package-downloads
CRAN Downloads
Steven P. Sanderson II, MPH - Date: 20 November, 2024
This repo contains the analysis of downloads of my R packages:
All of which follow the “analyses as
package”
philosophy this repo itself is an R package that can installed using
remotes::install_github().
I have forked this project itself from
ggcharts-analysis.
While I analyze healthyverse packages here, the functions are written
in a way that you can analyze any CRAN package with a slight
modification to the download_log function.
This file was last updated on November 20, 2024.
library(packagedownloads)
library(tidyverse)
library(patchwork)
library(timetk)
library(knitr)
library(leaflet)
library(htmltools)
library(tmaptools)
library(mapview)
library(countrycode)
library(htmlwidgets)
library(webshot)
library(rmarkdown)
library(dtplyr)
start_date <- Sys.Date() - 9 #as.Date("2020-11-15")
end_date <- Sys.Date() - 2
total_downloads <- download_logs(start_date, end_date)
interactive <- FALSE
pkg_release_date_tbl()
Last Full Day Data
downloads <- total_downloads |> filter(date == max(date))
daily_downloads <- compute_daily_downloads(downloads)
downloads_by_country <- compute_downloads_by_country(downloads)
p1 <- plot_cumulative_downloads(daily_downloads)
p2 <- plot_downloads_by_country(downloads_by_country)
f <- function(date) format(date, "%b %d, %Y")
patchwork_theme <- theme_classic(base_size = 24) +
theme(
plot.title = element_text(face = "bold"),
plot.caption = element_text(size = 14)
)
p1 + p2 +
plot_annotation(
title = "healthyverse Packages - Last Full Day",
subtitle = "A Summary of Downloads from the RStudio CRAN Mirror",
caption = glue::glue("Source: RStudio CRAN Logs for {f(end_date)}"),
theme = patchwork_theme
)
downloads |>
count(package, version) |>
tidyr::pivot_wider(
id_cols = version
, names_from = package
, values_from = n
, values_fill = 0
) |>
arrange(version) |>
kable()
| version | RandomWalker | TidyDensity | healthyR | healthyR.ai | healthyR.data | healthyR.ts | healthyverse | tidyAML |
|---|---|---|---|---|---|---|---|---|
| 0.0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
| 0.1.0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 |
| 0.2.0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 0.2.2 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 |
| 0.3.1 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 |
| 1.1.0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 |
| 1.1.1 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 |
| 1.5.0 | 0 | 19 | 0 | 0 | 0 | 0 | 0 | 0 |
downloads |>
count(package, sort = TRUE) |>
tidyr::pivot_wider(
names_from = package,
values_from = n,
values_fill = 0
) |>
kable()
| TidyDensity | healthyR | RandomWalker | tidyAML | healthyR.ts | healthyR.ai | healthyverse | healthyR.data |
|---|---|---|---|---|---|---|---|
| 19 | 10 | 9 | 8 | 7 | 6 | 6 | 5 |
Current Trend
Here are the current 7 day trends for the healthyverse suite of
packages.
