Notification:

1. For Problem Set 2, we will implement peer grading using Kritik.

2. Please use R Markdown to build your submission. Be sure that for each R chunk in your markdown file has echo = TRUE so your code is included in your submission.  You can do this for all the R Chunks by using this setup at the top of the markdown file:

Important Notes

For each of your submission plots be sure to include the following:

• Title

• Labels on your axes, or a key

– Be sure the labels are meaningful outside of the context of your code.

• Make sure the elements in your plot are appropriately scaled (sized to increase interpret-ability).

• A one sentence summary about your plot and its contents.

Fuel Economy

You have been dealing with fuel economy data since bootcamp, using the mtcars and mpg built-in data sets. In the US, the home of fuel economy data is the Department of Energy website which has an extensive downloads page. The two files for this problem are vehicles.csv and index.html.

To get started, import vehicles.csv with the import > From Text(base) function in the Environment panel in RStudio.  index.html will display in your default browser when you double-click it in your file browser.

When you have loaded vehicles.csv into R, you will see that it has 45,471 rows of data and 83 variables. It covers cars from 1984 through 2023. Before you can do anything with this data you must explore it. The data dictionary is in index.html. In your browser, scroll down to get to the data dictionary.

Now use what you learned about sub-setting to explore. (We’ll talk about more modern approaches to this exploration next week.) But for this assignment, I’ll start you with some simple exploration.

Start by looking at vehicles made by Mazda:

m2 <- vehicles[vehicles$make == "Mazda",c("year", "make", "model", "mpgData")]

You can see in the mpgData variable that not every row will have mpgData. Don’t be too fast to get ride of the rows that don’t report mpg, however.

This data set include electric and hybrid vehicles – note the variables phevBlended and highwayE. Look them up in the data dictionary.

So now make another, more useful, subset if the vehicles data.

It includes phevBlended which I can see takes on logical values TRUE and FALSE.

m3 <- vehicles[vehicles$year == "2022",c("year", "make", "model", "mpgData", "phevBlended")]

When I search, however, I don’t get anything. Are there really no hybrid cars in the data? m3[m3$phevBlended == TRUE,] ## nothing is returned

## [1] year       make       model      mpgData    phevBlended

## <0 rows> (or 0-length row.names)

## and for further evidence, I try

try(sum(m3$phevBlended))

## Error in sum(m3$phevBlended) : invalid 'type' (character) of argument

## it throws a type error -- OH!! Of course. The csv file had strings

## confirm the problem

typeof(m3$phevBlended)

## [1] "character"

## fix it

m3$phevBlended <- as.logical(m3$phevBlended)

sum(m3$phevBlended)

## [1] 44

OK, for the year 2022 there are 44 hybrid vehicles in the dataset. Now look at eletric vehicles. Build another subset with variable you might have ignored at first. Again, look them up.

m6 <- vehicles[vehicles$year == "2022",

c("year", "make", "model", "mpgData",

"phevBlended", "highwayE",

"fuelType", "fuelType1", "fuelType2",

"barrels08", "barrelsA08", "charge120", "charge240")]

## don't forget to fix this problem again

m6$phevBlended <- as.logical(m6$phevBlended)

sum(m6$phevBlended)             ## reconfirm the fix

## [1] 44

## take a look at the hybrid cars

head(m6[m6$phevBlended == TRUE, ])

make

## 37319

## 37386

## 37534

## 37535

## 37537

## 37594

##

## 37319

## 37386

## 37534

## 37535

## 37537

## 37594

##

## 37319

## 37386

## 37534

## 37535

## 37537

## 37594

Your Assignment

1.  Do an analysis of Fuel economy over the 40 year span 1984 through 2023, inclusive.  You may want to do an analysis by type of fuel which will ignore hybrids and electric vehicles for most the the years under analysis.

2. Now, examine vehicle makers. Which ones have made the most progress?  Make at least two plots that address the questions above.  As you do your work, you may make many plots.  If you include plots in addition to the two that described above, make sure that they address different issues and are not simply intermediate steps you took as you made the to plots you’re submitting for questions 1 and 2.

NASDAQ Composite

The Nasdaq Composite (ticker symbol ^IXIC) is a stock market index that includes almost all stocks listed on the Nasdaq stock exchange. Along with the Dow Jones Industrial Average and S&P 500, it is one of the three most-followed stock market indices in the United States.

Your Assignment

Use ggplot to create a Candlestick chart with the Nasdaq Composite data from September 20, 2021 to September 20, 2022, using file IXIC21-22.csv. If you don’t know what the Candlestick chart is, the following link might give some information: Candlestick.

For your convenience, the following links provide some examples might help you to create the chart:

• Candlestick Example 1.

• Candlestick Example 2

Be aware of the following when you create your chart:

• Make sure the X-axis is Date and the Y-axis is Adjust Close.

• Include an appropriate title with your graph.

• Make sure your data is clean before you create the plot.

• The format for X label will be month-year or year-month eg, Jul-2022, 07-2022, 2022-07

• You might need to use tidyquant package to create this plot.

• In a sentence or two, what does this plot show?

Here is an example of the candlestick plot:

Rural Capacity Index

The Rural Capacity Index  (RCI) was created by Headwaters Economics to assess municipal  “capacity”. Capacity being “the staffing, resources, and expertise—to apply for funding, fulfill onerous reporting re- quirements, and design, build, and maintain infrastructure projects over the long term.”  (Hernandez, 2022). The RCI consolidates various data from the American Community Survey, conducted by the US Census: ed- ucation levels, populations, health insurance, and even broadband access. ruralCapacityData.csv contains

the RCI and its constituents about the counties in New Mexico.

New Mexican urban and rural communities have very disparate characteristics. Bernillio County, containing New Mexico’s most populous urban setting, has over 1500 times the population of Harding County. During your exploration of the Rural Capacity Index you may want to omit Berniliio County to better represent the more numerous and varied small communities.

Your Assignment

1.  Create a plot that emphasizes rural capacity indexes.   Choose your other variables to reflect their contribution to the rural capacity index.

2.  Create a plot that demonstrates the relationship between the number of houses with broadband and the percent of adults, 25 and older, with bachelor degrees. Include information about the rural capacity indexes.

3.  Explore different sizes of communities and their capacity indexes.  Create three plots that describe communities with total population < 16000, 16000 < total population < 55000, and total population > 55000. What facets of each population subsection stand out to you, demonstrate them in your plots.

Column descriptions about the RCI data:

•  cap_index – The Rural Capacity Index, between 0 and 100.

• pop_over_25 – Population 25 years and older.

• pop_bachelors – Population with a bachelors degree.

• per_over_25_with_bach – Percent of population 25 and over with a bachelors degree.

•  num_fam – Number of families.

• per_fam_below_pov – Percentage of families below the poverty level.

• tot_house – Total households.

•  house_broadband – Total households with broadband connection.

• pop_noninst – Non-institutionalized population.

• pop_insured – Population with health insurance.

• per_insured – Percentage of population with health insurance.

• pop_uninsured – Population without health insurance.

• per_uninsured – Percent of population without health insurance.

• pop_total – Total population.