Question

给定tbl_df个对象df包含多个变量（即Var.50，Var.100，Var.150和Var.200），测量两次（即P1和P2），我想mutate来自重复测量的一组新相同变量（例如，平均P1和P2，为每个相应变量创建P3）。

之前已经询问了{p> Similar questions，但似乎没有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"))

Answer 1

以下是gather方法

的选项

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

Answer 2

使用dplyr：

library(dplyr)
df1 <- df %>%
          rowwise() %>%
          mutate(P3.Var.50 = mean(c(P1.Var.50,P2.Var.50)),
                 P3.Var.100 = mean(c(P1.Var.100,P2.Var.100)),
                 P3.Var.150 = mean(c(P1.Var.150,P2.Var.150)),
                 P3.Var.200 = mean(c(P1.Var.200,P2.Var.200)))

<强> ----------- --------------编程

newcols <- sapply(seq(50,200,50), function(i) paste0("P3.Var.",i))

[1] "P3.Var.50"  "P3.Var.100" "P3.Var.150" "P3.Var.200"

df1 <- df %>%
          rowwise() %>%
          mutate_(.dots = setNames(paste0("mean(c(",gsub("P3","P1",newcols),",",gsub("P3","P2",newcols),"))"), newcols))

Answer 3

这不像Akrun的解决方案那么通用，但是如果你没有缺少列，并且你知道你的类别P和Vars它应该更快（和更短）。

它只使用基础R +管道：

np = 2
vars <- seq(50,200,by = 50)
df %>%
  unlist %>%
  matrix(ncol=np) %>%
  cbind(rowMeans(.)) %>%
  matrix(nrow=nrow(df)) %>%
  `colnames<-`(c(names(df),paste0("P",np+1,".Var.",vars))) %>%
  as.data.frame(stringsAsFactors=FALSE)

#   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
# 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

如何使用dplyr改变多个变量？

3 个答案: