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Cyclistic Bike Share Analysis

Du Pham

paste("First created on July 15, 2021. Updated on", Sys.Date())

'First created on July 15, 2021. Updated on 2021-07-21'

Goal: Provide graphs about how casual riders and annual members use Cyclistic bikes differently so that the team can come up with appropriate stategies to maximize the number of annual memberships.

1. Data Prep: we collect the data files for the year 2019 from the webpage "https://divvy-tripdata.s3.amazonaws.com/index.html" to avoid the bias in data of 2020 due to the COVID pandemic. We combine the data files in one data frame.
2. Data Analysis:

• We first find the mean, median of bike share usage for casual riders (custumers) and annual members (subscibers).
• We next divide into smaller groups of specific duration usage time and compare the numbers of casual vs annual riders. We will work this out using graphs.

1. Conclusion:
2. Further considerations:

1. Data Preparation:

• Read data files: we read in 4 data *.csv files and combine them in a data frame
• Great the data: we use glimpse and head commands to understand the structure and the type of the data table.
• Find missing data: we next skim through the columns and count the number of null/NA entries.

Read the data .csv files

options(warn=-1)
library(tidyverse)
library(readr)

Registered S3 method overwritten by 'rvest':
  method            from
  read_xml.response xml2
-- Attaching packages --------------------------------------- tidyverse 1.2.1 --
v ggplot2 3.3.5     v purrr   0.3.2
v tibble  3.1.1     v dplyr   1.0.6
v tidyr   1.1.3     v stringr 1.4.0
v readr   1.3.1     v forcats 0.4.0
-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

mydir  <-  "Divvy_Trips"
myfiles  <- list.files(path=mydir, pattern="*.csv", full.names=TRUE)
myfiles

1. 'Divvy_Trips/Divvy_Trips_2019_Q1.csv'
2. 'Divvy_Trips/Divvy_Trips_2019_Q2.csv'
3. 'Divvy_Trips/Divvy_Trips_2019_Q3.csv'
4. 'Divvy_Trips/Divvy_Trips_2019_Q4.csv'

for (f in myfiles){
                                dat  <-  read_csv(f)
}

Parsed with column specification:
cols(
  trip_id = col_double(),
  start_time = col_datetime(format = ""),
  end_time = col_datetime(format = ""),
  bikeid = col_double(),
  tripduration = col_number(),
  from_station_id = col_double(),
  from_station_name = col_character(),
  to_station_id = col_double(),
  to_station_name = col_character(),
  usertype = col_character(),
  gender = col_character(),
  birthyear = col_double()
)
Parsed with column specification:
cols(
  `01 - Rental Details Rental ID` = col_double(),
  `01 - Rental Details Local Start Time` = col_datetime(format = ""),
  `01 - Rental Details Local End Time` = col_datetime(format = ""),
  `01 - Rental Details Bike ID` = col_double(),
  `01 - Rental Details Duration In Seconds Uncapped` = col_number(),
  `03 - Rental Start Station ID` = col_double(),
  `03 - Rental Start Station Name` = col_character(),
  `02 - Rental End Station ID` = col_double(),
  `02 - Rental End Station Name` = col_character(),
  `User Type` = col_character(),
  `Member Gender` = col_character(),
  `05 - Member Details Member Birthday Year` = col_double()
)
Parsed with column specification:
cols(
  trip_id = col_double(),
  start_time = col_datetime(format = ""),
  end_time = col_datetime(format = ""),
  bikeid = col_double(),
  tripduration = col_number(),
  from_station_id = col_double(),
  from_station_name = col_character(),
  to_station_id = col_double(),
  to_station_name = col_character(),
  usertype = col_character(),
  gender = col_character(),
  birthyear = col_double()
)
Parsed with column specification:
cols(
  trip_id = col_double(),
  start_time = col_datetime(format = ""),
  end_time = col_datetime(format = ""),
  bikeid = col_double(),
  tripduration = col_number(),
  from_station_id = col_double(),
  from_station_name = col_character(),
  to_station_id = col_double(),
  to_station_name = col_character(),
  usertype = col_character(),
  gender = col_character(),
  birthyear = col_double()
)

