Cyclistic Bike Share

Libraries and conflict management

library(tidyverse) library(readxl) # helps wrangle data library(conflicted)

Set dplyr::filter and dplyr::lag as the default choices

conflict_prefer(“filter”, “dplyr”) conflict_prefer(“lag”, “dplyr”)

#===================== # STEP 1: COLLECT DATA #===================== # # Upload Divvy datasets (csv files) here q1_2019 <- read_excel(“Divvy_Trips_2019_Q1.xlsx”) q1_2020 <- read_excel(“Divvy_Trips_2020_Q1.xlsx”)

#==================================================== # STEP 2: WRANGLE DATA AND COMBINE INTO A SINGLE FILE #==================================================== # Compare column names each of the files # While the names don’t have to be in the same order, they DO need to match perfectly before we can use a command to join them into one file colnames(q1_2019) colnames(q1_2020)

Rename columns to make them consistent with q1_2020 (as this will be the supposed going-forward table design for Divvy)

q1_2019 <- rename(q1_2019 ,ride_id = trip_id ,rideable_type = bikeid ,started_at = start_time ,ended_at = end_time ,start_station_name = from_station_name ,start_station_id = from_station_id ,end_station_name = to_station_name ,end_station_id = to_station_id ,member_casual = usertype )

Inspect the dataframes and look for incongruencies

str(q1_2019) str(q1_2020)

Convert ride_id and rideable_type to character so that they can stack correctly

q1_2019 <- mutate(q1_2019, ride_id = as.character(ride_id) ,rideable_type = as.character(rideable_type))

Stack individual quarter’s data frames into one big data frame

all_trips <- bind_rows(q1_2019, q1_2020)#, q3_2019)#, q4_2019, q1_2020)

Remove lat, long, birthyear, and gender fields as this data was dropped beginning in 2020

all_trips <- all_trips %>% select(-c(start_lat, start_lng, end_lat, end_lng, birthyear, gender, “tripduration”))

#====================================================== # STEP 3: CLEAN UP AND ADD DATA TO PREPARE FOR ANALYSIS #====================================================== # Inspect the new table that has been created colnames(all_trips) #List of column names nrow(all_trips) #How many rows are in data frame? dim(all_trips) #Dimensions of the data frame? head(all_trips) #See the first 6 rows of data frame. Also tail(all_trips) str(all_trips) #See list of columns and data types (numeric, character, etc) summary(all_trips) #Statistical summary of data. Mainly for numerics

(1) In the “member_casual” column, there are two names for members (“member” and “Subscriber”) and two names for casual riders (“Customer” and “casual”). We will need to consolidate that from four to two labels.

(2) The data can only be aggregated at the ride-level, which is too granular. We will want to add some additional columns of data – such as day, month, year – that provide additional opportunities to aggregate the data.

(3) We will want to add a calculated field for length of ride since the 2020Q1 data did not have the “tripduration” column. We will add “ride_length” to the entire dataframe for consistency.

(4) There are some rides where tripduration shows up as negative,

In the “member_casual” column, replace “Subscriber” with “member” and “Customer” with “casual”

Before 2020, Divvy used different labels for these two types of riders … we will want to make our dataframe consistent with their current nomenclature

N.B.: “Level” is a special property of a column that is retained even if a subset does not contain any values from a specific level

Begin by seeing how many observations fall under each usertype

table(all_trips$member_casual)

Reassign to the desired values (we will go with the current 2020 labels)

all_trips <- all_trips %>% mutate(member_casual = recode(member_casual ,“Subscriber” = “member” ,“Customer” = “casual”))

Check to make sure the proper number of observations were reassigned

table(all_trips$member_casual)

Add columns that list the date, month, day, and year of each ride

This will allow us to aggregate ride data for each month, day, or year … before completing these operations we could only aggregate at the ride level

https://www.statmethods.net/input/dates.html more on date formats in R found at that link

all_trips\(date <- as.Date(all_trips\)started_at) #The default format is yyyy-mm-dd all_trips\(month <- format(as.Date(all_trips\)date), “%m”) all_trips\(day <- format(as.Date(all_trips\)date), “%d”) all_trips\(year <- format(as.Date(all_trips\)date), “%Y”) all_trips\(day_of_week <- format(as.Date(all_trips\)date), “%A”)

