用于简化以下代码操作的冗余元素
Redundant elements to simplify in the following code manipulation
我尝试使用 dplyr 库合并到不同的电子表格并进行以下操作。
db0 <- t0 %>%
mutate(GROUP = ifelse(GROUP == 'CONTROLLO', 'CONTROL','TRAINING')) %>%
mutate(GROUP = factor(GROUP, levels = c('CONTROL' ,"TRAINING"))) %>%
pivot_longer(c(ADAS_CogT0:SVF_T0), names_to = 'tests0', values_to = 'score0') %>%
arrange(tests0) %>%
pivot_longer(c(ADAS_CogT7:SVF_T7), names_to = 'tests7', values_to = 'score7') %>%
arrange(tests7) %>%
mutate(
Gender = factor(if_else(
Gender == "1", "M", "F"
), levels = c("M", "F"))
) %>% mutate(tests0 = factor(tests0,
levels = c('ADAS_CogT0',
'PVF_T0', 'ROCF_CT0', 'ROCF_IT0',
'ROCF_RT0', 'SVF_T0'))) %>%
mutate(tests7 = factor(tests7,
levels = c('ADAS_CogT7',
'PVF_T7', 'ROCF_CT7', 'ROCF_IT7',
'ROCF_RT7', 'SVF_T7')))
在您个人看来，这段代码中有哪些步骤可以简单化？任何可以删除的冗余。无论如何，我在这里报告已经合并的数据集
dput(head(db, 100))
structure(list(ID = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,
77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,
93, 94, 95, 96, 97, 98, 99, 100), GROUP = c("TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO"), Gender = c(1,
0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1,
0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,
0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,
1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1,
0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0), Age = c(74, 76,
81, 74, 69, 72, 75, 83, 78, 72, 82, 68, 72, 72, 73, 80, 69, 72,
70, 80, 75, 80, 78, 74, 82, 74, 80, 82, 78, 81, 66, 71, 70, 79,
78, 73, 72, 77, 77, 71, 83, 74, 70, 71, 77, 69, 67, 64, 79, 71,
77, 77, 73, 67, 68, 79, 81, 67, 84, 75, 80, 73, 68, 74, 77, 79,
79, 72, 73, 78, 76, 78, 77, 74, 78, 77, 77, 82, 77, 70, 77, 81,
79, 75, 74, 78, 69, 77, 73, 77, 70, 79, 70, 72, 77, 72, 71, 71,
73, 81), Education = c(18, 4, 8, 5, 8, 11, 5, 5, 4, 8, 8, 12,
5, 18, 13, 5, 13, 13, 5, 5, 13, 5, 3, 8, 17, 5, 8, 5, 5, 8, 17,
8, 18, 18, 13, 13, 13, 13, 15, 17, 8, 5, 5, 13, 8, 5, 11, 13,
8, 8, 8, 5, 13, 8, 5, 17, 8, 12, 13, 5, 8, 8, 8, 5, 3, 8, 18,
5, 8, 13, 8, 5, 17, 8, 5, 17, 5, 8, 11, 8, 8, 5, 12, 3, 8, 8,
8, 13, 5, 5, 8, 8, 13, 5, 5, 8, 13, 5, 8, 12), ADAS_CogT0 = c(14.66,
15.33, 17.33, 19, 7.66, 12.6, 18.67, 14.99, 17.99, 17.33, 13.66,
16.99, 10.66, 9.66, 14.99, 15.66, 13.33, 4.33, 14.33, 15.99,
16.33, 10.66, 14.66, 10.66, 19.33, 17.66, 15.99, 20.66, 20.6,
17, 10.33, 6.33, 6.66, 19.99, 13.33, 24.33, 12.33, 10.33, 12.33,
9.66, 10.99, 13.99, 23, 6.32, 11.32, 13.99, 14.66, 8.99, 14.33,
9.99, 7.33, 15.66, 14, 7.99, 23.32, 14.66, 9.99, 5.66, 6.99,
