I'm trying write a conditional statement that duplicates the row where df$food=1. Then changes the value "df$weight" of initial row to the value of df$prior_w and not the duplicate. Also i need to change the df$food value to 0 on the duplicate and prior_w to NA on the duplicate
df <- data.frame(date=("2022-01-01","2022-01-02","2022-01-03","2022-01-04","2022-01-05"),
food=(0,0,0,1,0),
prior_w=(NA,NA,NA,2,NA),
weight=(5,4,3,6,4))
Id like to have a data frame like this
df_2 <- data.frame(date=("2022-01-01","2022-01-02","2022-01-03","2022-01-04","2022-01-04","2022-01-05"),
food=(0,0,0,1,0,0),
prior_w=(NA,NA,NA,NA,2,NA),
weight=(5,4,3,2,6,4))
Im will translate what i need in words (Not actual code, sorry im struggling). I looked at a lot of stack overflow questions and answers but i cant seem to find the perfect mix. I know the rep function repeats, and that i can write conditional statements with case_when or ifelse.
df_1 <- df %>%
repeat row case_when df$food==1 %>%
mutate (the_first_row (df$weight=prior_w),
second_row (df$food=0, df$prior_w = NA))
thank you for your help
We may use uncount to replicate the rows and then change the values based on the number of rows by group
library(dplyr)
library(tidyr)
df %>%
uncount(1 + (food == 1)) %>%
group_by(date) %>%
mutate(food = if(n() > 1) replace(food, -1, 0) else food,
weight = if(n() == 2) replace(weight, 1, prior_w[n()]) else weight,
prior_w = if(n() ==2 ) lag(prior_w) else prior_w) %>%
ungroup
-output
# A tibble: 6 × 4
date food prior_w weight
<chr> <dbl> <dbl> <dbl>
1 2022-01-01 0 NA 5
2 2022-01-02 0 NA 4
3 2022-01-03 0 NA 3
4 2022-01-04 1 NA 2
5 2022-01-04 0 2 6
6 2022-01-05 0 NA 4