Using R/dplyr to filter columns?

Question

I have a simple Q... I have a dataset I need to filter by certain parameters. I was hoping for a solution in R?

Dummy case:

 colour age animal red 10 dog yellow 5 cat pink 6 cat

I want to classify this dataset eg by:

If colour is 'red' OR 'pink' AND age is <7 AND animal is 'cat' then = category 1. Else category 2.

Output would be:

 colour age animal category red 10 dog 2 yellow 5 cat 2 pink 6 cat 1

Is there a way to manipulate dplyr to achieve this? I'm a clinician not a bioinformatician so go easy!

Answer 1

You could also manipulate this as:

df$category <- with(df,!(colour %in% c('red', 'pink') & age < 7 & animal == 'cat')) + 1
df

#  colour age animal category
#1    red  10    dog        2
#2 yellow   5    cat        2
#3   pink   6    cat        1

And in dplyr :

df %>%
  mutate(category = as.integer(!(colour %in% c('red', 'pink') & 
                                 age < 7 & animal == 'cat')) + 1)

Answer 2

I like the case_when function in dplyr to set up more complex selections with mutate.

library(tidyverse)

df <- data.frame(colour = c("red", "yellow", "pink", "red", "pink"),
              age = c(10, 5, 6, 12, 10),
              animal = c("dog", "cat", "cat", "hamster", "cat"))
df
#>   colour age  animal
#> 1    red  10     dog
#> 2 yellow   5     cat
#> 3   pink   6     cat
#> 4    red  12 hamster
#> 5   pink  10     cat

df <- mutate(df, category = case_when( 
        ((colour == "red" | colour == "pink") & age < 7 & animal == "cat") ~ 1,
         (colour == "yellow" | age != 5 & animal == "dog")                 ~ 2,
         (colour == "pink" | animal == "cat")                              ~ 3,
         (TRUE)                                                            ~ 4) )
df
#>   colour age  animal category
#> 1    red  10     dog        2
#> 2 yellow   5     cat        2
#> 3   pink   6     cat        1
#> 4    red  12 hamster        4
#> 5   pink  10     cat        3

^{Created on 2021-01-17 by the reprex package (v0.3.0)}