1 Getting used to R and R Studio

My solutions follow the problems. Look ahead if you get stuck. Don’t forget library(tidyverse) if you need it:

1.1 Using R Studio online

Point your web browser at http://r.datatools.utoronto.ca. Click on the button to the left of “R Studio” (it will show blue), click the orange Log in to Start, and log in using your UTorID and password.

Solution

This is about what you should see first, before you click the orange thing:

You might see this next:

If you do, leave Identity Provider as University of Toronto and then click Log On, and then log in with your UTorID and password.

You will see a progress bar as things start up, and then you should see something like this:

This is R Studio, ready to go.

If you are already logged in to something else on the same browser that uses your UTorID and password, you may come straight here without needing to log in again.

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Take a look around, and create a new Project. Give the new project any name you like.

Solution

Select File and New Project to get this:

Click on New Directory (highlighted blue on mine). This will create a new folder to put your new project in, which is usually what you want to do. The idea is that a project is a container for a larger collection of work, such as all your assignments in this course. That brings you to this:

where you click on New Project (highlighted on mine), and:

Give your project a name, as I did. Then click Create Project. At this point, R Studio will be restarted in your new project. You can tell which project you are in by looking top right, and you’ll see the name of your project next to the R symbol:

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One last piece of testing: find the Console window (which is probably on the left). Click next to the blue >, and type library(tidyverse). Press Enter.

Solution

It may think a bit, and then you’ll see something like this:

Aside: I used to use a cloud R Studio called rstudio.cloud. If you see or hear any references to that, it means the same thing as R Studio on r.datatools or jupyter. (You can still use rstudio.cloud if you want; it used to be completely free, but now the free tier won’t last you very long; the utoronto.ca link is free as long as you are at U of T.) I’m trying to get rid of references to R Studio Cloud as I see them, but I am bound to miss some, and in the lecture videos they are rather hard to find.

Now we can get down to some actual work. For the remaining questions, you may be able to figure out what to do (most of it is following the instructions and seeing what happens), but if you can’t, scroll down to my solutions to find out what I did (and what happened when I did it).

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1.2 Getting started

This question is to get you started using R.

Start R Studio, in some project. (If you started up a new project in the previous question and still have that open, use that; if not, create a new project.)
We’re going to do some stuff in R here, just to get used to it. First, make a Quarto document by selecting File, New File and Quarto Document.
The top four lines, surrounded by --- above and below, are called a YAML block. This contains information about the document as a whole, and needs to be there. Go down to below that, moving the “Quarto” heading down a bit if you want. On an empty line, type a / and you should see a list of things appear that you might want to insert. Pressing Enter now will insert an R code chunk (sometimes called “code cell”). Do that.
Type the line of code shown below into the code chunk in the Quarto document:

mtcars

Run this command. To do that, look at the top right of your code chunk block (shaded in a slightly different colour). You should see a down arrow and a green “play button”. Click the play button. This will run the code, and show the output below the code chunk.
Something a little more interesting: summary obtains a summary of whatever you feed it (the five-number summary plus the mean for numerical variables). Obtain this for our data frame. To do this, create a new code chunk below the previous one, type summary(mtcars) into the code chunk, and run it.
Let’s make a boxplot of the gas mileage data. This is a “poor man’s boxplot”; we’ll see a nicer-looking way later. To do it this way, make another new code chunk, enter the code boxplot(mtcars$mpg) into it, and run the chunk.
Some aesthetics to finish with: delete the template document (all the stuff you didn’t type below your code chunks and output). Then add some narrative text above and below your code chunks. Above the code chunk is where you say what you are going to do (and maybe why you are doing it), and below is where you say what you conclude from the output you just obtained.
Save your document (the usual way with File and Save). This saves it on the r.datatools server (and not on your computer, unless you are running R Studio on your computer). This means that when you come back to r.datatools later, even from another device, this notebook will still be available to you. Now click Render. This produces a pretty HTML version of your notebook.
Optional extra: Practice handing in your rendered Quarto notebook, as if it were an assignment that was worth something. (It is good to get the practice in a low-stakes situation, so that you’ll know what to do next week.)
Optional extra. Something more ambitious: make a scatterplot of gas mileage mpg, on the $y$ axis, against horsepower, hp, on the $x$-axis.

