diff --git a/images/help.jpeg b/images/help.jpeg new file mode 100644 index 0000000..d876e44 Binary files /dev/null and b/images/help.jpeg differ diff --git a/images/rstudio.jpeg b/images/rstudio.jpeg new file mode 100644 index 0000000..a526077 Binary files /dev/null and b/images/rstudio.jpeg differ diff --git a/springWorkshop.Rmd b/springWorkshop.Rmd new file mode 100644 index 0000000..b44c7ca --- /dev/null +++ b/springWorkshop.Rmd @@ -0,0 +1,438 @@ +--- +title: "Spring 2021 Workshop" +output: + learnr::tutorial: + progressive: no + theme: cerulean + allow_skip: yes +runtime: shiny_prerendered + +--- + +```{r setup, include=FALSE} +library(learnr) +knitr::opts_chunk$set(echo = FALSE) +``` + +## Introduction + +This workshop is for students new to R and RStudio. + +Your introductory StatSci course this semester will march you through a thorough introduction to using R for statistical science and data science purposes, as well as for many other practical applications. You should already be familiar with the environment you are going to use for your course, whether it is RStudio in the Cloud, a Docker container, or software installed on your own computer. Your instructor should have given you that information and you should be ready to use it. + +The materials here are a compilation of advice and instructions from your professors Curry Hilton, Victoria Ellison, Yue Jiang, Shawn Santo, Bob Eisinger, and especially Maria Tackett, Mine Cetinkaya-Rundel, and TAs including Becky Tang, Jennifer Wilson, Frances Hu, and many helpful resources. + +Here are some key ideas: + +If you are in the habit of saying you are not a coder, that stops here. + +R is an open source programming language for statistical computing (http://cran-r-project.org). + +RStudio is the IDE or Interactive Development Environment we use to work in R. RStudio is available open source, but their company also provides some cloud-based servers for working in their environment, known as RStudio in the Cloud. + +## Getting Started + +If you have not ever used GitHub, you will first need to go in and create an account. +Look at the guidelines on Jenny Bryant's [Happy Git with R website](https://happygitwithr.com/github-acct.html). + +Create your git account and make sure you write down your username and password. + +Now in github.com, search for the following repository: https://github.com/DukeStatSci/Spring_2021_Workshop +and have it ready. It's okay to go ahead and double click on the HTML file called intro-r.html and follow along. + +## RStudio + +Your RStudio window generally has four panels. This is customizable so you might have changed it already. You can also drag the borders to resize each portion. + +* **Source**: Where R markdown files are produced and saved + +* **Console**: Where live R code is run; cannot be saved + +* **Environment**: Where R objects live and are stored + +* **Utility**: Where directories, plots, packages, and help can be found + +![](images/rstudio.jpeg){width=90%} + +Your R Markdown file (.Rmd) is in the upper left panel. If you want to view this .Rmd file in a nicer format, click the Knit button above. This will turn your .Rmd file into a HTML or PDF file. The .Rmd and .html files are separate but directly related; after you make any changes in the .Rmd file, click Knit to update the .html file. + +The panel in the upper right contains any data you have loaded, as well as a history of +the commands that you've previously entered. You can also use the **Git** interface in this panel. + +Any plots that you generate will show up in the panel in the lower right corner. +This is also where you can browse your files, access help, manage packages, etc. + +Your console, in the bottom left panel, can be used to run code. Do this with caution as your code will not be saved. + +## R Packages + +R is an open-source programming language, meaning that users can contribute +packages that make our lives easier, and we can use them for free. For this lab, +and many others in the future, we will use the following R packages: + +- `tidyverse`: A group of packages for data manipulation, exploration and visualization that share a common coding syntax and style. +- `dplyr`: A package in the tidyverse for data wrangling +- `ggplot2`: A package in the tidyverse for data visualization +- `usethis`: for workflow automation (and eventually, to help write your own packages!) + +You can install these packages using commands like +`install.packages` and `install_github`. Find out more about the tidyverse [here.](https://rviews.rstudio.com/2017/06/08/what-is-the-tidyverse/) When you open this .Rmd file in R Studio, it may prompt you to install these packages, with a yellow warning at the top of the screen. Click 'Install' if it asks you. Alternatively, simply uncomment whichever you need and run this code chunk to install them. A **comment** is a line of code with a hashtag (#) preceding the code. This tells R to ignore the line of code. Comments are a useful tool for explaining the purpose of your code, making your code readable to anyone reading your code. + +```{r install-packages, message = FALSE, warning = FALSE} +# The install.packages function calls are commented out. +# Remove the hashtags (#) below if you need to install these