Classify values if any or the columns satisfy the condition & keep NA values-CodePudding

I am trying to find out if any of my columns have a values overpassing certain value, e.g. 7. If yes, that classify it as more, otherwise be less . My problem is similar to question here but I can't figure out how to update it to my case. I need the values to be classified row-wise. Also, some of the rows can be filled in just by the NA/NaN values. I need to keep them in a dataframe, so I can't filter them before the calculation.

Example:

# Example: classify data if they fit the condition:
data <- data.frame(V1=c(1,5,7, NA),
                   V2=c(1,5,8, NA),
                   V3=c(3,9,10, NaN))


# Classify each column: 
ifelse(data$V1 >=7, "more", "less") 

# Classify based on all columns? NOT WORKING!
 data$NewCol <-  ( Reduce(`|`, lapply(data, >=, 7)))

Desired outcome:

  V1 V2 V3 newCol
1  1  1  3 less   # no value is >= 7
2  5  5  9 more
3  7  8 10 more
4 NA NA NaN NA

CodePudding user response：

You may use rowSums -

data$newCol <- ifelse(rowSums(data >= 7) > 0, 'more', 'less')

Or using dplyr with case_when if we have multiple conditions to check.

library(dplyr)

data %>%
  rowwise() %>%
  mutate(newCol = case_when(all(is.na(c_across())) ~ NA_character_, 
                            any(c_across() >= 7) ~ 'more', 
                            TRUE ~ 'less'))

#     V1    V2    V3 newCol
#  <dbl> <dbl> <dbl> <chr> 
#1     1     1     3 less  
#2     5     5     9 more  
#3     7     8    10 more  
#4    NA    NA   NaN NA