ICC ODI Rankings Visualization using ggplot2

Introduction

In this R Markdown document, we’ll demonstrate how to visualize ICC ODI rankings by country using various R packages. We’ll use geographic data and rankings from a CSV file to create a map showing the rankings.

Load Requied Libraries

library(sf)                # For handling spatial data

## Linking to GEOS 3.11.2, GDAL 3.8.2, PROJ 9.3.1; sf_use_s2() is TRUE

library(rnaturalearth)     # For accessing world map data
library(rnaturalearthdata) # Provides the data for rnaturalearth

## 
## Attaching package: 'rnaturalearthdata'

## The following object is masked from 'package:rnaturalearth':
## 
##     countries110

library(ggplot2)           # For creating the plot

In this code chunk, we load the libraries needed for our analysis:

‘sf’ : This package is using for handling spatial data and manipulating geographic information.
‘rnaturalearth’ and ‘rnaturalearthdata’ : These packages provideaccess to worl map data.
‘ggplot2’ ; A powerful package for creating wide range of plots and visualizations.

Load World Map Data

#Load world map data
world <- ne_countries(scale="medium", returnclass = "sf")

In the above given code chunk , we load the world map data using the ‘ne_countries’ function from the ‘rnaturalearth’ package. The ‘scale’ argument determines the detail level of the map and ‘returnclass = “sf” specifies that the data should be returned as an ’sf’ (simple feature) object, which is sutitable for spatial plotting.

Load ICC ODI Data from CSV file

# Load ICC ODI data form csv file using read.csv function
icc_odi <- read.csv("icc_odi_ranking.csv", header = TRUE)
head(icc_odi)

##   Position         Team Matches Points Rating
## 1        1        India      42   5117    122
## 2        2    Australia      34   3936    116
## 3        3 South Africa      30   3357    112
## 4        4     Pakistan      26   2762    106
## 5        5  New Zealand      33   3349    101
## 6        6      England      28   2672     95

In the above given code we read the ICC ODI ranking data from a csv file using ‘read.csv’. The ‘header = True’ argument indicates that the first row of the csv file contains column names. We then use ‘head(icc_odi)’ to display the first few rows of the data, so we can check its structure,

Prepare ICC ODI Data

# Reaname columns for clarity
colnames(icc_odi) <- c("Position","Team", "Matches","Points","Rating")
print(icc_odi)

##    Position         Team Matches Points Rating
## 1         1        India      42   5117    122
## 2         2    Australia      34   3936    116
## 3         3 South Africa      30   3357    112
## 4         4     Pakistan      26   2762    106
## 5         5  New Zealand      33   3349    101
## 6         6      England      28   2672     95
## 7         7    Sri Lanka      47   4363     93
## 8         8   Bangladesh      40   3453     86
## 9         9  Afghanistan      31   2477     80
## 10       10  West Indies      32   2205     69
## 11       11     Scotland      28   1450     52
## 12       12      Ireland      22   1091     50
## 13       13     Zimbabwe      24   1181     49
## 14       14  Netherlands      34   1482     44
## 15       15       Canada       9    320     36
## 16       16      Namibia      24    756     32
## 17       17        Nepal      35   1095     31
## 18       18         Oman      24    581     24
## 19       19       U.S.A.      20    410     21
## 20       20       U.A.E.      30    345     12

We rename or rearrange the columns of the ‘icc_odi’ data frame for clarity. The columns represent * ‘Position’ ; The ranking position of the team. * ‘Team’ : The name of the team. * ‘Mathces’m’Points’, and’Rating’ : Additional information about the team’s performance. We print the ‘icc_odi’ data frame to verify the changes.

Merge World Data with ICC ODI Data

# Merge the world data with the ICC data
world <- merge(world, icc_odi, by.x = "name", by.y = "Team", all.x = TRUE)
world$Position <- as.numeric(world$Position)

We merge the world map data with the ICC ODI data using the merge function. The by.x and by.y arguments specify the columns to match from each data frame (“name” from world and “Team” from icc_odi). After merging, we convert the Position column to numeric format to ensure it is suitable for plotting.

Plot the Data

# Plot the data
ggplot(data = world) +
  geom_sf(aes(fill = Position), color = "white") +
  scale_fill_gradient(low = "darkgreen", high = "lightpink", na.value = "grey90") +
  labs(title = "ICC ODI Rankings by Country 2024",
       fill = "Position") +
  theme_minimal() +
  theme(legend.position = "right",
        axis.text = element_blank(),
        axis.title = element_blank(),
        panel.grid = element_blank(),
        plot.title = element_text(hjust = 0.5))

In this final step, we use ggplot2 to create a map visualization:

geom_sf(aes(fill = Position), color = “white”): Plots the map with countries filled based on their ranking positions, with borders colored white.
scale_fill_gradient(low = “darkgreen”, high = “lightpink”, na.value = “grey90”): Defines a gradient color scale for the Position values. Positions with lower rankings(means first five values) are shown in dark green, and those with higher rankings in light pink. Missing values are displayed in grey.
labs(): Adds a title and label for the color legend.

##Explanation of Theme Elements theme_minimal(): Applies a minimal theme to the plot, which provides a clean and simple design without heavy gridlines or background colors.

theme(legend.position = “right”): Places the legend on the right side of the plot.
axis.text = element_blank(): Removes axis text labels to focus on the map and the color gradient.
axis.title = element_blank(): Removes axis titles to maintain a clean appearance.
panel.grid = element_blank(): Removes gridlines from the plot panel for a cleaner look.
plot.title = element_text(hjust = 0.5): Centers the plot title horizontally.

Conclusion

This R Markdown document demonstrates how to visualize ICC ODI rankings by country using R packages for spatial data and plotting. The resulting map provides a clear representation of team rankings globally.