R Package Vignette Counts

workflowr

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Last updated: 2022-03-16

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Knit directory: myTidyTuesday/

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File	Version	Author	Date	Message
Rmd	94faa4e	opus1993	2022-03-16	CRAN vignette poisson coefficients
html	09fb452	opus1993	2022-03-16	Build site.
Rmd	7de0d3a	opus1993	2022-03-16	add the CRAN packages dataviz tweet
html	eb7c15f	opus1993	2022-03-16	Build site.
Rmd	df80a1d	opus1993	2022-03-16	CRAN packages treemap with taller aspection ratio
html	102eb53	opus1993	2022-03-16	Build site.
Rmd	3ed6597	opus1993	2022-03-16	CRAN packages treemap filled by vignettes
Rmd	b8ca143	opus1993	2022-03-16	CRAN packages treemap

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This week’s dataset comes from Dr. Robert Flight and covers the evolution of the built in R package help documentation, often called Vignettes.

suppressPackageStartupMessages({
library(tidyverse) # clean and transform rectangular data
library(grumanlib) # my plot theme
library(treemapify)
library(tidymodels)
library(poissonreg)
})

Warning: package 'parsnip' was built under R version 4.1.3

Warning: package 'poissonreg' was built under R version 4.1.3

tidymodels_prefer()

source(here::here("code","_common.R"),
       verbose = FALSE,
       local = knitr::knit_global())

theme_set(grumanlib::theme_jim())

Load up the data to have a look

cran <- tidytuesdayR::tt_load("2022-03-15")$cran %>%
  mutate(
    MajorVersion = as.integer(str_extract(version, "^[0-9]\\.")),
    VignetteCount = as.integer(rmd + rnw),
    date = lubridate::parse_date_time(date,
      orders = c("Ymd HMS", "a b d HMS Y")
    ),
    package = str_to_lower(package),
    ReleaseYear = lubridate::year((lubridate::floor_date(date, "year")))
  ) %>%
  group_by(package, MajorVersion) %>%
  mutate(ReleaseType = case_when(
    row_number(date) == 1L ~ "Major",
    TRUE ~ "Minor"
  )) %>%
  ungroup()

    Downloading file 1 of 2: `cran.csv`
    Downloading file 2 of 2: `bioc.csv`

downloads <- cranlogs::cran_top_downloads("last-month", count = 100) %>%
  mutate(package = str_to_lower(package))

cran %>%
  mutate(has_vignette = VignetteCount > 0) %>%
  count(ReleaseYear, ReleaseType, has_vignette,
    name = "vignettes"
  ) %>%
  filter(ReleaseYear > 2003) %>%
  ggplot(aes(x = ReleaseYear, vignettes, fill = has_vignette)) +
  geom_col() +
  facet_wrap(~ReleaseType) +
  labs(
    caption = "2021 is a partial year, ending August 12th",
    y = "Count of Releases published on CRAN",
    fill = "Has A Vignette"
  ) +
  theme(legend.position = "top")

vignette_counts <- cran %>%
  arrange(package, date) %>%
  filter(!is.na(date)) %>%
  group_by(package) %>%
  summarise(
    vignettes = last(VignetteCount),
    born_on = lubridate::year(min(date)),
    age = round(
      lubridate::interval(min(date), Sys.Date()) / lubridate::years(1), 1
    ),
    releases = n(),
    .groups = "drop"
  ) %>%
  inner_join(downloads, by = "package")

vignette_counts %>%
  mutate(package = glue::glue("{ package } \n { vignettes }")) %>%
  ggplot(aes(
    area = count,
    fill = vignettes,
    label = package,
    subgroup = cut_interval(born_on, n = 4)
  )) +
  geom_treemap(show.legend = FALSE) +
  geom_treemap_subgroup_border() +
  geom_treemap_text(aes(color = after_scale(map_chr(fill, best_contrast))),
    place = "top",
    reflow = TRUE
  ) +
  geom_treemap_subgroup_text(
    color = "white",
    alpha = 0.2,
    place = "bottomleft",
    fontface = "italic",
    min.size = 0
  ) +
  labs(
    fill = NULL, title = "Top Most Downloaded R Packages from CRAN",
    subtitle = "Package Name and Number of Vignettes labeled. Size corresponds to the Total Number of Downloads from 14Feb-15Mar2022.",
    caption = "Data Source: Robert Flight and cranlogs"
  ) +
  theme(legend.position = "none")

tweetrmd::include_tweet("https://twitter.com/jim_gruman/status/1504143273925148672")

This week’s #TidyTuesday on the most popular #rstats CRAN extension package vignettes, also using the cranlogs popularity figures.

