Data Viz
a01_data_viz.RmdThis vignette shows how to work with the sdcoe package
to make data viz. To begin, use the set_sdcoe_defaults()
function to set defaults.
set_sdcoe_defaults(
style = "print",
base_size = 8.5,
base_family = "Roboto",
base_line_size = 0.5,
base_rect_size = 0.1,
scale = "continuous"
)This function will apply various defaults that will be used for all plots that follow. The examples below show how to make various example plots.
Single bar or Column Chart
Column chart
# Create a dataframe 'df_bar_1' with income categories and corresponding college completion rates
df_bar_1 <- tibble(
Income = c(
"$0–$24,600",
"$24,600–$45,300",
"$45,300–$66,400",
"$66,400–$97,500",
"$97,500-$145,000",
"$145,000 and above"
),
College.Completion.Rate = c(1.9, 7, 11.2, 15.2, 23.4, 29)
)
# Create the plot
fig <- df_bar_1 |>
# Convert 'Income' to factor and specify levels to maintain order
mutate(Income = factor(Income, levels = Income)) |>
# Set aesthetics
ggplot(aes(
x = Income,
y = College.Completion.Rate,
label = scales::percent(College.Completion.Rate / 100)
)) +
# Add column chart and set fill color
geom_col(fill = palette_sdcoe_main[3]) +
# Add text on chart
geom_text(position = position_stack(vjust = 0.5)) +
# Set y-axis limits and breaks
scale_y_continuous(
limits = c(0, 35),
n.breaks = 7,
# Delete the expansion space around the y-axis
expand = c(0, 0),
# Format y-axis labels as percentages
labels = percent_format(scale = 1)
) +
# Remove ticks from x and y axes
remove_ticks() +
# Remove both x and y axe's title
labs(x = NULL, y = NULL)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(
sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1,
heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
Column chart
# Create a dataframe 'df_bar_2' with income categories and corresponding college debt as a percentage of income
df_bar_2 <- tibble(
Income = c(
"$10,000–$15,000", "$20,000–$30,000",
"$30,000–$40,000", "$50,000–$75,000", "$75,000–$100,000",
"$200,000 and above"
),
`College.Debt.(as.%.of.income)` = c(
21.5, 23.2,
23, 22.2, 21.5, 15.6
)
)
# Plot
fig <- df_bar_2 |>
mutate(
# Convert 'Income' to factor and specify levels to maintain order
Income = factor(Income, levels = Income),
# Create labels for the text annotations
Labels = comma(`College.Debt.(as.%.of.income)`, suffix = "%")
) |>
# Set aesthetics
ggplot(aes(x = Income, y = `College.Debt.(as.%.of.income)`)) +
# Add column plot
geom_col() +
# Add text labels to the bars
geom_text(aes(label = Labels),
# Position labels within the stack
position = position_stack(vjust = 1.03),
size = 3,
show.legend = FALSE
) +
# Remove expansion space around y-axis and add limits
scale_y_continuous(expand = c(0, 0), limits = c(0, 25)) +
# Remove ticks from x and y axes
remove_ticks() +
# Remove both x and y axes
remove_axis() +
# Remove both x and y axe's title
labs(x = NULL, y = NULL)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1, heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
Column distribution chart
# Data
# Define categories and corresponding values
change <- tibble(
Levels = c(
"Lowest quintile", "Second quintile", "Middle quintile",
"Fourth quintile", "Top quintile", "All", "",
"Top 10 percent", "Top 5 percent", "Top 1 percent",
"Top 0.5 percent", "Top 0.1 percent"
),
Value = c(-9.8, -19.1, -11.6, -8.4, -9.1, -9.4, 0, -9.5, -10.4, -13.4, -13.9, -13.7)
)
# Data wrangling
change <- change |>
mutate(
# Assign Highlight based on category ('All' is highlighted)
Highlight = c(rep("0", 5), "1", rep("2", 6)),
# Convert 'Levels' to factor and specify levels to maintain order
Levels = factor(Levels, levels = Levels)
)
# Plot
fig <- change |>
# Set aesthetics
ggplot(aes(x = Levels, y = Value, fill = Highlight)) +
# Add dodged column plot
geom_col(show.legend = FALSE, position = "dodge", width = 0.8) +
# Set y-axis limits, breaks and remove expansion space around y-axis
scale_y_continuous(expand = c(0, 0), limits = c(-20, 0), n.breaks = 6) +
# Move x-axis to the top, wrap text labels
scale_x_discrete(position = "top", labels = function(x) str_wrap(x, width = 10)) +
# Manually set fill colors for legend
scale_fill_manual(
# Specify legend colors
values = c("0" = palette_sdcoe_main[[3]], "1" = palette_sdcoe_main[[2]], "2" = palette_sdcoe_main[[1]])
) +
# Remove ticks from x and y axes
remove_ticks() +
# Remove axis labels
labs(x = NULL, y = NULL) +
theme(
# Customize x-axis text
axis.text.x = element_text()
)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1, heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
Grouped column chart
# Define categories and corresponding percentages
enrollement <- tibble(
Quantiles = c(
"Lowest Quintile", "Second Quintile", "Middle Quintile",
"Fourth Quintile", "Top Quintile", "",
"Top 1 Percent"
),
`African-American` = c(2.3, 7.2, 12.3, 15.6, 23.6, 0, 31.9),
`English.Learner` = c(3.5, 7.8, 12.5, 15.7, 22.2, 0, 27.2),
`Students.with.disabilities` = c(4.1, 9.3, 14.4, 17.6, 25.4, 0, 32.5)
)
# Plot
fig <- enrollement |>
