Google Career Certificate: Cyclist bike share 2021
Study the behaviour of the cyclist as per as usertype for year 2021
Sara Samy
2023-04-13
Preparing the data
The data of year 2021 was divided into twelve separate sheets as per as months of the year. These were merged into one sheet. The empty cells’ records were dropped after checking the alphabetic case (Lower and Upper)
The data was stored on Google drive and the computer as well. The data is considered fair as it is collected from records made from the trips’ details requested by the users of both types.
The tool used in this process is RStudio/2023.03.0+386.
Steps of Prepare Process
1-Libraries
These libraries are used for the whole project and not just this process
#uploading libraries and tools
library(tidyverse)
library(dplyr)
library(janitor)
library(skimr)
library(readr)
library(lubridate)
library(rmarkdown)
library(readxl)
library(writexl)
library(geosphere)
library(data.table)2-Read the monthly data
jan_data<- read_csv("1_2021.csv")
feb_data<- read_csv("2_2021.csv")
march_data<- read_csv("3_2021.csv")
april_data<- read_csv("4_2021.csv")
may_data<- read_csv("5_2021.csv")
june_data<- read_csv("6_2021.csv")
july_data<- read_csv("7_2021.csv")
aug_data<- read_csv("8_2021.csv")
sep_data<- read_csv("9_2021.csv")
oct_data<- read_csv("10_2021.csv")
nov_data<- read_csv("11_2021.csv")
dec_data<- read_csv("12_2021.csv")3-Change the column names in all the data worksheets to unify the header
#January data preparation - column names
colnames(jan_data)
jan_2021 <- rename(jan_data,
trip_id = "ride_id",
bikeid = "rideable_type",
starttime = "started_at",
stoptime = "ended_at",
from_station_name = "start_station_name",
from_station_id ="start_station_id",
to_station_name = "end_station_name",
to_station_id = "end_station_id",
usertype = "member_casual")
#February data preparation - column names
colnames(feb_data)
feb_2021 <- rename(feb_data,
trip_id = "ride_id",
bikeid = "rideable_type",
starttime = "started_at",
stoptime = "ended_at",
from_station_name = "start_station_name",
from_station_id ="start_station_id",
to_station_name = "end_station_name",
to_station_id = "end_station_id",
usertype = "member_casual")
#March data preparation - column names
colnames(march_data)
march_2021 <- rename(march_data,
trip_id = "ride_id",
bikeid = "rideable_type",
starttime = "started_at",
stoptime = "ended_at",
from_station_name = "start_station_name",
from_station_id ="start_station_id",
to_station_name = "end_station_name",
to_station_id = "end_station_id",
usertype = "member_casual")
#April data preparation - column names
colnames(april_data)
april_2021 <- rename(april_data,
trip_id = "ride_id",
bikeid = "rideable_type",
starttime = "started_at",
stoptime = "ended_at",
from_station_name = "start_station_name",
from_station_id ="start_station_id",
to_station_name = "end_station_name",
to_station_id = "end_station_id",
usertype = "member_casual")
#May data preparation - column names
colnames(may_data)
may_2021 <- rename(may_data,
trip_id = "ride_id",
bikeid = "rideable_type",
starttime = "started_at",
stoptime = "ended_at",
from_station_name = "start_station_name",
from_station_id ="start_station_id",
to_station_name = "end_station_name",
to_station_id = "end_station_id",
usertype = "member_casual")
#June data preparation - column names
colnames(june_data)
june_2021 <- rename(june_data,
trip_id = "ride_id",
bikeid = "rideable_type",
starttime = "started_at",
stoptime = "ended_at",
from_station_name = "start_station_name",
from_station_id ="start_station_id",
to_station_name = "end_station_name",
to_station_id = "end_station_id",
usertype = "member_casual")
#July data preparation - column names
colnames(july_data)
july_2021 <- rename(july_data,
trip_id = "ride_id",
bikeid = "rideable_type",
starttime = "started_at",
stoptime = "ended_at",
from_station_name = "start_station_name",
from_station_id ="start_station_id",
to_station_name = "end_station_name",
to_station_id = "end_station_id",
usertype = "member_casual")
#August data preparation - column names
colnames(aug_data)
aug_2021 <- rename(aug_data,
trip_id = "ride_id",
bikeid = "rideable_type",
starttime = "started_at",
stoptime = "ended_at",
from_station_name = "start_station_name",
from_station_id ="start_station_id",
to_station_name = "end_station_name",
to_station_id = "end_station_id",
usertype = "member_casual")
#September data preparation - column names
colnames(sep_data)
sep_2021 <- rename(sep_data,
trip_id = "ride_id",
bikeid = "rideable_type",
starttime = "started_at",
stoptime = "ended_at",
from_station_name = "start_station_name",
from_station_id ="start_station_id",
to_station_name = "end_station_name",
to_station_id = "end_station_id",
usertype = "member_casual")
#October data preparation - column names
colnames(oct_data)
oct_2021 <- rename(oct_data,
trip_id = "ride_id",
bikeid = "rideable_type",
starttime = "started_at",
stoptime = "ended_at",
from_station_name = "start_station_name",
from_station_id ="start_station_id",
to_station_name = "end_station_name",
to_station_id = "end_station_id",
usertype = "member_casual")
#November data preparation - column names
colnames(nov_data)
nov_2021 <- rename(nov_data,
trip_id = "ride_id",
bikeid = "rideable_type",
starttime = "started_at",
stoptime = "ended_at",
from_station_name = "start_station_name",
from_station_id ="start_station_id",
to_station_name = "end_station_name",
to_station_id = "end_station_id",
usertype = "member_casual")
#December data preparation - column names
colnames(dec_data)
dec_2021 <- rename(dec_data,
trip_id = "ride_id",
bikeid = "rideable_type",
starttime = "started_at",
stoptime = "ended_at",
from_station_name = "start_station_name",
from_station_id ="start_station_id",
to_station_name = "end_station_name",
to_station_id = "end_station_id",
usertype = "member_casual")
#Check on the colnames of all the 12 sheets
colnames(jan_2021)
colnames(feb_2021)
colnames(march_2021)
colnames(april_2021)
colnames(may_2021)
colnames(june_2021)
colnames(july_2021)
colnames(aug_2021)
colnames(sep_2021)
colnames(oct_2021)
colnames(nov_2021)
colnames(dec_2021)Results
#Check on the colnames of all the 12 sheets
> colnames(jan_2021)
[1] "trip_id" "bikeid" "starttime"
[4] "stoptime" "from_station_name" "from_station_id"
[7] "to_station_name" "to_station_id" "start_lat"
[10] "start_lng" "end_lat" "end_lng"
[13] "usertype"
> colnames(feb_2021)
[1] "trip_id" "bikeid" "starttime"
[4] "stoptime" "from_station_name" "from_station_id"
[7] "to_station_name" "to_station_id" "start_lat"
[10] "start_lng" "end_lat" "end_lng"
[13] "usertype"
> colnames(march_2021)
[1] "trip_id" "bikeid" "starttime"
[4] "stoptime" "from_station_name" "from_station_id"
[7] "to_station_name" "to_station_id" "start_lat"
[10] "start_lng" "end_lat" "end_lng"
[13] "usertype"
> colnames(april_2021)
[1] "trip_id" "bikeid" "starttime"
[4] "stoptime" "from_station_name" "from_station_id"
[7] "to_station_name" "to_station_id" "start_lat"
[10] "start_lng" "end_lat" "end_lng"
[13] "usertype"
> colnames(may_2021)
[1] "trip_id" "bikeid" "starttime"
[4] "stoptime" "from_station_name" "from_station_id"
[7] "to_station_name" "to_station_id" "start_lat"
[10] "start_lng" "end_lat" "end_lng"
[13] "usertype"
> colnames(june_2021)
[1] "trip_id" "bikeid" "starttime"
[4] "stoptime" "from_station_name" "from_station_id"
[7] "to_station_name" "to_station_id" "start_lat"
[10] "start_lng" "end_lat" "end_lng"
[13] "usertype"
> colnames(july_2021)
[1] "trip_id" "bikeid" "starttime"
[4] "stoptime" "from_station_name" "from_station_id"
[7] "to_station_name" "to_station_id" "start_lat"
[10] "start_lng" "end_lat" "end_lng"
[13] "usertype"
> colnames(aug_2021)
[1] "trip_id" "bikeid" "starttime"
[4] "stoptime" "from_station_name" "from_station_id"
[7] "to_station_name" "to_station_id" "start_lat"
[10] "start_lng" "end_lat" "end_lng"
[13] "usertype"
> colnames(sep_2021)
[1] "trip_id" "bikeid" "starttime"
[4] "stoptime" "from_station_name" "from_station_id"
[7] "to_station_name" "to_station_id" "start_lat"
[10] "start_lng" "end_lat" "end_lng"
[13] "usertype"
> colnames(oct_2021)
[1] "trip_id" "bikeid" "starttime"
[4] "stoptime" "from_station_name" "from_station_id"
[7] "to_station_name" "to_station_id" "start_lat"
[10] "start_lng" "end_lat" "end_lng"
[13] "usertype"
> colnames(nov_2021)
[1] "trip_id" "bikeid" "starttime"
[4] "stoptime" "from_station_name" "from_station_id"
[7] "to_station_name" "to_station_id" "start_lat"
[10] "start_lng" "end_lat" "end_lng"
[13] "usertype"
> colnames(dec_2021)
[1] "trip_id" "bikeid" "starttime"
[4] "stoptime" "from_station_name" "from_station_id"
[7] "to_station_name" "to_station_id" "start_lat"
[10] "start_lng" "end_lat" "end_lng"
[13] "usertype"4-Merge into one data-set
#Create a data set of trips for 2021
year_2021_df <- bind_rows(jan_2021, feb_2021,march_2021,april_2021,may_2021,
june_2021,july_2021,aug_2021,sep_2021,oct_2021,
nov_2021,dec_2021)5-Drop the missing values
year_2021_df_1 <- drop_na(year_2021_df)6-Check the cases of the text
#check that the text is all the same
year_2021_df_1$trip_id <- tolower(year_2021_df_1$trip_id)
year_2021_df_1$bikeid<-tolower(year_2021_df_1$bikeid)
year_2021_df_1$from_station_name<-tolower(year_2021_df_1$from_station_name)
year_2021_df_1$from_station_id<-tolower(year_2021_df_1$from_station_id)
year_2021_df_1$to_station_name<-tolower(year_2021_df_1$to_station_name)
year_2021_df_1$to_station_id<-tolower(year_2021_df_1$to_station_id)
year_2021_df_1$usertype<-tolower(year_2021_df_1$usertype)7-Remove Duplicates
#Remove duplicates
year_2021_df_2 <- distinct(year_2021_df_1)Process the data-set
In this process, some columns like time and stations were split and transformed. Also, a calculation for time duration in minutes and the distance in meters took place.Columns like order day,timing, month, season, year quarters were added.The tool used here is also RStudio/2023.03.0+386.
Steps of Processing data set Process
1-Change the Start_time and Stop_time data format
#Change the start_time and stop_time
year_2021_df_3 <- mutate(year_2021_df_2,
start_datetime = as.character.Date(year_2021_df_2$starttime),
stop_datetime = as.character.Date(year_2021_df_2$stoptime))2-Split the Start and stop times
Start Time
#Start datetime
year_2021_df_4 <- separate(year_2021_df_3,start_datetime,into = c('start_day','start_time'), sep = " ")Stop Time
#Stop datetime
year_2021_df_5 <- separate(year_2021_df_4,stop_datetime,into = c('stop_day','stop_time'), sep = " ")3-Calculate the time duration
year_2021_df_6 <- mutate(year_2021_df_5,
time_duration = as.numeric(difftime(stoptime, starttime, units = "mins")))4-Change the start day to weekdays
Start day
#Change the start day to weekdays
year_2021_df_6$day_of_order<- weekdays(strptime(year_2021_df_6$start_day, format = "%Y-%m-%d"))5-Calculate the distance between start and stop points using Lat-LNG, start and stop hours columns, and Months in English words
Start and Stop Hours
#separation of the hour only
year_2021_df_7 <- year_2021_df_6 %>%
rowwise%>%
mutate(start_hour = format(as.POSIXct(starttime), format = "%H"), stop_hour =format(as.POSIXct(stoptime), format = "%H")) Distance Calculation
#Calculation of the distance in meters using lat-LNG
year_2021_df_7 <- year_2021_df_7 %>%
rowwise %>%
#get the distance between the start and stop in meters
mutate(distance =distm(x=c(start_lat,start_lng),y=c(end_lat, end_lng), fun = distHaversine)) Months in Words
#Months
year_2021_df_7$month <-months(strptime(year_2021_df_7$start_day, format = "%Y-%m-%d"))6- Round the Time duration and distance and Correcting some negative values of distance
Round the time duration
#round the time in min
year_2021_df_7$time_duration <- round(year_2021_df_7$time_duration,0)Change the data type of the distance
#Change the vector to numeric
year_2021_df_7$distance <-as.numeric(year_2021_df_7$distance)Round the distance
#round the distance in meters
year_2021_df_7$distance <- round(as.numeric(year_2021_df_7$distance),2)Remove the Negative values of Time duration
#there are some distance values with negative time duration, which is a wrong entry
year_2021_df_7$time_duration <- abs(year_2021_df_7$time_duration)Remove the negative values of Distance
year_2021_df_7$distance <- abs(as.numeric(year_2021_df_7$distance))7- Remove zero-valued records for time duration or distance
year_2021_df_7 <-year_2021_df_7 %>%
#remove any zero time_duration and distance
filter(time_duration > 0) %>%
filter(round(distance,0) >0) 8- Adding a Start_timing and Stop_timing(Morning, Afternoon, Evening, Night) for start and stop hours
Start_Timing
#creating an empty column
year_2021_df_7$start_timing = NA# Setting the start_timing to values
year_2021_df_7$start_timing<- case_when(year_2021_df_7$start_hour =="00" ~ "Night",
year_2021_df_7$ start_hour =="01" ~ "Night",
year_2021_df_7$start_hour =="02" ~ "Night",
year_2021_df_7$start_hour =="03" ~ "Night",
year_2021_df_7$start_hour =="04" ~ "Night",
year_2021_df_7$start_hour =="05" ~ "Morning",
year_2021_df_7$start_hour =="06" ~ "Morning",
year_2021_df_7$start_hour =="07" ~ "Morning",
year_2021_df_7$start_hour =="08" ~ "Morning",
year_2021_df_7$start_hour =="09" ~ "Morning",
year_2021_df_7$start_hour =="10" ~ "Morning",
year_2021_df_7$start_hour =="11" ~ "Morning",
year_2021_df_7$start_hour =="12" ~ "Afternoon",
year_2021_df_7$start_hour =="13" ~ "Afternoon",
year_2021_df_7$start_hour =="14" ~ "Afternoon",
year_2021_df_7$start_hour =="15" ~ "Afternoon",
year_2021_df_7$start_hour =="16" ~ "Afternoon",
year_2021_df_7$start_hour =="17" ~ "Evening",
year_2021_df_7$start_hour =="18" ~ "Evening",
year_2021_df_7$start_hour =="19" ~ "Evening",
year_2021_df_7$start_hour =="20" ~ "Evening",
year_2021_df_7$start_hour =="21" ~ "Night",
year_2021_df_7$start_hour =="22" ~ "Night",
year_2021_df_7$start_hour =="23" ~ "Night")Stop_Timing
#creating an empty column
year_2021_df_7$stop_timing = NA# Setting the stop_timing to values
year_2021_df_7$stop_timing<- case_when(year_2021_df_7$stop_hour =="00" ~ "Night",
year_2021_df_7$ stop_hour =="01" ~ "Night",
year_2021_df_7$stop_hour =="02" ~ "Night",
year_2021_df_7$stop_hour =="03" ~ "Night",
year_2021_df_7$stop_hour =="04" ~ "Night",
year_2021_df_7$stop_hour =="05" ~ "Morning",
year_2021_df_7$stop_hour =="06" ~ "Morning",
year_2021_df_7$stop_hour =="07" ~ "Morning",
year_2021_df_7$stop_hour =="08" ~ "Morning",
year_2021_df_7$stop_hour =="09" ~ "Morning",
year_2021_df_7$stop_hour =="10" ~ "Morning",
year_2021_df_7$stop_hour =="11" ~ "Morning",
year_2021_df_7$stop_hour =="12" ~ "Afternoon",
year_2021_df_7$stop_hour =="13" ~ "Afternoon",
year_2021_df_7$stop_hour =="14" ~ "Afternoon",
year_2021_df_7$stop_hour =="15" ~ "Afternoon",
year_2021_df_7$stop_hour =="16" ~ "Afternoon",
year_2021_df_7$stop_hour =="17" ~ "Evening",
year_2021_df_7$stop_hour =="18" ~ "Evening",
year_2021_df_7$stop_hour =="19" ~ "Evening",
year_2021_df_7$stop_hour =="20" ~ "Evening",
year_2021_df_7$stop_hour =="21" ~ "Night",
year_2021_df_7$stop_hour =="22" ~ "Night",
year_2021_df_7$stop_hour =="23" ~ "Night")8- Adding season and year quarter columns
Season
#creating an empty column
year_2021_df_7$season =NA# Setting season values
year_2021_df_7$season<- case_when(year_2021_df_7$month=="January"~"Winter",
year_2021_df_7$month=="February"~"Winter",
year_2021_df_7$month=="March"~"Spring",
year_2021_df_7$month=="April"~"Spring",
year_2021_df_7$month=="May"~"Spring",
year_2021_df_7$month=="June"~"Summer",
year_2021_df_7$month=="July"~"Summer",
year_2021_df_7$month=="August"~"Summer",
year_2021_df_7$month=="September"~"Autumn",
year_2021_df_7$month=="October"~"Autumn",
year_2021_df_7$month=="November"~"Autumn",
year_2021_df_7$month=="December"~"Winter")Year_quarters
#creating an empty column
year_2021_df_7$year_quarter = NA#Setting year quarters values
year_2021_df_7$year_quarter<- case_when(year_2021_df_7$month=="January"~"Q1",
year_2021_df_7$month=="February"~"Q1",
year_2021_df_7$month=="March"~"Q1",
year_2021_df_7$month=="April"~"Q2",
year_2021_df_7$month=="May"~"Q2",
year_2021_df_7$month=="June"~"Q2",
year_2021_df_7$month=="July"~"Q3",
year_2021_df_7$month=="August"~"Q3",
year_2021_df_7$month=="September"~"Q3",
year_2021_df_7$month=="October"~"Q4",
year_2021_df_7$month=="November"~"Q4",
year_2021_df_7$month=="December"~"Q4")View the data-set
str(year_2021_df_7)Results
rowws_df [4,300,941 × 27] (S3: rowwise_df/tbl_df/tbl/data.frame)
$ trip_id : chr [1:4300941] "b9f73448dfbe0d45" "457c7f4b5d3da135" "57c750326f9fdabe" "4d518c65e338d070" ...
