I'm trying to create a mutate a new column called category with each of the product name classifications. And also I would like to mutate a column wherein caffeine_mg>0 has caffeine, but caffeine_mg=0 has no caffeine.
sbucks <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-12-21/starbucks.csv')
sbucks_new <- sbucks %>%
mutate(category = case_when(grepl("Tea", product_name) ~ "Tea",
grepl(c("coffee","Coffee","Caffè"), product_name) ~ "Coffee",
grepl("Smoothie", product_name) ~ "Smoothie",
grepl("Hot Chocolate", product_name) ~ "Hot Chocolate",
grepl("Espresso", product_name) ~ "Espresso",
grepl("Refreshers", product_name) ~ "Refreshers",
TRUE ~ "Others")) %>%
mutate(Caffeine = case_when(caffeine_mg=0 ~ "No",
caffeine_mg>0 ~ "Yes"))
There were several syntax errors in the OP's code:
In the second call to grep, the vector of patterns should be collapsed into a single character regex as with xxx|yyy|zzz.
In the second mutate, please see that the equality comparator would be == and not =.
bucks %>%
mutate(category = case_when(grepl("Tea", product_name) ~ "Tea",
grepl("coffee|Coffee|Caffè", product_name) ~ "Coffee",
grepl("Smoothie", product_name) ~ "Smoothie",
grepl("Hot Chocolate", product_name) ~ "Hot Chocolate",
grepl("Espresso", product_name) ~ "Espresso",
grepl("Refreshers", product_name) ~ "Refreshers",
TRUE ~ "Others")) %>%
mutate(Caffeine = case_when(caffeine_mg == 0 ~ "No",
caffeine_mg >0 ~ "Yes" ))
# A tibble: 1,147 × 17
product_name size milk whip serv_size_…¹ calor…² total…³ satur…⁴ trans…⁵ chole…⁶ sodiu…⁷ total…⁸ fiber_g sugar_g caffe…⁹ categ…˟ Caffe…˟
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <chr> <chr>
1 brewed coffee - dark roast short 0 0 236 3 0.1 0 0 0 5 0 0 0 130 Coffee Yes
2 brewed coffee - dark roast tall 0 0 354 4 0.1 0 0 0 10 0 0 0 193 Coffee Yes
3 brewed coffee - dark roast grande 0 0 473 5 0.1 0 0 0 10 0 0 0 260 Coffee Yes
4 brewed coffee - dark roast venti 0 0 591 5 0.1 0 0 0 10 0 0 0 340 Coffee Yes
5 brewed coffee - decaf pike place roast short 0 0 236 3 0.1 0 0 0 5 0 0 0 15 Coffee Yes
6 brewed coffee - decaf pike place roast tall 0 0 354 4 0.1 0 0 0 10 0 0 0 20 Coffee Yes
7 brewed coffee - decaf pike place roast grande 0 0 473 5 0.1 0 0 0 10 0 0 0 25 Coffee Yes
8 brewed coffee - decaf pike place roast venti 0 0 591 5 0.1 0 0 0 10 0 0 0 30 Coffee Yes
9 brewed coffee - medium roast short 0 0 236 3 0.1 0 0 0 5 0 0 0 155 Coffee Yes
10 brewed coffee - medium roast tall 0 0 354 4 0.1 0 0 0 5 0 0 0 235 Coffee Yes
# … with 1,137 more rows, and abbreviated variable names ¹serv_size_m_l, ²calories, ³total_fat_g, ⁴saturated_fat_g, ⁵trans_fat_g, ⁶cholesterol_mg, ⁷sodium_mg,
# ⁸total_carbs_g, ⁹caffeine_mg, ˟category, ˟Caffeine
# ℹ Use `print(n = ...)` to see more rows