1.3 Reading data from a file

In this question, we read a file from the web and do some descriptive statistics and a graph. This is very like what you will be doing on future assignments, so it’s good to practice it now.

Take a look at the data file at http://ritsokiguess.site/datafiles/jumping.txt. These are measurements on 30 rats that were randomly made to do different amounts of jumping by group (we’ll see the details later in the course). The control group did no jumping, and the other groups did “low jumping” and “high jumping”. The first column says which jumping group each rat was in, and the second is the rat’s bone density (the experimenters’ supposition was that more jumping should go with higher bone density).

What are the two columns of data separated by? (The fancy word is “delimited”).
Make a new Quarto document. Leave the first four or so lines at the top between the ---, but get rid of the rest of the template document. Start with a code chunk containing library(tidyverse). Run it.
Put the URL of the data file in a variable called my_url. Then use read_delim to read in the file. (See solutions for how.) read_delim reads data files where the data values are always separated by the same single character, here a space. Save the data frame in a variable rats.
Take a look at your data frame, by making a new code chunk and putting the data frame’s name in it (as we did with mtcars).
Find the mean bone density for rats that did each amount of jumping.
Make a boxplot of bone density for each jumping group.

1.4 Reading files different ways

This question is about different ways of reading data files. If you’re using r.datatools (online), start at the beginning and go as far as (d). If you’re using R Studio running on your own computer, start at part (here).

Log in to r.datatools.utoronto.ca. Open up a project (or start a new one), and watch the spinning circles for a few minutes. When that’s done, create a new Quarto Document with File, New File, Quarto Document. Delete the “template” document, but keep the top lines with title: and possibly output: in them. Add a code chunk that contains library(tidyverse) and run it.
* The easiest kind of files to read in are ones on the Internet, with a URL address that begins with http or https. I have a small file at link. Click the link to see it, and keep the tab open for the next part of this question. This is a text file with three things on each line, each separated by exactly one space. Read the data file into a data frame, and display your data frame.
You might have data in a file on your own computer. To read data from such a file, you first have to upload it to r.datatools, and then read it from there. To practice this: open a text editor (like Notepad or TextEdit). Go back to the web browser tab containing the data you used in the previous part. Copy the data from there and paste it into the text editor. Save it somewhere on your computer (like the Desktop). Upload that file to r.datatools, read in the data and verify that you get the right thing. (For this last part, see the Solution.)
You might have a spreadsheet on your computer. Create a .csv file from it, and use the ideas of the last part to read it into R Studio.
* Start here if you downloaded R and R Studio and they are running on your own computer. Open a web browser and point it at link. Click the link to see it, and keep the tab open for the next part of this question. This is a text file with three things on each line, each separated by exactly one space. Read the data file into a data frame, and display your data frame.
You might have data in a file on your own computer. To read data from such a file, R has to know where to find it. Each R project lives in a folder, and one way of specifying where a data file is is to give its complete path relative to the folder that R Studio is running its current project in. This is rather complicated, so we will try a simpler way. To set this up, open a text editor (like Notepad or TextEdit). Go back to the web browser tab containing the data you used in the previous part. Copy the data from there and paste it into the text editor. Save it somewhere on your computer (like the Desktop). Follow the steps in the solution below to read the data into R.
You might have a spreadsheet on your computer. Create a .csv file from it, and use the ideas of the last part to read it into R Studio.

My solutions follow:

1.5 Getting started

This question is to get you started using R.

Start R Studio at r.datatools or on your computer, in some project. (If you started up a new project in the previous question and are still logged in, use that; if not, create a new project with File, New Project, and New Directory. Then select New Project and give it a name. Click Create Project. This will give you an empty workspace to start from.)

