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Change color scheme of Shiny app to black/white
authorNaeem Model <me@nmode.ca>
Wed, 15 May 2024 21:51:44 +0000 (21:51 +0000)
committerNaeem Model <me@nmode.ca>
Wed, 15 May 2024 21:51:44 +0000 (21:51 +0000)
inst/app/index.html patch | blob | blame | history
inst/app/scripts/data.R patch | blob | blame | history
inst/app/www/styles.css patch | blob | blame | history

diff --git a/inst/app/index.html b/inst/app/index.html
index 83029c52d2e9781334ea14a140bd916c9c869594..b0f12455a8804f87e333f80372d1abedc5b484b3 100644 (file)
--- a/inst/app/index.html
+++ b/inst/app/index.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
 <html>
   <head>
-    {{ bootstrapLib(theme = bslib::bs_theme(primary = "red")) }}
+    {{ bootstrapLib(theme = bslib::bs_theme(primary = "black")) }}
     {{ headContent() }}
     <title>Rnaught Web</title>
     <link rel="stylesheet" type="text/css" href="styles.css">
diff --git a/inst/app/scripts/data.R b/inst/app/scripts/data.R
index 06cb256be1d3a24071a8c930fe5fa7cc72c3d3ff..eebd14fd6ba4bd830c68915ffa5436ba85d5690d 100644 (file)
--- a/inst/app/scripts/data.R
+++ b/inst/app/scripts/data.R
@@ -27,7 +27,7 @@ render_plot <- function(input, output) {
     counts <- tokenize_counts(input$data_counts)
     if (length(counts) > 0 && !anyNA(counts) && all(counts >= 0)) {
       output$data_plot <- renderPlot(
-        plot(seq_along(counts) - 1, counts, type = "o", pch = 16, col = "red",
+        plot(seq_along(counts) - 1, counts, type = "o", pch = 16, col = "black",
           xlab = input$data_units, ylab = "Cases", cex.lab = 1.5,
           xlim = c(0, max(length(counts) - 1, 1)), ylim = c(0, max(counts, 1))
         )
diff --git a/inst/app/www/styles.css b/inst/app/www/styles.css
index a3533d328fbfababcef2de6c794644ac49717e0e..f6c4407851f4d7d59a9a8d577232ab4ccb731cd8 100644 (file)
--- a/inst/app/www/styles.css
+++ b/inst/app/www/styles.css
@@ -21,6 +21,6 @@ noscript {
 }
 
 td.selected {
-  background-color: red;
+  background-color: black;
   color: white;
 }
[GitHub/MI2YorkU] An R package and web application for estimating the basic reproduction number (R0) of infectious diseases.

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