以下是一些虛構的資料：
tibble(fruit = rep(c("apple", "pear", "orange"), each = 3),
size = rep(c("big", "medium", "small"), times = 3),
# summer stock
shopA_summer_wk1 = abs(round(rnorm(9, 10, 5), 0)),
shopA_summer_wk2 = abs(round(rnorm(9, 10, 5), 0)),
shopB_summer_wk1 = abs(round(rnorm(9, 10, 5), 0)),
shopB_summer_wk2 = abs(round(rnorm(9, 10, 5), 0)),
shopC_summer_wk1 = abs(round(rnorm(9, 10, 5), 0)),
shopC_summer_wk2 = abs(round(rnorm(9, 10, 5), 0)),
# winter stock
shopA_winter_wk1 = abs(round(rnorm(9, 8, 4), 0)),
shopA_winter_wk2 = abs(round(rnorm(9, 8, 4), 0)),
shopA_winter_wk3 = abs(round(rnorm(9, 8, 4), 0)),
shopB_winter_wk1 = abs(round(rnorm(9, 8, 4), 0)),
shopB_winter_wk2 = abs(round(rnorm(9, 8, 4), 0)),
shopB_winter_wk3 = abs(round(rnorm(9, 8, 4), 0)),
shopC_winter_wk1 = abs(round(rnorm(9, 8, 4), 0)),
shopC_winter_wk2 = abs(round(rnorm(9, 8, 4), 0)),
shopC_winter_wk3 = abs(round(rnorm(9, 8, 4), 0)))
在夏季 2 周和冬季 3 周內收集了 3 家商店（A、B、C）的一些資料。收集的資料是該商店在特定一周內每種尺寸（大、中、小）的水果（蘋果、梨、橙子）數量。
以下是資料集的前 6 行：
# fruit size shopA_summer_wk1 shopA_summer_wk2 shopB_summer_wk1 shopB_summer_wk2 shopC_summer_wk1 shopC_summer_wk2 shopA_winter_wk1 shopA_winter_wk2 shopA_winter_wk3
# <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 apple big 9 12 12 16 15 5 14 4 0
# 2 apple medium 21 16 16 1 12 11 8 8 9
# 3 apple small 10 6 18 18 22 12 4 2 0
# 4 pear big 13 7 4 12 13 6 10 6 2
# 5 pear medium 13 12 8 0 8 5 11 7 3
# 6 pear small 16 18 4 3 13 8 7 5 0
我想使用pivot_longer()R 中的函式來重構這個資料集。鑒于有很多組類別，我很難為此撰寫代碼。
我希望它看起來像下面這樣：
我將不勝感激任何輸入:)
uj5u.com熱心網友回復：
使用names_pattern引數，我們可以這樣做：
pivot_longer(df, c(-fruit, -size), names_pattern = '(^.*)_wk(.*$)',
names_to = c('Shop_season', 'week'))
#> # A tibble: 135 x 5
#> fruit size Shop_season week value
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 apple big shopA_summer 1 11
#> 2 apple big shopA_summer 2 8
#> 3 apple big shopB_summer 1 4
#> 4 apple big shopB_summer 2 24
#> 5 apple big shopC_summer 1 9
#> 6 apple big shopC_summer 2 10
#> 7 apple big shopA_winter 1 9
#> 8 apple big shopA_winter 2 12
#> 9 apple big shopA_winter 3 5
#> 10 apple big shopB_winter 1 5
#> # ... with 125 more rows
您可能還想separate購物和季節，因為這些實際上是兩個不同的變數：
pivot_longer(df, c(-fruit, -size), names_pattern = '(^.*)_wk(.*$)',
names_to = c('Shop_season', 'week')) %>%
separate(Shop_season, into = c('shop', 'season'))
#> # A tibble: 135 x 6
#> fruit size shop season week value
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 apple big shopA summer 1 11
#> 2 apple big shopA summer 2 8
#> 3 apple big shopB summer 1 4
#> 4 apple big shopB summer 2 24
#> 5 apple big shopC summer 1 9
#> 6 apple big shopC summer 2 10
#> 7 apple big shopA winter 1 9
#> 8 apple big shopA winter 2 12
#> 9 apple big shopA winter 3 5
#> 10 apple big shopB winter 1 5
#> #... with 125 more rows
uj5u.com熱心網友回復：
如果資料是dt，那么
pivot_longer(
data = dt,
cols = -c(fruit:size),
names_to = c("shop_season", "week"),
names_pattern = "(.*)_(.*)"
)
輸出：
# A tibble: 135 x 5
fruit size shop_season week value
<chr> <chr> <chr> <chr> <dbl>
1 apple big shopA_summer wk1 13
2 apple big shopA_summer wk2 12
3 apple big shopB_summer wk1 9
4 apple big shopB_summer wk2 9
5 apple big shopC_summer wk1 7
6 apple big shopC_summer wk2 17
7 apple big shopA_winter wk1 10
8 apple big shopA_winter wk2 17
9 apple big shopA_winter wk3 12
10 apple big shopB_winter wk1 8
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