dplyr group_by逻辑值

本文介绍了dplyr group_by逻辑值的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我正在尝试通过一些声明简化这一过程。不知道如何去做（或者如果可以的话），但是任何可以使我接近或尽可能少地采取步骤的人。我正在使用dplyr和lubridate。我有一个名为OutofRange（示例）的数据库；

I'm trying to simplify this with a few statements. Not sure how to go about it (or if i can), but anyone that can get me close or as few steps as possible. I am using dplyr and lubridate. I have a database called OutofRange (sample);

OutOfRange %>% select(OutRange, TouchVPOC)
Source: local data frame [341 x 2]

   OutRange TouchVPOC

  (lgl)     (lgl)

1     FALSE      TRUE
2     FALSE     FALSE
3     FALSE      TRUE
4     FALSE     FALSE
5     FALSE      TRUE

OutOfRange %>% select(OutRange, TouchVPOC) %>% filter(OutRange == T) %>% tally
Source: local data frame [1 x 1]

      n  (int)
1    37

OutOfRange %>% select(OutRange, TouchVPOC) %>% filter(OutRange == T, TouchVPOC == T) %>% tally
Source: local data frame [1 x 1]

      n  (int)
1    15

15/37
[1] 0.4054054

因此，如果可能的话，我正在寻找类似这样的最终结果，其中CountofDataFrame是所有行的计数；其中OutRange& TouchVPOC是TRUE值的计数；和Pct = TouchVPOC / OutRange。

So, if possible I'm looking for a final outcome of something like this, where CountofDataFrame is the count of all rows; where OutRange & TouchVPOC are the count of TRUE Values; and Pct = TouchVPOC/OutRange.

CountOfDataFrame OutRange   TouchVPOC    Pct
             341       37          15    .40

我确实意识到，我可能会问很多..而我对此并不陌生，欢迎提出任何建议。

I do realize, I may be asking alot.. and I'm new to this, any suggestions are welcome. Just looking for a basis or a start in the right direction.

推荐答案

我建议您先将数据整理成整齐的格式，然后使用group_by / summarize / mutate进行汇总和百分比计算，如下所示。

I would suggest you first get the data into tidy format first, then use group_by/summarize/mutate to do aggregation and percentage calculation like below.

a <- data.frame(OutRange = c(TRUE, FALSE, FALSE, FALSE, FALSE),
           TouchVPOC = c(TRUE, TRUE, TRUE, FALSE, FALSE))

> a
  OutRange TouchVPOC
1     TRUE      TRUE
2    FALSE      TRUE
3    FALSE      TRUE
4    FALSE     FALSE
5    FALSE     FALSE

library(tidyr)
a %>%
  gather(type, value, OutRange:TouchVPOC) %>%
  group_by(type) %>%
  summarize(true_count = sum(value)) %>%
  mutate(total = sum(true_count), Pct = true_count / total)

Source: local data frame [2 x 4]

       type true_count total   Pct
      (chr)      (int) (int) (dbl)
1  OutRange          1     4  0.25
2 TouchVPOC          3     4  0.75

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