我想計算前一行之后的下一行的值差異。但是，我收到此錯誤：
錯誤
mutate()：！計算時出現問題..1 = across(where(is.numeric), diff)。? 組 1 發生錯誤：vs = 0 由錯誤引起across()：！計算列時出現問題mpg。由錯誤引起dplyr_internal_error()：運行rlang::last_error()以查看錯誤發生的位置。
這是我嘗試過的：
mtcars %>% group_by(vs) %>% mutate(across(where(is.numeric), diff))
這似乎可以解決問題：
mtcars %>% group_by(vs) %>% aggregate(. ~ vs, data=., diff) %>% as.data.frame() %>% unnest()
#//--
# A tibble: 30 × 11
vs mpg cyl disp hp drat wt qsec am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 0 0 0 0 0 0.255 0.560 0 0 0
2 0 -2.3 2 200 65 -0.75 0.565 0 -1 -1 -2
3 0 -4.4 0 0 70 0.0600 0.130 -1.18 0 0 2
4 0 2.10 0 -84.2 -65 -0.140 0.500 1.56 0 0 -1
5 0 0.900 0 0 0 0 -0.340 0.200 0 0 0
6 0 -2.10 0 0 0 0 0.0500 0.400 0 0 0
7 0 -4.8 0 196. 25 -0.140 1.47 -0.0200 0 0 1
8 0 0 0 -12 10 0.0700 0.174 -0.160 0 0 0
9 0 4.3 0 -20 15 0.23 -0.0790 -0.400 0 0 0
10 0 0.800 0 -122 -80 -0.47 -1.82 -0.550 0 0 -2
# … with 20 more rows
uj5u.com熱心網友回復：
您可以使用明確定義計算lag。或者您可以在基礎 R 中執行此操作：
library(tidyverse)
#tidyverse
mtcars %>%
group_by(vs) %>%
mutate(across(where(is.numeric), ~.-lag(., default = first(.)))) |>
arrange(vs)
#> # A tibble: 32 x 11
#> # Groups: vs [2]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0 0 0 0 0 0 0 0 0
#> 2 0 0 0 0 0 0.255 0.560 0 0 0 0
#> 3 -2.3 2 200 65 -0.75 0.565 0 0 -1 -1 -2
#> 4 -4.4 0 0 70 0.0600 0.130 -1.18 0 0 0 2
#> 5 2.10 0 -84.2 -65 -0.140 0.500 1.56 0 0 0 -1
#> 6 0.900 0 0 0 0 -0.340 0.200 0 0 0 0
#> 7 -2.10 0 0 0 0 0.0500 0.400 0 0 0 0
#> 8 -4.8 0 196. 25 -0.140 1.47 -0.0200 0 0 0 1
#> 9 0 0 -12 10 0.0700 0.174 -0.160 0 0 0 0
#> 10 4.3 0 -20 15 0.23 -0.0790 -0.400 0 0 0 0
#> # ... with 22 more rows
#base R
by(mtcars, mtcars$vs, \(x) apply(x, 2, diff)) |>
do.call(what = rbind.data.frame)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 0.Mazda RX4 Wag 0.0 0 0.0 0 0.00 0.255 0.56 0 0 0 0
#> 0.Hornet Sportabout -2.3 2 200.0 65 -0.75 0.565 0.00 0 -1 -1 -2
#> 0.Duster 360 -4.4 0 0.0 70 0.06 0.130 -1.18 0 0 0 2
#> 0.Merc 450SE 2.1 0 -84.2 -65 -0.14 0.500 1.56 0 0 0 -1
#> 0.Merc 450SL 0.9 0 0.0 0 0.00 -0.340 0.20 0 0 0 0
#> 0.Merc 450SLC -2.1 0 0.0 0 0.00 0.050 0.40 0 0 0 0
#> 0.Cadillac Fleetwood -4.8 0 196.2 25 -0.14 1.470 -0.02 0 0 0 1
#> 0.Lincoln Continental 0.0 0 -12.0 10 0.07 0.174 -0.16 0 0 0 0
#> 0.Chrysler Imperial 4.3 0 -20.0 15 0.23 -0.079 -0.40 0 0 0 0
#> 0.Dodge Challenger 0.8 0 -122.0 -80 -0.47 -1.825 -0.55 0 0 0 -2
#> 0.AMC Javelin -0.3 0 -14.0 0 0.39 -0.085 0.43 0 0 0 0
#> 0.Camaro Z28 -1.9 0 46.0 95 0.58 0.405 -1.89 0 0 0 2
#> 0.Pontiac Firebird 5.9 0 50.0 -70 -0.65 0.005 1.64 0 0 0 -2
#> 0.Porsche 914-2 6.8 -4 -279.7 -84 1.35 -1.705 -0.35 0 1 2 0
#> 0.Ford Pantera L -10.2 4 230.7 173 -0.21 1.030 -2.20 0 0 0 2
#> 0.Ferrari Dino 3.9 -2 -206.0 -89 -0.60 -0.400 1.00 0 0 0 2
#> 0.Maserati Bora -4.7 2 156.0 160 -0.08 0.800 -0.90 0 0 0 2
#> 1.Hornet 4 Drive -1.4 2 150.0 17 -0.77 0.895 0.83 0 -1 -1 0
#> 1.Valiant -3.3 0 -33.0 -5 -0.32 0.245 0.78 0 0 0 0
#> 1.Merc 240D 6.3 -2 -78.3 -43 0.93 -0.270 -0.22 0 0 1 1
#> 1.Merc 230 -1.6 0 -5.9 33 0.23 -0.040 2.90 0 0 0 0
#> 1.Merc 280 -3.6 2 26.8 28 0.00 0.290 -4.60 0 0 0 2
#> 1.Merc 280C -1.4 0 0.0 0 0.00 0.000 0.60 0 0 0 0
#> 1.Fiat 128 14.6 -2 -88.9 -57 0.16 -1.240 0.57 0 1 0 -3
#> 1.Honda Civic -2.0 0 -3.0 -14 0.85 -0.585 -0.95 0 0 0 1
#> 1.Toyota Corolla 3.5 0 -4.6 13 -0.71 0.220 1.38 0 0 0 -1
#> 1.Toyota Corona -12.4 0 49.0 32 -0.52 0.630 0.11 0 -1 -1 0
#> 1.Fiat X1-9 5.8 0 -41.1 -31 0.38 -0.530 -1.11 0 1 1 0
#> 1.Lotus Europa 3.1 0 16.1 47 -0.31 -0.422 -2.00 0 0 1 1
#> 1.Volvo 142E -9.0 0 25.9 -4 0.34 1.267 1.70 0 0 -1 0
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