MATH 204 Introduction to Statistics

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.title[
# MATH 204 Introduction to Statistics
]
.subtitle[
## Lecture 3: Intro to R
]
.author[
### JMG
]

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## Goals for Lecture

* Learn the basics of R necessary for Chapter 2 on summarizing data.

* That is, we introduce:

* Arithmetic in R.

* How R represents data. 
    
    * Data frames in R.
    
    * Working with data in R. 
    
    * Installing and loading packages.
    
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* Two helpful references are the online books [Applied Statistics with R](https://book.stat420.org/) and [Probability, Statistics, and Data
A Fresh Approach Using R](https://mathstat.slu.edu/~speegle/_book/preface.html).

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## What is R?

[R](https://www.r-project.org/) is a free software environment for statistical computing.

* We can use R for

* Basic statistical calculations
    
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* Statistical plots
    
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* Statistical modeling 
    
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* Presenting and sharing results of data analyses

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## What is RStudio?

[RStudio](https://www.rstudio.com/) is an integrated development environment (IDE) for R.

* We can use RStudio for

* Easier coding with R (plus some other languages)
    
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* Working with R in a more interactive fashion
    
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* Accessing R online via [RStudio cloud](https://www.rstudio.com/products/cloud/) projects

* R plus RStudio and the ever growing R ecosystem facilitate work in statistics and data science. These tools are used by professionals and academics in a variety of fields, and gaining experience with R can be valuable for your future.

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## Try It

Let's go to an [RStudio cloud](https://www.rstudio.com/products/cloud/) project and do some coding.

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## Summary

In this lecture, we covered the basics of R necessary for working with data and obtaining numerical and graphical summaries as will be covered in Chapter 2.

* In particular, we learned

* How to do basic arithmetic in R.

* How R represents data. 
    
    * About data frames in R.
    
    * About working with data in R. 
    
    * How to install and load R packages.

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## Next Time

* In the next lecture, we will begin our discussion of Chapter 2 on summarizing data.

* We will cover

* Numerical summaries such as mean, median, variance, and standard deviation.
    
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* Tables.
    
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* Visual data summaries such as histograms, box plots, bar plots, scatter plots, and mosaic plots. 
    
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* To get a head start on Chapter 2 content, see the videos embedded in the next few slides.

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## Video: Numerical Data Summaries

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## Video: Categorical Data Summaries

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## Notes

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## Notes

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## Notes