CODEWITHSUNDEEP
rmissing-dataweighted-average
milaske
milaske

Reputation: 65

Weighted mean calculation in R with missing values

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

Upvotes: 1

Views: 3862

Answers (2)

markdly
markdly

Reputation: 4534

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

Upvotes: 2

Rui Barradas
Rui Barradas

Reputation: 76402

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)
})

Upvotes: 0

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