I’m confused about the syntax of dplyr when attempting to compute a weighted mean.
I am following David’s suggestion here. The syntax is very transparent and therefore attractive, but it does not appear to work as I expected: Below the weigthed mean is computed for the whole data, rather than grouping by the B variable.
head(df)
# A tibble: 4 × 3
A B P
<dbl> <dbl> <dbl>
1 1 10 0.4
2 2 10 0.6
3 1 20 0.2
4 2 20 0.8
library(dplyr)
df %>% group_by(B) %>%
summarise(wm = weighted.mean(A, P))
# wm
# 1 1.7
I can achieve the desired result in several other ways. How can I use dplyr to replicate the calculations below?
# with a slit/apply routine:
sapply(split(df, df$B), function(x) weighted.mean(x$A, x$P))
# 10 20
# 1.6 1.8
# with data.table
library(data.table)
setDT(df)[, .(wm = weighted.mean(A, P)), B]
# B wm
# 1: 10 1.6
# 2: 20 1.8
# with plyr:
library(plyr)
ddply(df, .(B), summarise, wm = weighted.mean(A, P))
# B wm
# 1 10 1.6
# 2 20 1.8
# with aggregate | the formula approach is mysterious
df$wm <- 1:nrow(df)
aggregate(wm ~ B, data=df, function(x) weighted.mean(df$A[x], df$P[x]))
# B wm
# 1 10 1.6
# 2 20 1.8
df$wm <- NULL # no longer needed
Here is the toy data (a tibble, rather than a standard dataframe):
library(tidyverse)
df = structure(list(A = c(1, 2, 1, 2), B = c(10, 10, 20, 20), P = c(0.4, 0.6, 0.2, 0.8)),
row.names = c(NA, -4L), class = c("tbl_df", "tbl", "data.frame"))
Here is one and another post about calculating a mean by group with dplyr, but I did not see how they could shed light on my problem.
>Solution :
This is very common thing that happens when package plyr is loaded because plyr::summarise can override dplyr::summarise function. Just use dplyr::summarise. It’s the first thing to check if summarise outputs unexpected results.
Another way is to detach the plyr package before using dplyr:
detach("package:plyr")
library("dplyr")
df %>% group_by(B) %>%
summarise(wm = weighted.mean(A, P))
# B wm
# <dbl> <dbl>
# 1 10 1.6
# 2 20 1.8