CODEWITHSUNDEEP
rdplyrtidyverse
Slavistan Slavistan
Slavistan Slavistan

Reputation: 97

Find row-wise minimum positive non-zero number in data.frame using dplyr

Given a numeric data frame 

A <- c(1.1, 3.0, 2.0, 4.0, 0.0, 1.3)
B <- c(0.2, 1.0, 2.4, 1.1, 1.3, 0.0)
C <- c(5.2, 1.3, 3.7, 1.7, 1.3, 1.0)

data <- data.frame(A, B, C) %>% as_tibble()

how can I create another column containing the row-wise minimum positive non-zero number (using dplyr if possible) to obtain the following data frame?

## A tibble: 6 x 4
#      A     B     C posmin
#  <dbl> <dbl> <dbl>  <dbl>
#1   1.1   0.2   5.2    0.2
#2   3     1     1.3    1  
#3   2     0     3.7    2  
#4   4     1.1   1.7    1.1
#5   0     1.3   1.3    1.3  
#6   1.3   0     1      1

What's concise and almost does the job is

data %>% mutate(posmin = pmin(A, B, C))

which has two issues, however:

Is there something like pminpos and if there isn't how can I create it so that it can be called just like pmin in the code above? And how do I specify many contiguous columns without passing a comma-separated list of their names?

Thank you very much.

edit: I clearly missed to stress the point, that I'm looking for non-zero positive numbers, i.e. numbers strictly greater than > 0. Hence the sought-after values for rows #5 and #6 are not zero.

Upvotes: 3

Views: 1591

Answers (4)

akrun
akrun

Reputation: 886938

One option would be to convert the column names to symbols and then evaluate (!!!)

library(dplyr)
data %>% 
   mutate_all(funs(replace(., .==0, NA))) %>% 
   transmute(posmin = pmin(!!! rlang::syms(names(.)), na.rm = TRUE)) %>%
   bind_cols(data, .)

# A tibble: 6 x 4
#      A     B     C posmin
#  <dbl> <dbl> <dbl>  <dbl>
#1   1.1   0.2   5.2    0.2
#2   3     1     1.3    1  
#3   2     2.4   3.7    2  
#4   4     1.1   1.7    1.1
#5   0     1.3   1.3    1.3  
#6   1.3   0     1      1

Or use map/reduce

map(data, na_if, 0) %>% 
    reduce(pmin, na.rm = TRUE) %>% 
    bind_cols(data, posmin = .)

Or without using any external packages, we can call pmin within do.call in a single line

data$posmin <- do.call(pmin, c(NA^ (data == 0) * data, na.rm = TRUE))
data$posmin
#[1] 0.2 1.0 2.0 1.1 1.3 1.0

Or based on @Moody_Mudskipper's comments, instead of assigning value that are 0 to NA, change it to a larger value (Inf) and then use pmin

data$posmin <- do.call(pmin, '[<-'(data, data <=0, value=Inf))

Upvotes: 2

Jilber Urbina
Jilber Urbina

Reputation: 61154

apply is suitable here:

> data$posmin <- apply(data, 1, function(x) min(x[x>0]))
> data
# A tibble: 6 x 4
      A     B     C posmin
  <dbl> <dbl> <dbl>  <dbl>
1   1.1   0.2   5.2    0.2
2   3     1     1.3    1  
3   2     2.4   3.7    2  
4   4     1.1   1.7    1.1
5   0     1.3   1.3    1.3
6   1.3   0     1      1

Upvotes: 0

Onyambu
Onyambu

Reputation: 79188

you can use invoke and use na_if as commented by @markus

   data %>% 
       mutate(posmin = invoke(pmin, na_if(., 0), na.rm = TRUE))

# A tibble: 6 x 4
      A     B     C posmin
  <dbl> <dbl> <dbl>  <dbl>
1   1.1   0.2   5.2    0.2
2   3     1     1.3    1  
3   2     2.4   3.7    2  
4   4     1.1   1.7    1.1
5   0     1.3   1.3    1.3
6   1.3   0     1      1

Upvotes: 1

Mike S
Mike S

Reputation: 312

Here's a tidyverse solution similar to apply(), using the purrr package:

data %>% mutate(posmin = pmap(data, min))

Upvotes: 0

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