packages. + +#install.packages('tidyverse') +#install.packages('usethis') +``` + +Next, you need to load the packages in your working environment. We do this with +the `library` function. Note that you only need to **install** packages once, but +you need to **load** them each time you relaunch RStudio. + +```{r load-packages, echo = TRUE, message = FALSE, warning = FALSE} +library(tidyverse) +library(usethis) +``` + +To do so, you can + +- click on the green arrow at the top of the code chunk in the R Markdown (Rmd) +file, or +- highlight these lines, and hit the **Run** button on the upper right corner of the +pane, or +- hit `CTRL` + `ENTER` to run the current line of code +- hit `CTRL` + `SHIFT` + `ENTER` to run the current chunk of code +- type the code in the console. + +Going forward you will be asked to load any relevant packages at the beginning +of each lab. + +## Git and Github + +You will use GitHub to obtain the RMarkdown or .Rmd file for this workshop. If you already +have a GitHub account, great! If not, take a minute to start one. But first, some user name advice... + +Jenny Bryan is an RStudio software engineer who wrote 'Happy Git and Github for the useR'. +She was a keynote speaker at the [RStudio Conference in 2020](https://rstudio.com/resources/rstudioconf-2020/object-of-type-closure-is-not-subsettable/). +Her page of advice on creating a free Github account and choosing a user name that works is [here.](https://happygitwithr.com/github-acct.html) Register your free account. +You'll have to confirm the request in email. + +Now the workshop facilitators will help you fork the repository that contains the software you need. + +You will use the `use_git_config()` function from the `usethis` package. + +Type the following lines of code in the console in RStudio filling in your name and email address. + +The email address is the one tied to your GitHub account. +```{r get your github congfigured, echo=TRUE, eval=FALSE} +library(usethis) +use_git_config(user.name = "GitHub username", user.email="your email") +``` + +For example, George's would be: + +```{r github-usethis-ex, echo=TRUE, eval = FALSE} +library(usethis) +use_git_config(user.name="george-lindner", user.email="george.lindner@duke.edu") +``` + +Take a minute to configure your GitHub account into RStudio! + +## Diving into the Data + +We are going to use a dataset called `diamonds`. This dataset can be found in the `ggplot2` package! Simply run `library(tidyverse)` or `library(ggplot2)` if you haven't already, and then call `data(diamonds)` to load in the dataset. +To get you started, run the following command to load the data and to inspect the first ten rows of the file. + +```{r load-diamonds-data} +# Load the diamonds file +data(diamonds) + +# Display the data +diamonds +``` + +To do so, once again, you can + +* click on the green arrow at the top of the code chunk in the R Markdown (Rmd) +file, or +* put your cursor on this line, and hit the **Run** button on the upper right +corner of the pane, or +* type the code in the console. + +This command instructs R to load some data. You should see that the workspace area in the upper righthand corner of the RStudio window now lists a data set called `diamonds` that has +53940 observations on 10 variables. As you interact with R, you will create a series of objects. Sometimes you load them as we have done here, and sometimes you create them yourself as the byproduct of a computation or some analysis you have performed. + +However printing the whole dataset in the console is not that useful. +One advantage of RStudio is that it comes with a built-in data viewer. Click on +the name `diamonds` in the *Environment* pane (upper right window) that lists +the objects in your workspace. This will bring up an alternative display of the +data set in the *Data Viewer* (upper left window). You can close the data viewer +by clicking on the *x* in the upper lefthand corner. + +What you should see are eleven columns of data, each row representing a +different diamond: the first entry in each row is simply the row number (an index +we can use to access the data from individual stones if we want), the second is +price in dollars, and the third is the weight in carats of that gem. +Use the scrollbar on the right side of the console +window to examine the complete data set. + +Note that the row numbers in the first column are not part of the `diamonds` data. +R adds them as part of its printout to help you make visual comparisons. You can +think of them as the index that you see on the left side of a spreadsheet. In +fact, the comparison to a spreadsheet will generally be helpful. R has stored +the 'diamonds' data in a kind of spreadsheet or table called a *data frame*. + +You can see the dimensions of this data frame by typing: + +```{r dim-data, echo = TRUE} +dim(diamonds) +``` + +This command should output `[1] 53940 10`, indicating that there are 53940 rows or observations and 10 columns or fieldnames. +You can see the names of these columns (or variables) by typing: + +```{r names-data, echo=TRUE} +names(diamonds) +``` + + +```{r quiz1} +quiz( + question("How many variables are included in this data set?", + answer("2"), + answer("9"), + answer("10"), + answer("82"), + answer("53940", correct = TRUE), + allow_retry = TRUE + ) +) +``` + + +**Exercise:** What are some of the values for `cut` included in this dataset? + +Hint: Take a look at the `cut` variable in the Data Viewer to answer this question. + +You should see that the data frame contains the columns `price`, `carat`, and +`cut`. At this point, you might notice that many of the commands in R look a +lot like functions from math class; that is, invoking R commands means supplying +a function with some number of arguments. The `dim` and `names` commands, for +example, each took a single argument, the name of a data frame. + +**Tip: ** If you use the up and down arrow keys, you can scroll through your +previous commands, your so-called command history. You can also access it +by clicking on the history tab in the upper right panel. This will save +you a lot of typing in the future. + +## R Markdown + +The console is a greatplace for playing around with code, however it is not a good place for +documenting your work. Working in the console exclusively makes it difficult to +document your work as you go, and reproduce it later. + +R Markdown is a great solution for this problem. And, you already have worked with +an R Markdown document -- this lab! Type code in the code chunks provided in the R Markdown (Rmd) document, and **Knit** +the document to see the results. Knit should produce an HTML file for you that +lets you view the output of your code. + +## Data Exploration + +Let's start to examine the data a little more closely. +To find out more about this dataset, you could use some help. Try typing the name of the dataset with a '?' in front of it. + +```{r help-diamonds, exercise=TRUE} + +``` + +You should see the documentation on the dataset in the Help window. Try out the other tabs there briefly. +We can access the data in a single column of a data frame separately using a command like + +```{r view-clarity, exercise=TRUE} +diamonds %>% + select(clarity) %>% + slice(1:100) +``` + + +The `%>%` operator is called the **piping** +operator. Basically, it takes the output of the current line and tells R to continue into +the following line of code. Reading the code in plain English: From the diamonds data frame, we select the column `clarity`. We want the first 100 rows of the column `clarity`. + +**Try selecting a different column to observe and only view the first 50 rows.** + +```{r new-column, exercise=TRUE} + +``` + +```{r quiz2} +quiz( + question("What command would you use to extract just the data on diamond length? ", + answer("diamonds %>% choose(x)"), + answer("diamonds %>% x"), + answer("diamonds[x]"), + answer("diamonds %>% select(x)", correct = TRUE), + allow_retry = TRUE + ) +) + +``` + +### Mutate + +We can create new columns in our data frame through the `mutate()` function. + +```{r new-columns, exercise = TRUE} +diamonds <- diamonds %>% + mutate(total = x + y + z) + +diamonds +``` + +Notice the new column on the end of our table? + +### Filtering + +We can use just part of the dataset by filtering. Here, we are going to filter on a particular value of cut and color and plot price vs carat. Try filtering another variable. + +```{r try-filtering-diamonds, exercise = TRUE} +diamonds %>% + filter(cut == "Fair" & color =="G") +``` + + +### Booleans + +In addition to simple mathematical operators like subtraction and +division, you can ask R to make logical comparisons like greater than, `>`, less than, +`<`, and equality, `==`. For example, we can ask if the x dimension is greater than +the y dimension for these stones with the expression + +```{r x-greater-than-y, exercise = TRUE} +diamonds %>% + mutate(more_x = x > y) +``` + +This command add a new variable to the `diamonds` data frame containing the values +of either `TRUE` if that stone was longer than its width, or `FALSE` if it was not (the answer may surprise you). + +This variable contains different kind of +data than we have considered so far. All other columns in the `diamonds` data +frame have values that are numerical (carat, table and width) or text (color, cut). Here, +we've asked R to create *logical* data, data where the values are either `TRUE` +or `FALSE`. In general, data analysis will involve many different kinds of data +types, and one reason for using R is that it is able to represent and compute +with many of them. + +## Data Visualization + +R has some powerful functions for making graphics. We can create a simple plot +of the price of diamonds by their weight measured in carats with the command + +```{r plot-price-vs-carat, echo=TRUE} +ggplot(data = diamonds, aes(x = carat, y = price, color = cut)) + + geom_point() + + theme_classic() + + labs(title = "Carat vs. Price", + subtitle = "RStudio Spring Workshop", + caption = "Data Source: Diamonds Dataset", + x = "Carat", + y = "Price ($)", + color = "Cut") +``` + + +We use the `ggplot()` function to build plots. If you run the +plotting code in your console, you should see the plot appear under the *Plots* tab +of the lower right panel of RStudio. Notice that the command above again looks like +a function, this time with arguments separated by commas. + +* The first argument is always the dataset: `data = diamonds` in this case +* Provide the variables from the dataset to be assigned to `aes`thetic +elements of the plot, e.g. the x and the y axes. +* Use another layer, separated by a `+` to specify the `geom`etric +object for the plot. Since we want to scatterplot, we use `geom_point`. +* Add a theme to your plot to make it look professional: `theme_classic()` +* Finally, add labels through `labs()` + +The graph looks nice, but is pretty cluttered! If we want to graph a **categorical variable** such as `cut`, we can use boxplots. + +```{r better-plots-2, echo=TRUE} +ggplot(data = diamonds, aes(x = cut, y = price, fill = cut)) + + geom_boxplot() + + theme_minimal() + + labs(title = 'Cut vs. Price', + subtitle = 'RStudio Spring Workshop', + caption = 'Diamonds Dataset', + x = "", + y = "Price ($)", + fill = "Cut") +``` + + +You might wonder how you are supposed to know the syntax for the `ggplot` function. +Thankfully, R documents all of its functions extensively. To read what a function +does and learn the arguments that are available to you, just type in a question mark +followed by the name of the function that you're interested in. Type the following in +your console: + +```{r plot-help, echo=TRUE} +?ggplot +``` + +![](images/help.jpeg){width=90%} + +Notice that the help file replaces the plot in the lower right panel. You can +toggle between plots and help files using the tabs at the top of that panel. + +**Exercise:** Now play with mutate and piping and create a plot of interest to you. What is the difference between calling `fill = cut` and `color = cut`? + +```{r plot-of-something-interesting, exercise=TRUE} +ggplot(data = ____, aes(x = ____, y = ____)) + + ____ + +``` + + +For help with plotting with the `ggplot2` package, check out [this cheatsheet!](https://rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf) The +best (and easiest) way to learn the syntax is to take a look at the sample plots +provided on that page, and modify the code to fit your needs. + + + +## Resources for Learning R + +That was a short introduction to R and RStudio, but your instructors will provide you with more functions and a more complete sense of the language as your course progresses. You +might find the following tips and resources helpful. + +* Many courses use the Tidyverse while other courses will expect you to learn to work in base R, rather than in the RStudio IDE. In Tidyverse-flavored courses, you will use `dplyr` (for data wrangling) and `ggplot2` (for +data visualization) extensively. If you are googling for R code, make sure +to also include these package names in your search query. For example, instead +of googling "scatterplot in R", google "scatterplot in R with ggplot2". + +* There is a terrific resource on Medium called Towards Data Science, and you can find good information there, although you may have a limited number of free articles to read. https://towardsdatascience.com/data-science/home + +* Do a quick search on "Free books on R" and you will find a huge array of choices. + +* Lots of R users post their own guides that you can find by searching. Here's one from David Romney at Harvard: https://scholar.harvard.edu/dromney/online-resources-learning-r + +* The r-statistics blog is very helpful: https://www.r-statistics.com/ In fact, you will find that the R community by its very nature is beginner friendly. + +* The following cheatsheets may come in handy throughout your courses. Note that some +of the code on these cheatsheets may be too advanced for your needs now, however the +majority of it will become useful as you progress through your statistical science courses. + - [Data wrangling cheatsheet](http://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) + - [Data visualization cheatsheet](https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf) + - [R Markdown](https://github.com/rstudio/cheatsheets/raw/master/rmarkdown-2.0.pdf) + +## Troubleshooting + +If you run into problems with your RStudio work, start by inspecting the console for error messages. See what it tells you. Then you can try to look up that error message. Sites like Stack Overflow https://stackoverflow.com/questions/tagged/rstudio provide lots of resources and answers to questions people have already asked. You will learn R more quickly if you try to solve problems yourself. We all need help at times. + +If you need help with a problem in your labs, please ask your TA or your classmates. Don't be the person who posts a question to a website like Stack Overflow for which the answers can be readily found on the internet via a simple search. Those kinds of questions will be ignored or will result in heaps of abuse. Some sites are more beginner-friendly than others. Your TAs will have some suggestions for you. + +Whatever you do, please don't post your assigned questions onto paid answer-sharing services. That's a violation of the Community Standard as well as a very poor way to learn to use R. + +## Acknowledgements +This is derived from the `Learning R Tutorials` website. Thanks to Joan Durso and Maria Tackett for all their help. diff --git a/springWorkshop.html b/springWorkshop.html new file mode 100644 index 0000000..a1e4c2d --- /dev/null +++ b/springWorkshop.html @@ -0,0 +1,751 @@ + + + + + + + + + + + + + + + +Spring 2021 Workshop + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+