# Convert 'Quantiles' to factor and specify levels to maintain order
mutate(Quantiles = factor(Quantiles, levels = Quantiles)) |>
# Reshape dataframe from wide to long format
pivot_longer(
cols = -"Quantiles",
names_to = "Category",
values_to = "Percentage"
) |>
# Arrange rows by descending 'Percentage'
arrange(desc(Percentage)) |>
# Convert 'Category' to factor and specify levels to control legend order
mutate(Category = factor(Category, levels = c("African-American", "English.Learner", "Students.with.disabilities"))) |>
# Set aesthetics
ggplot(aes(x = Quantiles, y = Percentage, fill = Category)) +
# Add dodged column plot
geom_col(position = "dodge") +
# Set y-axis limits and breaks
scale_y_continuous(limits = c(0, 35), n.breaks = 7, expand = c(0, 0)) +
# Remove ticks from x and y axes
remove_ticks() +
# Manually set fill colors for legend
scale_fill_manual(
values = c(palette_sdcoe_main[[1]], palette_sdcoe_diverging[[5]], palette_sdcoe_main[[4]]),
breaks = c("African-American", "English.Learner", "Students.with.disabilities"),
) +
# Remove axis labels
labs(x = NULL, y = NULL) +
# Customize plot theme
theme(
# Set plot margins
plot.margin = margin(t = 30, r = 10, l = 20, b = 20),
# Set legend position
legend.position = c(0.2, 0.8),
# Set size of legend keys
legend.key.size = unit(0.3, "cm"),
# Customize x-axis line color
axis.line.x.bottom = element_line(color = palette_sdcoe_main[[2]]),
# Set legend direction
legend.direction = "vertical",
# Set spacing between legend keys
legend.key.spacing.y = unit(1, "cm")
)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1, heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
Horizontal bar chart
# Create a dataframe of countries and their corresponding PISA math scores
pisa_maths <- tibble(
Country = c(
"Denmark",
"Sweden", "Belgium", "Italy", "France",
"Finland", "Austria", "Norway", "Hungary",
"Netherlands", "Slovenia", "Germany", "Iceland",
"Czech Republic", "United Kingdom", "Luxembourg",
"Portugal", "OECD-Total", "Poland", "Israel",
"New Zealand", "Spain", "Greece", "Canada",
"Slovak Republic", "Switzerland", "Ireland",
"Japan", "Australia", "Korea", "United States",
"Turkey", "Chile", "Mexico"
),
`PISA.Math.%` = c(
48.2,
46.3, 44.2, 43.3, 43.2, 43.1, 42.7, 42.6, 40.2,
39.1, 37.2, 37, 36.8, 36, 35.7, 35.5, 35.2,
34.8, 34.3, 33.8, 33.7, 33.3, 32.6, 32.3, 29.3,
29.1, 28.8, 28.1, 27.1, 26.5, 26.1, 24.2, 22.5,
21
)
)
# Data wrangling
pisa_maths <- pisa_maths |>
# Arrange rows by descending 'PISA.Math.%'
arrange(desc(`PISA.Math.%`)) |>
mutate(
# Convert 'Country' to factor and specify levels to maintain order
Country = factor(Country, levels = Country),
# Create 'Highlight' column based on specific conditions
Highlight = case_match(
Country,
"United States" ~ "1",
"OECD-Total" ~ "2",
# No highlight for other countries
.default = "0"
),
# Create labels for highlighted countries
Labels = ifelse(Highlight != "0", paste0(Country, " ; ", round(`PISA.Math.%`), "%"), "")
)
# Plot
fig <- pisa_maths |>
# Set aesthetics
ggplot(aes(x = `PISA.Math.%`, y = Country, fill = Highlight)) +
# Add stacked column plot
geom_col(show.legend = F, position = position_stack(), width = 0.8) +
# Format x-axis labels as percentages
scale_x_continuous(expand = c(0, 0), limits = c(0, 50), labels = percent_format(scale = 1)) +
# Adjust y-axis limits
scale_y_discrete(expand = c(0, 0.03)) +
# Remove ticks and labels from x and y axes
remove_ticks() +
labs(x = NULL, y = NULL) +
# Add text labels to the bars
geom_text(aes(label = Labels),
# Position labels within the stack
position = position_stack(),
# Adjust horizontal alignment of labels
hjust = -0.1,
# Set text size
size = 3,
# Hide legend for the text labels
show.legend = FALSE
) +
# Add gridlines for x-axis
scatter_grid() +
# Manually set fill colors for legend
scale_fill_manual(
values = c("0" = palette_sdcoe_main[[1]], "1" = palette_sdcoe_main[[4]], "2" = palette_sdcoe_diverging[[5]])
) +
theme(
# Set plot margins
plot.margin = margin(t = 30, r = 10, l = 20, b = 20),
# Remove x-axis line
axis.line.x = element_blank(),
# Customize y-axis line color
axis.line.y = element_line(color = palette_sdcoe_lt_gray[[6]], linewidth = 0.2),
# Adjust y-axis text size
axis.text.y = element_text(size = 6),
# Remove major gridlines for y-axis
panel.grid.major.y = element_blank()
)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1, heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
Stacked column chart and stacked column alternative
# Data for stacked column chart
demographics_1 <- tibble(
Year = c(1975L, 1980L, 1985L, 1990L, 1995L, 2000L, 2005L, 2010L),
`American Indian` = c(532981157, 993592692, 1701107541, 2406261909, 259165177, 247106515, 140697169, 87915690),
Asian = c(18408678, 19938663, 43229817, 98839078, 29636829, 54384155, 97705509, 77519245),
`Black or African American` = c(21700352, 25325109, 36121650, 72587881, 14828368, 25394180, 29763550, 22026735),
Hispanic = c(84654869, 162558075, 372383223, 392854628, 20672404, 20495522, 7081396, 3650251),
