The-Cyclic-Bikeshare-data

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##<<Install & load required packages>>####

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install.packages("data.table")# For loading and subsequently, combining the spreadsheets conveniently( and efficiently). install.packages("readr") # For reading the spreadsheets install.packages("tidyverse") # tidyverse for data import and wrangling! install.packages("lubridate")# lubridate for date functions!

library(data.table) library(readr) library(tidyverse)
library(lubridate)

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STEP 1: COLLECT DATA

#===================== #Set windows destination & Upload Divvy datasets (csv files) here

setwd("C:/LAB/Capstone project/Extracted Data/R uploads") trips <- list.files(pattern="*.csv") print(trips_) #Rename the above printed files to an appropriate dataframe names apr_2023<- read_csv("202304-Trips.csv") mar_2023<- read_csv("202303-Trips.csv") feb_2023<- read_csv("202302-Trips.csv") jan_2023<- read_csv("202301-Trips.csv") dec_2022 <- read_csv("202212-Trips.csv") nov_2022<- read_csv("202211-Trips.csv") oct_2022<- read_csv("202210-Trips.csv") sep_2022<- read_csv("202209-Trips.csv") aug_2022<- read_csv("202208-Trips.csv") jul_2022<- read_csv("202207-Trips.csv") jun_2022<- read_csv("202206-Trips.csv") may_2022 <- read_csv("202205-Trips.csv")

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STEP 2: INSPECTED THE DATA FOR INCONSISTENCIES AND COMBINE INTO A SINGLE FILE

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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

str (apr_2023) str (mar_2023) str (feb_2023) str (jan_2023) str (dec_2022) str (nov_2022) str (oct_2022) str (sep_2022) str (jul_2022) str (jun_2022) str (jun_2022) str (may_2022)

Because there were no inconsistencies or discrepancies in the data’s column names, I continued to bind the data in the coding script below:

trips_<- list.files("C:/LAB/Capstone project/Extracted Data/R uploads", pattern="*.csv") print(filenames) #inspect the loaded files for any incongruencies.

trips_ <- rbindlist(lapply(trips_, fread))

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

trips <- bind_rows(apr_2023,mar_2023,feb_2023,jan_2023,dec_2022,nov_2022,oct_2022,sep_2022,jul_2022,jun_2022,may_2022) names(trips) #======================================================

STEP 3: CLEAN AND ADD DATA TO PREPARE FOR ANALYSIS

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#_In doing this step I removed columns that I deemed as not useful for this particular analysis, through the data exploration (I did bearing in mind the main business task/challenge at hand):

Remove ride_id, rideable_type,start_station_id, end_station_id

trips_ <- trips_%>%
select(-c(ride_id, rideable_type,start_station_id, end_station_id))

Inspect the new table that remains

str(trips_)#See list of columns and data types (numeric, character, etc) summary(trips_) #Statistical summary of data. Mainly numerical

#Upon inspection I saw it fit to rename the member_casual column to rider_type as its more precisely descriptive

colnames(trips_)[colnames(trips_) == "member_casual"] <- "rider_type"

names(trips_) #Inspect to see if indeed the member_casual column is changed to rider_type

#I also removed duplicate rows as I deemed them unnecessary and rather cumbersome for the processing of data nrow(trips_) [1] 5859061 trips_<- distinct(trips_) #Remove duplicate rows. nrow(trips_) [1] 5859027 #The above is showing that 21 rows (5859061-5859027) around were removed, cleaning effort highlight.

#_After the above, I did addition of insightful layers(columns)

(1) One of the main layers I dimmed necessary to add was a column for length of trip. I will name it "trip_length”.

trips_$trip_length <- difftime(trips_$ended_at, trips_$started_at, units = "hours")

df <- arrange(df, column_name) trips_<- arrange(trips_, trip_length)

Inspect the new added column for any anomalies. summary(trips_$trip_length)

#I found that I cannot even obtain statistical summaries from the new added column then that means we need to convert it to format which we can be able to obtain statistical summaries from

Converted "trip_length" from Factor to numeric so we can run calculations on the data

is.factor(trips_$ride_length) trips_$trip_length <- as.numeric(as.character(trips_$trip_length)) is.numeric(trips_$trip_length)

The above step is very paramount otherwise you won’t be able to find the statistical attributes from the column data like mean,min,max and Q3.

summary(trips_$trip_length) #now I was able to obtain statistical summaries and also found anomalies in the data such as a minimum value of -172 hours of ride length ,this obviously calls for further inspection of the data and in particular the trip length aspect of the data.

negative_values <- trips_ [trips_$ trip_length <= 0, ] nrow(negative_values) #found out that there are 544 negative values or values with zero print(negative_values$trip_length) summary(negative_values$trip_length)

Remove "bad" data

nrow(trips_)= 5859027 trips_ <- trips_[!(trips_$trip_length <= 0)] nrow(trips_)= 5858483 #so around 544 trip duration values with zero or negative figures were removed, cleaning effort highlight.

(2) I continued to add other additional columns(layers) to the data -- such as day, day_of_week and month_year -- that provided me with additional insights about the data.

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

trips_$date <- as.Date(trips_$started_at) #The default format is yyyy-mm-dd trips_$month_year <- format(as.Date(trips_$started_at), " %Y-%m") trips_$day_of_week <- format(as.Date(trips_$date), "%A") trips_$day <- format(as.Date(trips_$date), "%d")

Inspect the structure of the columns

str(trips_)

Upon inspection we can see that column such as months, day and day of week are in character class of which is not ideal for statistical

Specify the file path and name for the CSV file

csv_file <- "trips_data.csv"

Write the data frame to a CSV file

write.csv(trips_, file = csv_file, row.names = FALSE)

Print a message to confirm the CSV file creation

cat("CSV file created:", csv_file, "\n")

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