Introduction

+

This workshop is for students new to R and RStudio.

+

Your introductory StatSci course this semester will march you through a thorough introduction to using R for statistical science and data science purposes, as well as for many other practical applications. You should already be familiar with the environment you are going to use for your course, whether it is RStudio in the Cloud, a Docker container, or software installed on your own computer. Your instructor should have given you that information and you should be ready to use it.

+

The materials here are a compilation of advice and instructions from your professors Curry Hilton, Victoria Ellison, Yue Jiang, Shawn Santo, Bob Eisinger, and especially Maria Tackett, Mine Cetinkaya-Rundel, and TAs including Becky Tang, Jennifer Wilson, Frances Hu, and many helpful resources.

+

Here are some key ideas:

+

If you are in the habit of saying you are not a coder, that stops here.

+

R is an open source programming language for statistical computing (http://cran-r-project.org).

+

RStudio is the IDE or Interactive Development Environment we use to work in R. RStudio is available open source, but their company also provides some cloud-based servers for working in their environment, known as RStudio in the Cloud.

+
+
+

Getting Started

+

If you have not ever used GitHub, you will first need to go in and create an account. Look at the guidelines on Jenny Bryant’s Happy Git with R website.

+

Create your git account and make sure you write down your username and password.

+

Now in github.com, search for the following repository: https://github.com/DukeStatSci/Spring_2021_Workshop and have it ready. It’s okay to go ahead and double click on the HTML file called intro-r.html and follow along.

+
+
+

RStudio

+

Your RStudio window generally has four panels. This is customizable so you might have changed it already. You can also drag the borders to resize each portion.

+
    +

  • Source: Where R markdown files are produced and saved

    • +

  • Console: Where live R code is run; cannot be saved

    • +

  • Environment: Where R objects live and are stored

    • +

  • Utility: Where directories, plots, packages, and help can be found

    • +
+

+

Your R Markdown file (.Rmd) is in the upper left panel. If you want to view this .Rmd file in a nicer format, click the Knit button above. This will turn your .Rmd file into a HTML or PDF file. The .Rmd and .html files are separate but directly related; after you make any changes in the .Rmd file, click Knit to update the .html file.

+

The panel in the upper right contains any data you have loaded, as well as a history of the commands that you’ve previously entered. You can also use the Git interface in this panel.

+

Any plots that you generate will show up in the panel in the lower right corner. This is also where you can browse your files, access help, manage packages, etc.

+

Your console, in the bottom left panel, can be used to run code. Do this with caution as your code will not be saved.