White = c(62433325, 40330965, 73922053, 280095044, 112903459, 179572522, 136383742, 93504435),
`Two or more races` = c(770549, 3639482, 14563900, 86396313, 50219578, 128743191, 325815123, 265285834)
)
# Data wrangling
demographics_1 <- demographics_1 |>
pivot_longer(
# Pivot all columns except 'Year'
cols = -Year,
# Rename pivoted columns to 'Community'
names_to = "Community",
# Rename pivoted values to 'Population'
values_to = "Population"
) |>
# Group data by 'Year'
group_by(Year) |>
# Calculate percentage of population for each community in each year
mutate(Population_pct = 100 * Population / sum(Population)) |>
# Ungroup data
ungroup() |>
# Arrange data by ascending 'Population_pct'
arrange(Population_pct) |>
# Convert 'Community' to factor and specify levels to control stacking order
mutate(Community = factor(Community,
levels = c("Two or more races", "White", "Hispanic", "Black or African American", "Asian", "American Indian")
))
# Plot stacked column
fig <- demographics_1 |>
# Set aesthetics
ggplot(aes(x = Year, y = Population_pct, fill = Community)) +
# Add stacked column plot
geom_col(position = "stack") +
# Format y-axis labels as percentages
scale_y_continuous(expand = c(0, 0), labels = percent_format(scale = 1), n.breaks = 10) +
# Set x-axis breaks
scale_x_continuous(n.breaks = 10) +
# Remove axis labels
labs(x = NULL, y = NULL) +
# Manually set fill colors for legend
scale_fill_manual(
# Reverse palette to match ascending order of 'Population_pct'
values = rev(palette_sdcoe_categorical)
) +
# Customize plot theme
theme(
# Set legend position
legend.position = "right",
# Set legend direction
legend.direction = "vertical",
# Adjust legend justification
legend.justification = "top",
# Set spacing between legend keys
legend.key.spacing.y = unit(1, "cm")
)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1, heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
# Data for stacked column chart
demographics_2 <- tibble(
Year = c(1975L, 1980L, 1985L, 1990L, 1995L, 2000L, 2005L, 2010L),
`American Indian` = c(532981157, 993592692, 1701107541, 2406261909, 259165177, 247106515, 140697169, 87915690),
Asian = c(18408678, 19938663, 43229817, 98839078, 29636829, 54384155, 97705509, 77519245),
`Black or African American` = c(21700352, 25325109, 36121650, 72587881, 14828368, 25394180, 29763550, 22026735),
Hispanic = c(84654869, 162558075, 372383223, 392854628, 20672404, 20495522, 7081396, 3650251),
White = c(62433325, 40330965, 73922053, 280095044, 112903459, 179572522, 136383742, 93504435),
`Two or more races` = c(770549, 3639482, 14563900, 86396313, 50219578, 128743191, 325815123, 265285834)
)
# Data wrangling
demographics_2 <- demographics_2 |>
# Pivot all columns except 'Year'
pivot_longer(
cols = -Year,
names_to = "Community",
values_to = "Population"
) |>
# Group data by 'Year'
group_by(Year) |>
# Calculate percentage of population for each community in each year
mutate(Population_pct = 100 * Population / sum(Population)) |>
# Ungroup data
ungroup() |>
# Arrange data by ascending 'Population_pct'
arrange(Population_pct) |>
# Convert 'Community' to factor and specify levels to control stacking order
mutate(Community = factor(Community,
levels = c("American Indian", "Asian", "Black or African American", "Hispanic", "White", "Two or more races")
))
# Plot dodge column
fig <- demographics_2 |>
# Set aesthetics
ggplot(aes(x = Year, y = Population_pct, fill = Community)) +
# Add dodged column plot
geom_col(width = 4, position = position_dodge()) +
# Format y-axis labels as percentages
scale_y_continuous(expand = c(0, 0), limits = c(0, 90), labels = percent_format(scale = 1), n.breaks = 10) +
# Set x-axis breaks
scale_x_continuous(expand = c(0.01, 0), n.breaks = 10) +
# Remove axis labels
labs(x = NULL, y = NULL) +
# Manually set fill colors for legend
scale_fill_manual(
# Use predefined palette
values = (palette_sdcoe_categorical)
) +
# Remove ticks from axes
remove_ticks() +
# Customize plot theme
theme(
# Set plot margins
plot.margin = margin(t = 30, r = 10, l = 20, b = 20),
# Adjust legend text size
legend.text = element_text(size = 7),
# Set size of legend keys
legend.key.size = unit(0.2, "cm"),
# Set spacing between legend keys
legend.key.spacing.x = unit(0.5, "cm"),
# Customize x-axis line color
axis.line.x.bottom = element_line(color = palette_sdcoe_main[[2]])
) +
# Specify legend layout
guides(fill = guide_legend(nrow = 1))
# Create SDCOE plot with annotations and formatting
sdcoe_plot(sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1, heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
Stacked area chart
# Create a dataframe 'revenue' with funding data for different years
revenue <- tibble(
check.names = FALSE,
Year = c(
1990L, 1991L, 1992L,
1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L,
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L,
2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L,
2015L, 2016L, 2017L, 2018L, 2019L, 2020L, 2021L
),
"Federal Funding" = c(
3.4, 3.2, 2.9, 2.5,
3.9, 3.4, 2.8, 2.6, 2.6, 2.3, 2.8, 2.7, 5.2, 7.3, 7.5,
