upgrade metar and small fixes

.github/workflows/main.yaml

@@ -7,8 +7,8 @@ jobs:
     defaults:
       run:
         shell: Rscript {0}
-    # this needs to be the same as in Dockerfile
     container:
+      # this needs to be the same as in Dockerfile
       image: subugoe/muggle-onbuild:f60004c31979947dab53fbf9cfbb0e7f1c4bea55
     steps:
       - uses: actions/checkout@v2

DESCRIPTION

@@ -49,22 +49,20 @@ LazyData: true
 Roxygen: list(markdown = TRUE)
 Suggests:
     rmarkdown,
-    knitr,
     jsonlite,
     ggalt,
     scales,
     forcats,
     cowplot,
     readxl,
-    bookdown,
     flexdashboard,
     viridis,
     plotly,
     shiny,
     DT,
     writexl,
     echarts4r,
-    metaR,
+    metar,
     DBI,
     dbplyr,
     haven,
@@ -90,4 +88,4 @@ Imports:
     ggplot2,
     rlang
 Remotes:
-    subugoe/metaR@5543b34c49b339c8e91217912d07662415384cbe
+    subugoe/metar@7ccc2a0

R/plots.R

@@ -1,5 +1,5 @@
 #' Color palettes
-#' Colors used in the hybrid open access dashboard, based on [metaR::ugoe_pal()]
+#' Colors used in the hybrid open access dashboard, based on [metar::ugoe_pal()]
 #' @family visualisation functions
 #' @name colors
 NULL

README.md

@@ -3,7 +3,6 @@
 <!-- badges: start -->
 [![Main](https://github.com/subugoe/hoad/workflows/.github/workflows/main.yaml/badge.svg)](https://github.com/subugoe/hoad/actions)
 [![CRAN status](https://www.r-pkg.org/badges/version/hoad)](https://CRAN.R-project.org/package=hoad)
-[![Codecov test coverage](https://codecov.io/gh/subugoe/hoad/branch/master/graph/badge.svg)](https://codecov.io/gh/subugoe/hoad)
 [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
 [![Codecov test coverage](https://codecov.io/gh/subugoe/hoad/branch/master/graph/badge.svg)](https://codecov.io/gh/subugoe/hoad?branch=master)
 <!-- badges: end -->

inst/app/dashboard.Rmd

@@ -4,7 +4,7 @@ output:
   flexdashboard::flex_dashboard:
     orientation: rows
     vertical_layout: fill
-    css: ugoe_bootswatch.css
+    css: !expr metar::subugoe_template()$assets$css
 runtime: shiny
 ---
 
@@ -28,7 +28,7 @@ library(stringr)
 hybrid_df <- readr::read_csv(file = path_extdat("hybrid_publications.csv")) %>%
   mutate(license = fct_infreq(license)) %>%
   mutate(publisher = fct_infreq(publisher)) %>%
-  mutate(year = factor(issued, levels = c("2013", "2014", "2015","2016", "2017", "2018", "2019", "2020"))) %>%
+  mutate(year = factor(issued, levels = c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020"))) %>%
   mutate(hybrid_type = ifelse(hybrid_type == "SCOAP", "SCOAP<sup>3</sup>", hybrid_type)) %>%
   mutate(hybrid_type = factor(hybrid_type, levels = c("Open APC (TA)", "Open APC (Hybrid)", "SCOAP<sup>3</sup>"))) %>%
   mutate(domain = paste(domain, suffix, sep = ".")) %>%
@@ -86,13 +86,13 @@ jn_f <- reactive({
 
 # prepare dataset for plotting
 hybrid_sub <- reactive({
-if (length(unique(jn_f()$publisher)) > 1) {
+  if (length(unique(jn_f()$publisher)) > 1) {
     hybrid_sub <- jn_f() %>%
       group_by(year, yearly_all, license) %>%
       count() %>%
       mutate(prop = n / yearly_all)
   } else if (length(unique(jn_f()$journal_title)) > 1 &&
-             length(unique(jn_f()$publisher)) == 1) {
+    length(unique(jn_f()$publisher)) == 1) {
     hybrid_sub <- jn_f() %>%
       group_by(year, license, yearly_publisher_volume) %>%
       count() %>%
@@ -146,7 +146,8 @@ renderValueBox({
 renderValueBox({
   journal_n <- length(unique(jn_f()$journal_title))
   valueBox(format(journal_n, big.mark = " ", scientific = FALSE),
-           icon = "fa-filter")
+    icon = "fa-filter"
+  )
 })
 ```
 
@@ -156,7 +157,8 @@ renderValueBox({
 renderValueBox({
   hybrid_n <- nrow(jn_f())
   valueBox(format(hybrid_n, big.mark = " ", scientific = FALSE),
-           icon = "fa-creative-commons")
+    icon = "fa-creative-commons"
+  )
 })
 ```
 
