如何用RStudio(tidyverse)和ggplot2按年份计算平均进球并绘制趋势图

Got it, let's walk through how to turn that messy raw match data into a clean, interpretable yearly average goals trend plot using tidyverse and ggplot2. Here's a step-by-step breakdown:

1. Install & Load Required Packages #

First, ensure you have the tidyverse package (which includes both dplyr for data manipulation and ggplot2 for plotting) installed and loaded:

# Install tidyverse if it's not already on your system
if (!require(tidyverse)) {
  install.packages("tidyverse")
}

# Load the package into your R session
library(tidyverse)

2. Calculate Yearly Average Goals #

We'll use dplyr functions to group the raw match data by year and compute the average goals per match. We'll also add an optional count of matches per year for context:

# Process the SoccerTeam dataset to get yearly averages
yearly_avg_goals <- SoccerTeam %>%
  # Group data by the year the match was played
  group_by(YearPlayed) %>%
  # Calculate average goals (ignore missing values with na.rm = TRUE)
  summarize(
    avg_goals = mean(Goals, na.rm = TRUE),
    num_matches = n()  # Optional: track how many matches were played each year
  ) %>%
  # Ungroup to avoid unexpected behavior in later steps
  ungroup()

3. Create the Time Trend Plot #

Now use ggplot2 to visualize the yearly average goals. We'll add both a line (to show the overall trend) and points (to highlight individual year values) for clarity:

# Build the plot
ggplot(yearly_avg_goals, aes(x = YearPlayed, y = avg_goals)) +
  # Add a solid line for the trend
  geom_line(color = "#2c3e50", linewidth = 1) +
  # Add points to mark each year's average
  geom_point(size = 2.5, color = "#e74c3c") +
  # Customize labels and title
  labs(
    title = "Yearly Average Goals Scored (1950-2020)",
    x = "Year",
    y = "Average Goals Per Match",
    caption = "Data source: SoccerTeam match records"
  ) +
  # Use a clean, minimal theme
  theme_minimal() +
  # Tweak text styling for readability
  theme(
    plot.title = element_text(hjust = 0.5, size = 14, face = "bold"),
    axis.title = element_text(size = 12),
    axis.text = element_text(size = 10)
  )

Notes for Edge Cases #

• If your YearPlayed column is stored as a character instead of numeric, convert it first with mutate(YearPlayed = as.numeric(YearPlayed)) before grouping.
• If you want to highlight years with an unusually high/low number of matches, you could map num_matches to point size with geom_point(aes(size = num_matches)).

内容的提问来源于stack exchange，提问作者Kevin