8.1, 12.1, 14.3, 16.8, 22.6, 27.6, 29.8, 30.6,
33.6, 33.4, 32.5, 33.1, 37.8, 37.3, 37.7, 38.7, 39.1
),
"State Funding" = c(
1.3, 1.4, 0.8, 0.8,
1.8, 1.5, 1.4, 1.2, 1, 0.9, 1.1, 1, 0.8, 2.8, 3.2,
3.5, 4.3, 4.4, 4.9, 6.1, 6.8, 7.3, 7.5, 6.4, 5.2, 5.3,
4.9, 5, 5.1, 5, 5.1, 5.3
),
"Local Funding" = c(
0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
1.1, 5.7, 7.2, 10, 15.4, 16, 16.2
),
"Private Contributions" = c(
0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18.7,
23.5, 23.1, 25.5, 24.1, 25, 34.5, 33.1, 31.7
)
)
# Reshape the dataframe to long format for plotting convenience
revenue_long <- revenue |>
pivot_longer(
cols = -Year,
names_to = "Source",
values_to = "Revenue"
) |>
# Convert 'Source' column to factor to control order of plotting
mutate(Source = factor(Source, levels = c("Private Contributions", "Local Funding", "State Funding", "Federal Funding")))
# Create the plot
fig <- ggplot(data = revenue_long, aes(x = Year, y = Revenue, fill = Source)) +
# Create stacked area plot
geom_area(position = "stack", show.legend = FALSE) +
# Set axis and legend labels
labs(
x = "Year",
y = "Revenue",
fill = "Source"
) +
# Adjust x-axis limits and labels
scale_x_continuous(
expand = c(0.01, 0), breaks = 1990:2020,
# Set x-axis labels every 5 years
labels = ifelse(1990:2020 %in% seq(1990, 2020, 5), 1990:2020, "")
) +
# Adjust y-axis limits and breaks
scale_y_continuous(expand = c(0, 0), limits = c(0, 100), n.breaks = 7) +
# Allow text to extend beyond plot area
coord_cartesian(clip = "off") +
# Add labels at the end of each stack
geom_text(
data = revenue_long |>
group_by(Source) |>
summarise(Year = max(Year), Revenue = max(Revenue)),
aes(x = Year, y = Revenue, label = Source, color = Source),
position = position_stack(vjust = 0.5),
size = 3, fontface = "bold",
hjust = -0.1,
show.legend = FALSE
) +
# Set manual color scale
scale_color_manual(values = c(
"Federal Funding" = palette_sdcoe_main[[1]],
"Private Contributions" = palette_sdcoe_lt_gray[[7]],
"Local Funding" = palette_sdcoe_main[[3]],
"State Funding" = palette_sdcoe_main[[4]]
)) +
# Set manual fill scale
scale_fill_manual(values = c(
"Federal Funding" = palette_sdcoe_main[[1]],
"Private Contributions" = palette_sdcoe_main[[2]],
"Local Funding" = palette_sdcoe_main[[3]],
"State Funding" = palette_sdcoe_main[[4]]
)) +
theme(
# Remove axis titles
axis.title = element_blank(),
# Adjust x-axis ticks
axis.ticks.x = element_line(color = palette_sdcoe_lt_gray[[7]]),
# Set plot margins
plot.margin = margin(t = 30, r = 120, l = 20, b = 20),
# Remove major gridlines for x-axis
panel.grid.major.x = element_blank()
)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1, heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
100% Area chart
# Data frame containing spending data over fiscal years
spending <- tibble(
Fiscal.Year = c(
1960L,
1961L, 1962L, 1963L, 1964L, 1965L, 1966L, 1967L,
1968L, 1969L, 1970L, 1971L, 1972L, 1973L, 1974L,
1975L, 1976L, 1977L, 1978L, 1979L, 1980L, 1981L,
1982L, 1983L, 1984L, 1985L, 1986L, 1987L,
1988L, 1989L, 1990L, 1991L, 1992L, 1993L, 1994L,
1995L, 1996L, 1997L, 1998L, 1999L, 2000L, 2001L,
2002L, 2003L, 2004L, 2005L, 2006L, 2007L,
2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L,
2015L, 2016L, 2017L, 2018L, 2019L, 2020L
),
Staffing = c(
0.399,
0.419, 0.422, 0.428, 0.424, 0.439, 0.432, 0.445,
0.436, 0.463, 0.44, 0.438, 0.457, 0.447, 0.432,
0.418, 0.424, 0.413, 0.449, 0.467, 0.469, 0.461,
0.457, 0.447, 0.452, 0.439, 0.442, 0.443,
0.453, 0.47, 0.472, 0.477, 0.482, 0.481, 0.448,
0.456, 0.454, 0.46, 0.441, 0.45, 0.452, 0.443,
0.436, 0.442, 0.431, 0.437, 0.452, 0.467, 0.481,
0.481, 0.496, 0.499, 0.463, 0.445, 0.43, 0.431,
0.434, 0.453, 0.454, 0.435, 0.415
),
Operations = c(
0.265,
0.273, 0.321, 0.305, 0.303, 0.273, 0.28, 0.265,
0.252, 0.218, 0.232, 0.222, 0.206, 0.203, 0.209,
0.218, 0.23, 0.228, 0.187, 0.196, 0.17, 0.143,
0.155, 0.157, 0.147, 0.146, 0.139, 0.154, 0.15,
0.142, 0.125, 0.102, 0.08, 0.062, 0.085, 0.084,
0.082, 0.098, 0.104, 0.104, 0.091, 0.093,
0.092, 0.102, 0.112, 0.116, 0.118, 0.115, 0.11,
0.101, 0.102, 0.076, 0.08, 0.074, 0.101, 0.129,
0.147, 0.144, 0.121, 0.066, 0.089
),
Benefits = c(
0.11, 0.11,
0.097, 0.098, 0.103, 0.12, 0.125, 0.125, 0.141,
0.148, 0.159, 0.174, 0.171, 0.186, 0.195,
0.19, 0.195, 0.219, 0.222, 0.209, 0.23, 0.253,
0.254, 0.273, 0.285, 0.303, 0.305, 0.299, 0.303,
0.3, 0.305, 0.305, 0.326, 0.348, 0.359, 0.361,
0.369, 0.355, 0.368, 0.363, 0.368, 0.375, 0.379,
0.371, 0.367, 0.358, 0.351, 0.342, 0.332,
0.335, 0.322, 0.349, 0.378, 0.4, 0.39, 0.369, 0.348,
0.339, 0.357, 0.423, 0.4
),
"Capital Outlay" = c(
0.191,
0.168, 0.134, 0.142, 0.143, 0.14, 0.133, 0.132,
0.134, 0.133, 0.126, 0.126, 0.126, 0.124, 0.122,
0.125, 0.1, 0.092, 0.092, 0.081, 0.081, 0.089,
0.075, 0.07, 0.064, 0.059, 0.057, 0.049, 0.046,
0.04, 0.047, 0.068, 0.059, 0.059, 0.056, 0.049,
0.043, 0.038, 0.039, 0.035, 0.034, 0.04, 0.042,
0.042, 0.044, 0.043, 0.037, 0.036, 0.033,
0.039, 0.034, 0.033, 0.036, 0.038, 0.037, 0.034,
0.031, 0.025, 0.027, 0.03, 0.031
