dplyr does not group data by date

Question

I am trying to calculate the frequency of bikes that are taken by people using a dataset provided by Leada.

Here is the code:

library(dplyr)

setAs(\"cha         


        

Answer 1

The lubridate package is useful when dealing with dates. Here is the code to parse Start.Date and End.Date, extract week days, then group by week days:

Read dates as character vectors

library(dplyr)
library(lubridate)
# For some reason your instruction to load the csv directly from a url
# didn't work. I save the csv to a temporary directory.
d <- read.csv("/tmp/bike_trip_data.csv", colClasses = c("numeric", "numeric", "character", "factor", "numeric", "character", "factor", "numeric", "numeric", "factor", "character"), stringsAsFactors = T)

names(d)[9] <- "BikeNo"
d <- tbl_df(d)

Use lubridate to convert start date and end date

d <- d %>% 
  mutate(
    Start.Date = parse_date_time(Start.Date,"%m/%d/%y %H:%M"),
    End.Date = parse_date_time(End.Date,"%m/%d/%y %H:%M"),
    Weekday = wday(Start.Date, label=TRUE, abbr=FALSE))

Number of lines per week day

d %>%
  group_by(Weekday) %>%
  summarise(Total = n())

#     Weekday Total
# 1    Sunday 10587
# 2    Monday 23138
# 3   Tuesday 24678
# 4 Wednesday 23651
# 5  Thursday 25265
# 6    Friday 24283
# 7  Saturday 12413

Answer 2

I am sorry if this issue is long forgotten, but it weirds me out to see everyone recommending to convert to POSIX.ct or character when I have been using the much simpler solution of calling the arrange function from the plyr package using plyr::arrange, as it doesn't seem to have issues with the POSIXlt formats. As I am usually not the one finding the easiest solution for a problem in R, I am starting to think that there is something wrong with it. Does it not do the same as the dplyr version?