This homework is due Monday, Jan 27 at 11:59pm.
Go to the st295 organization on GitHub. Click on the repo with the prefix hw1. It contains the starter documents you need to complete the homework assignment.
Clone the repo and start a new project in RStudio (just like we do in class!).
Make sure to
Warning: package 'ggplot2' was built under R version 4.3.3Warning: package 'tidyr' was built under R version 4.3.3Warning: package 'readr' was built under R version 4.3.3Warning: package 'dplyr' was built under R version 4.3.3Warning: package 'stringr' was built under R version 4.3.3Warning: package 'lubridate' was built under R version 4.3.3Remember that continuing to develop a sound workflow for reproducible data analysis is important as you complete this homework and other assignments in this course. There will be reminders in this assignment for you to Render your document. The last thing you want to do is work an entire assignment before realizing you have an error somewhere that makes it so you can’t compile your document. Render after each completed question.
Use the duke_forest dataset in the openintro package for Exercises 1 and 2.
For the following two exercises you will work with data on houses that were sold in the Duke Forest neighborhood of Durham, NC in November 2020. The duke_forest dataset comes from the openintro package. You can see a list of the variables on the package website or by running ?duke_forest in your console.
We are going to first explore the duke_forest dataset by calculating a variety of summary statistics. Calculate the summary statistic associated with each scenario below.
Create a data frame that displays the mean house price across all categories of bedroom.
Create a data frame that displays the minimum and maximum lot area, in acres. Name your columns min_lot and max_lot.
Create a data frame that gives the number of homes for each combination of cooling system AND number of bathrooms. Name your count column n_count.
Copy your code from part c and add it to the code chunk in part d. Use the proper code chunk argument to make sure that the output does not show up in your rendered document. Write appropriate comments within the code chunk explaining what each line of code is doing.
Now is a good time to render, commit (with a descriptive and concise commit message), and push again. Make sure that you commit and push all changed documents and your Git pane is completely empty before proceeding.
Usually, we expect that within any market, larger houses will have higher prices. We can also expect that there exists a relation between the age of an house and its price. However, in some markets newer houses might be more expensive, while in other markets antique houses will have ‘more character’ than newer ones and have higher prices. In this question, we will explore the relations among age, size and price of houses.
Your family friend ask: “In Duke Forest, do houses that are bigger and more expensive tend to be newer than smaller and cheaper ones?”.
Once again, data visualization skills to the rescue!
price and area, also display information about year_built (that is conditioning for year_built, or your z variable).size = 3 within the appropriate geom function used to make a scatter plot to make your points bigger.geom_smooth() with the argument se = FALSE to add a smooth curve fit to the data and color the points by year_built.Use this dataset for Exercises 3 through 5.
The Behavioral Risk Factor Surveillance System (BRFSS) is the nation’s premier system of health-related telephone surveys that collect state data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.
Source: cdc.gov/brfss
In the following exercises we will work with data from the 2020 BRFSS survey. The data originally come from here, though we will work with a random sample of responses and a small number of variables from the data provided. These have already been sampled for you and the dataset you’ll use is called brfss.csv.
You will use the read_csv() function to read in your data from your data folder. You can find this code in your starter document.
Now is a good time to render, commit (with a descriptive and concise commit message), and push again. Make sure that you commit and push all changed documents and your Git pane is completely empty before proceeding.
brfss dataset?brfss dataset?Include the code and resulting output used to support your answer.
Write a sentence along with your code to answer your question
Now is a good time to render, commit (with a descriptive and concise commit message), and push again. Make sure that you commit and push all changed documents and your Git pane is completely empty before proceeding.
Do people who smoke more tend to have worse health conditions?
smoke_freq) and general health (general_health). Put smoke_freq on the x-axis.
general_health. Here we first convert general_health to a factor (how R stores categorical data) and then order the levels from Excellent to Poor. The same is done to smoke_freq, with the ordering being from Not at all to Every day.brfss |>
mutate(
general_health = as.factor(general_health),
general_health = fct_relevel(general_health, "Excellent", "Very good", "Good", "Fair", "Poor"),
smoke_freq = as.factor(smoke_freq),
smoke_freq = fct_relevel(smoke_freq, "Not at all", "Some days", "Every day")
) # add a pipe here to start creating your bar chartNow is a good time to render, commit (with a descriptive and concise commit message), and push again. Make sure that you commit and push all changed documents and your Git pane is completely empty before proceeding.
How are sleep and general health associated?
sleep and general_health.brfss |>
mutate(
general_health = as.factor(general_health),
general_health = fct_relevel(general_health, "Poor", "Fair", "Good",
"Very good", "Excellent")
) # add a pipe here to start creating your chartNow is a good time to render, commit (with a descriptive and concise commit message), and push again. Make sure that you commit and push all changed documents and your Git pane is completely empty before proceeding.
The “Workflow & formatting” grade is to assess the reproducible workflow. This includes:
%>%, |> and ggplot layers + should be followed by a new line%>% vs |>