Code
library(tidyverse)
library(readxl)
library(stringr)
::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE) knitr
Lai Wei
October 25, 2022
Today’s challenge is to
read in a dataset, and
describe the dataset using both words and any supporting information (e.g., tables, etc)
Read in one (or more) of the following data sets, using the correct R package and command.
Find the _data
folder, located inside the posts
folder. Then you can read in the data, using either one of the readr
standard tidy read commands, or a specialized package such as readxl
.
#Import Data
Error: `path` does not exist: 'D:/Umass Amherst/DACSS 601/601_Fall_2022/posts/_data/StateCounty2012.xls'
Error in head(StateCounty2012, -2): object 'StateCounty2012' not found
Error in as.data.frame(x): object 'StateCounty2012' not found
Add any comments or documentation as needed. More challenging data sets may require additional code chunks and documentation.
Using a combination of words and results of R commands, can you provide a high level description of the data? Describe as efficiently as possible where/how the data was (likely) gathered, indicate the cases and variables (both the interpretation and any details you deem useful to the reader to fully understand your chosen data).
#Get the colnames’ name
---
title: "Challenge 1"
author: "Lai Wei"
desription: "Reading in data and creating a post"
date: "10/25/2022"
format:
html:
toc: true
code-fold: true
code-copy: true
code-tools: true
categories:
- challenge_1
- railroads
- faostat
- wildbirds
---
```{r}
#| label: setup
#| warning: false
#| message: false
library(tidyverse)
library(readxl)
library(stringr)
knitr::opts_chunk$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`.
#Import Data
```{r}
StateCounty2012 <- read_excel("D:/Umass Amherst/DACSS 601/601_Fall_2022/posts/_data/StateCounty2012.xls", skip = 3) %>%
select(STATE,COUNTY,TOTAL) %>%
filter(!str_detect(STATE,"Total"))
StateCounty2012 <- head(StateCounty2012,-2)
View(StateCounty2012)
```
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).
#Get the colnames' name
```{r}
colnames(StateCounty2012)
StateCounty2012 %>%
select(STATE) %>%
distinct()
```