Question

数据集存在age, gender, state, income, group的数据。组表示每个用户所属的组：

     group      gender state age       income
 1       3      Female  CA     33  $75,000 - $99,999
 2       3        Male  MA     41  $50,000 - $74,999
 3       3        Male  KY     32  $35,000 - $49,999
 4       2      Female  CA     23  $35,000 - $49,999
 5       3        Male  KY     25  $50,000 - $74,999
 6       3        Male  MA     21  $75,000 - $99,999
 7       3      Female  CA     33  $75,000 - $99,999
 8       3        Male  MA     41  $50,000 - $74,999
 9       3        Male  KY     32  $35,000 - $49,999
10       2      Female  CA     23  $35,000 - $49,999
11       3        Male  KY     25  $50,000 - $74,999
12       3      Female  MA     21  $75,000 - $99,999

以上是虚拟数据，目标是使概念正确。

目标是按group, gender, income分组并获取计数，并为每个组获取属于该组的用户的平均年龄。然后按以下结构设置数据：＆＃34;扩展版本＆＃34;

    group  male female CA  MA  KY  $35,000 - $49,999  $50,000 - $74,999 $75,000 - $99,999  mean_age
     2      0     2     2   0   0          2                1              0                   23
...

以下是尝试：使用dplyr

> data %>% group_by(group, 
+ gender, 
+ state, 
+ income) %>% 
+ summarize(n()) %>% 
+ mutate(mean_age = mean(age))

我也在探索spread功能。

Answer 1

您可以在summarize()的一次通话中同时执行计数和平均值：

library(dplyr)    

data %>% group_by(group, 
                  gender, 
                  state, 
                  income) %>% 
  summarize(count = n(), mean_age = mean(age))

对于宽数据，样本中的变量名称不能唯一标识给定数据点的含义，因为唯一单位为group X gender X state X income，但每group只有一行。

由于您有两个摘要，摘要类型是唯一标识的附加层。因此，要将所有内容放在一行中，您将拥有[group]_[gender]_[state]_[income]_[summary]之类的变量名称。例如，2_Female_CA_$35,000 - $49,999_count。

可能有更好的广泛形状 - 您在宽数据框架上进行了哪种类型的计算？

Answer 2

除了@ treysp的答案，您还可以使用unite和spread来创建一个广泛（且不实用）的表格。（我只使用as.data.frame()强制打印所有列。）

require(tidyverse);
df %>%
    group_by(group, gender, state, income) %>%
    summarize(n = n(), mean_age = mean(age)) %>%
    unite(key, gender, state, income) %>%
    spread(key, n) %>% as.data.frame();
#  group mean_age Female_CA_$35,000 - $49,999 Female_CA_$75,000 - $99,999
#1     2       23                           2                          NA
#2     3       21                          NA                          NA
#3     3       25                          NA                          NA
#4     3       32                          NA                          NA
#5     3       33                          NA                           2
#6     3       41                          NA                          NA
#  Female_MA_$75,000 - $99,999 Male_KY_$35,000 - $49,999
#1                          NA                        NA
#2                           1                        NA
#3                          NA                        NA
#4                          NA                         2
#5                          NA                        NA
#6                          NA                        NA
#  Male_KY_$50,000 - $74,999 Male_MA_$50,000 - $74,999 Male_MA_$75,000 - $99,999
#1                        NA                        NA                        NA
#2                        NA                        NA                         1
#3                         2                        NA                        NA
#4                        NA                        NA                        NA
#5                        NA                        NA                        NA
#6                        NA                         2                        NA
#

样本数据

df <- read.table(text =
    "group      gender state age       income
 1       3      Female  CA     33  '$75,000 - $99,999'
 2       3        Male  MA     41  '$50,000 - $74,999'
 3       3        Male  KY     32  '$35,000 - $49,999'
 4       2      Female  CA     23  '$35,000 - $49,999'
 5       3        Male  KY     25  '$50,000 - $74,999'
 6       3        Male  MA     21  '$75,000 - $99,999'
 7       3      Female  CA     33  '$75,000 - $99,999'
 8       3        Male  MA     41  '$50,000 - $74,999'
 9       3        Male  KY     32  '$35,000 - $49,999'
10       2      Female  CA     23  '$35,000 - $49,999'
11       3        Male  KY     25  '$50,000 - $74,999'
12       3      Female  MA     21  '$75,000 - $99,999'", header = T, row.names = 1)

根据不同的列

2 个答案:

样本数据