Add Python versions chart to the Report

DESCRIPTION

@@ -1,15 +1,17 @@
 Package: connectViz
 Title: Visualize Your 'RStudio Connect' Server Usage Data
 Version: 0.0.0.9146
-Authors@R: 
-    person("David", "Granjon", , "[email protected]", role = c("aut", "cre"))
+Authors@R: c(
+    person("David", "Granjon", , "[email protected]", role = c("aut", "cre")),
+    person(given = "Christophe", family = "Regouby", role = c("ctb"), email = "[email protected]")
+    )
 Description: A collection of helper functions and 'htmlwidgets' to help admins or 
     user better understand how 'RStudio Connect' is used in their organization. The package
     provides plug and play visualizations that can be customized depending on needs.
 License: MIT + file LICENSE
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.1.2
+RoxygenNote: 7.2.3
 Suggests: 
     testthat (>= 3.0.0),
     shinydashboard

NAMESPACE

@@ -28,6 +28,7 @@ export(process_rsc_content)
 export(process_rsc_user)
 export(sort_content_by_access)
 export(sort_content_by_appmode)
+export(sort_content_by_pyversion)
 export(sort_content_by_rversion)
 export(sort_users_by_role)
 import(apexcharter)

R/helpers-data.R

@@ -444,7 +444,21 @@ sort_content_by_rversion <- function(content) {
     mutate(Percentage = round(n / sum(n) * 100, 1))
 }
 
-
+#' Sort RStudio Connect content by python version
+#'
+#'
+#' @param content Get from \link[connectapi]{get_content}.
+#'
+#' @return A tibble with content grouped by python version.
+#' @export
+#' @importFrom rlang .data
+sort_content_by_pyversion <- function(content) {
+  content %>%
+    filter(!is.na(.data$py_version)) %>%
+    group_by(.data$py_version) %>%
+    summarize(n = n()) %>%
+    mutate(Percentage = round(n / sum(n) * 100, 1))
+}
 
 #' Sort RStudio Connect content by app mode
 #'

R/helpers-tables.R

@@ -35,7 +35,7 @@ generate_table <- function(logs, sparkline = FALSE, pagination = 10, height = NU
       color = ifelse(n > !!threshold, "white", "black")
     ) %>%
     grid_summary(
-      column = "n",
+      columns = "n",
       stat = "avg",
       label = "Mean usage: ",
       position = "top"

inst/examples/simple-rmd/analysis.Rmd

@@ -242,6 +242,14 @@ r_versions <- card(
 )
 ```
 
+```{r content-python-version}
+py_versions <- card(
+  full_screen = TRUE,
+  card_header("Python versions"),
+  sort_content_by_pyversion(rsc_content) |> create_pie_chart("py_version")
+)
+```
+
 ```{r content-type}
 content_type <- card(
   full_screen = TRUE,
@@ -256,6 +264,7 @@ layout_column_wrap(
   user_roles,
   content_access,
   r_versions,
+  py_versions,
   content_type
 )
 ```

tests/testthat/test-helper-charts.R

@@ -0,0 +1,8 @@
+test_that("create_calendar_chart works with multi years", {
+  dates <- seq.Date(as.Date("2017-01-01"), as.Date("2018-12-31"), by = "day")
+  n <- rpois(length(dates), 20)
+
+  calendar_data <- data.frame(app_name = "my_app", Date = dates, Freq = n)
+
+  expect_error(create_calendar_chart(calendar_data), NA)
+})