R-如何将某一列中特定事件的总和添加到另一列中

Question

Using R.

This is a small subset of my dataset, simplified to only show relevant columns. The data is taken from Capital Bikeshare. The Start.Date column below has exact rental times for a bike.

Start.date              Member.type
2018-11-01 00:00:45     Member
2018-11-01 00:00:52     Casual
2018-11-01 00:01:46     Member
2018-11-01 01:00:02     Casual
2018-11-01 01:03:36     Member

What I'm trying to do is group all of the data by date, hour of day, number of each member type, and total number of member types (casual+member) for any given hour of any given day. So, in the end, I'll just have "Day - Hour - Number of Rentals per member type" so I can predict trends for hour of the day,

Here is my relevant code

library(dplyr)
bikeData <- read.csv("2011data.csv")

bikeData <- bikeData %>%
  mutate(Hour = format(strptime(
    bikeData$Start.date, "%Y-%m-%d %H:%M:%S"), "%m-%d %H")) %>%
  mutate(day = wday(Start.date, label=TRUE)) 

groupData <- bikeData %>%
  mutate(Start.date = ymd_hms(Start.date)) %>%
  count(date1 = as.Date(Start.date), Hour1 = hour(Start.date),
        member=(Member.type)) %>%
  group_by(date1, Hour1) %>%
  arrange(date1, Hour1) %>%
  summarise(total=sum(n))

What this gives me is the following new dataset, groupData

date1          Hour1     total 
2018-11-01         0        82
2018-11-01         1        43 
2018-11-01         2        17 
2018-11-01         3         4   
2018-11-02         0         5 
2018-11-02         1        24   

So I was able to do the total number of Member+Casual for all 24 hours of each day of my dataset, but how do I get another two columns that show the total number of casual and another that shows the total number of member? Thanks!

Desired below:

date1          Hour1     total     Casual     Member
2018-11-01         0        82        40          42
2018-11-01         1        43        20          23
2018-11-01         2        17        10           7
2018-11-01         3         4         1           3
2018-11-02         0         5         1           4
2018-11-02         1        24        20           4

Answer 1

groupData <- bikeData %>%
  mutate(Start.date = ymd_hms(Start.date)) %>%
  count(date1 = as.Date(Start.date), Hour1 = hour(Start.date),
        member=(Member.type)) %>%
  group_by(date1, Hour1) %>%
  arrange(date1, Hour1) %>%
  summarise(total=sum(n),members=sum(Member.type=="Member"),casuals=sum(Member.type=="Casual"))

You can simply add to your summarize call two variables that count the logical occurrences of Member.type equaling each of the options.