当变量是列名时清洗数据

Question

我经常得到这样的数据集，其中列 headers 中有变量，并且还包括相应的误差测量值。

https://drive.google.com/file/d/0BwSh24a5hm4kSERESlFkeHZXOFE/view?usp=sharing

我的问题是如何快速简单地整理这个数据集，使其看起来像这样：

https://drive.google.com/file/d/0BwSh24a5hm4kRDNiSFJoaWFub0E/view?usp=sharing

我对使用 dplyr + tidyr 和不使用 dplyr + tidyr 的答案很感兴趣。

感谢您的帮助！

Answer 1

蛮力我会说只使用 dplyr

library(dplyr)

df <- data.frame(Timepoint=c(0L, 7L, 14L, 21L, 28L), Group1=c(50L, 60L, 66L, 88L, 90L),
             Error_Group1=c(3, 4, 6, 8, 2), Group2=c(30L, 60L, 90L, 120L, 150L),
             Error_Group2=c(10L, 14L, 16L, 13L, 25L), Group3=c(44L, 78L, 64L, 88L, 91L),
             Error_Group3=c(2L, 13L, 16L, 4L, 9L))

df <- lapply(1:3, function(x){
  temp <- df %>% select(Timepoint, ends_with(as.character(x))) %>% mutate(Group=x)
  names(temp) <- c("Timepoint", "Measure", "Error", "Group")
  temp <- temp %>% select(Timepoint, Group, Measure, Error)
})

df <- do.call(rbind, df)
df

还有 tidyr 更优雅

library(dplyr); library(tidyr)
df <- df %>% gather(temp, Timepoint) 
names(df) <- c("Timepoint", "temp", "values")

df <- df %>% mutate(Group = sub("\D+", "", temp), temp=sub("\d", "", temp)) %>% 
  spread(temp, values)

names(df) <- c("Timepoint", "Group", "Error", "Measure")
df

Answer 2

从 v1.9.5 开始，data.table 可以同时 melt 多列。它既快速又节省内存。

require(data.table) ## v1.9.5+
melt(setDT(df), id=1L, measure=patterns("^Group", "^Error"), 
        variable.name="Group", value.name = c("Measure", "Error"))
#    Timepoint Group Measure Error
# 1:         0     1      50     3
# 2:         7     1      60     4
# 3:        14     1      66     6
# 4:        21     1      88     8
# 5:        28     1      90     2
# ...

Answer 3

与 dplyr 和 tidyr:

df %>%
  # 1. Pivot the table
  gather (g, m, -Timepoint) %>%
  # 2. Get the final Group ID in mGroup
  separate (g, c("Measure", "mGroup"), -2) %>% 
  # 3. Spread the actual Error and Measure in two columns
  spread (Measure, m) %>% 
  # 4. Assign the correct names to final columns
  select (Timepoint, Group = mGroup, Measure = Group, Error = Error_Group) %>%
  # 5. Sort as requested
  arrange (Group, Timepoint)

当变量是列名时清洗数据

Cleaning Data When Variables are Column Names

r

dplyr

data.table

tidyr