A Review of 2024

Author

Steven P Sanderson II, MPH

Published

May 28, 2024

2024 A Year in Review

The year 2024 was a big year for me. I did a lot of coding, a lot more than I typically do. The biggest push came personally in my ongoing development of my R packages that are in the healthyverse. To use the healthyverse simply do so in the familiar fashion:

install.packages("healthyverse")
library(healthyverse)

Here are links to all of the packages:

In order to start looking at some of the data that pertains to 2024 lets first get the data from the CRAN logs. Since I do this daily already, I can simply use the rds file I already have. I am going to go through the motions though, in case others might want to do something similar. The functions I am using to get the data can be found here

Now lets get that data!

library(tidyverse)
library(lubridate)
source("01_scripts/get_data_functions.R")
source("01_scripts/data_manipulation_functions.R")
source("01_scripts/mapping_functions.R")

get_cran_data()
get_package_release_data()
csv_to_rds()

Ok now that we have our data, lets ensure that we are only using the year 2024. We can do this by filtering out data by time with the timetk package.

Now lets filter our data below, some pre-processing may need to take place.

library(timetk)
data_tbl <- downloads_processed_tbl() %>%
  filter_by_time(
    .date_var = date,
    .start_date = "2024",
    .end_date = "2024"
  )

glimpse(data_tbl)
Rows: 39,040
Columns: 11
$ date      <date> 2024-01-01, 2024-01-01, 2024-01-01, 2024-01-01, 2024-01-01,…
$ time      <Period> 15H 9M 37S, 1M 5S, 1M 5S, 1M 19S, 1M 19S, 7H 40M 59S, 5H …
$ date_time <dttm> 2024-01-01 15:09:37, 2024-01-01 00:01:05, 2024-01-01 00:01:…
$ size      <int> 2116406, 2116405, 532, 529, 83289, 253032, 2367712, 54319, 2…
$ r_version <chr> "4.1.2", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "4.3.1", "4…
$ r_arch    <chr> "x86_64", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "aarch64",…
$ r_os      <chr> "mingw32", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "darwin20…
$ package   <chr> "healthyR.ts", "healthyR.ts", "healthyR.ts", "tidyAML", "tid…
$ version   <chr> "0.3.0", "0.3.0", "0.3.0", "0.0.3", "0.0.3", "0.0.12", "0.2.…
$ country   <chr> NA, "JP", "JP", "JP", "JP", "US", "KR", "KR", "KR", "KR", "U…
$ ip_id     <int> 2, 6074, 6074, 6074, 6074, 3446, 11, 11, 11, 11, 9833, 2, 22…

Now that we have our data, we have it for the year 2024 only with a start date of 2024-01-01 and an end date of 2024-12-29.

Package Information

The first thing we will do is look at how many downloads there were for each pacakge and it’s version.

library(knitr)

data_tbl %>% 
  count(package, version) %>% 
  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 0 122 0 127 0 0 0 117
0.0.10 0 0 0 126 0 0 0 0
0.0.11 0 0 0 129 0 0 0 0
0.0.12 0 0 0 130 0 0 0 0
0.0.13 0 0 0 2050 0 0 0 0
0.0.13.tar.gz%20H 0 0 0 5 0 0 0 0
0.0.2 0 0 0 126 0 0 0 183
0.0.3 0 0 0 127 0 0 0 213
0.0.4 0 0 0 118 0 0 0 941
0.0.5 0 0 0 128 0 0 0 2236
0.0.6 0 0 0 845 0 0 0 0
0.0.7 0 0 0 128 0 0 0 0
0.0.8 0 0 0 127 0 0 0 0
0.0.9 0 0 0 138 0 0 0 0
0.1.0 389 0 126 828 0 130 0 0
0.1.1 0 0 127 0 0 145 0 0
0.1.2 0 0 144 0 0 130 0 0
0.1.3 0 0 127 0 0 129 0 0
0.1.4 0 0 127 0 0 133 0 0
0.1.5 0 0 123 0 0 129 0 0
0.1.6 0 0 128 0 0 123 0 0
0.1.7 0 0 118 0 0 131 0 0
0.1.8 0 0 586 0 0 589 0 0
0.1.9 0 0 127 0 0 134 0 0
0.2.0 1106 0 138 0 0 137 0 0
0.2.1 0 0 2086 0 0 134 0 0
0.2.1.tar.gz%20HT 0 0 5 0 0 0 0 0
0.2.10 0 0 0 0 0 126 0 0
0.2.11 0 0 0 0 0 135 0 0
0.2.2 0 0 1564 0 0 124 0 0
0.2.2.tar.gz%20 0 0 0 0 0 10 0 0
0.2.3 0 0 0 0 0 131 0 0
0.2.4 0 0 0 0 0 134 0 0
0.2.5 0 0 0 0 0 134 0 0
0.2.6 0 0 0 0 0 134 0 0
0.2.7 0 0 0 0 0 132 0 0
0.2.8 0 0 0 0 0 957 0 0
0.2.9 0 0 0 0 0 144 0 0
0.3.0 0 0 0 0 0 2465 0 0
0.3.0.tar.gz%20H 0 0 0 0 0 5 0 0
0.3.1 0 0 0 0 0 683 0 0
1.0.0 0 122 0 0 133 0 142 0
1.0.1 0 575 0 0 666 0 126 0
1.0.2 0 0 0 0 534 0 193 0
1.0.3 0 0 0 0 1541 0 128 0
1.0.4 0 0 0 0 0 0 1901 0
1.1.0 0 122 0 0 571 0 680 0
1.1.1 0 0 0 0 1107 0 0 0
1.2.0 0 124 0 0 0 0 0 0
1.2.1 0 124 0 0 0 0 0 0
1.2.2 0 113 0 0 0 0 0 0
1.2.3 0 122 0 0 0 0 0 0
1.2.4 0 562 0 0 0 0 0 0
1.2.5 0 123 0 0 0 0 0 0
1.2.6 0 271 0 0 0 0 0 0
1.3.0 0 1784 0 0 0 0 0 0
1.4.0 0 1225 0 0 0 0 0 0
1.4.0.tar.gz%20H 0 1 0 0 0 0 0 0
1.5.0 0 2724 0 0 0 0 0 0
1.5.0.tar.gz%20HT 0 3 0 0 0 0 0 0

Now lets see how many total downloads for the year there were for each package.

data_tbl %>%
  count(package) %>%
  set_names("Package","Total Downloads") %>%
  kable()
Package Total Downloads
RandomWalker 1495
TidyDensity 8117
healthyR 5526
healthyR.ai 5132
healthyR.data 4552
healthyR.ts 7358
healthyverse 3170
tidyAML 3690 
data_tbl %>%
  select(package, version) %>%
  group_by(package) %>%
  distinct() %>%
  mutate(release_count = n()) %>%
  ungroup() %>%
  select(package, release_count) %>%
  distinct() %>%
  set_names("Package", "Number of Releases") %>%
  kable()
Package Number of Releases
healthyR.ts 26
tidyAML 5
healthyR.ai 15
healthyR 14
healthyverse 6
TidyDensity 16
healthyR.data 6
RandomWalker 2 
total_number_of_releases <- data_tbl %>%
  select(package, version) %>%
  group_by(package) %>%
  distinct() %>%
  mutate(release_count = n()) %>%
  ungroup() %>%
  select(package, release_count) %>%
  distinct() %>%
  summarise(total = sum(release_count, na.rm = TRUE))

So all in all there was a total of 39,040 downloads of all the healthyverse packages in 2024. There were in total 90 package releases as well.

Graphs

Now lets graph the data out!

data_tbl %>%
  count(package, version) %>%
  ggplot(aes(x = version, y = n, alpha = 0.382)) +
  geom_col(aes(group = package, fill = package)) +
  facet_wrap(package ~., ncol = 2, scales = "free") +
  scale_y_continuous(labels = scales::label_number(big.mark = ",")) +
  theme_minimal() +
  theme(legend.position = "bottom") +
  labs(
    title = "Downloads by Package for 2024",
    subtitle = "Faceted by Package",
    x = "Version",
    y = "Downloads",
    fill = "Package"
  )

data_tbl %>%
  count(package, version) %>%
  group_by(package) %>%
  mutate(cumulative_downloads = cumsum(n)) %>%
  mutate(record = row_number()) %>%
  ungroup() %>%
  ggplot(aes(x = record, y = cumulative_downloads, alpha = 0.382)) +
  geom_col(aes(group = package, fill = package)) +
  facet_wrap(package ~., ncol = 2, scales = "free") +
  scale_y_continuous(labels = scales::label_number(big.mark = ",")) +
  theme_minimal() +
  theme(legend.position = "bottom") +
    labs(
    title = "Downloads by Package for 2024",
    subtitle = "Faceted by Package",
    x = "Relase Number",
    y = "Downloads",
    fill = "Package"
  )

data_tbl %>%
  count(package, version) %>%
  group_by(package) %>%
  mutate(cumulative_downloads = cumsum(n)) %>%
  mutate(record = row_number()) %>%
  ungroup() %>%
  ggplot(aes(x = record, y = cumulative_downloads, alpha = 0.382)) +
  geom_line(aes(color = package, group = package), size = 1) +
  scale_y_continuous(labels = scales::label_number(big.mark = ",")) +
  theme_minimal() +
  theme(legend.position = "bottom") +
  labs(
    title = "Cumulative Downloads by Package for 2024",
    subtitle = "Colored by Package",
    x = "Release Number",
    y = "Downloads",
    color = "Package"
  )

Time Series Graphs

Now lets get some time-series graphs.

library(healthyR.ts)
pkg_tbl <- readRDS("00_data/pkg_release_tbl.rds")

data_tbl %>% 
  summarise_by_time(.date_var = date, n = n()) %>% 
  ts_calendar_heatmap_plot(.date_col = date, .value_col = n, .interactive = FALSE)

data_tbl %>%
  ts_downloads_tbl(.by_time = "day", package) %>%
  ggplot(aes(date, log1p(value))) +
  geom_point(aes(group = package, color = package), size = 1) +
  ggtitle(paste("Package Downloads: {healthyverse}")) +
  geom_smooth(method = "loess", color = "black",  se = FALSE) +
  geom_vline(
    data = pkg_tbl
    , aes(xintercept = as.Date(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")

data_tbl %>%
  ts_downloads_tbl(.by_time = "day") %>%
  rename(Actual = value) %>%
  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_vline(
    data = pkg_tbl
    , aes(xintercept = as.Date(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")

data_tbl %>%
  ts_downloads_tbl(.by_time = "day") %>%
  plot_seasonal_diagnostics(
    .date_var = date,
    .value = log1p(value),
    .interactive = FALSE
  ) +
  theme_minimal() +
  labs(
    title = "Seasonal Diagnostics",
    subtitle = "Values are log1p"
  )

data_tbl %>%
  ts_downloads_tbl(.by_time = "day") %>%
  plot_stl_diagnostics(
    .date_var = date,
    .value = log1p(value),
    .interactive = FALSE
  ) +
  theme_minimal() +
  labs(
    title = "STL Diagnostics",
    subtitle = "Values are log1p"
  )

Mapping

So now that we have seen all the downloads in variaous ways, where did they all come from? Lets take a look.

library(tmaptools)
library(countrycode)
library(mapview)
library(htmlwidgets)
library(webshot)

# mapping_dataset(.data_year = "2024") %>%
#   head() %>%
#   knitr::kable()

l <- map_leaflet()
saveWidget(l, "downloads_map.html")
try(webshot("downloads_map.html", file = "map.png", cliprect = "viewport"))

There was a total of 147 different countries that downloaded healthyverse packages in 2024.