downloads <- total_downloads[date >= start_date]
daily_downloads <- compute_daily_downloads(downloads)
downloads_by_country <- compute_downloads_by_country(downloads)
p1 <- plot_daily_downloads(daily_downloads)
p2 <- plot_cumulative_downloads(daily_downloads)
p3 <- hist_daily_downloads(daily_downloads)
p4 <- plot_downloads_by_country(downloads_by_country)
f <- function(date) format(date, "%b %d, %Y")
patchwork_theme <- theme_classic(base_size = 24) +
theme(
plot.title = element_text(face = "bold"),
plot.caption = element_text(size = 14)
)
p1 + p2 + p3 + p4 +
plot_annotation(
title = "healthyverse Packages - 7 Day Trend",
subtitle = "A Summary of Downloads from the RStudio CRAN Mirror",
caption = glue::glue("Source: RStudio CRAN Logs ({f(start_date)} to {f(end_date)})"),
theme = patchwork_theme
)
Since Inception
start_date <- as.Date("2020-11-15")
daily_downloads <- compute_daily_downloads(downloads = total_downloads)
downloads_by_country <- compute_downloads_by_country(downloads = total_downloads)
p1 <- plot_daily_downloads(daily_downloads)
p2 <- plot_cumulative_downloads(daily_downloads)
p3 <- hist_daily_downloads(daily_downloads)
p4 <- plot_downloads_by_country(downloads_by_country)
f <- function(date) format(date, "%b %d, %Y")
patchwork_theme <- theme_classic(base_size = 24) +
theme(
plot.title = element_text(face = "bold"),
plot.caption = element_text(size = 14)
)
p1 + p2 + p3 + p4 +
plot_annotation(
title = "healthyR packages are on the Rise",
subtitle = "A Summary of Downloads from the RStudio CRAN Mirror - Since Inception",
caption = glue::glue("Source: RStudio CRAN Logs ({f(start_date)} to {f(end_date)})"),
theme = patchwork_theme
)
By Release Date
pkg_tbl <- readRDS("pkg_release_tbl.rds")
dl_tbl <- total_downloads %>%
filter(
date != "2024-05-29" &
!(date == "2024-06-12" & package == "TidyDensity")
) |> # bad data on this for some reason
group_by(package) %>%
summarise_by_time(
.date_var = date,
.by = "week",
N = n()
) %>%
ungroup() %>%
select(date, package, N)
dl_tbl %>%
ggplot(aes(date, log1p(N))) +
theme_bw() +
geom_point(aes(group = package, color = package), size = 1) +
geom_line(aes(group = package, color = package)) +
ggtitle(paste("Package Downloads: {healthyverse}")) +
geom_smooth(method = "loess", color = "black", se = FALSE) +
geom_vline(
data = pkg_tbl
, aes(xintercept = as.numeric(date))
, color = "red"
, lwd = 1
, lty = "solid"
) +
facet_wrap(package ~., ncol = 2, scales = "free_x") +
theme_minimal() +
labs(
subtitle = "Vertical lines represent release dates",
x = "Date",
y = "log1p(Counts)",
color = "Package"
) +
theme(legend.position = "bottom")
dl_tbl %>%
select(date, N) %>%
summarise_by_time(
.date_var = date,
.by = "week",
Actual = sum(N, na.rm = TRUE)
) %>%
mutate(Actual = cumsum(Actual)) %>%
tk_augment_differences(.value = Actual, .differences = 1) %>%
tk_augment_differences(.value = Actual, .differences = 2) %>%
rename(velocity = contains("_diff1")) %>%
rename(acceleration = contains("_diff2")) %>%
pivot_longer(-date) %>%
mutate(name = str_to_title(name)) %>%
mutate(name = as_factor(name)) %>%
ggplot(aes(x = date, y = log1p(value), group = name)) +
geom_point(alpha = .2) +
geom_line(alpha = .2) +
geom_vline(
data = pkg_tbl
, aes(xintercept = as.numeric(date), color = package)
, lwd = 1
, lty = "solid"
) +
facet_wrap(name ~ ., ncol = 1, scale = "free") +
theme_minimal() +
labs(
title = "Total Downloads: Trend, Velocity, and Accelertion",
subtitle = "Vertical Lines Indicate a CRAN Release date for a package.",
x = "Date",
y = "",
color = ""
) +
theme(legend.position = "bottom")
Map of Downloads
A leaflet map of countries where a package has been downloaded.