11.66, 10.33, 6.99, 19.32, NA, 10, 17.66, 13.66, 10.32, NA, NA,
8.66, 9, 6.99, 14.99, 9.66, 13.66, 15.32, 12, 14, 13.66, 11.99,
15.66, 16, 15, 16.99, 20, 11, 7.99, 8.33, 8.32, 14.99, 18.66,
10.33, 11.99, 9.32, 17, 14.33, 14.66, 16.6, 9.99), ROCF_CT0 = c(32,
13.5, 34.7, 26.1, 35.4, 24.6, 28.9, 34.1, 12.1, 28.1, 22.7, 31.6,
30.4, 34.4, 37.2, 38.1, 18.6, 26.2, 34.8, 37.1, 26.2, 35.1, 37.1,
36, 11.2, 17.4, 32.7, 28.4, 21.1, 26.7, 31.9, 33.4, 27.4, 32.7,
33.9, 37.2, 28.2, 34.2, 29.7, 30, 16.2, 37.4, 34.8, 36.6, 33,
30.8, 34.6, 35.1, 29.7, 36.8, 28, 34.4, 33.7, 28.9, 31.8, 31.7,
10.2, 27.1, 36.8, 22.4, 35.7, 28, 30.9, 35.4, 26.9, 29.7, 26.7,
28.8, 32, 37.9, 27, 18.6, 33, 38, 25.1, 31, 27.1, 26.7, 37, 30.4,
37, 30.1, 36.9, 24.4, 34, 6.7, 20.4, 30.2, 31.1, 33.4, 17.4,
23.7, 30.6, 28.3, 19.4, 33.4, 31.6, 39.8, 27, 33.9), ROCF_IT0 = c(3.7,
7.6, 14.9, 13.6, 23.4, 4, 8.1, 23.9, 10.1, 7, 10, 9.2, 15.6,
10.8, 6.6, 13.6, 4, 15.3, 7.4, 0, 8.6, 22.9, 11.6, 10.1, 6.8,
12.1, 12.9, 0, 10.6, 5.9, 14.8, 10.9, 10.6, 10.6, 7.5, 7.3, 4.1,
3.1, 7.6, 13.4, 10.9, 19, 3.4, 11.7, 14.6, 9.4, 16.7, 15.7, 14.5,
12.5, 15.1, 22.6, 9.1, 15.5, 11, 6.1, 11.5, 5.6, 17.2, 18.1,
23.5, 15.6, 7.7, 13.5, 14.5, 11.9, 10.3, 11.5, 12.6, 19, 11.9,
15.4, 17.7, 8.4, 13.4, 5.2, 11.1, 16, 10.9, 8, 22.6, 13.6, 12.2,
6.5, 13.4, 11.5, 8, 18.1, 10, 11, 5.9, 14.9, 12, 11.4, 17, 5.9,
15, 10.9, 8.9, 15.7), ROCF_RT0 = c(3.9, 7.8, 16.3, 13.4, 24.1,
4.7, 7.8, 20.9, 9.8, 5.1, 9.8, 8.2, 16.3, 11.3, 5.9, 14.8, 4.7,
14.4, 6.7, 0, 4.9, 23.3, 14.8, 11.7, 6.4, 10.3, 14.3, 0, 10.8,
6.3, 15.4, 10.1, 5.3, 12, 6.9, 5.4, 4.4, 4.4, 7, 10.9, 11.9,
17.3, 3.7, 12.2, 6.7, 14.7, 16.2, 7.8, 10.8, 10.2, 16.7, 22.8,
9.1, 15.7, 10.7, 4, 8.8, 6.8, 14.6, 14.5, 24.8, 13.7, 0, 10.3,
17.3, 11.3, 8, 13.2, 15.2, 18.4, 4.3, 12.8, 18.9, 4.3, 11.8,
4.9, 11.8, 13.3, 10.3, 7.1, 23.7, 15.8, 16.4, 6.8, 20.3, 11.3,
7.6, 19.9, 10.8, 12.5, 5.6, 11.3, 11.7, 13.5, 12.3, 5.1, 14.2,
8.7, 12.3, 16.4), PVF_T0 = c(41.3, 35.4, 16.7, 28.4, 46.9, 33.6,
39.4, 27.4, 17.4, 37.9, 31.7, 37.6, 33.4, 21.5, 49.5, 40.4, 10.6,
40.5, 30.6, 28.4, 29.5, 22.4, 45.4, 29.7, 17.3, 39.4, 31.7, 24.4,
38.4, 22.7, 6.8, 19.9, 56.5, 35.3, 43.5, 32.5, 28.5, 38.5, 22.3,
32.3, 30.7, 23.4, 18.6, 46.6, 36.7, 35.6, 38.6, 16.9, 38.7, 37.6,
39.7, 34.4, 27.5, 16.2, 32.6, 48.3, 26.7, 49.9, 40.5, 34.4, 37.7,
39.7, 23.2, 19.4, 21.4, 33.7, 11.3, 41.6, 23.7, 31.5, 28.7, 34.4,
33.3, 27.7, 23.4, 32.3, 49.4, 35.7, 26.7, 31.9, 37.7, 35.4, 20.5,
28.4, 14.7, 30.7, 35.9, 16.5, 27.4, 49.4, 37.9, 27.7, 41.6, 31.6,
55.4, 42.9, 21.6, 25.6, 26.7, 43.5), SVF_T0 = c(40, 37, 30, 28,
49, 27, 34, 35, 29, 33, 20, 18, 36, 47, 43, 39, 36, 38, 48, 31,
25, 34, 31, 33, 24, 30, 45, 20, 33, 34, 29, 42, 40, 37, 37, 25,
28, 42, 19, 32, 37, 35, 20, 43, 33, 40, 40, 42, 27, 35, 29, 39,
31, 48, 41, 27, 32, 45, 39, 40, 36, 38, 27, 32, 47, 36, 24, 40,
38, 29, 43, 47, 38, 33, 44, 39, 46, 36, 32, 38, 38, 35, 26, 43,
29, 24, 33, 30, 33, 57, 40, 37, 44, 34, 46, 41, 33, 35, 29, 37
), ADAS_CogT7 = c(16, 9.32, 21.33, 17, 8.32, 11, 14.99, 10.99,
17, 18.33, 13.32, 14.34, 8.99, 7, 11.99, 15.33, 6.99, 5.33, 12.32,
13, 21.32, 7.99, 13.33, 11.99, 17.32, 16.32, 16.33, 14.66, 18.99,
17.33, 7.99, 9.33, 10.99, NA, 12.99, 16.33, 21.66, 9, 9.34, 8.66,
8.33, 13.66, 15.66, 6.66, 10.99, 13.33, 13.33, 7.99, 11.99, 11.32,
7.33, 9.66, 6.99, NA, 15.99, 15.66, 14.66, 6.32, 7, 11, 14, 10.33,
24.66, NA, 14.99, NA, 15.99, 9.32, NA, NA, 9.99, 9.33, 7.66,
17.33, 10.32, 16, 17, 12.99, 15, 14.33, 10, 14.99, 19, 13.99,
19.33, NA, 10, 6.99, 11.66, 6.66, 14.33, 16, 8.66, 10, NA, 20,
14.99, 19.66, 26.66, 8.99), ROCF_CT7 = c(33, 23.9, 28.7, 39.1,
36.4, 29.2, 11.4, 38.1, 18.1, 28.4, 22.2, 18.6, 37, 35, 33.2,
23.6, 21.1, 30.2, 28.8, 37.1, 32.2, 33.1, 26.1, 38, 23.6, 21.4,
37.3, 36.4, 15.1, 35.7, 29.9, 31.4, 32.4, NA, 37.9, 28.2, 37.2,
31.9, 36.7, 24.5, 19.2, 23.4, 32.8, 36.2, 31.7, 24.3, 31.6, 33.1,
19.2, 30.4, 32.7, 24.4, 36.2, NA, 28.1, 31.7, 19.7, 31.6, 28.3,
27.4, 37.7, 35, 33.4, 34.4, 32.1, NA, 33.7, 31.8, NA, 34.9, 15,
20.1, 31, 35, 37.1, 29, 32.6, 31.7, 28.4, 34.4, 35.7, 35.1, 35.9,
27.4, 34, NA, 17.4, 28.9, 35.4, 33.4, 30.4, 14.7, 33.2, 26.8,
NA, 20, 33.6, 26.8, 36, 34.9), ROCF_IT7 = c(1.7, 12.1, 5.9, 12.1,
24.4, 9.8, 7.6, 24.4, 9.6, 7, 11.5, 9.7, 17.1, 14.2, 4.6, 12.1,
4.5, 18.8, 8.4, 14.6, 6.1, 17.1, 15.1, 13.1, 11.8, 12.1, 15.9,
7.6, 11.1, 5.9, 23.8, 12.4, 11.1, NA, 11, 7.8, 5.6, 6.5, 10.1,
9.9, 14.4, 17, 6.4, 27.3, 16, 14.4, 19.7, 16.2, 8.5, 8, 20.5,
15.1, 15.6, NA, 9, 8.6, 8.5, 13.2, 17.7, 14.1, 11, 9.1, 7, 16,
20.4, NA, 12.3, 16, NA, 15, 8.4, 17.9, 19.7, 11.9, 18.6, 4.7,
12.1, 17.5, 9, 5.5, 17.4, 12.1, 9.7, 9.5, 13.4, NA, 7.5, 14.5,
11, 19.5, 4.9, 11.9, 26, 12, NA, 3.9, 5.5, 6.4, 4.9, 16.2), ROCF_RT7 = c(2.4,
9.3, 6.3, 12.4, 22.6, 8.9, 7.8, 21.9, 9.8, 5.1, 10.8, 4.2, 12.3,
18.9, 2.9, 13.8, 1.2, 19.9, 4.2, 9.8, 5.9, 21.3, 12.3, 10.2,
12.1, 7.8, 6.3, 7.8, 9.8, 6.3, 16.9, 11.6, 11.3, NA, 8.9, 16.9,
3.9, 6.4, 13, 9.9, 14.9, 18.4, 3.7, 15.4, 8.8, 14.2, 14.7, 14.3,
8.8, 5.1, 21.8, 14.3, 11.4, NA, 8.7, 9.5, 8.8, 14.2, 17, 13.8,