@@ -197,12 +199,13 @@ renderPlotly({
     ) +
     scale_x_discrete(drop = FALSE) +
     scale_y_continuous(
-      labels = function(x)
+      labels = function(x) {
         format(x, big.mark = " ", scientific = FALSE)
+      }
     ) +
     theme_minimal() +
     geom_col(position = position_stack(reverse = TRUE))
- # p
+  # p
   plotly::ggplotly(p)
 })
 ```
@@ -234,8 +237,9 @@ renderPlotly({
     ylab("Articles") +
     scale_x_discrete(drop = FALSE) +
     scale_y_continuous(
-      labels = function(x)
-        format(x, big.mark = " ", scientific = FALSE),
+      labels = function(x) {
+        format(x, big.mark = " ", scientific = FALSE)
+      },
       breaks = scales::pretty_breaks()
     ) +
     scale_fill_manual(
@@ -264,8 +268,10 @@ renderEcharts4r({
     count(suffix, domain) %>%
     filter(!is.na(domain)) %>%
     e_charts() %>%
-    e_treemap(suffix, domain, n, leafDepth = "1",
-              name = "Corresponding Email") %>%
+    e_treemap(suffix, domain, n,
+      leafDepth = "1",
+      name = "Corresponding Email"
+    ) %>%
     e_tooltip(trigger = "item")
 })
 ```
@@ -302,10 +308,12 @@ hybrid_df %>%
   geom_bar(aes(fill = "by Publisher", label = `Proportion in %`), color = "transparent", stat = "identity") +
   facet_wrap(~publisher_group, nrow = 1) +
   scale_fill_manual(values = c("#b3b3b3a0", "#153268"), name = "") +
-   labs(x = "Year", y = "Hybrid OA Articles") +
+  labs(x = "Year", y = "Hybrid OA Articles") +
   theme_minimal() +
-  theme(legend.position = "top",
-        legend.justification = "right") +
+  theme(
+    legend.position = "top",
+    legend.justification = "right"
+  ) +
   scale_y_continuous(labels = scales::number_format(big.mark = " ")) +
   theme(panel.grid.minor = element_blank()) +
   theme(axis.ticks = element_blank()) +
@@ -339,14 +347,14 @@ fillRow(
 
 output$pubplot <- renderPlotly({
   if (input$Year == "All") {
-    tt <-   hybrid_df %>%
+    tt <- hybrid_df %>%
       filter(!is.na(license)) %>%
       mutate(publisher = forcats::as_factor(publisher)) %>%
       group_by(year, publisher, journal_title, yearly_jn_volume) %>%
       summarize(oa = n()) %>%
       mutate(prop = oa / yearly_jn_volume)
   } else {
-    tt <-  hybrid_df %>%
+    tt <- hybrid_df %>%
       filter(!is.na(license)) %>%
       mutate(publisher = forcats::as_factor(publisher)) %>%
       filter(year %in% input$Year) %>%
@@ -366,8 +374,9 @@ output$pubplot <- renderPlotly({
     scale_x_discrete(
       drop = FALSE,
       limits = rev(levels(tt$publisher)),
-      labels = function(x)
+      labels = function(x) {
         str_wrap(x, width = 30)
+      }
     ) +
     scale_y_continuous(labels = scales::percent)
   if (input$rb == TRUE) {
@@ -404,13 +413,13 @@ fillCol(
 
