使用Tidyr/Dplyr汇总字符串组的计数

Question

我需要总结一下我分配给组的字符串数量,我知道我可以在dplyr/tidyr中完成,但我遗漏了一些东西.

示例数据集:

Owner = c('bob','julia','cheryl','bob','julia','cheryl')
Day = c('Mon', 'Tue') 
Locn = c('house','store','apartment','office','house','shop')
data <- data.frame(Owner, Day, Locn)

看起来像这样:

   Owner Day      Locn
1    bob Mon     house
2  julia Tue     store
3 cheryl Mon apartment
4    bob Tue    office
5  julia Mon     house
6 cheryl Tue      shop

我想按名称和日期分组,然后按列计算分组位置.在这个例子中,我希望"house"和"apartment"添加到标题为"Home"的列中,"store","office"和"shop"将计入"Work"列中.

我当前的代码(不起作用):

grouped_locn <- data %>%
  dplyr::arrange(Owner, Day) %>%
  dplyr::group_by(Owner, Day) %>%
  dplyr::summarize(Home = which(data$Locn %in% c('house', 'apartment')), 
               Work = which(data$Locn %in% c("store", "office", "apartment")))

我只是将我目前的尝试包括在总结步骤中,以显示我是如何接近它的.Home和Work代码当前返回包含组元素的行号的向量(即Home = 1 3 5)

我的预期输出:

   Owner Day   Home  Work
1    bob Mon      1     0
2    bob Tue      0     1
3  julia Mon      1     0
4  julia Tue      0     1
5 cheryl Mon      1     0
6 cheryl Tue      0     1

在实际数据集(30k +行)中,每个所有者每天有多个Locn值,因此Home和Work计数可以是1和0以外的数字(因此没有布尔值).

非常感谢.

Answer 1

这是一个简单有效的解决方案 data.table

对于旧版本(v <1.9.5)

library(data.table) # v < 1.9.5
setDT(data)[, Locn2 := c("Work", "Home")[(Locn %in% c('house', 'apartment')) + 1L]]
dcast.data.table(data, Owner + Day ~ Locn2, length)
#     Owner Day Home Work
# 1:    bob Mon    1    0
# 2:    bob Tue    0    1
# 3: cheryl Mon    1    0
# 4: cheryl Tue    0    1
# 5:  julia Mon    1    0
# 6:  julia Tue    0    1

对于较新的版本(v> = 1.9.5),您可以在一行中执行此操作

dcast(setDT(data), Owner + Day ~ c("Work", "Home")[(Locn %in% c('house', 'apartment')) + 1L], length)

这是tidyr另一种选择

library(dplyr)
library(tidyr)
data %>%
  mutate(temp = 1L, 
         Locn = ifelse(Locn %in% c('house', 'apartment'), "Home", "Work")) %>% 
  spread(Locn, temp, fill = 0L)

#    Owner Day Home Work
# 1    bob Mon    1    0
# 2    bob Tue    0    1
# 3 cheryl Mon    1    0
# 4 cheryl Tue    0    1
# 5  julia Mon    1    0
# 6  julia Tue    0    1

Answer 2

试试这个

data %>%
  group_by(Owner, Day) %>%
  summarise(Home = sum(Locn %in% c("house", "apartment")), 
            Work = sum(Locn %in% c("store", "office", "shop")))