See the data type and the first few row of data.

glimpse(dat)

Rows: 704,054
Columns: 12
$ trip_id           <dbl> 25223640, 25223641, 25223642, 25223643, 25223644, 25~
$ start_time        <dttm> 2019-10-01 00:01:39, 2019-10-01 00:02:16, 2019-10-0~
$ end_time          <dttm> 2019-10-01 00:17:20, 2019-10-01 00:06:34, 2019-10-0~
$ bikeid            <dbl> 2215, 6328, 3003, 3275, 5294, 1891, 1061, 1274, 6011~
$ tripduration      <dbl> 940, 258, 850, 2350, 1867, 373, 1072, 1458, 1437, 83~
$ from_station_id   <dbl> 20, 19, 84, 313, 210, 156, 84, 156, 156, 336, 77, 19~
$ from_station_name <chr> "Sheffield Ave & Kingsbury St", "Throop (Loomis) St ~
$ to_station_id     <dbl> 309, 241, 199, 290, 382, 226, 142, 463, 463, 336, 50~
$ to_station_name   <chr> "Leavitt St & Armitage Ave", "Morgan St & Polk St", ~
$ usertype          <chr> "Subscriber", "Subscriber", "Subscriber", "Subscribe~
$ gender            <chr> "Male", "Male", "Female", "Male", "Male", "Female", ~
$ birthyear         <dbl> 1987, 1998, 1991, 1990, 1987, 1994, 1991, 1995, 1993~

head(dat)

trip_id	start_time	end_time	bikeid	tripduration	from_station_id	from_station_name	to_station_id	to_station_name	usertype	gender	birthyear
25223640	2019-10-01 00:01:39	2019-10-01 00:17:20	2215	940	20	Sheffield Ave & Kingsbury St	309	Leavitt St & Armitage Ave	Subscriber	Male	1987
25223641	2019-10-01 00:02:16	2019-10-01 00:06:34	6328	258	19	Throop (Loomis) St & Taylor St	241	Morgan St & Polk St	Subscriber	Male	1998
25223642	2019-10-01 00:04:32	2019-10-01 00:18:43	3003	850	84	Milwaukee Ave & Grand Ave	199	Wabash Ave & Grand Ave	Subscriber	Female	1991
25223643	2019-10-01 00:04:32	2019-10-01 00:43:43	3275	2350	313	Lakeview Ave & Fullerton Pkwy	290	Kedzie Ave & Palmer Ct	Subscriber	Male	1990
25223644	2019-10-01 00:04:34	2019-10-01 00:35:42	5294	1867	210	Ashland Ave & Division St	382	Western Ave & Congress Pkwy	Subscriber	Male	1987
25223645	2019-10-01 00:04:38	2019-10-01 00:10:51	1891	373	156	Clark St & Wellington Ave	226	Racine Ave & Belmont Ave	Subscriber	Female	1994

We want to find if there is any COLUMNS with null values

NA_cols <- sapply(dat, function(x) sum(is.na(x))) 
NA_cols[NA_cols>0]

gender

66591

birthyear

61681

The missing data are in columns gender and birthyear which do not carry important numeric information for the analysis. We decide to leave the data as is.

2. Analysis:

• We first find the mean, median of bike share usages for casual riders (customers) and annual members (subscibers). We use summary command for each of the entries in usertype (customers, subscibers)

l <- as.list(strsplit(unique(dat$usertype)," "))
# print(l)
# typeof(l)
for (name in l){
                                cmd  <- sprintf("----------- usertype = % s -----", name)
                                print(eval(cmd))  
                                d <- dat  %>%     filter(usertype==name)

#   print(count(d, 'tripduration') )
                                print(summary(d[5]))

}

[1] "----------- usertype = Subscriber -----"
  tripduration    
 Min.   :     61  
 1st Qu.:    330  
 Median :    528  
 Mean   :    749  
 3rd Qu.:    851  
 Max.   :5169622  
[1] "----------- usertype = Customer -----"
  tripduration    
 Min.   :     61  
 1st Qu.:    727  
 Median :   1283  
 Mean   :   3665  
 3rd Qu.:   2135  
 Max.   :8585902  

We observe that the mean and median usage of the casual riders are 3,665 and 1,283, which are much higher than those of annual members, 749 and 528 resp.

