Warm up

Before we introduce the data, let’s warm up with some simple exercises. Update the YAML of your R Markdown file with your information, knit, commit, and push your changes. Make sure to commit with a meaningful commit message. Then, go to your repo on GitHub and confirm that your changes are visible in your Rmd and md files. If anything is missing, commit and push again.

Packages

We’ll use the tidyverse package for much of the data wrangling and visualisation and the data lives in the dsbox package. Remember to install these packages before loading using install.packages("tidyverse")'. Loading thedsboxpackage is a little more involved. We first need to installdevtools` and then install the package from Github.

install.packages("devtools")
library(devtools)

devtools::install_github("rstudio-education/dsbox")

You can then load them by running the following in your Console:

library(tidyverse)
library(dsbox)

Data

The data can be found in the dsbox package, and it’s called dcbikeshare. Since the dataset is distributed with the package, we don’t need to load it separately; it becomes available to us when we load the package.

In case there are problems with loading the dsbox package. I have also included the dataset in the data folder. You can load this in directly if you have any issues.

You can find out more about the dataset by inspecting its documentation, which you can access by running ?dcbikeshare in the Console or using the Help menu in RStudio to search for dcbikeshare. You can also find this information here.

The data include daily bike rental counts (by members and casual users) of Capital Bikeshare in Washington, DC in 2011 and 2012 as well as weather information on these days. The original data sources are http://capitalbikeshare.com/system-data and http://www.freemeteo.com.

Data wrangling

⊕Note: You will find it helpful to review the functions case_when and functions in the forcats for this exercize.

1. Recode the season variable to be a factor with meaningful level names as outlined in the codebook, with spring as the baseline level.

2. Recode the binary variables holiday and workingday to be factors with levels no (0) and yes (1), with no as the baseline level.

3. Recode the yr variable to be a factor with levels 2011 and 2012, with 2011 as the baseline level.

4. Recode the weathersit variable as 1 - clear, 2 - mist, 3 - light precipitation, and 4 - heavy precipitation, with clear as the baseline.

5. Calculate raw temperature, feeling temperature, humidity, and windspeed as their values given in the dataset multiplied by the maximum raw values stated in the codebook for each variable. Instead of writing over the existing variables, create new ones with concise but informative names.

6. Check that the sum of casual and registered adds up to cnt for each record. Hint: One way of doing this is to create a new column that takes on the value TRUE if they add up and FALSE if not, and then checking if all values in that column are TRUEs. But this is only one way, you might come up with another.

🧶 ✅ ⬆️ Knit, commit, and push your changes to GitHub with an appropriate commit message. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

Exploratory data analysis

7. Recreate the following visualization, and interpret it in context of the data. Hint: You will need to use one of the variables you created above. The temperature plotted is the feeling temperature.

8. Create a visualization displaying the relationship between bike rentals and season. Interpret the plot in context of the data.

🧶 ✅ ⬆️ Knit, commit, and push your changes to GitHub with an appropriate commit message. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

Modelling

9. Fit a linear model predicting total daily bike rentals from daily temperature. Write the linear model, interpret the slope and the intercept in context of the data, and determine and interpret the \(R^2\).

10. Fit another linear model predicting total daily bike rentals from daily feeling temperature. Write the linear model, interpret the slope and the intercept in context of the data, and determine and interpret the \(R^2\). Is temperature or feeling temperature a better predictor of bike rentals? Explain your reasoning.

11. Fit a model predicting total daily bike rentals from season, year, whether the day is holiday or not, whether the day is a workingday or not, the weather category, temperature, feeling temperature, humidity, and windspeed, as well as the interaction between feeling temperature and holiday. Record adjusted \(R^2\) of the model.

12. Write the linear models for holidays and non-holidays. Is the slope of temperature the same or different for these two models? How about the slope for feeling temperature? Why or why not?

13. Interpret the slopes of season and feeling temperature. If the slopes are different for holidays and non-holidays, make sure to interpret both. If the variable has multiple levels, make sure you interpret all of the slope coefficients associated with it.

14. Interpret the intercept. If the intercept is different for holidays and non-holidays, make sure to interpret both.

15. According to this model, assuming everything else is the same, in which season does the model predict total daily bike rentals to be highest and which to be the lowest?

🧶 ✅ ⬆️ Knit, commit, and push your changes to GitHub with an appropriate commit message. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards and review the md document on GitHub to make sure you’re happy with the final state of your work.