circular-line.utf8

A circular line chart may be useful when plotting cyclic dataseries resulting from the combination of a measurement variable and a time-related numerical/continuous variable. It usually helps highlighting a cyclic pattern, a repetition (or absence of repetition) in time. In principle, a circular line chart is a regular line chart which is projected into a polar coordinate system.

Before going any further, if you are not so familiar with line plots or polar coordinate systems, have a quick look at these two pages:

Here we will plot the temperatures (response variable) recorded hourly (predictor variable 1) at Lygra, Vestland, between Apr. 15^th and 22^nd, 2019 (predictor variable 2).

The dataframe for this tutorial is as follows:

# dataframe
df <- data.frame(day, time, temperature)
# structure of the dataframe
str(df)

## 'data.frame':    168 obs. of  3 variables:
##  $ day        : Factor w/ 7 levels "Monday","Tuesday",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ time       : Factor w/ 24 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ temperature: num  3.7 1.8 1.2 1.3 0.8 0.8 0.9 4.5 6.9 8.6 ...

Using the coding strategy presented here, we can start by building a multiple line chart. The function to use for drawing the lines is geom_line(). We must map the variables with the function aes() which will look more or less like this: aes(time, temperature, day). Our plan is thus to:

plot the predictor variable time on the X-axis,
plot the response variable temperature on the Y-axis,
use both group= and color= to display the data in colored lines based on the predictor variable day,
draw the lines with geom_line().

The code for the plot is as follows:

ggplot(df, aes(x = time, y = temperature, group = day, color = day)) +
  geom_line()

Then, to transform this regular line plot into a circular line plot, we project it into a polar coordinate system with coord_polar():

ggplot(df, aes(x = time, y = temperature, group = day, color = day)) +
  geom_line() +
  coord_polar()

This plot is almost acceptable, but the gap between days (which is materialized by the absence of line between 24 and 1 every day) is suboptimal. One solution is to use geom_polygon() instead of geom_line() as geom_polygon() links the first and the last data points of a series with a segment of corresponding color. Note that geom_polygon() must (in our case) be used with the argument fill = NA otherwise the polygone shape resulting from that code will not allow to see the overlapping series correctly:

ggplot(df, aes(time, temperature, group=day, color=day)) +
  geom_polygon(fill = NA) + 
  coord_polar()

Finally we may improve the global look of the plot by increasing the thickness of the lines with size= and by applying a nicer palette of colors such as viridis:

ggplot(df, aes(time, temperature, group=day, color=day)) +
  geom_polygon(size=1.2, fill = NA) + 
  scale_color_viridis_d() +
  coord_polar()

Adding plot title, axis titles, ticks, labels and other essential elements

In this section, you will learn how to set/modify all the necessary elements that make a plot complete and comprehensible. Such elements are: