Summary

flowchart LR
    A["`**Step 1**
    Scripts & Projects`"] --> B["`**Step 2**
    Working Directory`"] --> C["`**Step 3**
    Libraries`"] --> D["`**Step 4**
    Import & Export`"]
    style A fill:#1a1a1a,stroke:#1a1a1a,color:#fff
    style B fill:#1a1a1a,stroke:#1a1a1a,color:#fff
    style C fill:#1a1a1a,stroke:#1a1a1a,color:#fff
    style D fill:#1a1a1a,stroke:#1a1a1a,color:#fff
    linkStyle default stroke:#adb5bd,stroke-width:2px

A Quick and Dirty Setup

At this point, you should be familiar with working directories, libraries, and loading data. I like to start all of my R scripts by setting all of these, leaving the data cleaning, analysis, and visualization as a separate part of the script.

Here is how I recommend starting your scripts:

### Step 1: Open RStudio and open a script or project.

### Step 2: Set Working Directory
setwd("file_path/project")

### Step 3: Load Necessary Libraries
library(package_name_1)
library(package_name_2)

### Step 4: Load Data
data_object <- read_csv("data_file_name.csv")

If you receive no error messages, you should be ready to work with your data.

You should also note that if you set up everything as an R Project, you will not need to set your working directory. This allows you to just load your libraries and data without worrying about the working directory.

Conclusion

Using the four steps presented in this tutorial, you will be able to start your work in R and focus on cleaning, analyzing, visualizing, and communicating with data in R.

Quick Reference

Step	Command	Purpose
1	Open a script	Reproducible code
2	setwd("path")	Tell R where your files are
3	library(pkg)	Load package functions
4	read_csv("file")	Bring data into R