Google Analytics Cyclistic bike-share R script

Here is the R script I used to load, review, merge, add additional variables, filter and export to a new csv file.

#here are the R script I used to import the 12 months worth of CSV files

#review their structures to ensure all files have the same structures to merge together

#note - need to review each file's variables individually, which is not done in this script

install.packages("tidyverse")

install.packages("readr")

install.packages("janitor")

install.packages("lubridate")

library(tidyverse)

library(readr)

library(janitor)

library(lubridate)

df1 <- read_csv("202106-divvy-tripdata.csv")

df2 <- read_csv("202107-divvy-tripdata.csv")

df3 <- read_csv("202108-divvy-tripdata.csv")

df4 <- read_csv("202109-divvy-tripdata.csv")

df5 <- read_csv("202110-divvy-tripdata.csv")

df6 <- read_csv("202111-divvy-tripdata.csv")

df7 <- read_csv("202112-divvy-tripdata.csv")

df8 <- read_csv("202201-divvy-tripdata.csv")

df9 <- read_csv("202202-divvy-tripdata.csv")

df10 <- read_csv("202203-divvy-tripdata.csv")

df11 <- read_csv("202204-divvy-tripdata.csv")

df12 <- read_csv("202205-divvy-tripdata.csv")

df <- rbind(df1, df2, df3, df4, df5, df6, df7, df8, df9, df10, df11, df12)

#I ran below scripts for df1 to df12 to ensure all variables match before combining them. 

#there should be an easier / more automatic way to compare them instead of manual comparison.

str(df)

glimpse(df)

colnames(df)

summary(df)

table(df$rideable_type)

#convert variable started_at and ended_at to POSIXlt type

#calculate weekdays to see daily pattern

#calculate month of year to see monthly pattern

#calculate duration (ended_at - started_at) and convert the result to numeric

df$started_at <- as.POSIXlt(df$started_at)

df$ended_at <- as.POSIXlt(df$ended_at)

df$start_weekday <- weekdays(df$started_at)

df$start_month <- format(as.Date(df$started_at), "%m")

df$duration_min <- as.numeric((df$ended_at - df$started_at)/60)

summary(df)

#filter out any data that has duration less than or equal to 0

df_temp <- filter(df, duration_min > 0)

#select a smaller size of data

#after review, decided to keep only ride_id, rideable_type, started_at, ended_at, member_casual,

# start_weekday, duration_min and start_month

df1 <- select(df_temp, ride_id, rideable_type, started_at, ended_at, member_casual, start_weekday, 

              duration_min, start_month)

#save the file into file name "202106-202205-updatedbikedata.csv"

write.csv(df1,"202106-202205-updatedbikedata.csv",row.names=FALSE)