Removing matching observations where their adjacent column does not equal to 100

Question

I have ~4000 observations in my data frame, test_11, and have pasted part of the data frame below:

data frame snippit

The k_hidp column represents matching households, the k_fihhmnnet1_dv column is their reported household income and the percentage_income_rounded reports each participant's income contribution to the total household income

I want to filter my data to remove all k_hidp observations where their collective income in the percentage_income_rounded does not equal 100.

So for example, the first household 68632420 reported a contribution of 83% (65+13) instead of the 100% as the other households report.

Is there any way to remove these household observations so I am only left with households with a collective income of 100%?

Thank you!

Answer 1

Try this:

## Creating the dataframe
df=data.frame(k_hidp = c(68632420,68632420,68632420,68632420,68632420,68632420,68632422,68632422,68632422,68632422,68632428,68632428),
              percentage_income_rounded = c(65,18,86,14,49,51,25,25,25,25,50,50))

## Loading the libraries
library(dplyr)

## Aggregating and determining which household collective income is 100%
df1 = df %>%
  group_by(k_hidp) %>%
  mutate(TotalPercentage = sum(percentage_income_rounded)) %>%
  filter(TotalPercentage == 100)

Output

> df1
# A tibble: 6 x 3
# Groups:   k_hidp [2]
    k_hidp percentage_income_rounded TotalPercentage
     <dbl>                     <dbl>           <dbl>
1 68632422                        25             100
2 68632422                        25             100
3 68632422                        25             100
4 68632422                        25             100
5 68632428                        50             100
6 68632428                        50             100