dplyr不按日期分组数据 [英] dplyr does not group data by date

问题描述

我正在尝试计算由Leada提供的数据集使用的自行车频率。

  library（dplyr）
 
 setAs（character，POSIXlt，function（from）strptime（from，format = ％m /％d /％y％H：％M））
d < -  read.csv（http://mandrillapp.com/track/click/30315607/s3-us-west-1 .amazonaws.com p = eyJzIjoiemxlVjNUREczQ2l5UFVPeEFCalNUdmlDYTgwIiwidiI6MSwicCI6IntcInVcIjozMDMxNTYwNyxcInZcIjoxLFwidXJsXCI6XCJodHRwczpcXFwvXFxcL3MzLXVzLXdlc3QtMS5hbWF6b25hd3MuY29tXFxcL2RhdGF5ZWFyXFxcL2Jpa2VfdHJpcF9kYXRhLmNzdlwiLFwiaWRcIjpcImEyODNiNjMzOWJkOTQxMGM5ZjlkYzE0MmQ0NDQ5YmU4XCIsXCJ1cmxfaWRzXCI6W1wiMTVlYzMzNWM1NDRlMTM1ZDI0YjAwODE4ZjI5YTdkMmFkZjU2NWQ2MVwiXX0ifQ，
 colClasses = C（ 数字， 数字， POSIXlt， 因子， 数字， POSIXlt， 因子， 数字， 数字，因子，字符），
 stringsAsFactors = T）
名称（d）[9] < - BikeNo
 
d < -  tbl_df （d）
 
d<  -  d％>％mutate（Weekday = factor（工作日（Start.Date）））
d％>％group_by（工作日）
％>％summarize（Total = n（））
％>％select（工作日，总计）
  

但是dplyr不想按平日分组数据：

错误：列'Start.Date'不支持类型

为什么我关心Start.Date列，我按一个因素分组？
您可以在本地运行代码来重现错误：它将自动下载数据。

P.S。我正在使用dplyr版本：dplyr_0.3.0.2

解决方案

在处理日期时，lubridate包很有用。
这里是解析Start.Date和End.Date的代码，提取周日，然后按周分组：

读取日期作为字符向量< h3>

 库（dplyr）
库（lubridate）
＃由于某种原因，您的指令直接加载csv一个url 
＃没有工作。我将csv保存到临时目录。 
d<  -  read.csv（/ tmp / bike_trip_data.csv，colClasses = c（numeric，numeric，character，factor，numeric，character ，数字，数字，因子，字符），stringsAsFactors = T）
 
名称（d）[9]<  - BikeNo
d < -  tbl_df（d）
  

使用lubridate转换开始日期和结束日期

  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））
  

每周的行数

  d％>％
 group_by（工作日）％>％
总结（总计= n （））
 
＃平日合计
＃1星期日10587 
＃2星期一23138 
＃3星期二24678 
＃4星期三23651 
 ＃5星期四25265 
＃6星期五24283 
＃7星期六12413 
  

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("character", "POSIXlt", function(from) strptime(from, format = "%m/%d/%y %H:%M"))
d <- read.csv("http://mandrillapp.com/track/click/30315607/s3-us-west-1.amazonaws.com?p=eyJzIjoiemxlVjNUREczQ2l5UFVPeEFCalNUdmlDYTgwIiwidiI6MSwicCI6IntcInVcIjozMDMxNTYwNyxcInZcIjoxLFwidXJsXCI6XCJodHRwczpcXFwvXFxcL3MzLXVzLXdlc3QtMS5hbWF6b25hd3MuY29tXFxcL2RhdGF5ZWFyXFxcL2Jpa2VfdHJpcF9kYXRhLmNzdlwiLFwiaWRcIjpcImEyODNiNjMzOWJkOTQxMGM5ZjlkYzE0MmQ0NDQ5YmU4XCIsXCJ1cmxfaWRzXCI6W1wiMTVlYzMzNWM1NDRlMTM1ZDI0YjAwODE4ZjI5YTdkMmFkZjU2NWQ2MVwiXX0ifQ",
              colClasses = c("numeric", "numeric", "POSIXlt", "factor", "numeric", "POSIXlt", "factor", "numeric", "numeric", "factor", "character"),
              stringsAsFactors = T)
names(d)[9] <- "BikeNo"

d <- tbl_df(d)

d <- d %>% mutate(Weekday = factor(weekdays(Start.Date)))
d %>% group_by(Weekday) 
  %>% summarise(Total = n()) 
  %>% select(Weekday, Total)

It is strange but dplyr does not want to group data by Weekday saying:

Error: column 'Start.Date' has unsupported type

Why it cares about Start.Date column where I group by a factor? You can run the code locally to reproduce the error: it will download the data automatically.

P.S. I am using dplyr version: dplyr_0.3.0.2

解决方案

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

这篇关于dplyr不按日期分组数据的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！