$ bikeid : chr [1:4300941] "classic_bike" "electric_bike" "electric_bike" "electric_bike" ...
$ starttime : POSIXct[1:4300941], format: "2021-01-24 19:15:38" "2021-01-23 12:57:38" "2021-01-09 15:28:04" "2021-01-09 15:28:57" ...
$ stoptime : POSIXct[1:4300941], format: "2021-01-24 19:22:51" "2021-01-23 13:02:10" "2021-01-09 15:37:51" "2021-01-09 15:37:54" ...
$ from_station_name: chr [1:4300941] "california ave & cortez st" "california ave & cortez st" "california ave & cortez st" "california ave & cortez st" ...
$ from_station_id : chr [1:4300941] "17660" "17660" "17660" "17660" ...
$ to_station_name : chr [1:4300941] "wood st & augusta blvd" "california ave & north ave" "wood st & augusta blvd" "wood st & augusta blvd" ...
$ to_station_id : chr [1:4300941] "657" "13258" "657" "657" ...
$ start_lat : num [1:4300941] 41.9 41.9 41.9 41.9 41.9 ...
$ start_lng : num [1:4300941] -87.7 -87.7 -87.7 -87.7 -87.7 ...
$ end_lat : num [1:4300941] 41.9 41.9 41.9 41.9 41.9 ...
$ end_lng : num [1:4300941] -87.7 -87.7 -87.7 -87.7 -87.7 ...
$ usertype : chr [1:4300941] "member" "member" "casual" "casual" ...
$ start_day : chr [1:4300941] "2021-01-24" "2021-01-23" "2021-01-09" "2021-01-09" ...
$ start_time : chr [1:4300941] "19:15:38" "12:57:38" "15:28:04" "15:28:57" ...
$ stop_day : chr [1:4300941] "2021-01-24" "2021-01-23" "2021-01-09" "2021-01-09" ...
$ stop_time : chr [1:4300941] "19:22:51" "13:02:10" "15:37:51" "15:37:54" ...
$ time_duration : num [1:4300941] 7 5 10 9 10 21 6 4 18 17 ...
$ day_of_order : chr [1:4300941] "Sunday" "Saturday" "Saturday" "Saturday" ...
$ start_hour : chr [1:4300941] "19" "12" "15" "15" ...
$ stop_hour : chr [1:4300941] "19" "13" "15" "15" ...
$ distance : num [1:4300941] 2727.8 48.2 2728.5 2731.7 2727.8 ...
$ month : chr [1:4300941] "January" "January" "January" "January" ...
$ start_timing : chr [1:4300941] "Evening" "Afternoon" "Afternoon" "Afternoon" ...
$ stop_timing : chr [1:4300941] "Evening" "Afternoon" "Afternoon" "Afternoon" ...
$ season : chr [1:4300941] "Winter" "Winter" "Winter" "Winter" ...
$ year_quarter : chr [1:4300941] "Q1" "Q1" "Q1" "Q1" ...
- attr(*, "groups")= tibble [4,300,941 × 1] (S3: tbl_df/tbl/data.frame)
..$ .rows: list<int> [1:4300941]
.. ..$ : int 1
.. ..$ : int 2
.. ..$ : int 3
.. ..$ : int 4
.. ..$ : int 5
.. ..$ : int 6
.. ..$ : int 7
.. ..$ : int 8
.. ..$ : int 9
.. ..$ : int 10
.. ..$ : int 11
.. ..$ : int 12
.. ..$ : int 13
.. ..$ : int 14
.. ..$ : int 15
.. ..$ : int 16
.. ..$ : int 17
.. ..$ : int 18
.. ..$ : int 19
.. ..$ : int 20
.. ..$ : int 21
.. ..$ : int 22
.. ..$ : int 23
.. ..$ : int 24
.. ..$ : int 25
.. ..$ : int 26
.. ..$ : int 27
.. ..$ : int 28
.. ..$ : int 29
.. ..$ : int 30
.. ..$ : int 31
.. ..$ : int 32
.. ..$ : int 33
.. ..$ : int 34
.. ..$ : int 35
.. ..$ : int 36
.. ..$ : int 37
.. ..$ : int 38
.. ..$ : int 39
.. ..$ : int 40
.. ..$ : int 41
.. ..$ : int 42
.. ..$ : int 43
.. ..$ : int 44
.. ..$ : int 45
.. ..$ : int 46
.. ..$ : int 47
.. ..$ : int 48
.. ..$ : int 49
.. ..$ : int 50
.. ..$ : int 51
.. ..$ : int 52
.. ..$ : int 53
.. ..$ : int 54
.. ..$ : int 55
.. ..$ : int 56
.. ..$ : int 57
.. ..$ : int 58
.. ..$ : int 59
.. ..$ : int 60
.. ..$ : int 61
.. ..$ : int 62
.. ..$ : int 63
.. ..$ : int 64
.. ..$ : int 65
.. ..$ : int 66
.. ..$ : int 67
.. ..$ : int 68
.. ..$ : int 69
.. ..$ : int 70
.. ..$ : int 71
.. ..$ : int 72
.. ..$ : int 73
.. ..$ : int 74
.. ..$ : int 75
.. ..$ : int 76
.. ..$ : int 77
.. ..$ : int 78
.. ..$ : int 79
.. ..$ : int 80
.. ..$ : int 81
.. ..$ : int 82
.. ..$ : int 83
.. ..$ : int 84
.. ..$ : int 85
.. ..$ : int 86
.. ..$ : int 87
.. ..$ : int 88
.. ..$ : int 89
.. ..$ : int 90
.. ..$ : int 91
.. ..$ : int 92
.. ..$ : int 93
.. ..$ : int 94
.. ..$ : int 95
.. ..$ : int 96
.. ..$ : int 97
.. ..$ : int 98
.. ..$ : int 99
.. .. [list output truncated]
.. ..@ ptype: int(0) Analysis Process
In this process, the similarities and differences between the behavior of member and casual users were studied. This analysis included the number of rides done by both users in general and through different factors like through the day, week, month, season, and year quarter. Also, it studied the average time duration and distance per each user type, the most taken routes, start point and stop point. The tool used for this analysis is RStudio/2023.03.0+386. As for visualizations, it was RStudio/2023.03.0+386 and Tableau Public 2023.1. A dashboard was also created using Tableau Public 2023.1. Also, a focused tabled- report on Excel from Microsoft Professional Plus 2019.
A new column was created in tableau called path station, this joins the from_station and to_station together to find the most taken road.
Count of Rides Per each user_type
Total Number of rides
#----------------------------Total of rides--------------------
#count the total number of trips
nrow(year_2021_df_7)Results
[1] 4300941Total number of rides per user type
#count the total number of rides per each user-type
year_2021_df_7 %>%
count(usertype)Results
# A tibble: 2 × 2
# Rowwise:
usertype n
<chr> <int>
1 casual 1855831
2 member 2445110Visualization
#viz
ggplot(data = year_2021_df_7, mapping = aes(x= usertype, fill = usertype) ) +
#applied the mapping here an not in the geom_bar as it will be applied to the whole built-up layers
#as aes()in geom_function will be only applied here (hierarchy level)
geom_bar()+
labs(title = "Number of rides made by Casual VS Memeber", x=" User_type", y="Number of rides")+
theme(plot.title = element_text(hjust = 0.5))+ #centering the title
scale_y_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "User_type")+ # to change the name of the legend
geom_text(aes(label = after_stat(count)), stat = "count", vjust = 0, colour = "black")
#ggsave("plot1-Number of rides made by Casual VS Member.png")
Number of rides made
by different users
Total number of rides(bike type)
#count the total ride by each bike type
year_2021_df_7 %>%
count(bikeid)Results
# A tibble: 3 × 2
# Rowwise:
bikeid n
<chr> <int>
1 classic_bike 3032803
2 docked_bike 247001
3 electric_bike 1021137Visualization
#viz
ggplot(data = year_2021_df_7, mapping = aes(x= bikeid, fill = bikeid)) +
#applied the mapping here an not in the geom_bar as it will be applied to the whole built-up layers
#as aes()in geom_function will be only applied here (hierarchy level)
geom_bar()+
labs(title = "Number of rides made by bike type",x=" bike type", y="Number of rides")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = -90, vjust = 0.5, hjust = 1))+ #centering the title
scale_y_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "Bike_type")+ # to change the name of the legend
geom_text(aes(label = after_stat(count)), stat = "count", vjust = 0, colour = "black")
#ggsave("plot2-Number of rides per each bike type.png")
Number of rides per each bike type
Total of rides(user-type+bike)
#----------------------------Total of rides(user-type+bike)-------------------
#count the number of the rides for each bike type per user type
year_2021_df_7 %>%
group_by(usertype) %>%
count(bikeid)Results
# A tibble: 6 × 3
# Groups: user-type [2]
usertype bikeid n
<chr> <chr> <int>
1 casual classic_bike 1139872
2 casual docked_bike 247000
3 casual electric_bike 468959
4 member classic_bike 1892931
5 member docked_bike 1
6 member electric_bike 552178Visualization
#viz
ggplot(data = year_2021_df_7, mapping = aes(x= bikeid, fill = bikeid)) +
#applied the mapping here an not in the geom_bar as it will be applied to the whole built-up layers
#as aes()in geom_function will be only applied here (hierarchy level)
geom_bar()+
labs(title = "Number of rides made by bike type",caption = "Casual Vs Member",x=" bike type", y="Number of rides")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = -90, vjust = 0.5, hjust = 1))+ #centering the title
scale_y_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "Bike_type")+ # to change the name of the legend
geom_text(aes(label = after_stat(count)), stat = "count", vjust = 0, colour = "black")+
facet_wrap(~usertype)
#ggsave("plot3-Number of rides per each bike type Casual VS Member.png")
Number of rides per each bike type Casual VS Member
Total of rides(Start_timing)
#count the total number of rides per start_timing
year_2021_df_7 %>%
count(start_timing)Results
# A tibble: 4 × 2
# Rowwise:
start_timing n
<chr> <int>
1 Afternoon 1455981
2 Evening 1288296
3 Morning 1034497
4 Night 522167#count the total number of rides per start_timing and per user_type
year_2021_df_7 %>%
group_by(usertype) %>%
count(start_timing)Results
# A tibble: 8 × 3
# Groups: user type [2]
usertype start_timing n
<chr> <chr> <int>
1 casual Afternoon 673582
2 casual Evening 548857
3 casual Morning 340136
4 casual Night 293256
5 member Afternoon 782399
6 member Evening 739439
7 member Morning 694361
8 member Night 228911#count the total number of rides per start_timing for each bike
year_2021_df_7 %>%
group_by(bikeid) %>%
count(start_timing)Results
# A tibble: 12 × 3
# Groups: bikeid [3]
bikeid start_timing n
<chr> <chr> <int>
1 classic_bike Afternoon 1012520
2 classic_bike Evening 934369
3 classic_bike Morning 735620
4 classic_bike Night 350294
5 docked_bike Afternoon 103142
6 docked_bike Evening 65610
7 docked_bike Morning 41147
8 docked_bike Night 37102
9 electric_bike Afternoon 340319
10 electric_bike Evening 288317
11 electric_bike Morning 257730
12 electric_bike Night 134771#count the total number of rides per start_timing for each bike per user_type
year_2021_df_7 %>%
group_by(usertype,bikeid) %>%
count(start_timing) %>%
print(n=22)# to show the whole tibbleResults
# A tibble: 21 × 4
# Groups: usertype, bikeid [6]
usertype bikeid start_timing n
<chr> <chr> <chr> <int>
1 casual classic_bike Afternoon 405859
2 casual classic_bike Evening 349452
3 casual classic_bike Morning 204895
4 casual classic_bike Night 179666
5 casual docked_bike Afternoon 103142
6 casual docked_bike Evening 65609
7 casual docked_bike Morning 41147
8 casual docked_bike Night 37102
9 casual electric_bike Afternoon 164581
10 casual electric_bike Evening 133796
11 casual electric_bike Morning 94094
12 casual electric_bike Night 76488
13 member classic_bike Afternoon 606661
14 member classic_bike Evening 584917
15 member classic_bike Morning 530725
16 member classic_bike Night 170628
17 member docked_bike Evening 1
18 member electric_bike Afternoon 175738
19 member electric_bike Evening 154521
20 member electric_bike Morning 163636
21 member electric_bike Night 58283Visualization
#viz
ggplot(data = year_2021_df_7, mapping = aes(x= start_timing, fill = start_timing)) +
#applied the mapping here an not in the geom_bar as it will be applied to the whole built-up layers
#as aes()in geom_function will be only applied here (hierarchy level)
geom_bar()+
labs(title = "Number of rides made through the day",caption = "Casual Vs Member",x="start timing", y="Number of rides")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = -90, vjust = 0.5, hjust = 1))+ #centering the title
scale_y_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "Start_timing")+ # to change the name of the legend
facet_wrap(~usertype~bikeid)
#ggsave("plot4-Number of rides per each bike type Casual VS Member through the day.png")
Number of rides per each bike type Casual VS Member through the day
Total number of rides (Hour)
#----------------------------hour-------------------
#count the total number of rides per hour
year_2021_df_7 %>%
count(start_hour) %>%
print(n=25)Results
# A tibble: 24 × 2
# Rowwise:
start_hour n
<chr> <int>
1 00 61924
2 01 43236
3 02 26198
4 03 14039
5 04 11921
6 05 33161
7 06 87734
8 07 160728
9 08 191680
10 09 159838
11 10 178295
12 11 223061
13 12 261119
14 13 267120
15 14 268117
16 15 297452
17 16 362173
18 17 443229
19 18 380194
20 19 275733
21 20 189140
22 21 148577
23 22 125308
24 23 90964#count the total number of rides per hour for each user-type
year_2021_df_7 %>%
group_by(usertype) %>%
count(start_hour) %>%
print(n=49)Results
# A tibble: 48 × 3
# Groups: usertype [2]
usertype start_hour n
<chr> <chr> <int>
1 casual 00 37539
2 casual 01 27479
3 casual 02 17589
4 casual 03 9160
5 casual 04 6102
6 casual 05 8144
7 casual 06 18089
8 casual 07 33858
9 casual 08 46041
10 casual 09 55338
11 casual 10 77047
12 casual 11 101619
13 casual 12 121028
14 casual 13 129017
15 casual 14 131900
16 casual 15 138705
17 casual 16 152932
18 casual 17 179383
19 casual 18 160654
20 casual 19 121427
21 casual 20 87393
22 casual 21 74257
23 casual 22 68717
24 casual 23 52413
25 member 00 24385
26 member 01 15757
27 member 02 8609
28 member 03 4879
29 member 04 5819
30 member 05 25017
31 member 06 69645
32 member 07 126870
33 member 08 145639
34 member 09 104500
35 member 10 101248
36 member 11 121442
37 member 12 140091
