Initial Commit

.Rbuildignore

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+^.*\.Rproj$
+^\.Rproj\.user$
+^README\.Rmd$
+^data-raw$
+^LICENSE\.md$

.gitignore

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+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata
+.idea

DESCRIPTION

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+Package: rescuetimewrapper
+Type: Package
+Title: RescueTime Wrapper
+Version: 1.0.0
+Author: Niko Wenzl
+Maintainer: Niko Wenzl <[email protected]>
+Description: Wrapper for Accessing complete or anonymized data from the RescueTime API
+License: MIT + file LICENSE
+Encoding: UTF-8
+LazyData: true
+RoxygenNote: 7.1.1
+Imports: 
+    dplyr,
+    httr,
+    lubridate,
+    stringr
+Depends: 
+    R (>= 2.10)

LICENSE

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+YEAR: 2021
+COPYRIGHT HOLDER: Niko Wenzl

LICENSE.md

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+# MIT License
+
+Copyright (c) 2021 Niko Wenzl
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+export(get_productivity_index)
+export(get_rescue_time_data)
+export(get_rescue_time_data_anonymized)

R/main.R

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+#' Get RescueTime Data based on an API Key.
+#' The Data is returned unfiltered
+#'
+#' @param key string provided by RescueTime
+#' @param dateFrom string in iso date format "YYYY-mm-DDTHH:MM:ssZ"
+#' @param dateTo string in iso date format "YYYY-mm-DDTHH:MM:ssZ"
+#' @param scope string either "Overview", "Category", "Activity", "Productivity", "Document"
+#' @param transform boolean transform data to different structure
+
+#'
+#' @return table with complete data
+#' @export
+#'
+#' @examples
+#' data <- get_rescue_time_data("XYZ","2021-04-11T00:00:00Z","2021-04-12T23:59:59Z","Category")
+get_rescue_time_data <- function (key, dateFrom, dateTo, scope = "Activity", transform = FALSE) {
+  library(httr)
+  params <- list("key" = key,
+                 "format" = "csv",
+                 "restrict_begin" = dateFrom,
+                 "restrict_end" = dateTo ,
+                 "resolution_time" = "day",
+                 "perspective" = "interval",
+                 "restrict_kind" = tolower(scope))
+
+  res <- POST("https://www.rescuetime.com/anapi/data",
+              body = params,
+              encode = "form",
+              verbose()
+  )
+
+  # parse data
+  activity_data <- content(res, "parsed")
+
+  if (transform) {
+    colnames(activity_data)[colnames(activity_data) == "Time Spent (seconds)"] <- "Time"
+    colnames(activity_data) <- stringr::str_replace_all(colnames(activity_data),"[:punct:]|[:space:]|[/+]","")
+    activity_data <- transform(activity_data, Date = strftime(Date, "%Y-%m-%d"))
+    return(transform_data(activity_data, "Date", scope,  c("Time")))
+  } else {
+    return(activity_data)
+  }
+}
+
+#' Get RescueTime Data based on an API Key.
+#' The Data is return anonymized according to the category definitions on package level
+#'
+#'
+#' @param key string provided by RescueTime
+#' @param dateFrom string in iso date format "YYYY-mm-DDTHH:MM:ssZ"
+#' @param dateTo string in iso date format "YYYY-mm-DDTHH:MM:ssZ"
+#' @param scope string either "Category" or "SubCategory"
+#' @param transform boolean transform data to different structure
+#'
+#' @return table with an entry for each category for each day
+#' @export
+#'
+#' @examples
+#' data <- get_rescue_time_data_anonymized("XYZ","2021-04-11T00:00:00Z","2021-04-12T23:59:59Z","Category")
+get_rescue_time_data_anonymized <- function (key, dateFrom, dateTo, scope = "Category", transform = FALSE) {
+  library(dplyr)
+  activity_data <- get_rescue_time_data(key, dateFrom, dateTo, "Activity", FALSE)
+
+  csv_path <- system.file("categories.csv", package="rescuetimewrapper")
+  categories <- read.csv(csv_path)
+
+  # join with categories
+  joined_data <- merge(
+    categories,
+    activity_data,
+    by.x = "SubCategory",
+    by.y = "Category",
+    all = TRUE,
+  )
+
+  # productivity index
+  productivity_index <- activity_data %>%
+    group_by(Date) %>%
+    summarise(weighted.mean(Productivity, `Time Spent (seconds)`))
+  names(productivity_index)<-c("Date","Productivity Index")
