2.8 The Tidyverse

2.8.1 Introduction

The Tidyverse is a family of packages used to speed up the use of R.

You need to first install it (if you haven’t already done so) and then load it. To install, use Tools > Install packages or install.packages() then add tidyverse. To load a package, use the library() function.

Look at how many packages are installed within the Tidyverse. The messages you see are telling you which packages are loaded and which functions are in conflict (i.e., these are functions from other packages that are found within the Tidyverse). If you want to use the original function, simply add package_name::function.

2.8.2 Using piping

The difference between base R and the Tidyverse’s way of doing things is that base R can sometimes be more complex, while tidyverse is more straightforward and allows you to “see” within a dataframe easily. You need to learn how to use the “pipe” in magrittr that is part of the Tidyverse.

Pipes are written in R as %>% (note you must use a percentage sign before and after the pipe). To demonstrate what pipes do, have a look at the following pseudo code. You can use a shortcut in your keyboard, type Ctrl+Shift+m to add a pipe (for mac users, it is Cmd+Shift+m).

Since R version 4.1.0, there is a native pipe |>. It seems to be doing almost the same thing as the %>%. We will still use %>% as this is integrated within the Tidyverse.

2.8.3 Demo subsetting

Below are two code lines for how to subset the dataframe using base R and piping from the magrittr package.

With base R, we always need to refer to the dataset twice: once at the beginning and then to look into the dataset to select a variable.

df_Yes1 <- df[which(df$functionword == 'yes'),]
df_Yes1
##   word freq functionword length  logFreq
## 1    a  500          yes      1 6.214608
## 2  the  600          yes      3 6.396930
## 4  not  200          yes      3 5.298317
## 6   it  450          yes      2 6.109248
## 9   on  300          yes      2 5.703782

With the pipe, you only need to specify the dataset once: By adding the pipe, you can already look into the dataset and select the variable you need.

df %>% filter(functionword =='yes')
##   word freq functionword length  logFreq
## 1    a  500          yes      1 6.214608
## 2  the  600          yes      3 6.396930
## 3  not  200          yes      3 5.298317
## 4   it  450          yes      2 6.109248
## 5   on  300          yes      2 5.703782

And this is with the base R pipe (combined with code from the Tidyverse family)

df |> filter(functionword =='yes')
##   word freq functionword length  logFreq
## 1    a  500          yes      1 6.214608
## 2  the  600          yes      3 6.396930
## 3  not  200          yes      3 5.298317
## 4   it  450          yes      2 6.109248
## 5   on  300          yes      2 5.703782

As you can see, using the pipe (either within the Tidyverse or with base R) is a quick and easy way to do various operations.

Out of convenience and because we will use other packages integrated within the Tidyverse, we will use its pipe.

ReCap:

  • %>% is called a “pipe”
  • It passes the previous line into the data argument of the next line
  • It does not save any changes after output
  • If you want to save the output of a particular manipulation, simply save it with xx <-

2.8.4 Basic manipulations

We will use the pipe with the Tidyverse to obtain summaries. We will use an R built-in dataset. Type data() to see the full list of datasets installed by default in R. You can use data(package = .packages(all.available = TRUE)) to see all datasets installed within all packages.

2.8.4.1 First steps

Here is a list of all available datasets

data()
data(package = .packages(all.available = TRUE))

2.8.4.2 Loading dataset

We will use the dataset english from the package languageR. This is a package that contains many linguistically-oriented datasets. See details of the dataset here. Or by typing ?languageR::english (or simply ?english if the package is already loaded) in the console.

You can load the dataset after loading the package. Simply refer to it by its name.

?english

2.8.4.3 View

To see the dataset, run the code below to visualise it.

english %>% 
  View()

2.8.4.4 Structure

We can use str() to look at the structure of the dataset. Here we have a relatively large dataset with 4568 observations (=rows) and 36 variables (=columns).

english %>% 
  str()
## 'data.frame':    4568 obs. of  36 variables:
##  $ RTlexdec                       : num  6.54 6.4 6.3 6.42 6.45 ...
##  $ RTnaming                       : num  6.15 6.25 6.14 6.13 6.2 ...
##  $ Familiarity                    : num  2.37 4.43 5.6 3.87 3.93 3.27 3.73 5.67 3.1 4.43 ...
##  $ Word                           : Factor w/ 2197 levels "ace","act","add",..: 467 2124 1838 1321 1302 1347 434 468 15 1632 ...
##  $ AgeSubject                     : Factor w/ 2 levels "old","young": 2 2 2 2 2 2 2 2 2 2 ...
##  $ WordCategory                   : Factor w/ 2 levels "N","V": 1 1 1 1 1 1 1 1 1 1 ...
##  $ WrittenFrequency               : num  3.91 4.52 6.51 5.02 4.89 ...
##  $ WrittenSpokenFrequencyRatio    : num  1.022 0.35 2.089 -0.526 -1.045 ...
##  $ FamilySize                     : num  1.39 1.39 1.61 1.95 2.2 ...
##  $ DerivationalEntropy            : num  0.141 0.427 0.062 0.43 0.359 ...
##  $ InflectionalEntropy            : num  0.0211 0.942 1.4434 0 1.7539 ...
##  $ NumberSimplexSynsets           : num  0.693 1.099 2.485 1.099 2.485 ...
##  $ NumberComplexSynsets           : num  0 0 1.95 2.64 2.48 ...
##  $ LengthInLetters                : int  3 5 6 4 4 4 4 3 3 5 ...
##  $ Ncount                         : int  8 5 0 8 3 9 6 13 3 3 ...
##  $ MeanBigramFrequency            : num  7.04 9.54 9.88 8.31 7.94 ...
##  $ FrequencyInitialDiphone        : num  12 12.6 13.3 12.1 11.9 ...
##  $ ConspelV                       : int  10 20 10 5 17 19 10 13 1 7 ...
##  $ ConspelN                       : num  3.74 7.87 6.69 6.68 4.76 ...
##  $ ConphonV                       : int  41 38 13 6 17 21 13 7 11 14 ...
##  $ ConphonN                       : num  8.84 9.78 7.04 3.83 4.76 ...
##  $ ConfriendsV                    : int  8 20 10 4 17 19 10 6 0 7 ...
##  $ ConfriendsN                    : num  3.3 7.87 6.69 3.53 4.76 ...
##  $ ConffV                         : num  0.693 0 0 0.693 0 ...
##  $ ConffN                         : num  2.71 0 0 6.63 0 ...
##  $ ConfbV                         : num  3.5 2.94 1.39 1.1 0 ...
##  $ ConfbN                         : num  8.83 9.61 5.82 2.56 0 ...
##  $ NounFrequency                  : int  49 142 565 150 170 125 582 2061 144 522 ...
##  $ VerbFrequency                  : int  0 0 473 0 120 280 110 76 4 86 ...
##  $ CV                             : Factor w/ 2 levels "C","V": 1 1 1 1 1 1 1 1 2 1 ...
##  $ Obstruent                      : Factor w/ 2 levels "cont","obst": 2 2 2 2 2 2 2 2 1 2 ...
##  $ Frication                      : Factor w/ 4 levels "burst","frication",..: 1 2 2 1 1 1 1 1 3 2 ...
##  $ Voice                          : Factor w/ 2 levels "voiced","voiceless": 1 2 2 2 2 2 1 1 1 2 ...
##  $ FrequencyInitialDiphoneWord    : num  10.13 9.05 12.42 10.05 11.8 ...
##  $ FrequencyInitialDiphoneSyllable: num  10.41 9.15 13.13 11 12.16 ...
##  $ CorrectLexdec                  : int  27 30 30 30 26 28 30 28 25 29 ...

2.8.4.5 See first 6 rows

english %>% 
  head()
##   RTlexdec RTnaming Familiarity   Word AgeSubject WordCategory WrittenFrequency
## 1 6.543754 6.145044        2.37    doe      young            N         3.912023
## 2 6.397596 6.246882        4.43  whore      young            N         4.521789
## 3 6.304942 6.143756        5.60 stress      young            N         6.505784
## 4 6.424221 6.131878        3.87   pork      young            N         5.017280
## 5 6.450597 6.198479        3.93   plug      young            N         4.890349
## 6 6.531970 6.167726        3.27   prop      young            N         4.770685
##   WrittenSpokenFrequencyRatio FamilySize DerivationalEntropy
## 1                   1.0216512   1.386294             0.14144
## 2                   0.3504830   1.386294             0.42706
## 3                   2.0893560   1.609438             0.06197
## 4                  -0.5263339   1.945910             0.43035
## 5                  -1.0445451   2.197225             0.35920
## 6                   0.9248014   1.386294             0.06268
##   InflectionalEntropy NumberSimplexSynsets NumberComplexSynsets LengthInLetters
## 1             0.02114            0.6931472             0.000000               3
## 2             0.94198            1.0986123             0.000000               5
## 3             1.44339            2.4849066             1.945910               6
## 4             0.00000            1.0986123             2.639057               4
## 5             1.75393            2.4849066             2.484907               4
## 6             1.74730            1.6094379             1.386294               4
##   Ncount MeanBigramFrequency FrequencyInitialDiphone ConspelV ConspelN ConphonV
## 1      8            7.036333                12.02268       10 3.737670       41
## 2      5            9.537878                12.59780       20 7.870930       38
## 3      0            9.883931                13.30069       10 6.693324       13
## 4      8            8.309180                12.07807        5 6.677083        6
## 5      3            7.943717                11.92678       17 4.762174       17
## 6      9            8.349620                12.19724       19 6.234411       21
##   ConphonN ConfriendsV ConfriendsN    ConffV   ConffN   ConfbV   ConfbN
## 1 8.837826           8    3.295837 0.6931472 2.708050 3.496508 8.833900
## 2 9.775825          20    7.870930 0.0000000 0.000000 2.944439 9.614738
## 3 7.040536          10    6.693324 0.0000000 0.000000 1.386294 5.817111
## 4 3.828641           4    3.526361 0.6931472 6.634633 1.098612 2.564949
## 5 4.762174          17    4.762174 0.0000000 0.000000 0.000000 0.000000
## 6 6.249975          19    6.234411 0.0000000 0.000000 1.098612 2.197225
##   NounFrequency VerbFrequency CV Obstruent Frication     Voice
## 1            49             0  C      obst     burst    voiced
## 2           142             0  C      obst frication voiceless
## 3           565           473  C      obst frication voiceless
## 4           150             0  C      obst     burst voiceless
## 5           170           120  C      obst     burst voiceless
## 6           125           280  C      obst     burst voiceless
##   FrequencyInitialDiphoneWord FrequencyInitialDiphoneSyllable CorrectLexdec
## 1                   10.129308                       10.409763            27
## 2                    9.054388                        9.148252            30
## 3                   12.422026                       13.127395            30
## 4                   10.048151                       11.003649            30
## 5                   11.796336                       12.163092            26
## 6                   11.991567                       12.436772            28

2.8.4.6 See last 6 rows

english %>% 
  tail()
##      RTlexdec RTnaming Familiarity  Word AgeSubject WordCategory
## 4563 6.608770 6.503839        3.70   spy        old            V
## 4564 6.753998 6.446513        2.40   jag        old            V
## 4565 6.711022 6.506979        3.17  hash        old            V
## 4566 6.592332 6.386879        3.87  dash        old            V
## 4567 6.565561 6.519884        4.97 flirt        old            V
## 4568 6.667300 6.496624        3.03  hawk        old            V
##      WrittenFrequency WrittenSpokenFrequencyRatio FamilySize
## 4563         5.023881                   0.9703580   1.609438
## 4564         2.079442                  -1.6863990   1.386294
## 4565         3.663562                   0.4367177   1.609438
## 4566         5.043425                   0.5043947   1.945910
## 4567         3.135494                   0.0628009   1.945910
## 4568         4.276666                   1.0498221   1.945910
##      DerivationalEntropy InflectionalEntropy NumberSimplexSynsets
## 4563             0.08753             1.64317             1.609438
## 4564             0.30954             1.85123             1.098612
## 4565             0.15110             0.77890             1.386294
## 4566             0.63316             1.65739             2.564949
## 4567             0.99953             1.75885             1.609438
## 4568             0.95422             1.81367             1.945910
##      NumberComplexSynsets LengthInLetters Ncount MeanBigramFrequency
## 4563            0.6931472               3      5            6.838235
## 4564            0.0000000               3     18            6.229554
## 4565            1.7917595               4     11            8.825582
## 4566            1.6094379               4     10            8.356139
## 4567            0.6931472               5      1            8.751224
## 4568            3.0910425               4      4            7.426055
##      FrequencyInitialDiphone ConspelV ConspelN ConphonV ConphonN ConfriendsV
## 4563                11.50982       18 8.917981       42 9.516132          17
## 4564                 8.49433       20 4.744932       20 4.744932          19
## 4565                13.49254       25 5.141664       23 4.890349          21
## 4566                11.32815       25 5.141664       23 4.890349          21
## 4567                10.59918        7 4.624973       14 5.164786           7
## 4568                13.49254        3 2.772589       11 5.609472           3
##      ConfriendsN   ConffV   ConffN   ConfbV    ConfbN NounFrequency
## 4563    8.916774 0.000000 0.000000 3.218876 8.7181729           219
## 4564    4.736198 0.000000 0.000000 0.000000 0.0000000            10
## 4565    4.882802 1.609438 3.688879 1.098612 0.6931472            38
## 4566    4.882802 1.609438 3.688879 1.098612 0.6931472           113
## 4567    4.624973 0.000000 0.000000 2.079442 4.3040651            10
## 4568    2.772589 0.000000 0.000000 2.197225 5.5529596           109
##      VerbFrequency CV Obstruent Frication     Voice FrequencyInitialDiphoneWord
## 4563            88  C      obst frication voiceless                   12.030051
## 4564             7  C      obst frication    voiced                    8.311644
## 4565             7  C      obst frication voiceless                   12.567203
## 4566           231  C      obst     burst    voiced                    8.920923
## 4567            66  C      obst frication voiceless                   10.425639
## 4568            47  C      obst frication voiceless                    9.054388
##      FrequencyInitialDiphoneSyllable CorrectLexdec
## 4563                       12.492844            30
## 4564                        8.390041            29
## 4565                       12.665546            29
## 4566                        9.287764            29
## 4567                       10.932142            29
## 4568                        9.148252            30

2.8.4.7 Selecting variables

Here, we select a few variables to use. For variables or columns, use the function select

english %>% 
  select(RTlexdec, RTnaming, Familiarity) %>% 
  head(10)
##    RTlexdec RTnaming Familiarity
## 1  6.543754 6.145044        2.37
## 2  6.397596 6.246882        4.43
## 3  6.304942 6.143756        5.60
## 4  6.424221 6.131878        3.87
## 5  6.450597 6.198479        3.93
## 6  6.531970 6.167726        3.27
## 7  6.370586 6.123808        3.73
## 8  6.266859 6.096050        5.67
## 9  6.608648 6.117657        3.10
## 10 6.284843 6.179188        4.43

2.8.4.8 Selecting observations

If we want to select observations, we use the function filter. We will use select to select particular variables and then use filter to select specific observations. This example shows how the pipe chain works, by combining multiple functions and using pipes

english %>% 
  select(RTlexdec, RTnaming, Familiarity, AgeSubject) %>% 
  filter(AgeSubject == "old") %>% 
  head(10)
##      RTlexdec RTnaming Familiarity AgeSubject
## 1453 6.664894 6.422597        2.37        old
## 1454 6.677209 6.636603        4.43        old
## 1455 6.538617 6.487075        5.60        old
## 1456 6.546943 6.404402        3.87        old
## 1457 6.637428 6.423409        3.93        old
## 1458 6.757444 6.529273        3.27        old
## 1459 6.598073 6.471728        3.73        old
## 1460 6.572464 6.424058        5.67        old
## 1461 6.817349 6.470645        3.10        old
## 1462 6.662877 6.549937        4.43        old

2.8.4.9 Changing order of levels

Use some of the code above to manipulate the dataframe but now using code from the Tidyverse. As you will see, once you know how to manipulate a dataset with base R, you can easily apply the same techniques with the Tidyverse. The Tidyverse provides additional ways to manipulate a dataframe.

For example, if I want to check levels of a variable and change the reference level, I will use the following code

levels(english$AgeSubject)
## [1] "old"   "young"

To change levels of AgeSubject, we need to save a new dataset (do not override the original dataset!!). The mutate function means we are manipulating an object.

english2<- english %>% 
  mutate(AgeSubject = factor(AgeSubject, levels = c("young", "old")))
levels(english2$AgeSubject)
## [1] "young" "old"

2.8.4.10 Changing reference value

You can change the reference value by using fct_relevel. This is useful if you have many levels in one of the factors you are working with and you simply need to change the reference.

english2<- english %>% 
  mutate(AgeSubject = fct_relevel(AgeSubject, "old"))
levels(english2$AgeSubject)
## [1] "old"   "young"

2.8.5 String manipulation

We use the str_func variants from the stringr package within the Tidyverse.

topics <- c("Phonetics", "Phonology", "Morphology", "Syntax", "Semantics", "Pragmatics", "Psycholinguistics")

2.8.5.1 str_sub

Extracting substrings (2nd to 4th)

str_sub(topics, 2, 4)
## [1] "hon" "hon" "orp" "ynt" "ema" "rag" "syc"

2.8.5.2 str_detect

Detecting a particular pattern

str_detect(topics, "p")
## [1] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
str_detect(topics, "n")
## [1]  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
str_detect(topics, "pho")
## [1] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
str_detect(topics, "ling")
## [1] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE

2.8.5.3 str_locate

Locate specific characters

str_locate(topics, "n")
##      start end
## [1,]     4   4
## [2,]     4   4
## [3,]    NA  NA
## [4,]     3   3
## [5,]     5   5
## [6,]    NA  NA
## [7,]     9   9

2.8.5.4 str_locate_all

Locate all instances of characters

str_locate_all(topics, "n")
## [[1]]
##      start end
## [1,]     4   4
## 
## [[2]]
##      start end
## [1,]     4   4
## 
## [[3]]
##      start end
## 
## [[4]]
##      start end
## [1,]     3   3
## 
## [[5]]
##      start end
## [1,]     5   5
## 
## [[6]]
##      start end
## 
## [[7]]
##      start end
## [1,]     9   9

2.8.5.5 str_replace

replaces a single instance

str_replace(topics, "o", "O")
## [1] "PhOnetics"         "PhOnology"         "MOrphology"       
## [4] "Syntax"            "Semantics"         "Pragmatics"       
## [7] "PsychOlinguistics"

2.8.5.6 str_replace_all

replaces all instances

str_replace_all(topics, "o", "O")
## [1] "PhOnetics"         "PhOnOlOgy"         "MOrphOlOgy"       
## [4] "Syntax"            "Semantics"         "Pragmatics"       
## [7] "PsychOlinguistics"

2.8.6 Regular expressions

Regular expressions are wildcards that can be used to search for particular patterns. We can use them to identify all words that begin with a “p” or end with a “y” or “cs”? Or any other changes? You can already consult this cheat sheet Also, here

2.8.6.1 Initial and final

^ used to identify initial position $ used to identify final position

str_detect(topics, "^[Pp]")
## [1]  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE
str_detect(topics, "[y]$")
## [1] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
str_detect(topics, "[cs]$")
## [1]  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE

2.8.6.2 Other characters

The . is used as a place holder asking for any character in the sequence

str_detect(topics, "Ph.n")
## [1]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE

[a-z] will detect all characters between “a” and “z”

str_detect(topics, "[a-z]")
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE

[M-P] will detect all characters between “M” and “P”

str_detect(topics, "[M-P]")
## [1]  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE

[:lower:] will detect all characters in lower case

str_detect(topics, "[:lower:]")
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE
str_detect(topics, "[:upper:]")
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE

We can also use “[0-9]”, “[:digit:]”, “[:alpha:]”, “[:alnum:]”, “[:punct:]”, “[:graph:]”, “[:blank:]”, “[:space:]”, “[:print:]”, etc.. See PDF of cheat sheet!

2.8.7 Advanced manipulations

Sometimes, you may have a dataset that comes in a wide format (i.e., columns contain data from participants) and you want to change to long format (i.e., each row contains one observation with minimal number of columns). Let’s look at the functions pivot_longer and pivot_wider

2.8.7.1 Columns to rows

Let’s use the english dataset to transform it from wide to long and see the first 10 rows.

english %>% 
  select(Word, RTlexdec, RTnaming, Familiarity) %>% 
  pivot_longer(cols = c(RTlexdec, RTnaming, Familiarity), ## you can also add index, i.e., 2:4
               names_to = "variable",
               values_to = "values") %>% 
  head(10)
## # A tibble: 10 × 3
##    Word   variable    values
##    <fct>  <chr>        <dbl>
##  1 doe    RTlexdec      6.54
##  2 doe    RTnaming      6.15
##  3 doe    Familiarity   2.37
##  4 whore  RTlexdec      6.40
##  5 whore  RTnaming      6.25
##  6 whore  Familiarity   4.43
##  7 stress RTlexdec      6.30
##  8 stress RTnaming      6.14
##  9 stress Familiarity   5.6 
## 10 pork   RTlexdec      6.42

2.8.7.2 Rows to columns

Let’s use the same code above and change the code from long format, back to wide format. Pivot_wider allows you to go back to the original dataset. You will need to use unnest to get all rows in the correct place. Try without it to see the result.

english %>% 
  select(Word, RTlexdec, RTnaming, Familiarity) %>% 
  pivot_longer(cols = c(RTlexdec, RTnaming, Familiarity), ## you can also add index, i.e., 2:4
               names_to = "variable",
               values_to = "values") %>% 
  pivot_wider(names_from = "variable",
              values_from = "values") %>% 
  head(10)
## Warning: Values from `values` are not uniquely identified; output will contain list-cols.
## • Use `values_fn = list` to suppress this warning.
## • Use `values_fn = {summary_fun}` to summarise duplicates.
## • Use the following dplyr code to identify duplicates.
##   {data} |>
##   dplyr::summarise(n = dplyr::n(), .by = c(Word, variable)) |>
##   dplyr::filter(n > 1L)
## # A tibble: 10 × 4
##    Word   RTlexdec  RTnaming  Familiarity
##    <fct>  <list>    <list>    <list>     
##  1 doe    <dbl [2]> <dbl [2]> <dbl [2]>  
##  2 whore  <dbl [2]> <dbl [2]> <dbl [2]>  
##  3 stress <dbl [2]> <dbl [2]> <dbl [2]>  
##  4 pork   <dbl [2]> <dbl [2]> <dbl [2]>  
##  5 plug   <dbl [2]> <dbl [2]> <dbl [2]>  
##  6 prop   <dbl [2]> <dbl [2]> <dbl [2]>  
##  7 dawn   <dbl [2]> <dbl [2]> <dbl [2]>  
##  8 dog    <dbl [2]> <dbl [2]> <dbl [2]>  
##  9 arc    <dbl [2]> <dbl [2]> <dbl [2]>  
## 10 skirt  <dbl [2]> <dbl [2]> <dbl [2]>

But wait, where are the results? They are added in lists. We need to use the function unnest() to obtain the full results.

english %>% 
  select(Word, RTlexdec, RTnaming, Familiarity) %>% 
  pivot_longer(cols = c(RTlexdec, RTnaming, Familiarity), ## you can also add index, i.e., 2:4
               names_to = "variable",
               values_to = "values") %>% 
  pivot_wider(names_from = "variable",
              values_from = "values") %>% 
  unnest() %>% 
  head(10)
## Warning: Values from `values` are not uniquely identified; output will contain list-cols.
## • Use `values_fn = list` to suppress this warning.
## • Use `values_fn = {summary_fun}` to summarise duplicates.
## • Use the following dplyr code to identify duplicates.
##   {data} |>
##   dplyr::summarise(n = dplyr::n(), .by = c(Word, variable)) |>
##   dplyr::filter(n > 1L)
## Warning: `cols` is now required when using `unnest()`.
## ℹ Please use `cols = c(RTlexdec, RTnaming, Familiarity)`.
## # A tibble: 10 × 4
##    Word   RTlexdec RTnaming Familiarity
##    <fct>     <dbl>    <dbl>       <dbl>
##  1 doe        6.54     6.15        2.37
##  2 doe        6.66     6.42        2.37
##  3 whore      6.40     6.25        4.43
##  4 whore      6.68     6.64        4.43
##  5 stress     6.30     6.14        5.6 
##  6 stress     6.54     6.49        5.6 
##  7 pork       6.42     6.13        3.87
##  8 pork       6.55     6.40        3.87
##  9 plug       6.45     6.20        3.93
## 10 plug       6.64     6.42        3.93

Ah that is better. But we get warnings. What does the warnings tell us? These are simple warnings and not errors. You can use the suggestions the Tidyverse makes. By default, we are told that the results are shown as lists of columns (what we are after). The second warning tells you to use a specific specification with unnest().

