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documentation =======
documentation
For this project, your goal is to ask and answer three questions about the available bikeshare data from Washington, Chicago, and New York. This notebook can be submitted directly through the workspace when you are confident in your results.
You will be graded against the project Rubric by a mentor after you have submitted. To get you started, you can use the template below, but feel free to be creative in your solutions!
#upload the csv files into the environment
ny <- read.csv('new_york_city.csv')
wash <- read.csv('washington.csv')
chi <- read.csv('chicago.csv')
#add column called city to each table
ny['CITY']='New york'
wash['CITY']='Washington'
chi ['CITY']= 'Chicago'
#combine only same columns of all tables, to ignore the additional two columns from New York table
common_cols <- intersect(intersect(colnames(ny), colnames(wash)),colnames(chi))
#merge all three tables into one single data frame
cities <- rbind(subset(ny, select = common_cols),
subset(wash, select = common_cols),
subset(chi, select =common_cols))
#create an additional column to include the day of the week trip start time
cities$Weekday <- format(as.Date(cities$Start.Time), "%A")
library(ggplot2)What is the most common day? .
#get the count for each day by city
by(cities$Weekday, cities$CITY, summary)
cities$CITY: Chicago
Friday Monday Saturday Sunday Thursday Tuesday Wednesday
1285 1302 1150 1111 1254 1292 1236
------------------------------------------------------------
cities$CITY: New york
Friday Monday Saturday Sunday Thursday Tuesday Wednesday
8168 7570 6176 6597 8729 7898 9632
------------------------------------------------------------
cities$CITY: Washington
Friday Monday Saturday Sunday Thursday Tuesday Wednesday NA's
12926 11721 12133 11566 13204 13288 14212 1
#visualization to get the distribution of day of the week by City
ggplot(data = subset(cities, !is.na(Weekday))) +
geom_bar(mapping = aes(x=Weekday, fill = CITY),
position = "dodge") +
ggtitle("Day of the week trip start date by city")
Chicago drivers start most of their trips on Mondays but all days of the week have similar amount of bikers starting their trips, while Washington and New York start most of their trips on Wednesday. The weekends, Saturday and Sundays, are the least amount of bikers starting a trip at all cities as can be assumed during this time the travel had already started.
what are the counts of each user type?
#get the summary info for each user type by city
library("dplyr")
by(cities$User.Type, cities$CITY, summary)
cities$CITY: Chicago
Customer Subscriber
1 1746 6883
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cities$CITY: New york
Customer Subscriber
119 5558 49093
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cities$CITY: Washington
Customer Subscriber
1 23450 65600
#remove the blank user type shown in previous code
cities1 = cities %>% mutate_if(is.factor,trimws) %>% filter(User.Type!='')
unique(cities1$User.Type)- 'Subscriber'
- 'Customer'
#create visualiztion of user type by city
library(ggplot2)
ggplot(data = subset(cities1)) +
geom_bar(mapping = aes(x=CITY, fill = User.Type),
position = "dodge" ) +
ggtitle("User Types by city")
The dataframe contained mostly Subscribers and Customers. I removed the blank user type in order to get a more significant visualization. Results show that all cities have more Subscribers than customers. Washington has more customers all other cities. The difference of Subscribers bewteen Chicago and Washington is 58,717.
What is the average travel time for users in different cities?
#get trip duration by city summary
by(cities$Trip.Duration, cities$CITY, summary)
cities$CITY: Chicago
Min. 1st Qu. Median Mean 3rd Qu. Max.
60.0 394.2 670.0 937.2 1119.0 85408.0
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cities$CITY: New york
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
61.0 368.0 610.0 903.6 1051.0 1088634.0 1
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cities$CITY: Washington
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
60.3 410.9 707.0 1234.0 1233.2 904591.4 1
#visualization as box plot to get the average of trip duration per city.
library(ggplot2)
qplot(x = CITY, y = Trip.Duration ,
data = cities, geom ='boxplot') +
ggtitle("Trip Duration By city")
Warning message:
“Removed 2 rows containing non-finite values (stat_boxplot).”
#omiting outliers: limiting the y axis to 1500
library(ggplot2)
qplot(x = CITY, y = Trip.Duration ,
data = cities, geom ='boxplot') +
ggtitle("Trip Duration By city") +
coord_cartesian(ylim = c(50.0, 1500))
Warning message:
“Removed 2 rows containing non-finite values (stat_boxplot).”
The first graph shows all the values by city. Given that the trip durations ranges from 60 seconds to 1088634 seconds there are outliers that exceed the 75% quartile. In the second graph the y axis was limited to 1500 seconds to have a more concise visualization. The trip durations in Washington have the highest average with 1234 seconds, while New York have the shortest average.
Congratulations! You have reached the end of the Explore Bikeshare Data Project. You should be very proud of all you have accomplished!
Tip: Once you are satisfied with your work here, check over your report to make sure that it is satisfies all the areas of the rubric.
Before you submit your project, you need to create a .html or .pdf version of this notebook in the workspace here. To do that, run the code cell below. If it worked correctly, you should get a return code of 0, and you should see the generated .html file in the workspace directory (click on the orange Jupyter icon in the upper left).
Alternatively, you can download this report as .html via the File > Download as submenu, and then manually upload it into the workspace directory by clicking on the orange Jupyter icon in the upper left, then using the Upload button.
Once you've done this, you can submit your project by clicking on the "Submit Project" button in the lower right here. This will create and submit a zip file with this .ipynb doc and the .html or .pdf version you created. Congratulations!
system('python -m nbconvert Explore_bikeshare_data.ipynb')Note: Please fork the current Udacity repository so that you will have a remote repository in your Github account. Clone the remote repository to your local machine. Later, as a part of the project "Post your Work on Github", you will push your proposed changes to the remote repository in your Github account.
documentation =======
This is the README file for the master branch
refactoring
04-20-2022
Explore Bike Share Data
chicago.csv washington.csv new-york-city.csv
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