Challenge 1 Instructions

Code

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.xls ⭐⭐⭐⭐

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.

Railroad Data

library(readr) railroad_2012_clean_county <- read_csv(“posts/_data/railroad_2012_clean_county.csv”)

Birds Data

library(readr) birds.csv <- read_csv(“posts/_data/birds.csv”)

##FAOSTAT Data

library(readr) FAOSTAT_cattle_dairy.csv <- read_csv(“posts/_data/FAOSTAT_cattle_dairy.csv”)

library(readr) FAOSTAT_country_groups.csv <- read_csv(“posts/_data/FAOSTAT_country_groups.csv”)

library(readr) FAOSTAT_egg_chicken.csv <- read_csv(“posts/_data/FAOSTAT_egg_chicken.csv”)

library(readr) FAOSTAT_livestock.csv <- read_csv(“posts/_data/FAOSTAT_livestock.csv”)

Wild Birds Data

library(readxl) wild_bird_data <- read_excel(“posts/_data/wild_bird_data.xlsx”) View(wild_bird_data)

State County 2012 Data

library(readxl) StateCounty2012<-read_excel(“posts/_data/StateCounty2012.xls”)

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).

Connor’s Answer to Describing the Data

The data for all sets were either created in Excel or a plain text format called CSV (Comma Separated Values). To fully understand the datasets, first, we can make it easier by selecting specific columns using select().

An example is: select(railroad_2012_clean_county, contains(“state”))

To make it easier to view the StateCounty2012 dataset, we can rename column …2 to State to understand what values being looked at.

## Rename …2 to State

State <- select(StateCounty2012,…2)

Once we rename our column, we will make a table to interpret our data.

## Make Table of State

table(State)

This table will summarize the amount of workers on the railroad per county in 2012.