A Review of 2022

Author

Steven P Sanderson II, MPH

Published

May 28, 2024

2022 A Year in Review

The year 2022 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 2022 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 2022. 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 = "2022",
    .end_date = "2022"
  )

glimpse(data_tbl)
Rows: 27,799
Columns: 11
$ date      <date> 2022-01-01, 2022-01-01, 2022-01-01, 2022-01-01, 2022-01-01,…
$ time      <Period> 7H 9M 18S, 1H 40M 38S, 1H 40M 46S, 1H 41M 4S, 10H 52M 47S…
$ date_time <dttm> 2022-01-01 07:09:18, 2022-01-01 01:40:38, 2022-01-01 01:40:…
$ size      <int> 4866327, 1032739, 989862, 1028890, 13101, 14533, 14548, 1310…
$ r_version <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "4.1.2", "4.1.2", "4…
$ r_arch    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "i386", "i386", "i38…
$ r_os      <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "mingw32", "mingw32"…
$ package   <chr> "healthyR.data", "healthyR", "healthyR", "healthyR", "health…
$ version   <chr> "1.0.1", "0.1.6", "0.1.6", "0.1.6", "1.0.1", "0.1.6", "0.1.6…
$ country   <chr> "AU", "US", "US", "US", "US", "US", "US", "US", "US", "AU", …
$ ip_id     <int> 52, 48, 48, 48, 6414, 3451, 487, 817, 1251, 52, 1149, 1149, …

Now that we have our data, we have it for the year 2022 only with a start date of 2022-01-01 and an end date of 2022-12-31.

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 TidyDensity healthyR healthyR.ai healthyR.data healthyR.ts healthyverse
0.0.1 993 0 88 0 0 0
0.0.10 0 0 384 0 0 0
0.0.2 0 0 91 0 0 0
0.0.3 0 0 91 0 0 0
0.0.4 0 0 141 0 0 0
0.0.5 0 0 985 0 0 0
0.0.6 0 0 1082 0 0 0
0.0.7 0 0 706 0 0 0
0.0.8 0 0 836 0 0 0
0.0.9 0 0 607 0 0 0
0.1.0 0 88 0 0 87 0
0.1.1 0 90 0 0 99 0
0.1.2 0 101 0 0 87 0
0.1.3 0 89 0 0 87 0
0.1.4 0 90 0 0 87 0
0.1.5 0 90 0 0 90 0
0.1.6 0 137 0 0 90 0
0.1.7 0 970 0 0 881 0
0.1.8 0 1080 0 0 1056 0
0.1.9 0 876 0 0 485 0
0.2.0 0 1342 0 0 487 0
0.2.1 0 0 0 0 301 0
0.2.2 0 0 0 0 541 0
0.2.3 0 0 0 0 532 0
0.2.4 0 0 0 0 153 0
0.2.5 0 0 0 0 403 0
1.0.0 408 0 0 90 0 125
1.0.1 908 0 0 3618 0 217
1.0.2 0 0 0 0 0 3030
1.1.0 445 0 0 0 0 0
1.2.0 529 0 0 0 0 0
1.2.1 347 0 0 0 0 0
1.2.2 570 0 0 0 0 0
1.2.3 593 0 0 0 0 0
1.2.4 496 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
TidyDensity 5289
healthyR 4953
healthyR.ai 5011
healthyR.data 3708
healthyR.ts 5466
healthyverse 3372 
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.data 2
healthyR 11
healthyverse 3
healthyR.ai 10
healthyR.ts 16
TidyDensity 9 
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 27,799 downloads of all the healthyverse packages in 2022. There were in total 51 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 2022",
    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 2022",
    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 2022",
    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 = "2022") %>%
  head() %>%
  knitr::kable()
country latitude longitude display_name icon
Ghana 8.030028 -1.080027 Ghana https://nominatim.openstreetmap.org/ui/mapicons//poi_boundary_administrative.p.20.png
United States 39.783730 -100.445882 United States https://nominatim.openstreetmap.org/ui/mapicons//poi_boundary_administrative.p.20.png
Hong Kong SAR China 22.350627 114.184916 香港 Hong Kong, 中国 https://nominatim.openstreetmap.org/ui/mapicons//poi_boundary_administrative.p.20.png
Canada 61.066692 -107.991707 Canada https://nominatim.openstreetmap.org/ui/mapicons//poi_boundary_administrative.p.20.png
China 35.000074 104.999927 中国 https://nominatim.openstreetmap.org/ui/mapicons//poi_boundary_administrative.p.20.png
Jordan 31.166705 36.941628 الأردن https://nominatim.openstreetmap.org/ui/mapicons//poi_boundary_administrative.p.20.png 
# l <- map_leaflet(.data = data_tbl)
# mapshot(x = l, file = "map.png")

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 2022.