mapping_dataset() %>%
head() %>%
knitr::kable()
| country | latitude | longitude | display_name | icon |
|---|---|---|---|---|
| United States | 39.78373 | -100.445882 | United States | https://nominatim.openstreetmap.org/ui/mapicons/poi_boundary_administrative.p.20.png |
| United Kingdom | 54.70235 | -3.276575 | United Kingdom | https://nominatim.openstreetmap.org/ui/mapicons/poi_boundary_administrative.p.20.png |
| Germany | 51.16382 | 10.447831 | Deutschland | https://nominatim.openstreetmap.org/ui/mapicons/poi_boundary_administrative.p.20.png |
| Hong Kong SAR China | 22.35063 | 114.184916 | 香港 Hong Kong, 中国 | https://nominatim.openstreetmap.org/ui/mapicons/poi_boundary_administrative.p.20.png |
| Japan | 36.57484 | 139.239418 | 日本 | https://nominatim.openstreetmap.org/ui/mapicons/poi_boundary_administrative.p.20.png |
| Chile | -31.76134 | -71.318770 | Chile | https://nominatim.openstreetmap.org/ui/mapicons/poi_boundary_administrative.p.20.png |
l <- map_leaflet()
saveWidget(l, "downloads_map.html")
webshot("downloads_map.html", file = "map.png",
cliprect = "viewport")
To date there has been downloads in a total of 156 different countries.
Analysis by Package
healthyR
start_date <- as.Date("2020-11-15")
pkg <- "healthyR"
daily_downloads <- compute_daily_downloads(
downloads = total_downloads
, pkg = pkg)
downloads_by_country <- compute_downloads_by_country(
downloads = total_downloads
, pkg = pkg)
p1 <- plot_daily_downloads(daily_downloads)
p2 <- plot_cumulative_downloads(daily_downloads)
p3 <- hist_daily_downloads(daily_downloads)
p4 <- plot_downloads_by_country(downloads_by_country)
f <- function(date) format(date, "%b %d, %Y")
patchwork_theme <- theme_classic(base_size = 24) +
theme(
plot.title = element_text(face = "bold"),
plot.caption = element_text(size = 14)
)
p1 + p2 + p3 + p4 +
plot_annotation(
title = glue::glue("Package: {pkg}"),
subtitle = "A Summary of Downloads from the RStudio CRAN Mirror - Since Inception",
caption = glue::glue("Source: RStudio CRAN Logs ({f(start_date)} to {f(end_date)})"),
theme = patchwork_theme
)
healthyR.ts
start_date <- as.Date("2020-11-15")
pkg <- "healthyR.ts"
daily_downloads <- compute_daily_downloads(
downloads = total_downloads
, pkg = pkg)
downloads_by_country <- compute_downloads_by_country(
downloads = total_downloads
, pkg = pkg)
p1 <- plot_daily_downloads(daily_downloads)
p2 <- plot_cumulative_downloads(daily_downloads)
p3 <- hist_daily_downloads(daily_downloads)
p4 <- plot_downloads_by_country(downloads_by_country)
f <- function(date) format(date, "%b %d, %Y")
patchwork_theme <- theme_classic(base_size = 24) +
theme(
plot.title = element_text(face = "bold"),
plot.caption = element_text(size = 14)
)
p1 + p2 + p3 + p4 +
plot_annotation(
title = glue::glue("Package: {pkg}"),
subtitle = "A Summary of Downloads from the RStudio CRAN Mirror - Since Inception",
caption = glue::glue("Source: RStudio CRAN Logs ({f(start_date)} to {f(end_date)})"),
theme = patchwork_theme
)
healthyR.data
start_date <- as.Date("2020-11-15")
pkg <- "healthyR.data"
daily_downloads <- compute_daily_downloads(
downloads = total_downloads
, pkg = pkg)
downloads_by_country <- compute_downloads_by_country(
downloads = total_downloads
, pkg = pkg)
p1 <- plot_daily_downloads(daily_downloads)
p2 <- plot_cumulative_downloads(daily_downloads)
p3 <- hist_daily_downloads(daily_downloads)
p4 <- plot_downloads_by_country(downloads_by_country)
f <- function(date) format(date, "%b %d, %Y")
patchwork_theme <- theme_classic(base_size = 24) +
theme(
plot.title = element_text(face = "bold"),
plot.caption = element_text(size = 14)
)
p1 + p2 + p3 + p4 +
plot_annotation(
title = glue::glue("Package: {pkg}"),
subtitle = "A Summary of Downloads from the RStudio CRAN Mirror - Since Inception",
caption = glue::glue("Source: RStudio CRAN Logs ({f(start_date)} to {f(end_date)})"),
theme = patchwork_theme
)
healthyR.ai
start_date <- as.Date("2020-11-15")
pkg <- "healthyR.ai"
daily_downloads <- compute_daily_downloads(
downloads = total_downloads
, pkg = pkg)
downloads_by_country <- compute_downloads_by_country(
downloads = total_downloads
, pkg = pkg)
p1 <- plot_daily_downloads(daily_downloads)
p2 <- plot_cumulative_downloads(daily_downloads)
p3 <- hist_daily_downloads(daily_downloads)
p4 <- plot_downloads_by_country(downloads_by_country)
f <- function(date) format(date, "%b %d, %Y")
patchwork_theme <- theme_classic(base_size = 24) +
theme(
plot.title = element_text(face = "bold"),
plot.caption = element_text(size = 14)
)
p1 + p2 + p3 + p4 +
plot_annotation(
title = glue::glue("Package: {pkg}"),
subtitle = "A Summary of Downloads from the RStudio CRAN Mirror - Since Inception",
caption = glue::glue("Source: RStudio CRAN Logs ({f(start_date)} to {f(end_date)})"),
theme = patchwork_theme
)
healthyverse
start_date <- as.Date("2020-11-15")
pkg <- "healthyverse"
daily_downloads <- compute_daily_downloads(
downloads = total_downloads
, pkg = pkg)
downloads_by_country <- compute_downloads_by_country(
downloads = total_downloads
, pkg = pkg)
p1 <- plot_daily_downloads(daily_downloads)
p2 <- plot_cumulative_downloads(daily_downloads)
p3 <- hist_daily_downloads(daily_downloads)
p4 <- plot_downloads_by_country(downloads_by_country)
f <- function(date) format(date, "%b %d, %Y")
patchwork_theme <- theme_classic(base_size = 24) +
theme(
plot.title = element_text(face = "bold"),
plot.caption = element_text(size = 14)
)
p1 + p2 + p3 + p4 +
plot_annotation(
title = glue::glue("Package: {pkg}"),
subtitle = "A Summary of Downloads from the RStudio CRAN Mirror - Since Inception",
caption = glue::glue("Source: RStudio CRAN Logs ({f(start_date)} to {f(end_date)})"),
theme = patchwork_theme
)
TidyDensity
start_date <- as.Date("2020-11-15")
pkg <- "TidyDensity"
daily_downloads <- compute_daily_downloads(
downloads = total_downloads
, pkg = pkg)
downloads_by_country <- compute_downloads_by_country(
downloads = total_downloads
, pkg = pkg)
p1 <- plot_daily_downloads(daily_downloads)
p2 <- plot_cumulative_downloads(daily_downloads)
p3 <- hist_daily_downloads(daily_downloads)
p4 <- plot_downloads_by_country(downloads_by_country)
f <- function(date) format(date, "%b %d, %Y")
patchwork_theme <- theme_classic(base_size = 24) +
theme(
plot.title = element_text(face = "bold"),
plot.caption = element_text(size = 14)
)
p1 + p2 + p3 + p4 +
plot_annotation(
title = glue::glue("Package: {pkg}"),
subtitle = "A Summary of Downloads from the RStudio CRAN Mirror - Since Inception",
caption = glue::glue("Source: RStudio CRAN Logs ({f(start_date)} to {f(end_date)})"),
theme = patchwork_theme
)
tidyAML
start_date <- as.Date("2023-02-13")
pkg <- "tidyAML"
daily_downloads <- compute_daily_downloads(
downloads = total_downloads
, pkg = pkg)
downloads_by_country <- compute_downloads_by_country(
downloads = total_downloads
, pkg = pkg)
p1 <- plot_daily_downloads(daily_downloads)
p2 <- plot_cumulative_downloads(daily_downloads)
p3 <- hist_daily_downloads(daily_downloads)
p4 <- plot_downloads_by_country(downloads_by_country)
f <- function(date) format(date, "%b %d, %Y")
patchwork_theme <- theme_classic(base_size = 24) +
theme(
plot.title = element_text(face = "bold"),
plot.caption = element_text(size = 14)
)
p1 + p2 + p3 + p4 +
plot_annotation(
title = glue::glue("Package: {pkg}"),
subtitle = "A Summary of Downloads from the RStudio CRAN Mirror - Since Inception",
caption = glue::glue("Source: RStudio CRAN Logs ({f(start_date)} to {f(end_date)})"),
theme = patchwork_theme
)
RandomWalker
start_date <- as.Date("2023-02-13")
pkg <- "RandomWalker"
daily_downloads <- compute_daily_downloads(
downloads = total_downloads
, pkg = pkg)
downloads_by_country <- compute_downloads_by_country(
downloads = total_downloads
, pkg = pkg)
p1 <- plot_daily_downloads(daily_downloads)
p2 <- plot_cumulative_downloads(daily_downloads)
p3 <- hist_daily_downloads(daily_downloads)
p4 <- plot_downloads_by_country(downloads_by_country)
f <- function(date) format(date, "%b %d, %Y")
patchwork_theme <- theme_classic(base_size = 24) +
theme(
plot.title = element_text(face = "bold"),
plot.caption = element_text(size = 14)
)
p1 + p2 + p3 + p4 +
plot_annotation(
title = glue::glue("Package: {pkg}"),
subtitle = "A Summary of Downloads from the RStudio CRAN Mirror - Since Inception",
caption = glue::glue("Source: RStudio CRAN Logs ({f(start_date)} to {f(end_date)})"),
theme = patchwork_theme
)
Table Data
Downloads by Package and Version
total_downloads %>%
count(package, version) %>%
tidyr::pivot_wider(
id_cols = version
, names_from = package
, values_from = n
, values_fill = 0
) %>%
arrange(version) %>%
kable()
| version | RandomWalker | TidyDensity | healthyR | healthyR.ai | healthyR.data | healthyR.ts | healthyverse | tidyAML |
|---|---|---|---|---|---|---|---|---|
| 0.0.1 | 0 | 1258 | 0 | 579 | 0 | 0 | 0 | 892 |
| 0.0.10 | 0 | 0 | 0 | 707 | 0 | 0 | 0 | 0 |
| 0.0.11 | 0 | 0 | 0 | 547 | 0 | 0 | 0 | 0 |
| 0.0.12 | 0 | 0 | 0 | 799 | 0 | 0 | 0 | 0 |
| 0.0.13 | 0 | 0 | 0 | 4008 | 0 | 0 | 0 | 0 |
| 0.0.13.tar.gz%20H | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 |
| 0.0.2 | 0 | 0 | 0 | 1830 | 0 | 0 | 0 | 1972 |
| 0.0.3 | 0 | 0 | 0 | 591 | 0 | 0 | 0 | 665 |
