Assign column values based on condition and list with dplyr

I have a tibble with a column of dates along with other data. I would like to add a column with labels depending on dates falling within certain ranges. I could do this with mutate and nested conditionals, for instance:

data <- data %>%
    mutate(season = ifelse(date < "2020-03-01", "Winter 20",
                           ifelse(date < "2020-07-01", "Spring 20", 
                                  ifelse(date < "2020-11-01", "Fall 20", "Winter 21")

But this seems somewhat inelegant and also inflexible. Ideally, I would like to be able to specify a named list, e.g.

season_breaks <- c("Winter 20" = "2020-03-01", 
                   "Spring 20" = "2020-07-01", 
                   "Fall 20" = "2020-11-01", 
                   "Winter 21" = "2021-03-01")

And use this separately specified list to modify the tibble with the new season column. (I use this same set of date cutoffs in various other places, which is why it's helpful to have it as a separate list; also it's easier to modify in one place later if needed.)

Is there a way to create a new column like this?

Here is solution using cut:

season_breaks <- c("Winter 20" = as.Date("2020-03-01"), 
                   "Spring 20" = as.Date("2020-07-01"), 
                   "Fall 20" = as.Date("2020-11-01"), 
                   "Winter 21" = as.Date("2021-03-01"))


data %>% 
  mutate(season = cut(date, 
                      breaks = c(as.Date("1970-01-01"), unname(season_breaks)), 
                      labels = names(season_breaks)))

  date       season   
  <date>     <fct>    
1 2020-01-01 Winter 20
2 2020-04-01 Spring 20
3 2020-08-01 Fall 20  
4 2020-12-01 Winter 21

Using dplyr’s new (>= v1.1.0) rolling join feature:

library(dplyr)

# make table of season breaks
season_breaks <- data.frame(
  season = names(season_breaks),
  end_date = as.Date(unname(season_breaks))
)

data %>%
  left_join(season_breaks, join_by(closest(date < end_date))) %>%
  select(!end_date)

        date    season
1 2020-01-15 Winter 20
2 2020-04-15 Spring 20
3 2020-07-15   Fall 20
4 2020-10-15   Fall 20
5 2021-01-15 Winter 21

Example data:

data <- data.frame(
  date = seq(as.Date("2020-01-15"), length.out = 5, by = "3 months")
)

Example data:

library(tidyverse)                                
data <- data.frame(date = as.Date(c("2020-02-01",  "2020-06-01",  "2020-10-01",  "2020-11-01", "2021-02-01")))

I just show you the code as you may have different ways to define season:

Define season by quarter:

data %>% mutate(quarter = paste(quarter(date) %>% case_match(1 ~ "Spring", 2 ~ "Summer", 3 ~ "Fall", 4 ~ "Winter"), year(date)))

        date      season
1 2020-02-01 Spring 2020
2 2020-06-01 Summer 2020
3 2020-10-01 Winter 2020
4 2020-11-01 Winter 2020
5 2021-02-01 Spring 2021
> 

Define season by month:
You may need to adjust values to fit your definition of reasons.

data %>%
  mutate(season = paste(month(date) %>% case_match(
    c(3:5) ~ "Spring",
    c(6:8) ~ "Summer",
    c(9:10) ~ "Fall",
    c(11, 12, 1, 2) ~ "Winter"
  ), year(date)))

       date      season
1 2020-02-01 Winter 2020
2 2020-06-01 Summer 2020
3 2020-10-01   Fall 2020
4 2020-11-01 Winter 2020
5 2021-02-01 Winter 2021