Solution

You ought to see something like this.

Extra: if you want to tweak things, select Tools (at the top of the screen) and from it Global Options, then click Appearance. You can make the text bigger or smaller via Editor Font Size, and choose a different colour scheme by picking one of the Editor Themes (which previews on the right). My favourite is Tomorrow Night Blue. Click Apply or OK when you have found something you like. (I spend a lot of time in R Studio, and I like having a dark background to be easier on my eyes.)

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We’re going to do some stuff in R here, just to get used to it. First, make a Quarto Document by selecting File, New File and Quarto Document. Give it the title Playing, and add yourself as the Author, should you wish to do so. Otherwise, leave all the default selected, and click Create.

Solution

The first time, you might be invited to “install some packages” to make the Document thing work. Let it do that, if necessary, by clicking Yes. After that, you’ll have something like this:

This is a Quarto document, open in Visual Mode. This editing mode works a lot like Notion, or rather like Word or Google Docs: it is an approximation of what you will see in the finished document.

There is also a Source mode of editing, which you can get to by clicking on Source at the top left. Notice how the title Quarto has become ## Quarto, and the link at the end of the paragraph looks different. Behind the scenes, a Quarto document is what is called R Markdown. You can write in R Markdown (Source mode) or in Visual mode, and switch between them at any time. We are going to use Visual mode. I learned Source mode before there was a Visual mode.

Switch back to Visual mode for now.

Find the Run button along the top of the document window. We’ll be using that shortly.

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The top four lines, surrounded by --- above and below, are called a YAML block. This contains information about the document as a whole, and needs to be there. Go down to below that, moving the “Quarto” heading down a bit if you want. On an empty line, type a / and you should see a list of things appear that you might want to insert. Pressing Enter now will insert an R code chunk (sometimes called “code cell”). Do that.

Solution

Something like this:

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Type the line of code shown below into the chunk in the Document:

mtcars

Solution

What this will do: get hold of a built-in data set with information about some different models of car, and display it.

In approximately five seconds, you’ll be demonstrating that for yourself.

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Run this command. To do that, look at the top right of your code chunk block (shaded in a slightly different colour). You should see a down arrow and a green “play button”. Click the play button. This will run the code, and show the output below the code chunk.

Solution

Here’s what I get (yours will be about the same).

This is a rectangular array of rows and columns, with individuals in rows and variables in columns, known as a “data frame”. When you display a data frame in a Quarto document, you see 10 rows and as many columns as will fit on the screen. At the bottom, it says how many rows and columns there are altogether (here 32 rows and 11 columns), and which ones are being displayed. You can see more rows by clicking on Next, and if there are more columns, you’ll see a little arrow next to the rightmost column (as here next to am) that you can click on to see more columns. Try it and see. Or if you want to go to a particular collection of rows, click one of the numbers between Previous and Next: 1 is rows 1–10, 2 is rows 11–20, and so on. The column on the left without a header (containing the names of the cars) is called “row names”. These have a funny kind of status, kind of a column and kind of not a column; usually, if we need to use the names, we have to put them in a column first. In future solutions, rather than showing you a screenshot, expect me to show you something like this:

mtcars

The top bit is the code, the bottom bit is the output.

If you don’t see the “play button”, make sure that what you have really is a code chunk. If you can’t figure it out, delete this code chunk and make a new one. Sometimes R Studio gets confused.

On the code chunk, the other symbol is a down arrow, which runs all the chunks prior to this one (but not this one).

The output has its own little buttons. The first one pops the output out into its own window; the second one shows or hides the output, and the third one deletes the output (so that you have to run the chunk again to get it back). Experiment. You can’t do much damage here.

Extra: there is also a Run button to the top right of your document. This gives you a bit more flexibility: you can run just the line the cursor is on, or you can run the whole chunk (which might contain several lines of code), or you can run all the chunks in your document, and some other things. Some of these also have keyboard shortcuts attached: for example, control plus enter runs the line of code the cursor is on, and control plus shift plus enter (hold the control and shift down together) runs all the lines of code in the chunk.

