ggplot_calendar_heatmap: Plots a calendar heatmap

In Ather-Energy/ggTimeSeries: Time Series Visualisations Using the Grammar of Graphics

Description

A calendar heatmap provides context for weeks, and day of week which makes it a better way to visualise daily data than line charts. Largely uses Codoremifa's code from stackoverflow.com/questions/22815688/calendar-time-series-with-r.

Usage

ggplot_calendar_heatmap(dtDateValue, cDateColumnName = "",
  cValueColumnName = "", vcGroupingColumnNames = "Year",
  dayBorderSize = 0.25, dayBorderColour = "black",
  monthBorderSize = 2, monthBorderColour = "black",
  monthBorderLineEnd = "round")

Arguments

dtDateValue	Data set which may include other columns apart from date and values.
cDateColumnName	Column name of the dates.
cValueColumnName	Column name of the data.
vcGroupingColumnNames	The set of columns which together define the group for the chart to operate within If you plan to facet your plot, you should specify the same column names to this argument. The function will automatically add the veriable for the year to the facet.
dayBorderSize	Size of the border around each day
dayBorderColour	Colour of the border around each day
monthBorderSize	Size of the border around each month
monthBorderColour	Colour of the border around each month
monthBorderLineEnd	Line end for the border around each month

Value

Returns a gpplot friendly object which means the user can use ggplot scales to modify the look, add more geoms, etc.

Cosmetic Tips

The minimalist look can be achieved by appending the following chunk of code to the output object: + xlab(NULL) + ylab(NULL) + scale_fill_continuous(low = 'green', high = 'red') + theme( axis.text = element_blank(), axis.ticks = element_blank(), legend.position = 'none', strip.background = element_blank(), # strip.text = element_blank(), # useful if only one year of data plot.background = element_blank(), panel.border = element_blank(), panel.background = element_blank(), panel.grid = element_blank(), panel.border = element_blank() )

Also See

stat_calendar_heatmap, a flexible but less polished alternative.

Examples

{
library(data.table)
library(ggplot2)
set.seed(1)
dtData = data.table(
      DateCol = seq(
         as.Date("1/01/2014", "%d/%m/%Y"),
         as.Date("31/12/2015", "%d/%m/%Y"),
         "days"
      ),
      ValueCol = runif(730)
   )
# you could also try categorical data with
# ValueCol = sample(c('a','b','c'), 730, replace = T)
p1 = ggplot_calendar_heatmap(
   dtData,
   'DateCol',
   'ValueCol'
)
p1
# add new geoms
p1 +
geom_text(label = '!!!') +
scale_colour_continuous(low = 'red', high = 'green')
}

Ather-Energy/ggTimeSeries documentation built on June 22, 2019, 2:55 a.m.