mutate_at evaluation error when using group_by

Question

mutate_at() shows an evaluation error when used with group_by() and when imputing a numerical vector for column position as the first (.vars) argument.

• Issue shows up when using R3.4.2 and dplyr0.7.4 version
• Works fine when using R3.3.2 and dplyr0.5.0
• Works fine if .vars is character vector (column name)

Example:

# Create example dataframe
Id <- c('10_1', '10_2', '11_1', '11_2', '11_3', '12_1')
Month <- c(2, 3, 4, 6, 7, 8)
RWA <- c(0, 0, 0, 1.579, NA, 0.379)
dftest = data.frame(Id, Month, RWA)

# Define column to fill NAs
nacol = c('RWA')

# Fill NAs with last period
dftest_2 <- dftest %>%
  group_by(Id) %>%
  mutate_at(which(names(dftest) %in% nacol), 
            funs(ifelse(is.na(.),0,.)))

---

Error in mutate_impl(.data, dots) : 
Evaluation error: object 'NA' not found.

---

More sensible example demonstrating issue:

# Create example dataframe
Id <- c('10_1', '10_2', '11_1', '11_3', '11_3', '12_1')
Month <- c(2, 3, 4, 6, 7, 8)
RWA <- c(0, 0, 0, 1.579, NA, 0.379)
dftest = data.frame(Id, Month, RWA)

# Define column to fill NAs
nacol = c('RWA')

# Fill NAs with last period
dftest_2 <- dftest %>%
  group_by(Id) %>%
  mutate_at(which(names(dftest) %in% nacol), 
            funs(na.locf(., na.rm=F)))

Answer 1

The reason we are getting NA values is that the output we get from which is 3, but we grouped by 'Id' and so there are only 2 columns after that.

dftest %>%
     group_by(Id) %>% 
     mutate_at(which(names(dftest) %in% nacol)-1, funs(ifelse(is.na(.),0,.)))
# A tibble: 6 x 3
# Groups:   Id [6]
#      Id Month   RWA
#  <fctr> <dbl> <dbl>
#1   10_1     2 0.000
#2   10_2     3 0.000
#3   11_1     4 0.000
#4   11_2     6 1.579
#5   11_3     7 0.000
#6   12_1     8 0.379

The group_by is part is not needed here as we are changing NA values in other columns to 0

dftest %>%
    mutate_at(which(names(dftest) %in% nacol), funs(ifelse(is.na(.),0,.)))

It could be a bug and using the position based approach is sometimes risky. Better option would be to go with names

dftest %>%
    group_by(Id) %>% 
    mutate_at(intersect(names(.), nacol), funs(replace(., is.na(.), 0)))

NOTE: In all these cases, the group_by is not needed

---

Another option is replace_na from tidyr

dftest %>%
    tidyr::replace_na(as.list(setNames(0, nacol)))