 output$yearplot <- renderPlotly({
   if (input$Publisher == "All") {
-    tt <-   hybrid_df %>%
+    tt <- hybrid_df %>%
       filter(!is.na(license)) %>%
       group_by(year, publisher, journal_title, yearly_jn_volume) %>%
       summarize(oa = n()) %>%
       mutate(prop = oa / yearly_jn_volume)
   } else {
-    tt <-  hybrid_df %>%
+    tt <- hybrid_df %>%
       filter(!is.na(license)) %>%
       filter(publisher == input$Publisher) %>%
       group_by(year, publisher, journal_title, yearly_jn_volume) %>%
@@ -471,16 +480,16 @@ hybrid_data <- reactive({
 
 renderDataTable(
   expr = {
-  hybrid_data()
-},
-rownames = FALSE,
+    hybrid_data()
+  },
+  rownames = FALSE,
   filter = "bottom",
-options = list(
+  options = list(
     pageLength = 4,
     dom = "ftp",
-  columnDefs = list(list(
+    columnDefs = list(list(
       className = "dt-head-left", targets = "_all"
-))
+    ))
   )
 )
 ```
@@ -493,13 +502,13 @@ Institutional View
 my_data <- reactive({
   if (!is.null(input$plot_clicked_data$name)) {
     hybrid_df %>%
-    filter(suffix == input$plot_clicked_data$name | domain == input$plot_clicked_data$name) %>%
-    mutate(plot_title = ifelse(!input$plot_clicked_data$name %in% suffix,
-                               domain,
+      filter(suffix == input$plot_clicked_data$name | domain == input$plot_clicked_data$name) %>%
+      mutate(plot_title = ifelse(!input$plot_clicked_data$name %in% suffix,
+        domain,
         suffix
       ))
   } else {
-      hybrid_df
+    hybrid_df
   }
 })
 ```
@@ -512,16 +521,18 @@ Column
 ```{r}
 output$plot <- renderEcharts4r({
   hybrid_df %>%
-     count(suffix, domain) %>%
-     filter(!is.na(domain)) %>%
-     e_charts() %>%
-     e_treemap(suffix, domain, n, leafDepth = "1",
-               name = "Corresponding Email") %>%
-     e_tooltip(trigger = "item")
+    count(suffix, domain) %>%
+    filter(!is.na(domain)) %>%
+    e_charts() %>%
+    e_treemap(suffix, domain, n,
+      leafDepth = "1",
+      name = "Corresponding Email"
+    ) %>%
+    e_tooltip(trigger = "item")
 })
 
 output$oa_license <- renderPlotly({
-    ggplot(my_data(), aes(factor(year), fill = license)) +
+  ggplot(my_data(), aes(factor(year), fill = license)) +
     geom_bar(position = position_stack(reverse = TRUE)) +
     ggtitle(unique(my_data()$plot_title)) +
     scale_fill_manual(
@@ -530,11 +541,12 @@ output$oa_license <- renderPlotly({
     ) +
     scale_x_discrete("", drop = FALSE) +
     scale_y_continuous("Articles",
-      labels = function(x)
+      labels = function(x) {
         format(x, big.mark = " ", scientific = FALSE)
+      }
     ) +
     theme_minimal() -> p
-     tt <- plotly::ggplotly(p, tooltip = c("y"))
+  tt <- plotly::ggplotly(p, tooltip = c("y"))
   tt$x$data <- lapply(tt$x$data, function(x) {
     x$text <- paste(x$name, x$y, sep = ": ")
     x
@@ -549,16 +561,17 @@ output$oa_source <- renderPlotly({
     xlab("Year") +
     ylab("Articles") +
     scale_y_continuous(
-      labels = function(x)
+      labels = function(x) {
         format(x, big.mark = " ", scientific = FALSE)
+      }
     ) +
     scale_fill_manual(
       "Sources",
       values = colors_source_disclosure,
       na.value = "#b3b3b3a0"
     ) +
     theme_minimal()
-   tt <- plotly::ggplotly(p, tooltip = c("y"))
+  tt <- plotly::ggplotly(p, tooltip = c("y"))
   tt$x$data <- lapply(tt$x$data, function(x) {
     x$text <- paste(x$name, x$y, sep = ": ")
     x
@@ -584,7 +597,7 @@ Row
 ```{r}
 renderDataTable(
   expr = {
- my_data() %>%
+    my_data() %>%
       count(publisher) %>%
       arrange(desc(n)) %>%
       mutate(prop = round(n / sum(n) * 100, 2))