Cyclistic

Cyclistic is a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, they want me to understand how casual riders and annual members use Cyclistic bikes dierently. From these insights, my team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations.

Ask

Questions provided by the case study

1-How do annual members and casual riders use Cyclistic bikes differently?

Problem Type: Business question

2-Why would casual riders buy Cyclistic annual memberships?

Problem Type: Business question

3-How can Cyclistic use digital media to influence casual riders to become members?

Problem Type: Business question

Prepare

Analyze with R.O.C.C.C

Reliability-The data is reliable as it consists of all the data of their users

Original-The data is from kaggle and it is the original data

Comprehensive-Yes the data is comprehensive

Current-The dataset is currect and containes data from 2020 to 2021

Cited-Yes

How are you addressing licensing, privacy, security, and accessibility?

The company has their own licence over the dataset. Besides that, the dataset doesn't have any personal information about the riders.

Process

Check the data for errors and transform the data to work with it effectively.

The data set given consisted of various .csv files but for this case study I have considered 12 csv files which contain the users data for each month

Installing Packages 

  install.packages("tidyverse")
  install.packages("lubridate")
  install.packages("stringr")
  install.packages("scales")
  library(tidyverse)
  library(tidyr)
  library(dplyr)
  library(lubridate)
  library(stringr)
  library(scales)
  library(readr)

Converting the data to one datatype for merging the data

 m4_2020$start_station_id <- as.character(m4_2020$start_station_id)
 m4_2020$end_station_id <- as.character(m4_2020$end_station_id)
 m5_2020$end_station_id <- as.character(m5_2020$end_station_id)
 m5_2020$start_station_id <- as.character(m5_2020$start_station_id)
 m6_2020$start_station_id <- as.character(m6_2020$start_station_id)
 m6_2020$end_station_id <- as.character(m6_2020$end_station_id)
 m7_2020$end_station_id <- as.character(m7_2020$end_station_id)
 m7_2020$start_station_id <- as.character(m7_2020$start_station_id)
 m8_2020$start_station_id <- as.character(m8_2020$start_station_id)
 m8_2020$end_station_id <- as.character(m8_2020$end_station_id)
 m9_2020$end_station_id <- as.character(m9_2020$end_station_id)
 m9_2020$start_station_id <- as.character(m9_2020$start_station_id)
 m10_2020$start_station_id <- as.character(m10_2020$start_station_id)
 m10_2020$end_station_id <- as.character(m10_2020$end_station_id)
 m11_2020$end_station_id <- as.character(m11_2020$end_station_id)
 m11_2020$start_station_id <- as.character(m11_2020$start_station_id)
 m12_2020$start_station_id <- as.character(m12_2020$start_station_id)
 M12_2020$end_station_id <- as.character(M12_2020$end_station_id)

