The same plot in 14 other libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, Plotly, plotnine, Pygal, Seaborn; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts. Compare all 15 side by side: Basic Count Plot in Python, R, Julia and JavaScript.
A count plot displays the frequency of observations in each category of a categorical variable using vertical bars. Unlike a basic bar chart that requires pre-computed values, a count plot automatically counts occurrences from raw data. This makes it ideal for quick exploratory analysis of categorical distributions without manual aggregation.
#' anyplot.ai
#' count-basic: Basic Count Plot
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 87/100 | Updated: 2026-09-30
library(ggplot2)
library(dplyr)
library(scales)
library(ragg)
set.seed(42)
# --- Theme tokens -----------------------------------------------------------
THEME <- Sys.getenv("ANYPLOT_THEME", "light")
PAGE_BG <- if (THEME == "light") "#FAF8F1" else "#1A1A17"
INK <- if (THEME == "light") "#1A1A17" else "#F0EFE8"
INK_SOFT <- if (THEME == "light") "#4A4A44" else "#B8B7B0"
IMPRINT_PALETTE <- c("#009E73", "#C475FD", "#4467A3", "#BD8233",
"#AE3030", "#2ABCCD", "#954477", "#99B314")
BAR_FILL <- IMPRINT_PALETTE[1]
# Bar edge uses the ink token -- the style guide's endorsed outline pattern --
# never a custom hex; a crisp stroke for depth, not a second color encoding.
BAR_EDGE <- INK
# Faint gridline tone -- ggplot2 has no grid alpha, so blend INK ~15% into
# PAGE_BG instead of using full-opacity INK (which reads as bold as the axis).
GRID_COLOR <- colorRampPalette(c(PAGE_BG, INK))(100)[15]
# --- Data ---------------------------------------------------------------
# Raw, uncounted survey responses -- ggplot2's geom_bar() tallies them itself.
support_levels <- c(
"Strongly Agree", "Agree", "Neutral", "Disagree", "Strongly Disagree"
)
response_weights <- c(0.32, 0.30, 0.19, 0.13, 0.06)
survey_responses <- sample(
support_levels,
size = 480,
replace = TRUE,
prob = response_weights
)
df <- tibble::tibble(
response = factor(survey_responses, levels = support_levels)
)
# Order categories by descending frequency for readability
freq_order <- df %>%
count(response, name = "n") %>%
arrange(desc(n)) %>%
pull(response)
df$response <- factor(df$response, levels = freq_order)
n_total <- nrow(df)
# --- Plot -----------------------------------------------------------------
title_text <- "count-basic · r · ggplot2 · anyplot.ai"
# Basic variant: single categorical variable, one bar color, no reference
# line, highlighted bar or second grouping. geom_text(stat = "count") tallies
# and labels the bars in the same pass as geom_bar() -- no separate
# dplyr::count() table needed for the labels.
p <- ggplot(df, aes(x = response)) +
geom_bar(width = 0.62, fill = BAR_FILL, color = BAR_EDGE, linewidth = 0.4) +
geom_text(
stat = "count",
aes(
label = sprintf(
"%s (%s)",
scales::comma(after_stat(count)),
scales::percent(after_stat(count) / n_total, accuracy = 1)
),
# Most-frequent bar's label reads bolder and larger for a single focal
# point -- same fill color throughout, emphasis from type weight alone.
fontface = ifelse(after_stat(count) == max(after_stat(count)), "bold", "plain"),
size = ifelse(after_stat(count) == max(after_stat(count)), 3.8, 3.2)
),
vjust = -0.7,
color = INK
) +
scale_size_identity() +
# Extra headroom above the tallest bar (was 0.14) so its bold label sits
# clear of the panel edge -- a deliberate breathing-room refinement, not a
# change to the data-to-baseline mapping.
scale_y_continuous(expand = expansion(mult = c(0, 0.19))) +
labs(
title = title_text,
subtitle = sprintf("Likert-scale survey responses, n = %d", n_total),
x = "Survey Response",
y = "Count"
) +
theme_minimal(base_size = 8) +
theme(
plot.background = element_rect(fill = PAGE_BG, color = PAGE_BG),
panel.background = element_rect(fill = PAGE_BG, color = NA),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_line(color = GRID_COLOR, linewidth = 0.3),
axis.title = element_text(color = INK, size = 10),
axis.text = element_text(color = INK_SOFT, size = 8),
axis.text.x = element_text(margin = margin(t = 6)),
axis.ticks = element_blank(),
axis.line.x = element_line(color = INK_SOFT, linewidth = 0.3),
plot.title = element_text(color = INK, size = 12, face = "bold"),
plot.subtitle = element_text(color = INK_SOFT, size = 8.5, margin = margin(b = 8)),
# Title/subtitle flush to the full plot width (not just the panel) and a
# touch more canvas whitespace all around -- a spacing/typography-only
# refinement, no new marks or color.
plot.title.position = "plot",
plot.margin = margin(t = 14, r = 18, b = 10, l = 10)
)
# --- Save -------------------------------------------------------------------
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 8,
height = 4.5,
units = "in",
dpi = 400
)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/count-basic/ggplot2/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "count-basic",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/count-basic/r/ggplot2",
"hub": "https://anyplot.ai/count-basic",
"code_json": "https://api.anyplot.ai/specs/count-basic/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/count-basic",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/count-basic/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/count-basic/r/ggplot2/plot-dark.png",
"quality_score": 87.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Basic Count Plot on anyplot.ai.