Tukukkk

Suppose I want to create a mean variable in a given dataframe based on two vectors, one specifying the names of the variables to use, and one specifying weights by which these variables should go into the mean variable:

vars <- c("a", "b", "c","d"))

weights <- c(0.5, 0.7, 0.8, 0.2))

df <- data.frame(cbind(c(1,4,5,7), c(2,3,7,5), c(1,1,2,3), 

                       c(4,5,3,3), c(3,2,2,1), c(5,5,7,1)))

colnames(df) <- c("a","b","c","d","e","f")

How could I use dplyr::mutate() to create a mean variable that uses vars and weights to calculate a rowwise score? mutate() should specifically use the variables supplied by vars
The result should basically do the following:

df <- df %>% 

  rowwise() %>% 

  mutate(comp = mean(c(vars[1]*weights[1], vars[2]*weights[2], ...)))

Written out:

df2 <- df %>% 

  rowwise() %>% 

  mutate(comp = mean(c(0.5*a, 0.7*b, 0.8*c, 0.2*d)))

I can't figure out how to do this because, although vars contains the exact variable names that I want to use for mutate in my df, inside vars they are strings. How could I make mutate() understand that the strings vars contains relate to columns in my df? If you know another procedure not using mutate() that's fine also. Thanks!

You may use

df %>% mutate(wmean = apply(.[vars], 1, weighted.mean, weights))

#   a b c d e f     mean

# 1 1 2 1 4 3 5 1.590909

# 2 4 3 1 5 2 5 2.681818

# 3 5 7 2 3 2 7 4.363636

# 4 7 5 3 3 1 1 4.545455

but there is not much to gain with tidyverse as base R approaches can be almost the same and end up being shorter:

df$wmean <- apply(df[vars], 1, weighted.mean, weights)

or one of the following:

df$wmean <- colSums(t(df[vars]) * weights) / sum(weights)

df$wmean <- as.matrix(df[vars]) %*% weights / sum(weights)

df$wmean <- rowSums(sweep(df[vars], 2, weights, `*`)) / sum(weights)

Row-wise operations can be a bit tricky in the tidyverse. This is a case where some base R knowledge can be really handy. For example, you can do it in one line with apply (note that I corrected a typo in the line that creates weights and drop columns e and f, which do not have weights):

vars <- c("a", "b", "c","d")

weights <- c(0.5, 0.7, 0.8, 0.2)

df <- data.frame(cbind(c(1,4,5,7), c(2,3,7,5), c(1,1,2,3), 

                       c(4,5,3,3), c(3,2,2,1), c(5,5,7,1)))

colnames(df) <- c("a","b","c","d","e","f")



df$weighted.mean <- apply(df %>% select(-e, -f), 1, weighted.mean, weights)



  a b c d e f weighted.mean

1 1 2 1 4 3 5      1.590909

2 4 3 1 5 2 5      2.681818

3 5 7 2 3 2 7      4.363636

4 7 5 3 3 1 1      4.545455

If you really wanted to do it in the tidyverse, this should get you started:

library(tidyverse)



df.weights <- data.frame(vars, weights)



df.new <- df %>% 

  mutate(row.num = 1:n()) %>% 

  gather(variable, value, -row.num) %>% 

  left_join(df.weights, by = c(variable = 'vars')) %>% 

  filter(variable %in% vars) %>% 

  group_by(row.num) %>% 

  mutate(weighted.mean = weighted.mean(value, weights))

There should be a tidyverse solution using pmap, but it eludes me. Here's another approach using tidyverse packages purrr and tibble

library(tidyverse)



vars <- c("a", "b", "c", "d")

weights <- c(0.5, 0.7, 0.8, 0.2)

df <- data.frame(cbind(c(1,4,5,7), c(2,3,7,5), c(1,1,2,3), 

                       c(4,5,3,3), c(3,2,2,1), c(5,5,7,1)))

colnames(df) <- c("a","b","c","d","e","f")



df %>% 

 transpose() %>% 

  simplify_all() %>% 

  map_dbl(~weighted.mean(.x[vars], weights)) %>% 

  add_column(df, wmean = .)

#>   a b c d e f    wmean

#> 1 1 2 1 4 3 5 1.590909

#> 2 4 3 1 5 2 5 2.681818

#> 3 5 7 2 3 2 7 4.363636

#> 4 7 5 3 3 1 1 4.545455

^{Created on 2018-11-24 by the reprex package (v0.2.1)}