Merging the Data 

 total <- full_join(m1_2021,m2_2021, by=c('ride_id','rideable_type','started_at', 'ended_at',"start_station_name","start_station_id","end_station_name","end_station_id","start_lat", "start_lng","end_lat","end_lng","member_casual"),copy=FALSE)
 total <- full_join(m1_2021,m2_2021, by=c('ride_id','rideable_type','started_at', 'ended_at',"start_station_name","start_station_id","end_station_name","end_station_id","start_lat", "start_lng","end_lat","end_lng","member_casual"),copy=FALSE)
 total <- full_join(total,m3_2021, by=c('ride_id','rideable_type','started_at', 'ended_at',"start_station_name","start_station_id","end_station_name","end_station_id","start_lat", "start_lng","end_lat","end_lng","member_casual"),copy=FALSE)
 total <- full_join(total,m4_2020, by=c('ride_id','rideable_type','started_at', 'ended_at',"start_station_name","start_station_id","end_station_name","end_station_id","start_lat", "start_lng","end_lat","end_lng","member_casual"),copy=FALSE)
 total <- full_join(total,m5_2020, by=c('ride_id','rideable_type','started_at', 'ended_at',"start_station_name","start_station_id","end_station_name","end_station_id","start_lat", "start_lng","end_lat","end_lng","member_casual"),copy=FALSE)
 total <- full_join(total,m6_2020, by=c('ride_id','rideable_type','started_at', 'ended_at',"start_station_name","start_station_id","end_station_name","end_station_id","start_lat", "start_lng","end_lat","end_lng","member_casual"),copy=FALSE)
 total <- full_join(total,m6_2020, by=c('ride_id','rideable_type','started_at', 'ended_at',"start_station_name","start_station_id","end_station_name","end_station_id","start_lat", "start_lng","end_lat","end_lng","member_casual"),copy=FALSE)
 total <- full_join(total,m7_2020, by=c('ride_id','rideable_type','started_at', 'ended_at',"start_station_name","start_station_id","end_station_name","end_station_id","start_lat", "start_lng","end_lat","end_lng","member_casual"),copy=FALSE)
 total <- full_join(total,m8_2020, by=c('ride_id','rideable_type','started_at', 'ended_at',"start_station_name","start_station_id","end_station_name","end_station_id","start_lat", "start_lng","end_lat","end_lng","member_casual"),copy=FALSE)
 total <- full_join(total,m9_2020, by=c('ride_id','rideable_type','started_at', 'ended_at',"start_station_name","start_station_id","end_station_name","end_station_id","start_lat", "start_lng","end_lat","end_lng","member_casual"),copy=FALSE)
 total <- full_join(total,m10_2020, by=c('ride_id','rideable_type','started_at', 'ended_at',"start_station_name","start_station_id","end_station_name","end_station_id","start_lat", "start_lng","end_lat","end_lng","member_casual"),copy=FALSE)
 total <- full_join(total,m11_2020, by=c('ride_id','rideable_type','started_at', 'ended_at',"start_station_name","start_station_id","end_station_name","end_station_id","start_lat", "start_lng","end_lat","end_lng","member_casual"),copy=FALSE)
 total <- full_join(total,M12_2020, by=c('ride_id','rideable_type','started_at', 'ended_at',"start_station_name","start_station_id","end_station_name","end_station_id","start_lat", "start_lng","end_lat","end_lng","member_casual"),copy=FALSE)
 final <-na.omit(total)
 final$Time <- format(final$started_at,"%H:%M:%S")
 final$eTime <- format(final$ended_at,"%H:%M:%S")
 final$eTime <- as.POSIXct(final$eTime,format="%H:%M:%S")
 final$Time <- as.POSIXct(final$Time,format="%H:%M:%S")
 View(final %>%
       + mutate(diff= difftime(eTime,Time, units = "mins")))
 final$diff <- difftime(final$ended_at,final$started_at, units = "mins")

Analyse

Casuals vs Members 

    View(final %>% 
    group_by(member_casual) %>% 
    summarise(count = length(ride_id), '%' = (length(ride_id) / nrow(final)) * 100))
    
speed_calories

Amount of time Casual and Members use Cyclistic bikes in a week 

        ggplot(data=final,mapping=aes(x=member_casual,y=mean(diff),fill=member_casual))+
        geom_bar(stat="identity")+
        facet_wrap(~wday(started_at))
        
speed_calories

Amount of time Casual and Members use Cyclistic bikes per month 

            ggplot(data=final,mapping=aes(x=member_casual,y=mean(diff),fill=member_casual))+
            geom_bar(stat="identity")+
            facet_wrap(~month(started_at))
            
speed_calories

Amount of time Casual and Members use Cyclistic bikes per hour 

                ggplot(data=final,mapping=aes(x=member_casual,y=mean(diff), fill=member_casual))+
                geom_bar(stat="identity")+
                facet_wrap( ~hour(started_at))
                
speed_calories

Amount of time Casual and Members use Cyclistic bikes per hour by Ride Type 

                    ggplot(data=final,mapping=aes(x=member_casual,y=mean(diff), fill=member_casual))+
                    geom_bar(stat="identity")+
                    facet_wrap( ~hour(started_at))
                    
speed_calories

Types of Bikes members and non members use 

                    View(final %>%
                    group_by(rideable_type) %>% 
                    summarise(Count_Member= sum(member_casual=="member"),Count_Casual= sum(member_casual=="casual"),"casual_percent"=sum(member_casual=="casual")/nrow(final) *100 ,"member_percent"=sum(member_casual=="member")/nrow(final) *100))

                    
speed_calories

Share

From the above analysis we can concur:

> In all months we have more members' rides than casual rides.

> Temperature heavily influences the volume of rides in the month.

> The biggest volume of data is on the the weekend.

> Monday and Friday are days where activity among the users is low.

> There's a bigger volume of bikers in the evening.

How members differs from casuals

> Casual users use cyclic more on weekends, while members use them more on weekdays.

> We have more members during the morning, staring from 5AM. Casual users use it on a average from 11pm and 4am.

> During the weekend we have a bigger flow of casual users.

> Members and Casual users have a bigger preference for docked bikes

> Members might be using cyclic to go to work or use it as an exercise.

Act

How could your team and business apply your insights?

> We can prepare a marketing campaign for turning casual into members. The marketing can have a focus on workers as a green way to get to work.

> Increase benefits for riding during cold months. Coupons and discounts could be handed out.

> Ads campaigns could also be made showing people using the bikes for exercise during the weeks. The ads could focus on how practical and consistent the bikes can be.