Weighted mean calculation in R with missing values - r

Question

Does anyone know if it is possible to calculate a weighted mean in R when values are missing, and when values are missing, the weights for the existing values are scaled upward proportionately?
To convey this clearly, I created a hypothetical scenario. This describes the root of the question, where the scalar needs to be adjusted for each row, depending on which values are missing.
Image: Weighted Mean Calculation
File: Weighted Mean Calculation in Excel

Answer 1

Using weighted.mean from the base stats package with the argument na.rm = TRUE should get you the result you need. Here is a tidyverse way this could be done:
library(tidyverse)
scores <- tribble(
~student, ~test1, ~test2, ~test3,
"Mark", 90, 91, 92,
"Mike", NA, 79, 98,
"Nick", 81, NA, 83)
weights <- tribble(
~test, ~weight,
"test1", 0.2,
"test2", 0.4,
"test3", 0.4)
scores %>%
gather(test, score, -student) %>%
left_join(weights, by = "test") %>%
group_by(student) %>%
summarise(result = weighted.mean(score, weight, na.rm = TRUE))
#> # A tibble: 3 x 2
#> student result
#> <chr> <dbl>
#> 1 Mark 91.20000
#> 2 Mike 88.50000
#> 3 Nick 82.33333

Answer 2

The best way to post an example dataset is to use dput(head(dat, 20)), where dat is the name of a dataset. Graphic images are a really bad choice for that.
DATA.
dat <-
structure(list(Test1 = c(90, NA, 81), Test2 = c(91, 79, NA),
Test3 = c(92, 98, 83)), .Names = c("Test1", "Test2", "Test3"
), row.names = c("Mark", "Mike", "Nick"), class = "data.frame")
w <-
structure(list(Test1 = c(18, NA, 27), Test2 = c(36.4, 39.5, NA
), Test3 = c(36.8, 49, 55.3)), .Names = c("Test1", "Test2", "Test3"
), row.names = c("Mark", "Mike", "Nick"), class = "data.frame")
CODE.
You can use function weighted.mean in base package statsand sapply for this. Note that if your datasets of notes and weights are R objects of class matrix you will not need unlist.
sapply(seq_len(nrow(dat)), function(i){
weighted.mean(unlist(dat[i,]), unlist(w[i, ]), na.rm = TRUE)
})