Fill NA with previous value based on multiple column

Question

Im new with R.

Currently i'm working with dplyr package for manipulation data. But i'm stuck when want to do some calculation as below:

library(dplyr)
w18 <- c(1,2,3,4,5)
w19 <- c(10,10,NA,NA,NA)
temp_df <- data.frame(w18,w19)

I expect NA will replaced with value from w19 <- ifelse(is.na(w19), lag(w19)+ lag(w18) else w19

how ever when im tried with my code as below:

w19_function <- function(temp_df) {
isna <- is.na(temp_df)
lag_w19 <- tail(temp_df[!isna],1)
loc <- length(w18[!is.na(w18),])
temp_df[isna] <- lag_w19+ temp_df[loc,'w18']
return(temp_df)
}
w19_function(temp_df)

i expect result like this one :

w18,w19
1,10
2,10
3,12
4,15
5,19

but the code giving result :

w18,w19
1,10
2,10
3,12
4,12
5,12

what thing I should to add? please help me for solving this case.

Answer 1

This might work as well for you:

library(tidyverse)
w18 <- c(1,2,3,4,5)
w19 <- c(10,10,NA,NA,NA)
temp_df <- data.frame(w18,w19)
temp_df

temp_df %>%
  mutate(step = cumsum(if_else(is.na(w19), lag(w18), 0))) %>%
  fill(w19) %>%
  mutate(w19 = w19 + step) %>%
  select( -step)

# w18 w19
# 1   1  10
# 2   2  10
# 3   3  12
# 4   4  15
# 5   5  19

(modified: with repeats)

temp_df %>%
  mutate(grp = cumsum(if_else(!is.na(w19) & is.na(lag(w19)), 1, 0))) %>%
  group_by(grp) %>%
  mutate(step = cumsum(if_else(is.na(w19), lag(w18), 0))) %>%
  fill(w19) %>%
  mutate(w19 = w19 + step) %>%
  ungroup() %>%
  select( -step, -grp)

Answer 2

It's not easy to capture values which were changed on the fly.

Sometimes a traditional for loop can be helpful

for (i in seq_len(nrow(temp_df))) {
   if(is.na(temp_df$w19[i])) {
     temp_df$w19[i] <- temp_df$w18[i-1] + temp_df$w19[i-1]
   }
}

temp_df
#  w18 w19
#1   1  10
#2   2  10
#3   3  12
#4   4  15
#5   5  19