根据向量提供的变量名称和权重创建均值变量 [英] Creating a mean variable, from variable names and weights supplied by vectors

问题描述

假设我想基于两个向量在给定的数据框中创建一个均值变量，一个指定要使用的变量的名称，另一个指定这些变量应进入均值变量的权重:

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")

我如何使用 dplyr::mutate() 创建一个均值变量，该变量使用 vars 和 weights 来计算行得分?mutate() 应该专门使用 vars 提供的变量结果应该基本上做到以下几点:

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], ...)))

写出来:

df2 <- df %>% 
  rowwise() %>% 
  mutate(comp = mean(c(0.5*a, 0.7*b, 0.8*c, 0.2*d)))

我不知道如何做到这一点，因为虽然 vars 包含我想在 df 中用于 mutate 的确切变量名称，但在 vars 它们是字符串.我怎样才能让 mutate() 理解 vars 包含的字符串与我的 df 中的列有关?如果您知道另一个不使用 mutate() 的过程，那也没关系.谢谢！

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!

推荐答案

您可以使用

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

但是使用 tidyverse 并没有什么好处，因为基本的 R 方法几乎相同，但最终会更短:

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)

或以下之一:

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)

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