data-tidying-import-project

Project instructions

Important

This is the final project for course 2: data tidying and importing. This will not be graded, and is intended to provide you with the opportunity to test the skills you’ve acquired throughout the course in a less structured environment. This project is also structured for you to host on a webpage and link as proof of work on your resume or CV.

Hints to answer the research questions can be found Hints section. You are challanged not to use these hints unless needed.

A short self rubric can be found at the end of the README in the Rubric section to help the self-assessment of your project. Do not expand the rubric items until you have finished a draft of your project.

Before you start

Please complete the following steps before getting started if you have not downloaded and configured Git. If you have already completed these steps from course 1, please clone this repo and skip to the Instructions section.

Download Git

Note, if you are on Windows or Linux, you will need to install Git. If you do not have git installed, please do so:

Windows -> https://git-scm.com/download/win

Linux -> https://git-scm.com/download/linux

Configure git with RStudio

In the console, run the following:

install.packages("usethis")

usethis::use_git_config(
  user.name = "Your name", 
  user.email = "Email associated with your GitHub account"
 )

Please make sure that you replace "YourName" with your actual name, and "Email associated with your GitHub account" with your email used for your GitHub account.

Create and authenticate your personal access token

In the console, run the following to geneate your personal access token (PAT)

usethis::create_github_token()

Next, run the following and paste your PAT in:

gitcreds::gitcreds_set()

Getting started

Once you restart R, you will be able to clone this project repository and get working! To clone this project:

• Click the green code button on the repo
• Copy the HTTPS
• Open a new project in RStudio
• Click version control and then Git
• Paste the copied HTTPS in the Repository URL box.
• Name the project directory
• Browse and select where this project will be saved
• Click create project!

Instructions

This final project consists of an exploratory data analysis and write up of results you discover. The data sets you will be working with can be found in the data folder. Your write up, in index.qmd, will explore (at least) two questions.

Description

Inflation in the US

The Organisation for Economic Co-operation and Development defines inflation as follows:

Inflation is a rise in the general level of prices of goods and services that households acquire for the purpose of consumption in an economy over a period of time.

The main measure of inflation is the annual inflation rate which is the movement of the Consumer Price Index (CPI) from one month/period to the same month/period of the previous year expressed as percentage over time.

Source: OECD CPI FAQ

CPI is broken down into 12 divisions such as food, housing, health, etc.

The data you will need to explore US inflation are spread across two files:

• us-inflation.csv: Annual inflation rate for the US for 12 CPI divisions. Each division is identified by an ID number.
• cpi-divisions.xlsx: A "lookup table" of CPI division ID numbers and their descriptions.

A data table for each dataset can be seen below:

us-inflation

variable	description
country	location data; all values "United States"
cpi_division_id	numerical value to represent cpi division
2011	annual inflation percentage for the year 2011 by cpi division
2012	annual inflation percentage for the year 2012 by cpi division
...	...
2021	annual inflation percentage for the year 2021 by cpi division

cpi-divisions.xlsx

variable	description
id	numerical value to represent cpi division
description	description of the cpi value

Research Questions

You are tasked to use these data to produce the following:

1. Calculate the minimum and maximum annuel inflation percentage for each cpi division.

2. Recreate a plot of annual inflation vs. year for these divisions. Then, in a few sentences, describe the patterns you observe in the plot, particularly focusing on anything you find surprising or not surprising, based on your knowledge (or lack thereof) of inflation rates in the US over the last decade.

   

Important

- Version control with GitHub
This project also provides you with the opportunity to practice using version control, often expected to be used in a real-world context. We challenge you to Render; Commit; and Push after each visualization or summary statistics you create. If you are working on a project with more than one contributor, make sure that you Pull before making any progress to ensure your project matches up with the project repository.

Displaying code

Depending on the purpose of the project, it may be advantageous to either show or hide all of your code in your rendered document. To hide all of your code, set echo: false in your YAML. If you want to display your code, set echo: true.

Citations

When working on your project, you may want to include outside sources. When doing so, we need to make sure these sources are properly cited. In order to do so, we need to create a .bib file and specify it in your index.qmd's YAML heading. Your project files include a references.bib txt file. This is where you will put bibtex entries. An example of an entry to be referenced can be seen below:

@article{Cetinkaya2020,
	title        = {A Fresh Look at Introductory Data Science},
	author       = {Cetinkaya, Mine and Ellison, Victoria},
	year         = 2020,
	month        = {08},
	journal      = {Journal of Statistics Education},
	volume       = 29,
	pages        = {1--27},
	doi          = {10.1080/10691898.2020.1804497}
}

In this entry above, Cetinkaya2020 is the citation identifier. The default way to cite an entry in your text is with this syntax: [@citation-identifier].

To automatically generate a references section with your entries, the path to the .bib file needs to be specified in your YAML with bibliography: references.bib. This has been done for you.

Hints

RQ1 Hint (Calculation)

The way the data are currently set up, it is very difficult to operate row wise and answer the research question. We can use what we've learned about pivoting data to get these data in a more workable format!

RQ2 Hint (Reading in data)

The top of the cpi-divisions dataset has a note from the researcher. We don't want to include this in the data set. See https://readxl.tidyverse.org/reference/read_excel.html for how to skip lines when reading in data from excel. Skip the first line.

RQ2 Hint (Joining)

Start by using your new dataset from question 1 and joining it with the cpi-divisions dataset. Think critically about what variable is the same across both datasets, and use this as the "key".

RQ2 Hint (Subset CPIs)

To filter the joined dataset by only the IDs of CPI divisions in the final plot, create a vector using `c`. Then filter your joined data set by using the `filter()` function and the `%in%` operator.

RQ2 Hint (Legend)

If your legend has labels that are too long, you can try moving the legend to the bottom and stack the labels vertically. Hint: The legend.position and legend.direction arguments of the theme() functions will be useful.

ggplot(...) +
... +
theme(
  legend.position = "bottom", 
  legend.direction = "vertical"
)

Rubric

Self rubric

• Check your calculations: For id 1, minimum annual inflation is equal to -1.32; max is equal to 4.80 For id 2, minimum annual inflation is equal to 1.68; max is equal to 4.46 etc.

• Self assess the graph you made compared to the graph you are trying to recreate. Take note of details such as: Title and subtitle Axes labels Legend position Color scheme Categories of cpi division

Challenge

Using these data, come up with a third research question, add it to your written report, and investigate!