38 member 13 138103
39 member 14 136217
40 member 15 158747
41 member 16 209241
42 member 17 263846
43 member 18 219540
44 member 19 154306
45 member 20 101747
46 member 21 74320
47 member 22 56591
48 member 23 38551#count the total number of rides per hour for each bike id
year_2021_df_7 %>%
group_by(bikeid) %>%
count(start_hour) %>%
print(n=80)Results
# A tibble: 72 × 3
# Groups: bikeid [3]
bikeid start_hour n
<chr> <chr> <int>
1 classic_bike 00 40679
2 classic_bike 01 28571
3 classic_bike 02 16989
4 classic_bike 03 8806
5 classic_bike 04 7447
6 classic_bike 05 23054
7 classic_bike 06 62866
8 classic_bike 07 116619
9 classic_bike 08 137815
10 classic_bike 09 113360
11 classic_bike 10 125135
12 classic_bike 11 156771
13 classic_bike 12 182974
14 classic_bike 13 185598
15 classic_bike 14 185198
16 classic_bike 15 205376
17 classic_bike 16 253374
18 classic_bike 17 320914
19 classic_bike 18 279473
20 classic_bike 19 200270
21 classic_bike 20 133712
22 classic_bike 21 102281
23 classic_bike 22 84839
24 classic_bike 23 60682
25 docked_bike 00 4833
26 docked_bike 01 3385
27 docked_bike 02 2257
28 docked_bike 03 1287
29 docked_bike 04 759
30 docked_bike 05 656
31 docked_bike 06 1171
32 docked_bike 07 2329
33 docked_bike 08 3769
34 docked_bike 09 6830
35 docked_bike 10 11304
36 docked_bike 11 15088
37 docked_bike 12 18434
38 docked_bike 13 20517
39 docked_bike 14 21175
40 docked_bike 15 21188
41 docked_bike 16 21828
42 docked_bike 17 21876
43 docked_bike 18 17970
44 docked_bike 19 14609
45 docked_bike 20 11155
46 docked_bike 21 9410
47 docked_bike 22 8601
48 docked_bike 23 6570
49 electric_bike 00 16412
50 electric_bike 01 11280
51 electric_bike 02 6952
52 electric_bike 03 3946
53 electric_bike 04 3715
54 electric_bike 05 9451
55 electric_bike 06 23697
56 electric_bike 07 41780
57 electric_bike 08 50096
58 electric_bike 09 39648
59 electric_bike 10 41856
60 electric_bike 11 51202
61 electric_bike 12 59711
62 electric_bike 13 61005
63 electric_bike 14 61744
64 electric_bike 15 70888
65 electric_bike 16 86971
66 electric_bike 17 100439
67 electric_bike 18 82751
68 electric_bike 19 60854
69 electric_bike 20 44273
70 electric_bike 21 36886
71 electric_bike 22 31868
72 electric_bike 23 23712#count the total number of rides per hour for each bike id for each user-type
year_2021_df_7 %>%
group_by(usertype,bikeid) %>%
count(start_hour) %>%
print(n=122)Results
# A tibble: 121 × 4
# Groups: usertype, bikeid [6]
usertype bikeid start_hour n
<chr> <chr> <chr> <int>
1 casual classic_bike 00 22770
2 casual classic_bike 01 17022
3 casual classic_bike 02 10709
4 casual classic_bike 03 5238
5 casual classic_bike 04 3358
6 casual classic_bike 05 4636
7 casual classic_bike 06 10639
8 casual classic_bike 07 20129
9 casual classic_bike 08 27804
10 casual classic_bike 09 33138
11 casual classic_bike 10 46489
12 casual classic_bike 11 62060
13 casual classic_bike 12 73263
14 casual classic_bike 13 77858
15 casual classic_bike 14 79344
16 casual classic_bike 15 83311
17 casual classic_bike 16 92083
18 casual classic_bike 17 113091
19 casual classic_bike 18 104796
20 casual classic_bike 19 77464
21 casual classic_bike 20 54101
22 casual classic_bike 21 45739
23 casual classic_bike 22 42452
24 casual classic_bike 23 32378
25 casual docked_bike 00 4833
26 casual docked_bike 01 3385
27 casual docked_bike 02 2257
28 casual docked_bike 03 1287
29 casual docked_bike 04 759
30 casual docked_bike 05 656
31 casual docked_bike 06 1171
32 casual docked_bike 07 2329
33 casual docked_bike 08 3769
34 casual docked_bike 09 6830
35 casual docked_bike 10 11304
36 casual docked_bike 11 15088
37 casual docked_bike 12 18434
38 casual docked_bike 13 20517
39 casual docked_bike 14 21175
40 casual docked_bike 15 21188
41 casual docked_bike 16 21828
42 casual docked_bike 17 21876
43 casual docked_bike 18 17969
44 casual docked_bike 19 14609
45 casual docked_bike 20 11155
46 casual docked_bike 21 9410
47 casual docked_bike 22 8601
48 casual docked_bike 23 6570
49 casual electric_bike 00 9936
50 casual electric_bike 01 7072
51 casual electric_bike 02 4623
52 casual electric_bike 03 2635
53 casual electric_bike 04 1985
54 casual electric_bike 05 2852
55 casual electric_bike 06 6279
56 casual electric_bike 07 11400
57 casual electric_bike 08 14468
58 casual electric_bike 09 15370
59 casual electric_bike 10 19254
60 casual electric_bike 11 24471
61 casual electric_bike 12 29331
62 casual electric_bike 13 30642
63 casual electric_bike 14 31381
64 casual electric_bike 15 34206
65 casual electric_bike 16 39021
66 casual electric_bike 17 44416
67 casual electric_bike 18 37889
68 casual electric_bike 19 29354
69 casual electric_bike 20 22137
70 casual electric_bike 21 19108
71 casual electric_bike 22 17664
72 casual electric_bike 23 13465
73 member classic_bike 00 17909
74 member classic_bike 01 11549
75 member classic_bike 02 6280
76 member classic_bike 03 3568
77 member classic_bike 04 4089
78 member classic_bike 05 18418
79 member classic_bike 06 52227
80 member classic_bike 07 96490
81 member classic_bike 08 110011
82 member classic_bike 09 80222
83 member classic_bike 10 78646
84 member classic_bike 11 94711
85 member classic_bike 12 109711
86 member classic_bike 13 107740
87 member classic_bike 14 105854
88 member classic_bike 15 122065
89 member classic_bike 16 161291
90 member classic_bike 17 207823
91 member classic_bike 18 174677
92 member classic_bike 19 122806
93 member classic_bike 20 79611
94 member classic_bike 21 56542
95 member classic_bike 22 42387
96 member classic_bike 23 28304
97 member docked_bike 18 1
98 member electric_bike 00 6476
99 member electric_bike 01 4208
100 member electric_bike 02 2329
101 member electric_bike 03 1311
102 member electric_bike 04 1730
103 member electric_bike 05 6599
104 member electric_bike 06 17418
105 member electric_bike 07 30380
106 member electric_bike 08 35628
107 member electric_bike 09 24278
108 member electric_bike 10 22602
109 member electric_bike 11 26731
110 member electric_bike 12 30380
111 member electric_bike 13 30363
112 member electric_bike 14 30363
113 member electric_bike 15 36682
114 member electric_bike 16 47950
115 member electric_bike 17 56023
116 member electric_bike 18 44862
117 member electric_bike 19 31500
118 member electric_bike 20 22136
119 member electric_bike 21 17778
120 member electric_bike 22 14204
121 member electric_bike 23 10247#count the total number of rides per hour for each bike id for each user-type and
#start-timing to get the busy hour for each category
year_2021_df_7 %>%
group_by(usertype,bikeid,start_timing) %>%
count(start_hour) %>%
print(n=122)Results
# A tibble: 121 × 5
# Groups: usertype, bikeid, start_timing [21]
usertype bikeid start_timing start_hour n
<chr> <chr> <chr> <chr> <int>
1 casual classic_bike Afternoon 12 73263
2 casual classic_bike Afternoon 13 77858
3 casual classic_bike Afternoon 14 79344
4 casual classic_bike Afternoon 15 83311
5 casual classic_bike Afternoon 16 92083
6 casual classic_bike Evening 17 113091
7 casual classic_bike Evening 18 104796
8 casual classic_bike Evening 19 77464
9 casual classic_bike Evening 20 54101
10 casual classic_bike Morning 05 4636
11 casual classic_bike Morning 06 10639
12 casual classic_bike Morning 07 20129
13 casual classic_bike Morning 08 27804
14 casual classic_bike Morning 09 33138
15 casual classic_bike Morning 10 46489
16 casual classic_bike Morning 11 62060
17 casual classic_bike Night 00 22770
18 casual classic_bike Night 01 17022
19 casual classic_bike Night 02 10709
20 casual classic_bike Night 03 5238
21 casual classic_bike Night 04 3358
22 casual classic_bike Night 21 45739
23 casual classic_bike Night 22 42452
24 casual classic_bike Night 23 32378
25 casual docked_bike Afternoon 12 18434
26 casual docked_bike Afternoon 13 20517
27 casual docked_bike Afternoon 14 21175
28 casual docked_bike Afternoon 15 21188
29 casual docked_bike Afternoon 16 21828
30 casual docked_bike Evening 17 21876
31 casual docked_bike Evening 18 17969
32 casual docked_bike Evening 19 14609
33 casual docked_bike Evening 20 11155
34 casual docked_bike Morning 05 656
35 casual docked_bike Morning 06 1171
36 casual docked_bike Morning 07 2329
37 casual docked_bike Morning 08 3769
38 casual docked_bike Morning 09 6830
39 casual docked_bike Morning 10 11304
40 casual docked_bike Morning 11 15088
41 casual docked_bike Night 00 4833
42 casual docked_bike Night 01 3385
43 casual docked_bike Night 02 2257
44 casual docked_bike Night 03 1287
45 casual docked_bike Night 04 759
46 casual docked_bike Night 21 9410
47 casual docked_bike Night 22 8601
48 casual docked_bike Night 23 6570
49 casual electric_bike Afternoon 12 29331
50 casual electric_bike Afternoon 13 30642
51 casual electric_bike Afternoon 14 31381
52 casual electric_bike Afternoon 15 34206
53 casual electric_bike Afternoon 16 39021
54 casual electric_bike Evening 17 44416
55 casual electric_bike Evening 18 37889
56 casual electric_bike Evening 19 29354
57 casual electric_bike Evening 20 22137
58 casual electric_bike Morning 05 2852
59 casual electric_bike Morning 06 6279
60 casual electric_bike Morning 07 11400
61 casual electric_bike Morning 08 14468
62 casual electric_bike Morning 09 15370
63 casual electric_bike Morning 10 19254
64 casual electric_bike Morning 11 24471
65 casual electric_bike Night 00 9936
66 casual electric_bike Night 01 7072
67 casual electric_bike Night 02 4623
68 casual electric_bike Night 03 2635
69 casual electric_bike Night 04 1985
70 casual electric_bike Night 21 19108
71 casual electric_bike Night 22 17664
72 casual electric_bike Night 23 13465
73 member classic_bike Afternoon 12 109711
74 member classic_bike Afternoon 13 107740
75 member classic_bike Afternoon 14 105854
76 member classic_bike Afternoon 15 122065
77 member classic_bike Afternoon 16 161291
78 member classic_bike Evening 17 207823
79 member classic_bike Evening 18 174677
80 member classic_bike Evening 19 122806
81 member classic_bike Evening 20 79611
82 member classic_bike Morning 05 18418
83 member classic_bike Morning 06 52227
84 member classic_bike Morning 07 96490
85 member classic_bike Morning 08 110011
86 member classic_bike Morning 09 80222
87 member classic_bike Morning 10 78646
88 member classic_bike Morning 11 94711
89 member classic_bike Night 00 17909
90 member classic_bike Night 01 11549
91 member classic_bike Night 02 6280
92 member classic_bike Night 03 3568
93 member classic_bike Night 04 4089
94 member classic_bike Night 21 56542
95 member classic_bike Night 22 42387
96 member classic_bike Night 23 28304
97 member docked_bike Evening 18 1
98 member electric_bike Afternoon 12 30380
99 member electric_bike Afternoon 13 30363
100 member electric_bike Afternoon 14 30363
101 member electric_bike Afternoon 15 36682
102 member electric_bike Afternoon 16 47950
103 member electric_bike Evening 17 56023
104 member electric_bike Evening 18 44862
105 member electric_bike Evening 19 31500
106 member electric_bike Evening 20 22136
107 member electric_bike Morning 05 6599
108 member electric_bike Morning 06 17418
109 member electric_bike Morning 07 30380
110 member electric_bike Morning 08 35628
111 member electric_bike Morning 09 24278
112 member electric_bike Morning 10 22602
113 member electric_bike Morning 11 26731
114 member electric_bike Night 00 6476
115 member electric_bike Night 01 4208
116 member electric_bike Night 02 2329
117 member electric_bike Night 03 1311
118 member electric_bike Night 04 1730
119 member electric_bike Night 21 17778
120 member electric_bike Night 22 14204
121 member electric_bike Night 23 10247Visualization
#viz
ggplot(data = year_2021_df_7, mapping = aes(y= start_hour, fill = start_hour)) +
#applied the mapping here an not in the geom_bar as it will be applied to the whole built-up layers
#as aes()in geom_function will be only applied here (hierarchy level)
geom_bar()+
labs(title = "Number of rides made per hour",caption = "Casual Vs Member",y="Hour of order", x="Number of rides")+
theme(plot.title = element_text(hjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "Hour")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("plot5-Number of rides per hour.png")
Number of rides per hour
Total Number of rides(Day)
#----------------------------day-------------------
#count the total number of rides per each day
year_2021_df_7 %>%
count(day_of_order)Results
# A tibble: 7 × 2
# Rowwise:
day_of_order n
<chr> <int>
1 Friday 619393
2 Monday 537042
3 Saturday 768328
4 Sunday 656482
5 Thursday 567250
6 Tuesday 569082
7 Wednesday 583364#count the total number of rides per each day for each user-type
year_2021_df_7 %>%
group_by(usertype) %>%
count(day_of_order)Results
# A tibble: 14 × 3
# Groups: usertype [2]
usertype day_of_order n
<chr> <chr> <int>
1 casual Friday 266591
2 casual Monday 204025
3 casual Saturday 425797
4 casual Sunday 359535
5 casual Thursday 206358
6 casual Tuesday 194436
7 casual Wednesday 199089
8 member Friday 352802
9 member Monday 333017
10 member Saturday 342531
11 member Sunday 296947
12 member Thursday 360892
13 member Tuesday 374646
14 member Wednesday 384275#count the total number of rides per each day for each bike id
year_2021_df_7 %>%
group_by(bikeid) %>%
count(day_of_order) %>%
print(n=22)Results
# A tibble: 21 × 3
# Groups: bikeid [3]
bikeid day_of_order n
<chr> <chr> <int>
1 classic_bike Friday 430521
2 classic_bike Monday 377183
3 classic_bike Saturday 545073
4 classic_bike Sunday 467606
5 classic_bike Thursday 401147
6 classic_bike Tuesday 398373
7 classic_bike Wednesday 412900
8 docked_bike Friday 34368
9 docked_bike Monday 27905
10 docked_bike Saturday 63685
11 docked_bike Sunday 54545
12 docked_bike Thursday 22129
13 docked_bike Tuesday 22654
14 docked_bike Wednesday 21715
15 electric_bike Friday 154504
16 electric_bike Monday 131954
17 electric_bike Saturday 159570
18 electric_bike Sunday 134331
19 electric_bike Thursday 143974
20 electric_bike Tuesday 148055
21 electric_bike Wednesday 148749#count the total number of rides per each day for each bike id for each user-type
year_2021_df_7 %>%
group_by(usertype,bikeid) %>%
count(day_of_order) %>%
print(n=36)Results
# A tibble: 36 × 4
# Groups: usertype, bikeid [6]
usertype bikeid day_of_order n
<chr> <chr> <chr> <int>
1 casual classic_bike Friday 161224
2 casual classic_bike Monday 119641
3 casual classic_bike Saturday 273369
4 casual classic_bike Sunday 229010
5 casual classic_bike Thursday 125237
6 casual classic_bike Tuesday 112669
7 casual classic_bike Wednesday 118722
8 casual docked_bike Friday 34368
9 casual docked_bike Monday 27905
10 casual docked_bike Saturday 63685
11 casual docked_bike Sunday 54545
12 casual docked_bike Thursday 22129
13 casual docked_bike Tuesday 22654
14 casual docked_bike Wednesday 21714
15 casual electric_bike Friday 70999
16 casual electric_bike Monday 56479
17 casual electric_bike Saturday 88743
18 casual electric_bike Sunday 75980
19 casual electric_bike Thursday 58992
20 casual electric_bike Tuesday 59113
21 casual electric_bike Wednesday 58653
22 member classic_bike Friday 269297
23 member classic_bike Monday 257542
24 member classic_bike Saturday 271704
25 member classic_bike Sunday 238596
26 member classic_bike Thursday 275910
27 member classic_bike Tuesday 285704
28 member classic_bike Wednesday 294178
29 member docked_bike Wednesday 1
30 member electric_bike Friday 83505
31 member electric_bike Monday 75475
32 member electric_bike Saturday 70827
33 member electric_bike Sunday 58351
34 member electric_bike Thursday 84982
35 member electric_bike Tuesday 88942
36 member electric_bike Wednesday 90096#count the total number of rides to know the busy day for start timing
year_2021_df_7 %>%
group_by(day_of_order) %>%
count(start_timing) %>%
print(n=2000)Results
# A tibble: 28 × 3
# Groups: day_of_order [7]
day_of_order start_timing n
<chr> <chr> <int>
1 Friday Afternoon 206081
2 Friday Evening 190997
3 Friday Morning 142968
4 Friday Night 79347
5 Monday Afternoon 173540
6 Monday Evening 177448
7 Monday Morning 137065
8 Monday Night 48989
9 Saturday Afternoon 303651
10 Saturday Evening 174823
11 Saturday Morning 166744
12 Saturday Night 123110
13 Sunday Afternoon 276742
14 Sunday Evening 145008
15 Sunday Morning 138489
16 Sunday Night 96243
17 Thursday Afternoon 162224
18 Thursday Evening 195288
19 Thursday Morning 143724
20 Thursday Night 66014
21 Tuesday Afternoon 167637
22 Tuesday Evening 199117
23 Tuesday Morning 151849
24 Tuesday Night 50479
25 Wednesday Afternoon 166106
26 Wednesday Evening 205615
27 Wednesday Morning 153658
28 Wednesday Night 57985#count the total number of rides per each day for each hour per start timing
year_2021_df_7 %>%
group_by(start_hour, start_timing ) %>%
count(day_of_order) %>%
print(n=200)Results
# A tibble: 168 × 4
# Groups: start_hour, start_timing [24]
start_hour start_timing day_of_order n
<chr> <chr> <chr> <int>
1 00 Night Friday 7405
2 00 Night Monday 4927
3 00 Night Saturday 17064
4 00 Night Sunday 20972
5 00 Night Thursday 4589
6 00 Night Tuesday 3157
7 00 Night Wednesday 3810
8 01 Night Friday 4454
9 01 Night Monday 2889
10 01 Night Saturday 14704
11 01 Night Sunday 15751
12 01 Night Thursday 2194
13 01 Night Tuesday 1609
14 01 Night Wednesday 1635
15 02 Night Friday 2181
16 02 Night Monday 1774
17 02 Night Saturday 8856
18 02 Night Sunday 10352
19 02 Night Thursday 1138
20 02 Night Tuesday 924
21 02 Night Wednesday 973
22 03 Night Friday 1272
23 03 Night Monday 1182
24 03 Night Saturday 4047
25 03 Night Sunday 5478
26 03 Night Thursday 770
27 03 Night Tuesday 652
28 03 Night Wednesday 638
29 04 Night Friday 1384
30 04 Night Monday 1650
31 04 Night Saturday 2240
32 04 Night Sunday 3085
33 04 Night Thursday 1193
34 04 Night Tuesday 1176
35 04 Night Wednesday 1193
36 05 Morning Friday 4933
37 05 Morning Monday 5190
38 05 Morning Saturday 2619
39 05 Morning Sunday 2717
40 05 Morning Thursday 5519
41 05 Morning Tuesday 6222
42 05 Morning Wednesday 5961
43 06 Morning Friday 13543
44 06 Morning Monday 14218
45 06 Morning Saturday 5608
46 06 Morning Sunday 4915
47 06 Morning Thursday 15348
48 06 Morning Tuesday 17179
49 06 Morning Wednesday 16923
50 07 Morning Friday 23976
51 07 Morning Monday 25574
52 07 Morning Saturday 10503
53 07 Morning Sunday 8205
54 07 Morning Thursday 28638
55 07 Morning Tuesday 31919
56 07 Morning Wednesday 31913
57 08 Morning Friday 27909
58 08 Morning Monday 28628
59 08 Morning Saturday 18973
60 08 Morning Sunday 14140
61 08 Morning Thursday 32128
62 08 Morning Tuesday 34571
63 08 Morning Wednesday 35331
64 09 Morning Friday 21496
65 09 Morning Monday 19320
66 09 Morning Saturday 30865
67 09 Morning Sunday 24324
68 09 Morning Thursday 20528
69 09 Morning Tuesday 21307
70 09 Morning Wednesday 21998
71 10 Morning Friday 22508
72 10 Morning Monday 19307
73 10 Morning Saturday 44276
74 10 Morning Sunday 36983
75 10 Morning Thursday 18571
76 10 Morning Tuesday 18143
77 10 Morning Wednesday 18507
78 11 Morning Friday 28603
79 11 Morning Monday 24828
80 11 Morning Saturday 53900
81 11 Morning Sunday 47205
82 11 Morning Thursday 22992
83 11 Morning Tuesday 22508
84 11 Morning Wednesday 23025
85 12 Afternoon Friday 34814
86 12 Afternoon Monday 30144
87 12 Afternoon Saturday 60219
88 12 Afternoon Sunday 53407
89 12 Afternoon Thursday 27150
90 12 Afternoon Tuesday 27527
91 12 Afternoon Wednesday 27858
92 13 Afternoon Friday 36197
93 13 Afternoon Monday 30247
94 13 Afternoon Saturday 63062
95 13 Afternoon Sunday 55796
96 13 Afternoon Thursday 26573
97 13 Afternoon Tuesday 27873
98 13 Afternoon Wednesday 27372
99 14 Afternoon Friday 37471
100 14 Afternoon Monday 30286
101 14 Afternoon Saturday 62400
102 14 Afternoon Sunday 55659
103 14 Afternoon Thursday 27117
104 14 Afternoon Tuesday 27754
105 14 Afternoon Wednesday 27430
106 15 Afternoon Friday 43134
107 15 Afternoon Monday 35225
108 15 Afternoon Saturday 60031
109 15 Afternoon Sunday 57282
110 15 Afternoon Thursday 33420
111 15 Afternoon Tuesday 34541
112 15 Afternoon Wednesday 33819
113 16 Afternoon Friday 54465
114 16 Afternoon Monday 47638
115 16 Afternoon Saturday 57939
116 16 Afternoon Sunday 54598
117 16 Afternoon Thursday 47964
118 16 Afternoon Tuesday 49942
119 16 Afternoon Wednesday 49627
120 17 Evening Friday 64906
121 17 Evening Monday 63112
122 17 Evening Saturday 55037
123 17 Evening Sunday 49406
124 17 Evening Thursday 66298
125 17 Evening Tuesday 72130
126 17 Evening Wednesday 72340
127 18 Evening Friday 56900
128 18 Evening Monday 53031
129 18 Evening Saturday 49619
130 18 Evening Sunday 41159
131 18 Evening Thursday 58512
132 18 Evening Tuesday 59757
133 18 Evening Wednesday 61216
134 19 Evening Friday 41291
135 19 Evening Monday 36945
136 19 Evening Saturday 40155
137 19 Evening Sunday 31636
138 19 Evening Thursday 41820
139 19 Evening Tuesday 40960
140 19 Evening Wednesday 42926
141 20 Evening Friday 27900
142 20 Evening Monday 24360
143 20 Evening Saturday 30012
144 20 Evening Sunday 22807
145 20 Evening Thursday 28658
146 20 Evening Tuesday 26270
147 20 Evening Wednesday 29133
148 21 Night Friday 22370
149 21 Night Monday 17432
150 21 Night Saturday 25650
151 21 Night Sunday 17580
152 21 Night Thursday 23024
153 21 Night Tuesday 19433
154 21 Night Wednesday 23088
155 22 Night Friday 20551
156 22 Night Monday 12402
157 22 Night Saturday 25947
158 22 Night Sunday 14278
159 22 Night Thursday 19902
160 22 Night Tuesday 15125
161 22 Night Wednesday 17103
162 23 Night Friday 19730
163 23 Night Monday 6733
164 23 Night Saturday 24602
165 23 Night Sunday 8747
166 23 Night Thursday 13204
167 23 Night Tuesday 8403
168 23 Night Wednesday 9545#count the total number of rides per each day for each hour per start timing for each bike id for each user-type
year_2021_df_7 %>%
group_by(usertype, bikeid,start_hour, start_timing ) %>%
count(day_of_order) %>%
print(n=200)Results
# A tibble: 841 × 6
# Groups: usertype, bikeid, start_hour, start_timing [121]
usertype bikeid start_hour start_timing day_of_order n
<chr> <chr> <chr> <chr> <chr> <int>
1 casual classic_bike 00 Night Friday 2472
2 casual classic_bike 00 Night Monday 1721
3 casual classic_bike 00 Night Saturday 6616
4 casual classic_bike 00 Night Sunday 8602
5 casual classic_bike 00 Night Thursday 1394
6 casual classic_bike 00 Night Tuesday 870
7 casual classic_bike 00 Night Wednesday 1095
8 casual classic_bike 01 Night Friday 1589
9 casual classic_bike 01 Night Monday 1032
10 casual classic_bike 01 Night Saturday 6217
# … with 831 more rows
# ℹ Use `print(n = ...)` to see more rowsVisualization
#viz
ggplot(data = year_2021_df_7, mapping = aes(y= day_of_order, fill = day_of_order)) +
#applied the mapping here an not in the geom_bar as it will be applied to the whole built-up layers
#as aes()in geom_function will be only applied here (hierarchy level)
geom_bar()+
labs(title = "Number of rides made per day",caption = "Casual Vs Member",y="day_of_order", x="Number of rides")+
theme(plot.title = element_text(hjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "Hour")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("plot6-Number of rides per day.png")
Number of rides per day
Total number of rides(Month)
#----------------------------Month-------------------
#count the total number of rides per each month
year_2021_df_7 %>%
count(month)Results
# A tibble: 12 × 2
# Rowwise:
month n
<chr> <int>
1 April 273257
2 August 633825
3 December 169964
4 February 40128
5 January 79652
6 July 646656
7 June 564492
8 March 189092
9 May 414823
10 November 246379
11 October 455885
12 September 586788#count the total number of rides per each month per user-type
year_2021_df_7 %>%
group_by(usertype) %>%
count(month) %>%
print(n=24)Results
# A tibble: 24 × 3
# Groups: usertype [2]
usertype month n
<chr> <chr> <int>
1 casual April 103298
2 casual August 313231
3 casual December 42237
4 casual February 7547
5 casual January 13230
6 casual July 336030
7 casual June 272036
8 casual March 64713
9 casual May 190354
10 casual November 65961
11 casual October 176865
12 casual September 270329
13 member April 169959
14 member August 320594
15 member December 127727
16 member February 32581
17 member January 66422
18 member July 310626
19 member June 292456
20 member March 124379
21 member May 224469
22 member November 180418
23 member October 279020
24 member September 316459#count the total number of rides per each month per bike
year_2021_df_7 %>%
group_by(bikeid) %>%
count(month) %>%
print(n=36) Results
# A tibble: 36 × 3
# Groups: bikeid [3]
bikeid month n
<chr> <chr> <int>
1 classic_bike April 196948
2 classic_bike August 471082
3 classic_bike December 95577
4 classic_bike February 32149
5 classic_bike January 58246
6 classic_bike July 472362
7 classic_bike June 402555
8 classic_bike March 140357
9 classic_bike May 283957
10 classic_bike November 146550
11 classic_bike October 298343
12 classic_bike September 434677
13 docked_bike April 17523
14 docked_bike August 36801
15 docked_bike December 4088
16 docked_bike February 969
17 docked_bike January 1589
18 docked_bike July 46743
19 docked_bike June 40142
20 docked_bike March 11218
21 docked_bike May 32669
22 docked_bike November 6515
23 docked_bike October 19430
24 docked_bike September 29314
25 electric_bike April 58786
26 electric_bike August 125942
27 electric_bike December 70299
28 electric_bike February 7010
29 electric_bike January 19817
30 electric_bike July 127551
31 electric_bike June 121795
32 electric_bike March 37517
33 electric_bike May 98197
34 electric_bike November 93314
35 electric_bike October 138112
36 electric_bike September 122797#count the total number of rides per each month to know the busy day
year_2021_df_7 %>%
group_by(month) %>%
count(day_of_order) %>%
print(n=100) Results
# A tibble: 84 × 3
# Groups: month [12]
month day_of_order n
<chr> <chr> <int>
1 April Friday 48121
2 April Monday 35358
3 April Saturday 44142
4 April Sunday 40211
5 April Thursday 33082
6 April Tuesday 41763
7 April Wednesday 30580
8 August Friday 87681
9 August Monday 85360
10 August Saturday 108119
11 August Sunday 112191
12 August Thursday 80416
13 August Tuesday 87152
14 August Wednesday 72906
15 December Friday 28629
16 December Monday 21228
17 December Saturday 20047
18 December Sunday 15768
19 December Thursday 33061
20 December Tuesday 20113
21 December Wednesday 31118
22 February Friday 6471
23 February Monday 3699
24 February Saturday 8034
25 February Sunday 4363
26 February Thursday 5760
27 February Tuesday 5488
28 February Wednesday 6313
29 January Friday 12618
30 January Monday 10964
31 January Saturday 13269
32 January Sunday 9491
33 January Thursday 11883
34 January Tuesday 10361
35 January Wednesday 11066
36 July Friday 106684
37 July Monday 74002
38 July Saturday 129396
39 July Sunday 85169
40 July Thursday 96085
41 July Tuesday 76465
42 July Wednesday 78855
43 June Friday 77544
44 June Monday 60849
45 June Saturday 94343
46 June Sunday 88126
47 June Thursday 70206
48 June Tuesday 84793
49 June Wednesday 88631
50 March Friday 21555
51 March Monday 28528
52 March Saturday 37511
53 March Sunday 28940
54 March Thursday 18127
55 March Tuesday 28090
56 March Wednesday 26341
57 May Friday 51368
58 May Monday 59015
59 May Saturday 87746
60 May Sunday 80493
61 May Thursday 45988
62 May Tuesday 41098
63 May Wednesday 49115
64 November Friday 28725
65 November Monday 41463
66 November Saturday 33924
67 November Sunday 29827
68 November Thursday 28555
69 November Tuesday 46955
70 November Wednesday 36930
71 October Friday 71008
72 October Monday 45730
73 October Saturday 94529
74 October Sunday 72634
75 October Thursday 48909
76 October Tuesday 61918
77 October Wednesday 61157
78 September Friday 78989
79 September Monday 70846
80 September Saturday 97268
81 September Sunday 89269
82 September Thursday 95178
83 September Tuesday 64886
84 September Wednesday 90352#count the total number of rides per each month to know the busy day for start timing and hour
year_2021_df_7 %>%
group_by(month,day_of_order) %>%
count(start_timing) %>%
print(n=2000) Results
# A tibble: 336 × 4
# Groups: month, day_of_order [84]
month day_of_order start_timing n
<chr> <chr> <chr> <int>
1 April Friday Afternoon 16614
2 April Friday Evening 16417
3 April Friday Morning 10696
4 April Friday Night 4394
5 April Monday Afternoon 10885
6 April Monday Evening 13517
7 April Monday Morning 8098
8 April Monday Night 2858
9 April Saturday Afternoon 19525
10 April Saturday Evening 10508
# … with 326 more rows
# ℹ Use `print(n = ...)` to see more rows#count the total number of rides per each month to know the busy day for start timing and hour
year_2021_df_7 %>%
group_by(month,day_of_order, start_timing) %>%
count(start_hour) %>%
print(n=2000) Results
# A tibble: 2,016 × 5
# Groups: month, day_of_order, start_timing [336]
month day_of_order start_timing start_hour n
<chr> <chr> <chr> <chr> <int>
1 April Friday Afternoon 12 2655
2 April Friday Afternoon 13 2829
3 April Friday Afternoon 14 3024
4 April Friday Afternoon 15 3588
5 April Friday Afternoon 16 4518
6 April Friday Evening 17 5887
7 April Friday Evening 18 5057
8 April Friday Evening 19 3540
9 April Friday Evening 20 1933
10 April Friday Morning 05 418
# … with 2,006 more rows
# ℹ Use `print(n = ...)` to see more rows#count the total number of rides per each month to know the busy day for start timing and hour, user-type and bike id
year_2021_df_7 %>%
group_by(usertype, bikeid,month,day_of_order, start_timing) %>%
count(start_hour) %>%
print(n=2000) Results
# A tibble: 9,953 × 7
# Groups: usertype, bikeid, month, day_of_order, start_timing [1,681]
usertype bikeid month day_of_order start_timing start_hour n
<chr> <chr> <chr> <chr> <chr> <chr> <int>
1 casual classic_bike April Friday Afternoon 12 574
2 casual classic_bike April Friday Afternoon 13 685
3 casual classic_bike April Friday Afternoon 14 762
4 casual classic_bike April Friday Afternoon 15 805
5 casual classic_bike April Friday Afternoon 16 927
6 casual classic_bike April Friday Evening 17 1408
7 casual classic_bike April Friday Evening 18 1178
8 casual classic_bike April Friday Evening 19 875
9 casual classic_bike April Friday Evening 20 423
10 casual classic_bike April Friday Morning 05 34
# … with 9,943 more rows
# ℹ Use `print(n = ...)` to see more rowsVisualization
#viz
ggplot(data = year_2021_df_7, mapping = aes(y= month, fill = month)) +
#applied the mapping here an not in the geom_bar as it will be applied to the whole built-up layers
#as aes()in geom_function will be only applied here (hierarchy level)
geom_bar()+
labs(title = "Number of rides made per month",caption = "Casual Vs Member",y="Month", x="Number of rides")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "Month")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("plot7-Number of rides per Month.png") 
Number of rides per Month
Total rides(Season)
#----------------------------Season-------------------
#count the total number of rides per season
year_2021_df_7 %>%
count(season)Results
# A tibble: 4 × 2
# Rowwise:
season n
<chr> <int>
1 Autumn 1289052
2 Spring 877172
3 Summer 1844973
4 Winter 289744#count the number of rides for each user-type per season
year_2021_df_7 %>%
group_by(usertype) %>%
count(season)Results
# A tibble: 8 × 3
# Groups: usertype [2]
usertype season n
<chr> <chr> <int>
1 casual Autumn 513155
2 casual Spring 358365
3 casual Summer 921297
4 casual Winter 63014
5 member Autumn 775897
6 member Spring 518807
7 member Summer 923676
8 member Winter 226730#count the number of rides for each bike id per season
year_2021_df_7 %>%
group_by(bikeid) %>%
count(season)Results
# A tibble: 12 × 3
# Groups: bikeid [3]
bikeid season n
<chr> <chr> <int>
1 classic_bike Autumn 879570
2 classic_bike Spring 621262
3 classic_bike Summer 1345999
4 classic_bike Winter 185972
5 docked_bike Autumn 55259
6 docked_bike Spring 61410
7 docked_bike Summer 123686
8 docked_bike Winter 6646
9 electric_bike Autumn 354223
10 electric_bike Spring 194500
11 electric_bike Summer 375288
12 electric_bike Winter 97126#count the number of rides for each bike id per season for each user-type
year_2021_df_7 %>%
group_by(usertype,bikeid) %>%
count(season) %>%
print(n=22)Results
# A tibble: 21 × 4
# Groups: usertype, bikeid [6]
usertype bikeid season n
<chr> <chr> <chr> <int>
1 casual classic_bike Autumn 305328
2 casual classic_bike Spring 207978
3 casual classic_bike Summer 596506
4 casual classic_bike Winter 30060
5 casual docked_bike Autumn 55259
6 casual docked_bike Spring 61410
7 casual docked_bike Summer 123686
8 casual docked_bike Winter 6645
9 casual electric_bike Autumn 152568
10 casual electric_bike Spring 88977
11 casual electric_bike Summer 201105
12 casual electric_bike Winter 26309
13 member classic_bike Autumn 574242
14 member classic_bike Spring 413284
15 member classic_bike Summer 749493
16 member classic_bike Winter 155912
17 member docked_bike Winter 1
18 member electric_bike Autumn 201655
19 member electric_bike Spring 105523
20 member electric_bike Summer 174183
21 member electric_bike Winter 70817#find the peak month for each season according to each user-type
year_2021_df_7 %>%
group_by(usertype,month) %>%
count(season) %>%
print(n=24)Results
# A tibble: 24 × 4
# Groups: usertype, month [24]
usertype month season n
<chr> <chr> <chr> <int>
1 casual April Spring 103298
2 casual August Summer 313231
3 casual December Winter 42237
4 casual February Winter 7547
5 casual January Winter 13230
6 casual July Summer 336030
7 casual June Summer 272036
8 casual March Spring 64713
9 casual May Spring 190354
10 casual November Autumn 65961
11 casual October Autumn 176865
12 casual September Autumn 270329
13 member April Spring 169959
14 member August Summer 320594
15 member December Winter 127727
16 member February Winter 32581
17 member January Winter 66422
18 member July Summer 310626
19 member June Summer 292456
20 member March Spring 124379
21 member May Spring 224469
22 member November Autumn 180418
23 member October Autumn 279020
24 member September Autumn 316459Visualization
#viz
ggplot(data = year_2021_df_7, mapping = aes(y= season, fill = season)) +
#applied the mapping here an not in the geom_bar as it will be applied to the whole built-up layers
#as aes()in geom_function will be only applied here (hierarchy level)
geom_bar()+
labs(title = "Number of rides made per season",caption = "Casual Vs Member",y="season", x="Number of rides")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "season")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("plot8-Number of rides per season.png") 
Number of rides per season
Total rides(Year_quarters)
#----------------------------Year Quarter-------------------
#count the total number of rides per quarter
year_2021_df_7 %>%
count(year_quarter)Results
# A tibble: 4 × 2
# Rowwise:
year_quarter n
<chr> <int>
1 Q1 308872
2 Q2 1252572
3 Q3 1867269
4 Q4 872228#count the number of rides for each user-type per quarter
year_2021_df_7 %>%
group_by(usertype) %>%
count(year_quarter)Results
# A tibble: 8 × 3
# Groups: usertype [2]
usertype year_quarter n
<chr> <chr> <int>
1 casual Q1 85490
2 casual Q2 565688
3 casual Q3 919590
4 casual Q4 285063
5 member Q1 223382
6 member Q2 686884
7 member Q3 947679
8 member Q4 587165#count the number of rides for each bike id per quarter
year_2021_df_7 %>%
group_by(bikeid) %>%
count(year_quarter)Results
# A tibble: 12 × 3
# Groups: bikeid [3]
bikeid year_quarter n
<chr> <chr> <int>
1 classic_bike Q1 230752
2 classic_bike Q2 883460
3 classic_bike Q3 1378121
4 classic_bike Q4 540470
5 docked_bike Q1 13776
6 docked_bike Q2 90334
7 docked_bike Q3 112858
8 docked_bike Q4 30033
9 electric_bike Q1 64344
10 electric_bike Q2 278778
11 electric_bike Q3 376290
12 electric_bike Q4 301725#count the number of rides for each bike id per quarter for each user-type
year_2021_df_7 %>%
group_by(usertype,bikeid) %>%
count(year_quarter) %>%
print(n=22)Results
# A tibble: 21 × 4
# Groups: usertype, bikeid [6]
usertype bikeid year_quarter n
<chr> <chr> <chr> <int>
1 casual classic_bike Q1 51172
2 casual classic_bike Q2 336560
3 casual classic_bike Q3 608146
4 casual classic_bike Q4 143994
5 casual docked_bike Q1 13775
6 casual docked_bike Q2 90334
7 casual docked_bike Q3 112858
8 casual docked_bike Q4 30033
9 casual electric_bike Q1 20543
10 casual electric_bike Q2 138794
11 casual electric_bike Q3 198586
12 casual electric_bike Q4 111036
13 member classic_bike Q1 179580
14 member classic_bike Q2 546900
15 member classic_bike Q3 769975
16 member classic_bike Q4 396476
17 member docked_bike Q1 1
18 member electric_bike Q1 43801
19 member electric_bike Q2 139984
20 member electric_bike Q3 177704
21 member electric_bike Q4 190689 #find the peak month for each quarter according to each user-type
year_2021_df_7 %>%
group_by(usertype,month) %>%
count(year_quarter) %>%
print(n=24)Results
# A tibble: 24 × 4
# Groups: usertype, month [24]
usertype month year_quarter n
<chr> <chr> <chr> <int>
1 casual April Q2 103298
2 casual August Q3 313231
3 casual December Q4 42237
4 casual February Q1 7547
5 casual January Q1 13230
6 casual July Q3 336030
7 casual June Q2 272036
8 casual March Q1 64713
9 casual May Q2 190354
10 casual November Q4 65961
11 casual October Q4 176865
12 casual September Q3 270329
13 member April Q2 169959
14 member August Q3 320594
15 member December Q4 127727
16 member February Q1 32581
17 member January Q1 66422
18 member July Q3 310626
19 member June Q2 292456
20 member March Q1 124379
21 member May Q2 224469
22 member November Q4 180418
23 member October Q4 279020
24 member September Q3 316459Visualization
#viz
ggplot(data = year_2021_df_7, mapping = aes(y= year_quarter, fill = year_quarter)) +
#applied the mapping here an not in the geom_bar as it will be applied to the whole built-up layers
#as aes()in geom_function will be only applied here (hierarchy level)
geom_bar()+
labs(title = "Number of rides made per season",caption = "Casual Vs Member",y="year quarter", x="Number of rides")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "year quarter")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("plot9-Number of rides per year quarter.png") 
Number of rides per year quarters
Total rides(From_station)
#----------------------------station-------------------
#count the number of the rides by from_station
year_2021_df_7 %>%
count(from_station_name) %>%
print(n=836) Results
# A tibble: 836 × 2
# Rowwise:
from_station_name n
<chr> <int>
1 2112 w peterson ave 802
2 63rd st beach 1244
3 900 w harrison st 7685
4 aberdeen st & jackson blvd 10997
5 aberdeen st & monroe st 10359
6 aberdeen st & randolph st 9218
7 ada st & 113th st 30
8 ada st & washington blvd 8414
9 adler planetarium 12773
10 albany ave & 26th st 228
# … with 826 more rows
# ℹ Use `print(n = ...)` to see more rows#count the number of rides by from_station for each user-type
year_2021_df_7 %>%
group_by(usertype) %>%
count(from_station_name) %>%
print(n=1637) Results
# A tibble: 1,637 × 3
# Groups: usertype [2]
usertype from_station_name n
<chr> <chr> <int>
1 casual 2112 w peterson ave 320
2 casual 63rd st beach 727
3 casual 900 w harrison st 2273
4 casual aberdeen st & jackson blvd 3285
5 casual aberdeen st & monroe st 3562
6 casual aberdeen st & randolph st 3598
7 casual ada st & 113th st 20
8 casual ada st & washington blvd 2807
9 casual adler planetarium 7986
10 casual albany ave & 26th st 127
# … with 1,627 more rows
# ℹ Use `print(n = ...)` to see more rows#count the rides used by each type of bikes from the stations
year_2021_df_7 %>%
group_by(bikeid) %>%
count(from_station_name) %>%
print(n=3000)Results
# A tibble: 2,193 × 3
# Groups: bikeid [3]
bikeid from_station_name n
<chr> <chr> <int>
1 classic_bike 2112 w peterson ave 504
2 classic_bike 63rd st beach 811
3 classic_bike 900 w harrison st 5688
4 classic_bike aberdeen st & jackson blvd 7942
5 classic_bike aberdeen st & monroe st 7461
6 classic_bike aberdeen st & randolph st 6397
7 classic_bike ada st & 113th st 7
8 classic_bike ada st & washington blvd 6174
9 classic_bike adler planetarium 8609
10 classic_bike albany ave & 26th st 143
# … with 2,183 more rows
# ℹ Use `print(n = ...)` to see more rows#count the rides using the timing, day and season per each user-type
year_2021_df_7 %>%
group_by(usertype, bikeid, start_timing) %>%
count(from_station_name) %>%
print(n=4000)Results
# A tibble: 13,700 × 5
# Groups: usertype, bikeid, start_timing [21]
usertype bikeid start_timing from_station_name n
<chr> <chr> <chr> <chr> <int>
1 casual classic_bike Afternoon 2112 w peterson ave 61
2 casual classic_bike Afternoon 63rd st beach 175
3 casual classic_bike Afternoon 900 w harrison st 441
4 casual classic_bike Afternoon aberdeen st & jackson blvd 739
5 casual classic_bike Afternoon aberdeen st & monroe st 787
6 casual classic_bike Afternoon aberdeen st & randolph st 640
7 casual classic_bike Afternoon ada st & 113th st 3
8 casual classic_bike Afternoon ada st & washington blvd 492
9 casual classic_bike Afternoon adler planetarium 2296
10 casual classic_bike Afternoon albany ave & 26th st 23
# … with 13,690 more rows
# ℹ Use `print(n = ...)` to see more rowsyear_2021_df_7 %>%
group_by(usertype, bikeid, day_of_order) %>%
count(from_station_name) %>%
print(n=4000)Results
# A tibble: 23,385 × 5
# Groups: usertype, bikeid, day_of_order [36]
usertype bikeid day_of_order from_station_name n
<chr> <chr> <chr> <chr> <int>
1 casual classic_bike Friday 2112 w peterson ave 16
2 casual classic_bike Friday 63rd st beach 46
3 casual classic_bike Friday 900 w harrison st 192
4 casual classic_bike Friday aberdeen st & jackson blvd 280
5 casual classic_bike Friday aberdeen st & monroe st 325
6 casual classic_bike Friday aberdeen st & randolph st 337
7 casual classic_bike Friday ada st & washington blvd 276
8 casual classic_bike Friday adler planetarium 616
9 casual classic_bike Friday albany ave & 26th st 11
10 casual classic_bike Friday albany ave & bloomingdale ave 144
# … with 23,375 more rows
# ℹ Use `print(n = ...)` to see more rowsyear_2021_df_7 %>%
group_by(usertype, bikeid, season) %>%
count(from_station_name) %>%
print(n=4000)Results
# A tibble: 13,146 × 5
# Groups: usertype, bikeid, season [21]
usertype bikeid season from_station_name n
<chr> <chr> <chr> <chr> <int>
1 casual classic_bike Autumn 2112 w peterson ave 53
2 casual classic_bike Autumn 63rd st beach 81
3 casual classic_bike Autumn 900 w harrison st 488
4 casual classic_bike Autumn aberdeen st & jackson blvd 637
5 casual classic_bike Autumn aberdeen st & monroe st 512
6 casual classic_bike Autumn aberdeen st & randolph st 592
7 casual classic_bike Autumn ada st & washington blvd 502
8 casual classic_bike Autumn adler planetarium 1044
9 casual classic_bike Autumn albany ave & 26th st 19
10 casual classic_bike Autumn albany ave & bloomingdale ave 239
# … with 13,136 more rows
# ℹ Use `print(n = ...)` to see more rowsVisualization
#viz
#create a data set for the top 5 stations filtered per user-type
top_10_stations <- year_2021_df_7 %>%
group_by(usertype) %>%
count(from_station_name, sort= TRUE) %>%
slice_head(n=5)
str(top_10_stations)Results
# A tibble: 10 × 3
# Groups: usertype [2]
usertype from_station_name n
1 casual streeter dr & grand ave 54102
2 casual millennium park 26792
3 casual michigan ave & oak st 23576
4 casual shedd aquarium 20009
5 casual wells st & concord ln 18177
6 member clark st & elm st 23172
7 member kingsbury st & kinzie st 22229
8 member wells st & concord ln 22189
9 member wells st & elm st 19627
10 member dearborn st & erie st 18178#Visualization
ggplot(data = top_10_stations, mapping = aes(y= from_station_name,x= n, fill =from_station_name )) +
#applied the mapping here an not in the geom_bar as it will be applied to the whole built-up layers
#as aes()in geom_function will be only applied here (hierarchy level)
geom_col()+
labs(title = "Top 10 start points per no. of rides",caption = "Casual Vs Member",x="number of rides", y="station")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
facet_grid(~usertype)
#ggsave("plot10-Top 10 start points per no. of rides.png")
Top 10 start points per no. of rides
Time duration taken per each user_type under different factors
Time duration (General)
#-----------------------------------Time duration of the trip---------------
max(year_2021_df_7$time_duration)
min(year_2021_df_7$time_duration)
mean(year_2021_df_7$time_duration)
sum(year_2021_df_7$time_duration)
summary(year_2021_df_7$time_duration)Results
max(year_2021_df_7$time_duration)
55944
min(year_2021_df_7$time_duration)
1
mean(year_2021_df_7$time_duration)
20.54499
sum(year_2021_df_7$time_duration)
88362808
summary(year_2021_df_7$time_duration)
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.00 7.00 12.00 20.54 21.00 55944.00 Time duration (user-type summary)
#summary(user-type)
year_2021_df_7 %>%
group_by(usertype) %>%
summarise(first_quartile = as.numeric(quantile(time_duration,0.25)),
second_quartile = as.numeric(quantile(time_duration,0.50)),
median_time_duration = median(time_duration),
mean_time_duration = mean(time_duration),
third_quartile = as.numeric(quantile(time_duration,0.75)),
IQR = as.numeric(quantile(time_duration,0.75))-as.numeric(quantile(time_duration,0.25)),
#allowed limits so if the values fall outside them they aren't considered as outliers
max_time_duration = as.numeric(quantile(time_duration,0.75)) + 1.5*(as.numeric(quantile(time_duration,0.75))-as.numeric(quantile(time_duration,0.25))),
min_time_duration=as.numeric(quantile(time_duration,0.25))- 1.5*(as.numeric(quantile(time_duration,0.75))-as.numeric(quantile(time_duration,0.25))),
max_time_duration_outlier = max(time_duration),
min_time_duration_outlier = min(time_duration))Results
# A tibble: 2 × 11
usertype first_quartile second_quartile median_time_du…¹ mean_…² third…³ IQR max_t…⁴ min_t…⁵ max_t…⁶ min_t…⁷
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 casual 9 16 16 30.4 28 19 56.5 -19.5 55944 1
2 member 6 10 10 13.1 16 10 31 -9 1496 1
# … with abbreviated variable names ¹median_time_duration, ²mean_time_duration, ³third_quartile,
# ⁴max_time_duration, ⁵min_time_duration, ⁶max_time_duration_outlier, ⁷min_time_duration_outlierVisualization
#viz
ggplot(year_2021_df_7, mapping = aes(x=usertype, y=time_duration, fill= usertype))+
geom_boxplot()+
stat_boxplot(geom = "errorbar")+
labs(title = "summary of the Time duration", x=" User_type", y="Time_duration(min)")+
theme(plot.title = element_text(hjust = 0.5))+ #centering the title
scale_fill_discrete(name = "User_type")+
coord_cartesian(ylim = c(as.numeric(quantile(year_2021_df_7$time_duration,0.25))+10-1.5*(as.numeric(quantile(year_2021_df_7$time_duration,0.75))-as.numeric(quantile(year_2021_df_7$time_duration,0.25))),as.numeric(quantile(year_2021_df_7$time_duration,0.75))+40+1.5*(as.numeric(quantile(year_2021_df_7$time_duration,0.75))-as.numeric(quantile(year_2021_df_7$time_duration,0.25)))))
#saving the plot
#ggsave("plot11-Statistical summary of the Time duration of trips for each user type.png")
Statistical summary of the Time duration of trips for each user type
Total sum of time duration per user_type
#total of duration for the whole trips for each user-type
year_2021_df_7 %>%
group_by(usertype) %>%
summarize(sum(time_duration))Results
# A tibble: 2 × 2
usertype `sum(time_duration)`
<chr> <dbl>
1 casual 56374447
2 member 31988361Total sum of time duration taken by each bike for each user_type
#total of duration for the whole trips for each user-type for each bike
year_2021_df_7 %>%
group_by(usertype, bikeid) %>%
summarize(sum(time_duration))Results
# A tibble: 6 × 3
# Groups: usertype [2]
usertype bikeid `sum(time_duration)`
<chr> <chr> <dbl>
1 casual classic_bike 27741897
2 casual docked_bike 19250722
3 casual electric_bike 9381828
4 member classic_bike 25309308
5 member docked_bike 3
6 member electric_bike 6679050Average time duration per user_type by hour, timing, day, month, season, from and to stations
Hour
#hour(total)
year_2021_df_7 %>%
group_by(start_hour) %>%
summarize(mean(time_duration)) %>%
print(n=24)Results
# A tibble: 24 × 2
start_hour `mean(time_duration)`
<chr> <dbl>
1 00 24.7
2 01 28.2
3 02 33.0
4 03 32.6
5 04 30.5
6 05 13.1
7 06 13.0
8 07 13.4
9 08 14.0
10 09 17.2
11 10 21.0
12 11 21.9
13 12 22.1
14 13 23.0
15 14 23.5
16 15 22.6
17 16 20.6
18 17 19.4
19 18 19.5
20 19 20.0
21 20 21.0
22 21 21.4
23 22 22.8
24 23 24.2#hour(user-type)
year_2021_df_7 %>%
group_by(usertype,start_hour) %>%
summarize(mean(time_duration)) %>%
print(n=48)Results
# A tibble: 48 × 3
# Groups: usertype [2]
usertype start_hour `mean(time_duration)`
<chr> <chr> <dbl>
1 casual 00 32.5
2 casual 01 36.4
3 casual 02 42.4
4 casual 03 42.8
5 casual 04 48.1
6 casual 05 20.5
7 casual 06 18.9
8 casual 07 19.4
9 casual 08 21.5
10 casual 09 26.6
11 casual 10 31.4
12 casual 11 32.1
13 casual 12 32.7
14 casual 13 33.2
15 casual 14 33.6
16 casual 15 32.9
17 casual 16 30.1
18 casual 17 27.9
19 casual 18 27.7
20 casual 19 28.4
21 casual 20 30.0
22 casual 21 29.9
23 casual 22 30.9
24 casual 23 32.6
25 member 00 12.7
26 member 01 14.0
27 member 02 13.9
28 member 03 13.5
29 member 04 12.0
30 member 05 10.7
31 member 06 11.4
32 member 07 11.7
33 member 08 11.6
34 member 09 12.2
35 member 10 13.0
36 member 11 13.3
37 member 12 13.0
38 member 13 13.4
39 member 14 13.7
40 member 15 13.6
41 member 16 13.6
42 member 17 13.7
43 member 18 13.6
44 member 19 13.4
45 member 20 13.3
46 member 21 12.9
47 member 22 13.0
48 member 23 12.8Visualization
#viz
ggplot(year_2021_df_7, aes(y=start_hour, x = mean(time_duration), fill= start_hour))+
geom_col()+
labs(title = "Mean duration taken per hour",caption = "Casual Vs Member",y="Hour", x="Mean Duration")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "Start_hour")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("Plot12-Average duration taken per each hour in mins.png")
Average duration taken per each hour in mins
Start_timing
#timing(total)
year_2021_df_7 %>%
group_by(start_timing) %>%
summarize(mean(time_duration)) %>%
print(n=24)Results
# A tibble: 4 × 2
start_timing `mean(time_duration)`
<chr> <dbl>
1 Afternoon 22.2
2 Evening 19.8
3 Morning 17.2
4 Night 24.3#timing(user-type)
year_2021_df_7 %>%
group_by(usertype,start_timing) %>%
summarize(mean(time_duration)) %>%
print(n=24)Results
# A tibble: 8 × 3
# Groups: usertype [2]
usertype start_timing `mean(time_duration)`
<chr> <chr> <dbl>
1 casual Afternoon 32.4
2 casual Evening 28.3
3 casual Morning 27.4
4 casual Night 33.1
5 member Afternoon 13.5
6 member Evening 13.5
7 member Morning 12.2
8 member Night 13.0Visualization
#viz
ggplot(year_2021_df_7, aes(y=start_timing, x = mean(time_duration), fill= start_timing))+
geom_col()+
labs(title = "Mean duration taken through the day",caption = "Casual Vs Member",y="Start_timing", x="Mean Duration")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "Start_timing")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("Plot13-Average duration taken through the day.png")
Average duration taken through the day
Day
#day(total)
year_2021_df_7 %>%
group_by(day_of_order) %>%
summarize(mean(time_duration)) %>%
print(n=24)Results
# A tibble: 7 × 2
day_of_order `mean(time_duration)`
<chr> <dbl>
1 Friday 19.8
2 Monday 19.2
3 Saturday 24.9
4 Sunday 26.2
5 Thursday 17.3
6 Tuesday 17.0
7 Wednesday 17.1#day(user-type)
year_2021_df_7 %>%
group_by(usertype,day_of_order) %>%
summarize(mean(time_duration)) %>%
print(n=24)Results
# A tibble: 14 × 3
# Groups: usertype [2]
usertype day_of_order `mean(time_duration)`
<chr> <chr> <dbl>
1 casual Friday 29.1
2 casual Monday 30.1
3 casual Saturday 33.0
4 casual Sunday 35.4
5 casual Thursday 26.1
6 casual Tuesday 26.1
7 casual Wednesday 26.2
8 member Friday 12.7
9 member Monday 12.6
10 member Saturday 14.8
11 member Sunday 15.1
12 member Thursday 12.3
13 member Tuesday 12.3
14 member Wednesday 12.4Visualization
#viz
ggplot(year_2021_df_7, aes(y=day_of_order, x = mean(time_duration), fill= day_of_order))+
geom_col()+
labs(title = "Mean duration taken per the day",caption = "Casual Vs Member",y="Order_day", x="Mean Duration")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "order day")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("Plot14-Average duration taken per the day.png")
Average duration taken per the day
Month
#month(total)
year_2021_df_7 %>%
group_by(month) %>%
summarize(mean(time_duration)) %>%
print(n=24)Results
# A tibble: 12 × 2
month `mean(time_duration)`
<chr> <dbl>
1 April 22.0
2 August 20.0
3 December 13.8
4 February 19.7
5 January 14.0
6 July 23.0
7 June 24.8
8 March 20.7
9 May 24.6
10 November 13.6
11 October 17.0
12 September 19.3#month(user-type)
year_2021_df_7 %>%
group_by(usertype,month) %>%
summarize(mean(time_duration)) %>%
print(n=24) Results
# A tibble: 24 × 3
# Groups: usertype [2]
usertype month `mean(time_duration)`
<chr> <chr> <dbl>
1 casual April 35.1
2 casual August 26.8
3 casual December 23.7
4 casual February 42.8
5 casual January 24.4
6 casual July 31.6
7 casual June 36.4
8 casual March 34.6
9 casual May 36.7
10 casual November 21.1
11 casual October 25.0
12 casual September 26.5
13 member April 14.1
14 member August 13.5
15 member December 10.5
16 member February 14.4
17 member January 11.9
18 member July 13.7
19 member June 14.1
20 member March 13.5
21 member May 14.2
22 member November 10.9
23 member October 12.0
24 member September 13.0Visualization
#viz
ggplot(year_2021_df_7, aes(y=month, x = mean(time_duration), fill= month))+
geom_col()+
labs(title = "Mean duration taken per month",caption = "Casual Vs Member",y="month", x="Mean Duration")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "month")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("Plot15-Average duration taken per month.png")
Average duration taken per month
Season
#season(total)
year_2021_df_7 %>%
group_by(season) %>%
summarize(mean(time_duration)) %>%
print(n=24)Results
# A tibble: 4 × 2
season `mean(time_duration)`
<chr> <dbl>
1 Autumn 17.4
2 Spring 22.9
3 Summer 22.5
4 Winter 14.7#season(user-type)
year_2021_df_7 %>%
group_by(usertype,season) %>%
summarize(mean(time_duration)) %>%
print(n=24)Results
# A tibble: 8 × 3
# Groups: usertype [2]
usertype season `mean(time_duration)`
<chr> <chr> <dbl>
1 casual Autumn 25.3
2 casual Spring 35.9
3 casual Summer 31.4
4 casual Winter 26.1
5 member Autumn 12.2
6 member Spring 14.0
7 member Summer 13.7
8 member Winter 11.5Visualization
#viz
ggplot(year_2021_df_7, aes(y=season, x = mean(time_duration), fill= season))+
geom_col()+
labs(title = "Mean duration taken per season",caption = "Casual Vs Member",y="season", x="Mean Duration")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "Season")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("Plot16-Average duration taken per season.png")Average duration taken per season
Year_Quarters
#year_quarter(total)
year_2021_df_7 %>%
group_by(year_quarter) %>%
summarize(mean(time_duration)) %>%
print(n=24)Results
# A tibble: 4 × 2
year_quarter `mean(time_duration)`
<chr> <dbl>
1 Q1 18.9
2 Q2 24.1
3 Q3 20.8
4 Q4 15.4#year_quarter(user-type)
year_2021_df_7 %>%
group_by(usertype,year_quarter) %>%
summarize(mean(time_duration)) %>%
print(n=24)Results
# A tibble: 8 × 3
# Groups: usertype [2]
usertype year_quarter `mean(time_duration)`
<chr> <chr> <dbl>
1 casual Q1 33.8
2 casual Q2 36.3
3 casual Q3 28.5
4 casual Q4 23.9
5 member Q1 13.1
6 member Q2 14.1
7 member Q3 13.4
8 member Q4 11.3#viz
ggplot(year_2021_df_7, aes(y=year_quarter, x = mean(time_duration), fill= year_quarter))+
geom_col()+
labs(title = "Mean duration taken per year_quarter",caption = "Casual Vs Member",y="year_quarter", x="Mean Duration")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "year_quarter")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("Plot17-Average duration taken per year_quarter.png")
Average duration taken per year quarter
From_Station
#station (total)
year_2021_df_7 %>%
group_by(from_station_name) %>%
summarize(mean(time_duration)) %>%
print(n=1000)Results
# A tibble: 836 × 2
from_station_name `mean(time_duration)`
<chr> <dbl>
1 2112 w peterson ave 20.1
2 63rd st beach 60.4
3 900 w harrison st 14.6
4 aberdeen st & jackson blvd 14.0
5 aberdeen st & monroe st 19.1
6 aberdeen st & randolph st 18.0
7 ada st & 113th st 24.6
8 ada st & washington blvd 14.6
9 adler planetarium 34.0
10 albany ave & 26th st 27.2
# … with 826 more rows
# ℹ Use `print(n = ...)` to see more rows#station (user-type)
year_2021_df_7 %>%
group_by(usertype,from_station_name) %>%
summarize(mean(time_duration)) %>%
print(n=2000)Results
# A tibble: 1,637 × 3
# Groups: usertype [2]
usertype from_station_name `mean(time_duration)`
<chr> <chr> <dbl>
1 casual 2112 w peterson ave 23.6
2 casual 63rd st beach 84.3
3 casual 900 w harrison st 24.0
4 casual aberdeen st & jackson blvd 21.2
5 casual aberdeen st & monroe st 34.1
6 casual aberdeen st & randolph st 27.3
7 casual ada st & 113th st 34.6
8 casual ada st & washington blvd 19.2
9 casual adler planetarium 40.9
10 casual albany ave & 26th st 32.8
# … with 1,627 more rows
# ℹ Use `print(n = ...)` to see more rowsVisualization
#viz
#Create a data set with top5 start point per each user-type
top_10_average <-year_2021_df_7 %>%
group_by(usertype, from_station_name) %>%
summarise(mean(time_duration))
top_10_average<-top_10_average[order(-top_10_average$`mean(time_duration)`),]#add the , to the end
top_10_average<-top_10_average %>%
group_by(usertype) %>%
slice_head(n=5)Results
# A tibble: 10 × 3
# Groups: usertype [2]
usertype from_station_name mean(time_duration)
1 casual throop st & 52nd st 1004.90000
2 casual south chicago ave & elliot ave 948.47059
3 casual central ave & harrison st 809.73077
4 casual clyde ave & 87th st 744.34694
5 casual racine ave & 65th st 650.63158
6 member pulaski rd & 60th st 84.00000
7 member western ave & 111th st 56.33333
8 member michigan ave & 114th st 49.00000
9 member racine ave & 61st st 48.87500
10 member cicero ave & lake st 40.80000#Visualization
ggplot(top_10_average, aes(y=top_10_average$`mean(time_duration)`, x=top_10_average$from_station_name, fill = top_10_average$from_station_name))+
geom_col(position = position_nudge(x=0.3))+
labs(title = "Mean duration taken per Top10 Start points",caption = "Casual Vs Member",x="station", y="Mean Duration")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_y_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "station")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("Plot18-Average duration taken per Top10 Start points.png")
Average duration taken per Top10 Start points
Distance taken per each user_type under different factors
Distance (General)
#--------------------------Distance(meter)-------------------
max(year_2021_df_7$distance)
min(year_2021_df_7$distance)
mean(year_2021_df_7$distance)
sum(year_2021_df_7$distance)
summary(year_2021_df_7$distance)Results
> max(year_2021_df_7$distance)
[1] 20932.4
> min(year_2021_df_7$distance)
[1] 0.51
> mean(year_2021_df_7$distance)
[1] 1510.91
> sum(year_2021_df_7$distance)
[1] 6498336852
> summary(year_2021_df_7$distance)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.51 497.98 1110.52 1510.91 2081.01 20932.40Distance (user-type summary)
#summary(user-type)
year_2021_df_7 %>%
group_by(usertype) %>%
summarise(first_quartile = as.numeric(quantile(distance,0.25)),
second_quartile = as.numeric(quantile(distance,0.50)),
median_time_duration = median(distance),
mean_time_duration = mean(distance),
third_quartile = as.numeric(quantile(distance,0.75)),
IQR = as.numeric(quantile(distance,0.75))-as.numeric(quantile(distance,0.25)),
#allowed limits so if the values fall outside them they aren't considered as outliers
max_time_duration = as.numeric(quantile(distance,0.75)) + 1.5*(as.numeric(quantile(distance,0.75))-as.numeric(quantile(distance,0.25))),
min_time_duration=as.numeric(quantile(distance,0.25))- 1.5*(as.numeric(quantile(distance,0.75))-as.numeric(quantile(distance,0.25))),
max_time_duration_outlier = max(distance),
min_time_duration_outlier = min(distance))Results
# A tibble: 2 × 11
usertype first_quartile second_quartile median_time_du…¹ mean_…² third…³ IQR max_t…⁴ min_t…⁵ max_t…⁶ min_t…⁷
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 casual 517. 1179. 1179. 1575. 2167. 1651. 4643. -1959. 20932. 0.51
2 member 487. 1068. 1068. 1462. 2004. 1517. 4279. -1789. 20104. 0.51
# … with abbreviated variable names ¹median_time_duration, ²mean_time_duration, ³third_quartile,
# ⁴max_time_duration, ⁵min_time_duration, ⁶max_time_duration_outlier, ⁷min_time_duration_outlierVisualization
#viz
ggplot(year_2021_df_7, mapping = aes(x=usertype, y=distance, fill= usertype))+
geom_boxplot()+
stat_boxplot(geom = "errorbar")+
labs(title = "summary of the distance", x=" User_type", y="Distance(meters)")+
theme(plot.title = element_text(hjust = 0.5))+ #centering the title
scale_fill_discrete(name = "User_type")+
coord_cartesian(ylim = c(as.numeric(quantile(year_2021_df_7$distance,0.25))+10-1.5*(as.numeric(quantile(year_2021_df_7$distance,0.75))-as.numeric(quantile(year_2021_df_7$distance,0.25))),as.numeric(quantile(year_2021_df_7$distance,0.75))+40+1.5*(as.numeric(quantile(year_2021_df_7$distance,0.75))-as.numeric(quantile(year_2021_df_7$distance,0.25)))))
#saving the plot
#ggsave("plot19-Statistical summary of the distance of trips for each user type.png")
Statistical summary of the distance of trips for each user type
Total sum of distance per user_type
#total of distance for the whole trips for each user-type
year_2021_df_7 %>%
group_by(usertype) %>%
summarize(sum(distance))Results
# A tibble: 2 × 2
usertype `sum(distance)`
<chr> <dbl>
1 casual 2923315033.
2 member 3575021819.Total sum of distance taken by each bike for each user_type
#total of distance for the whole trips for each user-type for each bike
year_2021_df_7 %>%
group_by(usertype, bikeid) %>%
summarize(sum(distance))Results
# A tibble: 6 × 3
# Groups: usertype [2]
usertype bikeid `sum(distance)`
<chr> <chr> <dbl>
1 casual classic_bike 1769721273.
2 casual docked_bike 390299731.
3 casual electric_bike 763294029.
4 member classic_bike 2654930927.
5 member docked_bike 65.5
6 member electric_bike 920090827.Average distance per user_type by hour, timing, day, month, season, from and to stations
Hour
#hour(total)
year_2021_df_7 %>%
group_by(start_hour) %>%
summarize(mean(distance)) %>%
print(n=24)Results
# A tibble: 24 × 2
start_hour `mean(distance)`
<chr> <dbl>
1 00 1468.
2 01 1491.
3 02 1534.
4 03 1499.
5 04 1535.
6 05 1592.
7 06 1584.
8 07 1551.
9 08 1476.
10 09 1463.
11 10 1492.
12 11 1500.
13 12 1478.
14 13 1501.
15 14 1520.
16 15 1540.
17 16 1558.
18 17 1553.
19 18 1518.
20 19 1473.
21 20 1450.
22 21 1460.
23 22 1503.
24 23 1509.#hour(user-type)
year_2021_df_7 %>%
group_by(usertype,start_hour) %>%
summarize(mean(distance)) %>%
print(n=48)Results
# A tibble: 48 × 3
# Groups: usertype [2]
usertype start_hour `mean(distance)`
<chr> <chr> <dbl>
1 casual 00 1511.
2 casual 01 1543.
3 casual 02 1622.
4 casual 03 1608.
5 casual 04 1613.
6 casual 05 1660.
7 casual 06 1584.
8 casual 07 1575.
9 casual 08 1522.
10 casual 09 1549.
11 casual 10 1577.
12 casual 11 1608.
13 casual 12 1596.
14 casual 13 1598.
15 casual 14 1589.
16 casual 15 1597.
17 casual 16 1610.
18 casual 17 1616.
19 casual 18 1587.
20 casual 19 1533.
21 casual 20 1495.
22 casual 21 1491.
23 casual 22 1533.
24 casual 23 1539.
25 member 00 1402.
26 member 01 1400.
27 member 02 1352.
28 member 03 1292.
29 member 04 1453.
30 member 05 1570.
31 member 06 1584.
32 member 07 1544.
33 member 08 1462.
34 member 09 1417.
35 member 10 1427.
36 member 11 1409.
37 member 12 1376.
38 member 13 1411.
39 member 14 1454.
40 member 15 1490.
41 member 16 1520.
42 member 17 1510.
43 member 18 1468.
44 member 19 1426.
45 member 20 1411.
46 member 21 1430.
47 member 22 1466.
