---
title: "Introduction to R and RStudio"
output:
  html_document:
    css: ../lab.css
    highlight: pygments
    theme: cerulean
    toc: true
    toc_float: true
---

```{r global_options, include=FALSE}
knitr::opts_chunk$set(eval = FALSE)
library(dplyr)
library(ggplot2)
library(oilabs)
```


## The RStudio Interface

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 clarify
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.

Go ahead and launch RStudio. You should see a window that looks like the image 
shown below.

![](more/r-interface-2016.png)
<br>


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 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:

- `dplyr`: for data wrangling
- `ggplot2`: for data visualization
- `oilabs`: for data and custom functions with the OpenIntro labs

If these packages are not already available in your R environment, 
install them by typing the following three lines of code into
the console of your RStudio session, pressing the enter/return key after each one. 
Note that you can check to see which packages (and which versions) are installed by
inspecting the *Packages* tab in the lower right panel of RStudio. 

```{r install-packages, message = FALSE, eval=FALSE}
install.packages("dplyr")
install.packages("ggplot2")
install.packages("oilabs")
```

You may need to select a server from which to download; any of them will work.
Next, you need to load these packages in your working environment. We do this with
the `library` function. Run the following three lines in your console.

```{r load-packages, message = FALSE, eval=TRUE}
library(dplyr)
library(ggplot2)
library(oilabs)
```

Note that you only need to *install* packages once, but you need to *load* 
them each time you relaunch RStudio. 


### Creating a reproducible lab report

We will be using R Markdown to create reproducible lab reports. See the
following videos describing why and how:

[**Why use R Markdown for Lab Reports?**](https://youtu.be/lNWVQ2oxNho)
<iframe width="560" height="315" src="https://www.youtube.com/embed/lNWVQ2oxNho" frameborder="0" allowfullscreen></iframe>

[**Using R Markdown for Lab Reports in RStudio**](https://youtu.be/o0h-eVABe9M)
<iframe width="560" height="315" src="https://www.youtube.com/embed/o0h-eVABe9M" frameborder="0" allowfullscreen></iframe>

Going forward you should refrain from typing your code directly in the console, and
instead type any code (final correct answer, or anything you're just trying out) in
the R Markdown file and run the chunk using either the Run button on the chunk 
(green sideways triangle) or by highlighting the code and clicking Run on the top 
right corner of the R Markdown editor. If at any point you need to start over, you 
can Run All Chunks above the chunk you're working in by clicking on the down
arrow in the code chunk.

## Dr. Arbuthnot's Baptism Records

To get you started, run the following command to load the data.

```{r load-abrbuthnot-data, eval=TRUE}
data(arbuthnot)
```

You can do this by

- clicking on the green arrow at the top right of the code chunk in the R Markdown (Rmd) 
file, or
- putting your cursor on this line, and hit the **Run** button on the upper right 
corner of the pane, or
- hitting `Ctrl-Shift-Enter`, or
- typing the code in the console.

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 18<sup>th</sup> 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 view the data by 
typing its name into the console.

```{r view-data}
arbuthnot
```

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 as well as the names of the variables and the first few observations by typing:

```{r glimpse-data}
glimpse(arbuthnot)
```

This command should output the following

```{r glimpse-data-result, echo=FALSE, eval=TRUE}
glimpse(arbuthnot)
```

We can see that there are 82 observations and 3 variables in this dataset. The variable names are `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 `glimpse` command, for example, took a single argument, the name of a data frame. 

## Some Exploration

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

```{r view-boys}
arbuthnot$boys
```

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".

1. What command would you use to extract just the counts of girls baptized? Try
    it!
    
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.


### Data visualization

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

```{r plot-girls-vs-year}
qplot(x = year, y = girls, data = arbuthnot)
```

The `qplot()` function (meaning "quick plot") considers the type of data you have
provided it and makes the decision to visualize it with a scatterplot. The plot
should 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 three arguments
separated by commas. The first two arguments in the `qplot()` function specify 
the variables for the x-axis and the y-axis and the third provides the name of the
data set where they can be found. If we wanted to connect the data points with 
lines, we could add a fourth argument to specify the geometry that we'd like.

```{r plot-girls-vs-year-line}
qplot(x = year, y = girls, data = arbuthnot, geom = "line")
```

You might wonder how you are supposed to know that it was possible to add that 
fourth argument. 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.

```{r plot-help, tidy = FALSE}
?qplot
```

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.

2. Is there an apparent trend in the number of girls baptized over the years? 
How would you describe it? (To ensure that your lab report is comprehensive,
be sure to include the code needed to make the plot as well as your written
interpretation.)

### R as a big calculator

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

```{r calc-total-bapt-numbers}
5218 + 4683
```

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.

```{r calc-total-bapt-vars}
arbuthnot$boys + arbuthnot$girls
```

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.

### Adding a new variable to the data frame

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.

```{r calc-total-bapt-vars-save}
arbuthnot <- arbuthnot %>%
  mutate(total = boys + girls)
```

The `%>%` operator is called the **piping** 
operator. It takes the output of the previous expression and pipes it into 
the first argument of the function in the following one. 
To continue our analogy with mathematical functions, `x %>% f(y)` is 
equivalent to `f(x, y)`.

<div id="boxedtext">
**A note on piping: ** Note that we can read these three lines of code as the following: 

*"Take the `arbuthnot` dataset and **pipe** it into the `mutate` function. 
Mutate the `arbuthnot` data set by creating a new variable called `total` that is the sum of the variables
called `boys` and `girls`. Then assign the resulting dataset to the object
called `arbuthnot`, i.e. overwrite the old `arbuthnot` dataset with the new one
containing the new variable."*

This is equivalent to going through each row and adding up the `boys` 
and `girls` counts for that year and recording that value in a new column called
`total`.
</div>

<div id="boxedtext">
**Where is the new variable? ** When you make changes to variables in your dataset, 
click on the name of the dataset again to update it in the data viewer.
</div>

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 command

```{r plot-total-vs-year}
qplot(x = year, y = total, data = arbuthnot, geom = "line")
```

Similarly to how we computed the total number of births, we can compute the ratio 
of the number of boys to the number of girls baptized in 1629 with

```{r calc-prop-boys-to-girls-numbers}
5218 / 4683
```

or we can act on the complete columns with the expression

```{r calc-prop-boys-to-girls-vars}
arbuthnot <- arbuthnot %>%
  mutate(boy_to_girl_ratio = boys / girls)
```

We can also compute the proportion of newborns that are boys in 1629

```{r calc-prop-boys-numbers}
5218 / (5218 + 4683)
```

or this may also be computed for all years simultaneously and append it to the dataset:

```{r calc-prop-boys-vars}
arbuthnot <- arbuthnot %>%
  mutate(boy_ratio = boys / total)
```

Note that we are using the new `total` variable we created earlier in our calculations.

3. Now, generate a plot of the proportion of boys born over time. What do you see? 

<div id="boxedtext">
**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.
</div>

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

```{r boys-more-than-girls}
arbuthnot <- arbuthnot %>%
  mutate(more_boys = boys > girls)
```

This command add a new variable to the `arbuthnot` dataframe 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 a different kind of 
data than we have encountered so far. All other columns in the `arbuthnot` data 
frame have values that 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.

* * *

## More Practice

In the previous few pages, you recreated some of the displays and preliminary 
analysis of Arbuthnot's baptism data. Your assignment involves repeating these 
steps, but for present day birth records in the United States. Load the 
present day data with the following command.

```{r load-present-data}
data(present)
```

The data are stored in a data frame called `present`.

4.  What years are included in this data set? What are the dimensions of the 
    data frame? What are the variable (column) names?

5.  How do these counts compare to Arbuthnot's? Are they of a similar magnitude?

6.  Make a plot that displays the proportion of boys born over time. What do you see? 
    Does Arbuthnot's observation about boys being born in greater proportion than girls 
    hold up in the U.S.? Include the plot in your response. *Hint:* You should be
    able to reuse your code from Ex 3 above, just replace the dataframe name.

7.  In what year did we see the most total number of births in the U.S.? *Hint:*
    First calculate the totals and save it as a new variable. Then, sort your
    dataset in descending order based on the total column. You can do this
    interactively in the data viewer by clicking on the arrows next to the 
    variable names. To include the sorted result in your report you will need 
    to use two new functions: `arrange` (for sorting). We can arrange the data 
    in a descending order with another function: `desc` (for descending order).
    Sample code provided below.
    
```{r eval=FALSE}
present %>%
  arrange(desc(total))
```

These data come from reports by the Centers for Disease Control. You can learn more about them
by bringing up the help file using the command `?present`.


<div id="license">
This is a product of OpenIntro that is released under a 
[Creative Commons Attribution-ShareAlike 3.0 Unported](http://creativecommons.org/licenses/by-sa/3.0). 
This lab was adapted for OpenIntro by Andrew Bray and Mine &Ccedil;etinkaya-Rundel
from a lab written by Mark Hansen of UCLA Statistics.
</div>

* * *

## Resources for learning R and working in RStudio

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. 

In this course we will be using R packages called `dplyr` for data wrangling 
and `ggplot2` for data visualization. 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".

These cheatsheets may come in handy throughout the semester:

- [RMarkdown cheatsheet](http://www.rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf)
- [Data wrangling cheatsheet](http://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf)
- [Data visualization cheatsheet](http://www.rstudio.com/wp-content/uploads/2015/12/ggplot2-cheatsheet-2.0.pdf)

Chester Ismay has put together a resource for new users of R, RStudio, and R Markdown
[here](https://ismayc.github.io/rbasics-book).  It includes examples showing working with R Markdown files
in RStudio recorded as GIFs.

Note that some of the code on these cheatsheets may be too advanced for this course,
however majority of it will become useful throughout the semester.