2.8.8 Basic descriptive statistics

2.8.8.1 Basic summaries

We can use summary() to obtain basic summaries of the dataset. For numeric variables, this will give you the minimum, maximum, mean, median, 1st and 3rd quartiles; for factors/characters, this will be the count. If there are missing values, you will get number of NAs. Look at the summaries of the dataset below.

english %>% 
  summary()
##     RTlexdec        RTnaming      Familiarity         Word      AgeSubject  
##  Min.   :6.205   Min.   :6.022   Min.   :1.100   arm    :   4   old  :2284  
##  1st Qu.:6.426   1st Qu.:6.149   1st Qu.:3.000   barge  :   4   young:2284  
##  Median :6.550   Median :6.342   Median :3.700   bark   :   4               
##  Mean   :6.550   Mean   :6.323   Mean   :3.796   bear   :   4               
##  3rd Qu.:6.653   3rd Qu.:6.490   3rd Qu.:4.570   beef   :   4               
##  Max.   :7.188   Max.   :6.696   Max.   :6.970   bind   :   4               
##                                                  (Other):4544               
##  WordCategory WrittenFrequency WrittenSpokenFrequencyRatio   FamilySize    
##  N:2904       Min.   : 0.000   Min.   :-6.55393            Min.   :0.6931  
##  V:1664       1st Qu.: 3.761   1st Qu.:-0.07402            1st Qu.:1.0986  
##               Median : 4.832   Median : 0.68118            Median :1.7918  
##               Mean   : 5.021   Mean   : 0.67763            Mean   :1.8213  
##               3rd Qu.: 6.247   3rd Qu.: 1.44146            3rd Qu.:2.3026  
##               Max.   :11.357   Max.   : 5.63071            Max.   :5.5175  
##                                                                            
##  DerivationalEntropy InflectionalEntropy NumberSimplexSynsets
##  Min.   :0.00000     Min.   :0.0000      Min.   :0.000       
##  1st Qu.:0.03932     1st Qu.:0.7442      1st Qu.:1.099       
##  Median :0.41097     Median :1.0982      Median :1.609       
##  Mean   :0.54089     Mean   :1.1186      Mean   :1.708       
##  3rd Qu.:0.89323     3rd Qu.:1.6325      3rd Qu.:2.197       
##  Max.   :5.20728     Max.   :2.4514      Max.   :4.357       
##                                                              
##  NumberComplexSynsets LengthInLetters     Ncount       MeanBigramFrequency
##  Min.   :0.000        Min.   :2.000   Min.   : 0.000   Min.   : 5.390     
##  1st Qu.:0.000        1st Qu.:4.000   1st Qu.: 2.000   1st Qu.: 8.100     
##  Median :1.386        Median :4.000   Median : 5.000   Median : 8.559     
##  Mean   :1.568        Mean   :4.342   Mean   : 6.266   Mean   : 8.490     
##  3rd Qu.:2.565        3rd Qu.:5.000   3rd Qu.: 9.000   3rd Qu.: 8.973     
##  Max.   :6.111        Max.   :7.000   Max.   :22.000   Max.   :10.283     
##                                                                           
##  FrequencyInitialDiphone    ConspelV        ConspelN         ConphonV    
##  Min.   : 4.143          Min.   : 0.00   Min.   : 0.000   Min.   : 0.00  
##  1st Qu.:11.277          1st Qu.: 6.00   1st Qu.: 4.519   1st Qu.:10.00  
##  Median :12.023          Median :11.00   Median : 5.710   Median :16.00  
##  Mean   :11.963          Mean   :11.71   Mean   : 5.605   Mean   :18.26  
##  3rd Qu.:12.697          3rd Qu.:17.00   3rd Qu.: 6.997   3rd Qu.:24.00  
##  Max.   :14.654          Max.   :32.00   Max.   :10.492   Max.   :66.00  
##                                                                          
##     ConphonN       ConfriendsV     ConfriendsN         ConffV      
##  Min.   : 0.000   Min.   : 0.00   Min.   : 0.000   Min.   :0.0000  
##  1st Qu.: 5.268   1st Qu.: 4.00   1st Qu.: 4.159   1st Qu.:0.0000  
##  Median : 6.340   Median :10.00   Median : 5.487   Median :0.0000  
##  Mean   : 6.318   Mean   :10.42   Mean   : 5.265   Mean   :0.4109  
##  3rd Qu.: 7.491   3rd Qu.:15.00   3rd Qu.: 6.642   3rd Qu.:0.6931  
##  Max.   :10.600   Max.   :31.00   Max.   :10.303   Max.   :3.3322  
##                                                                    
##      ConffN           ConfbV           ConfbN       NounFrequency     
##  Min.   : 0.000   Min.   :0.0000   Min.   : 0.000   Min.   :    0.00  
##  1st Qu.: 0.000   1st Qu.:0.6931   1st Qu.: 0.000   1st Qu.:   28.75  
##  Median : 0.000   Median :1.3863   Median : 4.143   Median :  108.00  
##  Mean   : 1.308   Mean   :1.5570   Mean   : 3.890   Mean   :  600.19  
##  3rd Qu.: 1.386   3rd Qu.:2.5649   3rd Qu.: 6.242   3rd Qu.:  424.75  
##  Max.   :10.347   Max.   :4.1897   Max.   :10.600   Max.   :35351.00  
##                                                                       
##  VerbFrequency      CV       Obstruent       Frication          Voice     
##  Min.   :     0.0   C:4446   cont:1068   burst    :1840   voiced   :2060  
##  1st Qu.:     0.0   V: 122   obst:3500   frication:1660   voiceless:2508  
##  Median :    30.0                        long     :  88                   
##  Mean   :   881.0                        short    : 980                   
##  3rd Qu.:   164.2                                                         
##  Max.   :242066.0                                                         
##                                                                           
##  FrequencyInitialDiphoneWord FrequencyInitialDiphoneSyllable CorrectLexdec  
##  Min.   : 3.091              Min.   : 3.367                  Min.   : 1.00  
##  1st Qu.: 9.557              1st Qu.:10.000                  1st Qu.:27.00  
##  Median :10.517              Median :10.972                  Median :29.00  
##  Mean   :10.359              Mean   :10.789                  Mean   :27.05  
##  3rd Qu.:11.320              3rd Qu.:11.703                  3rd Qu.:30.00  
##  Max.   :13.925              Max.   :13.930                  Max.   :30.00  
##

2.8.8.2 Summary for a specific variable

english %>% 
  summarise(count = n(),
            range_RTlexdec = range(RTlexdec),
            mean_RTlexdec = mean(RTlexdec),
            sd_RTlexdec = sd(RTlexdec),
            var_RTlexdec = var(RTlexdec),
            min_RTlexdec = min(RTlexdec),
            max_RTlexdec = max(RTlexdec),
            quart1_RTlexdec = quantile(RTlexdec, 0.25),
            quart1_RTlexdec = quantile(RTlexdec, 0.75),
            median_RTlexdec = median(RTlexdec))
## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()` always returns an ungrouped data
##   frame and adjust accordingly.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
##   count range_RTlexdec mean_RTlexdec sd_RTlexdec var_RTlexdec min_RTlexdec
## 1  4568       6.205325      6.550097   0.1569188   0.02462351     6.205325
## 2  4568       7.187808      6.550097   0.1569188   0.02462351     6.205325
##   max_RTlexdec quart1_RTlexdec median_RTlexdec
## 1     7.187808        6.653211        6.550466
## 2     7.187808        6.653211        6.550466

As you can see, we can add use summarise to obtain summaries of the dataset. We asked here for the mean, sd, variance, minimum and maximum values, etc.. In the dataset english, we have many numeric variables, and if we want to obtain summaries for all of numeric variables, we can use summarise_all.

2.8.8.3 Summarise_all

If you want to add another level of summaries, e.g., for length, you can either add them as another level (with a new variable name) or use summarise_all to do that for you. We need to select only numeric variables to do that. This is the function to only select numeric variables where(is.numeric). If you do not use it, you will get an error message

english %>% 
  select(where(is.numeric)) %>% 
  summarise_all(funs(mean = mean, sd = sd, var = var, min = min, max = max,
                     range = range, median = median, Q1 = quantile(., probs = 0.25), Q3 = quantile(., probs = 0.75)))
## Warning: `funs()` was deprecated in dplyr 0.8.0.
## ℹ Please use a list of either functions or lambdas:
## 
## # Simple named list: list(mean = mean, median = median)
## 
## # Auto named with `tibble::lst()`: tibble::lst(mean, median)
## 
## # Using lambdas list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
##   RTlexdec_mean RTnaming_mean Familiarity_mean WrittenFrequency_mean
## 1      6.550097      6.322505         3.795582              5.021145
## 2      6.550097      6.322505         3.795582              5.021145
##   WrittenSpokenFrequencyRatio_mean FamilySize_mean DerivationalEntropy_mean
## 1                        0.6776251        1.821324                0.5408901
## 2                        0.6776251        1.821324                0.5408901
##   InflectionalEntropy_mean NumberSimplexSynsets_mean NumberComplexSynsets_mean
## 1                 1.118614                  1.707797                  1.567777
## 2                 1.118614                  1.707797                  1.567777
##   LengthInLetters_mean Ncount_mean MeanBigramFrequency_mean
## 1             4.342382    6.265762                 8.489792
## 2             4.342382    6.265762                 8.489792
##   FrequencyInitialDiphone_mean ConspelV_mean ConspelN_mean ConphonV_mean
## 1                     11.96292      11.71235      5.605324      18.25963
## 2                     11.96292      11.71235      5.605324      18.25963
##   ConphonN_mean ConfriendsV_mean ConfriendsN_mean ConffV_mean ConffN_mean
## 1      6.317727         10.41813         5.265387    0.410887    1.308101
## 2      6.317727         10.41813         5.265387    0.410887    1.308101
##   ConfbV_mean ConfbN_mean NounFrequency_mean VerbFrequency_mean
## 1    1.556996    3.889859           600.1883           881.0201
## 2    1.556996    3.889859           600.1883           881.0201
##   FrequencyInitialDiphoneWord_mean FrequencyInitialDiphoneSyllable_mean
## 1                         10.35905                              10.7892
## 2                         10.35905                              10.7892
##   CorrectLexdec_mean RTlexdec_sd RTnaming_sd Familiarity_sd WrittenFrequency_sd
## 1           27.05166   0.1569188   0.1784815       1.149326            1.843559
## 2           27.05166   0.1569188   0.1784815       1.149326            1.843559
##   WrittenSpokenFrequencyRatio_sd FamilySize_sd DerivationalEntropy_sd
## 1                       1.165004     0.8205113              0.5573451
## 2                       1.165004     0.8205113              0.5573451
##   InflectionalEntropy_sd NumberSimplexSynsets_sd NumberComplexSynsets_sd
## 1              0.5660556               0.6693279                1.308999
## 2              0.5660556               0.6693279                1.308999
##   LengthInLetters_sd Ncount_sd MeanBigramFrequency_sd
## 1          0.8418279  4.893778              0.6982397
## 2          0.8418279  4.893778              0.6982397
##   FrequencyInitialDiphone_sd ConspelV_sd ConspelN_sd ConphonV_sd ConphonN_sd
## 1                    1.11962    7.090395     1.99571    11.83507    1.980334
## 2                    1.11962    7.090395     1.99571    11.83507    1.980334
##   ConfriendsV_sd ConfriendsN_sd ConffV_sd ConffN_sd ConfbV_sd ConfbN_sd
## 1       6.923388       2.076256 0.6881427  2.413261  1.125484  3.131148
## 2       6.923388       2.076256 0.6881427  2.413261  1.125484  3.131148
##   NounFrequency_sd VerbFrequency_sd FrequencyInitialDiphoneWord_sd
## 1         1858.115         6852.356                       1.583577
## 2         1858.115         6852.356                       1.583577
##   FrequencyInitialDiphoneSyllable_sd CorrectLexdec_sd RTlexdec_var RTnaming_var
## 1                           1.598599         4.302697   0.02462351   0.03185564
## 2                           1.598599         4.302697   0.02462351   0.03185564
##   Familiarity_var WrittenFrequency_var WrittenSpokenFrequencyRatio_var
## 1         1.32095              3.39871                        1.357234
## 2         1.32095              3.39871                        1.357234
##   FamilySize_var DerivationalEntropy_var InflectionalEntropy_var
## 1      0.6732388               0.3106336                0.320419
## 2      0.6732388               0.3106336                0.320419
##   NumberSimplexSynsets_var NumberComplexSynsets_var LengthInLetters_var
## 1                0.4479998                 1.713479           0.7086742
## 2                0.4479998                 1.713479           0.7086742
##   Ncount_var MeanBigramFrequency_var FrequencyInitialDiphone_var ConspelV_var
## 1   23.94906               0.4875387                     1.25355     50.27371
## 2   23.94906               0.4875387                     1.25355     50.27371
##   ConspelN_var ConphonV_var ConphonN_var ConfriendsV_var ConfriendsN_var
## 1     3.982859     140.0688     3.921722         47.9333         4.31084
## 2     3.982859     140.0688     3.921722         47.9333         4.31084
##   ConffV_var ConffN_var ConfbV_var ConfbN_var NounFrequency_var
## 1  0.4735403   5.823826   1.266714   9.804086           3452593
## 2  0.4735403   5.823826   1.266714   9.804086           3452593
##   VerbFrequency_var FrequencyInitialDiphoneWord_var
## 1          46954789                        2.507715
## 2          46954789                        2.507715
##   FrequencyInitialDiphoneSyllable_var CorrectLexdec_var RTlexdec_min
## 1                            2.555518          18.51321     6.205325
## 2                            2.555518          18.51321     6.205325
##   RTnaming_min Familiarity_min WrittenFrequency_min
## 1     6.021751             1.1                    0
## 2     6.021751             1.1                    0
##   WrittenSpokenFrequencyRatio_min FamilySize_min DerivationalEntropy_min
## 1                       -6.553933      0.6931472                       0
## 2                       -6.553933      0.6931472                       0
##   InflectionalEntropy_min NumberSimplexSynsets_min NumberComplexSynsets_min
## 1                       0                        0                        0
## 2                       0                        0                        0
##   LengthInLetters_min Ncount_min MeanBigramFrequency_min
## 1                   2          0                5.390053
## 2                   2          0                5.390053
##   FrequencyInitialDiphone_min ConspelV_min ConspelN_min ConphonV_min
## 1                     4.14313            0            0            0
## 2                     4.14313            0            0            0
##   ConphonN_min ConfriendsV_min ConfriendsN_min ConffV_min ConffN_min ConfbV_min
## 1            0               0               0          0          0          0
## 2            0               0               0          0          0          0
##   ConfbN_min NounFrequency_min VerbFrequency_min
## 1          0                 0                 0
## 2          0                 0                 0
##   FrequencyInitialDiphoneWord_min FrequencyInitialDiphoneSyllable_min
## 1                        3.091042                            3.367296
## 2                        3.091042                            3.367296
##   CorrectLexdec_min RTlexdec_max RTnaming_max Familiarity_max
## 1                 1     7.187808     6.695675            6.97
## 2                 1     7.187808     6.695675            6.97
##   WrittenFrequency_max WrittenSpokenFrequencyRatio_max FamilySize_max
## 1             11.35666                        5.630714       5.517453
## 2             11.35666                        5.630714       5.517453
##   DerivationalEntropy_max InflectionalEntropy_max NumberSimplexSynsets_max
## 1                 5.20728                 2.45141                 4.356709
## 2                 5.20728                 2.45141                 4.356709
##   NumberComplexSynsets_max LengthInLetters_max Ncount_max
## 1                 6.111467                   7         22
## 2                 6.111467                   7         22
##   MeanBigramFrequency_max FrequencyInitialDiphone_max ConspelV_max ConspelN_max
## 1                10.28277                    14.65437           32     10.49202
## 2                10.28277                    14.65437           32     10.49202
##   ConphonV_max ConphonN_max ConfriendsV_max ConfriendsN_max ConffV_max
## 1           66     10.59975              31        10.30304   3.332205
## 2           66     10.59975              31        10.30304   3.332205
##   ConffN_max ConfbV_max ConfbN_max NounFrequency_max VerbFrequency_max
## 1   10.34744   4.189655   10.59975             35351            242066
## 2   10.34744   4.189655   10.59975             35351            242066
##   FrequencyInitialDiphoneWord_max FrequencyInitialDiphoneSyllable_max
## 1                         13.9249                            13.92962
## 2                         13.9249                            13.92962
##   CorrectLexdec_max RTlexdec_range RTnaming_range Familiarity_range
## 1                30       6.205325       6.021751              1.10
## 2                30       7.187808       6.695675              6.97
##   WrittenFrequency_range WrittenSpokenFrequencyRatio_range FamilySize_range
## 1                0.00000                         -6.553933        0.6931472
## 2               11.35666                          5.630714        5.5174529
##   DerivationalEntropy_range InflectionalEntropy_range
## 1                   0.00000                   0.00000
## 2                   5.20728                   2.45141
##   NumberSimplexSynsets_range NumberComplexSynsets_range LengthInLetters_range
## 1                   0.000000                   0.000000                     2
## 2                   4.356709                   6.111467                     7
##   Ncount_range MeanBigramFrequency_range FrequencyInitialDiphone_range
## 1            0                  5.390053                       4.14313
## 2           22                 10.282767                      14.65437
##   ConspelV_range ConspelN_range ConphonV_range ConphonN_range ConfriendsV_range
## 1              0        0.00000              0        0.00000                 0
## 2             32       10.49202             66       10.59975                31
##   ConfriendsN_range ConffV_range ConffN_range ConfbV_range ConfbN_range
## 1           0.00000     0.000000      0.00000     0.000000      0.00000
## 2          10.30304     3.332205     10.34744     4.189655     10.59975
##   NounFrequency_range VerbFrequency_range FrequencyInitialDiphoneWord_range
## 1                   0                   0                          3.091042
## 2               35351              242066                         13.924902
##   FrequencyInitialDiphoneSyllable_range CorrectLexdec_range RTlexdec_median
## 1                              3.367296                   1        6.550466
## 2                             13.929620                  30        6.550466
##   RTnaming_median Familiarity_median WrittenFrequency_median
## 1        6.342023                3.7                4.832298
## 2        6.342023                3.7                4.832298
##   WrittenSpokenFrequencyRatio_median FamilySize_median
## 1                           0.681184          1.791759
## 2                           0.681184          1.791759
##   DerivationalEntropy_median InflectionalEntropy_median
## 1                   0.410975                    1.09821
## 2                   0.410975                    1.09821
##   NumberSimplexSynsets_median NumberComplexSynsets_median
## 1                    1.609438                    1.386294
## 2                    1.609438                    1.386294
##   LengthInLetters_median Ncount_median MeanBigramFrequency_median
## 1                      4             5                    8.55855
## 2                      4             5                    8.55855
##   FrequencyInitialDiphone_median ConspelV_median ConspelN_median
## 1                       12.02268              11        5.710427
## 2                       12.02268              11        5.710427
##   ConphonV_median ConphonN_median ConfriendsV_median ConfriendsN_median
## 1              16        6.340359                 10            5.48685
## 2              16        6.340359                 10            5.48685
##   ConffV_median ConffN_median ConfbV_median ConfbN_median NounFrequency_median
## 1             0             0      1.386294      4.143135                  108
## 2             0             0      1.386294      4.143135                  108
##   VerbFrequency_median FrequencyInitialDiphoneWord_median
## 1                   30                           10.51651
## 2                   30                           10.51651
##   FrequencyInitialDiphoneSyllable_median CorrectLexdec_median RTlexdec_Q1
## 1                               10.97207                   29    6.425525
## 2                               10.97207                   29    6.425525
##   RTnaming_Q1 Familiarity_Q1 WrittenFrequency_Q1 WrittenSpokenFrequencyRatio_Q1
## 1    6.148682              3              3.7612                    -0.07401695
## 2    6.148682              3              3.7612                    -0.07401695
##   FamilySize_Q1 DerivationalEntropy_Q1 InflectionalEntropy_Q1
## 1      1.098612               0.039325              0.7441725
## 2      1.098612               0.039325              0.7441725
##   NumberSimplexSynsets_Q1 NumberComplexSynsets_Q1 LengthInLetters_Q1 Ncount_Q1
## 1                1.098612                       0                  4         2
## 2                1.098612                       0                  4         2
##   MeanBigramFrequency_Q1 FrequencyInitialDiphone_Q1 ConspelV_Q1 ConspelN_Q1
## 1               8.099924                   11.27694           6    4.519056
## 2               8.099924                   11.27694           6    4.519056
##   ConphonV_Q1 ConphonN_Q1 ConfriendsV_Q1 ConfriendsN_Q1 ConffV_Q1 ConffN_Q1
## 1          10    5.267858              4       4.158883         0         0
## 2          10    5.267858              4       4.158883         0         0
##   ConfbV_Q1 ConfbN_Q1 NounFrequency_Q1 VerbFrequency_Q1
## 1 0.6931472         0            28.75                0
## 2 0.6931472         0            28.75                0
##   FrequencyInitialDiphoneWord_Q1 FrequencyInitialDiphoneSyllable_Q1
## 1                       9.556808                           10.00048
## 2                       9.556808                           10.00048
##   CorrectLexdec_Q1 RTlexdec_Q3 RTnaming_Q3 Familiarity_Q3 WrittenFrequency_Q3
## 1               27    6.653211    6.489699           4.57            6.247074
## 2               27    6.653211    6.489699           4.57            6.247074
##   WrittenSpokenFrequencyRatio_Q3 FamilySize_Q3 DerivationalEntropy_Q3
## 1                        1.44146      2.302585               0.893225
## 2                        1.44146      2.302585               0.893225
##   InflectionalEntropy_Q3 NumberSimplexSynsets_Q3 NumberComplexSynsets_Q3
## 1               1.632455                2.197225                2.564949
## 2               1.632455                2.197225                2.564949
##   LengthInLetters_Q3 Ncount_Q3 MeanBigramFrequency_Q3
## 1                  5         9               8.972658
## 2                  5         9               8.972658
##   FrequencyInitialDiphone_Q3 ConspelV_Q3 ConspelN_Q3 ConphonV_Q3 ConphonN_Q3
## 1                   12.69734          17    6.997358          24    7.491413
## 2                   12.69734          17    6.997358          24    7.491413
##   ConfriendsV_Q3 ConfriendsN_Q3 ConffV_Q3 ConffN_Q3 ConfbV_Q3 ConfbN_Q3
## 1             15       6.642077 0.6931472  1.386294  2.564949  6.241734
## 2             15       6.642077 0.6931472  1.386294  2.564949  6.241734
##   NounFrequency_Q3 VerbFrequency_Q3 FrequencyInitialDiphoneWord_Q3
## 1           424.75           164.25                       11.31995
## 2           424.75           164.25                       11.31995
##   FrequencyInitialDiphoneSyllable_Q3 CorrectLexdec_Q3
## 1                           11.70264               30
## 2                           11.70264               30

As you can see, in this example, we see the chains of commands in the Tidyverse. We can continue to add commands each time we want to investigate something in particular. Keep adding pipes and commands. The most important point is that the dataset english did not change at all. If you want to create a new dataset with the results, simply use the assignment function <- at the beginning or -> at the end and give a name to the new dataset.

