R语言：为数据框新增列并按月份分组划分季节

Hey there, let's get that Season column added to your weather_grouped tibble. Mapping months to seasons is a common task, and there are a couple of clean ways to do this with R's tidyverse tools (since you're working with a tibble, I assume you're already using dplyr). Here are my go-to approaches:

1. Use case_when() for explicit, readable logic #

This method is great because it's super clear exactly which months map to which season—no guessing about interval boundaries.

library(dplyr)

# Update the tibble with the new Season column
weather_grouped <- weather_grouped %>%
  mutate(Season = case_when(
    Month %in% 1:3  ~ "冬季",   # Jan-Mar
    Month %in% 4:6  ~ "春季",   # Apr-Jun
    Month %in% 7:9  ~ "夏季",   # Jul-Sep
    Month %in% 10:12 ~ "秋季",  # Oct-Dec
    TRUE ~ NA_character_        # Catch any unexpected Month values
  ))

The TRUE ~ NA_character_ line is a safety net—if you have any Month values outside 1-12, they'll get marked as NA instead of breaking the code.

2. Use cut() for concise interval-based grouping #

If you prefer a more compact approach, cut() works perfectly here since we're dealing with continuous month values that fit cleanly into fixed intervals.

weather_grouped <- weather_grouped %>%
  mutate(Season = cut(
    x = Month,
    breaks = c(0, 3, 6, 9, 12),  # Define the interval boundaries
    labels = c("冬季", "春季", "夏季", "秋季"),  # Match labels to intervals
    include.lowest = TRUE        # Ensure Month=1 is included in the first interval
  ))

The include.lowest = TRUE is important here—it makes sure that the lowest value (1) falls into the first interval (0-3) instead of being excluded.

Verify the result #

Once you run either of these, you can check the updated tibble with:

head(weather_grouped)

You should see the new Season column populated with the correct season for each row's Month value.

内容的提问来源于stack exchange，提问作者Sara Hanna