An Introduction to R

# An Introduction to R

## Why Learn to Code?

### &#183; Alex Douglas &#183;

### University of Aberdeen

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### Find me at...

.medium[
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[<svg viewBox="0 0 512 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;">  <path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"></path></svg> @Scedacity](https://twitter.com/scedacity)  
[<svg viewBox="0 0 512 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;">  <path d="M440 6.5L24 246.4c-34.4 19.9-31.1 70.8 5.7 85.9L144 379.6V464c0 46.4 59.2 65.5 86.6 28.6l43.8-59.1 111.9 46.2c5.9 2.4 12.1 3.6 18.3 3.6 8.2 0 16.3-2.1 23.6-6.2 12.8-7.2 21.6-20 23.9-34.5l59.4-387.2c6.1-40.1-36.9-68.8-71.5-48.9zM192 464v-64.6l36.6 15.1L192 464zm212.6-28.7l-153.8-63.5L391 169.5c10.7-15.5-9.5-33.5-23.7-21.2L155.8 332.6 48 288 464 48l-59.4 387.3z"></path></svg> a.douglas@abdn.ac.uk](mailto:a.douglas@abdn.ac.uk)
]

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# Let's get started...

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# learning outcomes

- introduce you to using R and RStudio ✔️

- help your research become more robust and reproducible ✔️

- show you how to import your data ✔️

- show you how to summarise, visualise and analyse your data ✔️

**this course (definitely) won't ...**

- teach you everything there is to know about R ❌

- make you feel completely comfortable with R ❌

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# course structure

at the heart of this course is our open access  
[Introduction to R](https://alexd106.github.io/Rbook/) book

- introduction to R and RStudio environments

- perform basic operations in R

- importing exploring, wrangling and summarising data into R

- visualising data using base R and ggplot graphics

- introduce some simple statistics using R

- also other Chapters on programming in R,  
using R markdown and version control with Git and GitHub
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# course structure

and also our [intro2R](https://alexd106.github.io/intro2R/) website

- series of exercises (with solutions) to work through

- exercises cover material from each Chapter

- short how-to videos covering key R skills

- tutorials covering additional topics

- many links to other resources

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# suggested approach

- work through material for each Chapter

- once you've finished a Chapter, test your  
understanding by completing the  
associated exercise

- don't peek at the solutions too quickly 😄

- create an R script to document your code  
(preferably in an RStudio Project)

- use R's help pages and Google when you get stuck!

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# What is R?

<br>
.medium[

- answer depends on who you ask and what they use R for

- environment for statistical computing, graphics and programming

- originally created by Ross Ihaka and Robert Gentleman (1996)

- currently maintained by international R-core development team

- many, many people also contribute to R and the wider  
R community

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- more information can be found at  
https://www.r-project.com

- download R from the CRAN website  
https://cran.uk.r-project.org

- see [here](https://alexd106.github.io/intro2R/howto.html) for a short video describing  
how to download and install R

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- high initial investment in time to learn R

- unfamiliar command line environment

- frustrating (and sometimes) inconsistent syntax

- analysis and figures can take longer (initially!)

- relatively steep learning curve

- it's not actually that hard, just unfamiliar
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# why you should learn R

- it’s the software of choice for many students,  
academics, industries and charities worldwide

- highly flexible and extensive

- encourages you think about your research  
questions, data and analyses

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.footnote[
[Muenchen (2019)](http://r4stats.com/articles/popularity/)
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<br>
# why you should learn R

- transparent

- other people* can reproduce your analysis

- easily share your code (GitHub)

- [open science](https://en.wikipedia.org/wiki/Open_science)
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# why you should learn R

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# why you should learn R

- opportunity to get involved  
with a fantastic and supportive  
community
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<br>

# using R - objects

- commands are typed in the editor and then sourced into the console  
at the > prompt

```r
> 2 + 2
## [1] 4
```

- R is object orientated. You can create variable and assign value(s) to them

```r
> a <- 2 + 2
> a
## [1] 4
```
]

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<br>

# using R - objects

- once created, operations can be performed on variables

```r
> a <- 2 + 2
> b <- 3 * 2
> 
> a + b
## [1] 10
```

- this is very powerful and flexible

- much of the functionality of R is enhanced by using variables called functions

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<br>

# using R - functions

- you can use functions that are inbuilt in R

```r
> numbers <- c(2, 3, 4, 5, 6)   # use the concatenate function
> numbers
## [1] 2 3 4 5 6
> 
> mean(numbers)                 # use mean function
## [1] 4
> 
> var(numbers)                  # use variance function
## [1] 2.5
```

- comments (#) are ignored by R
]

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# using R - functions

- there are packages for almost anything

<br><br><br><br>
- install packages from [CRAN](https://cran.r-project.org/), [Bioconductor](https://www.bioconductor.org/) and [GitHub](https://github.com/)

- packages are easy to install in R
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<br>

# using R - functions

- or write your own functions

- function to calculate standard error

<br>

```r
> numbers <- c(2, 3, 4, 5, 6)    # use the concatenate function
> 
> se.fnc <- function(x){         # start function for se
+  std.x <- sd(x)		             # calculate SD
+  nos.x <- length(x)		         # calculate number obs
+  se.x <- std.x/(sqrt(nos.x))   # calculate SE
+  print(se.x)	
+ }
> 
> se.fnc(numbers)                # use your new function
## [1] 0.7071068
```
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<br>

# using R - syntax

- R is case sensitive `A` is not the same as `a`

- commands are generally separated by a new line, but you can also use a `;` (rare)

- anything that follows the hash symbol (#) will be ignored by R. Use this to comment your code

- a series of commands can be grouped using braces `{ }`

- write pretty code by following a [code style guide](https://google.github.io/styleguide/Rguide.html)

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# using R - plots

- by default plots are displayed in the 'Plots' pane in RStudio

- plots can be copied and pasted into your word processor of choice

- better to export plots to an external file (jpeg, pdf, png etc)

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<br>
# using R - help

- R has extensive help facilities

- from within R the main method of getting help is to use the `help()` function

```r
> help("plot")    # open help file for the function plot
```

or equivalently

```r
> ?plot
```

- or to search the help files use `help.search("plot")` or `??plot`
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<br>
# using R - help

- [Google it!](http://lmgtfy.com/?q=R+Error+in+library+there+is+no+package+called): Try Googling any error messages you get. It's not cheating and everyone does it! You'll be surprised how many other people have probably had the same problem and solved it.

- [Stack Overflow](http://stackoverflow.com/questions/tagged/r): There are many thousands of questions relevant to R on Stack Overflow

- [Here](https://stackoverflow.com/questions/tagged/r?sort=votes) are the most popular ones, ranked by vote. Make sure you search for similar questions before asking your own

- make sure you include a [reproducible example](http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example/5963610#5963610) to get the most useful advice. A reproducible example is a minimal example that lets others who are trying to help you to see the error themselves.
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# tips for learning R

- use R often and use it regularly

- learning R is not a memory test, you have your R scripts

- you don’t need to know everything there is to know about R to use it productively

- staring at code for hours is not good for you. Walk away for a few minutes

- there are many ways to tackle a particular problem. See what others have done

- recognise that there will be times when things will get a little tough or frustrating. Try to accept these periods as part of the natural process of learning a new skill (we’ve all been there) and remember, the time and energy you invest now will be more than payed back in the not too distant future.
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# Let's get started...

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