我需要同时根据两列的值向我的数据集添加值
这是我的数据集样本
# A tibble: 15 × 5
# Groups: nquest, nord [15]
nquest nord tpens ireg sector
<int> <int> <dbl> <int> <dbl>
1 173 1 1800 18 1
2 2886 1 1211 13 4
3 2886 2 2100 13 3
4 5416 1 700 8 4
5 7886 1 2000 9 2
6 20297 1 1200 5 2
7 20711 2 2000 4 3
8 22169 1 880 15 4
9 22276 1 1200 8 1
10 22286 1 850 8 4
11 22286 2 650 8 2
12 22657 1 1400 16 4
13 22657 2 1500 16 1
14 23490 1 1400 5 1
15 24147 1 1730 4 4
我需要根据他具体拥有的变量
iregsector sector == 1 sector == 2 sector == 3 sector ==4
ireg == 1 1944.5 27973.4 5328.4 79542.3
ireg == 2 49.3 561.7 237.1 3132.8
ireg == 3 3785.3 73596.3 13563.1 240420.8
ireg == 4 1661.1 7157.5 2054.2 27174.2
ireg == 5 3016.1 37700.8 6429.1 89338.0
ireg == 6 504.5 8160.5 1386.9 22491.7
ireg == 7 442.6 6954.7 2160.0 32398.8
ireg == 8 3419.0 36172.4 5416.6 89411.6
ireg == 9 2266.7 20603.2 4269.7 74176.9
ireg == 10 551.7 4019.0 1060.2 13843.0
ireg == 11 642.8 9484.3 1569.9 23637.0
ireg == 12 1962.6 17565.5 5863.1 145318.8
ireg == 13 843.9 5562.1 1520.3 19911.1
ireg == 14 308.7 831.5 295.6 4021.4
ireg == 15 2524.2 12507.0 4441.0 75844.6
ireg == 16 2763.3 8485.3 3581.4 49263.3
ireg == 17 607.4 2481.2 555.3 6689.7
ireg == 18 1474.0 2272.0 1217.7 23005.5
ireg == 19 3303.8 6552.2 3444.8 63552.4
ireg == 20 1232.1 3212.3 1449.7 23478.9
我已经尝试为每个单元格使用
case_whendataset <- dataset %>%
mutate(VA = case_when(
(ireg == 1 & nace == 1) ~ 1944.5,
(ireg == 1 & nace == 2 ) ~ 27973.4,
(ireg == 1 & nace == 3 ) ~ 5328.4,
(ireg == 1 & nace == 4 ) ~ 79542.3,
(ireg == ... & nace == ... ) ~ ....
有更快的方法吗？？
您可以通过
mutate()library(dplyr)
library(readr)
dataset %>%
rowwise() %>%
mutate(VA = m[ireg, sector]) %>%
ungroup()
#> # A tibble: 15 × 6
#> nquest nord tpens ireg sector VA
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 173 1 1800 18 1 1474
#> 2 2886 1 1211 13 4 19911.
#> 3 2886 2 2100 13 3 1520.
#> 4 5416 1 700 8 4 89412.
#> 5 7886 1 2000 9 2 20603.
#> 6 20297 1 1200 5 2 37701.
#> 7 20711 2 2000 4 3 2054.
#> 8 22169 1 880 15 4 75845.
#> 9 22276 1 1200 8 1 3419
#> 10 22286 1 850 8 4 89412.
#> 11 22286 2 650 8 2 36172.
#> 12 22657 1 1400 16 4 49263.
#> 13 22657 2 1500 16 1 2763.
#> 14 23490 1 1400 5 1 3016.
#> 15 24147 1 1730 4 4 27174.
示例数据：
dataset <- read_table("
nquest nord tpens ireg sector
173 1 1800 18 1
2886 1 1211 13 4
2886 2 2100 13 3
5416 1 700 8 4
7886 1 2000 9 2
20297 1 1200 5 2
20711 2 2000 4 3
22169 1 880 15 4
22276 1 1200 8 1
22286 1 850 8 4
22286 2 650 8 2
22657 1 1400 16 4
22657 2 1500 16 1
23490 1 1400 5 1
24147 1 1730 4 4")
m <- read_table(col_names = F, "
1944.5 27973.4 5328.4 79542.3
49.3 561.7 237.1 3132.8
3785.3 73596.3 13563.1 240420.8
1661.1 7157.5 2054.2 27174.2
3016.1 37700.8 6429.1 89338.0
504.5 8160.5 1386.9 22491.7
442.6 6954.7 2160.0 32398.8
3419.0 36172.4 5416.6 89411.6
2266.7 20603.2 4269.7 74176.9
551.7 4019.0 1060.2 13843.0
642.8 9484.3 1569.9 23637.0
1962.6 17565.5 5863.1 145318.8
843.9 5562.1 1520.3 19911.1
308.7 831.5 295.6 4021.4
2524.2 12507.0 4441.0 75844.6
2763.3 8485.3 3581.4 49263.3
607.4 2481.2 555.3 6689.7
1474.0 2272.0 1217.7 23005.5
3303.8 6552.2 3444.8 63552.4
1232.1 3212.3 1449.7 23478.9") %>% as.matrix()
创建于 2023-03-12 与 reprex v2.0.2
在
base Rdat$VA <- m1[as.matrix(dat[c("ireg", "sector")])]
-输出
> dat
# A tibble: 15 × 6
nquest nord tpens ireg sector VA
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 173 1 1800 18 1 1474
2 2886 1 1211 13 4 19911.
3 2886 2 2100 13 3 1520.
4 5416 1 700 8 4 89412.
5 7886 1 2000 9 2 20603.
6 20297 1 1200 5 2 37701.
7 20711 2 2000 4 3 2054.
8 22169 1 880 15 4 75845.
9 22276 1 1200 8 1 3419
10 22286 1 850 8 4 89412.
11 22286 2 650 8 2 36172.
12 22657 1 1400 16 4 49263.
13 22657 2 1500 16 1 2763.
14 23490 1 1400 5 1 3016.
15 24147 1 1730 4 4 27174.