| 0.0.4 | 0 | 0 | 0 | 677 | 0 | 0 | 0 | 928 |
| 0.0.5 | 0 | 0 | 0 | 1256 | 0 | 0 | 0 | 2056 |
| 0.0.6 | 0 | 0 | 0 | 2305 | 0 | 0 | 0 | 0 |
| 0.0.7 | 0 | 0 | 0 | 928 | 0 | 0 | 0 | 0 |
| 0.0.8 | 0 | 0 | 0 | 1054 | 0 | 0 | 0 | 0 |
| 0.0.9 | 0 | 0 | 0 | 842 | 0 | 0 | 0 | 0 |
| 0.1.0 | 376 | 0 | 470 | 610 | 0 | 702 | 0 | 0 |
| 0.1.1 | 0 | 0 | 1520 | 0 | 0 | 2220 | 0 | 0 |
| 0.1.2 | 0 | 0 | 1743 | 0 | 0 | 1214 | 0 | 0 |
| 0.1.3 | 0 | 0 | 544 | 0 | 0 | 1332 | 0 | 0 |
| 0.1.4 | 0 | 0 | 592 | 0 | 0 | 902 | 0 | 0 |
| 0.1.5 | 0 | 0 | 1239 | 0 | 0 | 739 | 0 | 0 |
| 0.1.6 | 0 | 0 | 2444 | 0 | 0 | 476 | 0 | 0 |
| 0.1.7 | 0 | 0 | 1230 | 0 | 0 | 1471 | 0 | 0 |
| 0.1.8 | 0 | 0 | 2043 | 0 | 0 | 2020 | 0 | 0 |
| 0.1.9 | 0 | 0 | 1098 | 0 | 0 | 713 | 0 | 0 |
| 0.2.0 | 473 | 0 | 2347 | 0 | 0 | 716 | 0 | 0 |
| 0.2.1 | 0 | 0 | 4384 | 0 | 0 | 532 | 0 | 0 |
| 0.2.1.tar.gz%20HT | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 |
| 0.2.10 | 0 | 0 | 0 | 0 | 0 | 614 | 0 | 0 |
| 0.2.11 | 0 | 0 | 0 | 0 | 0 | 649 | 0 | 0 |
| 0.2.2 | 0 | 0 | 1307 | 0 | 0 | 758 | 0 | 0 |
| 0.2.2.tar.gz%20 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 |
| 0.2.3 | 0 | 0 | 0 | 0 | 0 | 764 | 0 | 0 |
| 0.2.4 | 0 | 0 | 0 | 0 | 0 | 387 | 0 | 0 |
| 0.2.5 | 0 | 0 | 0 | 0 | 0 | 686 | 0 | 0 |
| 0.2.6 | 0 | 0 | 0 | 0 | 0 | 545 | 0 | 0 |
| 0.2.7 | 0 | 0 | 0 | 0 | 0 | 934 | 0 | 0 |
| 0.2.8 | 0 | 0 | 0 | 0 | 0 | 2112 | 0 | 0 |
| 0.2.9 | 0 | 0 | 0 | 0 | 0 | 831 | 0 | 0 |
| 0.3.0 | 0 | 0 | 0 | 0 | 0 | 2948 | 0 | 0 |
| 0.3.0.tar.gz%20H | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 |
| 0.3.1 | 0 | 0 | 0 | 0 | 0 | 420 | 0 | 0 |
| 1.0.0 | 0 | 622 | 0 | 0 | 3099 | 0 | 2576 | 0 |
| 1.0.1 | 0 | 1852 | 0 | 0 | 10018 | 0 | 2380 | 0 |
| 1.0.2 | 0 | 0 | 0 | 0 | 1855 | 0 | 3744 | 0 |
| 1.0.3 | 0 | 0 | 0 | 0 | 3304 | 0 | 565 | 0 |
| 1.0.4 | 0 | 0 | 0 | 0 | 0 | 0 | 3445 | 0 |
| 1.1.0 | 0 | 658 | 0 | 0 | 552 | 0 | 478 | 0 |
| 1.1.1 | 0 | 0 | 0 | 0 | 944 | 0 | 0 | 0 |
| 1.2.0 | 0 | 744 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1.2.1 | 0 | 560 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1.2.2 | 0 | 776 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1.2.3 | 0 | 807 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1.2.4 | 0 | 2980 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1.2.5 | 0 | 1999 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1.2.6 | 0 | 1207 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1.3.0 | 0 | 1766 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1.4.0 | 0 | 1212 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1.4.0.tar.gz%20H | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1.5.0 | 0 | 2267 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1.5.0.tar.gz%20HT | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
total_downloads %>%
count(package, sort = TRUE) %>%
tidyr::pivot_wider(
names_from = package,
values_from = n
) |>
kable()
| healthyR.ts | healthyR | healthyR.data | TidyDensity | healthyR.ai | healthyverse | tidyAML | RandomWalker |
|---|---|---|---|---|---|---|---|
