The goal of this lab is to introduce you to R and RStudio, which you'll be using throughout the course both to learn the statistical concepts discussed in the course and to analyze real data and come to informed conclusions. To straighten out which is which: R is the name of the programming language itself and RStudio is a convenient interface.
As the labs progress, you are encouraged to explore beyond what the labs dictate; a willingness to experiment will make you a much better programmer. Before we get to that stage, however, you need to build some basic fluency in R. Today we begin with the fundamental building blocks of R and RStudio: the interface, reading in data, and basic commands.
Your RStudio window has four panels.
Your R Markdown file (this document) is in the upper left panel.
The panel on the lower left is where the action happens. It's called the console. Everytime you launch RStudio, it will have the same text at the top of the console telling you the version of R that you're running. Below that information is the prompt. As its name suggests, this prompt is really a request, a request for a command. Initially, interacting with R is all about typing commands and interpreting the output. These commands and their syntax have evolved over decades (literally) and now provide what many users feel is a fairly natural way to access data and organize, describe, and invoke statistical computations.
The panel in the upper right contains your workspace as well as a history of the commands that you've previously entered.
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.
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:
statsr: for data files and functions used in this coursedplyr: for data wranglingggplot2: for data visualizationYou should have already installed these packages using commands like
install.packages and install_github.
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.
In [1]:
library(dplyr)
library(ggplot2)
library(statsr)
To do so, you can
Going forward you will be asked to load any relevant packages at the beginning of each lab.
To get you started, run the following command to load the data.
In [ ]:
data(arbuthnot)
To do so, once again, you can
This command instructs R to load some data. The Arbuthnot baptism counts for boys
and girls. You should see that the workspace area in the upper righthand corner of
the RStudio window now lists a data set called arbuthnot that has 82 observations
on 3 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.
The Arbuthnot data set refers to Dr. John Arbuthnot, an 18th century physician, writer, and mathematician. He was interested in the ratio of newborn boys to newborn girls, so he gathered the baptism records for children born in London for every year from 1629 to 1710. We can take a look at the data by typing its name into the console.
In [2]:
arbuthnot
Out[2]:
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 arbuthnot 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 four columns of numbers, each row representing a different year: the first entry in each row is simply the row number (an index we can use to access the data from individual years if we want), the second is the year, and the third and fourth are the numbers of boys and girls baptized that year, respectively. 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 Arbuthnot's 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 Arbuthnot's data in a kind of spreadsheet or table called a data frame.
You can see the dimensions of this data frame by typing:
In [3]:
dim(arbuthnot)
Out[3]:
This command should output [1] 82 3, indicating that there are 82 rows and 3
columns (we'll get to what the [1] means in a bit), just as it says next to
the object in your workspace. You can see the names of these columns (or
variables) by typing:
In [4]:
names(arbuthnot)
Out[4]:
You should see that the data frame contains the columns year, boys, and
girls. 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.
So far we asked you to type your commands in the console. The console is a great place for playing around with some 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! Going forward type the code for the questions in the code chunks provided in the R Markdown (Rmd) document for the lab, and Knit the document to see the results.
Let's start to examine the data a little more closely. We can access the data in a single column of a data frame separately using a command like
In [5]:
arbuthnot$boys
Out[5]:
This command will only show the number of boys baptized each year. The dollar sign basically says "go to the data frame that comes before me, and find the variable that comes after me".
In [6]:
# type your code for the Question 2 here, and Knit
arbuthnot$girls
Out[6]:
Notice that the way R has printed these data is different. When we looked at the complete data frame, we saw 82 rows, one on each line of the display. These data are no longer structured in a table with other variables, so they are displayed one right after another. Objects that print out in this way are called vectors; they represent a set of numbers. R has added numbers in [brackets] along the left side of the printout to indicate locations within the vector. For example, 5218 follows [1], indicating that 5218 is the first entry in the vector. And if [43] starts a line, then that would mean the first number on that line would represent the 43rd entry in the vector.
R has some powerful functions for making graphics. We can create a simple plot of the number of girls baptized per year with the command
In [7]:
ggplot(data = arbuthnot, aes(x = year, y = girls)) +
geom_point()
Before we review the code for this plot, let's summarize the trends we see in the data.
Back to the code... 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.
aesthetic
elements of the plot, e.g. the x and the y axes. + to specify the geometric
object for the plot. Since we want to scatterplot, we use geom_point.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. Try the following in
your console:
In [8]:
?ggplot
Out[8]:
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.
Now, suppose we want to plot the total number of baptisms. To compute this, we could use the fact that R is really just a big calculator. We can type in mathematical expressions like
In [9]:
5218 + 4683
Out[9]:
to see the total number of baptisms in 1629. We could repeat this once for each year, but there is a faster way. If we add the vector for baptisms for boys to that of girls, R will compute all sums simultaneously.