2.8.8.4 Group_by

2.8.8.5 One variable

What if you want to obtain all results summarised by a specific grouping? Let’s obtain the results grouped by the levels of AgeSubject.

english %>% 
  group_by(AgeSubject) %>% 
  summarise(count = n(),
            range_RTlexdec = range(RTlexdec),
            mean_RTlexdec = mean(RTlexdec),
            sd_RTlexdec = sd(RTlexdec),
            var_RTlexdec = var(RTlexdec),
            min_RTlexdec = min(RTlexdec),
            max_RTlexdec = max(RTlexdec),
            quart1_RTlexdec = quantile(RTlexdec, 0.25),
            quart1_RTlexdec = quantile(RTlexdec, 0.75),
            median_RTlexdec = median(RTlexdec))
## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()` always returns an ungrouped data
##   frame and adjust accordingly.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
## `summarise()` has grouped output by 'AgeSubject'. You can override using the
## `.groups` argument.
## # A tibble: 4 × 10
## # Groups:   AgeSubject [2]
##   AgeSubject count range_RTlexdec mean_RTlexdec sd_RTlexdec var_RTlexdec
##   <fct>      <int>          <dbl>         <dbl>       <dbl>        <dbl>
## 1 old         2284           6.40          6.66       0.116       0.0134
## 2 old         2284           7.19          6.66       0.116       0.0134
## 3 young       2284           6.21          6.44       0.106       0.0113
## 4 young       2284           6.88          6.44       0.106       0.0113
## # ℹ 4 more variables: min_RTlexdec <dbl>, max_RTlexdec <dbl>,
## #   quart1_RTlexdec <dbl>, median_RTlexdec <dbl>

2.8.8.6 Multiple variables

What if you want to obtain all results summarised by multiple groupings? Let’s obtain the results grouped by the levels of AgeSubject, WordCategory and Voice and we want to save the output.

english %>% 
  group_by(AgeSubject, WordCategory, Voice) %>% 
  summarise(count = n(),
            range_RTlexdec = range(RTlexdec),
            mean_RTlexdec = mean(RTlexdec),
            sd_RTlexdec = sd(RTlexdec),
            var_RTlexdec = var(RTlexdec),
            min_RTlexdec = min(RTlexdec),
            max_RTlexdec = max(RTlexdec),
            quart1_RTlexdec = quantile(RTlexdec, 0.25),
            quart1_RTlexdec = quantile(RTlexdec, 0.75),
            median_RTlexdec = median(RTlexdec)) -> dfMeans
## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()` always returns an ungrouped data
##   frame and adjust accordingly.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
## `summarise()` has grouped output by 'AgeSubject', 'WordCategory', 'Voice'. You can override using the
## `.groups` argument.
dfMeans
## # A tibble: 16 × 12
## # Groups:   AgeSubject, WordCategory, Voice [8]
##    AgeSubject WordCategory Voice  count range_RTlexdec mean_RTlexdec sd_RTlexdec
##    <fct>      <fct>        <fct>  <int>          <dbl>         <dbl>       <dbl>
##  1 old        N            voiced   676           6.45          6.67      0.121 
##  2 old        N            voiced   676           7.15          6.67      0.121 
##  3 old        N            voice…   776           6.45          6.66      0.120 
##  4 old        N            voice…   776           7.19          6.66      0.120 
##  5 old        V            voiced   354           6.40          6.66      0.112 
##  6 old        V            voiced   354           7.14          6.66      0.112 
##  7 old        V            voice…   478           6.46          6.65      0.101 
##  8 old        V            voice…   478           7.01          6.65      0.101 
##  9 young      N            voiced   676           6.21          6.45      0.116 
## 10 young      N            voiced   676           6.88          6.45      0.116 
## 11 young      N            voice…   776           6.21          6.44      0.102 
## 12 young      N            voice…   776           6.79          6.44      0.102 
## 13 young      V            voiced   354           6.24          6.43      0.106 
## 14 young      V            voiced   354           6.75          6.43      0.106 
## 15 young      V            voice…   478           6.21          6.43      0.0982
## 16 young      V            voice…   478           6.83          6.43      0.0982
## # ℹ 5 more variables: var_RTlexdec <dbl>, min_RTlexdec <dbl>,
## #   max_RTlexdec <dbl>, quart1_RTlexdec <dbl>, median_RTlexdec <dbl>

2.8.9 Summary tables

2.8.9.1 Using summarytools

Using the package summarytools you can obtain nice table ready for publications!

2.8.9.1.1 Summary of full dataset with graphs
english %>% 
  dfSummary(graph.col = TRUE, style = "grid") %>% 
  stview(method = "render")
## Error in table(names(candidates))[["tested"]]: subscript out of bounds
## Warning in parse_call(mc = match.call(), var_name = get_var_info, var_label =
## get_var_info, : metadata extraction terminated unexpectedly; inspect results
## carefully

Data Frame Summary

Dimensions: 4568 x 36
Duplicates: 0
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
1 RTlexdec [numeric]
Mean (sd) : 6.6 (0.2)
min ≤ med ≤ max:
6.2 ≤ 6.6 ≤ 7.2
IQR (CV) : 0.2 (0)
 4019 distinct values 4568 (100.0%) 0 (0.0%)
2 RTnaming [numeric]
Mean (sd) : 6.3 (0.2)
min ≤ med ≤ max:
6 ≤ 6.3 ≤ 6.7
IQR (CV) : 0.3 (0)
 1892 distinct values 4568 (100.0%) 0 (0.0%)
3 Familiarity [numeric]
Mean (sd) : 3.8 (1.1)
min ≤ med ≤ max:
1.1 ≤ 3.7 ≤ 7
IQR (CV) : 1.6 (0.3)
 254 distinct values 4568 (100.0%) 0 (0.0%)
4 Word [factor]
1. ace
2. act
3. add
4. age
5. aid
6. aide
7. ail
8. aim
9. air
10. aisle
[ 2187 others ]
2(0.0%)
2(0.0%)
2(0.0%)
2(0.0%)
2(0.0%)
2(0.0%)
2(0.0%)
2(0.0%)
2(0.0%)
2(0.0%)
4548(99.6%)
 4568 (100.0%) 0 (0.0%)
5 AgeSubject [factor]
1. old
2. young
2284(50.0%)
2284(50.0%)
 4568 (100.0%) 0 (0.0%)
6 WordCategory [factor]
1. N
2. V
2904(63.6%)
1664(36.4%)
 4568 (100.0%) 0 (0.0%)
7 WrittenFrequency [numeric]
Mean (sd) : 5 (1.8)
min ≤ med ≤ max:
0 ≤ 4.8 ≤ 11.4
IQR (CV) : 2.5 (0.4)
 882 distinct values 4568 (100.0%) 0 (0.0%)
8 WrittenSpokenFrequencyRatio [numeric]
Mean (sd) : 0.7 (1.2)
min ≤ med ≤ max:
-6.6 ≤ 0.7 ≤ 5.6
IQR (CV) : 1.5 (1.7)
 1595 distinct values 4568 (100.0%) 0 (0.0%)
9 FamilySize [numeric]
Mean (sd) : 1.8 (0.8)
min ≤ med ≤ max:
0.7 ≤ 1.8 ≤ 5.5
IQR (CV) : 1.2 (0.5)
 75 distinct values 4568 (100.0%) 0 (0.0%)
10 DerivationalEntropy [numeric]
Mean (sd) : 0.5 (0.6)
min ≤ med ≤ max:
0 ≤ 0.4 ≤ 5.2
IQR (CV) : 0.9 (1)
 1644 distinct values 4568 (100.0%) 0 (0.0%)
11 InflectionalEntropy [numeric]
Mean (sd) : 1.1 (0.6)
min ≤ med ≤ max:
0 ≤ 1.1 ≤ 2.5
IQR (CV) : 0.9 (0.5)
 1966 distinct values 4568 (100.0%) 0 (0.0%)
12 NumberSimplexSynsets [numeric]
Mean (sd) : 1.7 (0.7)
min ≤ med ≤ max:
0 ≤ 1.6 ≤ 4.4
IQR (CV) : 1.1 (0.4)
 47 distinct values 4568 (100.0%) 0 (0.0%)
13 NumberComplexSynsets [numeric]
Mean (sd) : 1.6 (1.3)
min ≤ med ≤ max:
0 ≤ 1.4 ≤ 6.1
IQR (CV) : 2.6 (0.8)
 106 distinct values 4568 (100.0%) 0 (0.0%)
14 LengthInLetters [integer]
Mean (sd) : 4.3 (0.8)
min ≤ med ≤ max:
2 ≤ 4 ≤ 7
IQR (CV) : 1 (0.2)
2:6(0.1%)
3:676(14.8%)
4:2020(44.2%)
5:1504(32.9%)
6:338(7.4%)
7:24(0.5%)
 4568 (100.0%) 0 (0.0%)
15 Ncount [integer]
Mean (sd) : 6.3 (4.9)
min ≤ med ≤ max:
0 ≤ 5 ≤ 22
IQR (CV) : 7 (0.8)
 23 distinct values 4568 (100.0%) 0 (0.0%)
16 MeanBigramFrequency [numeric]
Mean (sd) : 8.5 (0.7)
min ≤ med ≤ max:
5.4 ≤ 8.6 ≤ 10.3
IQR (CV) : 0.9 (0.1)
 2196 distinct values 4568 (100.0%) 0 (0.0%)
17 FrequencyInitialDiphone [numeric]
Mean (sd) : 12 (1.1)
min ≤ med ≤ max:
4.1 ≤ 12 ≤ 14.7
IQR (CV) : 1.4 (0.1)
 166 distinct values 4568 (100.0%) 0 (0.0%)
18 ConspelV [integer]
Mean (sd) : 11.7 (7.1)
min ≤ med ≤ max:
0 ≤ 11 ≤ 32
IQR (CV) : 11 (0.6)
 29 distinct values 4568 (100.0%) 0 (0.0%)
19 ConspelN [numeric]
Mean (sd) : 5.6 (2)
min ≤ med ≤ max:
0 ≤ 5.7 ≤ 10.5
IQR (CV) : 2.5 (0.4)
 310 distinct values 4568 (100.0%) 0 (0.0%)
20 ConphonV [integer]
Mean (sd) : 18.3 (11.8)
min ≤ med ≤ max:
0 ≤ 16 ≤ 66
IQR (CV) : 14 (0.6)
 41 distinct values 4568 (100.0%) 0 (0.0%)
21 ConphonN [numeric]
Mean (sd) : 6.3 (2)
min ≤ med ≤ max:
0 ≤ 6.3 ≤ 10.6
IQR (CV) : 2.2 (0.3)
 279 distinct values 4568 (100.0%) 0 (0.0%)
22 ConfriendsV [integer]
Mean (sd) : 10.4 (6.9)
min ≤ med ≤ max:
0 ≤ 10 ≤ 31
IQR (CV) : 11 (0.7)
 31 distinct values 4568 (100.0%) 0 (0.0%)
23 ConfriendsN [numeric]
Mean (sd) : 5.3 (2.1)
min ≤ med ≤ max:
0 ≤ 5.5 ≤ 10.3
IQR (CV) : 2.5 (0.4)
 485 distinct values 4568 (100.0%) 0 (0.0%)
24 ConffV [numeric]
Mean (sd) : 0.4 (0.7)
min ≤ med ≤ max:
0 ≤ 0 ≤ 3.3
IQR (CV) : 0.7 (1.7)
 23 distinct values 4568 (100.0%) 0 (0.0%)
25 ConffN [numeric]
Mean (sd) : 1.3 (2.4)
min ≤ med ≤ max:
0 ≤ 0 ≤ 10.3
IQR (CV) : 1.4 (1.8)
 141 distinct values 4568 (100.0%) 0 (0.0%)
26 ConfbV [numeric]
Mean (sd) : 1.6 (1.1)
min ≤ med ≤ max:
0 ≤ 1.4 ≤ 4.2
IQR (CV) : 1.9 (0.7)
 48 distinct values 4568 (100.0%) 0 (0.0%)
27 ConfbN [numeric]
Mean (sd) : 3.9 (3.1)
min ≤ med ≤ max:
0 ≤ 4.1 ≤ 10.6
IQR (CV) : 6.2 (0.8)
 365 distinct values 4568 (100.0%) 0 (0.0%)
28 NounFrequency [integer]
Mean (sd) : 600.2 (1858.1)
min ≤ med ≤ max:
0 ≤ 108 ≤ 35351
IQR (CV) : 396 (3.1)
 822 distinct values 4568 (100.0%) 0 (0.0%)
29 VerbFrequency [integer]
Mean (sd) : 881 (6852.4)
min ≤ med ≤ max:
0 ≤ 30 ≤ 242066
IQR (CV) : 164.2 (7.8)
 594 distinct values 4568 (100.0%) 0 (0.0%)
30 CV [factor]
1. C
2. V
4446(97.3%)
122(2.7%)
 4568 (100.0%) 0 (0.0%)
31 Obstruent [factor]
1. cont
2. obst
1068(23.4%)
3500(76.6%)
 4568 (100.0%) 0 (0.0%)
32 Frication [factor]
1. burst
2. frication
3. long
4. short
1840(40.3%)
1660(36.3%)
88(1.9%)
980(21.5%)
 4568 (100.0%) 0 (0.0%)
33 Voice [factor]
1. voiced
2. voiceless
2060(45.1%)
2508(54.9%)
 4568 (100.0%) 0 (0.0%)
34 FrequencyInitialDiphoneWord [numeric]
Mean (sd) : 10.4 (1.6)
min ≤ med ≤ max:
3.1 ≤ 10.5 ≤ 13.9
IQR (CV) : 1.8 (0.2)
 401 distinct values 4568 (100.0%) 0 (0.0%)
35 FrequencyInitialDiphoneSyllable [numeric]
Mean (sd) : 10.8 (1.6)
min ≤ med ≤ max:
3.4 ≤ 11 ≤ 13.9
IQR (CV) : 1.7 (0.1)
 402 distinct values 4568 (100.0%) 0 (0.0%)
36 CorrectLexdec [integer]
Mean (sd) : 27.1 (4.3)
min ≤ med ≤ max:
1 ≤ 29 ≤ 30
IQR (CV) : 3 (0.2)
 30 distinct values 4568 (100.0%) 0 (0.0%)

Generated by summarytools 1.1.4 (R version 4.5.1)
2025-07-17

2.8.9.1.2 Descriptive statistics for numeric variables
english %>% 
  descr() %>% 
  stview(method = "render")
## Error in table(names(candidates))[["tested"]]: subscript out of bounds
## Warning in parse_call(mc = match.call(), var_name = (ncol(xx) == 1), var_label
## = (ncol(xx) == : metadata extraction terminated unexpectedly; inspect results
## carefully
## Non-numerical variable(s) ignored: Word, AgeSubject, WordCategory, CV, Obstruent, Frication, Voice

Descriptive Statistics

N: 4568
ConfbN ConfbV ConffN ConffV ConfriendsN ConfriendsV ConphonN ConphonV ConspelN ConspelV CorrectLexde
c		 Derivational
Entropy		 Familiarity FamilySize FrequencyIni
tialDiphone		 FrequencyIni
tialDiphoneS
yllable		 FrequencyIni
tialDiphoneW
ord		 Inflectional
Entropy		 LengthInLett
ers		 MeanBigramFr
equency		 Ncount NounFrequenc
y		 NumberComple
xSynsets		 NumberSimple
xSynsets		 RTlexdec RTnaming VerbFrequenc
y		 WrittenFrequ
ency		 WrittenSpoke
nFrequencyRa
tio
Mean 3.89 1.56 1.31 0.41 5.27 10.42 6.32 18.26 5.61 11.71 27.05 0.54 3.80 1.82 11.96 10.79 10.36 1.12 4.34 8.49 6.27 600.19 1.57 1.71 6.55 6.32 881.02 5.02 0.68
Std.Dev 3.13 1.13 2.41 0.69 2.08 6.92 1.98 11.84 2.00 7.09 4.30 0.56 1.15 0.82 1.12 1.60 1.58 0.57 0.84 0.70 4.89 1858.12 1.31 0.67 0.16 0.18 6852.36 1.84 1.17
Min 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 1.10 0.69 4.14 3.37 3.09 0.00 2.00 5.39 0.00 0.00 0.00 0.00 6.21 6.02 0.00 0.00 -6.55
Q1 0.00 0.69 0.00 0.00 4.16 4.00 5.27 10.00 4.52 6.00 27.00 0.04 3.00 1.10 11.28 10.00 9.55 0.74 4.00 8.10 2.00 28.50 0.00 1.10 6.43 6.15 0.00 3.76 -0.07
Median 4.14 1.39 0.00 0.00 5.49 10.00 6.34 16.00 5.71 11.00 29.00 0.41 3.70 1.79 12.02 10.97 10.52 1.10 4.00 8.56 5.00 108.00 1.39 1.61 6.55 6.34 30.00 4.83 0.68
Q3 6.24 2.56 1.39 0.69 6.65 15.00 7.51 24.00 7.00 17.00 30.00 0.89 4.57 2.30 12.70 11.70 11.32 1.63 5.00 8.97 9.00 425.50 2.56 2.20 6.65 6.49 164.50 6.25 1.44
Max 10.60 4.19 10.35 3.33 10.30 31.00 10.60 66.00 10.49 32.00 30.00 5.21 6.97 5.52 14.65 13.93 13.92 2.45 7.00 10.28 22.00 35351.00 6.11 4.36 7.19 6.70 242066.00 11.36 5.63
MAD 4.09 1.20 0.00 0.00 1.86 8.90 1.68 10.38 1.81 8.90 1.48 0.61 1.14 0.90 1.11 1.18 1.32 0.70 1.48 0.64 4.45 146.78 1.75 0.76 0.17 0.26 44.48 1.81 1.12
IQR 6.24 1.87 1.39 0.69 2.48 11.00 2.22 14.00 2.48 11.00 3.00 0.85 1.57 1.20 1.42 1.70 1.76 0.89 1.00 0.87 7.00 396.00 2.56 1.10 0.23 0.34 164.25 2.49 1.52
CV 0.80 0.72 1.84 1.67 0.39 0.66 0.31 0.65 0.36 0.61 0.16 1.03 0.30 0.45 0.09 0.15 0.15 0.51 0.19 0.08 0.78 3.10 0.83 0.39 0.02 0.03 7.78 0.37 1.72
Skewness 0.20 0.16 1.71 1.76 -0.41 0.44 -0.40 1.10 -0.41 0.39 -2.66 1.42 0.25 0.79 -0.60 -0.96 -0.86 -0.33 0.23 -0.53 0.75 9.08 0.45 0.39 0.35 0.06 22.80 0.30 -0.21
SE.Skewness 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
Kurtosis -1.11 -1.08 1.60 2.46 0.14 -0.47 0.72 1.74 0.23 -0.41 7.83 4.00 -0.41 0.59 1.94 1.88 1.72 -0.84 -0.20 0.46 -0.26 117.84 -0.71 0.01 -0.09 -1.67 705.39 -0.04 0.98
N.Valid 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568
N 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568 4568
Pct.Valid 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00

Generated by summarytools 1.1.4 (R version 4.5.1)
2025-07-17

2.8.9.1.3 Frequencies for non-numeric variables
english %>% 
  freq() %>% 
    stview(method = "render")
## Error in table(names(candidates))[["tested"]]: subscript out of bounds
## Warning in parse_call(mc = match.call(), var_name = FALSE, var_label = FALSE, :
## metadata extraction terminated unexpectedly; inspect results carefully
## Variable(s) ignored: RTlexdec, RTnaming, Familiarity, WrittenFrequency, WrittenSpokenFrequencyRatio, FamilySize, DerivationalEntropy, InflectionalEntropy, NumberSimplexSynsets, NumberComplexSynsets, MeanBigramFrequency, FrequencyInitialDiphone, ConspelV, ConspelN, ConphonV, ConphonN, ConfriendsV, ConfriendsN, ConffN, ConfbV, ConfbN, NounFrequency, VerbFrequency