---

We now partition the users into groups of specific time windows and carry the plots to compare the numbers of users between the casual and annual riders.

dat <- dat %>%
                                mutate(duration_group = case_when(
#     tripduration <= 60 ~ "a. <= 1h",
                                tripduration <= 60*8 ~ "a. <= 8h",  
                                tripduration <= 60*24 ~ "b. 8-24h",
                                tripduration <= 60*168 ~ "c. 1d- 1w",  
                                tripduration <= 60*4704 ~ "d. 1-4w", 
                                TRUE ~ "e. > 4w"
                                ))

dat <- dat %>%
                                mutate(duration_day = tripduration/(24*60))

dat %>%
                                filter(usertype=="Customer") %>%
                                group_by(duration_group) %>% 
                                summarise_at(vars(tripduration), funs(n(),mean(., na.rm=TRUE),median(., na.rm=TRUE),sd(., na.rm=TRUE) ))

duration_group	n	mean	median	sd
a. <= 8h	12696	345.8951	360	9.311473e+01
b. 8-24h	46563	924.2205	903	2.732324e+02
c. 1d- 1w	44908	2956.3664	2275	1.721485e+03
d. 1-4w	1921	37481.1478	18453	4.299967e+04
e. > 4w	106	1292997.6981	643713	1.512685e+06

dat %>%
                                filter(usertype=="Subscriber") %>%
                                group_by(duration_group) %>% 
                                summarise_at(vars(tripduration), funs(n(),mean(., na.rm=TRUE),median(., na.rm=TRUE),sd(., na.rm=TRUE) ))

duration_group	n	mean	median	sd
a. <= 8h	266973	308.5608	312.0	9.992137e+01
b. 8-24h	283339	801.5121	740.0	2.505926e+02
c. 1d- 1w	47095	1952.6450	1798.0	6.149719e+02
d. 1-4w	427	53480.4215	33989.0	5.308332e+04
e. > 4w	26	914154.7308	503003.5	1.250441e+06

ggplot(data = dat) + 
#   geom_histogram(mapping = aes(x = tripduration),binwidth = 1000, color = "black", fill = "gray") 
                                geom_bar(mapping = aes(x = duration_group,fill=usertype)) +
#   facet_wrap(~usertype) +
#   theme(axis.text.x = element_text(angle = 45)) +
                                xlab("Trip duration groups") +
                                ylab("Number of riders") +
                                labs(title = "Casual Riders (Customers) vs Annual Riders (Subscribers)",subtitle ="Same scales for groups", fill= "Rider Type")

Here is a plot when the scales of each group are different to see clearer the groups d and f

ggplot(data = dat) + 
#   geom_histogram(mapping = aes(x = tripduration),binwidth = 1000, color = "black", fill = "gray") 
                                geom_bar(mapping = aes(x = duration_group,fill = usertype)) +
                                facet_wrap(~duration_group, scales = "free") + 
                                scale_x_discrete(breaks= c("", "", "", "", ""))+
                                xlab("Trip duration groups") +
                                ylab("Number of riders") +
                                labs(title = "Casual Riders (Customers) vs Annual Riders (Subscribers)", subtitle ="Different scales for groups", fill= "Rider Type")

3. Conclusion

• By grouping data in short and long riders and comparing the bike share usage for each group, we see that the annual users are using the share bike service a whole lot more. We also see that they tend to use the service for everyday commute more.
• The conclusion is drawn both from graphs and data statistical summary.

4. Further Consideration

• Here the data is given in the trip id form. We can collect data in customer id form, where we can analyse how a casual rider's bike usage differs from annual user's one.
• We can analyse the bike usage of each age group for market target.