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R: Mutate non zero minimum in grouped column

I am trying to calculate the non zero minimum of a column within groups using dplyr. I have seen a few similar questions around, however, none seem to work. Let's see an example with iris. I would like to create a new column :min_length, with the minimum pedal length higher than 1 for each species (I did not use 0 for this example to fit the dataset). I tried:

iris <- iris %>% group_by(Species) %>% mutate(min_length = min(Petal.Length>1))

But I get:

> iris$min_length
  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1
 [58] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[115] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Also, I tried an answer suggested in Find minimum non-zero value in a column R :

iris$min_length <- iris %>% group_by(Species) %>% min(iris[iris$Sepal.Length>1, "Sepal.Length"])

But I get:

Error in FUN(X[[i]], ...) : only defined on a data frame with all numeric variables

The intended output would be:

iris$min_length
  [1] 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1
 [29] 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 3.0 3.0 3.0 3.0 3.0 3.0
 [57] 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0
 [85] 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5
[113] 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5
[141] 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5

Any ideas?

Solution

  • You almost got it

    library(tidyverse)

iris %>% group_by(Species) %>% mutate(min_length = min(Petal.Length[Petal.Length > 1]))