使用purrr在两个列表上迭代，然后通过数据帧列表导入dplyr:：filter

这是问题的继续：“使用Purrr迭代两个列表，然后通过管道进入Dplyr:：Filter”

使用下面的示例数据，我首先创建一个数据框（

所需的

），其中包含要提供给

dplyr:：filter

的值。然后，我使用下面的代码创建结果的数据框

library(tidyverse)
library(purrr)

然而，我的实际数据来自三个不同的时间段，跨越三个不同的数据集。对于这个例子，我只是制作了另外两个DF副本，然后将它们放入一个列表中

map2_dfr(wanted$School, wanted$Code, ~filter(DF, School == .x, Code == .y)) %>% 
group_by(School, Code) %>% 
summarise_all(sum)

这就是我被困的地方。我想将上述两种方法结合起来，以便迭代

所需的

，将这些值输入到

dplyr:：filter

，但现在我必须跨数据帧列表执行此操作，并输出三个数据帧的列表

我正在与下面的代码做斗争…这不起作用。有什么建议吗？使用如此多的

地图
似乎也不是最好的方法

DFList %>%
   map(~filter(.x, School == "School1", Code == "B344")) %>%
   map(~group_by(.x, School, Code)) %>%
   map(~summarise(.x, Count = sum(Question1)))

样本数据：

map2_dfr(Wanted$School, Wanted$Code, 
    ~DFList %>% 
        map(~filter(.x, School == .x, Code == .y) %>% 
        map(~group_by(.x, Code, School) %>% 
        map(~summarise(.x, Count = sum(Question1))))))

code由于列表中的数据帧的格式相同，只需使用
dplyr:：bind_rows
将它们强制为单个数据帧，通过传递
.id
参数保存元素名称，该参数可通过加入
所需的
进行过滤后用于分组：

库（tidyverse）
DF%
分组人（身份证、学校、代码）%>%
总结所有（总和）
#>加入，由=c（“代码”、“学校”）
#>#tibble:6 x 5
#>#组：id、学校[？]
#>id学校代码问题1问题2
#>                 
#>1学校1 B344 7.00 8.00
#>2 1学校2 S300 15.0 14.0
#>3 2学校1 B344 7.00 8.00
#>4 2学校2 S300 15.0 14.0
#>5 3学校1 B344 7.00 8.00
#>6 3学校2 S300 15.0 14.0

map2_dfr(Wanted$School, Wanted$Code, 
    ~DFList %>% 
        map(~filter(.x, School == .x, Code == .y) %>% 
        map(~group_by(.x, Code, School) %>% 
        map(~summarise(.x, Count = sum(Question1))))))

Code <- c("B344","B555","S300","T220","B888","B888","B555","B344","B344","T220","B555","B555","S300","B555","S300","S300","S300","S300","B344","B344","B888","B888","B888")
School <- c("School1","School1","School2","School3","School4","School4","School1","School1","School3","School3","School4","School1","School1","School3","School2","School2","    School4","School2","School3","School4","School3","School1","School2")
Question1 <- c(3,4,5,4,5,5,5,4,5,3,4,5,4,5,4,3,3,3,4,5,4,3,3)
Question2 <- c(5,4,3,4,3,5,4,3,2,3,4,5,4,5,4,3,4,4,5,4,3,3,4)
DF <- data_frame(Code, School, Question1, Question2)

wanted <- data_frame(School = c("School2", "School1"),
                     Code = c("S300", "B344"))