12.8, 9.7, 5.1, 15.3, 22.8, NA, 6.4, 12.7, NA, 20.9, 7.8, 11.8,
22.4, 4.3, 17.8, 4.9, 13.8, 16.4, 7.4, 5.6, 24.8, 11.8, 9.4,
11.8, 20.3, NA, 7.1, 13.9, 12.3, 23.5, 2.6, 14.8, 15.2, 11.7,
NA, 4.3, 6.7, 5.2, 7.3, 18.4), PVF_T7 = c(38.3, 49.4, 16.7, 31.4,
60.9, 29.5, 48.4, 28.4, 17.4, 32.9, 40.7, 42.6, 42.4, 32.3, 38.5,
40.4, 20.4, 40.5, 42.6, 28.4, 27.5, 24.4, 40.4, 28.7, 5.3, 38.4,
46.7, 26.4, 37.4, 23.7, 9.8, 34.9, 55.5, NA, 38.5, 38.5, 25.5,
43.5, 36.3, 30.3, 32.7, 15.4, 21.6, 40.5, 38.7, 48.6, 47.6, 26.9,
35.7, 31.9, 47.7, 35.4, 29.5, NA, 33.6, 48.3, 27.7, 55.6, 39.5,
30.4, 36.7, 38.7, 23.9, 26.4, 28.4, NA, 18.3, 47.6, NA, 35.5,
29.7, 39.4, 44.3, 26.7, 29.4, 31.3, 44.4, 30.7, 11.5, 40.9, 37.7,
40.4, 14.5, 21.4, 14.7, NA, 29.9, 20.5, 34.4, 55.4, 32.9, 20.7,
27.6, 31.6, NA, 7.7, 25.6, 22.6, 22.7, 34.5), SVF_T7 = c(26,
48, 30, 29, 43, 25, 42, 36, 31, 27, 22, 21, 40, 46, 32, 29, 32,
38, 46, 31, 20, 36, 35, 32, 12, 28, 47, 20, 33, 30, 22, 44, 35,
NA, 36, 21, 24, 45, 25, 25, 30, 27, 34, 50, 31, 42, 34, 40, 25,
31, 41, 37, 38, NA, 38, 19, 29, 46, 40, 38, 36, 34, 31, 40, 39,
NA, 19, 40, NA, 29, 44, 43, 43, 31, 45, 28, 35, 31, 33, 36, 28,
35, 34, 34, 29, NA, 26, 30, 34, 57, 40, 36, 41, 36, NA, 22, 31,
29, 26, 31)), row.names = c(NA, -100L), class = c("tbl_df", "tbl",
"data.frame"))
>
此处两个不同电子表格中的数据已合并。如果你愿意，可以用现在的数据来推算一下。
根据显示的代码，当我们按顺序执行多个 pivot_longer 时，行会扩展。如果我们想一次对每个 key/value 对执行此操作，一个选项是 to_long
library(dplyr)
library(tibble)
library(sjmisc)
library(forcats)
out <- db %>%
mutate(GROUP = fct_recode(GROUP, CONTROL = "CONTROLLO")) %>%
to_long(keys = c("tests0", "tests7"),
values =c("score0", "score7"),
grep("T0$", names(.), value = TRUE),
grep("T7$", names(.), value = TRUE)) %>%
as_tibble %>%
mutate(Gender = factor(Gender, levels = c(1, 0), labels = c("M", "F")),
across(c(tests0, tests7), factor))
-输出
> out
# A tibble: 600 × 9
ID GROUP Gender Age Education tests0 score0 tests7 score7
<dbl> <fct> <fct> <dbl> <dbl> <fct> <dbl> <fct> <dbl>
1 1 TRAINING M 74 18 ADAS_CogT0 14.7 ADAS_CogT7 16
2 2 TRAINING F 76 4 ADAS_CogT0 15.3 ADAS_CogT7 9.32
3 3 TRAINING M 81 8 ADAS_CogT0 17.3 ADAS_CogT7 21.3
4 4 TRAINING F 74 5 ADAS_CogT0 19 ADAS_CogT7 17
5 5 TRAINING M 69 8 ADAS_CogT0 7.66 ADAS_CogT7 8.32
6 6 TRAINING F 72 11 ADAS_CogT0 12.6 ADAS_CogT7 11
7 7 TRAINING F 75 5 ADAS_CogT0 18.7 ADAS_CogT7 15.0
8 8 TRAINING M 83 5 ADAS_CogT0 15.0 ADAS_CogT7 11.0
9 9 TRAINING F 78 4 ADAS_CogT0 18.0 ADAS_CogT7 17
10 10 TRAINING F 72 8 ADAS_CogT0 17.3 ADAS_CogT7 18.3
# … with 590 more rows
我尝试使用 dplyr 库合并到不同的电子表格并进行以下操作。
db0 <- t0 %>%
mutate(GROUP = ifelse(GROUP == 'CONTROLLO', 'CONTROL','TRAINING')) %>%
mutate(GROUP = factor(GROUP, levels = c('CONTROL' ,"TRAINING"))) %>%
pivot_longer(c(ADAS_CogT0:SVF_T0), names_to = 'tests0', values_to = 'score0') %>%
arrange(tests0) %>%
pivot_longer(c(ADAS_CogT7:SVF_T7), names_to = 'tests7', values_to = 'score7') %>%
arrange(tests7) %>%
mutate(
Gender = factor(if_else(
Gender == "1", "M", "F"
), levels = c("M", "F"))
) %>% mutate(tests0 = factor(tests0,
levels = c('ADAS_CogT0',
'PVF_T0', 'ROCF_CT0', 'ROCF_IT0',
'ROCF_RT0', 'SVF_T0'))) %>%
mutate(tests7 = factor(tests7,
levels = c('ADAS_CogT7',
'PVF_T7', 'ROCF_CT7', 'ROCF_IT7',
'ROCF_RT7', 'SVF_T7')))
在您个人看来，这段代码中有哪些步骤可以简单化？任何可以删除的冗余。无论如何，我在这里报告已经合并的数据集
dput(head(db, 100))
structure(list(ID = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,
77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,
93, 94, 95, 96, 97, 98, 99, 100), GROUP = c("TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING",
"TRAINING", "TRAINING", "TRAINING", "TRAINING", "TRAINING", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO",
"CONTROLLO", "CONTROLLO", "CONTROLLO", "CONTROLLO"), Gender = c(1,
0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1,
0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,
0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,
1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1,
0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0), Age = c(74, 76,
81, 74, 69, 72, 75, 83, 78, 72, 82, 68, 72, 72, 73, 80, 69, 72,
70, 80, 75, 80, 78, 74, 82, 74, 80, 82, 78, 81, 66, 71, 70, 79,
78, 73, 72, 77, 77, 71, 83, 74, 70, 71, 77, 69, 67, 64, 79, 71,
77, 77, 73, 67, 68, 79, 81, 67, 84, 75, 80, 73, 68, 74, 77, 79,
79, 72, 73, 78, 76, 78, 77, 74, 78, 77, 77, 82, 77, 70, 77, 81,
79, 75, 74, 78, 69, 77, 73, 77, 70, 79, 70, 72, 77, 72, 71, 71,
73, 81), Education = c(18, 4, 8, 5, 8, 11, 5, 5, 4, 8, 8, 12,
5, 18, 13, 5, 13, 13, 5, 5, 13, 5, 3, 8, 17, 5, 8, 5, 5, 8, 17,
8, 18, 18, 13, 13, 13, 13, 15, 17, 8, 5, 5, 13, 8, 5, 11, 13,
8, 8, 8, 5, 13, 8, 5, 17, 8, 12, 13, 5, 8, 8, 8, 5, 3, 8, 18,
5, 8, 13, 8, 5, 17, 8, 5, 17, 5, 8, 11, 8, 8, 5, 12, 3, 8, 8,
8, 13, 5, 5, 8, 8, 13, 5, 5, 8, 13, 5, 8, 12), ADAS_CogT0 = c(14.66,
15.33, 17.33, 19, 7.66, 12.6, 18.67, 14.99, 17.99, 17.33, 13.66,
16.99, 10.66, 9.66, 14.99, 15.66, 13.33, 4.33, 14.33, 15.99,
16.33, 10.66, 14.66, 10.66, 19.33, 17.66, 15.99, 20.66, 20.6,
17, 10.33, 6.33, 6.66, 19.99, 13.33, 24.33, 12.33, 10.33, 12.33,
9.66, 10.99, 13.99, 23, 6.32, 11.32, 13.99, 14.66, 8.99, 14.33,