Frequencies

.$Word

Type: Factor
Valid Total
Word Freq % % Cum. % % Cum.
ace 2 0.044 0.044 0.044 0.044
act 2 0.044 0.088 0.044 0.088
add 2 0.044 0.13 0.044 0.13
age 2 0.044 0.18 0.044 0.18
aid 2 0.044 0.22 0.044 0.22
aide 2 0.044 0.26 0.044 0.26
ail 2 0.044 0.31 0.044 0.31
aim 2 0.044 0.35 0.044 0.35
air 2 0.044 0.39 0.044 0.39
aisle 2 0.044 0.44 0.044 0.44
ale 2 0.044 0.48 0.044 0.48
angst 2 0.044 0.53 0.044 0.53
ant 2 0.044 0.57 0.044 0.57
ape 2 0.044 0.61 0.044 0.61
arc 2 0.044 0.66 0.044 0.66
arch 2 0.044 0.70 0.044 0.70
are 2 0.044 0.74 0.044 0.74
arm 4 0.088 0.83 0.088 0.83
art 2 0.044 0.88 0.044 0.88
ash 2 0.044 0.92 0.044 0.92
ask 2 0.044 0.96 0.044 0.96
ass 2 0.044 1.01 0.044 1.01
axe 2 0.044 1.05 0.044 1.05
babe 2 0.044 1.09 0.044 1.09
back 2 0.044 1.14 0.044 1.14
badge 2 0.044 1.18 0.044 1.18
bag 2 0.044 1.23 0.044 1.23
bail 2 0.044 1.27 0.044 1.27
bait 2 0.044 1.31 0.044 1.31
bale 2 0.044 1.36 0.044 1.36
ball 2 0.044 1.40 0.044 1.40
ban 2 0.044 1.44 0.044 1.44
band 2 0.044 1.49 0.044 1.49
bang 2 0.044 1.53 0.044 1.53
bank 2 0.044 1.58 0.044 1.58
bar 2 0.044 1.62 0.044 1.62
bard 2 0.044 1.66 0.044 1.66
barge 4 0.088 1.75 0.088 1.75
bark 4 0.088 1.84 0.088 1.84
barn 2 0.044 1.88 0.044 1.88
base 2 0.044 1.93 0.044 1.93
bash 2 0.044 1.97 0.044 1.97
bat 2 0.044 2.01 0.044 2.01
batch 2 0.044 2.06 0.044 2.06
bath 2 0.044 2.10 0.044 2.10
bay 2 0.044 2.15 0.044 2.15
beach 2 0.044 2.19 0.044 2.19
bead 2 0.044 2.23 0.044 2.23
beak 2 0.044 2.28 0.044 2.28
beam 2 0.044 2.32 0.044 2.32
bean 2 0.044 2.36 0.044 2.36
bear 4 0.088 2.45 0.088 2.45
beard 2 0.044 2.50 0.044 2.50
beast 2 0.044 2.54 0.044 2.54
beat 2 0.044 2.58 0.044 2.58
beau 2 0.044 2.63 0.044 2.63
beck 2 0.044 2.67 0.044 2.67
bed 2 0.044 2.71 0.044 2.71
bee 2 0.044 2.76 0.044 2.76
beech 2 0.044 2.80 0.044 2.80
beef 4 0.088 2.89 0.088 2.89
beep 2 0.044 2.93 0.044 2.93
beer 2 0.044 2.98 0.044 2.98
beet 2 0.044 3.02 0.044 3.02
beg 2 0.044 3.06 0.044 3.06
belch 2 0.044 3.11 0.044 3.11
bell 2 0.044 3.15 0.044 3.15
belt 2 0.044 3.20 0.044 3.20
bench 2 0.044 3.24 0.044 3.24
bend 2 0.044 3.28 0.044 3.28
berth 2 0.044 3.33 0.044 3.33
bet 2 0.044 3.37 0.044 3.37
bib 2 0.044 3.42 0.044 3.42
bid 2 0.044 3.46 0.044 3.46
bide 2 0.044 3.50 0.044 3.50
bile 2 0.044 3.55 0.044 3.55
bilge 2 0.044 3.59 0.044 3.59
bill 2 0.044 3.63 0.044 3.63
bin 2 0.044 3.68 0.044 3.68
bind 4 0.088 3.77 0.088 3.77
binge 2 0.044 3.81 0.044 3.81
birch 2 0.044 3.85 0.044 3.85
bird 2 0.044 3.90 0.044 3.90
bit 2 0.044 3.94 0.044 3.94
bitch 4 0.088 4.03 0.088 4.03
bite 2 0.044 4.07 0.044 4.07
blade 2 0.044 4.12 0.044 4.12
blame 2 0.044 4.16 0.044 4.16
blanch 2 0.044 4.20 0.044 4.20
blast 2 0.044 4.25 0.044 4.25
blaze 2 0.044 4.29 0.044 4.29
bleat 2 0.044 4.33 0.044 4.33
bleed 2 0.044 4.38 0.044 4.38
bleep 2 0.044 4.42 0.044 4.42
blend 2 0.044 4.47 0.044 4.47
bless 2 0.044 4.51 0.044 4.51
blight 2 0.044 4.55 0.044 4.55
blink 2 0.044 4.60 0.044 4.60
bliss 2 0.044 4.64 0.044 4.64
blitz 2 0.044 4.68 0.044 4.68
bloat 2 0.044 4.73 0.044 4.73
blob 2 0.044 4.77 0.044 4.77
block 2 0.044 4.82 0.044 4.82
bloke 2 0.044 4.86 0.044 4.86
blood 2 0.044 4.90 0.044 4.90
bloom 2 0.044 4.95 0.044 4.95
blot 2 0.044 4.99 0.044 4.99
blouse 2 0.044 5.04 0.044 5.04
blow 2 0.044 5.08 0.044 5.08
bluff 2 0.044 5.12 0.044 5.12
blur 2 0.044 5.17 0.044 5.17
blush 2 0.044 5.21 0.044 5.21
boar 2 0.044 5.25 0.044 5.25
board 2 0.044 5.30 0.044 5.30
boast 2 0.044 5.34 0.044 5.34
boat 2 0.044 5.39 0.044 5.39
bob 2 0.044 5.43 0.044 5.43
bog 2 0.044 5.47 0.044 5.47
boil 4 0.088 5.56 0.088 5.56
bolt 2 0.044 5.60 0.044 5.60
bomb 2 0.044 5.65 0.044 5.65
bond 2 0.044 5.69 0.044 5.69
bone 2 0.044 5.74 0.044 5.74
boo 2 0.044 5.78 0.044 5.78
book 2 0.044 5.82 0.044 5.82
boom 2 0.044 5.87 0.044 5.87
boon 2 0.044 5.91 0.044 5.91
boost 2 0.044 5.95 0.044 5.95
boot 2 0.044 6.00 0.044 6.00
booth 2 0.044 6.04 0.044 6.04
booze 2 0.044 6.09 0.044 6.09
bore 2 0.044 6.13 0.044 6.13
boss 2 0.044 6.17 0.044 6.17
bough 2 0.044 6.22 0.044 6.22
bounce 2 0.044 6.26 0.044 6.26
bound 2 0.044 6.30 0.044 6.30
bout 2 0.044 6.35 0.044 6.35
bowl 2 0.044 6.39 0.044 6.39
box 2 0.044 6.44 0.044 6.44
boy 2 0.044 6.48 0.044 6.48
brace 2 0.044 6.52 0.044 6.52
brad 2 0.044 6.57 0.044 6.57
brag 2 0.044 6.61 0.044 6.61
braid 2 0.044 6.65 0.044 6.65
brain 2 0.044 6.70 0.044 6.70
brake 2 0.044 6.74 0.044 6.74
bran 2 0.044 6.79 0.044 6.79
branch 2 0.044 6.83 0.044 6.83
brand 2 0.044 6.87 0.044 6.87
brass 2 0.044 6.92 0.044 6.92
brat 2 0.044 6.96 0.044 6.96
brawl 2 0.044 7.01 0.044 7.01
brawn 2 0.044 7.05 0.044 7.05
breach 2 0.044 7.09 0.044 7.09
bread 2 0.044 7.14 0.044 7.14
break 2 0.044 7.18 0.044 7.18
breast 2 0.044 7.22 0.044 7.22
breath 2 0.044 7.27 0.044 7.27
breed 2 0.044 7.31 0.044 7.31
breeze 2 0.044 7.36 0.044 7.36
brew 2 0.044 7.40 0.044 7.40
bribe 2 0.044 7.44 0.044 7.44
brick 2 0.044 7.49 0.044 7.49
bride 2 0.044 7.53 0.044 7.53
bridge 2 0.044 7.57 0.044 7.57
brig 2 0.044 7.62 0.044 7.62
brim 2 0.044 7.66 0.044 7.66
bring 2 0.044 7.71 0.044 7.71
brink 2 0.044 7.75 0.044 7.75
broach 2 0.044 7.79 0.044 7.79
broad 2 0.044 7.84 0.044 7.84
broil 2 0.044 7.88 0.044 7.88
bronze 2 0.044 7.92 0.044 7.92
brooch 2 0.044 7.97 0.044 7.97
brood 2 0.044 8.01 0.044 8.01
brook 4 0.088 8.10 0.088 8.10
broom 2 0.044 8.14 0.044 8.14
broth 2 0.044 8.19 0.044 8.19
brow 2 0.044 8.23 0.044 8.23
bruise 2 0.044 8.27 0.044 8.27
brunt 2 0.044 8.32 0.044 8.32
brush 2 0.044 8.36 0.044 8.36
brute 2 0.044 8.41 0.044 8.41
buck 2 0.044 8.45 0.044 8.45
bud 2 0.044 8.49 0.044 8.49
budge 2 0.044 8.54 0.044 8.54
buff 2 0.044 8.58 0.044 8.58
bug 2 0.044 8.63 0.044 8.63
build 2 0.044 8.67 0.044 8.67
bulb 2 0.044 8.71 0.044 8.71
bulge 2 0.044 8.76 0.044 8.76
bulk 2 0.044 8.80 0.044 8.80
bull 2 0.044 8.84 0.044 8.84
bum 2 0.044 8.89 0.044 8.89
bump 2 0.044 8.93 0.044 8.93
bun 2 0.044 8.98 0.044 8.98
bunch 2 0.044 9.02 0.044 9.02
bunk 2 0.044 9.06 0.044 9.06
burn 2 0.044 9.11 0.044 9.11
burr 2 0.044 9.15 0.044 9.15
burst 2 0.044 9.19 0.044 9.19
bus 2 0.044 9.24 0.044 9.24
bush 2 0.044 9.28 0.044 9.28
bust 4 0.088 9.37 0.088 9.37
butt 4 0.088 9.46 0.088 9.46
buy 2 0.044 9.50 0.044 9.50
buzz 2 0.044 9.54 0.044 9.54
cab 2 0.044 9.59 0.044 9.59
cad 2 0.044 9.63 0.044 9.63
cage 2 0.044 9.68 0.044 9.68
cake 2 0.044 9.72 0.044 9.72
calf 2 0.044 9.76 0.044 9.76
call 2 0.044 9.81 0.044 9.81
cam 2 0.044 9.85 0.044 9.85
camp 2 0.044 9.89 0.044 9.89
can 2 0.044 9.94 0.044 9.94
cane 2 0.044 9.98 0.044 9.98
cant 2 0.044 10.03 0.044 10.03
cap 2 0.044 10.07 0.044 10.07
cape 2 0.044 10.11 0.044 10.11
car 2 0.044 10.16 0.044 10.16
card 2 0.044 10.20 0.044 10.20
care 2 0.044 10.25 0.044 10.25
cart 2 0.044 10.29 0.044 10.29
carve 2 0.044 10.33 0.044 10.33
case 2 0.044 10.38 0.044 10.38
cash 2 0.044 10.42 0.044 10.42
cask 2 0.044 10.46 0.044 10.46
cast 2 0.044 10.51 0.044 10.51
caste 2 0.044 10.55 0.044 10.55
cat 2 0.044 10.60 0.044 10.60
catch 2 0.044 10.64 0.044 10.64
cause 2 0.044 10.68 0.044 10.68
cave 2 0.044 10.73 0.044 10.73
cease 2 0.044 10.77 0.044 10.77
cell 2 0.044 10.81 0.044 10.81
cent 2 0.044 10.86 0.044 10.86
chafe 2 0.044 10.90 0.044 10.90
chain 2 0.044 10.95 0.044 10.95
chair 2 0.044 10.99 0.044 10.99
chaise 2 0.044 11.03 0.044 11.03
chalk 2 0.044 11.08 0.044 11.08
champ 4 0.088 11.16 0.088 11.16
chance 2 0.044 11.21 0.044 11.21
change 2 0.044 11.25 0.044 11.25
chant 2 0.044 11.30 0.044 11.30
chap 2 0.044 11.34 0.044 11.34
char 2 0.044 11.38 0.044 11.38
charge 2 0.044 11.43 0.044 11.43
charm 2 0.044 11.47 0.044 11.47
chart 2 0.044 11.51 0.044 11.51
chase 2 0.044 11.56 0.044 11.56
chat 2 0.044 11.60 0.044 11.60
cheat 2 0.044 11.65 0.044 11.65
check 2 0.044 11.69 0.044 11.69
cheek 4 0.088 11.78 0.088 11.78
cheer 2 0.044 11.82 0.044 11.82
cheese 2 0.044 11.87 0.044 11.87
chef 2 0.044 11.91 0.044 11.91
chess 2 0.044 11.95 0.044 11.95
chest 2 0.044 12.00 0.044 12.00
chew 2 0.044 12.04 0.044 12.04
chide 2 0.044 12.08 0.044 12.08
chief 2 0.044 12.13 0.044 12.13
child 2 0.044 12.17 0.044 12.17
chime 2 0.044 12.22 0.044 12.22
chin 2 0.044 12.26 0.044 12.26
chip 2 0.044 12.30 0.044 12.30
chive 2 0.044 12.35 0.044 12.35
choice 2 0.044 12.39 0.044 12.39
choir 2 0.044 12.43 0.044 12.43
choke 2 0.044 12.48 0.044 12.48
chomp 2 0.044 12.52 0.044 12.52
choose 2 0.044 12.57 0.044 12.57
chop 4 0.088 12.65 0.088 12.65
chord 2 0.044 12.70 0.044 12.70
chore 2 0.044 12.74 0.044 12.74
chow 2 0.044 12.78 0.044 12.78
chrome 2 0.044 12.83 0.044 12.83
chuck 4 0.088 12.92 0.088 12.92
chum 2 0.044 12.96 0.044 12.96
chump 2 0.044 13.00 0.044 13.00
chunk 2 0.044 13.05 0.044 13.05
church 2 0.044 13.09 0.044 13.09
churn 2 0.044 13.13 0.044 13.13
chute 2 0.044 13.18 0.044 13.18
cinch 2 0.044 13.22 0.044 13.22
cite 2 0.044 13.27 0.044 13.27
claim 2 0.044 13.31 0.044 13.31
clam 2 0.044 13.35 0.044 13.35
clamp 2 0.044 13.40 0.044 13.40
clan 2 0.044 13.44 0.044 13.44
clang 2 0.044 13.49 0.044 13.49
clap 4 0.088 13.57 0.088 13.57
clash 2 0.044 13.62 0.044 13.62
class 2 0.044 13.66 0.044 13.66
clause 2 0.044 13.70 0.044 13.70
claw 2 0.044 13.75 0.044 13.75
clay 2 0.044 13.79 0.044 13.79
cleat 2 0.044 13.84 0.044 13.84
clench 2 0.044 13.88 0.044 13.88
clerk 2 0.044 13.92 0.044 13.92
click 2 0.044 13.97 0.044 13.97
cliff 2 0.044 14.01 0.044 14.01
climb 2 0.044 14.05 0.044 14.05
clinch 2 0.044 14.10 0.044 14.10
cling 2 0.044 14.14 0.044 14.14
clip 4 0.088 14.23 0.088 14.23
cloak 2 0.044 14.27 0.044 14.27
clock 2 0.044 14.32 0.044 14.32
clod 2 0.044 14.36 0.044 14.36
clog 2 0.044 14.40 0.044 14.40
clot 2 0.044 14.45 0.044 14.45
cloth 2 0.044 14.49 0.044 14.49
cloud 2 0.044 14.54 0.044 14.54
clout 2 0.044 14.58 0.044 14.58
clove 2 0.044 14.62 0.044 14.62
clown 2 0.044 14.67 0.044 14.67
club 2 0.044 14.71 0.044 14.71
cluck 2 0.044 14.75 0.044 14.75
clump 2 0.044 14.80 0.044 14.80
clutch 2 0.044 14.84 0.044 14.84
coach 2 0.044 14.89 0.044 14.89
coal 2 0.044 14.93 0.044 14.93
coast 2 0.044 14.97 0.044 14.97
coat 2 0.044 15.02 0.044 15.02
coax 2 0.044 15.06 0.044 15.06
cock 2 0.044 15.11 0.044 15.11
cod 4 0.088 15.19 0.088 15.19
code 2 0.044 15.24 0.044 15.24
coil 2 0.044 15.28 0.044 15.28
coin 2 0.044 15.32 0.044 15.32
coke 2 0.044 15.37 0.044 15.37
colt 2 0.044 15.41 0.044 15.41
comb 2 0.044 15.46 0.044 15.46
come 2 0.044 15.50 0.044 15.50
cone 2 0.044 15.54 0.044 15.54
cook 2 0.044 15.59 0.044 15.59
coop 2 0.044 15.63 0.044 15.63
cop 2 0.044 15.67 0.044 15.67
cope 4 0.088 15.76 0.088 15.76
cord 2 0.044 15.81 0.044 15.81
core 2 0.044 15.85 0.044 15.85
cork 2 0.044 15.89 0.044 15.89
corn 2 0.044 15.94 0.044 15.94
corps 2 0.044 15.98 0.044 15.98
corpse 2 0.044 16.02 0.044 16.02
cost 2 0.044 16.07 0.044 16.07
cot 2 0.044 16.11 0.044 16.11
couch 2 0.044 16.16 0.044 16.16
cough 2 0.044 16.20 0.044 16.20
count 4 0.088 16.29 0.088 16.29
coup 2 0.044 16.33 0.044 16.33
coupe 2 0.044 16.37 0.044 16.37
course 2 0.044 16.42 0.044 16.42
court 2 0.044 16.46 0.044 16.46
cove 2 0.044 16.51 0.044 16.51
cow 4 0.088 16.59 0.088 16.59
cowl 4 0.088 16.68 0.088 16.68
cox 2 0.044 16.73 0.044 16.73
crab 2 0.044 16.77 0.044 16.77
crack 2 0.044 16.81 0.044 16.81
craft 2 0.044 16.86 0.044 16.86
crag 2 0.044 16.90 0.044 16.90
cram 2 0.044 16.94 0.044 16.94
cramp 2 0.044 16.99 0.044 16.99
crane 2 0.044 17.03 0.044 17.03
crank 2 0.044 17.08 0.044 17.08
crap 2 0.044 17.12 0.044 17.12
crash 2 0.044 17.16 0.044 17.16
crate 2 0.044 17.21 0.044 17.21
crave 2 0.044 17.25 0.044 17.25
crawl 2 0.044 17.29 0.044 17.29
craze 4 0.088 17.38 0.088 17.38
creak 2 0.044 17.43 0.044 17.43
cream 2 0.044 17.47 0.044 17.47
crease 2 0.044 17.51 0.044 17.51
creed 2 0.044 17.56 0.044 17.56
creek 2 0.044 17.60 0.044 17.60
creep 2 0.044 17.64 0.044 17.64
crepe 2 0.044 17.69 0.044 17.69
crest 2 0.044 17.73 0.044 17.73
crew 2 0.044 17.78 0.044 17.78
crime 2 0.044 17.82 0.044 17.82
croak 2 0.044 17.86 0.044 17.86
crone 2 0.044 17.91 0.044 17.91
crook 2 0.044 17.95 0.044 17.95
croon 2 0.044 17.99 0.044 17.99
crop 2 0.044 18.04 0.044 18.04
cross 2 0.044 18.08 0.044 18.08
crouch 2 0.044 18.13 0.044 18.13
crow 4 0.088 18.21 0.088 18.21
crowd 2 0.044 18.26 0.044 18.26
crown 2 0.044 18.30 0.044 18.30
crumb 2 0.044 18.35 0.044 18.35
crunch 2 0.044 18.39 0.044 18.39
crush 2 0.044 18.43 0.044 18.43
crust 2 0.044 18.48 0.044 18.48
crutch 2 0.044 18.52 0.044 18.52
crux 2 0.044 18.56 0.044 18.56
cry 2 0.044 18.61 0.044 18.61
crypt 2 0.044 18.65 0.044 18.65
cub 2 0.044 18.70 0.044 18.70
cube 2 0.044 18.74 0.044 18.74
cud 2 0.044 18.78 0.044 18.78
cue 2 0.044 18.83 0.044 18.83
cuff 4 0.088 18.91 0.088 18.91
cull 2 0.044 18.96 0.044 18.96
cult 2 0.044 19.00 0.044 19.00
cup 2 0.044 19.05 0.044 19.05
cur 2 0.044 19.09 0.044 19.09
curb 2 0.044 19.13 0.044 19.13
curd 2 0.044 19.18 0.044 19.18
cure 4 0.088 19.26 0.088 19.26
curl 2 0.044 19.31 0.044 19.31
curse 2 0.044 19.35 0.044 19.35
curve 2 0.044 19.40 0.044 19.40
cusp 2 0.044 19.44 0.044 19.44
cut 2 0.044 19.48 0.044 19.48
cyst 2 0.044 19.53 0.044 19.53
czar 2 0.044 19.57 0.044 19.57
dad 2 0.044 19.61 0.044 19.61
dale 2 0.044 19.66 0.044 19.66
dam 2 0.044 19.70 0.044 19.70
dame 2 0.044 19.75 0.044 19.75
damn 2 0.044 19.79 0.044 19.79
damp 4 0.088 19.88 0.088 19.88
dance 2 0.044 19.92 0.044 19.92
dare 2 0.044 19.96 0.044 19.96
darn 2 0.044 20.01 0.044 20.01
dash 2 0.044 20.05 0.044 20.05
date 2 0.044 20.10 0.044 20.10
daunt 2 0.044 20.14 0.044 20.14
dawn 2 0.044 20.18 0.044 20.18
deal 2 0.044 20.23 0.044 20.23
dean 2 0.044 20.27 0.044 20.27
dearth 2 0.044 20.32 0.044 20.32
debt 2 0.044 20.36 0.044 20.36
deck 2 0.044 20.40 0.044 20.40
deed 2 0.044 20.45 0.044 20.45
deem 2 0.044 20.49 0.044 20.49
deer 2 0.044 20.53 0.044 20.53
dell 2 0.044 20.58 0.044 20.58
den 2 0.044 20.62 0.044 20.62
dent 2 0.044 20.67 0.044 20.67
desk 2 0.044 20.71 0.044 20.71
dial 2 0.044 20.75 0.044 20.75
dice 2 0.044 20.80 0.044 20.80
die 2 0.044 20.84 0.044 20.84
dig 2 0.044 20.88 0.044 20.88
dike 2 0.044 20.93 0.044 20.93
dill 2 0.044 20.97 0.044 20.97
dime 2 0.044 21.02 0.044 21.02
din 2 0.044 21.06 0.044 21.06
dine 2 0.044 21.10 0.044 21.10
dint 2 0.044 21.15 0.044 21.15
dip 2 0.044 21.19 0.044 21.19
dirge 2 0.044 21.23 0.044 21.23
dirt 2 0.044 21.28 0.044 21.28
disc 2 0.044 21.32 0.044 21.32
dish 4 0.088 21.41 0.088 21.41
ditch 2 0.044 21.45 0.044 21.45
dive 2 0.044 21.50 0.044 21.50
do 4 0.088 21.58 0.088 21.58
dock 4 0.088 21.67 0.088 21.67
dodge 2 0.044 21.72 0.044 21.72
doe 2 0.044 21.76 0.044 21.76
dog 2 0.044 21.80 0.044 21.80
dole 2 0.044 21.85 0.044 21.85
doll 2 0.044 21.89 0.044 21.89
dome 2 0.044 21.94 0.044 21.94
doom 2 0.044 21.98 0.044 21.98
door 2 0.044 22.02 0.044 22.02
dope 2 0.044 22.07 0.044 22.07
dose 2 0.044 22.11 0.044 22.11
doubt 2 0.044 22.15 0.044 22.15
dough 2 0.044 22.20 0.044 22.20
douse 2 0.044 22.24 0.044 22.24
draft 2 0.044 22.29 0.044 22.29
drag 2 0.044 22.33 0.044 22.33
drain 2 0.044 22.37 0.044 22.37
drake 2 0.044 22.42 0.044 22.42
dram 2 0.044 22.46 0.044 22.46
drape 2 0.044 22.50 0.044 22.50
draught 2 0.044 22.55 0.044 22.55
draw 2 0.044 22.59 0.044 22.59
drawl 2 0.044 22.64 0.044 22.64
dread 2 0.044 22.68 0.044 22.68
dream 2 0.044 22.72 0.044 22.72
dress 2 0.044 22.77 0.044 22.77
drift 2 0.044 22.81 0.044 22.81
drill 2 0.044 22.85 0.044 22.85
drink 2 0.044 22.90 0.044 22.90
drip 2 0.044 22.94 0.044 22.94
drive 2 0.044 22.99 0.044 22.99
drone 2 0.044 23.03 0.044 23.03
droop 2 0.044 23.07 0.044 23.07
drop 2 0.044 23.12 0.044 23.12
dross 2 0.044 23.16 0.044 23.16
drought 2 0.044 23.20 0.044 23.20
drove 2 0.044 23.25 0.044 23.25
drown 2 0.044 23.29 0.044 23.29
drum 2 0.044 23.34 0.044 23.34
duck 4 0.088 23.42 0.088 23.42
duct 2 0.044 23.47 0.044 23.47
dud 2 0.044 23.51 0.044 23.51
duel 2 0.044 23.56 0.044 23.56
dug 2 0.044 23.60 0.044 23.60
duke 2 0.044 23.64 0.044 23.64
dump 2 0.044 23.69 0.044 23.69
dun 2 0.044 23.73 0.044 23.73
dune 2 0.044 23.77 0.044 23.77
dung 2 0.044 23.82 0.044 23.82
dunk 2 0.044 23.86 0.044 23.86
dusk 2 0.044 23.91 0.044 23.91
dust 2 0.044 23.95 0.044 23.95
dwarf 2 0.044 23.99 0.044 23.99
dwell 2 0.044 24.04 0.044 24.04
ear 2 0.044 24.08 0.044 24.08
earl 2 0.044 24.12 0.044 24.12
earn 2 0.044 24.17 0.044 24.17
earth 2 0.044 24.21 0.044 24.21
ease 2 0.044 24.26 0.044 24.26
east 2 0.044 24.30 0.044 24.30
eat 2 0.044 24.34 0.044 24.34
ebb 2 0.044 24.39 0.044 24.39
edge 2 0.044 24.43 0.044 24.43
eel 2 0.044 24.47 0.044 24.47
egg 2 0.044 24.52 0.044 24.52
elk 2 0.044 24.56 0.044 24.56
elm 2 0.044 24.61 0.044 24.61
end 2 0.044 24.65 0.044 24.65
err 2 0.044 24.69 0.044 24.69
eve 2 0.044 24.74 0.044 24.74
ewe 2 0.044 24.78 0.044 24.78
face 2 0.044 24.82 0.044 24.82
fact 2 0.044 24.87 0.044 24.87
fad 2 0.044 24.91 0.044 24.91
fade 2 0.044 24.96 0.044 24.96
fail 2 0.044 25.00 0.044 25.00
fair 2 0.044 25.04 0.044 25.04
faith 2 0.044 25.09 0.044 25.09
fake 2 0.044 25.13 0.044 25.13
fame 2 0.044 25.18 0.044 25.18
fan 2 0.044 25.22 0.044 25.22
fang 2 0.044 25.26 0.044 25.26
farce 2 0.044 25.31 0.044 25.31
fare 4 0.088 25.39 0.088 25.39
farm 2 0.044 25.44 0.044 25.44
fast 2 0.044 25.48 0.044 25.48
fate 2 0.044 25.53 0.044 25.53
fault 2 0.044 25.57 0.044 25.57
fawn 4 0.088 25.66 0.088 25.66
faze 2 0.044 25.70 0.044 25.70
fear 2 0.044 25.74 0.044 25.74
feast 2 0.044 25.79 0.044 25.79
feat 2 0.044 25.83 0.044 25.83
fee 2 0.044 25.88 0.044 25.88
feed 2 0.044 25.92 0.044 25.92