),
Other = c(
0.034,
0.031, 0.026, 0.027, 0.027, 0.028, 0.03, 0.033,
0.037, 0.037, 0.042, 0.04, 0.04, 0.041, 0.042,
0.049, 0.051, 0.047, 0.05, 0.047, 0.049, 0.054,
0.06, 0.052, 0.052, 0.054, 0.058, 0.053, 0.048,
0.048, 0.051, 0.048, 0.053, 0.05, 0.052, 0.05,
0.052, 0.049, 0.048, 0.049, 0.054, 0.048, 0.051,
0.044, 0.046, 0.046, 0.042, 0.04, 0.044, 0.044,
0.045, 0.043, 0.043, 0.043, 0.042, 0.038,
0.04, 0.039, 0.042, 0.047, 0.065
)
)
# Create a plot from the spending data
fig <- spending |>
# Convert data from wide to long format for plotting
pivot_longer(
# Exclude Fiscal.Year from pivot operation
cols = -Fiscal.Year,
# New column name for categories
names_to = "Category",
# New column name for percentage values
values_to = "Percentage"
) |>
# Modify the data for plotting
mutate(
# Convert percentage values to percentages
Percentage = Percentage * 100,
# Specify order of categories for better visualization
Category = factor(Category, levels = c("Other", "Capital Outlay", "Benefits", "Operations", "Staffing"))
) |>
# Create ggplot object with specified aesthetics
ggplot(aes(x = Fiscal.Year, y = Percentage, fill = Category)) +
# Add filled area plot
geom_area(position = "fill") +
# Customize y-axis
scale_y_continuous(
# Remove y-axis expansion
expand = c(0, 0),
# Format y-axis labels as percentages
labels = percent_format(scale = 100),
# Set number of breaks on y-axis
n.breaks = 10
) +
# Customize x-axis
scale_x_continuous(
expand = c(0, 0),
# Set breaks for each year
breaks = 1960:2020,
# Show labels for every 5 years
labels = ifelse(1960:2020 %in% seq(1960, 2020, 5), 1960:2020, "")
) +
# Remove axis labels
labs(x = NULL, y = NULL) +
# Customize fill colors for each category
scale_fill_manual(values = c(
"Staffing" = palette_sdcoe_main[[1]],
"Capital Outlay" = palette_sdcoe_main[[2]],
"Benefits" = palette_sdcoe_main[[3]],
"Operations" = palette_sdcoe_main[[4]],
"Other" = palette_sdcoe_main[[5]]
)) +
# Customize plot guides
guides(
# Add minor ticks on x-axis
x = guide_axis(cap = "both", minor.ticks = TRUE)
) +
# Customize plot theme
theme(
# Set legend position
legend.position = "right",
# Set legend direction
legend.direction = "vertical",
# Set legend key width
legend.key.width = unit(0.3, "cm"),
# Set spacing between legend keys
legend.key.spacing.y = unit(0.5, "cm"),
# Customize axis ticks
axis.ticks = element_line(color = palette_sdcoe_lt_gray[[7]], linewidth = 0.2),
# Customize axis lines
axis.line = element_line(linewidth = 0.2, color = palette_sdcoe_lt_gray[[7]]),
# Customize major grid lines on y-axis
panel.grid.major.y = element_line(color = palette_sdcoe_lt_gray[[7]], linewidth = 0.2),
# Set plot margins
plot.margin = margin(t = 30, r = 10, l = 20, b = 20)
)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1, heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
Line chart
students <- tibble(
`1998` = c(9991081, 5311411),
`1999` = c(10622794, 5402864),
`2000` = c(10116952, 5457793),
`2001` = c(10821819, 5491464),
`2002` = c(11240815, 5710759),
`2003` = c(11425090, 5890821),
`2004` = c(11533235, 2540889),
`2005` = c(11237238, 2494145),
`2006` = c(11213184, 2453741),
`2007` = c(10991496, 2507728),
`2008` = c(11145436, 2537825),
`2009` = c(11602000, 2475785),
`2010` = c(11979290, 2355803),
`2011` = c(11740265, 2312909),
`2012` = c(11234147, 2262961),
`2013` = c(10429316, 2247747),
`2014` = c(10365372, 6698800),
`2015` = c(9822475, 6704300)
)
# Data wrangling
students <- students |>
# Transpose the data frame
t() |>
# Convert to data frame
as.data.frame() |>
# Convert row names to a column
rownames_to_column() |>
# Rename columns
`names<-`(c("Year", "Series 1", "Series 2")) |>
# Convert all columns to numeric
mutate(across(everything(), as.numeric)) |>
# Convert data from wide to long format for plotting
pivot_longer(
# Exclude Year from pivot operation
cols = -Year,
# New column name for series
names_to = "Series",
# New column name for counts
values_to = "Count"
)
# Plot
fig <- students |>
# Set aesthetics for ggplot
ggplot(aes(x = Year, y = Count, color = Series)) +
# Add line plot
geom_line(show.legend = FALSE, linewidth = 1.2) +
# Add text annotation for Series 1
annotate("text", x = 2011, y = 11e+06, label = "Series 1", size = 3.5) +
# Add text annotation for Series 2
annotate("text", x = 2011, y = 3e+06, label = "Series 2", size = 3.5) +
# Customize x-axis
scale_x_continuous(expand = c(0.01, 0), breaks = 1998:2015) +
# Customize y-axis
scale_y_continuous(expand = c(0, 0), limits = c(0, 12e+06), labels = comma, n.breaks = 7) +
# Customize plot guides
guides(
# Add minor ticks on x-axis
x = guide_axis(cap = "both")
) +
# Customize color scale
scale_color_manual(
values = c(
"Series 1" = palette_sdcoe_main[[3]],
"Series 2" = palette_sdcoe_main[[1]]
)
) +
# Remove axis labels
labs(x = NULL, y = NULL) +
# Customize plot theme
theme(
# Set plot margins
plot.margin = margin(t = 30, r = 10, l = 20, b = 20),
# Customize axis ticks
axis.ticks = element_line(color = palette_sdcoe_lt_gray[[7]], linewidth = 0.3),
