R Package Vignette Counts
Jim Gruman
March 14, 2022
Last updated: 2022-03-16
Checks: 7 0
Knit directory: myTidyTuesday/
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| Rmd | 7de0d3a | opus1993 | 2022-03-16 | add the CRAN packages dataviz tweet |
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| Rmd | df80a1d | opus1993 | 2022-03-16 | CRAN packages treemap with taller aspection ratio |
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| Rmd | 3ed6597 | opus1993 | 2022-03-16 | CRAN packages treemap filled by vignettes |
| Rmd | b8ca143 | opus1993 | 2022-03-16 | CRAN packages treemap |
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)
})
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 %>%
count(ReleaseYear, ReleaseType) %>%
filter(ReleaseYear > 2003) %>%
ggplot(aes(x = ReleaseYear, n, fill = ReleaseType)) +
geom_col() +
labs(
caption = "2021 is a partial year, ending August 12th",
y = "Count of Releases published on CRAN"
)
cran %>%
arrange(package, date) %>%
filter(!is.na(date)) %>%
inner_join(downloads, by = "package") %>%
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
),
downloads = sum(count),
.groups = "drop"
) %>%
mutate(package = glue::glue("{ package } \n { vignettes }")) %>%
ggplot(aes(
area = downloads,
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.",
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.
— Jim Gruman📚🚵♂️⚙📈 (@jim_gruman) March 16, 2022
Code: https://t.co/AY9Y0JfsYe pic.twitter.com/rcJ5OVFXTA
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] treemapify_2.5.5 grumanlib_0.1.0.9999 forcats_0.5.1
[4] stringr_1.4.0 dplyr_1.0.8 purrr_0.3.4
[7] readr_2.1.2 tidyr_1.2.0 tibble_3.1.6
[10] ggplot2_3.3.5 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] workflows_0.2.4 selectr_0.4-2 plyr_1.8.6
[7] tidytuesdayR_1.0.2 splines_4.1.2 listenv_0.8.0
[10] usethis_2.1.5 digest_0.6.29 foreach_1.5.2
[13] htmltools_0.5.2 yardstick_0.0.9 viridis_0.6.2
[16] parsnip_0.2.0 fansi_1.0.2 magrittr_2.0.2
[19] memoise_2.0.1 tune_0.1.6 tzdb_0.2.0
[22] recipes_0.2.0 globals_0.14.0 ggfittext_0.9.1
[25] modelr_0.1.8 gower_1.0.0 vroom_1.5.7
[28] R.utils_2.11.0 hardhat_0.2.0 rsample_0.1.1
[31] dials_0.1.0 colorspace_2.0-3 rvest_1.0.2
[34] textshaping_0.3.6 haven_2.4.3 xfun_0.29
[37] prismatic_1.1.0 callr_3.7.0 crayon_1.5.0
[40] jsonlite_1.8.0 survival_3.2-13 iterators_1.0.14
[43] glue_1.6.2 gtable_0.3.0 ipred_0.9-12
[46] R.cache_0.15.0 tweetrmd_0.0.9 future.apply_1.8.1
[49] scales_1.1.1 infer_1.0.0 DBI_1.1.2
[52] Rcpp_1.0.8.2 viridisLite_0.4.0 bit_4.0.4
[55] GPfit_1.0-8 lava_1.6.10 prodlim_2019.11.13
[58] httr_1.4.2 ellipsis_0.3.2 farver_2.1.0
[61] pkgconfig_2.0.3 R.methodsS3_1.8.1 nnet_7.3-16
[64] sass_0.4.0 dbplyr_2.1.1 utf8_1.2.2
[67] here_1.0.1 labeling_0.4.2 tidyselect_1.1.2
[70] rlang_1.0.1 DiceDesign_1.9 later_1.3.0
[73] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
[76] cachem_1.0.6 cli_3.2.0 generics_0.1.2
[79] broom_0.7.12 evaluate_0.15 fastmap_1.1.0
[82] yaml_2.3.4 ragg_1.2.2 bit64_4.0.5
[85] processx_3.5.2 knitr_1.37 fs_1.5.2
[88] workflowsets_0.1.0 future_1.24.0 whisker_0.4
[91] R.oo_1.24.0 xml2_1.3.3 compiler_4.1.2
[94] rstudioapi_0.13 curl_4.3.2 cranlogs_2.1.1
[97] reprex_2.0.1 lhs_1.1.4 bslib_0.3.1
[100] stringi_1.7.6 highr_0.9 ps_1.6.0
[103] lattice_0.20-45 Matrix_1.3-4 styler_1.7.0
[106] conflicted_1.1.0 vctrs_0.3.8 tidymodels_0.1.4
[109] pillar_1.7.0 lifecycle_1.0.1 furrr_0.2.3
[112] jquerylib_0.1.4 httpuv_1.6.5 R6_2.5.1
[115] promises_1.2.0.1 gridExtra_2.3 parallelly_1.30.0
[118] codetools_0.2-18 MASS_7.3-54 assertthat_0.2.1
[121] rprojroot_2.0.2 withr_2.5.0 parallel_4.1.2
[124] hms_1.1.1 grid_4.1.2 rpart_4.1-15
[127] timeDate_3043.102 class_7.3-19 rmarkdown_2.13
[130] git2r_0.29.0 getPass_0.2-2 pROC_1.18.0
[133] lubridate_1.8.0