First, you should load recharts:

library(recharts)

1 Introduction

Radar plot includes 1 basic types:

Radar

The keys are:

character x and numeric y and will be compacted using data.table::dcast with fun=sum
x is used as indicators, while facet is used to produce separate pies!
you can use setPolar to tune the polar system parameters.

2 Function Call

echartr(data, x, <y>, <series>, <facet>, <t>, <type>, <subtype>)

Arg	Requirement
data	source data in the form of data.frame
x	character independent variable. Each level of `x[,1]` is treated as polar axis labels. Other type will be coerced to factors. Only the first one is accepted if multiple variables are provided.
y	numeric dependent variable. Only the first one is accepted if multiple variables are provided.
series	series variable which will be coerced to factors. Each level of `series` is treated as a subsetting factor to produce data series. Only the first one is accepted if multiple variables are provided.
facet	facetting variable which will be coerced to factors. Each level of `facet` is treated as a subsetting factor to produce separate pies. Only the first one is accepted if multiple variables are provided.
t	timeline variable which will be coerced to factors. Only the first one is accepted if multiple variables are provided.
type	‘radar’.
subtype	radar: “fill” fill: fill the radar with color (default hollow)

3 Showcase

3.1 Data Preparation

Let’s look into mtcars dataset embeded in the package datasets. We want to compare some paramters (c(‘mpg’,‘disp’,‘hp’,‘qsec’,‘wt’,‘drat’)) among several models (Mercedes-benz 450 series).

cars = mtcars[c('Merc 450SE','Merc 450SL','Merc 450SLC'),
              c('mpg','disp','hp','qsec','wt','drat')]
cars$model <- rownames(cars)
cars <- data.table::melt(cars, id.vars='model')
names(cars) <- c('model', 'indicator', 'Parameter')
knitr::kable(cars)

model	indicator	Parameter
Merc 450SE	mpg	16.40
Merc 450SL	mpg	17.30
Merc 450SLC	mpg	15.20
Merc 450SE	disp	275.80
Merc 450SL	disp	275.80
Merc 450SLC	disp	275.80
Merc 450SE	hp	180.00
Merc 450SL	hp	180.00
Merc 450SLC	hp	180.00
Merc 450SE	qsec	17.40
Merc 450SL	qsec	17.60
Merc 450SLC	qsec	18.00
Merc 450SE	wt	4.07
Merc 450SL	wt	3.73
Merc 450SLC	wt	3.78
Merc 450SE	drat	3.07
Merc 450SL	drat	3.07
Merc 450SLC	drat	3.07

3.2 Radar Chart

3.2.1 Single Radar

Set type ‘radar’ and do not set series or facet. But such a radar chart makes little sense.

echartr(cars, indicator, Parameter, type='radar')

3.2.2 Radar with Multiple Series

So we always want to set data series to compare the parameters series by series.

echartr(cars, indicator, Parameter, model, type='radar', sub='fill') %>%
    setTitle('Merc 450SE  vs  450SL  vs  450SLC')

3.3 Multiple Radars

If you don’t set series, instead, set facet, you will yield a bunch of radars shoulder by shoulder.

echartr(cars, indicator, Parameter, facet=model, type='radar') %>%
        setTitle('Merc 450SE  vs  450SL  vs  450SLC')

3.4 With Timeline

You can set t to yield a radar chart with timeline.

echartr(cars, indicator, Parameter, t=model, type='radar') %>%
        setTitle('Merc 450SE  vs  450SL  vs  450SLC')

When it comes to timeline, you should pay special attention. In order to make timeline work properly, you need an expand.grid dataset to cover all the combinations of x, series and t levels.

carstat = data.table::dcast(data.table::data.table(mtcars),
              am + carb + gear ~., mean,
              value.var=c('mpg','disp','hp','qsec','wt','drat'))
carstat = data.table::melt(carstat, id=c('am', 'carb', 'gear'))
names(carstat) <- c('am', 'carb', 'gear', 'indicator', 'Parameter')
levels(carstat$indicator) <- gsub("_mean_\\.", "",
                                  levels(carstat$indicator))
carstat$am <- factor(carstat$am, labels=c('A', 'M'))
fullData <- data.frame(expand.grid(levels(carstat$indicator),
            levels(carstat$am), unique(carstat$carb)))
carstat <- merge(fullData, carstat, all.x=TRUE)
carstat$carb <- as.factor(carstat$carb)
carstat$gear <- as.factor(carstat$gear)
str(carstat)

## 'data.frame':    5616 obs. of  8 variables:
##  $ Var1     : Factor w/ 6 levels "mpg","disp","hp",..: 1 2 3 4 5 6 1 2 3 4 ...
##  $ Var2     : Factor w/ 2 levels "A","M": 1 1 1 1 1 1 2 2 2 2 ...
##  $ Var3     : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ am       : Factor w/ 2 levels "A","M": 1 1 1 1 1 1 1 1 1 1 ...
##  $ carb     : Factor w/ 6 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ gear     : Factor w/ 3 levels "3","4","5": 1 1 1 1 1 1 1 1 1 1 ...
##  $ indicator: Factor w/ 6 levels "mpg","disp","hp",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Parameter: num  20.3 20.3 20.3 20.3 20.3 ...

echartr(carstat, indicator, Parameter, am, facet=carb, t=gear, type='radar') %>%
    setTitle('Merc 450SE  vs  450SL  vs  450SLC')

4 Futher Setup

4.1 `setPolar`

The default radar chart is built on polygons. You can built it on circle using setPolar, which is only appliable for radar charts.

echartr(cars, indicator, Parameter, facet=model, type='radar') %>%
        setTitle('Merc 450SE  vs  450SL  vs  450SLC') %>%
    setPolar(type='circle')

You can explore setPolar by typing ?setPolar in R console.

Then you can configure the widgets, add markLines and/or markPoints, fortify the chart.

You can refer to related functions to play around on your own.

Basic Plots 23 - Radar Chart