Question

我有一个体育团队跟踪胜/负的数据，而其他团队的结构如下：

Game TotalWins TotalLosses Team1Win Team1Loss Team2Win Team2Loss ...
1    1         0           1        NA        NA       NA
2    1         1           NA       NA        NA       1
3    2         1           NA       NA        1        NA
4    2         2           NA       1         NA       NA
5    3         2           NA       NA        1        NA
...

我想创建一个因子变量，其中包含游戏所针对的团队，以便数据如下所示：

Game TotalWins TotalLosses Team1Win Team1Loss Team2Win Team2Loss Team
1    1         0           1        NA        NA       NA        Team1
2    1         1           NA       NA        NA       1         Team2
3    2         1           NA       NA        1        NA        Team2
4    2         2           NA       1         NA       NA        Team1
5    3         2           NA       NA        1        NA        Team2
...

我的想法（不可行的代码）基本上是这样的：

if (Team1Win == 1 | Team1Loss == 1), Team = "Team1"
if (Team2Win == 1 | Team2Loss == 1), Team = "Team2"

我真的在使用mutate在dplyr中如何做到这一点。我已尝试过ifelse，recode等各种方法，但我不断得到错误或结果不是我想要的。

在dplyr中使用此功能的正确且最有效的方法是什么？

Answer 1

与其他答案类似，但有一些有用的变化：

na.rm = TRUE

gather
sub工作得很好，不需要stringr
使用full_join按目标包含完整数据。

library(dplyr)
library(tidyr)

df = read.delim(text = 
"Game TotalWins TotalLosses Team1Win Team1Loss Team2Win Team2Loss
1 1 0 1 NA NA NA
2 1 1 NA NA NA 1
3 2 1 NA NA 1 NA
4 2 2 NA 1 NA NA
5 3 2 NA NA 1 NA", sep = " ")

df %>% 
  select(-starts_with("Total")) %>% 
  gather(Team, one, -Game, na.rm = TRUE) %>% 
  select(-one) %>% 
  mutate(Team = sub("Win|Loss", "", Team)) %>% 
  full_join(df, .)

#> Joining, by = "Game"
#>   Game TotalWins TotalLosses Team1Win Team1Loss Team2Win Team2Loss  Team
#> 1    1         1           0        1        NA       NA        NA Team1
#> 2    2         1           1       NA        NA       NA         1 Team2
#> 3    3         2           1       NA        NA        1        NA Team2
#> 4    4         2           2       NA         1       NA        NA Team1
#> 5    5         3           2       NA        NA        1        NA Team2

Answer 2

我现在是dplyr处理事情的傻瓜，因此我使用dplyr提供了一个解决方案，该解决方案延伸到了您可能拥有的许多团队。它还使用tidyr和stringr在下面的apom评论中有用地指出。

library(dplyr)
library(tidyr)
library(stringr)

df = read_delim(
"Game TotalWins TotalLosses Team1Win Team1Loss Team2Win Team2Loss
1 1 0 1 NA NA NA
2 1 1 NA NA NA 1
3 2 1 NA NA 1 NA
4 2 2 NA 1 NA NA
5 3 2 NA NA 1 NA",delim = " ")

df %>% 
  gather("Team",value,contains("Team")) %>% 
  filter(!is.na(value)) %>%
  mutate(Team = str_replace_all(Team,c("Win" = "","Loss" = ""))) %>%
  select(-value)

Answer 3

这可能就是你要找的东西。（不仅仅是2支球队的硬编码）

# solution 1 :
paste0("Team",ceiling(apply(df[-c(1:3)], 1, function(x) which(!is.na(x)))/2))
[1] "Team1" "Team2" "Team2" "Team1" "Team2"

# solution 2: using a apply() {basically a for loop itself]
apply(df[-c(1:3)], 1, function(x) gsub("(Team\\d)+.*", "\\1", colnames(df[-c(1:3)])[which(!is.na(x))]))
# [1] "Team1" "Team2" "Team2" "Team1" "Team2"

# solution 3: (long route to dplyr) [ you have indirectly taught me a lot in dplyr through my search for this solution]
func <- function(x){
  y = which(x == 1)             # get the location of where 1 appears
  z = rep(0, times = length(x)) # create a vector of 0's+location of 1
  z[y] = y                      # i.e. c(0,0,3,0,5) for Team2Win 
  z
}

df1 = df[-c(1:3)] %>% gather("key", "value", starts_with("Team")) %>% 
                group_by(key) %>%
                dplyr::mutate(x = func(value)) %>%
                filter(x != 0) %>% arrange(x) %>% select(key)

df$newcol = gsub("(Team\\d+).*", "\\1", df1$key)

Answer 4

您可以使用简单的循环来完成：

x = colnames(df)
df$team<- NA

for (i in 1:nrow(df))
{
 df$team[i] = x[which(df[i,]==1)]
}

然后在最后你可以修剪＆＃34;赢得＆＃34;和＆＃34;损失＆＃34;使用以下功能：

df$team<- gsub("win", "",df$team)
df$team<- gsub("loss", "",df$team)

Answer 5

我很确定您的数据中有两个以上的团队，团队名称不是通用的。您要做的是先将数据重新整形为长形，然后提取相关的团队名称。因此，您可能需要按照以下步骤进行操作。

library(dplyr)
library(tidyr)
new_df <- df %>% 
  gather(team,idx,Team1Win:Team100Loss) %>%
  filter(!is.na(idx)) %>%
  select(-idx) %>%
  mutate(team = gsub("Win|Loss","",team))

如果要保留这些宽列，则可以将新DF加入旧列。

使用dplyr以数值变量为条件变异因子变量

5 个答案: