R is software that we can use to perform statistical analyses

But to make our lives easier, we install RStudio too, which interfaces R, & then we forget that R exists

So once we have those both installed, we only ever need to open up RStudio

Within RStudio we have 4 panes, when you open it up for the first time, you'll see 3, and then when you start working on a document, another pane will appear, you can edit where you want the panes to be, what theme you want to use, text etc

To start with, we have the source pane, which is where our main documents will appear, really where we will work the most, and we use it to view, edit and run any code within an RMarkdown document or an R script

Then we have the console, which is for running any code that we dont want to keep, nothing we do there is saved, so generally it's good to use whenever you want to quickly check something, you wouldnt want to do your entire analysis in the console, but you might use it to quickly see the sample size, or to view some data or a table, or to install packages (we'll talk more about packages next week)

We have the environment pane, which we won't really use that much, but it contains a list of any of the objects we create within RStudio, which could be a dataset, or a plot, or a table etc.,

Then we have the files pane, we'll use this for navigating to different files & folders & looking at the help documentation

demo RStudio

typically when we're working with rstudio, we'll be working with data files created & saved elsewhere, and we'll have to use file paths to get to those objects

Navigating to different folders/directories across different devices can be a bit of a pain

UG students also really struggle with file paths 😢

So we can use R Projects (.Rproj) to make our lives easier

R projects are like folders that we can create in RStudio, and they save information about that folder & the files in it

Saves the previous state of the project so will open up the files you were working on previously

the r project will become your 'working directory', whenever we talk about directories, we really just mean folders, so wherever the R PRoj is located, this becomes the folder you're working in, and so it becomes the the default location where R will look for or save any files

the good thing about R projects is that we can use relative file paths which means that your project will work across devices & people if you share it with others, but i'll explain file paths later on when we have a go at reading in some data

We've taught students how to use R Projects, they've created them almost weekly for the past two years so they should be relatively well rehearsed with doing this, & you should get them to set one up for their dissertation to save their data into, and any analyses, or documents

generally, i would suggest creating an r project for any research project you're working on, and then you can keep anything related to that project in that one area

demo creating an R project, and opening r_docs & data

We can see what project we're in by looking at the top right corner

so now try this out yourselves, by creating an R project & creating r_docs & data folders, which you can do in the files pane

one of the best ways of working in rstudio, and the way we've predominantly taught students, is to use an RMarkdown document, in this types of documents, we can combine code and normal text, you'll know its an RMarkdown doc, because it has the file extension .Rmd, theyre made up of 3 components which is the yaml, body text, and code chunks

the yaml sets the options for our document, we can use it to specify the title, the author, and what type of output file we want, such as html, pdf, word doc etc., it's really easy to switch between different document types, so it's not a problem if you start off with html and then decide you want a word output instead, you can just change the option within the yaml

then we have body text, which we have to format using something called markdown, which confusingly, is just a way that we mark up text, so we have to use different symbols to denote different things, like what's a heading and at what level, and what we want to be in bold etc., i'm not going to describe all the ways you can format it but ive linked to a cheat sheet which you can refer to, or all the info is in the tutorial too

& then finally, our third component is our code chunk, this is where we have little fenced off boxes where we can write code inside them

demo Rmd creation & changing yaml options

To produce our nicely formatted document with our body text & code embedded into it, we have to render it, this is known as Kniting our document

knitting then runs all the code in a fresh session, and will compile the document, with any output of code, and any formatted text, because it runs everything in a new session, the order that you do things within it is very important, but i'll talk more about that next week

you can knit to pdfs, htmls, word docs etc., but often when students are working with a word doc, they'll have the knitted, formatted file open in word, and they'll try to knit it again in rstudio but it'll show an error, and they panic, so you just need to make sure to close the word doc version