+
+
+

R Packages

+

R is an open-source programming language, meaning that users can contribute packages that make our lives easier, and we can use them for free. For this lab, and many others in the future, we will use the following R packages:

+
    +
  • tidyverse: A group of packages for data manipulation, exploration and visualization that share a common coding syntax and style.
    • +
  • dplyr: A package in the tidyverse for data wrangling
    • +
  • ggplot2: A package in the tidyverse for data visualization
    • +
  • usethis: for workflow automation (and eventually, to help write your own packages!)
    • +
+

You can install these packages using commands like install.packages and install_github. Find out more about the tidyverse here. When you open this .Rmd file in R Studio, it may prompt you to install these packages, with a yellow warning at the top of the screen. Click ‘Install’ if it asks you. Alternatively, simply uncomment whichever you need and run this code chunk to install them. A comment is a line of code with a hashtag (#) preceding the code. This tells R to ignore the line of code. Comments are a useful tool for explaining the purpose of your code, making your code readable to anyone reading your code.

+

Next, you need to load the packages in your working environment. We do this with the library function. Note that you only need to install packages once, but you need to load them each time you relaunch RStudio.

+
library(tidyverse)
+library(usethis)
+

To do so, you can

+
    +
  • click on the green arrow at the top of the code chunk in the R Markdown (Rmd) file, or
    • +
  • highlight these lines, and hit the Run button on the upper right corner of the pane, or
    • +
  • hit CTRL + ENTER to run the current line of code
    • +
  • hit CTRL + SHIFT + ENTER to run the current chunk of code
    • +
  • type the code in the console.
    • +
+

Going forward you will be asked to load any relevant packages at the beginning of each lab.

+
+
+

Git and Github

+

You will use GitHub to obtain the RMarkdown or .Rmd file for this workshop. If you already have a GitHub account, great! If not, take a minute to start one. But first, some user name advice…

+

Jenny Bryan is an RStudio software engineer who wrote ‘Happy Git and Github for the useR’. She was a keynote speaker at the RStudio Conference in 2020.
+Her page of advice on creating a free Github account and choosing a user name that works is here. Register your free account.
+You’ll have to confirm the request in email.

+

Now the workshop facilitators will help you fork the repository that contains the software you need.

+

You will use the use_git_config() function from the usethis package.

+

Type the following lines of code in the console in RStudio filling in your name and email address.

+

The email address is the one tied to your GitHub account.

+
library(usethis)
+use_git_config(user.name = "GitHub username", user.email="your email")
+

For example, George’s would be:

+
library(usethis)
+use_git_config(user.name="george-lindner", user.email="george.lindner@duke.edu")
+

Take a minute to configure your GitHub account into RStudio!

+
+
+

Diving into the Data

+

We are going to use a dataset called diamonds. This dataset can be found in the ggplot2 package! Simply run library(tidyverse) or library(ggplot2) if you haven’t already, and then call data(diamonds) to load in the dataset. To get you started, run the following command to load the data and to inspect the first ten rows of the file.

+
+ +
+

To do so, once again, you can

+
    +
  • click on the green arrow at the top of the code chunk in the R Markdown (Rmd) file, or
    • +
  • put your cursor on this line, and hit the Run button on the upper right corner of the pane, or
    • +
  • type the code in the console.
    • +
+

This command instructs R to load some data. You should see that the workspace area in the upper righthand corner of the RStudio window now lists a data set called diamonds that has 53940 observations on 10 variables. As you interact with R, you will create a series of objects. Sometimes you load them as we have done here, and sometimes you create them yourself as the byproduct of a computation or some analysis you have performed.

+

However printing the whole dataset in the console is not that useful. One advantage of RStudio is that it comes with a built-in data viewer. Click on the name diamonds in the Environment pane (upper right window) that lists the objects in your workspace. This will bring up an alternative display of the data set in the Data Viewer (upper left window). You can close the data viewer by clicking on the x in the upper lefthand corner.

+

What you should see are eleven columns of data, each row representing a different diamond: the first entry in each row is simply the row number (an index we can use to access the data from individual stones if we want), the second is price in dollars, and the third is the weight in carats of that gem. Use the scrollbar on the right side of the console window to examine the complete data set.

+

Note that the row numbers in the first column are not part of the diamonds data. R adds them as part of its printout to help you make visual comparisons. You can think of them as the index that you see on the left side of a spreadsheet. In fact, the comparison to a spreadsheet will generally be helpful. R has stored the ‘diamonds’ data in a kind of spreadsheet or table called a data frame.

