英文:
Data wrangling widen and consolidate rows
问题
我想要扩展一些行，然后使用R来合并这些行。
当我使用pivot_wider(names_from = Role, values_from = c(Person, Payoff))时，数据看起来像这样：
然而，我希望数据看起来像下表。我应该使用不同的命令或另一种方式来使用pivot_wider吗？
英文:
I want to widen a number of rows and then consolidate the rows using R.
| Group ID | Person | Role | Payoff |
|---|---|---|---|
| group_1 | person_1 | A | 10 |
| group_1 | person_2 | B | 20 |
| group_2 | person_3 | A | 14 |
| group_2 | person_4 | B | 14 |
| group_3 | person_5 | A | 34 |
| group_3 | person_6 | B | 48 |
When I use pivot_wider(names_from = Role, values_from = c(Person, Payoff)) the data looks like this:
| Group ID | A | B | A Payoff | B Payoff |
|---|---|---|---|---|
| group_1 | person_1 | NA | 10 | NA |
| group_1 | NA | person_2 | NA | 20 |
| group_2 | person_3 | NA | 14 | NA |
| group_2 | NA | person_4 | NA | 14 |
| group_3 | person_5 | NA | 34 | NA |
| group_3 | NA | person_6 | Na | 48 |
However, I want data to look the table below. Is there a different command I should be using or another way pivot_wider should be used?
| Group ID | A | B | A Payoff | B Payoff |
|---|---|---|---|---|
| group_1 | person_1 | person_2 | 10 | 20 |
| group_2 | person_3 | person_4 | 14 | 14 |
| group_3 | person_5 | person_6 | 34 | 48 |
I also tried code similar to the following commands
data %>%
group_by('session_group_id') %>%
pivot_wider(names_from = Role, values_from = c(Person, Payoff))
and
data %>%
group_by('session_group_id') %>%
pivot_wider(names_from = Role,
values_from = c(Person, Payoff),
values_fn = list)
答案1
得分: 2
Using your code, I get the right answer (tidyr v 1.3.0, dplyr v 1.1.0):
library(tidyr)
library(dplyr)
dat <- read.table(header=TRUE,
text="
Group_ID Person Role Payoff
group_1 person_1 A 10
group_1 person_2 B 20
group_2 person_3 A 14
group_2 person_4 B 14
group_3 person_5 A 34
group_3 person_6 B 48
")
dat %>% pivot_wider(names_from = Role, values_from = c(Person, Payoff))
#> # A tibble: 3 × 5
#> Group_ID Person_A Person_B Payoff_A Payoff_B
#> <chr> <chr> <chr> <int> <int>
#> 1 group_1 person_1 person_2 10 20
#> 2 group_2 person_3 person_4 14 14
#> 3 group_3 person_5 person_6 34 48
<sup>Created on 2023-04-03 with reprex v2.0.2</sup>
英文:
Using your code, I get the right answer (tidyr v 1.3.0, dplyr v 1.1.0):
library(tidyr)
library(dplyr)
dat <- read.table(header=TRUE,
text="
Group_ID Person Role Payoff
group_1 person_1 A 10
group_1 person_2 B 20
group_2 person_3 A 14
group_2 person_4 B 14
group_3 person_5 A 34
group_3 person_6 B 48
")
dat %>% pivot_wider(names_from = Role, values_from = c(Person, Payoff))
#> # A tibble: 3 × 5
#> Group_ID Person_A Person_B Payoff_A Payoff_B
#> <chr> <chr> <chr> <int> <int>
#> 1 group_1 person_1 person_2 10 20
#> 2 group_2 person_3 person_4 14 14
#> 3 group_3 person_5 person_6 34 48
<sup>Created on 2023-04-03 with reprex v2.0.2</sup>
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