在多列中计数特定类别，并将它们乘以和行。

Question

我想数一数每个类别在我的数据中发生的频率。

要做到这一点，我需要计算每一行中的类别，并将这个数字乘以第5列的和。

(我的分析不需要列c4 )

优先产出将是：

分析= 131

Ads= 253

Identification= ..

我的数据如下：

tracker_category <- data.frame = c("Tracker1", "Tracker2", "Tracker3", "Tracker4","Tracker5","Tracker6"), 
c1 = c("Analytics", "Crash", "Location", "Identification", "Analytics", "Ads"), 
c2 = c("Ads", "Analytics", "Location", "Analytics", "Identification", "Ads"), 
c3 = c("Identification", "Analytics", "Ads", "Ads", "Analytics", "Location"),
c4 = c("url1.com","ur2.com","url3.com","url4.com","url5.com","url6.com"),
sum_tracker = c(1,20,100,0,5,76))

Answer 1

以下是你所追求的东西。

您可以将数据帧转换为“长”格式，然后添加事件(列5)。

data 备注:为了支持可重现性，我更正了您的数据帧定义。

tracker_category <- data.frame(
id = c("Tracker1", "Tracker2", "Tracker3", "Tracker4","Tracker5","Tracker6"), 
c1 = c("Analytics", "Crash", "Location", "Identification", "Analytics", "Ads"), 
c2 = c("Ads", "Analytics", "Location", "Analytics", "Identification", "Ads"), 
c3 = c("Identification", "Analytics", "Ads", "Ads", "Analytics", "Location"),
c4 = c("url1.com","ur2.com","url3.com","url4.com","url5.com","url6.com"),
sum_tracker = c(1,20,100,0,5,76)
)

强制采用长格式 {tidyr}，为此提供了pivot_longer()函数。

library(dplyr)
library(tidyr)

tracker_category %>% 
  select(-c4) %>%       # remove c4
  pivot_longer( cols = c(c1:c3)           # which cols to use
              , names_to = "action"       # where to store the names
              , values_to = "categories") # and values

这产生了：

# A tibble: 18 x 4
   id       sum_tracker action categories    
   <chr>          <dbl> <chr>  <chr>         
 1 Tracker1           1 c1     Analytics     
 2 Tracker1           1 c2     Ads           
 3 Tracker1           1 c3     Identification
 4 Tracker2          20 c1     Crash         
 5 Tracker2          20 c2     Analytics     
 6 Tracker2          20 c3     Analytics     
 7 Tracker3         100 c1     Location      
 8 Tracker3         100 c2     Location      
 9 Tracker3         100 c3     Ads           
10 Tracker4           0 c1     Identification
11 Tracker4           0 c2     Analytics     
12 Tracker4           0 c3     Ads           
13 Tracker5           5 c1     Analytics     
14 Tracker5           5 c2     Identification
15 Tracker5           5 c3     Analytics     
16 Tracker6          76 c1     Ads           
17 Tracker6          76 c2     Ads           
18 Tracker6          76 c3     Location

根据这种格式，您可以使用summarise()对您的组执行{dplyr}。

tracker_category %>% 
   select(-c4) %>% 
   pivot_longer(cols = c(c1:c3), names_to = "action", values_to = "categories") %>% 
#------------- group by your categories
   group_by(categories) %>% 
#------------- and sum over your tracked results, note to use sum and not multiplication as we used a long format
   summarise(total = sum(sum_tracker))

这产生了：

# A tibble: 5 x 2
  categories     total
  <chr>          <dbl>
1 Ads              253
2 Analytics         51
3 Crash             20
4 Identification     6
5 Location         276

请检查你的131分析例子是否真的正确.