First, you should load recharts:

library(recharts)

1 Introduction

Pie plot includes 3 basic types:

Pie: pie
Ring: ring
Nightingale rose chart: rose

The keys are:

character x and numeric y and will be compacted using data.table::dcast with fun=sum
x is used as series, series is used to produce multi-tier pies/rings, while facet is used to produce separate pies!
Pie and funnel charts can shift from one to another by clicking type shift buttons in the toolbox widget.

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` is treated as a data series. 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 tyre factor to produce rings towards the center. 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	‘pie’, ‘ring’, ‘rose’.
subtype	pie: c(“multi”,“clock”,“clockwise”) multi: multiple select mode clock/clokwise: pie/ring display clockwise ring: c(“info”,“multi”,“clock”,“clockwise”) info: infographic ring rose: c(“area”,“radius”,“multi”,“clock”,“clockwise”) area: rose chart in area mode radius: rose chart in radius mode

3 Showcase

3.1 Data Preparation

Let’s look into Titanic dataset embeded in the package datasets. The survival count by cabin class is shown as below:

titanic <- data.table::melt(apply(Titanic, c(1,4), sum))
names(titanic) <- c('Class', 'Survived', 'Count')
knitr::kable(titanic)

Class	Survived	Count
1st	No	122
2nd	No	167
3rd	No	528
Crew	No	673
1st	Yes	203
2nd	Yes	118
3rd	Yes	178
Crew	Yes	212

3.2 Pie Chart

3.2.1 Single Pie

type is set ‘pie’.

echartr(titanic, Class, Count, type='pie') %>%
    setTitle('Titanic: N by Cabin Class')

3.2.2 Multiple Pies

Pie chart uses facet as subsetting factor to produce seperate polar systems. So when we apply Class (containing 4 levels) as facet, we get 4 pies.

echartr(titanic, Survived, Count, facet=Class, type='pie') %>%
    setTitle('Titanic: Survival Outcome by Cabin Class')

3.2.3 Multiple Series without X

What if we only provide facet and y? We get several dichotomous pies.

echartr(titanic, y=Count, facet=Class, type='pie') %>%
    setTitle('Titanic: Propotion of Each Cabin Class')

3.2.4 Multi-ring Pie Charts

If series is given, you will yield a multi-ring pie chart. type and subtype are organized as vectors in association with levels of series.

echartr(titanic, Survived, Count, Class, type=c(rep('ring', 3), 'pie'),
        subtype='clock') %>% setTitle('Titanic: Survival Outcome by Cabin Class')

You can use series and facet to produce multiple multi-tier pies.

titanic_sex <- data.table::melt(apply(Titanic, c(1,2,4), sum))
names(titanic_sex)[4] <- "Count"
knitr::kable(titanic_sex)

Class	Sex	Survived	Count
1st	Male	No	118
2nd	Male	No	154
3rd	Male	No	422
Crew	Male	No	670
1st	Female	No	4
2nd	Female	No	13
3rd	Female	No	106
Crew	Female	No	3
1st	Male	Yes	62
2nd	Male	Yes	25
3rd	Male	Yes	88
Crew	Male	Yes	192
1st	Female	Yes	141
2nd	Female	Yes	93
3rd	Female	Yes	90
Crew	Female	Yes	20

echartr(titanic_sex, Survived, Count, Class, facet=Sex, 
        type=c(rep('ring', 3), 'pie'), subtype='clock') %>% 
    setTitle('Titanic: Cabin-specific Survival Outcome by Sex')

3.2.5 Pies With Timeline

We need another variable as timeline. Let’s say, ‘sex’.

echartr(titanic_sex, Survived, Count, facet=Class, t=Sex, type='pie') %>% 
    setTitle("Titanic: Survival Outcome by Cabin Class Across Sex") %>%
    setPolar(radius='30%')

3.3 Ring Chart

3.3.1 Regular Ring Chart

type is set ‘ring’.

echartr(titanic, Survived, Count, facet=Class, type='ring') %>%
    setTitle('Titanic: Survival Outcome by Cabin Class')

3.3.2 Infographic Ring Chart

Infographic ring chart is a special type. We need to carefully configure the aesthetic parameters step by step.

ds <- data.frame(q=c('68% feel good', '29% feel bad', '3% have no feelings'),
              a=c(68, 29, 3))

Build the base chart.

g <- echartr(ds, q, a, type='ring', subtype='info') %>% 
    setTheme('macarons', width=800, height=600) %>%
    setTitle('How do you feel?','ring_info', 
             pos=c('center','center', 'horizontal'))
g

But the legend is not at the right position. So we first set its pos=c('center','top','vertical'), then tune the location using relocLegend.

echartr uses tuneGrid function to adjust the sizing, positioning of all the widgets, so if you set the exact sizing and positionning paramemters using setLegend, it will be overrided. relocWidget is always recommended to be used at the end of the pipe chain call.

width = 800
height = 600
g %>% setLegend(pos=c('center','top','vertical'), itemGap=height/25) %>%
    relocLegend(x=width/2, y=height/8)

3.4 Nighingale Rose Chart

3.4.1 Radius Mode

type is set ‘rose’, subtype is set ‘radius’.

echartr(titanic_sex, Class, Count, facet=Survived, type='rose', 
        subtype='radius') %>%
    setTitle('Titanic: Survival Outcome by Cabin Class')

3.5 Area Mode

type is set ‘rose’, subtype is set ‘area’.

echartr(titanic_sex, Class, Count, facet=Survived, type='rose', 
        subtype='area') %>%
    setTitle('Titanic: Survival Outcome by Cabin Class')

4 Futher Setup

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 21 - Pie Chart