问题
I want to gather two seperate groups of columns into two key-value pairs. Here's some example data:
library(dplyr)
library(tidyr)
ID = c(1:5)
measure1 = c(1:5)
measure2 = c(6:10)
letter1 = c("a", "b", "c", "d", "e")
letter2 = c("f", "g", "h", "i", "j")
df = data.frame(ID, measure1, measure2, letter1, letter2)
df = tbl_df(df)
df$letter1 <- as.character(df$letter1)
df$letter2 <- as.character(df$letter2)
I want the values of the two measure columns (measure1 and measure2) to be in one column with a key-column next to it (the key-value pair). I also want the same for letter1 and letter2. I figured that I could use select() to create two different datasets, use gather seperately on both datasets and then join (this worked):
df_measure = df %>%
select(ID, measure1, measure2) %>%
gather(measure_time, measure, -ID) %>%
mutate(id.extra = c(1:10))
df_letter = df %>%
select(ID, letter1, letter2) %>%
gather(letter_time, letter, -ID) %>%
mutate(id.extra = c(1:10))
df_long = df_measure %>%
left_join(df_letter, by = "id.extra")
So this works perfectly (in this case), but i guess this could be done more elegantly (without stuff like splitting or creating 'id.extra').So please shed some light on it!
回答1:
You can use something like the following. I'm not sure from your current approach if this is exactly your desired output or not since it seems to contain a lot of redundant information.
df %>%
gather(val, var, -ID) %>%
extract(val, c("value", "time"), regex = "([a-z]+)([0-9]+)") %>%
spread(value, var)
# # A tibble: 10 × 4
# ID time letter measure
# * <int> <chr> <chr> <chr>
# 1 1 1 a 1
# 2 1 2 f 6
# 3 2 1 b 2
# 4 2 2 g 7
# 5 3 1 c 3
# 6 3 2 h 8
# 7 4 1 d 4
# 8 4 2 i 9
# 9 5 1 e 5
# 10 5 2 j 10
This is much more easily done with melt + patterns from "data.table":
library(data.table)
melt(as.data.table(df), measure.vars = patterns("measure", "letter"))
Or you can be old-school and just use reshape from base R. Note, however, that base R's reshape does not like "tibbles", so you have to convert it with as.data.frame).
reshape(as.data.frame(df), direction = "long", idvar = "ID",
varying = 2:ncol(df), sep = "")
回答2:
We can use melt from data.table which can take multiple measure patterns
library(data.table)
melt(setDT(df), measure = patterns("^measure", "^letter"),
value.name = c("measure", "letter"))
# ID variable measure letter
# 1: 1 1 1 a
# 2: 2 1 2 b
# 3: 3 1 3 c
# 4: 4 1 4 d
# 5: 5 1 5 e
# 6: 1 2 6 f
# 7: 2 2 7 g
# 8: 3 2 8 h
# 9: 4 2 9 i
#10: 5 2 10 j
来源：https://stackoverflow.com/questions/43293951/using-gather-to-gather-two-or-more-groups-of-columns-into-two-or-more-key