ggplot2 geom选择与数据处理咨询：按id拆分变量及颜色展示

Hey there! Let's work through your ggplot2 questions one by one.

First: Tidy Data is Non-Negotiable for ggplot2 #

First off, let's get this straight: ggplot2 is built for tidy data (where each row is a single observation, and each column is a single variable). Your 1000-row subset is totally manageable for ggplot2—no performance issues here—but if your current data has big and small as separate columns (wide format), you'll need to reshape it to long format using tidyr::pivot_longer(). No data.table required, don't worry.

For example, if your raw data looks like this:

# Sample wide-format data
df <- tibble(
  id = rep(1:100, each = 10),
  big = rnorm(1000),
  small = rnorm(1000, mean = 2),
  colors = sample(c("red", "blue", "green"), 1000, replace = TRUE)
)

You'd reshape it like this:

library(tidyr)
tidy_df <- df %>%
  pivot_longer(cols = c(big, small), 
               names_to = "size_group",  # Name for the new variable column
               values_to = "measurement") # Name for the new value column

This gives you a tidy frame with columns: id, size_group (either "big" or "small"), measurement, and colors—perfect for ggplot2.

Second: Visualizing big & small Split by id #

The right geom depends on what you want to show. Here are the most common options:

1. Compare distributions per id #

If you want to see how big and small values are distributed within each id, use geom_boxplot() or geom_violin() with facets:

library(ggplot2)
ggplot(tidy_df, aes(x = size_group, y = measurement, fill = size_group)) +
  geom_boxplot(alpha = 0.7) +
  facet_wrap(~id) # Splits plots into a grid by id

Add geom_jitter() if you want to overlay individual data points (great for spotting outliers):

ggplot(tidy_df, aes(x = size_group, y = measurement, color = size_group)) +
  geom_boxplot(alpha = 0.3) +
  geom_jitter(size = 0.5, alpha = 0.6) +
  facet_wrap(~id)

2. Compare paired big/small values per id #

If each id has a direct big vs small comparison (like paired measurements), use geom_point() + geom_line() to connect values for each id:

ggplot(tidy_df, aes(x = size_group, y = measurement, group = id, color = id)) +
  geom_point() +
  geom_line() +
  labs(x = "Size Category", y = "Value")

(Note: If you have 100+ ids, the color scale might get messy—stick to facets instead in that case.)

Third: Visualizing colors Split by id #

Again, pick a geom based on your goal:

1. Count of each color per id #

Use geom_bar() to show how many times each color appears in every id. Facets work great here:

ggplot(df, aes(x = colors, fill = colors)) +
  geom_bar() +
  facet_wrap(~id) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-labels for readability

If you want to see proportions instead of raw counts, use position = "fill":

ggplot(df, aes(x = id, fill = colors)) +
  geom_bar(position = "fill") +
  labs(y = "Proportion of Colors")

2. Combine colors with big/small per id #

To see how colors relate to your big/small measurements across ids, use geom_boxplot() with a facet grid:

ggplot(tidy_df, aes(x = colors, y = measurement, fill = colors)) +
  geom_boxplot() +
  facet_grid(id ~ size_group) # Rows = ids, columns = big/small

Quick Recap #

• 1000 rows is totally fine for ggplot2—no issues here.
• Always reshape to tidy data first with tidyr::pivot_longer() if your big/small are separate columns.
• Choose your geom based on what story you want to tell about the data (distributions, counts, comparisons).

内容的提问来源于stack exchange，提问作者hoze