Textrecipes series: lexicons

Photo by Moritz Schmidt on Unsplash

This is the second blog post in the textrecipes series where I go over the various text preprocessing workflows you can do with textrecipes. This post will be covering how to use lexicons to create features. This post will not cover end-to-end modeling but will only look at how to add lexicons information into your recipe.

Packages πŸ“¦

We are going fairly light package wise this time only needing tidymodels, textrecipes, and lastly tidytext for EDA. We will also be using textdata to provide lexicons.

library(tidymodels)
library(textrecipes)
library(tidytext)
library(textdata)
theme_set(theme_minimal())

What is a lexicon?

A lexicon is a list of words with one or more corresponding values for each word. You could imagine a sentiment lexicon having entries such as β€œawesome = 1”, β€œterrible = -1” and β€œokay = 0”. Having this information could be useful if you want to predict if some text is positively charged or negatively charged.

One real-world lexicon is the AFINN lexicon. It rates English words on a scale from -5 (negative) to 5 (positive). The words have been manually labeled by Finn Γ…rup Nielsen in 2009-2011. It is available in textdata as the function lexicon_afinn()

lexicon_afinn()
## # A tibble: 2,477 x 2
##    word       value
##    <chr>      <dbl>
##  1 abandon       -2
##  2 abandoned     -2
##  3 abandons      -2
##  4 abducted      -2
##  5 abduction     -2
##  6 abductions    -2
##  7 abhor         -3
##  8 abhorred      -3
##  9 abhorrent     -3
## 10 abhors        -3
## # … with 2,467 more rows

The first time you use a function in textdata you are given a prompt to download. Please carefully read the prompt to make sure you are able to conform to the license and the demands of the authors.

And we have plenty of words. Note that this list doesn’t give every possible word-value pair, this is partly because words with no apparent sentiment such as (cat, house, government) haven’t been encluded. Always make sure to manually inspect a premade lexicon before using it in your application. Make sure that the domain you are working in is similar to the domain the lexicon was created for. An example of a domain-specific lexicon is the Loughran-McDonald sentiment lexicon (lexicon_loughran()) which was created for use with financial documents.

The data

We will be using the data Animal Crossing data from the last post again.

user_reviews <- readr::read_tsv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/user_reviews.tsv')

user_reviews <- user_reviews %>%
  mutate(grade = factor(grade > 7, c(TRUE, FALSE), c("High", "Low")))

set.seed(1234)
review_split <- initial_split(user_reviews)

review_training <- training(review_split)
review_testing <- training(review_split)

We can use lexicons in our text mining with tidytext too. First, we will tokenize

review_tokens <- review_training %>%
  select(grade, user_name, text) %>%
  unnest_tokens(tokens, text)

review_tokens
## # A tibble: 270,013 x 3
##    grade user_name tokens 
##    <fct> <chr>     <chr>  
##  1 Low   mds27272  my     
##  2 Low   mds27272  gf     
##  3 Low   mds27272  started
##  4 Low   mds27272  playing
##  5 Low   mds27272  before 
##  6 Low   mds27272  me     
##  7 Low   mds27272  no     
##  8 Low   mds27272  option 
##  9 Low   mds27272  to     
## 10 Low   mds27272  create 
## # … with 270,003 more rows

then we can use a left_join() to add a sentiment variable

review_tokens %>%
  left_join(lexicon_afinn(), by = c("tokens" = "word"))
## # A tibble: 270,013 x 4
##    grade user_name tokens  value
##    <fct> <chr>     <chr>   <dbl>
##  1 Low   mds27272  my         NA
##  2 Low   mds27272  gf         NA
##  3 Low   mds27272  started    NA
##  4 Low   mds27272  playing    NA
##  5 Low   mds27272  before     NA
##  6 Low   mds27272  me         NA
##  7 Low   mds27272  no         -1
##  8 Low   mds27272  option     NA
##  9 Low   mds27272  to         NA
## 10 Low   mds27272  create     NA
## # … with 270,003 more rows

