Challenge 1 Instructions

challenge_1

Author

Meredith Rolfe

Published

August 15, 2022

Code

library(tidyverse)

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

Challenge Overview

Today’s challenge is to

read in a dataset, and
describe the dataset using both words and any supporting information (e.g., tables, etc)

Read in the Data

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

railroad_2012_clean_county.csv ⭐
birds.csv ⭐⭐
FAOstat*.csv ⭐⭐
wild_bird_data.xlsx ⭐⭐⭐
StateCounty2012.xlsx ⭐⭐⭐⭐

Find the _data folder, located inside the posts folder. Then you can read in the data, using either one of the readr standard tidy read commands, or a specialized package such as readxl.

Add any comments or documentation as needed. More challenging data sets may require additional code chunks and documentation.

Describe the data

Using a combination of words and results of R commands, can you provide a high level description of the data? Describe as efficiently as possible where/how the data was (likely) gathered, indicate the cases and variables (both the interpretation and any details you deem useful to the reader to fully understand your chosen data).

---
title: "Challenge 1 Instructions"
author: "Meredith Rolfe"
desription: "Reading in data and creating a post"
date: "08/15/2022"
format:
  html:
    toc: true
    code-fold: true
    code-copy: true
    code-tools: true
categories:
  - challenge_1
---

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

library(tidyverse)

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

## Challenge Overview

Today's challenge is to

1)  read in a dataset, and

2)  describe the dataset using both words and any supporting information (e.g., tables, etc)

## Read in the Data

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

-   railroad_2012_clean_county.csv ⭐
-   birds.csv ⭐⭐
-   FAOstat\*.csv ⭐⭐
-   wild_bird_data.xlsx ⭐⭐⭐
-   StateCounty2012.xlsx ⭐⭐⭐⭐

Find the `_data` folder, located inside the `posts` folder. Then you can read in the data, using either one of the `readr` standard tidy read commands, or a specialized package such as `readxl`.

```{r}
```



Add any comments or documentation as needed. More challenging data sets may require additional code chunks and documentation.

## Describe the data

Using a combination of words and results of R commands, can you provide a high level description of the data? Describe as efficiently as possible where/how the data was (likely) gathered, indicate the cases and variables (both the interpretation and any details you deem useful to the reader to fully understand your chosen data).

```{r}
#| label: summary

```