feel 2 0.044 25.96 0.044 25.96
feint 2 0.044 26.01 0.044 26.01
fell 4 0.088 26.09 0.088 26.09
fen 2 0.044 26.14 0.044 26.14
fence 2 0.044 26.18 0.044 26.18
fern 2 0.044 26.23 0.044 26.23
fetch 2 0.044 26.27 0.044 26.27
feud 2 0.044 26.31 0.044 26.31
fiend 2 0.044 26.36 0.044 26.36
fife 2 0.044 26.40 0.044 26.40
fig 2 0.044 26.44 0.044 26.44
fight 2 0.044 26.49 0.044 26.49
file 2 0.044 26.53 0.044 26.53
fill 2 0.044 26.58 0.044 26.58
film 2 0.044 26.62 0.044 26.62
filth 2 0.044 26.66 0.044 26.66
fin 2 0.044 26.71 0.044 26.71
find 2 0.044 26.75 0.044 26.75
fine 2 0.044 26.80 0.044 26.80
fink 2 0.044 26.84 0.044 26.84
fir 2 0.044 26.88 0.044 26.88
fire 2 0.044 26.93 0.044 26.93
firm 2 0.044 26.97 0.044 26.97
fish 2 0.044 27.01 0.044 27.01
fist 2 0.044 27.06 0.044 27.06
fix 2 0.044 27.10 0.044 27.10
flag 2 0.044 27.15 0.044 27.15
flail 2 0.044 27.19 0.044 27.19
flair 2 0.044 27.23 0.044 27.23
flake 2 0.044 27.28 0.044 27.28
flame 2 0.044 27.32 0.044 27.32
flange 2 0.044 27.36 0.044 27.36
flank 2 0.044 27.41 0.044 27.41
flare 2 0.044 27.45 0.044 27.45
flask 2 0.044 27.50 0.044 27.50
flaw 2 0.044 27.54 0.044 27.54
flax 2 0.044 27.58 0.044 27.58
flea 2 0.044 27.63 0.044 27.63
fleck 2 0.044 27.67 0.044 27.67
flee 2 0.044 27.71 0.044 27.71
fleet 4 0.088 27.80 0.088 27.80
flesh 2 0.044 27.85 0.044 27.85
flex 4 0.088 27.93 0.088 27.93
flick 2 0.044 27.98 0.044 27.98
flight 2 0.044 28.02 0.044 28.02
fling 2 0.044 28.06 0.044 28.06
flint 2 0.044 28.11 0.044 28.11
flip 2 0.044 28.15 0.044 28.15
flirt 2 0.044 28.20 0.044 28.20
float 2 0.044 28.24 0.044 28.24
flock 2 0.044 28.28 0.044 28.28
floe 2 0.044 28.33 0.044 28.33
flog 2 0.044 28.37 0.044 28.37
flood 2 0.044 28.42 0.044 28.42
floor 2 0.044 28.46 0.044 28.46
flop 2 0.044 28.50 0.044 28.50
flour 2 0.044 28.55 0.044 28.55
flow 2 0.044 28.59 0.044 28.59
fluff 2 0.044 28.63 0.044 28.63
fluke 2 0.044 28.68 0.044 28.68
flush 2 0.044 28.72 0.044 28.72
flute 2 0.044 28.77 0.044 28.77
flux 2 0.044 28.81 0.044 28.81
fly 4 0.088 28.90 0.088 28.90
foal 2 0.044 28.94 0.044 28.94
foam 2 0.044 28.98 0.044 28.98
foe 2 0.044 29.03 0.044 29.03
fog 2 0.044 29.07 0.044 29.07
foil 4 0.088 29.16 0.088 29.16
fold 2 0.044 29.20 0.044 29.20
folk 2 0.044 29.25 0.044 29.25
font 2 0.044 29.29 0.044 29.29
food 2 0.044 29.33 0.044 29.33
fool 2 0.044 29.38 0.044 29.38
foot 2 0.044 29.42 0.044 29.42
force 2 0.044 29.47 0.044 29.47
ford 2 0.044 29.51 0.044 29.51
forge 2 0.044 29.55 0.044 29.55
fork 2 0.044 29.60 0.044 29.60
form 2 0.044 29.64 0.044 29.64
fort 2 0.044 29.68 0.044 29.68
found 2 0.044 29.73 0.044 29.73
fowl 2 0.044 29.77 0.044 29.77
fox 2 0.044 29.82 0.044 29.82
frame 2 0.044 29.86 0.044 29.86
fraud 2 0.044 29.90 0.044 29.90
fray 2 0.044 29.95 0.044 29.95
freak 2 0.044 29.99 0.044 29.99
freeze 2 0.044 30.04 0.044 30.04
freight 2 0.044 30.08 0.044 30.08
fret 4 0.088 30.17 0.088 30.17
friend 2 0.044 30.21 0.044 30.21
frieze 2 0.044 30.25 0.044 30.25
fright 2 0.044 30.30 0.044 30.30
frill 2 0.044 30.34 0.044 30.34
fringe 2 0.044 30.39 0.044 30.39
frock 2 0.044 30.43 0.044 30.43
frog 2 0.044 30.47 0.044 30.47
front 2 0.044 30.52 0.044 30.52
frost 2 0.044 30.56 0.044 30.56
froth 2 0.044 30.60 0.044 30.60
frown 2 0.044 30.65 0.044 30.65
fruit 2 0.044 30.69 0.044 30.69
fry 4 0.088 30.78 0.088 30.78
fuel 2 0.044 30.82 0.044 30.82
full 2 0.044 30.87 0.044 30.87
fun 2 0.044 30.91 0.044 30.91
fund 2 0.044 30.95 0.044 30.95
funk 2 0.044 31.00 0.044 31.00
fur 2 0.044 31.04 0.044 31.04
fuse 2 0.044 31.09 0.044 31.09
fuss 2 0.044 31.13 0.044 31.13
fuzz 2 0.044 31.17 0.044 31.17
gab 2 0.044 31.22 0.044 31.22
gag 2 0.044 31.26 0.044 31.26
gain 2 0.044 31.30 0.044 31.30
gait 2 0.044 31.35 0.044 31.35
gal 2 0.044 31.39 0.044 31.39
gale 2 0.044 31.44 0.044 31.44
gall 2 0.044 31.48 0.044 31.48
game 2 0.044 31.52 0.044 31.52
gang 2 0.044 31.57 0.044 31.57
gap 2 0.044 31.61 0.044 31.61
garb 2 0.044 31.65 0.044 31.65
gas 2 0.044 31.70 0.044 31.70
gash 2 0.044 31.74 0.044 31.74
gasp 2 0.044 31.79 0.044 31.79
gate 2 0.044 31.83 0.044 31.83
gauge 2 0.044 31.87 0.044 31.87
gauze 2 0.044 31.92 0.044 31.92
gay 2 0.044 31.96 0.044 31.96
gaze 2 0.044 32.01 0.044 32.01
gear 2 0.044 32.05 0.044 32.05
gel 2 0.044 32.09 0.044 32.09
gem 2 0.044 32.14 0.044 32.14
gene 2 0.044 32.18 0.044 32.18
get 2 0.044 32.22 0.044 32.22
ghost 2 0.044 32.27 0.044 32.27
ghoul 2 0.044 32.31 0.044 32.31
gibe 2 0.044 32.36 0.044 32.36
gift 2 0.044 32.40 0.044 32.40
gig 2 0.044 32.44 0.044 32.44
gill 2 0.044 32.49 0.044 32.49
gilt 2 0.044 32.53 0.044 32.53
gin 2 0.044 32.57 0.044 32.57
gird 2 0.044 32.62 0.044 32.62
girl 2 0.044 32.66 0.044 32.66
gist 2 0.044 32.71 0.044 32.71
give 2 0.044 32.75 0.044 32.75
glance 2 0.044 32.79 0.044 32.79
gland 2 0.044 32.84 0.044 32.84
glare 2 0.044 32.88 0.044 32.88
glass 2 0.044 32.92 0.044 32.92
glaze 2 0.044 32.97 0.044 32.97
gleam 2 0.044 33.01 0.044 33.01
glean 2 0.044 33.06 0.044 33.06
glee 2 0.044 33.10 0.044 33.10
glen 2 0.044 33.14 0.044 33.14
glide 2 0.044 33.19 0.044 33.19
glimpse 2 0.044 33.23 0.044 33.23
glint 2 0.044 33.27 0.044 33.27
gloat 2 0.044 33.32 0.044 33.32
globe 2 0.044 33.36 0.044 33.36
gloom 2 0.044 33.41 0.044 33.41
gloss 2 0.044 33.45 0.044 33.45
glove 2 0.044 33.49 0.044 33.49
glow 2 0.044 33.54 0.044 33.54
glue 2 0.044 33.58 0.044 33.58
gnaw 2 0.044 33.63 0.044 33.63
gnome 2 0.044 33.67 0.044 33.67
go 2 0.044 33.71 0.044 33.71
goad 2 0.044 33.76 0.044 33.76
goal 2 0.044 33.80 0.044 33.80
goat 2 0.044 33.84 0.044 33.84
gob 2 0.044 33.89 0.044 33.89
god 2 0.044 33.93 0.044 33.93
gold 2 0.044 33.98 0.044 33.98
golf 2 0.044 34.02 0.044 34.02
gong 2 0.044 34.06 0.044 34.06
goon 2 0.044 34.11 0.044 34.11
goose 2 0.044 34.15 0.044 34.15
gore 4 0.088 34.24 0.088 34.24
gouge 2 0.044 34.28 0.044 34.28
gourd 2 0.044 34.33 0.044 34.33
gout 2 0.044 34.37 0.044 34.37
gown 2 0.044 34.41 0.044 34.41
grab 2 0.044 34.46 0.044 34.46
grace 2 0.044 34.50 0.044 34.50
grade 2 0.044 34.54 0.044 34.54
graft 2 0.044 34.59 0.044 34.59
grail 2 0.044 34.63 0.044 34.63
grain 2 0.044 34.68 0.044 34.68
gram 2 0.044 34.72 0.044 34.72
grant 2 0.044 34.76 0.044 34.76
grape 2 0.044 34.81 0.044 34.81
graph 2 0.044 34.85 0.044 34.85
grasp 2 0.044 34.89 0.044 34.89
grass 2 0.044 34.94 0.044 34.94
grate 4 0.088 35.03 0.088 35.03
grave 2 0.044 35.07 0.044 35.07
graze 2 0.044 35.11 0.044 35.11
grease 2 0.044 35.16 0.044 35.16
greed 2 0.044 35.20 0.044 35.20
greet 2 0.044 35.25 0.044 35.25
grid 2 0.044 35.29 0.044 35.29
grill 2 0.044 35.33 0.044 35.33
grille 2 0.044 35.38 0.044 35.38
grime 2 0.044 35.42 0.044 35.42
grin 2 0.044 35.46 0.044 35.46
grind 2 0.044 35.51 0.044 35.51
grip 2 0.044 35.55 0.044 35.55
gripe 2 0.044 35.60 0.044 35.60
grist 2 0.044 35.64 0.044 35.64
grit 2 0.044 35.68 0.044 35.68
groan 2 0.044 35.73 0.044 35.73
groin 2 0.044 35.77 0.044 35.77
groom 2 0.044 35.81 0.044 35.81
groove 2 0.044 35.86 0.044 35.86
grope 2 0.044 35.90 0.044 35.90
grouch 2 0.044 35.95 0.044 35.95
ground 2 0.044 35.99 0.044 35.99
group 2 0.044 36.03 0.044 36.03
grouse 2 0.044 36.08 0.044 36.08
grove 2 0.044 36.12 0.044 36.12
grow 2 0.044 36.16 0.044 36.16
growl 2 0.044 36.21 0.044 36.21
grub 4 0.088 36.30 0.088 36.30
grudge 2 0.044 36.34 0.044 36.34
grunt 2 0.044 36.38 0.044 36.38
guard 2 0.044 36.43 0.044 36.43
guess 2 0.044 36.47 0.044 36.47
guest 2 0.044 36.51 0.044 36.51
guide 2 0.044 36.56 0.044 36.56
guild 2 0.044 36.60 0.044 36.60
guile 2 0.044 36.65 0.044 36.65
guilt 2 0.044 36.69 0.044 36.69
guise 2 0.044 36.73 0.044 36.73
gulf 2 0.044 36.78 0.044 36.78
gull 2 0.044 36.82 0.044 36.82
gulp 2 0.044 36.87 0.044 36.87
gum 2 0.044 36.91 0.044 36.91
gun 2 0.044 36.95 0.044 36.95
gush 2 0.044 37.00 0.044 37.00
gust 2 0.044 37.04 0.044 37.04
gut 2 0.044 37.08 0.044 37.08
guy 2 0.044 37.13 0.044 37.13
gyp 2 0.044 37.17 0.044 37.17
hack 2 0.044 37.22 0.044 37.22
hag 2 0.044 37.26 0.044 37.26
hail 4 0.088 37.35 0.088 37.35
hair 2 0.044 37.39 0.044 37.39
hall 2 0.044 37.43 0.044 37.43
halt 2 0.044 37.48 0.044 37.48
ham 2 0.044 37.52 0.044 37.52
hand 2 0.044 37.57 0.044 37.57
hang 2 0.044 37.61 0.044 37.61
hank 2 0.044 37.65 0.044 37.65
hark 2 0.044 37.70 0.044 37.70
harm 2 0.044 37.74 0.044 37.74
harp 2 0.044 37.78 0.044 37.78
hart 2 0.044 37.83 0.044 37.83
hash 2 0.044 37.87 0.044 37.87
haste 2 0.044 37.92 0.044 37.92
hat 2 0.044 37.96 0.044 37.96
hatch 4 0.088 38.05 0.088 38.05
hate 2 0.044 38.09 0.044 38.09
haul 2 0.044 38.13 0.044 38.13
haunt 2 0.044 38.18 0.044 38.18
have 2 0.044 38.22 0.044 38.22
haw 4 0.088 38.31 0.088 38.31
hawk 4 0.088 38.40 0.088 38.40
hay 2 0.044 38.44 0.044 38.44
haze 4 0.088 38.53 0.088 38.53
head 2 0.044 38.57 0.044 38.57
heal 2 0.044 38.62 0.044 38.62
heap 2 0.044 38.66 0.044 38.66
hear 2 0.044 38.70 0.044 38.70
heart 2 0.044 38.75 0.044 38.75
hearth 2 0.044 38.79 0.044 38.79
heat 2 0.044 38.84 0.044 38.84
heave 2 0.044 38.88 0.044 38.88
heck 2 0.044 38.92 0.044 38.92
hedge 2 0.044 38.97 0.044 38.97
heed 2 0.044 39.01 0.044 39.01
heel 2 0.044 39.05 0.044 39.05
height 2 0.044 39.10 0.044 39.10
heir 2 0.044 39.14 0.044 39.14
hell 2 0.044 39.19 0.044 39.19
helm 2 0.044 39.23 0.044 39.23
help 2 0.044 39.27 0.044 39.27
hem 2 0.044 39.32 0.044 39.32
hen 2 0.044 39.36 0.044 39.36
herb 2 0.044 39.40 0.044 39.40
herd 2 0.044 39.45 0.044 39.45
hick 2 0.044 39.49 0.044 39.49
hide 4 0.088 39.58 0.088 39.58
hike 2 0.044 39.62 0.044 39.62
hill 2 0.044 39.67 0.044 39.67
hilt 2 0.044 39.71 0.044 39.71
hind 2 0.044 39.75 0.044 39.75
hinge 2 0.044 39.80 0.044 39.80
hint 2 0.044 39.84 0.044 39.84
hip 2 0.044 39.89 0.044 39.89
hire 2 0.044 39.93 0.044 39.93
hiss 2 0.044 39.97 0.044 39.97
hit 2 0.044 40.02 0.044 40.02
hitch 2 0.044 40.06 0.044 40.06
hive 2 0.044 40.11 0.044 40.11
hob 2 0.044 40.15 0.044 40.15
hoe 2 0.044 40.19 0.044 40.19
hog 4 0.088 40.28 0.088 40.28
hoist 2 0.044 40.32 0.044 40.32
hold 2 0.044 40.37 0.044 40.37
hole 2 0.044 40.41 0.044 40.41
home 2 0.044 40.46 0.044 40.46
hone 2 0.044 40.50 0.044 40.50
hooch 2 0.044 40.54 0.044 40.54
hood 2 0.044 40.59 0.044 40.59
hoof 2 0.044 40.63 0.044 40.63
hook 2 0.044 40.67 0.044 40.67
hoop 2 0.044 40.72 0.044 40.72
hoot 2 0.044 40.76 0.044 40.76
hop 2 0.044 40.81 0.044 40.81
hope 2 0.044 40.85 0.044 40.85
horn 2 0.044 40.89 0.044 40.89
horse 2 0.044 40.94 0.044 40.94
hose 2 0.044 40.98 0.044 40.98
host 2 0.044 41.02 0.044 41.02
hound 2 0.044 41.07 0.044 41.07
hour 2 0.044 41.11 0.044 41.11
house 2 0.044 41.16 0.044 41.16
howl 2 0.044 41.20 0.044 41.20
hub 2 0.044 41.24 0.044 41.24
hue 2 0.044 41.29 0.044 41.29
huff 2 0.044 41.33 0.044 41.33
hug 2 0.044 41.37 0.044 41.37
hulk 2 0.044 41.42 0.044 41.42
hull 2 0.044 41.46 0.044 41.46
hum 2 0.044 41.51 0.044 41.51
hump 2 0.044 41.55 0.044 41.55
hunch 2 0.044 41.59 0.044 41.59
hunk 2 0.044 41.64 0.044 41.64
hunt 2 0.044 41.68 0.044 41.68
hurl 2 0.044 41.73 0.044 41.73
hurt 2 0.044 41.77 0.044 41.77
hush 2 0.044 41.81 0.044 41.81
hut 2 0.044 41.86 0.044 41.86
hymn 2 0.044 41.90 0.044 41.90
ice 2 0.044 41.94 0.044 41.94
inch 2 0.044 41.99 0.044 41.99
ink 2 0.044 42.03 0.044 42.03
inn 2 0.044 42.08 0.044 42.08
ire 2 0.044 42.12 0.044 42.12
isle 2 0.044 42.16 0.044 42.16
itch 2 0.044 42.21 0.044 42.21
jab 2 0.044 42.25 0.044 42.25
jack 2 0.044 42.29 0.044 42.29
jade 2 0.044 42.34 0.044 42.34
jag 2 0.044 42.38 0.044 42.38
jail 2 0.044 42.43 0.044 42.43
jam 2 0.044 42.47 0.044 42.47
jape 2 0.044 42.51 0.044 42.51
jar 2 0.044 42.56 0.044 42.56
jaw 2 0.044 42.60 0.044 42.60
jazz 2 0.044 42.64 0.044 42.64
jeep 2 0.044 42.69 0.044 42.69
jeer 2 0.044 42.73 0.044 42.73
jerk 2 0.044 42.78 0.044 42.78
jest 2 0.044 42.82 0.044 42.82
jet 2 0.044 42.86 0.044 42.86
jig 2 0.044 42.91 0.044 42.91
job 2 0.044 42.95 0.044 42.95
jog 2 0.044 42.99 0.044 42.99
join 2 0.044 43.04 0.044 43.04
joke 2 0.044 43.08 0.044 43.08
jolt 2 0.044 43.13 0.044 43.13
jot 2 0.044 43.17 0.044 43.17
joust 2 0.044 43.21 0.044 43.21
jowl 2 0.044 43.26 0.044 43.26
joy 2 0.044 43.30 0.044 43.30
judge 2 0.044 43.35 0.044 43.35
jug 2 0.044 43.39 0.044 43.39
juice 2 0.044 43.43 0.044 43.43
jump 2 0.044 43.48 0.044 43.48
junk 2 0.044 43.52 0.044 43.52
jute 2 0.044 43.56 0.044 43.56
kale 2 0.044 43.61 0.044 43.61
keel 2 0.044 43.65 0.044 43.65
keen 2 0.044 43.70 0.044 43.70
keep 2 0.044 43.74 0.044 43.74
keg 2 0.044 43.78 0.044 43.78
kelp 2 0.044 43.83 0.044 43.83
ken 2 0.044 43.87 0.044 43.87
key 2 0.044 43.91 0.044 43.91
kick 2 0.044 43.96 0.044 43.96
kid 4 0.088 44.05 0.088 44.05
kill 2 0.044 44.09 0.044 44.09
kilt 2 0.044 44.13 0.044 44.13
kin 2 0.044 44.18 0.044 44.18
kind 2 0.044 44.22 0.044 44.22
king 2 0.044 44.26 0.044 44.26
kiss 2 0.044 44.31 0.044 44.31
kit 2 0.044 44.35 0.044 44.35
kite 2 0.044 44.40 0.044 44.40
knack 2 0.044 44.44 0.044 44.44
knead 2 0.044 44.48 0.044 44.48
knee 2 0.044 44.53 0.044 44.53
kneel 2 0.044 44.57 0.044 44.57
knife 2 0.044 44.61 0.044 44.61
knight 2 0.044 44.66 0.044 44.66
knit 2 0.044 44.70 0.044 44.70
knob 2 0.044 44.75 0.044 44.75
knock 2 0.044 44.79 0.044 44.79
knoll 2 0.044 44.83 0.044 44.83
knot 2 0.044 44.88 0.044 44.88
know 2 0.044 44.92 0.044 44.92
lace 2 0.044 44.96 0.044 44.96
lack 2 0.044 45.01 0.044 45.01
lad 2 0.044 45.05 0.044 45.05
lag 2 0.044 45.10 0.044 45.10
lake 2 0.044 45.14 0.044 45.14
lamb 2 0.044 45.18 0.044 45.18
lame 2 0.044 45.23 0.044 45.23
lamp 2 0.044 45.27 0.044 45.27
lance 4 0.088 45.36 0.088 45.36
land 2 0.044 45.40 0.044 45.40
lane 2 0.044 45.45 0.044 45.45
lap 4 0.088 45.53 0.088 45.53
lapse 2 0.044 45.58 0.044 45.58
lard 2 0.044 45.62 0.044 45.62
lark 2 0.044 45.67 0.044 45.67
lash 2 0.044 45.71 0.044 45.71
lass 2 0.044 45.75 0.044 45.75
last 2 0.044 45.80 0.044 45.80
latch 2 0.044 45.84 0.044 45.84
lath 2 0.044 45.88 0.044 45.88
lathe 2 0.044 45.93 0.044 45.93
laugh 2 0.044 45.97 0.044 45.97
launch 4 0.088 46.06 0.088 46.06
law 2 0.044 46.10 0.044 46.10
lawn 2 0.044 46.15 0.044 46.15
lay 2 0.044 46.19 0.044 46.19
laze 2 0.044 46.23 0.044 46.23
leaf 2 0.044 46.28 0.044 46.28
leak 2 0.044 46.32 0.044 46.32
lean 2 0.044 46.37 0.044 46.37
leap 2 0.044 46.41 0.044 46.41
learn 2 0.044 46.45 0.044 46.45
lease 2 0.044 46.50 0.044 46.50
leash 2 0.044 46.54 0.044 46.54
leave 4 0.088 46.63 0.088 46.63
ledge 2 0.044 46.67 0.044 46.67
lee 2 0.044 46.72 0.044 46.72
leg 2 0.044 46.76 0.044 46.76
lend 2 0.044 46.80 0.044 46.80
lens 2 0.044 46.85 0.044 46.85
let 2 0.044 46.89 0.044 46.89
lick 2 0.044 46.94 0.044 46.94
lid 2 0.044 46.98 0.044 46.98
lie 2 0.044 47.02 0.044 47.02
life 2 0.044 47.07 0.044 47.07
lift 2 0.044 47.11 0.044 47.11
light 2 0.044 47.15 0.044 47.15
like 2 0.044 47.20 0.044 47.20
lilt 2 0.044 47.24 0.044 47.24
limb 2 0.044 47.29 0.044 47.29
lime 2 0.044 47.33 0.044 47.33
limp 2 0.044 47.37 0.044 47.37
line 2 0.044 47.42 0.044 47.42
link 2 0.044 47.46 0.044 47.46
lint 2 0.044 47.50 0.044 47.50
lip 2 0.044 47.55 0.044 47.55
lisle 2 0.044 47.59 0.044 47.59
list 4 0.088 47.68 0.088 47.68
load 2 0.044 47.72 0.044 47.72
loaf 4 0.088 47.81 0.088 47.81
loan 2 0.044 47.85 0.044 47.85
lob 2 0.044 47.90 0.044 47.90
lobe 2 0.044 47.94 0.044 47.94
lock 2 0.044 47.99 0.044 47.99
lodge 2 0.044 48.03 0.044 48.03
loft 2 0.044 48.07 0.044 48.07
log 2 0.044 48.12 0.044 48.12
loin 2 0.044 48.16 0.044 48.16
look 2 0.044 48.20 0.044 48.20
loom 4 0.088 48.29 0.088 48.29
loon 2 0.044 48.34 0.044 48.34
loop 2 0.044 48.38 0.044 48.38
loot 2 0.044 48.42 0.044 48.42
lop 2 0.044 48.47 0.044 48.47
lope 2 0.044 48.51 0.044 48.51
lord 2 0.044 48.56 0.044 48.56
lore 2 0.044 48.60 0.044 48.60
lose 2 0.044 48.64 0.044 48.64
loss 2 0.044 48.69 0.044 48.69
lot 2 0.044 48.73 0.044 48.73
lounge 2 0.044 48.77 0.044 48.77
louse 2 0.044 48.82 0.044 48.82
love 2 0.044 48.86 0.044 48.86
low 2 0.044 48.91 0.044 48.91
luck 2 0.044 48.95 0.044 48.95
lug 4 0.088 49.04 0.088 49.04
lull 2 0.044 49.08 0.044 49.08
lump 4 0.088 49.17 0.088 49.17
lung 2 0.044 49.21 0.044 49.21
lurch 2 0.044 49.26 0.044 49.26
lure 2 0.044 49.30 0.044 49.30
lurk 2 0.044 49.34 0.044 49.34
lust 2 0.044 49.39 0.044 49.39
lute 2 0.044 49.43 0.044 49.43
lye 2 0.044 49.47 0.044 49.47
lymph 2 0.044 49.52 0.044 49.52
lynch 2 0.044 49.56 0.044 49.56
maid 2 0.044 49.61 0.044 49.61
mail 2 0.044 49.65 0.044 49.65
make 2 0.044 49.69 0.044 49.69
mall 2 0.044 49.74 0.044 49.74
malt 2 0.044 49.78 0.044 49.78
man 2 0.044 49.82 0.044 49.82
mane 2 0.044 49.87 0.044 49.87
manse 2 0.044 49.91 0.044 49.91
map 2 0.044 49.96 0.044 49.96
mar 2 0.044 50.00 0.044 50.00
march 4 0.088 50.09 0.088 50.09
mare 2 0.044 50.13 0.044 50.13
mark 2 0.044 50.18 0.044 50.18
marsh 2 0.044 50.22 0.044 50.22
mart 2 0.044 50.26 0.044 50.26
mash 2 0.044 50.31 0.044 50.31
mask 2 0.044 50.35 0.044 50.35
mass 2 0.044 50.39 0.044 50.39
mast 2 0.044 50.44 0.044 50.44
mat 2 0.044 50.48 0.044 50.48
match 2 0.044 50.53 0.044 50.53
mate 2 0.044 50.57 0.044 50.57
maw 2 0.044 50.61 0.044 50.61
may 2 0.044 50.66 0.044 50.66
maze 2 0.044 50.70 0.044 50.70
mead 2 0.044 50.74 0.044 50.74
meal 2 0.044 50.79 0.044 50.79
mean 2 0.044 50.83 0.044 50.83
meat 2 0.044 50.88 0.044 50.88
meet 2 0.044 50.92 0.044 50.92
meld 2 0.044 50.96 0.044 50.96
melt 2 0.044 51.01 0.044 51.01
mend 2 0.044 51.05 0.044 51.05
merge 2 0.044 51.09 0.044 51.09
mesh 2 0.044 51.14 0.044 51.14
mess 2 0.044 51.18 0.044 51.18
mew 2 0.044 51.23 0.044 51.23