48 member 23 1469.Visualization
#viz
ggplot(year_2021_df_7, aes(y=start_hour, x = mean(distance), fill= start_hour))+
geom_col()+
labs(title = "Mean distance taken per hour",caption = "Casual Vs Member",y="Hour", x="Mean Distance")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "Start_hour")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("Plot20-Average distance taken per each hour in meters.png")
Average distance taken per each hour in meters
Start_timing
#timing(total)
year_2021_df_7 %>%
group_by(start_timing) %>%
summarize(mean(distance)) %>%
print(n=24)Results
# A tibble: 4 × 2
start_timing `mean(distance)`
<chr> <dbl>
1 Afternoon 1522.
2 Evening 1510.
3 Morning 1506.
4 Night 1489.#timing(user-type)
year_2021_df_7 %>%
group_by(usertype,start_timing) %>%
summarize(mean(distance)) %>%
print(n=24) Results
# A tibble: 8 × 3
# Groups: usertype [2]
usertype start_timing `mean(distance)`
<chr> <chr> <dbl>
1 casual Afternoon 1598.
2 casual Evening 1570.
3 casual Morning 1576.
4 casual Night 1531.
5 member Afternoon 1457.
6 member Evening 1466.
7 member Morning 1472.
8 member Night 1435.Visualization
#viz
ggplot(year_2021_df_7, aes(y=start_timing, x = mean(distance), fill= start_timing))+
geom_col()+
labs(title = "Mean distance taken through the day",caption = "Casual Vs Member",y="Start_timing", x="Mean distance")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "Start_timing")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("Plot21-Average distance taken through the day.png")
Average distance taken through the day
Day
#day(total)
year_2021_df_7 %>%
group_by(day_of_order) %>%
summarize(mean(distance)) %>%
print(n=24)Results
# A tibble: 7 × 2
day_of_order `mean(distance)`
<chr> <dbl>
1 Friday 1495.
2 Monday 1461.
3 Saturday 1580.
4 Sunday 1567.
5 Thursday 1482.
6 Tuesday 1471.
7 Wednesday 1487.#day(user-type)
year_2021_df_7 %>%
group_by(usertype,day_of_order) %>%
summarize(mean(distance)) %>%
print(n=24)Results
# A tibble: 14 × 3
# Groups: usertype [2]
usertype day_of_order `mean(distance)`
<chr> <chr> <dbl>
1 casual Friday 1560.
2 casual Monday 1507.
3 casual Saturday 1631.
4 casual Sunday 1613.
5 casual Thursday 1549.
6 casual Tuesday 1527.
7 casual Wednesday 1552.
8 member Friday 1446.
9 member Monday 1434.
10 member Saturday 1516.
11 member Sunday 1511.
12 member Thursday 1444.
13 member Tuesday 1442.
14 member Wednesday 1453.Visualization
#viz
ggplot(year_2021_df_7, aes(y=day_of_order, x = mean(distance), fill= day_of_order))+
geom_col()+
labs(title = "Mean distance taken per day",caption = "Casual Vs Member",y="Order_day", x="Mean distance")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "order_day")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("Plot22-Average distance taken per the day.png")
Average distance taken per the day
Month
#month(total)
year_2021_df_7 %>%
group_by(month) %>%
summarize(mean(distance)) %>%
print(n=24)Results
# A tibble: 12 × 2
month `mean(distance)`
<chr> <dbl>
1 April 1502.
2 August 1552.
3 December 1354.
4 February 1410.
5 January 1374.
6 July 1563.
7 June 1557.
8 March 1486.
9 May 1532.
10 November 1367.
11 October 1449.
12 September 1541.#month(user-type)
year_2021_df_7 %>%
group_by(usertype,month) %>%
summarize(mean(distance)) %>%
print(n=24) Results
# A tibble: 24 × 3
# Groups: usertype [2]
usertype month `mean(distance)`
<chr> <chr> <dbl>
1 casual April 1532.
2 casual August 1613.
3 casual December 1402.
4 casual February 1474.
5 casual January 1409.
6 casual July 1602.
7 casual June 1593.
8 casual March 1522.
9 casual May 1557.
10 casual November 1444.
11 casual October 1532.
12 casual September 1621.
13 member April 1484.
14 member August 1493.
15 member December 1339.
16 member February 1395.
17 member January 1367.
18 member July 1521.
19 member June 1524.
20 member March 1467.
21 member May 1511.
22 member November 1338.
23 member October 1396.
24 member September 1472.Visualization
#viz
ggplot(year_2021_df_7, aes(y=month, x = mean(distance), fill= month))+
geom_col()+
labs(title = "Mean distance taken through the day",caption = "Casual Vs Member",y="month", x="Mean distance")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "month")+ # to change the name of the legend
facet_grid(~usertype)
ggsave("Plot23-Average distance taken per month.png")Average distance taken per month
Season
#season(total)
year_2021_df_7 %>%
group_by(season) %>%
summarize(mean(distance)) %>%
print(n=24)Results
# A tibble: 4 × 2
season `mean(distance)`
<chr> <dbl>
1 Autumn 1475.
2 Spring 1513.
3 Summer 1558.
4 Winter 1367.#season(user-type)
year_2021_df_7 %>%
group_by(usertype,season) %>%
summarize(mean(distance)) %>%
print(n=24)Results
# A tibble: 8 × 3
# Groups: usertype [2]
usertype season `mean(distance)`
<chr> <chr> <dbl>
1 casual Autumn 1568.
2 casual Spring 1543.
3 casual Summer 1603.
4 casual Winter 1412.
5 member Autumn 1414.
6 member Spring 1492.
7 member Summer 1513.
8 member Winter 1355.Visualization
#viz
ggplot(year_2021_df_7, aes(y=season, x = mean(distance), fill= season))+
geom_col()+
labs(title = "Mean distance taken per season",caption = "Casual Vs Member",y="season", x="Mean distance")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "season")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("Plot24-Average distance taken per season.png")
Average distance taken per season
Year_Quarters
#year_quarter(total)
year_2021_df_7 %>%
group_by(year_quarter) %>%
summarize(mean(distance)) %>%
print(n=24)Results
# A tibble: 4 × 2
year_quarter `mean(distance)`
<chr> <dbl>
1 Q1 1447.
2 Q2 1537.
3 Q3 1552.
4 Q4 1407.#year_quarter(user-type)
year_2021_df_7 %>%
group_by(usertype,year_quarter) %>%
summarize(mean(distance)) %>%
print(n=24)Results
# A tibble: 8 × 3
# Groups: usertype [2]
usertype year_quarter `mean(distance)`
<chr> <chr> <dbl>
1 casual Q1 1500.
2 casual Q2 1570.
3 casual Q3 1611.
4 casual Q4 1492.
5 member Q1 1427.
6 member Q2 1510.
7 member Q3 1495.
8 member Q4 1366.Visualization
#viz
ggplot(year_2021_df_7, aes(y=year_quarter, x = mean(distance), fill= year_quarter))+
geom_col()+
labs(title = "Mean distance taken per year_quarter",caption = "Casual Vs Member",y="year_quarter", x="Mean distance")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_x_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "year_quarter")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("Plot25-Average distance taken per year_quarter.png")
Average distance taken per year quarter
From_Station
#station (total)
year_2021_df_7 %>%
group_by(from_station_name) %>%
summarize(mean(distance)) %>%
print(n=1000)Results
# A tibble: 836 × 2
from_station_name `mean(distance)`
<chr> <dbl>
1 2112 w peterson ave 1848.
2 63rd st beach 2548.
3 900 w harrison st 1351.
4 aberdeen st & jackson blvd 1490.
5 aberdeen st & monroe st 1984.
6 aberdeen st & randolph st 1724.
7 ada st & 113th st 1256.
8 ada st & washington blvd 2060.
9 adler planetarium 1954.
10 albany ave & 26th st 3469.
# … with 826 more rows
# ℹ Use `print(n = ...)` to see more rows#station (user-type)
year_2021_df_7 %>%
group_by(usertype,from_station_name) %>%
summarize(mean(distance)) %>%
print(n=2000)Results
# A tibble: 1,637 × 3
# Groups: usertype [2]
usertype from_station_name `mean(distance)`
<chr> <chr> <dbl>
1 casual 2112 w peterson ave 2028.
2 casual 63rd st beach 2395.
3 casual 900 w harrison st 1494.
4 casual aberdeen st & jackson blvd 1659.
5 casual aberdeen st & monroe st 2237.
6 casual aberdeen st & randolph st 1985.
7 casual ada st & 113th st 1882.
8 casual ada st & washington blvd 2142.
9 casual adler planetarium 1889.
10 casual albany ave & 26th st 3281.
# … with 1,627 more rows
# ℹ Use `print(n = ...)` to see more rowsVisualization
#viz
#Create a data set with top5 start point per each user-type
top_10_avg_dist <-year_2021_df_7 %>%
group_by(usertype, from_station_name) %>%
summarise(mean(distance))
top_10_avg_dist<-top_10_avg_dist[order(-top_10_avg_dist$`mean(distance)`),]#add the , to the end
top_10_avg_dist<-top_10_avg_dist %>%
group_by(usertype) %>%
slice_head(n=5)Results
# A tibble: 10 × 3
# Groups: usertype [2]
usertype from_station_name mean(distance)
1 casual tripp ave & 15th st 10721.571
2 casual komensky ave & 31st st 8933.972
3 casual nordica & medill 7762.697
4 casual narragansett ave & school st 7512.171
5 casual w 103rd st & s avers ave 7466.370
6 member major ave & bloomingdale ave 13762.520
7 member sayre & diversey 13690.804
8 member francisco ave & 47th st 12500.280
9 member long ave & belden ave 11327.120
10 member central ave & madison st 11199.833#Visualization
ggplot(top_10_average, aes(y=top_10_avg_dist$`mean(distance)`, x=top_10_avg_dist$from_station_name, fill = top_10_avg_dist$from_station_name))+
geom_col(position = position_nudge(x=0.3))+
labs(title = "Mean distance taken per Top10 Start points",caption = "Casual Vs Member",x="station", y="Mean Distance")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5))+ #centering the title
scale_y_continuous(labels = scales :: comma)+ #removing the E expression
scale_fill_discrete(name = "station")+ # to change the name of the legend
facet_grid(~usertype)
#ggsave("Plot26-Average distance taken per Top10 Start points.png")
Average distance taken per Top10 Start points
Focused results on the user-types’ behavior
This excel sheet contains a summarized analysis of casual and member behavior through different times, hours, days, months, seasons, and year quarters. It also shows the different stations, most trips started and ended, most taken routes, and the type of the bikes used. It also has the average duration and distance taken at different states. This worksheet can be observed through this link: Summary Analysis of the behavior between casual and member worksheet
Dashboard of the data-set to test different factors
This dashboard was founded to summarize and integrate the analysis. Also, It is working a real time dashboard that can assist i further analysis. It contains both the count of the trips, and average time duration and distance. It can be observed through this link:Tableau Dashboards or by clicking on that image below.
Observations on the different users’ behavior
Casual users are 43.15% from the total number of users.
Classic bike considered to be the most used bike for both users (70.51%), then electric and then docked bikes.
5.74% of casual users used docked bikes, while 10.9% used electric ones.
Peak hours are 16, 17, 18 for total users.
Afternoon(33.85%) is considered the most requested trips order time.
Sat, Sun, Fri are peak days for casuals while Wed, Tues, Thurs are the ones for members.
Peak month in general is July, Summer is the peak season, and Q3 is the highest quarter of trip count.
Start point is Streeter Dr & Grand Ave for casual users(42.97% of casuals) while Clark St & Elm St(24.89% of members) is the most used start station for the members.
Stop point is Streeter Dr & Grand Ave for casual users(40% of casuals) while Clark St & Elm St(29.3% of members) is the most used start station for the members.
Most taken routes are “Streeter Dr & Grand Ave To Millennium Park”for casuals (10.46%) and “Ellis Ave & 55th St&&Ellis Ave & 60th St” for members(26.67%).
Average duration is 30 minutes for casuals while 13 minutes for members.
Average distance is 1575 meters for casual while 1462 meters for members.
Classic bikes have the largest distance and time duration.
Top hours from the longest duration perspective are 04 for casuals(48.13 minutes), and 01 for members(14 minutes).
Top hours from the longest distance perspective are 05 for casuals(1.03 mile), and 01 for members(0.98 mile).
Top routes from the longest duration perspective are “Millennium Park To Streeter Dr & Grand Ave” for casuals(53.1 minutes), and “Shedd Aquarium To Streeter Dr & Grand Ave” for members(21.63 minutes).
Top routes from the longest distance perspective are “Millennium Park To Streeter Dr & Grand Ave” for both users(0.83 mile).
Deep observations on casuals’ behavior
At mornings, 18.33% Of the casuals, 11 is considered the peak hour, Average duration is 27 minutes and distance is 1576 meters, classic bike is the most used, stop and start stations are Streeter Dr & Grand Ave, and the top most taken route is “DuSable Lake Shore Dr & Monroe St To Streeter Dr & Grand”.
At Afternoons(highest timing through the day), %36.30 Of the casuals, 16 is considered the peak hour, Average duration is 32 minutes and distance is 1598 meters, classic bike is the most used, stop and start stations are Streeter Dr & Grand Ave, and the top most taken route is “Shedd Aquarium To Streeter Dr & Grand Ave”.
At Evenings, 29.57% Of the casuals, 17 is considered the peak hour, Average duration is 28 minutes and distance is 1570 meters, classic bike is the most used, stop and start stations are Streeter Dr & Grand Ave, and the top most taken route is “Streeter Dr & Grand Ave To Millennium Park”.
At Nights, 15.80% Of the casuals, 21 is considered the peak hour, Average duration is 33 minutes and distance is 1531 meters, classic bike is the most used, Docked bikes had the most usage percent, stop and start stations are Streeter Dr & Grand Ave and Wells St & Concord Ln simultaneously, and the top most taken route is “Ellis Ave & 55th St&&Ellis Ave & 60th St”.
Weekdays are all on average of 10-11% of casuals but become higher on Fridays 14.37%, while weekends are almost double the weekdays percentage, the highest on Saturdays.
Winter is the least season from the number of rides. December is considered to be the peak month. The top most taken route is “DuSable Lake Shore Dr & Monroe St To Streeter Dr & Grand”.
Summer is the top season from the number of rides. July is considered to be the peak month. The top most taken route is “Lake Shore Dr & Monroe St To Streeter Dr & Grand Ave”.
Spring is the second-placed season from the number of rides. May is considered to be the peak month. The top most taken route is “Lake Shore Dr & Monroe St To Streeter Dr & Grand Ave”.
Fall is the third-placed season from the number of rides. September is considered to be the peak month. The top most taken route is “DuSable Lake Shore Dr & Monroe St To Streeter Dr & Grand Ave”.
Docked bikes record a percentage of usage higher than that of the members.
Summary of the study
Afternoon time is the most order timing. 16 is considered the peak hour.
Classic bike is the most used type, followed by the electrics and then the docked bikes.
“Streeter Dr & Grand Ave To Millennium Park” is the most taken route.
Winter(Dec, Jan, Feb), the count decreases gradually.
Summer, July is the peak month. Fall, September is the highest month.
Spring(March To May), it increases gradually more the winters, till the count reaches its ultimate in the summers.
Weekends (Saturdays) are the busiest days, and Fridays accordingly.
Conclusions
Number of classic bikes should increase specially in Summer at July.
More bikes could be added at Streeter Dr & Grand Ave station.
Afternoons and Evenings also needed more bikes to avoid any lack of service.
Docked bikes should increase.
Lake and park streets should be considered.