Question

改变列表的正确方法是什么？在这种特定情况下，列表由split

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library(dplyr)
csv<-data.frame(participant_number=c(1,1,1,2,2),SelfEsteem=c(3,4,2,1,3))
csv<-csv%>%split(.,.$participant_number)%>%mutate(.,var(.$SelfEsteem))

错误：

Error in UseMethod("mutate_") : 
  no applicable method for 'mutate_' applied to an object of class "list"

更新回复评论：我打算计算每个组SelfEsteem的方差（分组变量为participant_number）。如果我尝试group_by我没有得到预期的结果....为什么？

library(dplyr)

#Please note I changed the dataframe to make my point about variance differences more obvious
csv<-data.frame(participant_number=c(1,1,1,2,2),SelfEsteem=c(3,4,2,1,3))

csv<-csv%>%group_by(participant_number)%>%mutate(.,SE_variance=var(.$SelfEsteem))

#var(c(3,4,2)) #1
#var(c(1,3))  #2

预期：

        participant_number SelfEsteem SE_variance
(dbl)            (dbl)       (dbl)
1                  1          3         1
2                  1          4         1
3                  1          2         1
4                  2          1         2
5                  2          3         2

Answer 1

group_by的尝试失败，因为您覆盖了mutate的搜索路径。 Mutate使用非标准评估，因此它将首先在其data参数的列中查找变量。

使用竖线（%>%）时，点.表示整个数据框，.$SelfEsteem表示整个< / em>来自整个数据框的SelfEsteem列。

您只需要简化一点（不要覆盖默认值）以获得预期的结果。

csv %>% group_by(participant_number) %>% mutate(SE_variance = var(SelfEsteem)) # Source: local data frame [5 x 3] # Groups: participant_number [2] # # participant_number SelfEsteem SE_variance # (dbl) (dbl) (dbl) # 1 1 3 1 # 2 1 4 1 # 3 1 2 1 # 4 2 1 2 # 5 2 3 2

Answer 2

如果您确实需要使用list个对象，可以使用mutate list map来library(dplyr) library(purr) csv %>% split(.,.$participant_number) # your list map(~mutate(.,var(.$SelfEsteem))) # this will mutate a list：

bind_rows()

现在，您可以将所有内容与csv %>% split(.,.$participant_number) %>% map(~mutate(.,var(.$SelfEsteem))) %>% bind_rows()

放在一起

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private SessionManager session;
private SQLiteHandler db;

@Override
protected void onCreate(Bundle savedInstanceState) {
    super.onCreate(savedInstanceState);
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    // session manager
    session = new SessionManager(getApplicationContext());

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你得到了你需要的东西。

如何在列表中使用mutate？

2 个答案: