Improved the formatting of dates in R plots. Switched to a functionnal approach in the line plot. Broke the bar plot while trying to improve the formatting of dates :( Added the tidyverse R package to the Nix flake.

flake.nix

@@ -28,6 +28,7 @@
               requests
 	      kapkgs.execo
             ]))
+            (rWrapper.override { packages = with rPackages; [ tidyverse reshape2 ]; })
           ];
         };
         latex = pkgs.mkShell {

plot/bar_plot.r

@@ -1,17 +1,26 @@
 #!/usr/bin/env Rscript
 
+# Libraries:
+library(tidyverse)
+
 # Parsing command line arguments:
 options = commandArgs(trailingOnly = TRUE)
 filename = options[1]
 table_header = options[-1]
 
 # Loading files:
-table = read.csv(filename, header = FALSE, row.names = length(table_header) + 1) # The last column of the table gives the timestamp, thus the row names
+table = read.csv(filename, col_names = table_header, row.names = length(table_header)) # The last column of the table gives the timestamp, thus the row names
 
 # Setting up the table so it can be plotted:
-colnames(table) = table_header
 # Transposing for bar plotting:
 table = t(as.matrix(table))
 
 # Plotting:
-barplot(table)
+barplot(table)
+# legend("topright", legend = c("Level 1", "Level 2"), fill = c("red", "darkblue"))
+
+# read.csv(filename, col_names = table_header, row.names = length(table_header) + 1) %>% # The last column of the table gives the timestamp, thus the row names
+#     mutate(timestamp = as_date(as_datetime(timestamp))) %>% # Formatting the date
+#     as.matrix() %>% # Converting to matrix to transpose
+#     t() %>% # Transposing for bar plotting
+#     barplot()

plot/line_plot.r

@@ -1,20 +1,16 @@
 #!/usr/bin/env Rscript
 
 # Libraries:
-library(ggplot2)
 library(reshape2)
+library(tidyverse)
 
 # Parsing command line arguments:
 options = commandArgs(trailingOnly = TRUE)
 filename = options[1]
 table_header = options[-1]
 
-# Loading files:
-table = read.csv(filename, header = FALSE)
-
-# Setting up the table so it can be plotted:
-colnames(table) = table_header
-melted_table = melt(table, id.vars = "timestamp", variable.name = "category")
-
-# Plotting:
-ggplot(melted_table, aes(timestamp, value)) + geom_line(aes(colour = category))
+read_csv(filename, col_names=table_header) %>%
+    mutate(timestamp = as_date(as_datetime(timestamp))) %>% # Formatting the date
+    melt(id.vars = "timestamp", variable.name = "category", value.name = "amount") %>% # Formatting the table to plot each category
+    ggplot(aes(x = timestamp, y = amount)) +
+    geom_line(aes(color = category))