Programming Assignment 2

Assignment Introduction

In this assignment, you’ll practice some of the R skills you’ll need for the final project. The assignment is broken into 3 parts:

Part 1: Reading Package Docs (20 points)
Part 2: Getting and Cleaning Data (40 points)
Part 3: Reading Remote Files (40 points)

Part 1: Reading Package Docs

Description

In the final project, you’ll be asked to use some external packages to create an R processing pipeline that gets data, cleans it, analyzes it, and present results. In this assignment, we’re going to work with some of those packages to get familiar with them.

You can access R function documentation using syntax like ?function. This documentation typically contains a description of the function’s purpose, a list of parameters, and the function signature. In addition, many package authors include examples, small blocks of code using the package’s functionality that you can copy, paste, and run directly.

In this part of the assignment, I’d like you to practice reading and running these examples. Please copy one example of each of the following functions from its documentation. Try running these yourself, but you do not need to include the output in your submission.

ymd_hms() from the {lubridate} package
str_extract() from the {stringr} package
setnames() from the {data.table} package
map_dbl() from the {purrr} package
Two functions from the “Data Retrieval and Transformation” section of the package list (link)
- NOTE: must be from two different packages
Two functions from the “Math and Statistics” section of the package list (link)
- NOTE: must be from two different packages
Two functions from the “Visualization, Presentation, and Reporting” section of the package list (link)
- NOTE: must be from two different packages

NOTE: Please, just COPY from these packages’ documentation… do not write your own custom examples.

Submission

Upload a file to the Programming Assignment 2 dropbox on D2L with a name like {Firstname}_{Lastname}_Assignment2pt1.R (example: James_Lamb_Assignment2pt1.R). This file should contain 10 sections, one for each of the functions above. Please copy one example per function into your script, and use the format given in the example below:

#==============================================================================#
# package: data.table
# function: setnames

# Example:
DT <- data.table(a=1:2,b=3:4,c=5:6) # compare to data.table
try(tracemem(DT))                  # by reference, no deep or shallow copies
setnames(DT,"b","B")               # by name, no match() needed (warning if "b" is missing)
#==============================================================================#
#==============================================================================#
# package: stats
# function: cor

# Example:
cor(1:10, 2:11) # == 1
#==============================================================================#

Part 2: Getting and Cleaning Data

Description

As you go further into the world of data science, you’ll hear this said often: most of a data scientist’s time is spent getting and cleaning data. Let’s practice that crucial activity.

In the second part of this assignment, we’ll be working with data from the St. Louis Federal Reserve (FRED).

Using the resources I’ve provided below, your goal is to download two series: Japanese inflation (YOY percent change) and U.S. inflation (YOY percent change).

Then you’ll join them into a single data.frame and plot them against each other on a line plot using R’s base plotting system.

Brief R + {quantmod} + FRED tutorial: https://www.quantmod.com/documentation/getSymbols.FRED.html
Tutorial on creating line plots in R’s base plotting system: https://www.harding.edu/fmccown/r/

Submission

Upload a file to the Programming Assignment 2 dropbox on D2L with a name like {Firstname}_{Lastname}_Assignment2pt2.R (example: James_Lamb_Assignment2pt2.R). In the example script below, I’ve filled in the pieces needed to pull the U.S. data. Your task is to fill out the rest of this script so that it produces a plot comparing Japan and the U.S.

You need to complete all of the following steps, indicated by the phrase ### FILL THIS IN ### in the example script below:

add the command to pull the Japanese inflation data (https://fred.stlouisfed.org/series/FPCPITOTLZGJPN)
rename the columns of the Japanese data so that the inflation series is called “JPN_INF_YOY”
add the Japanese data to the merge() command to create a single dataset for plotting
- please look at the documentation for ?merge
ensure the plot is a line plot (hint: see ?graphics::plot)
set the plot title to “Inflation Data”
set the plot y-axis label to “CPI Inflation (%yoy)”
set the plot x-axis label to “time”
add the U.S. data to the plot with lines(type = "l")
- use the col argument within lines() to set the color of this line to “red”