Add a “ride_length” calculation to all_trips (in seconds)

https://stat.ethz.ch/R-manual/R-devel/library/base/html/difftime.html

all_trips\(ride_length <- difftime(all_trips\)ended_at,all_trips$started_at)

Inspect the structure of the columns

str(all_trips)

Convert “ride_length” from Factor to numeric so we can run calculations on the data

is.factor(all_trips\(ride_length) all_trips\)ride_length <- as.numeric(as.character(all_trips\(ride_length)) is.numeric(all_trips\)ride_length)

Remove “bad” data

The dataframe includes a few hundred entries when bikes were taken out of docks and checked for quality by Divvy or ride_length was negative

We will create a new version of the dataframe (v2) since data is being removed

https://www.datasciencemadesimple.com/delete-or-drop-rows-in-r-with-conditions-2/

all_trips_v2 <- all_trips[!(all_trips\(start_station_name == "HQ QR" | all_trips\)ride_length<0),]

#===================================== # STEP 4: CONDUCT DESCRIPTIVE ANALYSIS #===================================== # Descriptive analysis on ride_length (all figures in seconds) mean(all_trips_v2\(ride_length) #straight average (total ride length / rides) median(all_trips_v2\)ride_length) #midpoint number in the ascending array of ride lengths max(all_trips_v2\(ride_length) #longest ride min(all_trips_v2\)ride_length) #shortest ride

You can condense the four lines above to one line using summary() on the specific attribute

summary(all_trips_v2$ride_length)

Compare members and casual users

aggregate(all_trips_v2\(ride_length ~ all_trips_v2\)member_casual, FUN = mean) aggregate(all_trips_v2\(ride_length ~ all_trips_v2\)member_casual, FUN = median) aggregate(all_trips_v2\(ride_length ~ all_trips_v2\)member_casual, FUN = max) aggregate(all_trips_v2\(ride_length ~ all_trips_v2\)member_casual, FUN = min)

See the average ride time by each day for members vs casual users

aggregate(all_trips_v2\(ride_length ~ all_trips_v2\)member_casual + all_trips_v2$day_of_week, FUN = mean)

Notice that the days of the week are out of order. Let’s fix that.

all_trips_v2\(day_of_week <- ordered(all_trips_v2\)day_of_week, levels=c(“Sunday”, “Monday”, “Tuesday”, “Wednesday”, “Thursday”, “Friday”, “Saturday”))

Now, let’s run the average ride time by each day for members vs casual users

aggregate(all_trips_v2\(ride_length ~ all_trips_v2\)member_casual + all_trips_v2$day_of_week, FUN = mean)

analyze ridership data by type and weekday

all_trips_v2 %>% mutate(weekday = wday(started_at, label = TRUE)) %>% #creates weekday field using wday() group_by(member_casual, weekday) %>% #groups by usertype and weekday summarise(number_of_rides = n() #calculates the number of rides and average duration ,average_duration = mean(ride_length)) %>% # calculates the average duration arrange(member_casual, weekday) # sorts

Let’s visualize the number of rides by rider type

all_trips_v2 %>% mutate(weekday = wday(started_at, label = TRUE)) %>% group_by(member_casual, weekday) %>% summarise(number_of_rides = n() ,average_duration = mean(ride_length)) %>% arrange(member_casual, weekday) %>% ggplot(aes(x = weekday, y = number_of_rides, fill = member_casual)) + geom_col(position = “dodge”)

Let’s create a visualization for average duration

all_trips_v2 %>% mutate(weekday = wday(started_at, label = TRUE)) %>% group_by(member_casual, weekday) %>% summarize(number_of_rides = n() ,average_duration = mean(ride_length)) %>% arrange(member_casual, weekday) %>% ggplot(aes(x = weekday, y = average_duration, fill = member_casual)) + geom_col(position = “dodge”)

#================================================= # STEP 5: EXPORT SUMMARY FILE FOR FURTHER ANALYSIS #================================================= # Create a csv file that we will visualize in Excel, Tableau, or my presentation software # N.B.: This file location is for a Mac. If you are working on a PC, change the file location accordingly (most likely “C:_USERNAME...”) to export the data. You can read more here: https://datatofish.com/export-dataframe-to-csv-in-r/ counts <- aggregate(all_trips_v2\(ride_length ~ all_trips_v2\)member_casual + all_trips_v2$day_of_week, FUN = mean) write.csv(counts, file = ‘avg_ride_length.csv’)