# Load required packages
library(dplyr)
library(ggplot2)
library(scales)
library(stringr)Developing Custom Charting Functions with ggplot2
R
Data Visualization
ggplot2
R has plenty of visualization options, but ggplot2 stands out for its flexibility, quality, and composability. In this post, let’s build reusable custom charting functions on top of ggplot2 — the kind you can drop straight into a production analytics workflow.
Required Packages
Dataset Acquisition and Preparation
Let’s use a summarized version of the COVID-19 Data Repository from Johns Hopkins University to build and test the custom charts.
# Load COVID-19 data
df <- read.csv("https://bit.ly/3G8G63u")
# Get top 5 countries by death count
top_countries <- df %>%
group_by(country) %>%
summarise(count = sum(deaths_daily)) %>%
top_n(5) %>%
.$country
print(top_countries)[1] "Brazil" "India" "Mexico" "Russia" "US" Let’s calculate a 7-day centered moving average of daily confirmed cases for these five countries:
# Create a data frame with the required information
# Note that a centered 7-day moving average is used
plotdf <- df %>%
mutate(date = as.Date(date, format = "%m/%d/%Y")) %>%
filter(country %in% top_countries) %>%
group_by(country, date) %>%
summarise(count = sum(confirmed_daily)) %>%
arrange(country, date) %>%
group_by(country) %>%
mutate(MA = zoo::rollapply(count, FUN = mean, width = 7, by = 1, fill = NA, align = "center"))Fundamental Line Chart Function Development
The initial implementation demonstrates the creation of a basic line chart function. Note the utilization of aes_string() instead of aes(), which enables the provision of arguments to ggplot2 as string parameters, thereby enhancing function flexibility and programmability.
# Function definition
line_chart <- function(df,
x,
y,
group_color = NULL,
line_width = 1,
line_type = 1){
ggplot(df, aes(x = !! sym(x),
y = !! sym(y),
color = !! sym(group_color))) +
geom_line(linewidth = line_width,
linetype = line_type)
}
# Test run
line_chart(plotdf,
x = "date",
y = "MA",
group_color = "country",
line_type = 1,
line_width = 1.2)
Custom Theme Development
Now let’s build a custom theme on top of those chart functions. Because custom_theme() returns a standard ggplot2 object, it can be applied to any chart type — making it straightforward to keep a consistent look across a report or dashboard.
custom_theme <- function(plt,
base_size = 11,
base_line_size = 1,
palette = "Set1"){
# Note the use of "+" and not "%>%"
plt +
# Adjust overall font size
theme_minimal(base_size = base_size,
base_line_size = base_line_size) +
# Put legend at the bottom
theme(legend.position = "bottom") +
# Different colour scale
scale_color_brewer(palette = palette)
}
# Test run
line_chart(plotdf, "date", "MA", "country") %>% custom_theme()
Advanced Function Enhancement
The following section demonstrates the expansion of the line_chart() function to incorporate additional features and parameters, thereby increasing its versatility and applicability across diverse visualization requirements:
line_chart <- function(df,
x, y,
group_color = NULL,
line_width = 1,
line_type = 1,
xlab = NULL,
ylab = NULL,
title = NULL,
subtitle = NULL,
caption = NULL){
# Base plot
ggplot(df, aes(x = !! sym(x),
y = !! sym(y),
color = !! sym(group_color))) +
# Line chart
geom_line(size = line_width,
linetype = line_type) +
# Titles and subtitles
labs(x = xlab,
y = ylab,
title = title,
subtitle = subtitle,
caption = caption)
}Correspondingly, we enhance the custom_theme() function to accommodate diverse axis formatting options and advanced styling parameters:
custom_theme <- function(plt,
palette = "Set1",
format_x_axis_as = NULL,
format_y_axis_as = NULL,
x_axis_scale = 1,
y_axis_scale = 1,
x_axis_text_size = 10,
y_axis_text_size = 10,
base_size = 11,
base_line_size = 1,
x_angle = 45){
mappings <- names(unlist(plt$mapping))
p <- plt +
# Adjust overall font size
theme_minimal(base_size = base_size,
base_line_size = base_line_size) +
# Put legend at the bottom
theme(legend.position = "bottom",
axis.text.x = element_text(angle = x_angle)) +
# Different colour palette
{if("colour" %in% mappings) scale_color_brewer(palette = palette)}+
{if("fill" %in% mappings) scale_fill_brewer(palette = palette)}+
# Change some theme options
theme(plot.background = element_rect(fill = "#f7f7f7"),
plot.subtitle = element_text(face = "italic"),
axis.title.x = element_text(face = "bold",
size = x_axis_text_size),
axis.title.y = element_text(face = "bold",
size = y_axis_text_size)) +
# Change x-axis formatting
{if(!is.null(format_x_axis_as))
switch(format_x_axis_as,
"date" = scale_x_date(breaks = pretty_breaks(n = 12)),
"number" = scale_x_continuous(labels = number_format(accuracy = 0.1,
decimal.mark = ",",
scale = x_axis_scale)),
"percent" = scale_x_continuous(labels = percent))} +
# Change y-axis formatting
{if(!is.null(format_y_axis_as))
switch(format_y_axis_as,
"date" = scale_y_date(breaks = pretty_breaks(n = 12)),
"number" = scale_y_continuous(labels = number_format(accuracy = 0.1,
decimal.mark = ",",
scale = y_axis_scale)),
"percent" = scale_y_continuous(labels = percent))}
# Capitalise all names using labs()
capitalized_labels <- lapply(p$labels, str_to_title)
p <- p + do.call(labs, capitalized_labels)
return(p)
}Integrated Function Implementation
The following demonstration illustrates the coordinated application of our enhanced functions to generate a polished, publication-ready visualization:
line_chart(plotdf,
x = "date",
y = "MA",
group_color = "country",
xlab = "Date",
ylab = "Moving Avg. (in '000)",
title = "Daily COVID19 Case Load",
subtitle = "Top 5 countries by volume") %>%
custom_theme(format_x_axis_as = "date",
format_y_axis_as = "number",
y_axis_scale = 0.001)
Cross-Chart Type Theme Application
The architectural design of our custom_theme() function enables its universal application to any ggplot2 object, regardless of visualization type. The following example demonstrates this flexibility through bar chart implementation:
p <- plotdf %>%
mutate(month = format(date, "%m-%b")) %>%
ggplot(aes(x = month, y = MA, fill = country)) +
geom_col(position = "dodge") +
labs(title = "Monthly COVID19 Case load trend",
subtitle = "Top 5 countries",
x = "Month",
y = "Moving Average ('000)")
custom_theme(p,
palette = "Set2",
format_y_axis_as = "number",
y_axis_scale = 0.001)
Why use Custom Charting Functions
The development of custom charting functions utilizing ggplot2 provides substantial advantages for analytical workflows:
Visual Consistency: Ensures uniform appearance and styling across all visualizations within reports or analytical dashboards.
Less repetition: Wrapping common chart patterns into a function means less boilerplate code each time you need that chart type.
Maintenance Optimization: Facilitates centralized style updates through single function modifications, propagating changes across all implementations.
Accessibility Enhancement: Abstracts ggplot2 complexity for team members with varying levels of package familiarity, democratizing visualization capabilities.