Lollipop Charts

# Sample data set
set.seed(1)
df1 <- tibble(x = LETTERS[1:10],
                 y = sample(20:35, 10, replace = TRUE))
df1

## # A tibble: 10 × 2
##    x         y
##    <chr> <int>
##  1 A        28
##  2 B        23
##  3 C        26
##  4 D        20
##  5 E        21
##  6 F        32
##  7 G        26
##  8 H        30
##  9 I        33
## 10 J        21

ggplot(df1) + 
  geom_segment(aes(x = x, xend = x, y = 0, yend = y)) + 
  geom_point(aes(x = x, y = y, colour = x), size = 5)

We can flip this horizontally and reorder the \(x\) categories in order of decreasing ( or increasing ) \(y\), using forcats::fct_reorder:

ggplot(df1) + 
  geom_segment(aes(x = fct_reorder(x, -y), # in decreasing order of y
                   xend = fct_reorder(x, -y), 
                   y = 0, 
                   yend = y)) + 
  geom_point(aes(x = x, y = y, colour = x), size = 5) +
  coord_flip() + 
  xlab("Group") +
  ylab("") +
  theme_minimal()

Bump Charts

Bump Charts track the ranking of several objects based on other parameters, such as time/month or even category. We can think of this a a Basis for ranking. For instance, what is the opinion score of a set of products across various categories of users:

year <- rep(2019:2021, 4)
position <- c(4, 2, 2, 3, 1, 4, 2, 3, 1, 1, 4, 3)
product <- c("A", "A", "A",
            "B", "B", "B", 
            "C", "C", "C",
            "D", "D", "D")

df2 <- tibble(x = year,
                 y = position,
                 group = product)

df2

## # A tibble: 12 × 3
##        x     y group
##    <int> <dbl> <chr>
##  1  2019     4 A    
##  2  2020     2 A    
##  3  2021     2 A    
##  4  2019     3 B    
##  5  2020     1 B    
##  6  2021     4 B    
##  7  2019     2 C    
##  8  2020     3 C    
##  9  2021     1 C    
## 10  2019     1 D    
## 11  2020     4 D    
## 12  2021     3 D

We need to use a new package called, what else, ggbump to create our Bump Charts:

library(ggbump)

ggplot(df2) +
  geom_bump(aes(x = x, y = y, color = group)) +
  geom_point(aes(x = x, y = y, color = group), size = 6) +
  theme_minimal() +
  xlab("Time") +
  ylab("Product Rank") +
  scale_color_brewer(palette = "RdBu") # Change Colour Scale

We can add labels along the “bump lines” and remove the legend altogether:

ggplot(df2) +
  geom_bump(aes(x = x, y = y, color = group)) +
  geom_point(aes(x = x, y = y, color = group), size = 6) +
  theme_minimal() +
  scale_color_brewer(palette = "RdBu") + # Change Colour Scale
# Same as before up to her
# Add the labels at start and finish

  geom_text(data = df2 %>% filter(x == min(x)),
            aes(x = x - 0.1, label = group, y = y),
            size = 5, hjust = 1) +
  geom_text(data = df2 %>% filter(x == max(x)),
            aes(x = x + 0.1, label = group, y = y),
            size = 5, hjust = 0) +
  xlab("Time") +
  ylab("Product Rank") +
  theme(legend.position = "none")

Radar Charts

The same data can be plotted on a roughly circular set of axes, with the radial distance defining the rank against each axes.

Of course, we can use ggradar, which is at this time (Feb 2023) a development version and not yet part of CRAN. We will still try it, and another package ggiraphExtra which IS a part of CRAN.

#library(ggradar)

set.seed(4)
df3 <- tibble(Product = c("G1", "G2", "G3"),
              Power = runif(3), 
              Cost = runif(3),
              Harmony = runif(3),
              Style = runif(3),
              Size = runif(3),
              Manufacturability = runif(3),
              Durability = runif(3),
              Universality = runif(3))
df3

## # A tibble: 3 × 9
##   Product   Power  Cost Harmony  Style  Size Manufacturability Durabil…¹ Unive…²
##   <chr>     <dbl> <dbl>   <dbl>  <dbl> <dbl>             <dbl>     <dbl>   <dbl>
## 1 G1      0.586   0.277   0.724 0.0731 0.100             0.455     0.962   0.997
## 2 G2      0.00895 0.814   0.906 0.755  0.954             0.971     0.762   0.506
## 3 G3      0.294   0.260   0.949 0.286  0.416             0.584     0.715   0.490
## # … with abbreviated variable names ¹​Durability, ²​Universality

ggradar::ggradar(plot.data = df3,
                 axis.label.size = 3, # Titles of Params
                 grid.label.size = 4, # Score Values/Circles
                 group.point.size = 3,# Product Points Sizes
                 group.line.width = 1, # Product Line Widths
                 fill = TRUE, # fill the radar polygons
                 fill.alpha = 0.3, # Not too dark, Arvind
                 legend.title = "Product") +
  theme_void()

From the ggiraphExtra website:

Package ggiraphExtra contains many useful functions for exploratory plots. These functions are made by both ‘ggplot2’ and ‘ggiraph’ packages. You can make a static ggplot or an interactive ggplot by setting the parameter interactive=TRUE.

# library(ggiraphExtra)

ggiraphExtra::ggRadar(data = df3,
        aes(colour = Product),
        rescale = FALSE,
          )  + # recale = TRUE makes it look different...try!!
  theme_minimal()

Both render very similar-looking radar charts and the syntax is not too intimidating!!

Your Turn

1. Take the HELPrct dataset from our well used mosaicData package. Plot ranking charts using each of the public health issues that you can see in that dataset. What choice will you make for the the axes?
2. Try the SaratogaHouses dataset also from mosaicData.