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Something a little more interesting: summary obtains a summary of whatever you feed it (the five-number summary plus the mean for numerical variables). Obtain this for our data frame. To do this, create a new code chunk below the previous one, type summary(mtcars) into the code chunk, and run it.

Solution

This is what you should see:

summary(mtcars)

      mpg             cyl             disp             hp       
 Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
 1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
 Median :19.20   Median :6.000   Median :196.3   Median :123.0  
 Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
 3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
 Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
      drat             wt             qsec             vs        
 Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
 1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
 Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
 Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
 3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
 Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
       am              gear            carb      
 Min.   :0.0000   Min.   :3.000   Min.   :1.000  
 1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
 Median :0.0000   Median :4.000   Median :2.000  
 Mean   :0.4062   Mean   :3.688   Mean   :2.812  
 3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :1.0000   Max.   :5.000   Max.   :8.000

For the gas mileage column mpg, the mean is bigger than the median, and the largest value is unusually large compared with the others, suggesting a distribution that is skewed to the right.

There are 11 numeric (quantitative) variables, so we get the five-number summary plus mean for each one. Categorical variables, if we had any here, would be displayed a different way.

(In case you are wondering, the way without screenshots is obtained by my writing a document with code chunks and running them, so this output genuinely is obtained by running the code you see.)

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Let’s make a boxplot of the gas mileage data. This is a “poor man’s boxplot”; we’ll see a nicer-looking way later. To do it this way, make another new code chunk, enter the code boxplot(mtcars$mpg) into it, and run the chunk.

Solution

This is what you should see:

boxplot(mtcars$mpg)

The long upper whisker supports our guess from before that the distribution is right-skewed.

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Some aesthetics to finish with: delete the template document (all the stuff you didn’t type below your code chunks and output). Then add some narrative text above and below your code chunks. Above the code chunk is where you say what you are going to do (and maybe why you are doing it), and below is where you say what you conclude from the output you just obtained.

Solution

My complete document is at http://ritsokiguess.site/datafiles/a0-notebook-1.Rmd.¹ Take a look at it. I added one extra thing: my variable names have “backticks” around them. You’ll see the effect of this in a moment. Backtick is on the key to the left of 1 and below Esc on your keyboard, along with a “squiggle” symbol that we’ll be using later.

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Save your document (the usual way with File and Save). This saves it on the r.datatools server (and not on your computer). This means that when you come back to R Studio later, even from another device, this document will still be available to you. Now click Render. This will work a bit, and then produces a pretty HTML version of your document, that should display on the right of your R Studio in the Viewer.

Solution

My HTML document is at http://ritsokiguess.site/datafiles/a0-notebook-1.nb.html.

Here’s where you see the effect of the backticks: all the variable names are in typewriter font so that you can see they are variable names and not something else. If you want to try this notebook out yourself, you have a couple of options: (i) make a new Quarto Document on r.datatools and copy-paste the contents of my file (it’s just text), or (ii) download my document onto your computer, and then upload it to r.datatools. Look in the Files pane bottom right, and next to New Folder you should see Upload. Upload the file from wherever it got saved to when you downloaded it.

You might find that your dataframe doesn’t display as nicely as mine, just as a list of values rather than one where you can page up and down and left and right. To fix that, go up to the YAML block at the very top, delete where it says

format: html

and replace it with

format:
  html:
    theme: cosmo
    df-print: paged

and then render again.

If you’re feeling extra-ambitious, you can try Knit to PDF. This produces something that looks as if it was written in LaTeX, but actually wasn’t. To make this work, if you have a library(tidyverse) line somewhere, as you probably will, you may need to find the code chunk it’s in, and make it look like this:

Then it will work.

THis should “just work” on r.datatools. On your own computer, you will need to have a version of LaTeX installed. The easiest one is Tinytex, which you can install from within R. Click the link, and follow the two lines of instructions in the code chunk below “For R Users”.