| 24700 | 20966 | 19772 | 18712 | 16738 | 13188 | 6513 | 849 |
Cumulative Downloads by Package
p1 <- plot_cumulative_downloads_pkg(total_downloads, pkg = "healthyR")
p2 <- plot_cumulative_downloads_pkg(total_downloads, pkg = "healthyR.ts")
p3 <- plot_cumulative_downloads_pkg(total_downloads, pkg = "healthyR.data")
p4 <- plot_cumulative_downloads_pkg(total_downloads, pkg = "healthyverse")
p5 <- plot_cumulative_downloads_pkg(total_downloads, pkg = "healthyR.ai")
p6 <- plot_cumulative_downloads_pkg(total_downloads, pkg = "TidyDensity")
p7 <- plot_cumulative_downloads_pkg(total_downloads, pkg = "tidyAML")
p8 <- plot_cumulative_downloads_pkg(total_downloads, pkg = "RandomWalker")
f <- function(date) format(date, "%b %d, %Y")
patchwork_theme <- theme_classic(base_size = 24) +
theme(
plot.title = element_text(face = "bold"),
plot.caption = element_text(size = 14)
)
p1 + p2 + p3 + p4 + p5 + p6 + p7 + p8 +
plot_annotation(
title = "healthyR packages are on the Rise",
subtitle = "A Summary of Downloads from the RStudio CRAN Mirror - Since Inception",
caption = glue::glue("Source: RStudio CRAN Logs ({f(start_date)} to {f(end_date)})"),
theme = patchwork_theme
)
Thirty Day Run Post Release
pkg_rel <- readRDS("pkg_release_tbl.rds") |>
# Filter out bad data, not sure why it occurrs.
filter(
date != "2024-05-29" &
!(date == "2024-06-12" & package == "TidyDensity")
) |>
arrange(date) |>
group_by(package) |>
mutate(rel_no = row_number()) |>
ungroup()
thirty_day_runup_tbl <- total_downloads |>
lazy_dt() |>
select(date, package, version) |>
group_by(date, package, version) |>
summarise(dl_count = n()) |>
ungroup() |>
arrange(date) |>
group_by(package, version) |>
mutate(rec_no = row_number()) |>
mutate(cum_dl = cumsum(dl_count)) |>
filter(rec_no < 31) |>
ungroup() |>
mutate(pkg_ver = paste0(package, "-", version)) |>
collect()
release_tbl <- left_join(
x = thirty_day_runup_tbl,
y = pkg_rel
) |>
group_by(package) |>
fill(release_record, .direction = "down") |>
fill(rel_no, .direction = "down") |>
mutate(
release_record = as.factor(release_record),
rel_no = as.factor(rel_no)
) |>
ungroup()
latest_group_tbl <- release_tbl |>
group_by(package) |>
arrange(date, rec_no) |>
mutate(group_no = as.numeric(rel_no)) |>
filter(group_no == max(group_no)) |>
ungroup()
joined_tbl <- left_join(
x = thirty_day_runup_tbl,
y = latest_group_tbl
) |>
mutate(group_no = ifelse(is.na(group_no), FALSE, TRUE))
joined_tbl |>
ggplot(aes(x = rec_no, y = dl_count, group = as.factor(pkg_ver))) +
facet_wrap(~ package, scales = "free", ncol = 3) +
geom_line(aes(col = group_no)) +
scale_color_manual(values = c("FALSE" = "grey", "TRUE" = "red")) +
theme_minimal() +
labs(
y = "Downloads",
x = "Day After Version Release",
col = "Latest Release"
)
joined_tbl |>
ggplot(aes(x = rec_no, y = cum_dl, group = as.factor(pkg_ver))) +
facet_wrap(~ package, scales = "free", ncol = 3) +
geom_line(aes(col = group_no)) +
scale_color_manual(values = c("FALSE" = "grey", "TRUE" = "red")) +
theme_minimal() +
labs(
y = "Downloads",
x = "Day After Version Release",
col = "Latest Release"
)