might 2 0.044 51.27 0.044 51.27
mile 2 0.044 51.31 0.044 51.31
milk 2 0.044 51.36 0.044 51.36
mill 2 0.044 51.40 0.044 51.40
mime 2 0.044 51.44 0.044 51.44
mince 2 0.044 51.49 0.044 51.49
mind 2 0.044 51.53 0.044 51.53
mine 2 0.044 51.58 0.044 51.58
mink 2 0.044 51.62 0.044 51.62
mint 2 0.044 51.66 0.044 51.66
mirth 2 0.044 51.71 0.044 51.71
miss 2 0.044 51.75 0.044 51.75
mist 2 0.044 51.80 0.044 51.80
mite 2 0.044 51.84 0.044 51.84
mitt 2 0.044 51.88 0.044 51.88
mix 2 0.044 51.93 0.044 51.93
moan 2 0.044 51.97 0.044 51.97
mob 2 0.044 52.01 0.044 52.01
mock 2 0.044 52.06 0.044 52.06
mode 2 0.044 52.10 0.044 52.10
mole 2 0.044 52.15 0.044 52.15
moll 2 0.044 52.19 0.044 52.19
mom 2 0.044 52.23 0.044 52.23
monk 2 0.044 52.28 0.044 52.28
month 2 0.044 52.32 0.044 52.32
moo 2 0.044 52.36 0.044 52.36
mood 2 0.044 52.41 0.044 52.41
moon 2 0.044 52.45 0.044 52.45
moose 2 0.044 52.50 0.044 52.50
moot 2 0.044 52.54 0.044 52.54
mop 2 0.044 52.58 0.044 52.58
moss 2 0.044 52.63 0.044 52.63
moth 2 0.044 52.67 0.044 52.67
mould 2 0.044 52.71 0.044 52.71
mount 2 0.044 52.76 0.044 52.76
mourn 2 0.044 52.80 0.044 52.80
mouse 2 0.044 52.85 0.044 52.85
mouth 2 0.044 52.89 0.044 52.89
move 2 0.044 52.93 0.044 52.93
mow 2 0.044 52.98 0.044 52.98
muck 2 0.044 53.02 0.044 53.02
mud 2 0.044 53.06 0.044 53.06
muff 2 0.044 53.11 0.044 53.11
mug 2 0.044 53.15 0.044 53.15
mulch 2 0.044 53.20 0.044 53.20
mule 2 0.044 53.24 0.044 53.24
mum 2 0.044 53.28 0.044 53.28
munch 2 0.044 53.33 0.044 53.33
muse 2 0.044 53.37 0.044 53.37
mush 2 0.044 53.42 0.044 53.42
must 2 0.044 53.46 0.044 53.46
myth 2 0.044 53.50 0.044 53.50
nab 2 0.044 53.55 0.044 53.55
nail 2 0.044 53.59 0.044 53.59
name 2 0.044 53.63 0.044 53.63
nap 2 0.044 53.68 0.044 53.68
nape 2 0.044 53.72 0.044 53.72
naught 2 0.044 53.77 0.044 53.77
neck 2 0.044 53.81 0.044 53.81
need 2 0.044 53.85 0.044 53.85
nerve 2 0.044 53.90 0.044 53.90
nest 2 0.044 53.94 0.044 53.94
news 2 0.044 53.98 0.044 53.98
newt 2 0.044 54.03 0.044 54.03
nick 2 0.044 54.07 0.044 54.07
niece 2 0.044 54.12 0.044 54.12
night 2 0.044 54.16 0.044 54.16
nil 2 0.044 54.20 0.044 54.20
nip 2 0.044 54.25 0.044 54.25
nod 2 0.044 54.29 0.044 54.29
node 2 0.044 54.33 0.044 54.33
noise 2 0.044 54.38 0.044 54.38
nonce 2 0.044 54.42 0.044 54.42
nook 2 0.044 54.47 0.044 54.47
noon 2 0.044 54.51 0.044 54.51
noose 2 0.044 54.55 0.044 54.55
norm 2 0.044 54.60 0.044 54.60
north 2 0.044 54.64 0.044 54.64
nose 2 0.044 54.68 0.044 54.68
notch 2 0.044 54.73 0.044 54.73
note 2 0.044 54.77 0.044 54.77
noun 2 0.044 54.82 0.044 54.82
nudge 2 0.044 54.86 0.044 54.86
nun 2 0.044 54.90 0.044 54.90
nurse 2 0.044 54.95 0.044 54.95
nut 2 0.044 54.99 0.044 54.99
nymph 2 0.044 55.04 0.044 55.04
oak 2 0.044 55.08 0.044 55.08
oath 2 0.044 55.12 0.044 55.12
oil 2 0.044 55.17 0.044 55.17
ooze 2 0.044 55.21 0.044 55.21
orb 2 0.044 55.25 0.044 55.25
ore 2 0.044 55.30 0.044 55.30
ought 2 0.044 55.34 0.044 55.34
ounce 2 0.044 55.39 0.044 55.39
oust 2 0.044 55.43 0.044 55.43
owl 2 0.044 55.47 0.044 55.47
pace 2 0.044 55.52 0.044 55.52
pack 2 0.044 55.56 0.044 55.56
pact 2 0.044 55.60 0.044 55.60
pad 2 0.044 55.65 0.044 55.65
page 2 0.044 55.69 0.044 55.69
pail 2 0.044 55.74 0.044 55.74
pain 2 0.044 55.78 0.044 55.78
paint 2 0.044 55.82 0.044 55.82
pair 2 0.044 55.87 0.044 55.87
pal 2 0.044 55.91 0.044 55.91
pale 2 0.044 55.95 0.044 55.95
pall 4 0.088 56.04 0.088 56.04
palm 2 0.044 56.09 0.044 56.09
pan 2 0.044 56.13 0.044 56.13
pane 2 0.044 56.17 0.044 56.17
pang 2 0.044 56.22 0.044 56.22
pant 2 0.044 56.26 0.044 56.26
pap 2 0.044 56.30 0.044 56.30
par 2 0.044 56.35 0.044 56.35
pare 2 0.044 56.39 0.044 56.39
park 2 0.044 56.44 0.044 56.44
part 2 0.044 56.48 0.044 56.48
pass 2 0.044 56.52 0.044 56.52
paste 2 0.044 56.57 0.044 56.57
pat 2 0.044 56.61 0.044 56.61
patch 2 0.044 56.65 0.044 56.65
pate 2 0.044 56.70 0.044 56.70
path 2 0.044 56.74 0.044 56.74
paunch 2 0.044 56.79 0.044 56.79
pause 2 0.044 56.83 0.044 56.83
pave 2 0.044 56.87 0.044 56.87
paw 2 0.044 56.92 0.044 56.92
pawn 2 0.044 56.96 0.044 56.96
pay 2 0.044 57.01 0.044 57.01
pea 2 0.044 57.05 0.044 57.05
peace 2 0.044 57.09 0.044 57.09
peach 2 0.044 57.14 0.044 57.14
peak 2 0.044 57.18 0.044 57.18
peal 2 0.044 57.22 0.044 57.22
pear 2 0.044 57.27 0.044 57.27
pearl 2 0.044 57.31 0.044 57.31
peat 2 0.044 57.36 0.044 57.36
peck 2 0.044 57.40 0.044 57.40
pee 2 0.044 57.44 0.044 57.44
peel 2 0.044 57.49 0.044 57.49
peep 2 0.044 57.53 0.044 57.53
peer 4 0.088 57.62 0.088 57.62
peg 2 0.044 57.66 0.044 57.66
pelt 4 0.088 57.75 0.088 57.75
pen 2 0.044 57.79 0.044 57.79
perch 2 0.044 57.84 0.044 57.84
perk 2 0.044 57.88 0.044 57.88
pest 2 0.044 57.92 0.044 57.92
pet 2 0.044 57.97 0.044 57.97
pew 2 0.044 58.01 0.044 58.01
phase 2 0.044 58.06 0.044 58.06
phrase 2 0.044 58.10 0.044 58.10
pick 2 0.044 58.14 0.044 58.14
pie 2 0.044 58.19 0.044 58.19
piece 2 0.044 58.23 0.044 58.23
pier 2 0.044 58.27 0.044 58.27
pierce 2 0.044 58.32 0.044 58.32
pig 2 0.044 58.36 0.044 58.36
pike 2 0.044 58.41 0.044 58.41
pile 2 0.044 58.45 0.044 58.45
pill 2 0.044 58.49 0.044 58.49
pimp 2 0.044 58.54 0.044 58.54
pin 2 0.044 58.58 0.044 58.58
pinch 2 0.044 58.63 0.044 58.63
pine 4 0.088 58.71 0.088 58.71
pint 2 0.044 58.76 0.044 58.76
pip 2 0.044 58.80 0.044 58.80
pipe 2 0.044 58.84 0.044 58.84
piss 2 0.044 58.89 0.044 58.89
pit 2 0.044 58.93 0.044 58.93
pitch 2 0.044 58.98 0.044 58.98
pith 2 0.044 59.02 0.044 59.02
place 2 0.044 59.06 0.044 59.06
plaid 2 0.044 59.11 0.044 59.11
plain 2 0.044 59.15 0.044 59.15
plan 2 0.044 59.19 0.044 59.19
plane 2 0.044 59.24 0.044 59.24
plank 2 0.044 59.28 0.044 59.28
plant 2 0.044 59.33 0.044 59.33
plate 2 0.044 59.37 0.044 59.37
play 2 0.044 59.41 0.044 59.41
plea 2 0.044 59.46 0.044 59.46
plead 2 0.044 59.50 0.044 59.50
please 2 0.044 59.54 0.044 59.54
pleat 2 0.044 59.59 0.044 59.59
pledge 2 0.044 59.63 0.044 59.63
plod 2 0.044 59.68 0.044 59.68
plot 2 0.044 59.72 0.044 59.72
plough 2 0.044 59.76 0.044 59.76
pluck 2 0.044 59.81 0.044 59.81
plug 2 0.044 59.85 0.044 59.85
plum 2 0.044 59.89 0.044 59.89
plume 2 0.044 59.94 0.044 59.94
plunge 2 0.044 59.98 0.044 59.98
plush 2 0.044 60.03 0.044 60.03
poach 2 0.044 60.07 0.044 60.07
pod 2 0.044 60.11 0.044 60.11
point 2 0.044 60.16 0.044 60.16
poise 2 0.044 60.20 0.044 60.20
poke 2 0.044 60.25 0.044 60.25
pole 2 0.044 60.29 0.044 60.29
poll 2 0.044 60.33 0.044 60.33
pomp 2 0.044 60.38 0.044 60.38
pond 2 0.044 60.42 0.044 60.42
pool 2 0.044 60.46 0.044 60.46
pop 2 0.044 60.51 0.044 60.51
pope 2 0.044 60.55 0.044 60.55
porch 2 0.044 60.60 0.044 60.60
pore 2 0.044 60.64 0.044 60.64
pork 2 0.044 60.68 0.044 60.68
port 2 0.044 60.73 0.044 60.73
pose 2 0.044 60.77 0.044 60.77
post 2 0.044 60.81 0.044 60.81
pot 2 0.044 60.86 0.044 60.86
pouch 2 0.044 60.90 0.044 60.90
pound 4 0.088 60.99 0.088 60.99
pour 2 0.044 61.03 0.044 61.03
pout 2 0.044 61.08 0.044 61.08
praise 2 0.044 61.12 0.044 61.12
prank 2 0.044 61.16 0.044 61.16
pray 2 0.044 61.21 0.044 61.21
preach 2 0.044 61.25 0.044 61.25
prep 2 0.044 61.30 0.044 61.30
press 2 0.044 61.34 0.044 61.34
prey 2 0.044 61.38 0.044 61.38
price 2 0.044 61.43 0.044 61.43
prick 2 0.044 61.47 0.044 61.47
pride 2 0.044 61.51 0.044 61.51
priest 2 0.044 61.56 0.044 61.56
prime 2 0.044 61.60 0.044 61.60
prince 2 0.044 61.65 0.044 61.65
print 2 0.044 61.69 0.044 61.69
prize 2 0.044 61.73 0.044 61.73
probe 2 0.044 61.78 0.044 61.78
prod 2 0.044 61.82 0.044 61.82
prop 2 0.044 61.87 0.044 61.87
prose 2 0.044 61.91 0.044 61.91
prove 2 0.044 61.95 0.044 61.95
prow 2 0.044 62.00 0.044 62.00
prowl 2 0.044 62.04 0.044 62.04
pry 2 0.044 62.08 0.044 62.08
psalm 2 0.044 62.13 0.044 62.13
pub 2 0.044 62.17 0.044 62.17
puck 2 0.044 62.22 0.044 62.22
puff 2 0.044 62.26 0.044 62.26
puke 2 0.044 62.30 0.044 62.30
pull 2 0.044 62.35 0.044 62.35
pulp 2 0.044 62.39 0.044 62.39
pulse 2 0.044 62.43 0.044 62.43
pump 2 0.044 62.48 0.044 62.48
pun 2 0.044 62.52 0.044 62.52
punch 2 0.044 62.57 0.044 62.57
punk 2 0.044 62.61 0.044 62.61
punt 2 0.044 62.65 0.044 62.65
pup 2 0.044 62.70 0.044 62.70
purge 2 0.044 62.74 0.044 62.74
purse 2 0.044 62.78 0.044 62.78
pus 2 0.044 62.83 0.044 62.83
push 2 0.044 62.87 0.044 62.87
put 2 0.044 62.92 0.044 62.92
putt 2 0.044 62.96 0.044 62.96
pyre 2 0.044 63.00 0.044 63.00
quack 2 0.044 63.05 0.044 63.05
quake 2 0.044 63.09 0.044 63.09
quart 2 0.044 63.13 0.044 63.13
quartz 2 0.044 63.18 0.044 63.18
quay 2 0.044 63.22 0.044 63.22
queen 2 0.044 63.27 0.044 63.27
quell 2 0.044 63.31 0.044 63.31
quench 2 0.044 63.35 0.044 63.35
quest 2 0.044 63.40 0.044 63.40
queue 2 0.044 63.44 0.044 63.44
quill 2 0.044 63.49 0.044 63.49
quince 2 0.044 63.53 0.044 63.53
quirk 2 0.044 63.57 0.044 63.57
quiz 2 0.044 63.62 0.044 63.62
quote 2 0.044 63.66 0.044 63.66
race 2 0.044 63.70 0.044 63.70
rack 2 0.044 63.75 0.044 63.75
raft 2 0.044 63.79 0.044 63.79
rag 2 0.044 63.84 0.044 63.84
rage 2 0.044 63.88 0.044 63.88
raid 2 0.044 63.92 0.044 63.92
rail 2 0.044 63.97 0.044 63.97
rain 2 0.044 64.01 0.044 64.01
raise 2 0.044 64.05 0.044 64.05
rake 2 0.044 64.10 0.044 64.10
ram 2 0.044 64.14 0.044 64.14
ranch 2 0.044 64.19 0.044 64.19
range 2 0.044 64.23 0.044 64.23
rank 2 0.044 64.27 0.044 64.27
rant 2 0.044 64.32 0.044 64.32
rap 2 0.044 64.36 0.044 64.36
rape 2 0.044 64.40 0.044 64.40
rash 2 0.044 64.45 0.044 64.45
rasp 2 0.044 64.49 0.044 64.49
rat 2 0.044 64.54 0.044 64.54
rate 2 0.044 64.58 0.044 64.58
rave 2 0.044 64.62 0.044 64.62
ray 2 0.044 64.67 0.044 64.67
reach 2 0.044 64.71 0.044 64.71
realm 2 0.044 64.75 0.044 64.75
ream 2 0.044 64.80 0.044 64.80
reap 2 0.044 64.84 0.044 64.84
rear 2 0.044 64.89 0.044 64.89
reed 2 0.044 64.93 0.044 64.93
reef 2 0.044 64.97 0.044 64.97
reek 2 0.044 65.02 0.044 65.02
reel 2 0.044 65.06 0.044 65.06
reign 2 0.044 65.11 0.044 65.11
rein 2 0.044 65.15 0.044 65.15
rend 2 0.044 65.19 0.044 65.19
rent 2 0.044 65.24 0.044 65.24
rest 2 0.044 65.28 0.044 65.28
retch 2 0.044 65.32 0.044 65.32
rhyme 2 0.044 65.37 0.044 65.37
rib 2 0.044 65.41 0.044 65.41
rice 2 0.044 65.46 0.044 65.46
ride 2 0.044 65.50 0.044 65.50
ridge 2 0.044 65.54 0.044 65.54
rift 2 0.044 65.59 0.044 65.59
rig 2 0.044 65.63 0.044 65.63
right 2 0.044 65.67 0.044 65.67
rile 2 0.044 65.72 0.044 65.72
rim 2 0.044 65.76 0.044 65.76
rime 2 0.044 65.81 0.044 65.81
rind 2 0.044 65.85 0.044 65.85
ring 4 0.088 65.94 0.088 65.94
rink 2 0.044 65.98 0.044 65.98
rinse 2 0.044 66.02 0.044 66.02
rip 2 0.044 66.07 0.044 66.07
rise 2 0.044 66.11 0.044 66.11
risk 2 0.044 66.16 0.044 66.16
rite 2 0.044 66.20 0.044 66.20
roach 2 0.044 66.24 0.044 66.24
road 2 0.044 66.29 0.044 66.29
roam 2 0.044 66.33 0.044 66.33
roar 2 0.044 66.37 0.044 66.37
roast 2 0.044 66.42 0.044 66.42
rob 2 0.044 66.46 0.044 66.46
robe 2 0.044 66.51 0.044 66.51
rock 4 0.088 66.59 0.088 66.59
rod 2 0.044 66.64 0.044 66.64
roe 2 0.044 66.68 0.044 66.68
role 2 0.044 66.73 0.044 66.73
roll 2 0.044 66.77 0.044 66.77
romp 2 0.044 66.81 0.044 66.81
roof 2 0.044 66.86 0.044 66.86
rook 2 0.044 66.90 0.044 66.90
room 2 0.044 66.94 0.044 66.94
roost 2 0.044 66.99 0.044 66.99
root 2 0.044 67.03 0.044 67.03
rope 2 0.044 67.08 0.044 67.08
rose 2 0.044 67.12 0.044 67.12
rot 2 0.044 67.16 0.044 67.16
rouse 2 0.044 67.21 0.044 67.21
rout 2 0.044 67.25 0.044 67.25
rove 2 0.044 67.29 0.044 67.29
rub 2 0.044 67.34 0.044 67.34
rue 2 0.044 67.38 0.044 67.38
rug 2 0.044 67.43 0.044 67.43
rule 2 0.044 67.47 0.044 67.47
rum 2 0.044 67.51 0.044 67.51
rump 2 0.044 67.56 0.044 67.56
run 2 0.044 67.60 0.044 67.60
rung 2 0.044 67.64 0.044 67.64
runt 2 0.044 67.69 0.044 67.69
ruse 2 0.044 67.73 0.044 67.73
rush 2 0.044 67.78 0.044 67.78
rust 2 0.044 67.82 0.044 67.82
rut 2 0.044 67.86 0.044 67.86
rye 2 0.044 67.91 0.044 67.91
sack 2 0.044 67.95 0.044 67.95
sag 2 0.044 67.99 0.044 67.99
sail 2 0.044 68.04 0.044 68.04
saint 2 0.044 68.08 0.044 68.08
sake 2 0.044 68.13 0.044 68.13
sale 2 0.044 68.17 0.044 68.17
salt 2 0.044 68.21 0.044 68.21
salve 2 0.044 68.26 0.044 68.26
sand 2 0.044 68.30 0.044 68.30
sap 2 0.044 68.35 0.044 68.35
sash 2 0.044 68.39 0.044 68.39
sauce 2 0.044 68.43 0.044 68.43
save 2 0.044 68.48 0.044 68.48
saw 2 0.044 68.52 0.044 68.52
sax 2 0.044 68.56 0.044 68.56
say 2 0.044 68.61 0.044 68.61
scald 2 0.044 68.65 0.044 68.65
scale 2 0.044 68.70 0.044 68.70
scalp 2 0.044 68.74 0.044 68.74
scan 2 0.044 68.78 0.044 68.78
scar 2 0.044 68.83 0.044 68.83
scare 2 0.044 68.87 0.044 68.87
scarf 2 0.044 68.91 0.044 68.91
scene 2 0.044 68.96 0.044 68.96
scent 2 0.044 69.00 0.044 69.00
scheme 2 0.044 69.05 0.044 69.05
school 2 0.044 69.09 0.044 69.09
scoop 2 0.044 69.13 0.044 69.13
scope 2 0.044 69.18 0.044 69.18
score 2 0.044 69.22 0.044 69.22
scorn 2 0.044 69.26 0.044 69.26
scotch 2 0.044 69.31 0.044 69.31
scour 2 0.044 69.35 0.044 69.35
scourge 2 0.044 69.40 0.044 69.40
scout 2 0.044 69.44 0.044 69.44
scrap 2 0.044 69.48 0.044 69.48
scrape 2 0.044 69.53 0.044 69.53
scratch 2 0.044 69.57 0.044 69.57
scream 2 0.044 69.61 0.044 69.61
screech 2 0.044 69.66 0.044 69.66
screen 2 0.044 69.70 0.044 69.70
screw 2 0.044 69.75 0.044 69.75
scribe 4 0.088 69.83 0.088 69.83
script 2 0.044 69.88 0.044 69.88
scrub 2 0.044 69.92 0.044 69.92
scuff 2 0.044 69.96 0.044 69.96
sea 2 0.044 70.01 0.044 70.01
seal 2 0.044 70.05 0.044 70.05
seam 2 0.044 70.10 0.044 70.10
sear 2 0.044 70.14 0.044 70.14
search 2 0.044 70.18 0.044 70.18
seat 2 0.044 70.23 0.044 70.23
sect 2 0.044 70.27 0.044 70.27
see 2 0.044 70.32 0.044 70.32
seed 2 0.044 70.36 0.044 70.36
seek 2 0.044 70.40 0.044 70.40
seem 2 0.044 70.45 0.044 70.45
seep 2 0.044 70.49 0.044 70.49
seize 2 0.044 70.53 0.044 70.53
self 2 0.044 70.58 0.044 70.58
sell 2 0.044 70.62 0.044 70.62
send 2 0.044 70.67 0.044 70.67
sense 2 0.044 70.71 0.044 70.71
serf 2 0.044 70.75 0.044 70.75
serge 2 0.044 70.80 0.044 70.80
serve 2 0.044 70.84 0.044 70.84
set 4 0.088 70.93 0.088 70.93
sew 2 0.044 70.97 0.044 70.97
sex 2 0.044 71.02 0.044 71.02
shack 2 0.044 71.06 0.044 71.06
shade 2 0.044 71.10 0.044 71.10
shaft 2 0.044 71.15 0.044 71.15
shag 4 0.088 71.23 0.088 71.23
shah 2 0.044 71.28 0.044 71.28
shake 2 0.044 71.32 0.044 71.32
shall 2 0.044 71.37 0.044 71.37
sham 2 0.044 71.41 0.044 71.41
shame 2 0.044 71.45 0.044 71.45
shank 2 0.044 71.50 0.044 71.50
shape 2 0.044 71.54 0.044 71.54
shard 2 0.044 71.58 0.044 71.58
share 2 0.044 71.63 0.044 71.63
shark 2 0.044 71.67 0.044 71.67
shave 2 0.044 71.72 0.044 71.72
shawl 2 0.044 71.76 0.044 71.76
shay 2 0.044 71.80 0.044 71.80
sheaf 2 0.044 71.85 0.044 71.85
shear 2 0.044 71.89 0.044 71.89
sheath 2 0.044 71.94 0.044 71.94
shed 4 0.088 72.02 0.088 72.02
sheen 2 0.044 72.07 0.044 72.07
sheep 2 0.044 72.11 0.044 72.11
sheer 2 0.044 72.15 0.044 72.15
sheet 2 0.044 72.20 0.044 72.20
shelf 2 0.044 72.24 0.044 72.24
shell 2 0.044 72.29 0.044 72.29
shield 2 0.044 72.33 0.044 72.33
shift 2 0.044 72.37 0.044 72.37
shin 2 0.044 72.42 0.044 72.42
shine 2 0.044 72.46 0.044 72.46
ship 2 0.044 72.50 0.044 72.50
shirt 2 0.044 72.55 0.044 72.55
shoal 2 0.044 72.59 0.044 72.59
shock 2 0.044 72.64 0.044 72.64
shoe 2 0.044 72.68 0.044 72.68
shoot 2 0.044 72.72 0.044 72.72
shop 2 0.044 72.77 0.044 72.77
shore 2 0.044 72.81 0.044 72.81
shot 2 0.044 72.85 0.044 72.85
should 2 0.044 72.90 0.044 72.90
shout 2 0.044 72.94 0.044 72.94
shove 2 0.044 72.99 0.044 72.99
show 2 0.044 73.03 0.044 73.03
shred 2 0.044 73.07 0.044 73.07
shrimp 2 0.044 73.12 0.044 73.12
shrine 2 0.044 73.16 0.044 73.16
shrink 2 0.044 73.20 0.044 73.20
shrub 2 0.044 73.25 0.044 73.25
shrug 2 0.044 73.29 0.044 73.29
shuck 2 0.044 73.34 0.044 73.34
shun 2 0.044 73.38 0.044 73.38
shunt 2 0.044 73.42 0.044 73.42
shut 2 0.044 73.47 0.044 73.47
side 2 0.044 73.51 0.044 73.51
siege 2 0.044 73.56 0.044 73.56
sigh 2 0.044 73.60 0.044 73.60
sight 2 0.044 73.64 0.044 73.64
sign 2 0.044 73.69 0.044 73.69
silk 2 0.044 73.73 0.044 73.73
sill 2 0.044 73.77 0.044 73.77
sin 2 0.044 73.82 0.044 73.82
sine 2 0.044 73.86 0.044 73.86
sing 2 0.044 73.91 0.044 73.91
sink 2 0.044 73.95 0.044 73.95
sip 2 0.044 73.99 0.044 73.99
sir 2 0.044 74.04 0.044 74.04
sit 2 0.044 74.08 0.044 74.08
site 2 0.044 74.12 0.044 74.12
size 2 0.044 74.17 0.044 74.17
skate 2 0.044 74.21 0.044 74.21
skeet 2 0.044 74.26 0.044 74.26
sketch 2 0.044 74.30 0.044 74.30
ski 2 0.044 74.34 0.044 74.34
skid 2 0.044 74.39 0.044 74.39
skiff 2 0.044 74.43 0.044 74.43
skill 2 0.044 74.47 0.044 74.47
skin 2 0.044 74.52 0.044 74.52
skip 2 0.044 74.56 0.044 74.56
skirt 2 0.044 74.61 0.044 74.61
skit 2 0.044 74.65 0.044 74.65
skulk 2 0.044 74.69 0.044 74.69
skull 2 0.044 74.74 0.044 74.74
skunk 2 0.044 74.78 0.044 74.78
sky 2 0.044 74.82 0.044 74.82
slab 2 0.044 74.87 0.044 74.87
slam 2 0.044 74.91 0.044 74.91
slang 2 0.044 74.96 0.044 74.96
slant 2 0.044 75.00 0.044 75.00
slap 2 0.044 75.04 0.044 75.04
slash 2 0.044 75.09 0.044 75.09
slat 2 0.044 75.13 0.044 75.13
slate 2 0.044 75.18 0.044 75.18
slave 2 0.044 75.22 0.044 75.22
sleep 2 0.044 75.26 0.044 75.26
sleeve 2 0.044 75.31 0.044 75.31
slice 2 0.044 75.35 0.044 75.35
slide 2 0.044 75.39 0.044 75.39
slip 2 0.044 75.44 0.044 75.44
slit 2 0.044 75.48 0.044 75.48
slob 2 0.044 75.53 0.044 75.53
sloe 2 0.044 75.57 0.044 75.57
sloop 2 0.044 75.61 0.044 75.61
slop 2 0.044 75.66 0.044 75.66