If we want to look at the overall document-wise sentiment level we can sum the values within each document

review_tokens_sentiment <- review_tokens %>%
  left_join(lexicon_afinn(), by = c("tokens" = "word")) %>%
  group_by(user_name, grade) %>% 
  summarise(sentiment = sum(value, na.rm = TRUE))

review_tokens_sentiment
## # A tibble: 2,250 x 3
## # Groups:   user_name [2,250]
##    user_name     grade sentiment
##    <chr>         <fct>     <dbl>
##  1 11_11         Low          16
##  2 12hwilso      Low           3
##  3 1mooey        High         10
##  4 24ths         Low           1
##  5 3nd3r02       Low          25
##  6 425_Flex      Low         -11
##  7 7kurtis7      Low          -3
##  8 7Swords       Low          -2
##  9 8bheotapus    Low          -7
## 10 A_Mighty_Pleb Low           0
## # … with 2,240 more rows

Since the AFINN lexicon is centered around 0 we can very generally say that positive scores tend to be more positive and a negative score will tend to accompany negative texts.

There are many oversimplifications going on here. We are not taking sentence length into account. There is no reason to believe a 100-word review with a score of 10 is any less positive than a 1000-word review with a score of 100. It is also not obvious that β€œa breathtaking(5) bastard(-5)” is a neutral statement.

We can visualize the final distribution

review_tokens_sentiment %>% 
  ggplot(aes(sentiment)) +
  geom_bar()

But it would be more informative if we include grade to see if there is a difference

review_tokens_sentiment %>% 
  ggplot(aes(sentiment, fill = grade)) +
  geom_boxplot()

It appears that the lexicon is not entirely useless. The sentiments for highly-rated reviews are a little bit higher.

Reshaping a lexicon

A lexicon needs to be in a specific format to be used in textrecipes. We need a tibble with the first column containing tokens and any additional columns should contain the numerics. lexicon_afinn() already meets the demand and can be used directly. The lexicon_loughran() doesn’t give us the information we want.

lexicon_loughran()
## # A tibble: 4,150 x 2
##    word         sentiment
##    <chr>        <chr>    
##  1 abandon      negative 
##  2 abandoned    negative 
##  3 abandoning   negative 
##  4 abandonment  negative 
##  5 abandonments negative 
##  6 abandons     negative 
##  7 abdicated    negative 
##  8 abdicates    negative 
##  9 abdicating   negative 
## 10 abdication   negative 
## # … with 4,140 more rows

With the sentiment being a character denoting the sentiment of the word. What might not be obvious at first glance of this lexicon is that a word can have multiple sentiments such as the word β€œencumber” which has 3

lexicon_loughran() %>%
  filter(word == "encumber")
## # A tibble: 3 x 2
##   word     sentiment   
##   <chr>    <chr>       
## 1 encumber negative    
## 2 encumber litigious   
## 3 encumber constraining

We can use tidyr to turn this into a wide format.

lexicon_loughran_wide <- lexicon_loughran() %>%
  mutate(var = 1) %>% 
  tidyr::pivot_wider(names_from = sentiment, 
                     values_from = var, 
                     values_fill = list(var = 0))

lexicon_loughran_wide
## # A tibble: 3,917 x 7
##    word         negative positive uncertainty litigious constraining superfluous
##    <chr>           <dbl>    <dbl>       <dbl>     <dbl>        <dbl>       <dbl>
##  1 abandon             1        0           0         0            0           0
##  2 abandoned           1        0           0         0            0           0
##  3 abandoning          1        0           0         0            0           0
##  4 abandonment         1        0           0         0            0           0
##  5 abandonments        1        0           0         0            0           0
##  6 abandons            1        0           0         0            0           0
##  7 abdicated           1        0           0         0            0           0
##  8 abdicates           1        0           0         0            0           0
##  9 abdicating          1        0           0         0            0           0
## 10 abdication          1        0           0         0            0           0
## # … with 3,907 more rows

This is now be used. Textrecipes are able to handle multi-axis lexicons with no problems.

Using textrecipes

To use these lexicons in our modeling step will we use the step_word_embeddings() step. This is normally used for word embeddings, but you can treat a lexicon (when transformed according to the last section) as a selection of word vector or in other words a word embedding.

To see the effect lets create a minimal recipe that only sums along the lexicons using the AFINN lexicon

recipe(~ text, data = review_training) %>%
  step_tokenize(text) %>%
  step_word_embeddings(text, embeddings = lexicon_afinn()) %>%
  prep() %>%
  juice()
## # A tibble: 2,250 x 1
##    w_embed_sum_value
##                <dbl>
##  1               -11
##  2                 7
##  3                -5
##  4                 9
##  5                 2
##  6                16
##  7                11
##  8                 0
##  9                 4
## 10                -3
## # … with 2,240 more rows

This gives us 1 column of the sum of the values. If we instead used the lexicon_loughran_wide lexicon the get back 6 variables.

recipe(~ text, data = review_training) %>%
  step_tokenize(text) %>%
  step_word_embeddings(text, embeddings = lexicon_loughran_wide, prefix = "loughran") %>%
  prep() %>%
  juice()
## # A tibble: 2,250 x 6
##    loughran_sum_ne… loughran_sum_po… loughran_sum_un… loughran_sum_li…
##               <dbl>            <dbl>            <dbl>            <dbl>
##  1                2                0                0                0
##  2                2                8                0                0
##  3                2                0                0                0
##  4                0                3                0                0
##  5                2                2                1                0
##  6                2                5                6                0
##  7                0                4                0                0
##  8                1                0                0                0
##  9                2                5                0                0
## 10                0                0                0                0
## # … with 2,240 more rows, and 2 more variables:
## #   loughran_sum_constraining <dbl>, loughran_sum_superfluous <dbl>

To use the lexicon values along with side term frequencies can we use step_mutate() to create a separate variable to be used for lexicon calculations.

rec_spec <- recipe(grade ~ text + date, review_training) %>%
  # Days since release
  step_mutate(date = as.numeric(date - as.Date("2020-03-20"))) %>%
  # Tokenize to words
  step_tokenize(text) %>%
  
  # Create copy of text variable
  step_mutate(text_lexicon = text) %>%
  # Apply lexicon counting
  step_word_embeddings(text_lexicon, embeddings = lexicon_afinn(), prefix = "afinn") %>%
  
  # Remove stopwords
  step_stopwords(text) %>%
  # Remove less frequent words
  step_tokenfilter(text, max_tokens = 100) %>%
  # Calculate term frequencies
  step_tf(text, weight_scheme = "binary")

rec_spec
## Data Recipe
## 
## Inputs:
## 
##       role #variables
##    outcome          1
##  predictor          2
## 
## Operations:
## 
## Variable mutation for date
## Tokenization for text
## Variable mutation for text_lexicon
## Word embeddings aggregated from text_lexicon
## Stop word removal for text
## Text filtering for text
## Term frequency with text

By inspectiong the results we get:

