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.
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 stats
and 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)
})
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
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