我想将两个独立的列组合成两个键值对。这是一些示例数据:
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)
我希望两个度量列(measure1和measure2)的值位于一列中,其旁边有一个键列(键值对)。我也希望letter1和letter2也一样。我想我可以使用select()来创建两个不同的数据集,在两个数据集上单独使用聚集然后加入(这有效):
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")
所以这完全有效(在这种情况下),但我想这可以更优雅地完成(没有分裂或创建'id.extra'之类的东西)。所以请详细说明它!
答案 0 :(得分:3)
您可以使用以下内容。我不确定你当前的方法是否正好是你想要的输出,因为它似乎包含很多冗余信息。
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
使用来自&#34; data.table&#34;的melt
+ patterns
可以轻松完成此操作:
library(data.table)
melt(as.data.table(df), measure.vars = patterns("measure", "letter"))
或者你可以老去,只使用基地R的reshape
。但是,请注意,基地R reshape
不喜欢&#34; tibbles&#34;,所以你必须用as.data.frame
)转换它。
reshape(as.data.frame(df), direction = "long", idvar = "ID",
varying = 2:ncol(df), sep = "")
答案 1 :(得分:1)
我们可以使用melt
中的data.table
,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
git clone https://github.com/cudamat/cudamat.git