Extracting columns having greater than certain values in R dataframe

Question

I have a dataframe:

Alix    Blim    Jux Gyno
0.105   0.234   0.67    0.89
0.01    0.542   0.11    0.65
0.003   0.002   0.6     0.67
0.009   0.123   0.09    0.01
         


        

Answer 1

We could use colSums to subset columns using base R

df[colSums(df > 0.6) > 0]

# Jux Gyno
#1 0.67 0.89
#2 0.11 0.65
#3 0.60 0.67
#4 0.09 0.01

---

Or with dplyr, select_if

library(dplyr)
df %>% select_if(~any(. > 0.6))

Answer 2

here is a base R option with Filter

Filter(function(x) any(x > 0.6), df)
#  Jux Gyno
#1 0.67 0.89
#2 0.11 0.65
#3 0.60 0.67
#4 0.09 0.01

---

Or using transmute_if

library(dplyr)
df %>% 
    transmute_if(~ any(.x > 0.6), I)

Or with keep

library(purrr)
keep(df, map_lgl(df, ~ any(.x > 0.6)))

Answer 3

df <- read.table(text='Alix    Blim    Jux Gyno
0.105   0.234   0.67    0.89
0.01    0.542   0.11    0.65
0.003   0.002   0.6     0.67
0.009   0.123   0.09    0.01
', header=T)

We can use sapply to find the max value in each column, then check if it's greater than 0.6. This gives a logical vector which we can use to subset df by column:

df[,sapply(df, max) > 0.6]

   Jux Gyno
1 0.67 0.89
2 0.11 0.65
3 0.60 0.67
4 0.09 0.01

Answer 4

Please provide a reproducible example for future questions.

Here is my dplyr solution:

library(tidyverse)

df <- tibble(First = 0:5,
             Second = 10:15,
             Third = 20:25)

is_greater_than <- function(x) any(x > 10)

select_if(df, is_greater_than)

Answer 5

> df[ , sapply(df, function(x) any(x>0.6))]
   Jux Gyno
1 0.67 0.89
2 0.11 0.65
3 0.60 0.67
4 0.09 0.01