如何使用dplyr突变多个变量? [英] How can I mutate multiple variables using dplyr?

问题描述

给出一个包含多个变量(即Var.50，Var.100，Var.150和Var.200)的 tbl_df 对象 df ，并对其进行两次测量(即P1和P2)，我想通过重复的测量对新的一组相同变量进行 mutate (例如，平均值P1和P2，为每个对应的变量创建P3).

Given a tbl_df object df containing multiple variables (i.e. Var.50, Var.100, Var.150 and Var.200), measured twice (i.e. P1 and P2), I want to mutate a new set of the same variables from repeated measurements (for example, average P1 and P2, creating P3 for each corresponding variable).

类似的问题，但似乎没有使用 dplyr 的清晰答案.

Similar questions have been asked before, but there does not seem to have clear answers using dplyr.

示例数据:

df <- structure(list(P1.Var.50 = c(134.242050170898, 52.375, 177.126017252604
), P1.Var.100 = c(395.202219645182, 161.636606852214, 538.408426920573
), P1.Var.150 = c(544.40028889974, 266.439168294271, 718.998555501302
), P1.Var.200 = c(620.076151529948, 333.218780517578, 837.109700520833
), P2.Var.50 = c(106.133892059326, 113.252154032389, 172.384114583333
), P2.Var.100 = c(355.226725260417, 277.197153727214, 502.086781819661
), P2.Var.150 = c(481.993103027344, 329.575764973958, 709.315409342448
), P2.Var.200 = c(541.859161376953, 372.05473836263, 829.299621582031
)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-3L), .Names = c("P1.Var.50", "P1.Var.100", "P1.Var.150", "P1.Var.200", 
"P2.Var.50", "P2.Var.100", "P2.Var.150", "P2.Var.200"))

推荐答案

以下是 gather 方法

library(tidyverse)
rownames_to_column(df, 'rn') %>% 
    gather( key, value, -rn) %>%
    separate(key, into = c('key1', 'key2'), extra = 'merge', remove = FALSE) %>% 
    group_by(rn, key2) %>%
    summarise(key3 = 'P3', value = mean(value)) %>% 
    unite(key, key3, key2)  %>%
    spread(key, value) %>%
    ungroup() %>% 
    select(-rn) %>% 
    select(order(as.numeric(sub(".*\\.(\\d+)$", "\\1", names(.))))) %>%
    bind_cols(df, .)
# A tibble: 3 x 12
#  P1.Var.50 P1.Var.100 P1.Var.150 P1.Var.200 P2.Var.50 P2.Var.100 P2.Var.150 P2.Var.200 P3_Var.50 P3_Var.100 P3_Var.150 P3_Var.200
#      <dbl>      <dbl>      <dbl>      <dbl>     <dbl>      <dbl>      <dbl>      <dbl>     <dbl>      <dbl>      <dbl>      <dbl>
#1  134.2421   395.2022   544.4003   620.0762  106.1339   355.2267   481.9931   541.8592 120.18797   375.2145   513.1967   580.9677
#2   52.3750   161.6366   266.4392   333.2188  113.2522   277.1972   329.5758   372.0547  82.81358   219.4169   298.0075   352.6368
#3  177.1260   538.4084   718.9986   837.1097  172.3841   502.0868   709.3154   829.2996 174.75507   520.2476   714.1570   833.2047

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