# Customize x-axis line
axis.line.x = element_line(color = palette_sdcoe_lt_gray[[7]], linewidth = 0.3),
)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1, heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
Bar chart single line irregular year intervals
# Create a dataframe 'dropout' with dropout rates for different years
dropout <- tibble(
Year = c(1976L, 1980L, 1985L, 1990L, 1995L, 2001L, 2006L),
Rates = c(4.2, 4.7, 6.4, 4.6, 5.4, 6.5, 5.7)
)
# plots line with markers
fig = dropout |>
# Set aesthetics for ggplot
ggplot(aes(x = Year, y = Rates)) +
# Add line plot
geom_line(show.legend = FALSE, linewidth = 1.2) +
# Add point markers
geom_point(shape = 21,stroke = 1.5, size = 3, color = palette_sdcoe_main[[1]], fill = "white") +
# Customize y-axis
scale_y_continuous(expand = c(0, 0), limits = c(0.0, 7.0),
labels = label_number(accuracy = 0.1),
n.breaks = 7) +
# Customize x-axis
scale_x_continuous(
# Set expansion limits for better visualization
expand = c(0.02, 0),
# Set breaks for each year
breaks = 1976:2006,
# Show labels for every 5 years
labels = ifelse(1976:2006 %in% c(1976, seq(1960, 1995, 5), 2001, 2006), 1976:2006, "")
)+
# Customize plot guides
guides(
# Add minor ticks on x-axis
x = guide_axis(cap = "both")
)+
# Remove axis labels
labs(x = NULL, y = NULL) +
# Customize plot theme
theme(
# Set plot margins
plot.margin = margin(t = 30, r = 10, l = 20, b = 20),
# Customize axis ticks
axis.ticks = element_line(color = palette_sdcoe_lt_gray[[7]], linewidth = 0.3),
# Customize x-axis line
axis.line.x = element_line(color = palette_sdcoe_lt_gray[[7]], linewidth = 0.3)
)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1, heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
Point chart and dot charts
Scatter plot
# Data for scatter plot
df_scatter <- tibble(
mpg = c(
21, 21, 22.8,
21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8,
16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4, 30.4,
33.9, 21.5, 15.5, 15.2, 13.3, 19.2, 27.3, 26, 30.4,
15.8, 19.7, 15, 21.4
),
wt = c(
2.62, 2.875,
2.32, 3.215, 3.44, 3.46, 3.57, 3.19, 3.15, 3.44,
3.44, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 2.2,
1.615, 1.835, 2.465, 3.52, 3.435, 3.84, 3.845,
1.935, 2.14, 1.513, 3.17, 2.77, 3.57, 2.78
)
)
# Plot
fig <- df_scatter |>
# Set aesthetics for ggplot
ggplot(aes(x = mpg, y = wt)) +
# Add point markers
geom_point(size = 4, color = palette_sdcoe_main[[1]], fill = "white") +
# Customize x-axis, set expansion, limits and breaks
scale_x_continuous(
expand = c(0, 0), limits = c(0, 40),
n.breaks = 10
) +
# Customize y-axis, set expansion, limits and breaks
scale_y_continuous(
expand = c(0, 0), limits = c(0, 6),
n.breaks = 6
) +
# Customize plot guides
guides(
# Add minor ticks on x-axis
x = guide_axis(cap = "both")
) +
# Remove axis labels
labs(x = NULL, y = NULL) +
# Customize plot theme
theme(
# Set plot margins
plot.margin = margin(t = 30, r = 10, l = 20, b = 20),
# Customize axis ticks
axis.ticks = element_line(color = palette_sdcoe_lt_gray[[7]], linewidth = 0.3),
# Customize axis lines
axis.line = element_line(color = palette_sdcoe_lt_gray[[7]], linewidth = 0.3)
)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1, heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
Slope chart
# Data for slope chart
df_slop <- tibble(
Fields = c(
"English 9", "Geography", "Biology", "PE", "Spanish I"
),
`2018` = c(0.748, 0.813, 0.744, 0.894, 0.688),
`2022` = c(0.812, 0.677, 0.759, 0.904, 0.546)
)
# Data wrangling
df_slop <- df_slop |>
# Convert data from wide to long format for plotting
pivot_longer(
# Exclude Fields from pivot operation
cols = -Fields,
# New column name for years
names_to = "Year",
# New column name for values
values_to = "Value"
) |>
# Modify the data for plotting
mutate(
# Convert Year to numeric
Year = as.numeric(Year),
# Format Value as percentage
Labels = percent(Value, accuracy = 0.1),
# Specify order of fields for better visualization
Fields = factor(Fields, levels = c("English 9", "Geography", "Biology", "PE", "Spanish I"))
)
# Plot
fig <- df_slop |>
# Set aesthetics for ggplot
ggplot(aes(x = Year, y = Value, color = Fields)) +
# Add line plot and set line width
geom_line(linewidth = 1.2) +
# Add point markers and set point size
geom_point(size = 2) +
# Add text annotations for each field at the extremities of the lines
annotate("text",
x = ifelse(df_slop$Year == 2018, df_slop$Year - 0.3, df_slop$Year + 0.3),
y = c(df_slop$Value[1] + 0.02, df_slop$Value[-1]), label = df_slop$Labels, size = 3.5
) +
# Allow text to extend beyond plot area
coord_cartesian(clip = "off") +
# Set plot theme
theme_minimal() +
# Customize x-axis and y-axis
scale_x_continuous(expand = c(0.01, 0), breaks = c(2018, 2022), labels = c("2018", "2022")) +
scale_y_continuous(expand = c(0, 0)) +
# Customize plot guides
guides(
# Add minor ticks on x-axis
x = guide_axis(cap = "both")
) +
# Customize color scale
scale_color_manual(values = c(
"English 9" = palette_sdcoe_main[[1]],
"Geography" = palette_sdcoe_main[[2]],
"Biology" = palette_sdcoe_main[[3]],
"PE" = palette_sdcoe_main[[7]],
"Spanish I" = palette_sdcoe_main[[8]]
)) +
# Customize plot theme
theme(
# Set plot margins
plot.margin = margin(t = 5, r = 40, l = 20, b = 40),
# Customize axis ticks
axis.ticks.x = element_line(color = palette_sdcoe_lt_gray[[7]], linewidth = 0.3),
# Remove y-axis ticks
axis.ticks.y = element_blank(),
# Customize x-axis line
axis.line.x = element_line(color = palette_sdcoe_lt_gray[[7]], linewidth = 0.3),
# Remove y-axis line
axis.line.y = element_blank(),
# Remove axis titles
axis.title = element_blank(),
# Remove y-axis text
axis.text.y = element_blank(),
# Remove gridlines
panel.grid = element_blank(),
# Set legend position
legend.position = "top",
# Remove legend title
legend.title = element_blank()
)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1, heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
Dumbbell chart
# Data
passing_rate <- tibble(
Fields = c("English 9", "Geography", "Biology", "PE", "Spanish I"),
pre = c(0.748, 0.813, 0.94, 0.894, 0.688),
post = c(0.812, 0.677, 0.759, 0.97, 0.546),
pre.y.value = c(2.5, 2, 1.5, 1, 0.5),
post.y.value = c(2.5, 2, 1.5, 1, 0.5)
)
# Create a new column for the difference between pre and post passing rates
passing_rate <- passing_rate |>
mutate(Fields = factor(Fields, levels = c("Spanish I", "PE", "Biology", "Geography", "English 9")))
# Create a ggplot object for the dumbbell chart
fig <- ggplot(passing_rate, aes(x = pre, xend = post, y = Fields, yend = Fields)) +
# Add segments connecting pre and post points
geom_segment(size = 1, color = palette_sdcoe_main[[2]]) +
# Add pre points
geom_point(aes(x = pre, color = palette_sdcoe_main[[1]]), size = 5) +
# Add post points
geom_point(aes(x = post, color = palette_sdcoe_main[[3]]), size = 5) +
# Customize x-axis
scale_x_continuous(limits = c(0, 1), breaks = seq(0, 1, 0.2), labels = percent) +
# Set color scale
scale_color_manual(
values = c(palette_sdcoe_main[[3]], palette_sdcoe_main[[1]]),
labels = c("start", "end")
) +
# Set plot theme to minimal
theme_minimal() +
# Customize plot guides
guides(
# Add minor ticks on x-axis
x = guide_axis(cap = "both")
) +
# Customize y-axis text
theme(
# Set y-axis text size and font face to bold
axis.text.y = element_text(size = 10, face = "bold"),
# Customize x-axis line
axis.line.x = element_line(color = palette_sdcoe_lt_gray[[7]], linewidth = 0.3),
# Customize axis ticks
axis.ticks.x = element_line(color = palette_sdcoe_lt_gray[[7]], linewidth = 0.3),
# Remove axis titles
axis.title = element_blank(),
# Set legend position
legend.position = "top",
# Remove legend title
legend.title = element_blank(),
# Remove grid lines
panel.grid = element_blank()
)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1, heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
Pie charts and pie alternatives
Pie Chart
# Data
pie_data <- tibble(
Services = c(688955L),
Transfers = c(45613L),
Salaries = c(612600L),
Total = c(1347168L)
)
# Data wrangling
pie_data <- pie_data |>
# Convert data from wide to long format, excluding Total
pivot_longer(
cols = -Total,
names_to = "Category",
values_to = "Amount",
) |>
# Arrange data in ascending order of Amount
arrange(Amount) |>
# Wrangle data for plotting
mutate(
# Calculate percentage of total for each category
Percentage = Amount / Total,
# Reorder categories for better visualization
Category = factor(Category, levels = unique(Category)),
# Create labels with category name, amount, and percentage
Labels = paste0(
Category, ", ",
comma(Amount, prefix = "$"), ", ",
percent(Percentage, accuracy = 1)
),
# Calculate cumulative sum of percentages in reverse order
csum = rev(cumsum(rev(Percentage))),
# Calculate position for labels
pos = Percentage / 2 + lead(csum, 1),
# Replace NA positions with half of the percentage
pos = if_else(is.na(pos), Percentage / 2, pos)
)
# Plotting
fig <- ggplot(pie_data, aes(x = "", y = Percentage)) +
# Create pie slices
geom_col(width = 1, color = "white", fill = palette_sdcoe_main[[3]]) +
# Set polar coordinates
coord_polar("y", start = -(pi + 0.1), clip = "off") +
# Customize y-axis with labels and breaks
scale_y_continuous(
breaks = pie_data$pos + 0.016, limits = c(0, 1),
labels = str_wrap(pie_data$Labels, width = 10)
) +
# Adjust x-axis scale
scale_x_discrete(expand = c(0, 0.5)) +
theme(
# Remove axis title
axis.title = element_blank(),
# Customize axis text size
axis.text = element_text(size = 7),
# Remove axis lines
axis.line = element_blank(),
# Remove gridlines
panel.grid = element_blank()
)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1, heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
Bar Chart
# Data
bar_data <- tibble(
Services = c(688955L),
Transfers = c(45613L),
Salaries = c(612600L),
Total = c(1347168L)
)
# Data wrangling
bar_data <- bar_data |>
# Convert data from wide to long format, excluding Total
pivot_longer(
cols = -Total,
names_to = "Category",
values_to = "Amount"
) |>
# Arrange data in ascending order of Amount
arrange(Amount) |>
mutate(