Code: https://t.co/AY9Y0JfsYe pic.twitter.com/rcJ5OVFXTA

— Jim Gruman📚🚵‍♂️⚙📈 (@jim_gruman) March 16, 2022

Let’s build on Julia Silge’s video from this week on Machine Learning with tidymodels.

A regression model of counts is often best modeled with the poisson distribution

poisson_wf <- workflow(vignettes ~ releases, poisson_reg())

fit(poisson_wf, data = vignette_counts)

== Workflow [trained] ==========================================================
Preprocessor: Formula
Model: poisson_reg()

-- Preprocessor ----------------------------------------------------------------
vignettes ~ releases

-- Model -----------------------------------------------------------------------

Call:  stats::glm(formula = ..y ~ ., family = stats::poisson, data = data)

Coefficients:
(Intercept)     releases  
    0.19996      0.02019  

Degrees of Freedom: 99 Total (i.e. Null);  98 Residual
Null Deviance:      341.8 
Residual Deviance: 285.4    AIC: 452.5

Let’s try a zero inflated model, with the zero inflated terms, that is, those with a probability of being zero, modeled by born_on year.

zip_spec <- poisson_reg() %>% set_engine("zeroinfl")

zip_wf <- workflow() %>%
  add_model(zip_spec, formula = vignettes ~ releases | releases) %>%
  add_variables(
    outcomes = vignettes,
    predictors = c(releases, born_on)
  )

fit(zip_wf, data = vignette_counts)

== Workflow [trained] ==========================================================
Preprocessor: Variables
Model: poisson_reg()

-- Preprocessor ----------------------------------------------------------------
Outcomes: vignettes
Predictors: c(releases, born_on)

-- Model -----------------------------------------------------------------------

Call:
pscl::zeroinfl(formula = vignettes ~ releases | releases, data = data)

Count model coefficients (poisson with log link):
(Intercept)     releases  
     0.9023       0.0120  

Zero-inflation model coefficients (binomial with logit link):
(Intercept)     releases  
    0.47398     -0.04801  

Bootstrapping to better understand the model coefficients for inference

folds <- bootstraps(vignette_counts, times = 100)

Let’s speed this up as best as we can with parallel processing.

all_cores <- parallelly::availableCores(omit = 1)
all_cores

system 
    11 

future::plan("multisession", workers = all_cores) # on Windows

get_coefficients <- function(x) {
  extract_fit_engine(x) %>%
    tidy(type = "zero")
}

ctrl <- control_resamples(
  extract = get_coefficients
)

zip_res <- fit_resamples(zip_wf,
  folds,
  control = ctrl
)

For all of the bootstraps, what is the estimate of the coefficient statistic? Let’s make a plot:

zip_res %>%
  select(id, .extracts) %>%
  unnest(.extracts) %>%
  unnest(.extracts) %>%
  ggplot(aes(x = estimate, fill = term)) +
  geom_histogram(show.legend = FALSE, bins = 35) +
  facet_wrap(~term, scales = "free_x") +
  geom_vline(
    xintercept = 0,
    lty = 2,
    size = 1.2,
    color = "gray70"
  ) +
  labs(
    caption = "Zero Inflated Poisson model for Vignette Counts on R Packages",
    subtitle = "More releases means more vignettes"
  )

Session information

sessionInfo()

