ggTimeSeries (version 1.0.2)

ggplot_calendar_heatmap: Plots a calendar heatmap

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

# NOT RUN {
{
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')
}
# }