Challenge 8: Joining Data

challenge_8

railroads

snl

debt

Joining Data

Author

Teresa Lardo

Published

April 26, 2023

library(tidyverse)
library(ggplot2)
library(readr)
library(dplyr)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Challenge Overview

Today’s challenge is to:

read in multiple data sets, and describe the data set using both words and any supporting information (e.g., tables, etc)
tidy data (as needed, including sanity checks)
mutate variables as needed (including sanity checks)
join two or more data sets and analyze some aspect of the joined data

(be sure to only include the category tags for the data you use!)

Read in data

Read in one (or more) of the following datasets, using the correct R package and command.

military marriages ⭐⭐
faostat ⭐⭐
railroads ⭐⭐⭐
fed_rate ⭐⭐⭐
debt ⭐⭐⭐
us_hh ⭐⭐⭐⭐
snl ⭐⭐⭐⭐⭐

Briefly describe the data

Tidy Data (as needed)

Is your data already tidy, or is there work to be done? Be sure to anticipate your end result to provide a sanity check, and document your work here.

Are there any variables that require mutation to be usable in your analysis stream? For example, do you need to calculate new values in order to graph them? Can string values be represented numerically? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?

Document your work here.

Join Data

Be sure to include a sanity check, and double-check that case count is correct!

---
title: "Challenge 8: Joining Data"
author: "Teresa Lardo"
description: "Joining Data"
date: "04/26/2023"
format:
  html:
    toc: true
    code-copy: true
    code-tools: true
categories:
  - challenge_8
  - railroads
  - snl
  - debt
---

```{r}
#| label: setup
#| warning: false
#| message: false

library(tidyverse)
library(ggplot2)
library(readr)
library(dplyr)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)
```

## Challenge Overview

Today's challenge is to:

1)  read in multiple data sets, and describe the data set using both words and any supporting information (e.g., tables, etc)
2)  tidy data (as needed, including sanity checks)
3)  mutate variables as needed (including sanity checks)
4)  join two or more data sets and analyze some aspect of the joined data

(be sure to only include the category tags for the data you use!)

## Read in data

Read in one (or more) of the following datasets, using the correct R package and command.

  - military marriages ⭐⭐
  - faostat ⭐⭐
  - railroads  ⭐⭐⭐
  - fed_rate ⭐⭐⭐
  - debt ⭐⭐⭐
  - us_hh ⭐⭐⭐⭐
  - snl ⭐⭐⭐⭐⭐

```{r}
```

### Briefly describe the data

## Tidy Data (as needed)

Is your data already tidy, or is there work to be done? Be sure to anticipate your end result to provide a sanity check, and document your work here.

```{r}
```

Are there any variables that require mutation to be usable in your analysis stream? For example, do you need to calculate new values in order to graph them? Can string values be represented numerically? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?

Document your work here.

```{r}
```

## Join Data

Be sure to include a sanity check, and double-check that case count is correct!