simplified selection of working directory

user is prompted for 1st .csv => script reuses same path to find other
files

Author: KonScience Podcast

summarize-flattr-reports.R

@@ -1,35 +1,27 @@
-# Creates Flattr summary from "Monthly Revenue" reports
-# Started by Katrin from the KonScience Podcast in Sept. 2014
-
-# IMPORTANT: Adjust folder path that contains all the downloaded .csv files here
-#   Windows: Please use double backslashes like C:\\User\\YOU...
-#   Linux & Mac: /Users/YOU/ can be abbreviated as '~/...'
-path_to_flattr_reports <- "/Users/YOU/Flattr/"
-
+# READ ME: https://github.com/KonScience/Summarize-Flattr-Reports#summarize-flattr-reports
 
+# assumes all .csv files were downloaded into same folder
+first_flattr_file <- file.choose()
+flattr_dir <- dirname(first_flattr_file) #learned from http://stackoverflow.com/a/18003224
 
 # saves original working directory and sets new one as provided above
 original_wd <- getwd()
-setwd(path_to_flattr_reports)
+setwd(flattr_dir)
 
 # get filenames of Flattr Monthly Revenue CSVs
-Flattr_filenames <- list.files(path_to_flattr_reports,
+Flattr_filenames <- list.files(flattr_dir,
                                pattern = "flattr-revenue-[0-9]*.csv"
                                )
 
-# // TODO find easier way to select path, e.g.
-# - auto-use folder where script runs from // TODO adjust ReadMe.md
-# - prompt user for path, all files (file.choose selects only one) or 1st file and find others in same folder // TODO remove original_wd code
-
 # read data from CSVs into data frame
 raw <- do.call("rbind",
-                      lapply(Flattr_filenames,
-                             read.csv,
-                             sep = ";",
-                             dec = ",", # convert decimal separator from , to . for following calculations
-                             stringsAsFactors = FALSE # 
-                             )
-                      ) # Function structure learned from https://stat.ethz.ch/pipermail/r-help/2010-October/255593.html
+               lapply(Flattr_filenames,
+                      read.csv,
+                      sep = ";",
+                      dec = ",", # convert decimal separator from , to . for following calculations
+                      stringsAsFactors = FALSE # 
+                      )
+               ) # Function structure learned from https://stat.ethz.ch/pipermail/r-help/2010-October/255593.html
 
 # append 1st days to months & convert to date format
 # learned from http://stackoverflow.com/a/4594269
@@ -56,8 +48,8 @@ export_csv <- function(data_source, filename) {
               sep = ";",
               dec = ",",
               row.names = FALSE
-              )}
-
+              )
+}
 
 # exports summary to same folder
 export_csv(per_thing_ordered, "flattr-revenue-summary.csv")
@@ -75,21 +67,19 @@ per_period <- ddply(raw,
 
 # plots Flattr clicks over time
 # // TODO colorize by "title"-category
-# // TODO spread elements out randomly over 25 days in their months (leave visible gap to next month)
 per_period$EUR_per_click <- (per_period$all_revenue / per_period$all_clicks)
 library(ggplot2)
 qplot(x = per_period$period,
       y = per_period$EUR_per_click,
       xlab = "time",
       ylab = "EUR per click"
       )
-ggsave("flattr-revenue-clicks.pdf")
+ggsave("flattr-revenue-clicks.png")
 
 # orders by title 
 per_period_orderd <- per_period[order(per_period$title),]
 
 # export to same folder
-# // TODO functionalize export with dataframe & filename objects
 export_csv(per_period_orderd, "flattr-revenue-click-value.csv")
 
 # restore original working directory