rec_spec %>%
  prep() %>%
  juice()
## # A tibble: 2,250 x 103
##     date grade afinn_sum_value tf_text_1 tf_text_10 tf_text_2 tf_text_able
##    <dbl> <fct>           <dbl> <lgl>     <lgl>      <lgl>     <lgl>       
##  1     0 Low               -11 FALSE     FALSE      FALSE     FALSE       
##  2     0 Low                 7 TRUE      FALSE      FALSE     TRUE        
##  3     0 Low                -5 FALSE     FALSE      FALSE     FALSE       
##  4     0 Low                 9 FALSE     FALSE      FALSE     FALSE       
##  5     0 Low                 2 FALSE     FALSE      FALSE     TRUE        
##  6     0 Low                16 FALSE     FALSE      FALSE     FALSE       
##  7     0 Low                11 FALSE     FALSE      FALSE     FALSE       
##  8     0 Low                 0 FALSE     FALSE      FALSE     FALSE       
##  9     0 Low                 4 FALSE     FALSE      FALSE     FALSE       
## 10     0 Low                -3 FALSE     FALSE      FALSE     FALSE       
## # … with 2,240 more rows, and 96 more variables: tf_text_absolutely <lgl>,
## #   tf_text_account <lgl>, tf_text_actually <lgl>, tf_text_also <lgl>,
## #   tf_text_amazing <lgl>, tf_text_animal <lgl>, tf_text_another <lgl>,
## #   tf_text_anything <lgl>, tf_text_back <lgl>, tf_text_bad <lgl>,
## #   tf_text_best <lgl>, tf_text_bought <lgl>, tf_text_buy <lgl>,
## #   tf_text_can <lgl>, tf_text_console <lgl>, tf_text_crossing <lgl>,
## #   tf_text_day <lgl>, tf_text_else <lgl>, tf_text_enjoy <lgl>,
## #   tf_text_even <lgl>, tf_text_ever <lgl>, tf_text_every <lgl>,
## #   tf_text_everyone <lgl>, tf_text_everything <lgl>, tf_text_expand <lgl>,
## #   tf_text_experience <lgl>, tf_text_fact <lgl>, tf_text_family <lgl>,
## #   tf_text_feel <lgl>, tf_text_first <lgl>, tf_text_full <lgl>,
## #   tf_text_fun <lgl>, tf_text_game <lgl>, tf_text_games <lgl>,
## #   tf_text_get <lgl>, tf_text_gets <lgl>, tf_text_give <lgl>,
## #   tf_text_go <lgl>, tf_text_going <lgl>, tf_text_good <lgl>,
## #   tf_text_great <lgl>, tf_text_horizons <lgl>, tf_text_island <lgl>,
## #   tf_text_islands <lgl>, `tf_text_it’s` <lgl>, tf_text_just <lgl>,
## #   tf_text_let <lgl>, tf_text_like <lgl>, tf_text_little <lgl>,
## #   tf_text_lot <lgl>, tf_text_love <lgl>, tf_text_made <lgl>,
## #   tf_text_make <lgl>, tf_text_makes <lgl>, tf_text_many <lgl>,
## #   tf_text_money <lgl>, tf_text_much <lgl>, tf_text_multiplayer <lgl>,
## #   tf_text_multiple <lgl>, tf_text_never <lgl>, tf_text_new <lgl>,
## #   tf_text_nintendo <lgl>, tf_text_now <lgl>, tf_text_one <lgl>,
## #   tf_text_people <lgl>, tf_text_per <lgl>, tf_text_person <lgl>,
## #   tf_text_play <lgl>, tf_text_played <lgl>, tf_text_player <lgl>,
## #   tf_text_players <lgl>, tf_text_playing <lgl>, tf_text_progress <lgl>,
## #   tf_text_really <lgl>, tf_text_review <lgl>, tf_text_save <lgl>,
## #   tf_text_second <lgl>, tf_text_see <lgl>, tf_text_series <lgl>,
## #   tf_text_share <lgl>, tf_text_since <lgl>, tf_text_single <lgl>,
## #   tf_text_start <lgl>, tf_text_still <lgl>, tf_text_switch <lgl>,
## #   tf_text_system <lgl>, tf_text_thing <lgl>, tf_text_things <lgl>,
## #   tf_text_think <lgl>, tf_text_time <lgl>, tf_text_two <lgl>,
## #   tf_text_us <lgl>, tf_text_want <lgl>, tf_text_way <lgl>,
## #   tf_text_well <lgl>, tf_text_wife <lgl>

session information


─ Session info ───────────────────────────────────────────────────────────────
 setting  value                       
 version  R version 4.0.0 (2020-04-24)
 os       macOS Mojave 10.14.6        
 system   x86_64, darwin17.0          
 ui       X11                         
 language (EN)                        
 collate  en_US.UTF-8                 
 ctype    en_US.UTF-8                 
 tz       America/Los_Angeles         
 date     2020-05-11                  

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version    date       lib source                     
 assertthat      0.2.1      2019-03-21 [1] CRAN (R 4.0.0)             
 backports       1.1.6      2020-04-05 [1] CRAN (R 4.0.0)             
 base64enc       0.1-3      2015-07-28 [1] CRAN (R 4.0.0)             
 bayesplot       1.7.1      2019-12-01 [1] CRAN (R 4.0.0)             
 blogdown        0.18       2020-03-04 [1] CRAN (R 4.0.0)             
 bookdown        0.18       2020-03-05 [1] CRAN (R 4.0.0)             
 boot            1.3-25     2020-04-26 [1] CRAN (R 4.0.0)             
 broom         * 0.5.6      2020-04-20 [1] CRAN (R 4.0.0)             
 callr           3.4.3      2020-03-28 [1] CRAN (R 4.0.0)             
 class           7.3-17     2020-04-26 [1] CRAN (R 4.0.0)             
 cli             2.0.2      2020-02-28 [1] CRAN (R 4.0.0)             
 clipr           0.7.0      2019-07-23 [1] CRAN (R 4.0.0)             
 codetools       0.2-16     2018-12-24 [1] CRAN (R 4.0.0)             
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 colourpicker    1.0        2017-09-27 [1] CRAN (R 4.0.0)             
 crayon          1.3.4      2017-09-16 [1] CRAN (R 4.0.0)             
 crosstalk       1.1.0.1    2020-03-13 [1] CRAN (R 4.0.0)             
 desc            1.2.0      2018-05-01 [1] CRAN (R 4.0.0)             
 details       * 0.2.1      2020-01-12 [1] CRAN (R 4.0.0)             
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 DiceDesign      1.8-1      2019-07-31 [1] CRAN (R 4.0.0)             
 digest          0.6.25     2020-02-23 [1] CRAN (R 4.0.0)             
 dplyr         * 0.8.5      2020-03-07 [1] CRAN (R 4.0.0)             
 DT              0.13       2020-03-23 [1] CRAN (R 4.0.0)             
 dygraphs        1.1.1.6    2018-07-11 [1] CRAN (R 4.0.0)             
 ellipsis        0.3.0      2019-09-20 [1] CRAN (R 4.0.0)             
 emo             0.0.0.9000 2020-05-12 [1] Github (hadley/emo@3f03b11)
 evaluate        0.14       2019-05-28 [1] CRAN (R 4.0.0)             
 fansi           0.4.1      2020-01-08 [1] CRAN (R 4.0.0)             
 fastmap         1.0.1      2019-10-08 [1] CRAN (R 4.0.0)             
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 furrr           0.1.0      2018-05-16 [1] CRAN (R 4.0.0)             
 future          1.17.0     2020-04-18 [1] CRAN (R 4.0.0)             
 generics        0.0.2      2018-11-29 [1] CRAN (R 4.0.0)             
 ggplot2       * 3.3.0      2020-03-05 [1] CRAN (R 4.0.0)             
 ggridges        0.5.2      2020-01-12 [1] CRAN (R 4.0.0)             
 globals         0.12.5     2019-12-07 [1] CRAN (R 4.0.0)             
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 GPfit           1.0-8      2019-02-08 [1] CRAN (R 4.0.0)             
 gridExtra       2.3        2017-09-09 [1] CRAN (R 4.0.0)             
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 gtools          3.8.2      2020-03-31 [1] CRAN (R 4.0.0)             
 htmltools       0.4.0      2019-10-04 [1] CRAN (R 4.0.0)             
 htmlwidgets     1.5.1      2019-10-08 [1] CRAN (R 4.0.0)             
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 httr            1.4.1      2019-08-05 [1] CRAN (R 4.0.0)             
 igraph          1.2.5      2020-03-19 [1] CRAN (R 4.0.0)             
 infer         * 0.5.1      2019-11-19 [1] CRAN (R 4.0.0)             
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 janeaustenr     0.1.5      2017-06-10 [1] CRAN (R 4.0.0)             
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 listenv         0.8.0      2019-12-05 [1] CRAN (R 4.0.0)             
 lme4            1.1-23     2020-04-07 [1] CRAN (R 4.0.0)             
 loo             2.2.0      2019-12-19 [1] CRAN (R 4.0.0)             
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 magrittr        1.5        2014-11-22 [1] CRAN (R 4.0.0)             
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 MASS            7.3-51.6   2020-04-26 [1] CRAN (R 4.0.0)             
 Matrix          1.2-18     2019-11-27 [1] CRAN (R 4.0.0)             
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 nnet            7.3-14     2020-04-26 [1] CRAN (R 4.0.0)             
 parsnip       * 0.1.1      2020-05-06 [1] CRAN (R 4.0.0)             
 pillar          1.4.4      2020-05-05 [1] CRAN (R 4.0.0)             
 pkgbuild        1.0.8      2020-05-07 [1] CRAN (R 4.0.0)             
 pkgconfig       2.0.3      2019-09-22 [1] CRAN (R 4.0.0)             
 plyr            1.8.6      2020-03-03 [1] CRAN (R 4.0.0)             
 png             0.1-7      2013-12-03 [1] CRAN (R 4.0.0)             
 prettyunits     1.1.1      2020-01-24 [1] CRAN (R 4.0.0)             
 pROC            1.16.2     2020-03-19 [1] CRAN (R 4.0.0)             
 processx        3.4.2      2020-02-09 [1] CRAN (R 4.0.0)             
 prodlim         2019.11.13 2019-11-17 [1] CRAN (R 4.0.0)             
 promises        1.1.0      2019-10-04 [1] CRAN (R 4.0.0)             
 ps              1.3.3      2020-05-08 [1] CRAN (R 4.0.0)             
 purrr         * 0.3.4      2020-04-17 [1] CRAN (R 4.0.0)             
 R6              2.4.1      2019-11-12 [1] CRAN (R 4.0.0)             
 Rcpp            1.0.4.6    2020-04-09 [1] CRAN (R 4.0.0)             
 recipes       * 0.1.12     2020-05-01 [1] CRAN (R 4.0.0)             
 reshape2        1.4.4      2020-04-09 [1] CRAN (R 4.0.0)             
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 rpart           4.1-15     2019-04-12 [1] CRAN (R 4.0.0)             
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 rsample       * 0.0.6      2020-03-31 [1] CRAN (R 4.0.0)             
 rsconnect       0.8.16     2019-12-13 [1] CRAN (R 4.0.0)             
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 rstantools      2.0.0      2019-09-15 [1] CRAN (R 4.0.0)             
 rstudioapi      0.11       2020-02-07 [1] CRAN (R 4.0.0)             
 scales        * 1.1.1      2020-05-11 [1] CRAN (R 4.0.0)             
 sessioninfo     1.1.1      2018-11-05 [1] CRAN (R 4.0.0)             
 shiny           1.4.0.2    2020-03-13 [1] CRAN (R 4.0.0)             
 shinyjs         1.1        2020-01-13 [1] CRAN (R 4.0.0)             
 shinystan       2.5.0      2018-05-01 [1] CRAN (R 4.0.0)             
 shinythemes     1.1.2      2018-11-06 [1] CRAN (R 4.0.0)             
 SnowballC       0.7.0      2020-04-01 [1] CRAN (R 4.0.0)             
 StanHeaders     2.19.2     2020-02-11 [1] CRAN (R 4.0.0)             
 statmod         1.4.34     2020-02-17 [1] CRAN (R 4.0.0)             
 stopwords       2.0        2020-04-14 [1] CRAN (R 4.0.0)             
 stringi         1.4.6      2020-02-17 [1] CRAN (R 4.0.0)             
 stringr         1.4.0      2019-02-10 [1] CRAN (R 4.0.0)             
 survival        3.1-12     2020-04-10 [1] CRAN (R 4.0.0)             
 textrecipes   * 0.2.2      2020-05-10 [1] CRAN (R 4.0.0)             
 threejs         0.3.3      2020-01-21 [1] CRAN (R 4.0.0)             
 tibble        * 3.0.1      2020-04-20 [1] CRAN (R 4.0.0)             
 tidymodels    * 0.1.0      2020-02-16 [1] CRAN (R 4.0.0)             
 tidyposterior   0.0.2      2018-11-15 [1] CRAN (R 4.0.0)             
 tidypredict     0.4.5      2020-02-10 [1] CRAN (R 4.0.0)             
 tidyr           1.0.3      2020-05-07 [1] CRAN (R 4.0.0)             
 tidyselect      1.1.0      2020-05-11 [1] CRAN (R 4.0.0)             
 tidytext      * 0.2.4      2020-04-17 [1] CRAN (R 4.0.0)             
 timeDate        3043.102   2018-02-21 [1] CRAN (R 4.0.0)             
 tokenizers      0.2.1      2018-03-29 [1] CRAN (R 4.0.0)             
 tune          * 0.1.0      2020-04-02 [1] CRAN (R 4.0.0)             
 usethis         1.6.1      2020-04-29 [1] CRAN (R 4.0.0)             
 vctrs           0.3.0      2020-05-11 [1] CRAN (R 4.0.0)             
 withr           2.2.0      2020-04-20 [1] CRAN (R 4.0.0)             
 workflows     * 0.1.1      2020-03-17 [1] CRAN (R 4.0.0)             
 xfun            0.13       2020-04-13 [1] CRAN (R 4.0.0)             
 xml2            1.3.2      2020-04-23 [1] CRAN (R 4.0.0)             
 xtable          1.8-4      2019-04-21 [1] CRAN (R 4.0.0)             
 xts             0.12-0     2020-01-19 [1] CRAN (R 4.0.0)             
 yaml            2.2.1      2020-02-01 [1] CRAN (R 4.0.0)             
 yardstick     * 0.0.6      2020-03-17 [1] CRAN (R 4.0.0)             
 zoo             1.8-8      2020-05-02 [1] CRAN (R 4.0.0)             

[1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library


Emil Hvitfeldt
Emil Hvitfeldt
Research Programmer