+

You can see the dimensions of this data frame by typing:

+
dim(diamonds)
+
## [1] 53940    10
+

This command should output [1] 53940 10, indicating that there are 53940 rows or observations and 10 columns or fieldnames. You can see the names of these columns (or variables) by typing:

+
names(diamonds)
+
##  [1] "carat"   "cut"     "color"   "clarity" "depth"   "table"   "price"  
+##  [8] "x"       "y"       "z"
+

Quiz

+
+
+
+
+
+ +
+
+

Exercise: What are some of the values for cut included in this dataset?

+

Hint: Take a look at the cut variable in the Data Viewer to answer this question.

+

You should see that the data frame contains the columns price, carat, and cut. At this point, you might notice that many of the commands in R look a lot like functions from math class; that is, invoking R commands means supplying a function with some number of arguments. The dim and names commands, for example, each took a single argument, the name of a data frame.

+

Tip: If you use the up and down arrow keys, you can scroll through your previous commands, your so-called command history. You can also access it by clicking on the history tab in the upper right panel. This will save you a lot of typing in the future.

+
+
+

R Markdown

+

The console is a greatplace for playing around with code, however it is not a good place for documenting your work. Working in the console exclusively makes it difficult to document your work as you go, and reproduce it later.

+

R Markdown is a great solution for this problem. And, you already have worked with an R Markdown document – this lab! Type code in the code chunks provided in the R Markdown (Rmd) document, and Knit the document to see the results. Knit should produce an HTML file for you that lets you view the output of your code.

+
+
+

Data Exploration

+

Let’s start to examine the data a little more closely. To find out more about this dataset, you could use some help. Try typing the name of the dataset with a ‘?’ in front of it.

+
+ +
+

You should see the documentation on the dataset in the Help window. Try out the other tabs there briefly.
+We can access the data in a single column of a data frame separately using a command like

+
+
diamonds %>%
+  select(clarity) %>%
+  slice(1:100)
+ +
+

The %>% operator is called the piping operator. Basically, it takes the output of the current line and tells R to continue into the following line of code. Reading the code in plain English: From the diamonds data frame, we select the column clarity. We want the first 100 rows of the column clarity.

+

Try selecting a different column to observe and only view the first 50 rows.

+
+ +
+

Quiz

+
+
+
+
+
+ +
+
+
+

Mutate

+

We can create new columns in our data frame through the mutate() function.

+
+
diamonds <- diamonds %>%
+  mutate(total = x + y + z)
+
+diamonds
+ +
+

Notice the new column on the end of our table?

+
+
+

Filtering

+

We can use just part of the dataset by filtering. Here, we are going to filter on a particular value of cut and color and plot price vs carat. Try filtering another variable.

+
+
diamonds %>%
+  filter(cut == "Fair" & color =="G")
+ +
+
+
+

Booleans

+

In addition to simple mathematical operators like subtraction and division, you can ask R to make logical comparisons like greater than, >, less than, <, and equality, ==. For example, we can ask if the x dimension is greater than the y dimension for these stones with the expression

+
+
diamonds %>%
+  mutate(more_x = x > y)
+ +
+

This command add a new variable to the diamonds data frame containing the values of either TRUE if that stone was longer than its width, or FALSE if it was not (the answer may surprise you).

+

This variable contains different kind of data than we have considered so far. All other columns in the diamonds data frame have values that are numerical (carat, table and width) or text (color, cut). Here, we’ve asked R to create logical data, data where the values are either TRUE or FALSE. In general, data analysis will involve many different kinds of data types, and one reason for using R is that it is able to represent and compute with many of them.

+
+
+
+

Data Visualization

+

R has some powerful functions for making graphics. We can create a simple plot of the price of diamonds by their weight measured in carats with the command

+
ggplot(data = diamonds, aes(x = carat, y = price, color = cut)) +
+  geom_point() +
+  theme_classic() +
+  labs(title = "Carat vs. Price",
+       subtitle = "RStudio Spring Workshop",
+       caption = "Data Source: Diamonds Dataset",
+       x = "Carat",
+       y = "Price ($)",
+       color = "Cut")
+

+

We use the ggplot() function to build plots. If you run the plotting code in your console, you should see the plot appear under the Plots tab of the lower right panel of RStudio. Notice that the command above again looks like a function, this time with arguments separated by commas.