Weird quirk of lab PCs is that you must store your RMarkdown files in OneDrive, unsure if this applies to office PCs yet...i'm still waiting on a pc, so i havent been able to test this with office computers, the lab computers work in an unusual way when knitting files, and i dont know if this applies to office pcs too yet so this might not work for some of you, and if it doesnt, dont worry

demo knitting & output * files pane

demo deleting stuff out

we'll cover functions etc., in the next tutorial & next week's session so dont worry about not understanding everything right now we'll also get lots of practice doing this, so this will become more familiar to you over time

when we write code in an rmarkdown document, we use code chunks, and this is what they look like: they have these back ticks, curly brackets, and this little r, generally, its good practice to have quite a few of these code chunks, it can make finding errors much easier when you separate it out into different parts, but you also shouldnt have a code chunk for each line of code,

to insert them theres a couple ways we can do it

demo inserting

to actually run whats in them, there's a couple days we can do it too

demo running

there's lots of options we can specify for these code chunks to decide whether we want the code to be run, or if we want to show only the output, or if we want to show the code etc., but i'm gonna go over that in the next few weeks

we can use code chunks to run all different commands, typically, our first code chunk in a document will be where we load any packages we want to use

packages are essentially add ons that increase the functionality of rstudio, Packages typically contain functions that we can use to tell R to do lots of different things, or they might include data, tutorials, or templates that we can use for different types of documents

to be able to use packages we first have to install them onto our pc, and then we can access them in rstudio by loading the,

to install packages for the first time, we need to use a function called install.packages, to briefly explain what a function is, it is a pre-defined command we can use to do different things in RStudio, so this function, takes the name of a package, and installs it for us

another function we'll pretty much always use is the library function, this command takes the name of the package that we want to use in rstudio, and loads it so that we can use any of the other functions, data, or components it contains within that session, we have to load packages in any document that we want to use them in

so to install a package in rstudio, we would use the console because we dont need to keep that information in the future, we can just install it once, and then we dont need to install it anymore unless we want to update it

demo install.packages

then to actually load it in a session so we can use it, we have to use the library command

demo library

the tutorial will go into more detail on packages and functions so i'll go over this more next week, so this will become clearer, but for now i just want you to have a go at running some code and using code chunks within rstudio

so using these examples above, have a go at installing and loading a package called tidyverse, this is a package that we'll use very often,

tidyverse will be the focus of the next tutorial, so im not gonna go into it too much now, but its basically a package of packages, and two of those packages are used to read in data, but to be able to use them, we need to know the file path of our datafile,

there are two types of file paths, we have absolute and we have relative, absolute start from the inner depths of our pc, and relative file paths are ones that are relative to our current location which look like these:

The ./ in the relative example is just a way of writing starting where you are, step into the data folder, if we need to jump up a level, we can use ../

Relative file paths are the best to use because they work across devices when we use R Projects, we dont have to worry about having different file structures across machines, theyre also nice and short to type out

when writing file paths, the easiest way to think of them, is to think about where you are currently located on your pc, and where you need to get to, and what steps you need to take to get there

to practice using these, i want you to download this file here, if you go to week 2 on the webpage, you can click on the link, then right click and save as, and then make sure it is in your data folder of the r project that we created earlier

once we have our data, and we know where we are, and where we need to get to, we can write this file path into one of our data reading functions, we have different functions for different data types, but the most common we all use are csv files, and sav files, these are what we might use in excel or spss

to read in these files, we can follow these examples here where we specify our function, and then within speech marks, we specify our file path

we'll learn more about how functions work in the following week, so dont worry too much, for now i just want you to get used to running lines of code in RStudio

so based on these examples, have a go at reading in the CSV, you only need to use one of these functions that works with CSV files, you should do this in a new code chunk, so have a go at creating one, if you need a reminder let me know, and then try running it, which you can do by the little green play button or selecting the line and pressing ctrl + enter

download data, n demo read csv

From now on, we'll use tutorials that we open from within RStudio

We'll be using discovr - an R package written by andy field that covers all key analyses/skills

So to do this weeks tutorial, we need to download the discovr package