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R Studio Script
---
title: "google_case_study_1"
author: "Angela Martinez"
date: "2022-10-07"
output: html_document
---
This is my process in completing the first case study in the Google Data Analytics course. The question we're trying to answer here is: How do annual members and casual riders use Cyclistic bikes differently
I start by installing the packages needed...
```{r}
install.packages("tidyverse",repos = "http://cran.us.r-project.org")
install.packages("lubridate",repos = "http://cran.us.r-project.org")
install.packages("janitor",repos = "http://cran.us.r-project.org")
install.packages("ggplot2",repos = "http://cran.us.r-project.org")
```
...and loading them.
```{r}
library(tidyverse)
library(lubridate)
library(janitor)
library(ggplot2)
```
Then, I downloaded data sets for the last 12 months locally and unzipped them. I renamed each file and imported them.
```{r}
Sep2021 <- read_csv("C:/Users/angel/OneDrive/Documents/googleCaseStudy1/TripData/2021_09.csv")
Oct2021 <- read_csv("C:/Users/angel/OneDrive/Documents/googleCaseStudy1/TripData/2021_10.csv")
Nov2021 <- read_csv("C:/Users/angel/OneDrive/Documents/googleCaseStudy1/TripData/2021_11.csv")
Dec2021 <- read_csv("C:/Users/angel/OneDrive/Documents/googleCaseStudy1/TripData/2021_12.csv")
Jan2022 <- read_csv("C:/Users/angel/OneDrive/Documents/googleCaseStudy1/TripData/2022_01.csv")
Feb2022 <- read_csv("C:/Users/angel/OneDrive/Documents/googleCaseStudy1/TripData/2022_02.csv")
Mar2022 <- read_csv("C:/Users/angel/OneDrive/Documents/googleCaseStudy1/TripData/2022_03.csv")
Apr2022 <- read_csv("C:/Users/angel/OneDrive/Documents/googleCaseStudy1/TripData/2022_04.csv")
May2022 <- read_csv("C:/Users/angel/OneDrive/Documents/googleCaseStudy1/TripData/2022_05.csv")
Jun2022 <- read_csv("C:/Users/angel/OneDrive/Documents/googleCaseStudy1/TripData/2022_06.csv")
Jul2022 <- read_csv("C:/Users/angel/OneDrive/Documents/googleCaseStudy1/TripData/2022_07.csv")
Aug2022 <- read_csv("C:/Users/angel/OneDrive/Documents/googleCaseStudy1/TripData/2022_08.csv")
```
Now, I'll check that the column names are consistent with each other.
```{r}
colnames(Sep2021)
colnames(Oct2021)
colnames(Nov2021)
colnames(Dec2021)
colnames(Jan2022)
colnames(Feb2022)
colnames(Mar2022)
colnames(Apr2022)
colnames(May2022)
colnames(Jun2022)
colnames(Jul2022)
colnames(Aug2022)
```
And check the data types for consistency
```{r}
str(Sep2021)
str(Oct2021)
str(Nov2021)
str(Dec2021)
str(Jan2022)
str(Feb2022)
str(Mar2022)
str(Apr2022)
str(May2022)
str(Jun2022)
str(Jul2022)
str(Aug2022)
```
Stack data into one big data frame
```{r}
all_trips <- bind_rows(Sep2021, Oct2021, Nov2021, Dec2021,
Jan2022, Feb2022, Mar2022, Apr2022,
May2022, Jun2022, Jul2022, Aug2022)
```
Clean data frame be removing columns we don't need: latatude and longitude
```{r}
all_trips <- all_trips %>%
select(-c(start_lat, start_lng, end_lat, end_lng))
```
Inspect the new data frame
```{r}
colnames(all_trips)
nrow(all_trips)
dim(all_trips)
head(all_trips)
str(all_trips)
summary(all_trips)
```
Check to make sure only "member" or "casual" are in the member_casual column
```{r}
table(all_trips$member_casual)
```
Add columns for date, month, day, year, and day of week
```{r}
all_trips$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$date), "%A")
```
Add a "ride_length" calculation to all_trips (in seconds)
```{r}
all_trips$ride_length <- difftime(all_trips$ended_at,all_trips$started_at)
```
Inspect the structure of the columns
```{r}
str(all_trips)
```
Convert "ride_length" to numeric so we can run calculations
```{r}
is.numeric(all_trips$ride_length) #check if ride_length is numeric
all_trips$ride_length <- as.numeric(as.character(all_trips$ride_length))
```
Create new data frame with removed ride length values of 0
```{r}
all_trips_v2 <- all_trips[!(all_trips$ride_length<=0),]
```
Descriptive analysis on ride_length (all figures in seconds)
```{r}
summary(all_trips_v2$ride_length)
```
Compare members and casual users
```{r}
aggregate(all_trips_v2$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$member_casual, FUN = min)
```
Fix order or days of the week
```{r}
all_trips_v2$day_of_week <- ordered(all_trips_v2$day_of_week,
levels=c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"))
```
See the average ride time by each day for members vs casual users
```{r}
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual +
all_trips_v2$day_of_week, FUN = mean)
```
Analyze ridership data by type and weekday
```{r}
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
```
Visualize the number of rides by rider type
```{r}
options(scipen = 999)
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")
```
Visualize the average duration
```{r}
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 = average_duration, fill = member_casual)) +
geom_col(position = "dodge")
```
Create a CSV file for tableu
```{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 = '~/googleCaseStudy1/avg_ride_length.csv')
```
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