9.99, 7.33, 15.66, 14, 7.99, 23.32, 14.66, 9.99, 5.66, 6.99,
11.66, 10.33, 6.99, 19.32, NA, 10, 17.66, 13.66, 10.32, NA, NA,
8.66, 9, 6.99, 14.99, 9.66, 13.66, 15.32, 12, 14, 13.66, 11.99,
15.66, 16, 15, 16.99, 20, 11, 7.99, 8.33, 8.32, 14.99, 18.66,
10.33, 11.99, 9.32, 17, 14.33, 14.66, 16.6, 9.99), ROCF_CT0 = c(32,
13.5, 34.7, 26.1, 35.4, 24.6, 28.9, 34.1, 12.1, 28.1, 22.7, 31.6,
30.4, 34.4, 37.2, 38.1, 18.6, 26.2, 34.8, 37.1, 26.2, 35.1, 37.1,
36, 11.2, 17.4, 32.7, 28.4, 21.1, 26.7, 31.9, 33.4, 27.4, 32.7,
33.9, 37.2, 28.2, 34.2, 29.7, 30, 16.2, 37.4, 34.8, 36.6, 33,
30.8, 34.6, 35.1, 29.7, 36.8, 28, 34.4, 33.7, 28.9, 31.8, 31.7,
10.2, 27.1, 36.8, 22.4, 35.7, 28, 30.9, 35.4, 26.9, 29.7, 26.7,
28.8, 32, 37.9, 27, 18.6, 33, 38, 25.1, 31, 27.1, 26.7, 37, 30.4,
37, 30.1, 36.9, 24.4, 34, 6.7, 20.4, 30.2, 31.1, 33.4, 17.4,
23.7, 30.6, 28.3, 19.4, 33.4, 31.6, 39.8, 27, 33.9), ROCF_IT0 = c(3.7,
7.6, 14.9, 13.6, 23.4, 4, 8.1, 23.9, 10.1, 7, 10, 9.2, 15.6,
10.8, 6.6, 13.6, 4, 15.3, 7.4, 0, 8.6, 22.9, 11.6, 10.1, 6.8,
12.1, 12.9, 0, 10.6, 5.9, 14.8, 10.9, 10.6, 10.6, 7.5, 7.3, 4.1,
3.1, 7.6, 13.4, 10.9, 19, 3.4, 11.7, 14.6, 9.4, 16.7, 15.7, 14.5,
12.5, 15.1, 22.6, 9.1, 15.5, 11, 6.1, 11.5, 5.6, 17.2, 18.1,
23.5, 15.6, 7.7, 13.5, 14.5, 11.9, 10.3, 11.5, 12.6, 19, 11.9,
15.4, 17.7, 8.4, 13.4, 5.2, 11.1, 16, 10.9, 8, 22.6, 13.6, 12.2,
6.5, 13.4, 11.5, 8, 18.1, 10, 11, 5.9, 14.9, 12, 11.4, 17, 5.9,
15, 10.9, 8.9, 15.7), ROCF_RT0 = c(3.9, 7.8, 16.3, 13.4, 24.1,
4.7, 7.8, 20.9, 9.8, 5.1, 9.8, 8.2, 16.3, 11.3, 5.9, 14.8, 4.7,
14.4, 6.7, 0, 4.9, 23.3, 14.8, 11.7, 6.4, 10.3, 14.3, 0, 10.8,
6.3, 15.4, 10.1, 5.3, 12, 6.9, 5.4, 4.4, 4.4, 7, 10.9, 11.9,
17.3, 3.7, 12.2, 6.7, 14.7, 16.2, 7.8, 10.8, 10.2, 16.7, 22.8,
9.1, 15.7, 10.7, 4, 8.8, 6.8, 14.6, 14.5, 24.8, 13.7, 0, 10.3,
17.3, 11.3, 8, 13.2, 15.2, 18.4, 4.3, 12.8, 18.9, 4.3, 11.8,
4.9, 11.8, 13.3, 10.3, 7.1, 23.7, 15.8, 16.4, 6.8, 20.3, 11.3,
7.6, 19.9, 10.8, 12.5, 5.6, 11.3, 11.7, 13.5, 12.3, 5.1, 14.2,
8.7, 12.3, 16.4), PVF_T0 = c(41.3, 35.4, 16.7, 28.4, 46.9, 33.6,
39.4, 27.4, 17.4, 37.9, 31.7, 37.6, 33.4, 21.5, 49.5, 40.4, 10.6,
40.5, 30.6, 28.4, 29.5, 22.4, 45.4, 29.7, 17.3, 39.4, 31.7, 24.4,
38.4, 22.7, 6.8, 19.9, 56.5, 35.3, 43.5, 32.5, 28.5, 38.5, 22.3,
32.3, 30.7, 23.4, 18.6, 46.6, 36.7, 35.6, 38.6, 16.9, 38.7, 37.6,
39.7, 34.4, 27.5, 16.2, 32.6, 48.3, 26.7, 49.9, 40.5, 34.4, 37.7,
39.7, 23.2, 19.4, 21.4, 33.7, 11.3, 41.6, 23.7, 31.5, 28.7, 34.4,
33.3, 27.7, 23.4, 32.3, 49.4, 35.7, 26.7, 31.9, 37.7, 35.4, 20.5,
28.4, 14.7, 30.7, 35.9, 16.5, 27.4, 49.4, 37.9, 27.7, 41.6, 31.6,
55.4, 42.9, 21.6, 25.6, 26.7, 43.5), SVF_T0 = c(40, 37, 30, 28,
49, 27, 34, 35, 29, 33, 20, 18, 36, 47, 43, 39, 36, 38, 48, 31,
25, 34, 31, 33, 24, 30, 45, 20, 33, 34, 29, 42, 40, 37, 37, 25,
28, 42, 19, 32, 37, 35, 20, 43, 33, 40, 40, 42, 27, 35, 29, 39,
31, 48, 41, 27, 32, 45, 39, 40, 36, 38, 27, 32, 47, 36, 24, 40,
38, 29, 43, 47, 38, 33, 44, 39, 46, 36, 32, 38, 38, 35, 26, 43,
29, 24, 33, 30, 33, 57, 40, 37, 44, 34, 46, 41, 33, 35, 29, 37
), ADAS_CogT7 = c(16, 9.32, 21.33, 17, 8.32, 11, 14.99, 10.99,
17, 18.33, 13.32, 14.34, 8.99, 7, 11.99, 15.33, 6.99, 5.33, 12.32,
13, 21.32, 7.99, 13.33, 11.99, 17.32, 16.32, 16.33, 14.66, 18.99,
17.33, 7.99, 9.33, 10.99, NA, 12.99, 16.33, 21.66, 9, 9.34, 8.66,
8.33, 13.66, 15.66, 6.66, 10.99, 13.33, 13.33, 7.99, 11.99, 11.32,
7.33, 9.66, 6.99, NA, 15.99, 15.66, 14.66, 6.32, 7, 11, 14, 10.33,
24.66, NA, 14.99, NA, 15.99, 9.32, NA, NA, 9.99, 9.33, 7.66,
17.33, 10.32, 16, 17, 12.99, 15, 14.33, 10, 14.99, 19, 13.99,
19.33, NA, 10, 6.99, 11.66, 6.66, 14.33, 16, 8.66, 10, NA, 20,
14.99, 19.66, 26.66, 8.99), ROCF_CT7 = c(33, 23.9, 28.7, 39.1,
36.4, 29.2, 11.4, 38.1, 18.1, 28.4, 22.2, 18.6, 37, 35, 33.2,
23.6, 21.1, 30.2, 28.8, 37.1, 32.2, 33.1, 26.1, 38, 23.6, 21.4,
37.3, 36.4, 15.1, 35.7, 29.9, 31.4, 32.4, NA, 37.9, 28.2, 37.2,
31.9, 36.7, 24.5, 19.2, 23.4, 32.8, 36.2, 31.7, 24.3, 31.6, 33.1,
19.2, 30.4, 32.7, 24.4, 36.2, NA, 28.1, 31.7, 19.7, 31.6, 28.3,
27.4, 37.7, 35, 33.4, 34.4, 32.1, NA, 33.7, 31.8, NA, 34.9, 15,
20.1, 31, 35, 37.1, 29, 32.6, 31.7, 28.4, 34.4, 35.7, 35.1, 35.9,
27.4, 34, NA, 17.4, 28.9, 35.4, 33.4, 30.4, 14.7, 33.2, 26.8,
NA, 20, 33.6, 26.8, 36, 34.9), ROCF_IT7 = c(1.7, 12.1, 5.9, 12.1,
24.4, 9.8, 7.6, 24.4, 9.6, 7, 11.5, 9.7, 17.1, 14.2, 4.6, 12.1,
4.5, 18.8, 8.4, 14.6, 6.1, 17.1, 15.1, 13.1, 11.8, 12.1, 15.9,
7.6, 11.1, 5.9, 23.8, 12.4, 11.1, NA, 11, 7.8, 5.6, 6.5, 10.1,
9.9, 14.4, 17, 6.4, 27.3, 16, 14.4, 19.7, 16.2, 8.5, 8, 20.5,
15.1, 15.6, NA, 9, 8.6, 8.5, 13.2, 17.7, 14.1, 11, 9.1, 7, 16,
20.4, NA, 12.3, 16, NA, 15, 8.4, 17.9, 19.7, 11.9, 18.6, 4.7,
12.1, 17.5, 9, 5.5, 17.4, 12.1, 9.7, 9.5, 13.4, NA, 7.5, 14.5,
11, 19.5, 4.9, 11.9, 26, 12, NA, 3.9, 5.5, 6.4, 4.9, 16.2), ROCF_RT7 = c(2.4,
9.3, 6.3, 12.4, 22.6, 8.9, 7.8, 21.9, 9.8, 5.1, 10.8, 4.2, 12.3,
18.9, 2.9, 13.8, 1.2, 19.9, 4.2, 9.8, 5.9, 21.3, 12.3, 10.2,
12.1, 7.8, 6.3, 7.8, 9.8, 6.3, 16.9, 11.6, 11.3, NA, 8.9, 16.9,
3.9, 6.4, 13, 9.9, 14.9, 18.4, 3.7, 15.4, 8.8, 14.2, 14.7, 14.3,
8.8, 5.1, 21.8, 14.3, 11.4, NA, 8.7, 9.5, 8.8, 14.2, 17, 13.8,
12.8, 9.7, 5.1, 15.3, 22.8, NA, 6.4, 12.7, NA, 20.9, 7.8, 11.8,
22.4, 4.3, 17.8, 4.9, 13.8, 16.4, 7.4, 5.6, 24.8, 11.8, 9.4,
11.8, 20.3, NA, 7.1, 13.9, 12.3, 23.5, 2.6, 14.8, 15.2, 11.7,
NA, 4.3, 6.7, 5.2, 7.3, 18.4), PVF_T7 = c(38.3, 49.4, 16.7, 31.4,
60.9, 29.5, 48.4, 28.4, 17.4, 32.9, 40.7, 42.6, 42.4, 32.3, 38.5,
40.4, 20.4, 40.5, 42.6, 28.4, 27.5, 24.4, 40.4, 28.7, 5.3, 38.4,
46.7, 26.4, 37.4, 23.7, 9.8, 34.9, 55.5, NA, 38.5, 38.5, 25.5,
43.5, 36.3, 30.3, 32.7, 15.4, 21.6, 40.5, 38.7, 48.6, 47.6, 26.9,
35.7, 31.9, 47.7, 35.4, 29.5, NA, 33.6, 48.3, 27.7, 55.6, 39.5,
30.4, 36.7, 38.7, 23.9, 26.4, 28.4, NA, 18.3, 47.6, NA, 35.5,
29.7, 39.4, 44.3, 26.7, 29.4, 31.3, 44.4, 30.7, 11.5, 40.9, 37.7,
40.4, 14.5, 21.4, 14.7, NA, 29.9, 20.5, 34.4, 55.4, 32.9, 20.7,
27.6, 31.6, NA, 7.7, 25.6, 22.6, 22.7, 34.5), SVF_T7 = c(26,
48, 30, 29, 43, 25, 42, 36, 31, 27, 22, 21, 40, 46, 32, 29, 32,
38, 46, 31, 20, 36, 35, 32, 12, 28, 47, 20, 33, 30, 22, 44, 35,
NA, 36, 21, 24, 45, 25, 25, 30, 27, 34, 50, 31, 42, 34, 40, 25,
31, 41, 37, 38, NA, 38, 19, 29, 46, 40, 38, 36, 34, 31, 40, 39,
NA, 19, 40, NA, 29, 44, 43, 43, 31, 45, 28, 35, 31, 33, 36, 28,
35, 34, 34, 29, NA, 26, 30, 34, 57, 40, 36, 41, 36, NA, 22, 31,
29, 26, 31)), row.names = c(NA, -100L), class = c("tbl_df", "tbl",
"data.frame"))
>
此处两个不同电子表格中的数据已合并。如果你愿意，可以用现在的数据来推算一下。
根据显示的代码，当我们按顺序执行多个 pivot_longer 时，行会扩展。如果我们想一次对每个 key/value 对执行此操作，一个选项是 to_long
library(dplyr)
library(tibble)
library(sjmisc)
library(forcats)
out <- db %>%
mutate(GROUP = fct_recode(GROUP, CONTROL = "CONTROLLO")) %>%
to_long(keys = c("tests0", "tests7"),
values =c("score0", "score7"),
grep("T0$", names(.), value = TRUE),
grep("T7$", names(.), value = TRUE)) %>%
as_tibble %>%
mutate(Gender = factor(Gender, levels = c(1, 0), labels = c("M", "F")),
across(c(tests0, tests7), factor))
-输出
> out
# A tibble: 600 × 9
ID GROUP Gender Age Education tests0 score0 tests7 score7
<dbl> <fct> <fct> <dbl> <dbl> <fct> <dbl> <fct> <dbl>
1 1 TRAINING M 74 18 ADAS_CogT0 14.7 ADAS_CogT7 16
2 2 TRAINING F 76 4 ADAS_CogT0 15.3 ADAS_CogT7 9.32
3 3 TRAINING M 81 8 ADAS_CogT0 17.3 ADAS_CogT7 21.3
4 4 TRAINING F 74 5 ADAS_CogT0 19 ADAS_CogT7 17
5 5 TRAINING M 69 8 ADAS_CogT0 7.66 ADAS_CogT7 8.32
6 6 TRAINING F 72 11 ADAS_CogT0 12.6 ADAS_CogT7 11
7 7 TRAINING F 75 5 ADAS_CogT0 18.7 ADAS_CogT7 15.0
8 8 TRAINING M 83 5 ADAS_CogT0 15.0 ADAS_CogT7 11.0
9 9 TRAINING F 78 4 ADAS_CogT0 18.0 ADAS_CogT7 17
10 10 TRAINING F 72 8 ADAS_CogT0 17.3 ADAS_CogT7 18.3
# … with 590 more rows