slope 2 0.044 75.70 0.044 75.70
slot 2 0.044 75.74 0.044 75.74
slouch 2 0.044 75.79 0.044 75.79
slough 2 0.044 75.83 0.044 75.83
sludge 2 0.044 75.88 0.044 75.88
slug 2 0.044 75.92 0.044 75.92
sluice 2 0.044 75.96 0.044 75.96
slum 2 0.044 76.01 0.044 76.01
slump 2 0.044 76.05 0.044 76.05
smack 2 0.044 76.09 0.044 76.09
smart 2 0.044 76.14 0.044 76.14
smash 2 0.044 76.18 0.044 76.18
smear 2 0.044 76.23 0.044 76.23
smell 2 0.044 76.27 0.044 76.27
smelt 4 0.088 76.36 0.088 76.36
smile 2 0.044 76.40 0.044 76.40
smirk 2 0.044 76.44 0.044 76.44
smoke 2 0.044 76.49 0.044 76.49
snack 2 0.044 76.53 0.044 76.53
snag 2 0.044 76.58 0.044 76.58
snail 2 0.044 76.62 0.044 76.62
snake 2 0.044 76.66 0.044 76.66
snap 2 0.044 76.71 0.044 76.71
snare 2 0.044 76.75 0.044 76.75
snatch 2 0.044 76.80 0.044 76.80
sneak 2 0.044 76.84 0.044 76.84
sneer 2 0.044 76.88 0.044 76.88
sniff 2 0.044 76.93 0.044 76.93
snip 2 0.044 76.97 0.044 76.97
snob 2 0.044 77.01 0.044 77.01
snoop 2 0.044 77.06 0.044 77.06
snort 2 0.044 77.10 0.044 77.10
snout 2 0.044 77.15 0.044 77.15
snow 2 0.044 77.19 0.044 77.19
snug 2 0.044 77.23 0.044 77.23
soak 2 0.044 77.28 0.044 77.28
soap 2 0.044 77.32 0.044 77.32
sob 2 0.044 77.36 0.044 77.36
sock 2 0.044 77.41 0.044 77.41
sod 2 0.044 77.45 0.044 77.45
sole 2 0.044 77.50 0.044 77.50
solve 2 0.044 77.54 0.044 77.54
son 2 0.044 77.58 0.044 77.58
song 2 0.044 77.63 0.044 77.63
soot 2 0.044 77.67 0.044 77.67
soothe 2 0.044 77.71 0.044 77.71
sop 2 0.044 77.76 0.044 77.76
sort 2 0.044 77.80 0.044 77.80
soul 2 0.044 77.85 0.044 77.85
sound 2 0.044 77.89 0.044 77.89
soup 2 0.044 77.93 0.044 77.93
source 2 0.044 77.98 0.044 77.98
south 2 0.044 78.02 0.044 78.02
soy 2 0.044 78.06 0.044 78.06
spa 2 0.044 78.11 0.044 78.11
space 2 0.044 78.15 0.044 78.15
spade 2 0.044 78.20 0.044 78.20
span 2 0.044 78.24 0.044 78.24
spare 2 0.044 78.28 0.044 78.28
spark 2 0.044 78.33 0.044 78.33
spat 2 0.044 78.37 0.044 78.37
spate 2 0.044 78.42 0.044 78.42
speak 2 0.044 78.46 0.044 78.46
spear 2 0.044 78.50 0.044 78.50
speck 2 0.044 78.55 0.044 78.55
speech 2 0.044 78.59 0.044 78.59
speed 2 0.044 78.63 0.044 78.63
spell 4 0.088 78.72 0.088 78.72
spend 2 0.044 78.77 0.044 78.77
sphere 2 0.044 78.81 0.044 78.81
sphinx 2 0.044 78.85 0.044 78.85
spice 2 0.044 78.90 0.044 78.90
spike 2 0.044 78.94 0.044 78.94
spill 2 0.044 78.98 0.044 78.98
spin 2 0.044 79.03 0.044 79.03
spine 2 0.044 79.07 0.044 79.07
spire 2 0.044 79.12 0.044 79.12
spit 4 0.088 79.20 0.088 79.20
spite 2 0.044 79.25 0.044 79.25
splash 2 0.044 79.29 0.044 79.29
spleen 2 0.044 79.33 0.044 79.33
splice 2 0.044 79.38 0.044 79.38
split 2 0.044 79.42 0.044 79.42
splurge 2 0.044 79.47 0.044 79.47
spoil 2 0.044 79.51 0.044 79.51
spoke 2 0.044 79.55 0.044 79.55
spoof 2 0.044 79.60 0.044 79.60
spool 2 0.044 79.64 0.044 79.64
spoon 2 0.044 79.68 0.044 79.68
sport 2 0.044 79.73 0.044 79.73
spot 2 0.044 79.77 0.044 79.77
spouse 2 0.044 79.82 0.044 79.82
spout 2 0.044 79.86 0.044 79.86
sprawl 2 0.044 79.90 0.044 79.90
spray 2 0.044 79.95 0.044 79.95
spread 2 0.044 79.99 0.044 79.99
spree 2 0.044 80.04 0.044 80.04
sprig 2 0.044 80.08 0.044 80.08
spring 2 0.044 80.12 0.044 80.12
sprite 2 0.044 80.17 0.044 80.17
sprout 2 0.044 80.21 0.044 80.21
spur 2 0.044 80.25 0.044 80.25
spurt 2 0.044 80.30 0.044 80.30
spy 2 0.044 80.34 0.044 80.34
squad 2 0.044 80.39 0.044 80.39
squall 2 0.044 80.43 0.044 80.43
square 2 0.044 80.47 0.044 80.47
squash 4 0.088 80.56 0.088 80.56
squat 2 0.044 80.60 0.044 80.60
squaw 2 0.044 80.65 0.044 80.65
squawk 2 0.044 80.69 0.044 80.69
squeak 2 0.044 80.74 0.044 80.74
squeal 2 0.044 80.78 0.044 80.78
squeeze 2 0.044 80.82 0.044 80.82
squint 2 0.044 80.87 0.044 80.87
squire 2 0.044 80.91 0.044 80.91
squirm 2 0.044 80.95 0.044 80.95
squirt 2 0.044 81.00 0.044 81.00
stab 2 0.044 81.04 0.044 81.04
stack 2 0.044 81.09 0.044 81.09
staff 2 0.044 81.13 0.044 81.13
stag 2 0.044 81.17 0.044 81.17
stage 2 0.044 81.22 0.044 81.22
stain 2 0.044 81.26 0.044 81.26
stair 2 0.044 81.30 0.044 81.30
stake 2 0.044 81.35 0.044 81.35
stalk 2 0.044 81.39 0.044 81.39
stall 2 0.044 81.44 0.044 81.44
stamp 2 0.044 81.48 0.044 81.48
stance 2 0.044 81.52 0.044 81.52
stanch 2 0.044 81.57 0.044 81.57
stand 2 0.044 81.61 0.044 81.61
star 2 0.044 81.65 0.044 81.65
starch 2 0.044 81.70 0.044 81.70
stare 2 0.044 81.74 0.044 81.74
start 2 0.044 81.79 0.044 81.79
starve 2 0.044 81.83 0.044 81.83
state 4 0.088 81.92 0.088 81.92
staunch 2 0.044 81.96 0.044 81.96
stave 2 0.044 82.01 0.044 82.01
stay 2 0.044 82.05 0.044 82.05
stead 2 0.044 82.09 0.044 82.09
steak 2 0.044 82.14 0.044 82.14
steal 2 0.044 82.18 0.044 82.18
steam 2 0.044 82.22 0.044 82.22
steed 2 0.044 82.27 0.044 82.27
steel 2 0.044 82.31 0.044 82.31
steer 4 0.088 82.40 0.088 82.40
stem 2 0.044 82.44 0.044 82.44
stench 2 0.044 82.49 0.044 82.49
step 2 0.044 82.53 0.044 82.53
stew 2 0.044 82.57 0.044 82.57
stick 2 0.044 82.62 0.044 82.62
still 2 0.044 82.66 0.044 82.66
stilt 2 0.044 82.71 0.044 82.71
sting 2 0.044 82.75 0.044 82.75
stink 2 0.044 82.79 0.044 82.79
stint 2 0.044 82.84 0.044 82.84
stir 2 0.044 82.88 0.044 82.88
stitch 2 0.044 82.92 0.044 82.92
stock 2 0.044 82.97 0.044 82.97
stole 2 0.044 83.01 0.044 83.01
stone 2 0.044 83.06 0.044 83.06
stool 2 0.044 83.10 0.044 83.10
stoop 2 0.044 83.14 0.044 83.14
stop 2 0.044 83.19 0.044 83.19
store 2 0.044 83.23 0.044 83.23
stork 2 0.044 83.27 0.044 83.27
storm 2 0.044 83.32 0.044 83.32
stout 2 0.044 83.36 0.044 83.36
stove 2 0.044 83.41 0.044 83.41
stow 2 0.044 83.45 0.044 83.45
strafe 2 0.044 83.49 0.044 83.49
strain 2 0.044 83.54 0.044 83.54
strand 4 0.088 83.63 0.088 83.63
strap 2 0.044 83.67 0.044 83.67
straw 2 0.044 83.71 0.044 83.71
stray 2 0.044 83.76 0.044 83.76
streak 2 0.044 83.80 0.044 83.80
stream 2 0.044 83.84 0.044 83.84
street 2 0.044 83.89 0.044 83.89
stress 2 0.044 83.93 0.044 83.93
stretch 2 0.044 83.98 0.044 83.98
stride 2 0.044 84.02 0.044 84.02
strife 2 0.044 84.06 0.044 84.06
strike 2 0.044 84.11 0.044 84.11
string 2 0.044 84.15 0.044 84.15
strip 2 0.044 84.19 0.044 84.19
stripe 2 0.044 84.24 0.044 84.24
strive 2 0.044 84.28 0.044 84.28
stroke 2 0.044 84.33 0.044 84.33
stroll 2 0.044 84.37 0.044 84.37
strut 2 0.044 84.41 0.044 84.41
stub 2 0.044 84.46 0.044 84.46
stud 2 0.044 84.50 0.044 84.50
stuff 2 0.044 84.54 0.044 84.54
stump 2 0.044 84.59 0.044 84.59
stunt 4 0.088 84.68 0.088 84.68
style 2 0.044 84.72 0.044 84.72
sub 2 0.044 84.76 0.044 84.76
suck 2 0.044 84.81 0.044 84.81
sue 2 0.044 84.85 0.044 84.85
suit 2 0.044 84.89 0.044 84.89
suite 2 0.044 84.94 0.044 84.94
sulk 2 0.044 84.98 0.044 84.98
sum 2 0.044 85.03 0.044 85.03
sun 2 0.044 85.07 0.044 85.07
sup 2 0.044 85.11 0.044 85.11
surf 2 0.044 85.16 0.044 85.16
surge 2 0.044 85.20 0.044 85.20
swamp 2 0.044 85.25 0.044 85.25
swan 2 0.044 85.29 0.044 85.29
swap 2 0.044 85.33 0.044 85.33
swarm 2 0.044 85.38 0.044 85.38
swath 2 0.044 85.42 0.044 85.42
sway 2 0.044 85.46 0.044 85.46
swear 2 0.044 85.51 0.044 85.51
sweat 2 0.044 85.55 0.044 85.55
sweep 2 0.044 85.60 0.044 85.60
swell 2 0.044 85.64 0.044 85.64
swerve 2 0.044 85.68 0.044 85.68
swig 2 0.044 85.73 0.044 85.73
swim 2 0.044 85.77 0.044 85.77
swine 2 0.044 85.81 0.044 85.81
swing 2 0.044 85.86 0.044 85.86
swipe 2 0.044 85.90 0.044 85.90
swirl 2 0.044 85.95 0.044 85.95
switch 2 0.044 85.99 0.044 85.99
swoop 2 0.044 86.03 0.044 86.03
sword 2 0.044 86.08 0.044 86.08
tab 2 0.044 86.12 0.044 86.12
tack 2 0.044 86.16 0.044 86.16
tact 2 0.044 86.21 0.044 86.21
tag 2 0.044 86.25 0.044 86.25
tail 2 0.044 86.30 0.044 86.30
taint 2 0.044 86.34 0.044 86.34
take 2 0.044 86.38 0.044 86.38
tale 2 0.044 86.43 0.044 86.43
talk 2 0.044 86.47 0.044 86.47
tamp 2 0.044 86.51 0.044 86.51
tan 2 0.044 86.56 0.044 86.56
tang 2 0.044 86.60 0.044 86.60
tank 2 0.044 86.65 0.044 86.65
tape 2 0.044 86.69 0.044 86.69
tar 2 0.044 86.73 0.044 86.73
tart 2 0.044 86.78 0.044 86.78
task 2 0.044 86.82 0.044 86.82
taste 2 0.044 86.87 0.044 86.87
taunt 2 0.044 86.91 0.044 86.91
tax 2 0.044 86.95 0.044 86.95
tea 2 0.044 87.00 0.044 87.00
teach 2 0.044 87.04 0.044 87.04
teak 2 0.044 87.08 0.044 87.08
team 2 0.044 87.13 0.044 87.13
tease 2 0.044 87.17 0.044 87.17
tech 2 0.044 87.22 0.044 87.22
tee 2 0.044 87.26 0.044 87.26
teens 2 0.044 87.30 0.044 87.30
tell 2 0.044 87.35 0.044 87.35
tempt 2 0.044 87.39 0.044 87.39
tend 2 0.044 87.43 0.044 87.43
tense 2 0.044 87.48 0.044 87.48
tent 2 0.044 87.52 0.044 87.52
test 2 0.044 87.57 0.044 87.57
text 2 0.044 87.61 0.044 87.61
thank 2 0.044 87.65 0.044 87.65
thaw 2 0.044 87.70 0.044 87.70
theft 2 0.044 87.74 0.044 87.74
theme 2 0.044 87.78 0.044 87.78
thief 2 0.044 87.83 0.044 87.83
thigh 2 0.044 87.87 0.044 87.87
thin 2 0.044 87.92 0.044 87.92
thing 2 0.044 87.96 0.044 87.96
think 2 0.044 88.00 0.044 88.00
thirst 2 0.044 88.05 0.044 88.05
thong 2 0.044 88.09 0.044 88.09
thorn 2 0.044 88.13 0.044 88.13
thought 2 0.044 88.18 0.044 88.18
thrash 2 0.044 88.22 0.044 88.22
thread 2 0.044 88.27 0.044 88.27
threat 2 0.044 88.31 0.044 88.31
thrift 2 0.044 88.35 0.044 88.35
thrill 2 0.044 88.40 0.044 88.40
thrive 2 0.044 88.44 0.044 88.44
throat 2 0.044 88.49 0.044 88.49
throne 2 0.044 88.53 0.044 88.53
throng 2 0.044 88.57 0.044 88.57
throw 2 0.044 88.62 0.044 88.62
thrush 2 0.044 88.66 0.044 88.66
thrust 2 0.044 88.70 0.044 88.70
thud 2 0.044 88.75 0.044 88.75
thug 2 0.044 88.79 0.044 88.79
thumb 2 0.044 88.84 0.044 88.84
thump 2 0.044 88.88 0.044 88.88
thwack 2 0.044 88.92 0.044 88.92
thwart 2 0.044 88.97 0.044 88.97
tick 2 0.044 89.01 0.044 89.01
tide 2 0.044 89.05 0.044 89.05
tie 2 0.044 89.10 0.044 89.10
tile 2 0.044 89.14 0.044 89.14
till 2 0.044 89.19 0.044 89.19
tilt 2 0.044 89.23 0.044 89.23
time 2 0.044 89.27 0.044 89.27
tin 2 0.044 89.32 0.044 89.32
tint 2 0.044 89.36 0.044 89.36
tip 2 0.044 89.40 0.044 89.40
tire 2 0.044 89.45 0.044 89.45
toad 2 0.044 89.49 0.044 89.49
toast 2 0.044 89.54 0.044 89.54
toe 2 0.044 89.58 0.044 89.58
toil 2 0.044 89.62 0.044 89.62
toll 2 0.044 89.67 0.044 89.67
tomb 2 0.044 89.71 0.044 89.71
tome 2 0.044 89.75 0.044 89.75
ton 2 0.044 89.80 0.044 89.80
tone 2 0.044 89.84 0.044 89.84
tongue 2 0.044 89.89 0.044 89.89
tool 2 0.044 89.93 0.044 89.93
toot 2 0.044 89.97 0.044 89.97
tooth 2 0.044 90.02 0.044 90.02
top 2 0.044 90.06 0.044 90.06
torch 2 0.044 90.11 0.044 90.11
torque 2 0.044 90.15 0.044 90.15
toss 2 0.044 90.19 0.044 90.19
tote 4 0.088 90.28 0.088 90.28
touch 2 0.044 90.32 0.044 90.32
tour 2 0.044 90.37 0.044 90.37
tout 2 0.044 90.41 0.044 90.41
town 2 0.044 90.46 0.044 90.46
toy 2 0.044 90.50 0.044 90.50
trace 2 0.044 90.54 0.044 90.54
track 2 0.044 90.59 0.044 90.59
tract 2 0.044 90.63 0.044 90.63
trade 2 0.044 90.67 0.044 90.67
trail 2 0.044 90.72 0.044 90.72
train 4 0.088 90.81 0.088 90.81
trait 2 0.044 90.85 0.044 90.85
tramp 2 0.044 90.89 0.044 90.89
trance 2 0.044 90.94 0.044 90.94
trap 2 0.044 90.98 0.044 90.98
trash 2 0.044 91.02 0.044 91.02
tray 2 0.044 91.07 0.044 91.07
tread 2 0.044 91.11 0.044 91.11
treat 2 0.044 91.16 0.044 91.16
tree 2 0.044 91.20 0.044 91.20
trench 2 0.044 91.24 0.044 91.24
trend 2 0.044 91.29 0.044 91.29
tribe 2 0.044 91.33 0.044 91.33
trick 2 0.044 91.37 0.044 91.37
trill 2 0.044 91.42 0.044 91.42
trip 2 0.044 91.46 0.044 91.46
tripe 2 0.044 91.51 0.044 91.51
troll 2 0.044 91.55 0.044 91.55
troop 2 0.044 91.59 0.044 91.59
trot 2 0.044 91.64 0.044 91.64
trough 2 0.044 91.68 0.044 91.68
trout 2 0.044 91.73 0.044 91.73
truce 2 0.044 91.77 0.044 91.77
truck 2 0.044 91.81 0.044 91.81
trump 2 0.044 91.86 0.044 91.86
trunk 2 0.044 91.90 0.044 91.90
trust 2 0.044 91.94 0.044 91.94
try 2 0.044 91.99 0.044 91.99
tryst 2 0.044 92.03 0.044 92.03
tub 2 0.044 92.08 0.044 92.08
tube 4 0.088 92.16 0.088 92.16
tuck 2 0.044 92.21 0.044 92.21
tug 2 0.044 92.25 0.044 92.25
tune 2 0.044 92.29 0.044 92.29
turf 2 0.044 92.34 0.044 92.34
turn 2 0.044 92.38 0.044 92.38
tusk 2 0.044 92.43 0.044 92.43
twain 2 0.044 92.47 0.044 92.47
tweed 2 0.044 92.51 0.044 92.51
twin 2 0.044 92.56 0.044 92.56
twinge 2 0.044 92.60 0.044 92.60
twist 2 0.044 92.64 0.044 92.64
twitch 2 0.044 92.69 0.044 92.69
type 2 0.044 92.73 0.044 92.73
urge 2 0.044 92.78 0.044 92.78
urn 2 0.044 92.82 0.044 92.82
use 2 0.044 92.86 0.044 92.86
vale 2 0.044 92.91 0.044 92.91
valve 2 0.044 92.95 0.044 92.95
vamp 2 0.044 92.99 0.044 92.99
van 2 0.044 93.04 0.044 93.04
vase 2 0.044 93.08 0.044 93.08
vault 2 0.044 93.13 0.044 93.13
veal 2 0.044 93.17 0.044 93.17
veer 2 0.044 93.21 0.044 93.21
veil 2 0.044 93.26 0.044 93.26
vein 2 0.044 93.30 0.044 93.30
vent 2 0.044 93.35 0.044 93.35
verb 2 0.044 93.39 0.044 93.39
verge 2 0.044 93.43 0.044 93.43
verse 2 0.044 93.48 0.044 93.48
verve 2 0.044 93.52 0.044 93.52
vest 2 0.044 93.56 0.044 93.56
vet 2 0.044 93.61 0.044 93.61
vex 2 0.044 93.65 0.044 93.65
vice 2 0.044 93.70 0.044 93.70
vie 2 0.044 93.74 0.044 93.74
view 2 0.044 93.78 0.044 93.78
vine 2 0.044 93.83 0.044 93.83
voice 2 0.044 93.87 0.044 93.87
volt 2 0.044 93.91 0.044 93.91
vote 2 0.044 93.96 0.044 93.96
vow 2 0.044 94.00 0.044 94.00
wad 2 0.044 94.05 0.044 94.05
wade 2 0.044 94.09 0.044 94.09
wag 2 0.044 94.13 0.044 94.13
wage 2 0.044 94.18 0.044 94.18
wail 2 0.044 94.22 0.044 94.22
waist 2 0.044 94.26 0.044 94.26
wait 2 0.044 94.31 0.044 94.31
waive 2 0.044 94.35 0.044 94.35
wake 2 0.044 94.40 0.044 94.40
walk 2 0.044 94.44 0.044 94.44
wall 2 0.044 94.48 0.044 94.48
waltz 2 0.044 94.53 0.044 94.53
wand 2 0.044 94.57 0.044 94.57
wane 2 0.044 94.61 0.044 94.61
want 2 0.044 94.66 0.044 94.66
war 2 0.044 94.70 0.044 94.70
ward 2 0.044 94.75 0.044 94.75
ware 2 0.044 94.79 0.044 94.79
warn 2 0.044 94.83 0.044 94.83
warp 2 0.044 94.88 0.044 94.88
wart 2 0.044 94.92 0.044 94.92
wash 2 0.044 94.96 0.044 94.96
wasp 2 0.044 95.01 0.044 95.01
waste 2 0.044 95.05 0.044 95.05
watch 2 0.044 95.10 0.044 95.10
watt 2 0.044 95.14 0.044 95.14
wave 2 0.044 95.18 0.044 95.18
wax 2 0.044 95.23 0.044 95.23
way 2 0.044 95.27 0.044 95.27
wealth 2 0.044 95.32 0.044 95.32
wear 2 0.044 95.36 0.044 95.36
weave 2 0.044 95.40 0.044 95.40
web 2 0.044 95.45 0.044 95.45
wed 2 0.044 95.49 0.044 95.49
wedge 2 0.044 95.53 0.044 95.53
weed 2 0.044 95.58 0.044 95.58
week 2 0.044 95.62 0.044 95.62
weep 2 0.044 95.67 0.044 95.67
weigh 2 0.044 95.71 0.044 95.71
weight 2 0.044 95.75 0.044 95.75
weld 4 0.088 95.84 0.088 95.84
well 2 0.044 95.88 0.044 95.88
welt 2 0.044 95.93 0.044 95.93
west 2 0.044 95.97 0.044 95.97
whack 4 0.088 96.06 0.088 96.06
wharf 2 0.044 96.10 0.044 96.10
wheat 2 0.044 96.15 0.044 96.15
wheel 2 0.044 96.19 0.044 96.19
whelp 2 0.044 96.23 0.044 96.23
whiff 2 0.044 96.28 0.044 96.28
while 2 0.044 96.32 0.044 96.32
whim 2 0.044 96.37 0.044 96.37
whine 2 0.044 96.41 0.044 96.41
whip 2 0.044 96.45 0.044 96.45
whirl 2 0.044 96.50 0.044 96.50
whit 2 0.044 96.54 0.044 96.54
whiz 2 0.044 96.58 0.044 96.58
whoop 2 0.044 96.63 0.044 96.63
whoosh 2 0.044 96.67 0.044 96.67
whore 2 0.044 96.72 0.044 96.72
whorl 2 0.044 96.76 0.044 96.76
wick 2 0.044 96.80 0.044 96.80
wield 2 0.044 96.85 0.044 96.85
wife 2 0.044 96.89 0.044 96.89
wig 2 0.044 96.94 0.044 96.94
will 2 0.044 96.98 0.044 96.98
wilt 2 0.044 97.02 0.044 97.02
win 2 0.044 97.07 0.044 97.07
wine 2 0.044 97.11 0.044 97.11
wing 2 0.044 97.15 0.044 97.15
wink 2 0.044 97.20 0.044 97.20
wipe 2 0.044 97.24 0.044 97.24
wire 2 0.044 97.29 0.044 97.29
wish 2 0.044 97.33 0.044 97.33
wisp 2 0.044 97.37 0.044 97.37
wit 4 0.088 97.46 0.088 97.46
witch 2 0.044 97.50 0.044 97.50
woe 2 0.044 97.55 0.044 97.55
wolf 2 0.044 97.59 0.044 97.59
womb 2 0.044 97.64 0.044 97.64
woo 2 0.044 97.68 0.044 97.68
wood 2 0.044 97.72 0.044 97.72
wool 2 0.044 97.77 0.044 97.77
word 2 0.044 97.81 0.044 97.81
work 2 0.044 97.85 0.044 97.85
world 2 0.044 97.90 0.044 97.90
worm 2 0.044 97.94 0.044 97.94
would 2 0.044 97.99 0.044 97.99
wow 2 0.044 98.03 0.044 98.03
wrack 2 0.044 98.07 0.044 98.07
wrap 2 0.044 98.12 0.044 98.12
wrath 2 0.044 98.16 0.044 98.16
wreak 2 0.044 98.20 0.044 98.20
wreath 2 0.044 98.25 0.044 98.25
wreck 2 0.044 98.29 0.044 98.29
wren 2 0.044 98.34 0.044 98.34
wrest 2 0.044 98.38 0.044 98.38
wretch 2 0.044 98.42 0.044 98.42
wring 2 0.044 98.47 0.044 98.47
wrist 2 0.044 98.51 0.044 98.51
writ 2 0.044 98.56 0.044 98.56
write 2 0.044 98.60 0.044 98.60
writhe 2 0.044 98.64 0.044 98.64
yacht 2 0.044 98.69 0.044 98.69
yak 4 0.088 98.77 0.088 98.77
yam 2 0.044 98.82 0.044 98.82
yang 2 0.044 98.86 0.044 98.86
yank 2 0.044 98.91 0.044 98.91
yard 2 0.044 98.95 0.044 98.95
yarn 2 0.044 98.99 0.044 98.99
yaw 2 0.044 99.04 0.044 99.04
yawl 2 0.044 99.08 0.044 99.08
yawn 2 0.044 99.12 0.044 99.12
yea 2 0.044 99.17 0.044 99.17
year 2 0.044 99.21 0.044 99.21
yearn 2 0.044 99.26 0.044 99.26
yeast 2 0.044 99.30 0.044 99.30
yell 2 0.044 99.34 0.044 99.34
yelp 2 0.044 99.39 0.044 99.39
yen 2 0.044 99.43 0.044 99.43
yes 2 0.044 99.47 0.044 99.47
yield 2 0.044 99.52 0.044 99.52
yoke 2 0.044 99.56 0.044 99.56
yolk 2 0.044 99.61 0.044 99.61
yore 2 0.044 99.65 0.044 99.65
youth 2 0.044 99.69 0.044 99.69
zeal 2 0.044 99.74 0.044 99.74
zest 2 0.044 99.78 0.044 99.78
zinc 2 0.044 99.82 0.044 99.82
zing 2 0.044 99.87 0.044 99.87
zip 2 0.044 99.91 0.044 99.91
zone 2 0.044 99.96 0.044 99.96
zoo 2 0.044 100.00 0.044 100.00
<NA> 0 0.00 100.00
Total 4568 100.00 100.00 100.00 100.00