# Calculate percentage of total for each category
Percentage = Amount / Total,
# Specify order of categories for better visualization
Category = factor(Category, levels = unique(Category)),
# Create labels with category name, amount, and percentage
Labels = comma(Amount, prefix = "$")
)
# Plot
fig <- bar_data |>
# Set aesthetics
ggplot(aes(x = Category, y = Percentage)) +
# Add column plot
geom_col(
position = position_stack(),
fill = palette_sdcoe_main[[3]],
width = 1,
color = "white"
) +
# Add text labels to the bars
geom_text(aes(label = Labels),
# Position labels above the bars
nudge_y = 0.01,
# Set text size
size = 3,
# Hide legend for the text labels
show.legend = FALSE
) +
# Allow text to extend beyond plot area
coord_cartesian(clip = "off") +
# Remove expansion space around y-axis
scale_y_continuous(expand = c(0, 0)) +
# Remove expansion space around x-axis
scale_x_discrete(expand = c(0, 0)) +
# Remove ticks from x and y axes
remove_ticks() +
# Remove both x and y axes
remove_axis() +
# Remove axis labels
labs(x = NULL, y = NULL) +
theme(
# Set axis line width
axis.line.x = element_line(linewidth = 0.1)
)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1, heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
Pie charts and pie chart alternatives (many categories)
Pie Chart
# Data
spending <- tibble(
Category = c("Special Funds", "Federal", "Fees", "Other", "LCFF", "Categorical"),
Amount = c(3650251, 22026735, 77519245, 87915690, 93504435, 265285834)
)
# Data wrangling
spending <- spending |>
# Arrange data in descending order of Amount
arrange(desc(Amount)) |>
mutate(
# Calculate percentage of total spending
Percentage = Amount / sum(Amount),
# Highlight LCFF category
Highlights = ifelse(Category == "LCFF", "1", "0"),
# Specify order of categories for better visualization
Category = factor(Category, levels = unique(Category)),
# Create labels with category name, amount, and percentage
Labels = paste0(
Category,
", ",
comma(Amount, prefix = "$"),
", ",
percent(Percentage, accuracy = 1)
)
) |>
# create position for text
arrange(desc(Highlights)) |>
mutate(text_y = cumsum(Amount) - Amount / 2)
# Plotting
fig <-
ggplot(spending, aes(x = "", y = Amount, fill = Highlights)) +
# Create pie slices
geom_col(show.legend = FALSE,
width = 1,
color = "white") +
# add lebels
geom_text(
aes(label = scales::percent(Percentage), y = text_y),
nudge_x = 0.8,
size = 4,
show.legend = F
) +
# add arrows
geom_segment(aes(
x = 1.52,
y = text_y,
xend = 1.6,
yend = text_y
),
size = .5) +
# Set polar coordinates
coord_polar(theta = "y",
start = -(pi + 1),
clip = "off") +
# Adjust x-axis scale
scale_x_discrete(expand = c(0, 0.5)) +
theme(
# Remove axis title
axis.title = element_blank(),
# Remove axis text
axis.text = element_blank(),
# Remove axis lines
axis.line = element_blank(),
# Remove gridlines
panel.grid = element_blank()
)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(
sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1,
heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)
Bar Chart
# Data
spending <- tibble(
Category = c("Special Funds", "Federal", "Fees", "Other", "LCFF", "Categorical"),
Amount = c(3650251, 22026735, 77519245, 87915690, 93504435, 265285834)
)
# Data wrangling
spending <- spending |>
# Arrange data in ascending order of Amount
arrange(Amount) |>
mutate(
# Highlight LCFF and Categorical categories
Highlights = ifelse(Category == "LCFF", "1", "0"),
# Specify order of categories for better visualization
Category = factor(Category, levels = unique(Category))
)
# Plot
fig <- spending |>
# Set aesthetics for ggplot
ggplot(aes(x = Category, y = Amount, fill = Highlights)) +
# Add column plot
geom_col(position = "dodge", width = 1, color = "white") +
# Customize fill colors
scale_fill_manual(values = c("0" = palette_sdcoe_main[[1]], "1" = palette_sdcoe_main[[4]])) +
# Customize y-axis
scale_y_continuous(expand = c(0, 0), limits = c(0, 300e+06), labels = dollar, n.breaks = 6) +
# Add minor ticks on x-axis
guides(
x = guide_axis(cap = "both", minor.ticks = TRUE)
) +
# Remove axis labels
labs(x = NULL, y = NULL) +
# Customize plot theme
theme(
# Set axis ticks color and width
axis.ticks = element_line(color = palette_sdcoe_lt_gray[[7]], linewidth = 0.3),
# Remove legend
legend.position = "none",
# Remove y-axis line
axis.line.y = element_blank(),
# Customize x-axis line
axis.line.x = element_line(linewidth = 0.1),
# Set plot margins
plot.margin = margin(t = 10, r = 10, l = 10, b = 10)
)
# Create SDCOE plot with annotations and formatting
sdcoe_plot(sdcoe_figure("FIGURE X"),
# Add title
sdcoe_title("Title in 12pt Title Case"),
# Add subtitle
sdcoe_subtitle("Subtitle in 9.5pt sentence case"),
# Add y-axis title
sdcoe_y_title("Unit if needed (Thousands of dollars) 8.5pt italic"),
# Add the previously created plot
fig,
# Add SDCOE logo
sdcoe_logo_text(),
# Add data source
sdcoe_source("This is source of the chart"),
# Add note
sdcoe_note("This is note of the chart"),
# Set layout parameters
ncol = 1, heights = c(1, 5, 1, 1, 30, 1, 1, 1)
)