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Optional extra: practice handing in your rendered Quarto document, as if it were an assignment that was worth something. (It is good to get the practice in a low-stakes situation, so that you’ll know what to do next week.)

Solution

There are two steps: download the HTML file onto your computer, and then handing it in on Quercus.

To download: find the HTML file that you want to hand in in the Files pane bottom right. There should be two files starting with the same thing, eg. test1.qmd, which is the document you wrote, and test1.html, which is the rendered version of it, and is the one you want to download. (The test1 part is the name you chose when you saved it.) Click the checkbox to the left of the HTML file. Now click on More above the bottom-right pane. This pops up a menu from which you choose Export. This will pop up another window called Export Files, where you put the name that the file will have on your computer. (I usually leave the name the same.) Click Download. The file will go to your Downloads folder, or wherever things you download off the web go.

Now, to hand it in. Open up Quercus at q.utoronto.ca, log in and navigate to this course. Click Assignments. Click (the title of) Assignment 0. There is a big blue Submit Assignment button top right. Click it. You’ll get a File Upload at the bottom of the screen. Click Choose File and find the HTML file that you downloaded. Click Open (or equivalent on your system). The name of the file should appear next to Choose File. Click Submit Assignment. You’ll see Submitted at the top right.

If you want to try this again, you can Re-submit Assignment as many times as you like. (For the real thing, you can use this if you realize you made a mistake in something you submitted. The graders’ instructions, for the real thing, are to grade the last file submitted, so in that case you need to make sure that the last thing submitted includes everything that you want graded. Here, though, it doesn’t matter.)

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Optional extra. Something more ambitious: make a scatterplot of gas mileage mpg, on the $y$ axis, against horsepower, hp, on the $x$-axis.

Solution

That goes like this. I’ll explain the steps below.

library(tidyverse)
ggplot(mtcars, aes(x=hp, y=mpg))+geom_point()

This shows a somewhat downward trend, which is what you’d expect, since a larger hp value means a more powerful engine, which will probably consume more gas and get fewer miles per gallon. As for the code: to make a ggplot plot, as we will shortly see in class, you first need a ggplot statement that says what to plot. The first thing in a ggplot is a data frame (mtcars here), and then the aes says that the plot will have hp on the $x$-axis and mpg on the $y$-axis, taken from the data frame that you specified. That’s all of the what-to-plot. The last thing is how to plot it; geom_point() says to plot the data values as points.

You might like to add a regression line to the plot. That is a matter of adding this to the end of the plotting command:

ggplot(mtcars, aes(x=hp, y=mpg))+geom_point()+geom_smooth(method="lm")

`geom_smooth()` using formula = 'y ~ x'

The line definitely goes downhill. Decide for yourself how well you think a line fits these data.

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1.6 Reading data from a file

In this question, we read a file from the web and do some descriptive statistics and a graph. This is very like what you will be doing on future assignments, so it’s good to practice it now.

What are the two columns of data separated by? (The fancy word is “delimited”).

Solution

Exactly one space. This is true all the way down, as you can check.

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Make a new Quarto document. Leave the lines at the top between the ---, but get rid of the rest of the template document. Start with a code chunk containing library(tidyverse). Run it.

Solution

You will get either the same message as before or nothing. (I got nothing because I had already loaded the tidyverse in this session.)

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Put the URL of the data file in a variable called my_url. Then use read_delim to read in the file. (See solutions for how.) read_delim reads data files where the data values are always separated by the same single character, here a space. Save the data frame in a variable rats.

Solution

Like this:

my_url <- "http://ritsokiguess.site/datafiles/jumping.txt"
rats <- read_delim(my_url," ")

Rows: 30 Columns: 2
── Column specification ────────────────────────────────────────────────────────
Delimiter: " "
chr (1): group
dbl (1): density

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

The second thing in read_delim is what separates the data values. Often when you use read_delim it’ll be a space.

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Take a look at your data frame, by making a new code chunk and putting the data frame’s name in it (as we did with mtcars).