+  productivity_index <- transform(productivity_index, Date = strftime(Date, "%Y-%m-%d"))
+
+  # anonymize data by aggreagting over categories
+  anomymized_data <- aggregate(x = joined_data$`Time Spent (seconds)`,
+                               by = list(joined_data[,scope], joined_data$Date),
+                               FUN = sum)
+  colnames(anomymized_data) <- c("Category", "Date","Time Spent (seconds)")
+
+  all_data <- expand.grid(unique(na.omit(categories[,scope])),
+                          unique(na.omit(joined_data$Date)),
+                          "Time Spent (seconds)"=NA)
+  colnames(all_data) <- c("Category", "Date","Time Spent (seconds)")
+  result <- select(merge(all_data, anomymized_data, by = c("Category", "Date"), all=TRUE),-"Time Spent (seconds).x")
+  colnames(result) <- c("Category", "Date","Time")
+
+  # add number of Applications for each row
+  for (row in 1:nrow(result)) {
+    current_category <- toString(result[row, "Category"])
+    current_date <- result[row, "Date"]
+    result[row, "Number of Applications"] <- length(which(joined_data[,scope] == current_category & joined_data$Date == current_date))
+  }
+
+  if (transform) {
+    colnames(result)[colnames(result) == "Time Spent (seconds)"] <- "Time"
+    colnames(result) <- stringr::str_replace_all(colnames(result),"[:punct:]|[:space:]|[+]","")
+    result <- transform(result, Date = strftime(Date, "%Y-%m-%d"))
+    transformed_result <- transform_data(result, "Date", scope, c("Time", "NumberofApplications"))
+    data_with_productivity <- merge(transformed_result, productivity_index, by = "Date", all = TRUE)
+    return(data_with_productivity)
+  } else {
+    return(result)
+  }
+}
+
+#' Transform data frame based on row and column definitions
+#'
+#' @param data data.frame with rows and columns
+#' @param row_column string the column that contains the values for the transformed row
+#' @param col_column string the column that contains the values for the transformed columns
+#' @param ignored_columns array list of columns to ignore as value columns
+#'
+#' @return a data frame that has a row for each entry of the values in the row_column
+#' and columns for each value of the col_column
+#'
+#' @examples new_data <- transform_data(data, "Date", "Category")
+transform_data <- function(data, row_column, col_column, value_columns = c("Time")) {
+  library(lubridate)
+  # create table with unique values for row_column as rows
+  transformed_data <- data.frame(unique(na.omit(data[, row_column])))
+  colnames(transformed_data) <- c(row_column)
+
+
+  # for each column that values are converted to columns themselves
+  unique_items <- data.frame(unique(na.omit(data[,col_column])))
+  colnames(unique_items) <- c(col_column)
+
+  for(i in 1:nrow(transformed_data)) {
+    for (j in 1:nrow(unique_items)) {
+      col_value <- unique_items[j, col_column]
+      current_row <- data[which(grepl(pattern = toString(transformed_data[i, row_column]), x=data[,row_column], fixed=TRUE)
+                                & grepl(pattern = toString(col_value), x = data[, col_column], fixed=TRUE)),]
+      for (col in value_columns) {
+        transformed_data[i, paste(col_value, col, sep = "_")] <- current_row[1, col]
+      }
+    }
+  }
+  return(transformed_data)
+}
+
+#' Function that calculates a productivity index (weighted mean) for a given time period
+#'
+#' @param key string provided by RescueTime
+#' @param dateFrom string in iso date format "YYYY-mm-DDTHH:MM:ssZ"
+#' @param dateTo string in iso date format "YYYY-mm-DDTHH:MM:ssZ"
+#'
+#' @return data.frame with a productivity for each date
+#' @export
+#'
+#' @examples productivity_index <- get_productivity_index("XYZ","2021-04-11T00:00:00Z","2021-04-12T23:59:59Z")
+
+get_productivity_index <- function(key, dateFrom, dateTo) {
+  library(dplyr)
+  activity_data <- get_rescue_time_data(key, dateFrom, dateTo, "Activity", FALSE)
+
+  # productivity index
+  productivity_index <- activity_data %>%
+    group_by(Date) %>%
+    summarise(weighted.mean(Productivity, `Time Spent (seconds)`))
+  names(productivity_index)<-c("Date","Productivity Index")
+  productivity_index <- transform(productivity_index, Date = strftime(Date, "%Y-%m-%d"))
+
+  return(productivity_index)
+}

