Mutate Column to indicate if condition is met?-CodePudding

I have a dataset similar to this:

> dput(df)
structure(list(Person_id = c("A", "A", "A", "A", "A", "B", "B", 
"B"), Weight = c(170L, 164L, 160L, 150L, 149L, 250L, 225L, 230L
)), class = "data.frame", row.names = c(NA, -8L))

I want to create a column that indicates if the individual hits 150 lbs or below. We see that Person A eventually does reach 150 lbs, so that individual should be marked as a 'yes'--even though they did not hit this threshold the first three observations. Person B never reaches the 150 lb threshold, so they should be marked as a 'no'.

The desired output should look like this:

> dput(df)
structure(list(Person_id = c("A", "A", "A", "A", "A", "B", "B", 
"B"), Weight = c(170L, 164L, 160L, 150L, 149L, 250L, 225L, 230L
), Condition_met = c("Yes", "Yes", "Yes", "Yes", "Yes", "No", 
"No", "No")), class = "data.frame", row.names = c(NA, -8L))

CodePudding user response：

Using dplyr you could do:

library(dplyr)

dat %>%
  group_by(Person_id) %>%
  mutate(Condition_met = if_else(any(Weight <= 150), "Yes", "no")) %>%
  ungroup()
#> # A tibble: 8 × 3
#>   Person_id Weight Condition_met
#>   <chr>      <int> <chr>        
#> 1 A            170 Yes          
#> 2 A            164 Yes          
#> 3 A            160 Yes          
#> 4 A            150 Yes          
#> 5 A            149 Yes          
#> 6 B            250 no           
#> 7 B            225 no           
#> 8 B            230 no

Or a similar base R approach using ave:

dat$Condition_met <- ave(dat$Weight, dat$Person_id, FUN = function(x) ifelse(any(x <= 150), "Yes", "No"))

dat
#>   Person_id Weight Condition_met
#> 1         A    170           Yes
#> 2         A    164           Yes
#> 3         A    160           Yes
#> 4         A    150           Yes
#> 5         A    149           Yes
#> 6         B    250            No
#> 7         B    225            No
#> 8         B    230            No