In andrewjpfeiffer/timetrackr: Takes time tracking data and produces a pretty graph

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

timetrackr

The goal of timetrackr is to visualise simple time-tracking data.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("andrewjpfeiffer/timetrackr")

Example

The timetrackr package assumes you have time-tracking data in CSV format, with no column names, and the following columns in the specified order:

1. Start datetime, in the format %Y-%m-%d %H:%M %z
2. Activity, to categorise time-tracking events
3. Notes, to provide additional detail.

The package also assumes that the data are sorted by start datetime in ascending order. This is the natural ordering if you are using an iOS app like Shortcuts to track time by repeatedly appending to the CSV.

To read data, use the function time_1read(). This function also:

• omits the last row, because we don't know when the last event finished
• adds the end datetime based on the start datetime
• adds an indicator that specifies whether or not the end datetime is after the start datetime

library(timetrackr)
library(magrittr)

example_csv <- paste(
  c(paste(c("2019-10-11 18:00 +1030", "01. Sleep", "I'm asleep"), collapse = ","),
    paste(c("2019-10-12 06:00 +1030", "02. Eat",   "I'm awake"),  collapse = ","),
    paste(c("2019-10-12 22:00 +1030", "03. Party", "I'm tired"),  collapse = ","),
    paste(c("2019-10-13 03:00 +1030", "01. Sleep", "Zzzzzzzzz"),  collapse = ","),
    paste(c("2019-10-13 10:00 +1030", "02. Eat",   "Nomnomnom"),  collapse = ",")),
  collapse = "\n"
)

example_data <- time_1read(example_csv)

example_data

To graph this data, use the functions time_2transform() and time_3plot() in succession.

example_data_transform <- time_2transform(example_data) 

time_3plot(example_data_transform, date_start = "2019-10-12", date_end = "2019-10-12")

If the start and/or end date are omitted from time_3plot(), the minimum and/or maximum date are used respectively:

time_3plot(example_data_transform, date_end = "2019-10-12")

time_3plot(example_data_transform, date_start = "2019-10-12")

By default, the activities will appear in the legend in alphabetical order, and the fill colours are the default colours used by ggplot2: see this StackOverflow answer for more detail.

This behaviour can be customised by using the df_palette_in argument in time_3plot():

• This argument should be a tibble with two variables: activity and fill.
• The activities will appear in the legend in the order specified in this palette tibble.
• The fill colours will be as specified in this palette tibble.

my_palette <- tibble::tibble(activity = c("02. Eat", "03. Party", "01. Sleep"),
                             fill = c("blue", "green", "black"))

time_3plot(example_data_transform, df_palette_in = my_palette)

Note well:

• Any activities in the time-tracking data that do not appear in the palette tibble will be included at the end of the legend.
• The fill colour for these activities will be white, to alert you to the fact that you may need to update the palette tibble.
• If the fill colour specified in the palette tibble will also default to white if R cannot interpret the string as a colour using grDevices::col2rgb(). For example, you may like to use:
  • a six-digit hex code such as "#AABBCC".
  • a predefined colour name that R recognises (run grDevices::colors(), and see http://bc.bojanorama.pl/wp-content/uploads/2013/04/rcolorsheet.pdf for an illustration of these colours).

Shiny Application

The shiny application for this package can be found at https://andrewjpfeiffer.shinyapps.io/timeplot. To use the application:

• Upload the CSV file, as per the aforementioned specifications. (The application will tell you if your rows aren't ordered correctly.)
• Optionally, select the start and end dates to plot (arguments to time_3plot()) using the date pickers.
• Optionally, upload a two-column CSV file with no headers to change the colours of the plot (time_3plot() will automatically read this CSV file with the appropriate column names).
• Click the button "Create time tracking plot" to generate the plot in the application.
• Click the button "Download plot" to save the plot.

andrewjpfeiffer/timetrackr documentation built on Feb. 21, 2020, 4:22 a.m.