Solution

rats

There are 30 rows and two columns, as there should be.

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Find the mean bone density for rats that did each amount of jumping.

Solution

This is something you’ll see a lot: group_by followed by summarize. Reminder: to get that funny thing with the percent signs (called the “pipe symbol”), type control-shift-M (or equivalent on a Mac):

rats %>% group_by(group) %>%
summarize(m=mean(density))

The mean bone density is clearly highest for the high jumping group, and not much different between the low-jumping and control groups.

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Make a boxplot of bone density for each jumping group.

Solution

On a boxplot, the groups go across and the values go up and down, so the right syntax is this:

ggplot(rats,aes(x=group, y=density))+geom_boxplot()

Given the amount of variability, the control and low-jump groups are very similar (with the control group having a couple of outliers), but the high-jump group seems to have a consistently higher bone density than the others.

This is more or less in line with what the experimenters were guessing, but it seems that it has to be high jumping to make a difference.

You might recognize that this is the kind of data where we would use analysis of variance, which we will do later on in the course: we are comparing several (here three) groups.

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1.7 Reading files different ways

This question is about different ways of reading data files. If you’re working online (using r.datatools or similar), start at the beginning. If you’re using R Studio running on your own computer, start at part (here).

Log in to r.datatools.utoronto.ca. Open up a project (or start a new one), and watch the spinning circles for a few minutes. When that’s done, create a new Quarto Document with File, New File, Quarto Document. Delete the “template” document, but not the top lines with title: and output: in them. Add a code chunk that contains library(tidyverse) and run it.

Solution

So far you (with luck) have something that looks like this:

If you have an error rather than that output, you probably need to install the tidyverse first. Make another code chunk, containing

install.packages("tidyverse")

and run it. Wait for it to finish. It may take a while. If it completes successfully (you might see the word DONE at the end), delete that code chunk (you don’t need it any more) and try again with the library(tidyverse) chunk. It should run properly this time.

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* The easiest kind of files to read in are ones on the Internet, with a URL address that begins with http or https. I have a small file at link. Click the link to see it, and keep the tab open for the next part of this question. This is a text file with three things on each line, each separated by exactly one space. Read the data file into a data frame, and display your data frame.

Solution

Data values separated by exactly one space is the kind of thing that read_delim reads, so make another code chunk and fill it with this:

my_url <- "http://ritsokiguess.site/datafiles/testing.txt"
testing <- read_delim(my_url, " ")

Rows: 6 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: " "
chr (1): g
dbl (2): x, y

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

testing

When you run that, you’ll see something like my output. The first part is read_delim telling you what it saw in the file: two columns of (whole) numbers and one column of text. The top line of the file is assumed to contain names, which are used as the names of the columns of your data frame. The bottom part of the output, obtained by putting the name of the data frame on a line by itself in your code chunk, is what the data frame actually looks like. You ought to get into the habit of eyeballing it and checking that it looks like the values in the data file.

The things on the left side of the <- symbol (that is meant to look like an arrow pointing left) are variables that you are creating in R. You get to choose the names for them. My habit is to use my_url for URLs of files that I am going to read in, and (usually) to give my data frames names that say something about what they contain, but this is your choice to make.

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You might have data in a file on your own computer. To read data from such a file, you first have to upload it to r.datatools, and then read it from there. To practice this: open a text editor (like Notepad or TextEdit). Go back to the web browser tab containing the data you used in the previous part. Copy the data from there and paste it into the text editor. Save it somewhere on your computer (like the Desktop). Upload that file, read in the data and verify that you get the right thing. (For this last part, see the Solution.)

Solution

I copied and pasted the data, and saved it in a file called testing.txt on my computer. I’m assuming that you’ve given it a similar name. Then go back to r.datatools. You should have a Files pane bottom right. If you don’t see a pane bottom right at all, press Control-Shift-0 to show all the panes. If you see something bottom right but it’s not Files (for example a plot), click the Files tab, and you should see a list of things that look like files, like this:

Click the Upload button (next to New Folder), click Choose File. Use the file finder to track down the file you saved on your computer, then click OK. The file should be uploaded to the same folder on r.datatools that your project is, and appear in the Files pane bottom right.