.$AgeSubject

Type: Factor
Valid Total
AgeSubject Freq % % Cum. % % Cum.
old 2284 50.00 50.00 50.00 50.00
young 2284 50.00 100.00 50.00 100.00
<NA> 0 0.00 100.00
Total 4568 100.00 100.00 100.00 100.00

.$WordCategory

Type: Factor
Valid Total
WordCategory Freq % % Cum. % % Cum.
N 2904 63.57 63.57 63.57 63.57
V 1664 36.43 100.00 36.43 100.00
<NA> 0 0.00 100.00
Total 4568 100.00 100.00 100.00 100.00

.$LengthInLetters

Type: Integer
Valid Total
LengthInLetters Freq % % Cum. % % Cum.
2 6 0.13 0.13 0.13 0.13
3 676 14.80 14.93 14.80 14.93
4 2020 44.22 59.15 44.22 59.15
5 1504 32.92 92.08 32.92 92.08
6 338 7.40 99.47 7.40 99.47
7 24 0.53 100.00 0.53 100.00
<NA> 0 0.00 100.00
Total 4568 100.00 100.00 100.00 100.00

.$Ncount

Type: Integer
Valid Total
Ncount Freq % % Cum. % % Cum.
0 348 7.62 7.62 7.62 7.62
1 444 9.72 17.34 9.72 17.34
2 448 9.81 27.15 9.81 27.15
3 440 9.63 36.78 9.63 36.78
4 420 9.19 45.97 9.19 45.97
5 296 6.48 52.45 6.48 52.45
6 308 6.74 59.19 6.74 59.19
7 252 5.52 64.71 5.52 64.71
8 254 5.56 70.27 5.56 70.27
9 226 4.95 75.22 4.95 75.22
10 174 3.81 79.03 3.81 79.03
11 160 3.50 82.53 3.50 82.53
12 184 4.03 86.56 4.03 86.56
13 140 3.06 89.62 3.06 89.62
14 128 2.80 92.43 2.80 92.43
15 96 2.10 94.53 2.10 94.53
16 88 1.93 96.45 1.93 96.45
17 60 1.31 97.77 1.31 97.77
18 54 1.18 98.95 1.18 98.95
19 24 0.53 99.47 0.53 99.47
20 6 0.13 99.61 0.13 99.61
21 8 0.18 99.78 0.18 99.78
22 10 0.22 100.00 0.22 100.00
<NA> 0 0.00 100.00
Total 4568 100.00 100.00 100.00 100.00

.$ConffV

Type: Numeric
Valid Total
ConffV Freq % % Cum. % % Cum.
0 3030 66.33 66.33 66.33 66.33
0.693147180559945 748 16.37 82.71 16.37 82.71
1.09861228866811 182 3.98 86.69 3.98 86.69
1.38629436111989 126 2.76 89.45 2.76 89.45
1.6094379124341 190 4.16 93.61 4.16 93.61
1.79175946922805 54 1.18 94.79 1.18 94.79
1.94591014905531 34 0.74 95.53 0.74 95.53
2.07944154167984 12 0.26 95.80 0.26 95.80
2.19722457733622 28 0.61 96.41 0.61 96.41
2.30258509299405 40 0.88 97.29 0.88 97.29
2.39789527279837 12 0.26 97.55 0.26 97.55
2.484906649788 8 0.18 97.72 0.18 97.72
2.56494935746154 34 0.74 98.47 0.74 98.47
2.63905732961526 4 0.088 98.56 0.088 98.56
2.70805020110221 12 0.26 98.82 0.26 98.82
2.77258872223978 6 0.13 98.95 0.13 98.95
2.83321334405622 24 0.53 99.47 0.53 99.47
2.89037175789616 10 0.22 99.69 0.22 99.69
3.04452243772342 2 0.044 99.74 0.044 99.74
3.09104245335832 4 0.088 99.82 0.088 99.82
3.13549421592915 4 0.088 99.91 0.088 99.91
3.2188758248682 2 0.044 99.96 0.044 99.96
3.3322045101752 2 0.044 100.00 0.044 100.00
<NA> 0 0.00 100.00
Total 4568 100.00 100.00 100.00 100.00

.$CV

Type: Factor
Valid Total
CV Freq % % Cum. % % Cum.
C 4446 97.33 97.33 97.33 97.33
V 122 2.67 100.00 2.67 100.00
<NA> 0 0.00 100.00
Total 4568 100.00 100.00 100.00 100.00

.$Obstruent

Type: Factor
Valid Total
Obstruent Freq % % Cum. % % Cum.
cont 1068 23.38 23.38 23.38 23.38
obst 3500 76.62 100.00 76.62 100.00
<NA> 0 0.00 100.00
Total 4568 100.00 100.00 100.00 100.00

.$Frication

Type: Factor
Valid Total
Frication Freq % % Cum. % % Cum.
burst 1840 40.28 40.28 40.28 40.28
frication 1660 36.34 76.62 36.34 76.62
long 88 1.93 78.55 1.93 78.55
short 980 21.45 100.00 21.45 100.00
<NA> 0 0.00 100.00
Total 4568 100.00 100.00 100.00 100.00

.$Voice

Type: Factor
Valid Total
Voice Freq % % Cum. % % Cum.
voiced 2060 45.10 45.10 45.10 45.10
voiceless 2508 54.90 100.00 54.90 100.00
<NA> 0 0.00 100.00
Total 4568 100.00 100.00 100.00 100.00

Generated by summarytools 1.1.4 (R version 4.5.1)
2025-07-17

2.8.9.2 Using gtsummary

We use the gtsummary package to create nice tables for publication. This package is very useful for creating publication ready tables with descriptive statistics, regression models, etc. See this link for more details on the various options possible with this package.

2.8.9.2.1 Basic summaries
english %>%  
  tbl_summary(include = c(AgeSubject, RTlexdec, Familiarity, WordCategory, Voice))
Characteristic N = 4,5681
AgeSubject
    old 2,284 (50%)
    young 2,284 (50%)
RTlexdec 6.55 (6.43, 6.65)
Familiarity 3.70 (3.00, 4.57)
WordCategory
    N 2,904 (64%)
    V 1,664 (36%)
Voice
    voiced 2,060 (45%)
    voiceless 2,508 (55%)
1 n (%); Median (Q1, Q3)
2.8.9.2.2 With statistics (comparing means)

We use the two numeric outcomes and assess differences between the two groups.

english %>%  
  tbl_summary(include = c(RTlexdec, RTnaming),
              by = AgeSubject, # split table by AgeSubject
              missing = "no" # don't list missing data separately
  ) %>%  
  add_n() %>%  # add column with total number of non-missing observations
  add_p() %>%  # test for a difference between groups
  modify_header(label = "**Variable**") %>%  # update the column header
  bold_labels()
Variable N old
N = 2,2841		 young
N = 2,2841		 p-value2
RTlexdec 4,568 6.64 (6.57, 6.73) 6.43 (6.36, 6.51) <0.001
RTnaming 4,568 6.49 (6.45, 6.53) 6.15 (6.12, 6.18) <0.001
1 Median (Q1, Q3)
2 Wilcoxon rank sum test

2.8.10 Visualisation

In the tidyverse, the package for making elegant plots is called ggplot2. It works a lot like how pipes work, but since it was originally designed as a separate package, it uses + instead of %>%.

2.8.10.1 First steps

2.8.10.1.1 Empty plot area

Let’s produce a basic plot with nothing drawn on it. This is the basic plotting area in R. We need to then add layers on top of it to show our plot

english %>% 
  ggplot() +
  theme_bw()

2.8.10.1.2 Adding x and y values

Let’s add the x and y values from our dataset. X = subjective familiarity rating, y = RT in Visual Lexical Decision task

english %>% 
  ggplot(aes(x = Familiarity, 
             y = RTlexdec)) +
  theme_bw()

There are no differences between the two. We need to tell ggplot2 to add a geometric function for plotting

2.8.10.1.3 Adding geoms

Geoms are integrated within ggplot2 to obtain various types of plots.

english %>% 
  ggplot(aes(x = Familiarity, 
             y = RTlexdec)) +
  theme_bw() +
  geom_point()

2.8.10.1.4 Adding line of best fit

We will add a line of best fit. This is used to evaluate presence/absence of a relationship between two numeric variables

english %>% 
  ggplot(aes(x = Familiarity, 
             y = RTlexdec)) +
  theme_bw() +
  geom_point() +
  geom_smooth(method = "lm") ## line of best fit based on the lm() method
## `geom_smooth()` using formula = 'y ~ x'

The result shows a nice negative correlation! RT lexical decision decreases when familiarity rating increases.

We can ask, are there differences related to the word category, i.e., verb vs noun?

2.8.10.1.5 By word category

We change colour by levels of word category;

english %>% 
  ggplot(aes(x = Familiarity, 
             y = RTlexdec,
             colour = WordCategory)) + ## add colour to the base aesthetics
  theme_bw() +
  geom_point() +
  geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'

2.8.10.1.6 Making final touches

Let’s add a title and a subtitle, change x and y labels, change size of overall plot, and colours of the categories.

english %>% 
  ggplot(aes(x = Familiarity, 
             y = RTlexdec,
             colour = WordCategory)) + ## add colour to the base aesthetics
  theme_bw() +
  geom_point() +
  geom_smooth(method = "lm") +
  labs(x = "Familiarity rating", y = "RT Lexical Decision", title = "Familiarity rating vs RT in a lexical decision task", subtitle = "with a trend line") + ## add labels
  theme(text = element_text(size = 15)) + ## increase size of plot
  theme(legend.position = "bottom", legend.title = element_blank()) + ## remove legend title and change position
  scale_color_manual(labels = c("Nouns", "Verbs"), values = c("blue", "red")) ## change colours and names of legend
## `geom_smooth()` using formula = 'y ~ x'

To choose colours, use the addin colourpicker from above. See this link for full list of colours available. Use colours that are colour-blind friendly here

2.8.10.2 Additional plots

We looked above at one example of plots (with points). We could use additional types of plots.

2.8.10.2.1 A bar plot

Will show barplots of the dataset

english %>%
  ggplot(aes(x = RTlexdec, 
             colour = AgeSubject)) +
  theme_bw() +
  geom_bar()

And another view with error bars! This is a nice example that shows how you can combine multiple chains with the pipe:

  • Group by Age of subject
  • Compute mean and SD
  • use ggplot2 syntax to plot a barplot and error bars
english %>%
  group_by(AgeSubject) %>%
  summarise(
    sd = sd(RTlexdec),
    RTlexdecM = mean(RTlexdec)
  ) %>% 
  ggplot(aes(x = AgeSubject, 
             y = RTlexdecM)) +
  theme_bw() +
  geom_col(fill = "lightgray", color = "black") +
  geom_errorbar(aes(ymin = RTlexdecM-sd, ymax = RTlexdecM+sd), width = 0.2)

2.8.10.2.2 A histogram

This looks at the distribution of the variable. We look at a histogram

english %>%
  ggplot(aes(x = RTlexdec, 
             colour = AgeSubject)) +
  theme_bw() +
  geom_histogram(fill = "white") +
  scale_color_manual(values = c("red", "blue"))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

2.8.10.2.3 A density plot

This looks at the distribution of the variable. We see that the two variables have different means. We can superpose the density plot on top of the histogram or have the density plot on its own.

2.8.10.2.3.1 Histogram and density plot
english %>%
  ggplot(aes(x = RTlexdec, 
             colour = AgeSubject)) +
  theme_bw() +
  geom_histogram(aes(y = ..density..), fill = "white") +
  scale_color_manual(values = c("red", "blue")) +
  geom_density()
## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

2.8.10.2.3.2 Density plot only
english %>%
  ggplot(aes(x = RTlexdec, 
             colour = AgeSubject)) +
  theme_bw() +
  geom_density()

2.8.10.2.4 A boxplot

This allows you to see various information, including the Median, SD, Quartiles (25% and 75%) and outliers. Looking at the medians, we see clear difference between the two distributions.

english %>%
  ggplot(aes(x = AgeSubject, 
             y = RTlexdec)) +
  theme_bw() +
  geom_boxplot()

2.8.10.2.5 A Violin plot

This allows you to see various information, including the Median, SD, Quartiles (25% and 75%) and outliers. Looking at the medians, we see clear difference between the two distributions.

english %>%
  ggplot(aes(x = AgeSubject, 
             y = RTlexdec)) +
  theme_bw() +
  geom_violin()

2.8.10.3 Advanced plots

We will use the package ggdist see herewhich allows us to obtain various plots adding the distributions.

2.8.10.3.1 A dotplot

This dotplot allow to display the distribution of the dataframe showing their density

english %>%
  ggplot(aes(x = AgeSubject, 
             y = RTlexdec)) +
  theme_bw() +
  stat_dotsinterval()

2.8.10.3.2 Rain cloud plots

Rain clouds are used to combine both a density plot with dotplots in the same figure. These can be informative as to the overall distribution of the dataset.

english %>%
  ggplot(aes(x = AgeSubject, 
             y = RTlexdec,
             fill = AgeSubject)) +
  theme_bw() +
  stat_slab(aes(thickness = after_stat(pdf*n)), scale = 0.7) +
  stat_dotsinterval(side = "bottom", scale = 0.7, slab_linewidth = NA) +
  scale_fill_brewer(palette = "Set2")

2.8.10.3.3 Eye plots and half-eye plots

We can also obtain density plots with eye plots and half-eye plots. These are similar to rain clouds but allow you to see the distribution of the data in a different way. For these plots, using stat_slabinterval() with side = "both", "top", "bottom", "right", or "left" you can adjust positions of the eye plots. Note that with "top", or "bottom", the y value is a categorical predictor and the x is a numeric outcome.

2.8.10.3.3.1 Eye plot

We can use stat_eye() or stat_slabinterval(side = "both") to obtain eye plots.

english %>%
  ggplot(aes(x = AgeSubject, 
             y = RTlexdec,
             fill = AgeSubject)) +
  theme_bw() +
  stat_eye() 

english %>%
  ggplot(aes(x = AgeSubject, 
             y = RTlexdec,
             fill = AgeSubject)) +
  theme_bw() +
  stat_slabinterval(side = "both")

2.8.10.3.3.2 Half-eye plot

We can use stat_halfeye() or stat_slabinterval() to obtain half-eye plots. Use stat_slabinterval(side = "left") or stat_slabinterval(side = "right") to obtain half-eye plots on the left or right side of the plot, respectively.

english %>%
  ggplot(aes(x = AgeSubject, 
             y = RTlexdec,
             fill = AgeSubject)) +
  theme_bw() +
  stat_halfeye() 

english %>%
  ggplot(aes(x = AgeSubject, 
             y = RTlexdec,
             fill = AgeSubject)) +
  theme_bw() +
  stat_slabinterval()

2.8.10.4 Facet_grid

The plots we used so far allowed to plot data as a function of one categorical variable, e.g., AgeSubject. What if we wanted to show the different patterns emerging when combining AgeSubject (old vs young), WordCategory (Noun or Verb), CV (Consonant or Vowel) and Voice (Voiced and Voiceless) ? What if we also wanted to modify the labels and order of levels of variables?

We will start slowly below to show how we can combine two categorical variables and extend them to additional ones

2.8.10.4.1 Two categorical variables
2.8.10.4.1.1 First steps

Here we obtain a boxplot with two categorical variables AgeSubject and WordCategory

english %>%
  ggplot(aes(x = AgeSubject, 
             y = RTlexdec)) +
  theme_bw() +
  geom_boxplot() +
  facet_grid(~ WordCategory)

2.8.10.4.1.2 Changing order of levels within a variable and its labels

What would you do to change both order of levels within a variable and its labels? We want to change order for AgeSubject to be Young vs Old (rather than old vs young) and change labels of WordCategory from N vs V to Noun vs Verb?

english %>%
  mutate(AgeSubject = factor(AgeSubject, levels = c("young", "old"), labels = c("Young", "Old")),
         WordCategory = factor(WordCategory, labels = c("Noun", "Verb"))) %>% 
  ggplot(aes(x = AgeSubject, 
             y = RTlexdec)) +
  theme_bw() +
  geom_boxplot() +
  facet_grid(~ WordCategory)

2.8.10.4.2 Three or more categorical variables

Let us obtain a boxplot with four categorical variables AgeSubject, WordCategory, CV and Voice. We still need to change names. We can also add margins = TRUE to obtain mean values for all categories (under all). We can also use scale = "free" to change limits of the y-axis.

Of course this figure is so complex that it needs a lot of interpretation. But it allows you to see how we can use facet_grid to get more categorical variables in. This visualisation suggests that there are no clear differences when plotting results by this 4-way interaction as we always have clear differences between “Young” and “Old” participants, with “Young” being faster than “Old” participants.

english %>%
  mutate(AgeSubject = factor(AgeSubject, levels = c("young", "old"), labels = c("Young", "Old")),
         WordCategory = factor(WordCategory, labels = c("Noun", "Verb")),
         CV = factor(CV, labels = c("Consonant", "Vowel"))) %>% 
  ggplot(aes(x = AgeSubject, 
             y = RTlexdec)) +
  theme_bw() +
  geom_boxplot() +
  facet_grid(CV + Voice ~ WordCategory, margins = TRUE, scales = "free")

2.8.10.4.3 Comparing two numeric outcomes

What if we want to compare performance in relation to reaction time for the lexical decision task (RTlexdec) and reaction time for naming (RTnaming). We want to see if there are differences related to the AgeSubject, WordCategory. We use pivot_longer here to do change the format of our table and then change names and use facet_grid.

english %>%
  select(RTlexdec, RTnaming, AgeSubject, WordCategory) %>% 
  pivot_longer(cols = c(RTlexdec, RTnaming),
               names_to = "variable",
               values_to = "values") %>% 
  mutate(AgeSubject = factor(AgeSubject, levels = c("young", "old"), labels = c("Young", "Old")),
         WordCategory = factor(WordCategory, labels = c("Noun", "Verb"))) %>% 
  ggplot(aes(x = variable, 
             y = values)) +
  theme_bw() +
  geom_boxplot() +
  facet_grid(AgeSubject ~ WordCategory, margins = TRUE, scales = "free")

2.8.10.4.4 Exporting images

When you use Rmarkdown, your figures are already embedded within the generated output. If you are using an R script and/or want to add the figure in a different document, you can use the following code:

jpeg(filename = paste0("outputs/test.jpeg"), width = 15, height = 15, units = "cm", res = 300)

english %>%
  select(RTlexdec, RTnaming, AgeSubject, WordCategory) %>% 
  pivot_longer(cols = c(RTlexdec, RTnaming),
               names_to = "variable",
               values_to = "values") %>% 
  mutate(AgeSubject = factor(AgeSubject, levels = c("young", "old"), labels = c("Young", "Old")),
         WordCategory = factor(WordCategory, labels = c("Noun", "Verb"))) %>% 
  ggplot(aes(x = variable, 
             y = values)) +
  theme_bw() +
  geom_boxplot() +
  facet_grid(AgeSubject ~ WordCategory, margins = TRUE, scales = "free")
dev.off()
## png 
##   2

The image is automatically saved into your working directory and you can import it to your word () document.

You can use any device to save the output. Jpeg, PNG, PDF, TIFF, etc.. From an R script, you can run the code and then the image will appear within the “Plots” area. Simply click on export and you will be able to save the image.

2.8.10.4.5 Conclusion

As you can see, visualisations in R using the Tidyverse provide you with many options and you can explore these further.

See here for a full list of geoms. This will help you in thinking about visualisation.

See extensions to ggplot2 here for additional plugins to enhance plots.