# Load dependencies
library(quantmod)

# Get Data
usaDF <- as.data.frame(
    quantmod::getSymbols(
        Symbols = "FPCPITOTLZGUSA"
        , src = "FRED"
        , auto.assign = FALSE
    )
)
usaDF["Date"] <- as.Date(row.names(usaDF))

jpnDF <- as.data.frame(
    quantmod::getSymbols(
    ### FILL THIS IN ###
    )
)
jpnDF["Date"] <- as.Date(row.names(jpnDF))

# rename columns
names(usaDF) <- c("USA_INF_YOY", "Date")
names(jpnDF) <- ### FILL THIS IN ###

# Combine the two data.frames with merge()
mergedDF <- merge(
    x = usaDF,
    y = ### FILL THIS IN ###,
    by = "Date"
)

# Plot the two series
plot(
    x = mergedDF$Date,
    y = mergedDF$JPN_INF_YOY,
    type = ### FILL THIS IN ###,
    main = ### FILL THIS IN ###,
    xlab = ### FILL THIS IN ###,
    ylab = ### FILL THIS IN ###
)

lines(
    x = ### FILL THIS IN ###,
    y = ### FILL THIS IN ###,
    type = "l",
    col = ### FILL THIS IN ###
)

legend(
    x = "topright"
    , col = c("black", "red")
    , legend = c("JPN", "USA")
    , lty = c(1, 1)
)

If this works correctly, you should see a plot similar to this:

Part 3: Reading Remote Files

Description

In the final project, you’ll be asked to submit code that reads in data from remote storage.

In this part of the assignment, you’ll practice this important skill.

hint: Read https://jameslamb.github.io/intro-to-r/code/programming-supplement.html#Working_with_Files for some information about reading data from files.

Submission

Upload a file to the Programming Assignment 2 dropbox on D2L with a name like {Firstname}_{Lastname}_Assignment2pt3.R (example: James_Lamb_Assignment2pt3.R).

In the file, implement 2 functions:

get_remote_data()
- takes 1 argument:
  - url: string containing a URL pointing to a CSV file on the internet
- prints the following message: "Downloaded data from '<url>'. Full dataset has <m> rows and <n> columns."
  - <url> is replaced with the URL passed via url
  - <m> is replaced with the number of rows in the data
  - <n> is replaced with the number of columns in the data
- returns a data.frame representation of the file’s contents
- does not directly call download.file(), or otherwise leave files on the filesystem
get_dataset()
- takes 2 arguments:
  - url: same interpretation as above
  - num_rows: an integer. If >=1, the function returns the first num_rows rows of the data. If <= 0, the function returns all rows.
- calls get_remote_data() and uses its result
- returns a data.frame with the first num_rows of the data indicated by url
- does not directly call download.file(), or otherwise leave files on the filesystem

Your submitted script should only contain library() calls (if you choose to use external packages) and your implementations of those 2 functions.

Follow the template below, only adding code between the ### comments.

Any code you add outside those ### comments will be ignored.

### BEGIN CODE ###
# (optional - add library() calls here)
### END CODE ###

get_remote_data <- function(url){
    ### BEGIN CODE ###
    # (add code here)
    ### END CODE ###
}

get_dataset <- function(url, num_rows){
    ### BEGIN CODE ###
    # (add code here)
    ### END CODE ###
}

Test your code using the following snippet.

Do not include this test code in your submission.

#--- test 1: should print and return the expected data ---#
fullDF <- get_remote_data(
    url = "https://raw.githubusercontent.com/jameslamb/intro-to-r/refs/heads/main/sample-data/iris.csv"
)
# "Downloaded data from 'https://raw.githubusercontent.com/jameslamb/intro-to-r/refs/heads/main/sample-data/iris.csv'. Dataset has 150 rows and 6 columns."

#--- test 2: should subset rows ---#
subsetDF <- get_dataset(
    url = "https://raw.githubusercontent.com/jameslamb/intro-to-r/refs/heads/main/sample-data/iris.csv"
    , num_rows = 27
)
stopifnot(dim(subsetDF) == c(27, 6))

#--- test 3: should return all rows if num_rows == -1 ---#
subsetDF <- get_dataset(
    url = "https://raw.githubusercontent.com/jameslamb/intro-to-r/refs/heads/main/sample-data/iris.csv"
    , num_rows = -1
)
stopifnot(dim(subsetDF) == c(150, 6))