R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22000)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] pscl_1.5.5           vctrs_0.3.8          rlang_1.0.1         
 [4] poissonreg_0.2.0     yardstick_0.0.9      workflowsets_0.1.0  
 [7] workflows_0.2.4      tune_0.1.6           rsample_0.1.1       
[10] recipes_0.2.0        parsnip_0.2.0        modeldata_0.1.1     
[13] infer_1.0.0          dials_0.1.0          scales_1.1.1        
[16] broom_0.7.12         tidymodels_0.1.4     treemapify_2.5.5    
[19] grumanlib_0.1.0.9999 forcats_0.5.1        stringr_1.4.0       
[22] dplyr_1.0.8          purrr_0.3.4          readr_2.1.2         
[25] tidyr_1.2.0          tibble_3.1.6         ggplot2_3.3.5       
[28] tidyverse_1.3.1      workflowr_1.7.0     

loaded via a namespace (and not attached):
  [1] readxl_1.3.1       backports_1.4.1    systemfonts_1.0.4 
  [4] selectr_0.4-2      plyr_1.8.6         tidytuesdayR_1.0.2
  [7] splines_4.1.2      listenv_0.8.0      usethis_2.1.5     
 [10] digest_0.6.29      foreach_1.5.2      htmltools_0.5.2   
 [13] viridis_0.6.2      fansi_1.0.2        magrittr_2.0.2    
 [16] memoise_2.0.1      tzdb_0.2.0         globals_0.14.0    
 [19] ggfittext_0.9.1    modelr_0.1.8       gower_1.0.0       
 [22] vroom_1.5.7        R.utils_2.11.0     hardhat_0.2.0     
 [25] colorspace_2.0-3   rvest_1.0.2        textshaping_0.3.6 
 [28] haven_2.4.3        xfun_0.29          prismatic_1.1.0   
 [31] callr_3.7.0        crayon_1.5.0       jsonlite_1.8.0    
 [34] survival_3.2-13    iterators_1.0.14   glue_1.6.2        
 [37] gtable_0.3.0       ipred_0.9-12       R.cache_0.15.0    
 [40] tweetrmd_0.0.9     future.apply_1.8.1 DBI_1.1.2         
 [43] Rcpp_1.0.8.2       viridisLite_0.4.0  bit_4.0.4         
 [46] GPfit_1.0-8        lava_1.6.10        prodlim_2019.11.13
 [49] httr_1.4.2         ellipsis_0.3.2     farver_2.1.0      
 [52] pkgconfig_2.0.3    R.methodsS3_1.8.1  nnet_7.3-16       
 [55] sass_0.4.0         dbplyr_2.1.1       utf8_1.2.2        
 [58] here_1.0.1         labeling_0.4.2     tidyselect_1.1.2  
 [61] DiceDesign_1.9     later_1.3.0        munsell_0.5.0     
 [64] cellranger_1.1.0   tools_4.1.2        cachem_1.0.6      
 [67] cli_3.2.0          generics_0.1.2     evaluate_0.15     
 [70] fastmap_1.1.0      yaml_2.3.4         ragg_1.2.2        
 [73] rematch2_2.1.2     bit64_4.0.5        processx_3.5.2    
 [76] knitr_1.37         fs_1.5.2           future_1.24.0     
 [79] whisker_0.4        R.oo_1.24.0        xml2_1.3.3        
 [82] compiler_4.1.2     rstudioapi_0.13    curl_4.3.2        
 [85] cranlogs_2.1.1     reprex_2.0.1       lhs_1.1.4         
 [88] bslib_0.3.1        stringi_1.7.6      highr_0.9         
 [91] ps_1.6.0           lattice_0.20-45    Matrix_1.3-4      
 [94] styler_1.7.0       conflicted_1.1.0   pillar_1.7.0      
 [97] lifecycle_1.0.1    furrr_0.2.3        jquerylib_0.1.4   
[100] httpuv_1.6.5       R6_2.5.1           promises_1.2.0.1  
[103] gridExtra_2.3      parallelly_1.30.0  codetools_0.2-18  
[106] MASS_7.3-54        assertthat_0.2.1   rprojroot_2.0.2   
[109] withr_2.5.0        parallel_4.1.2     hms_1.1.1         
[112] grid_4.1.2         rpart_4.1-15       timeDate_3043.102 
[115] class_7.3-19       rmarkdown_2.13     git2r_0.29.0      
[118] getPass_0.2-2      pROC_1.18.0        lubridate_1.8.0