Impute only certain NA's for a variable in a data frame [closed] - r

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I'm new to R and exploring different beautiful options in it. I'm working on a data frame where I have a variable with 900 missing values, i.e NAs.
I want to impute 3 different values for NAs;
1st 300 NA's with Value 1.
2nd 300 NA's with Value 2.
3rd 300 NA's with Value 3.
There are a total of 23272 rows in the data.
dim(data)
[1] 23272 2
colSums(is.na(data))
month year
884 884
summary(data$month)
1 2 3 4 5 6 7 8 9 10 11 12 NA's
1977 1658 1837 1584 1703 1920 1789 2046 1955 2026 1845 2048 884
If we check the month 8,10 and 12. There is no much differences, Hence thought of assigning these 3 months to NA by splitting at the ratio (300:300:284). Usually we go my MODE, but I want to try this approach.

Answer 1

I assume you mean you a have a long list, some of the values of which are NAs:
set.seed(42)
df <- data.frame(val = sample(c(1:3, NA_real_), size = 1000, replace = TRUE))
We can keep a running tally of NA's and assign those to the imputed value using integer division with %/%.
library(tidyverse)
df2 <- df %>%
mutate(NA_num = if_else(is.na(val),
cumsum(is.na(val)),
NA_integer_),
imputed = NA_num %/% 100 + 1)
Output:
df2 %>%
slice(397:410) # based on manual examination using this seed
val NA_num imputed
1 NA 98 1
2 NA 99 1
3 3 NA NA
4 1 NA NA
5 1 NA NA
6 3 NA NA
7 3 NA NA
8 2 NA NA
9 NA 100 2
10 1 NA NA
11 NA 101 2
12 2 NA NA
13 1 NA NA
14 2 NA NA

Answer 2

Without an example, I think this will work.
Basically, filter the NAs to a new table, do the calc and merge it back. Assume the new_dt is the OG data where you filter to only contain the NAs
library('tidyverse');
new_dt = data.frame(x1 =rep(1:900), x2= NA) %>% filter(is.na(x2)) %>%
mutate(23 = case_when(row_number()%/%300==0 ~1,
row_number()%/%300==1 ~2,
row_number()%/%300==2 ~3))
dt <- rbind(dt,new_dt)