README.md

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+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+
+
+# RescueTime R Wrapper
+
+<!-- badges: start -->
+<!-- badges: end -->
+
+The goal of RescueTime Wrapper is to provide access to Rescue Time Data through R. Also it is possible to get anonymized data from here.
+
+## Installation
+
+You can install the version via the remotes package.
+
+``` r
+remotes.install_github(repo = "tud-ise/rescuetime-r-wrapper")
+```
+
+## Example
+
+After loading the library you have two ways to access the RescueTime API. The Parameters are the same for both functions. 
+First, you need to supply your RescueTimeAPI Key, second and third the start and enddate provided as string in ISO Format (YYYY-mm-DDTHH:MM:SSZ).
+The fourth parameter varies between the function and defines the scope of the data returned. There is an optional fifth parameter that is discussed in the next paragraph.
+
+
+```r
+library(rescuetimewrapper)
+# complete
+data <- get_rescue_time_data("XYZ","2021-04-11T00:00:00Z","2021-04-12T23:59:59Z", 'Activity')
+# anonymized
+data <- get_rescue_time_data_anonymized("XYZ","2021-04-11T00:00:00Z","2021-04-12T23:59:59Z","Category")
+```
+
+The data returned is a table looking like this
+
+
+```r
+data <- get_rescue_time_data("XYZ","2021-04-11T00:00:00Z","2021-04-12T23:59:59Z", 'activity')
+#> Date                `Time Spent (seconds)` `Number of People` Activity             Category                           Productivity
+#> 1 2021-04-11 00:00:00                   1451                  1 idea64               Editing & IDEs                                2
+#> 2 2021-04-11 00:00:00                    924                  1 Windows Explorer     General Utilities                             1
+#> 3 2021-04-11 00:00:00                    892                  1 discord              General Communication & Scheduling            0
+#> 4 2021-04-11 00:00:00                    620                  1 brave                Browsers                                      0
+#> 5 2021-04-11 00:00:00                    574                  1 iTunes               Music                                        -2
+#> 6 2021-04-11 00:00:00                    495                  1 youtube.com          Video                                        -2
+
+data <- get_rescue_time_data_anonymized("XYZ","2021-04-12T00:00:00Z","2021-04-12T23:59:59Z", 'Category')
+#>                      Category       Date Time Number of Applications
+#> 1                    Business 2021-04-12  961                      4
+#> 2  Communication & Scheduling 2021-04-12  406                      2
+#> 3           Social Networking 2021-04-12   53                      1
+#> 4        Design & Composition 2021-04-12   NA                      0
+#> 5               Entertainment 2021-04-12 3157                      6
+#> 6              News & Opinion 2021-04-12   29                      2
+#> 7        Reference & Learning 2021-04-12  301                      5
+#> 8        Software Development 2021-04-12 7208                      8
+#> 9                    Shopping 2021-04-12   60                      2
+#> 10                  Utilities 2021-04-12 1489                      9
+#> 11              Uncategorized 2021-04-12  344                      8
+```
+
+### Transformed Data
+You can also query the data in a transformed way, with an optional Parameter. The transformed data will also contain the productivity index described below. The output will look like this:
+```r
+data <- get_rescue_time_data("XYZ","2021-04-11T00:00:00Z","2021-04-12T23:59:59Z", 'activity', TRUE)
+#> Date                   `idea64_Time`     `Windows Explorer_Time` ...
+#> 1 2021-04-11 00:00:00                   1451                  924
+
+data <- get_rescue_time_data_anonymized("XYZ","2021-04-12T00:00:00Z","2021-04-12T23:59:59Z", 'Category', TRUE)
+#> Date                   `Business_Time`     `Business_Number of Applications`   'Communication & Scheduling_Time' ...
+#> 1 2021-04-11 00:00:00                   961                  4                   406
+```
+
+### Productivity Index
+You can also calculate a productivity index (weighted mean) for a given time period. The value varies from -2 (very unproductive) to 2 (very productive)
+```r
+productivity_index <- get_productivity_index("XYZ","2021-04-11T00:00:00Z","2021-04-12T23:59:59Z")
+#> Date           Productivity Index
+#> 2021-04-11   1.75
+#> 2021-04-12   0.98
+```

RescueTimeRWrapper.Rproj

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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
+
+AutoAppendNewline: Yes
+StripTrailingWhitespace: Yes
+
+BuildType: Package
+PackageUseDevtools: Yes
+PackageInstallArgs: --no-multiarch --with-keep.source
+PackageRoxygenize: rd,collate,namespace,vignette