In [10]:
arbuthnot$boys + arbuthnot$girls
Out[10]:
What you will see are 82 numbers (in that packed display, because we aren't looking at a data frame here), each one representing the sum we're after. Take a look at a few of them and verify that they are right.
We'll be using this new vector to generate some plots, so we'll want to save it as a permanent column in our data frame.
In [11]:
arbuthnot <- arbuthnot %>%
mutate(total = boys + girls)
What in the world is going on here? The %>% operator is called the piping
operator. Basically, it takes the output of the current line and pipes it into
the following line of code.
You'll see that there is now a new column called total that has been tacked on
to the data frame. The special symbol <- performs an assignment, taking the
output of one line of code and saving it into an object in your workspace. In
this case, you already have an object called arbuthnot, so this command updates
that data set with the new mutated column.
We can make a plot of the total number of baptisms per year with the following command.
In [12]:
ggplot(data = arbuthnot, aes(x = year, y = total)) +
geom_line()
Note that using geom_line() instead of geom_point() results in a line plot instead
of a scatter plot. You want both? Just layer them on:
In [13]:
ggplot(data = arbuthnot, aes(x = year, y = total)) +
geom_line() +
geom_point()
In [14]:
# type your code for the Exercise here, and Knit
ggplot(data = arbuthnot, aes(x = year, y = boys / total)) +
geom_line() +
geom_point()
Finally, in addition to simple mathematical operators like subtraction and
division, you can ask R to make comparisons like greater than, >, less than,
<, and equality, ==. For example, we can ask if boys outnumber girls in each
year with the expression
In [15]:
arbuthnot <- arbuthnot %>%
mutate(more_boys = boys > girls)
In [19]:
arbuthnot$more_boys
Out[19]:
This command add a new variable to the arbuthnot data frame containing the values
of either TRUE if that year had more boys than girls, or FALSE if that year
did not (the answer may surprise you). This variable contains different kind of
data than we have considered so far. All other columns in the arbuthnot data
frame have values are numerical (the year, the number of boys and girls). Here,
we've asked R to create logical data, data where the values are either TRUE
or FALSE. In general, data analysis will involve many different kinds of data
types, and one reason for using R is that it is able to represent and compute
with many of them.
In the previous few pages, you recreated some of the displays and preliminary analysis of Arbuthnot's baptism data. Next you will do a similar analysis, but for present day birth records in the United States. Load up the present day data with the following command.
In [20]:
data(present)
The data are stored in a data frame called present which should now be loaded in
your workspace.
4
In [21]:
# type your code for Question 4 here, and Knit
str(present)
In [22]:
# type your code for Exercise here, and Knit
summary(present)
Out[22]:
In [23]:
range(present$year)
Out[23]:
5
variable called total in the present dataset. Then, calculate the proportion of
boys born each year and store these values in a new variable called prop_boys in
the same dataset. Plot these values over time and based on the plot determine if the
following statement is true or false: The proportion of boys born in the US has
decreased over time.
In [32]:
ggplot(data = present, aes(x = year, y = boys / total)) +
geom_line() +
geom_point()
In [24]:
# type your code for Question 5 here, and Knit
present$total = present$boys + present$girls
6
more_boys which contains the value of either TRUE
if that year had more boys than girls, or FALSE if that year did not. Based on this
variable which of the following statements is true?
In [25]:
# type your code for Question 6 here, and Knit
present$more_boys = present$boys > present$girls
7
prop_boy_girl in the present dataset. Plot these values over time. Which of the following best describes the trend?
In [34]:
sum(present$more_boys)/nrow(present)
Out[34]:
In [26]:
# type your code for Question 7 here, and Knit
present$prop_boy_girl = present$boys / present$girls
In [35]:
ggplot(data = present, aes(x = year, y = prop_boy_girl)) +
geom_line() +
geom_point()
8
total column. You can do this
interactively in the data viewer by clicking on the arrows next to the variable
names. Or to arrange the data in a descenting order with new function: descr (for
descending order).
In [31]:
# type your code for Question 8 here
# sample code is provided below, edit as necessary, uncomment, and then Knit
present %>% arrange(desc(total))
Out[31]:
That was a short introduction to R and RStudio, but we will provide you with more functions and a more complete sense of the language as the course progresses. You might find the following tips and resources helpful.
In this course we will be using the 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".
The following cheathseets may come in handy throughout the course. Note that some of the code on these cheatsheets may be too advanced for this course, however majority of it will become useful as you progress through the course material.
While you will get plenty of exercise working with these packages in the labs of this course, if you would like further opportunities to practice we recommend checking out the relevant courses at DataCamp.