To read it in, you supply the file name to read_delim thus:

testing2 <- read_delim("testing.txt", " ")

Rows: 6 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: " "
chr (1): g
dbl (2): x, y

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

and then you look at it in the same way as before:

testing2

Check.

$\blacksquare$

You might have a spreadsheet on your computer. Create a .csv file from it, and use the ideas of the last part to read it into R Studio.

Solution

Open the spreadsheet containing the data you want to read into R. If there are several sheets in the workbook, make sure you’re looking at the right one. Select File, Save As, select “CSV” or “comma-separated values” and give it a name. Save the resulting file somewhere.

Then follow the same steps as the previous part to upload it to your project on r.datatools. (If you look at the actual file, it will be plain text with the data values having commas between them, as the name suggests. You can open the file in R Studio by clicking on it in the Files pane; it should open top left.)

The final step is to read it into an R data frame. This uses read_csv; there are several read_ functions that read in different types of file, and you need to use an appropriate one.

My spreadsheet got saved as cars.csv, so:

cars <- read_csv("cars.csv")

Rows: 38 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): Car, Country
dbl (4): MPG, Weight, Cylinders, Horsepower

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

cars

Some information about different types of cars.

You are now done with this question.

$\blacksquare$

* Start here if you downloaded R and R Studio and they are running on your own computer. Open a web browser and point it at link. Click the link to see it, and keep the tab open for the next part of this question. This is a text file with three things on each line, each separated by exactly one space. Read the data file into a data frame, and display your data frame.

Solution

Data values separated by exactly one space is the kind of thing that read_delim reads, so make another code chunk and fill it with this:

my_url <- "http://ritsokiguess.site/datafiles/testing.txt"
testing <- read_delim(my_url, " ")

Rows: 6 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: " "
chr (1): g
dbl (2): x, y

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

testing

(This part is exactly the same whether you are running R Studio on r.datatools or have R Studio running on your computer. A remote file is obtained in exactly the same way regardless.)

$\blacksquare$

You might have data in a file on your own computer. To read data from such a file, R has to know where to find it. Each R project lives in a folder, and one way of specifying where a data file is is to give its complete path relative to the folder that R Studio is running its current project in. This is rather complicated, so we will try a simpler way. To set this up, open a text editor (like Notepad or TextEdit). Go back to the web browser tab containing the data you used in the previous part. Copy the data from there and paste it into the text editor. Save it somewhere on your computer (like the Desktop). Follow the steps in the solution below to read the data into R.

Solution

I copied and pasted the data, and saved it in a file called testing.txt on my computer. I’m assuming that you’ve given it a similar name. Go back to R Studio. Create a new code chunk containing this:

f <- file.choose()

Run this code chunk. You’ll see a file chooser. Find the file you saved on your computer, and click Open (or OK or whatever you see). This saves what R needs to access the file in the variable f. If you want to, you can look at it:

and you’ll see what looks like a file path in the appropriate format for your system (Windows, Mac, Linux). To read the data in, you supply the file path to read_delim thus:

testing2 <- read_delim(f, " ")

and then you look at it in the same way as before:

testing2

Check.

$\blacksquare$

You might have a spreadsheet on your computer. Create a .csv file from it, and use the ideas of the last part to read it into R Studio.

Solution

Then read it into an R data frame. This uses read_csv; there are several read_ functions that read in different types of file, and you need to use an appropriate one. Before that, though, again run

f <- file.choose()

to find the .csv file on your computer, and then

cars <- read_csv(f)

to read it in. My spreadsheet was

cars

Some information about different types of cars.

$\blacksquare$

This, as it stands, is an old-fashioned R Markdown document rather than a Quarto one, but they work basically the same.↩︎