+
    +
  • The first argument is always the dataset: data = diamonds in this case
    • +
  • Provide the variables from the dataset to be assigned to aesthetic elements of the plot, e.g. the x and the y axes.
    • +
  • Use another layer, separated by a + to specify the geometric object for the plot. Since we want to scatterplot, we use geom_point.
    • +
  • Add a theme to your plot to make it look professional: theme_classic()
    • +
  • Finally, add labels through labs()
    • +
+

The graph looks nice, but is pretty cluttered! If we want to graph a categorical variable such as cut, we can use boxplots.

+
ggplot(data = diamonds, aes(x = cut, y = price, fill = cut)) +
+  geom_boxplot() +
+  theme_minimal() +
+  labs(title = 'Cut vs. Price',
+       subtitle = 'RStudio Spring Workshop',
+       caption = 'Diamonds Dataset',
+       x = "",
+       y = "Price ($)",
+       fill = "Cut")
+

+

You might wonder how you are supposed to know the syntax for the ggplot function. Thankfully, R documents all of its functions extensively. To read what a function does and learn the arguments that are available to you, just type in a question mark followed by the name of the function that you’re interested in. Type the following in your console:

+
?ggplot
+

+

Notice that the help file replaces the plot in the lower right panel. You can toggle between plots and help files using the tabs at the top of that panel.

+

Exercise: Now play with mutate and piping and create a plot of interest to you. What is the difference between calling fill = cut and color = cut?

+
+
ggplot(data = ____, aes(x = ____, y = ____)) +
+  ____
+ +
+

For help with plotting with the ggplot2 package, check out this cheatsheet! The best (and easiest) way to learn the syntax is to take a look at the sample plots provided on that page, and modify the code to fit your needs.

+
+
+

Resources for Learning R

+

That was a short introduction to R and RStudio, but your instructors will provide you with more functions and a more complete sense of the language as your course progresses. You might find the following tips and resources helpful.

+
    +

  • Many courses use the Tidyverse while other courses will expect you to learn to work in base R, rather than in the RStudio IDE. In Tidyverse-flavored courses, you will use dplyr (for data wrangling) and ggplot2 (for data visualization) extensively. If you are googling for R code, make sure to also include these package names in your search query. For example, instead of googling “scatterplot in R”, google “scatterplot in R with ggplot2”.

    • +

  • There is a terrific resource on Medium called Towards Data Science, and you can find good information there, although you may have a limited number of free articles to read. https://towardsdatascience.com/data-science/home

    • +

  • Do a quick search on “Free books on R” and you will find a huge array of choices.

    • +

  • Lots of R users post their own guides that you can find by searching. Here’s one from David Romney at Harvard: https://scholar.harvard.edu/dromney/online-resources-learning-r

    • +

  • The r-statistics blog is very helpful: https://www.r-statistics.com/ In fact, you will find that the R community by its very nature is beginner friendly.

    • +

  • The following cheatsheets may come in handy throughout your courses. Note that some of the code on these cheatsheets may be too advanced for your needs now, however the majority of it will become useful as you progress through your statistical science courses.

    +
    • +
+
+
+

Troubleshooting

+

If you run into problems with your RStudio work, start by inspecting the console for error messages. See what it tells you. Then you can try to look up that error message. Sites like Stack Overflow https://stackoverflow.com/questions/tagged/rstudio provide lots of resources and answers to questions people have already asked. You will learn R more quickly if you try to solve problems yourself. We all need help at times.

+

If you need help with a problem in your labs, please ask your TA or your classmates. Don’t be the person who posts a question to a website like Stack Overflow for which the answers can be readily found on the internet via a simple search. Those kinds of questions will be ignored or will result in heaps of abuse. Some sites are more beginner-friendly than others. Your TAs will have some suggestions for you.

+

Whatever you do, please don’t post your assigned questions onto paid answer-sharing services. That’s a violation of the Community Standard as well as a very poor way to learn to use R.

+
+
+

Acknowledgements

+This is derived from the Learning R Tutorials website. Thanks to Joan Durso and Maria Tackett for all their help. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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