1_Getting_used_to_R.qmd

---
title: "Getting used to R"
---

<!-- COMMENT NOT SHOW IN ANY OUTPUT: Code chunk below sets overall defaults for .qmd file; these inlcude showing output by default and looking for files relative to .Rpoj file, not .qmd file, which makes putting filesin different folders easier  -->

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
```

### Overview

The focus of this overview is to get you used to tools we will be using
in class. Before completing it you should have a basic understanding of
using R. We will do an introduction in class [(download help
file)](../primer_materials/R_primer.R). You should also be comfortable
using Rstudio and github [(see help
file)](../primer_materials/tools_overview.qmd){target="_blank"}.

#### qmd basics

qmd files differ from R files in that they combine regular text with
code chunks. This is a code chunk

```{r}
print("this is a chunk")
```

Code chunks combine code with output. When combined with regular
text/prose, this makes it easier to produce a range of documents. You
set the output in the YAML header (the stuff between the 3 dashes you
see at top of this document).

After you write the file, you **Render** it to turn the qmd file into
the selected output. Try it now. Note the first time you do this in a
project you may be prompted to install a number of packages! If you are
using a webservice you may also need to allow pop-ups in your browser.
Don't be surprised if a new window pops up (it should).

**Note**: qmd is a newer form of rmd. Rmd files used to be **knit**.
Once qmd files were established, both those and Rmd files were rendered.
Some files in the book are still rmd but are being transitioned over
time. This has little impact on the user.

![The render button turns your .qmd file into other
products](/images/render.png)

The **Render** button saves the .qmd file and renders a new version
whose output depends on what you selected in the header. Here we have
html_document, so if everything works a preview of a webpage like
document should appear. The file also produces a github friendly .md
file. **This means you should only edit the qmd file! Everything else is
automatically produced and any changes you make will be overwritten by
your next render**.

When you **Render** a file, it runs in a totally new R instance. this
means anything you only added in your instance (like working in the
console) won't be available. In other words, its the best way to see
what a "new" user gets when they use your code.

However, you can also work with the file interactively. To do this,
press the green button next to an R chunk.

![The green arrows just runs the chunk in the console and shows the
output](https://lh3.googleusercontent.com/pw/AM-JKLUYgHbhk7YzhXdAZwV-fvLFlnOc4IcCMwt6U21qsHP7sXcjQ5xDL86NewZo2THSGAveP0Y1cL2PP4yysUTLn4N6iXoO6B1h_8RtAlqmNONY2W5V_j_4hqtQ8d3GhroTNJewT3oEqSVA-Vjh4IkDRqE-pw=w784-h73-no?authuser=0)

```{r}
print("this is a chunk")
```

Now we'll start changing the file to show you how rmarkdown works.
First, amend the file by replacing the **NAME** and **DATE** spots in
the header (top of the file between the --- markers) with your name and
the real date. Then **Knit** the file again. You should see your name in
the new preview.

Rstudio has a **Markdown Quick Reference** guide (look under the help
tab), but some general notes.

-   Pound/Hashtag signs denote headers
-   you can surround something double asterisks for bold or single
    asterisks for italics
-   lists are denoted by numbers or asterisks at beginning of line
    (followed by space!)
    -   and can be indented for sublevels
-   R code can be done inline, but is generally placed in stand-alone
    chunks
    -   these will, by default, show the code and output
-   lots of other options exist!

You can also switch to visual mode to more easily work with formatting.

![Selecting visual mode will show you what your markdown code actually
looks like](/images/visual_mode.png)

The main idea is qmd files allow you to combine code, text, graphs, etc
into multiple outputs that you can share (including with coding
illiterate colleagues who just want output).

To practice working with qmd files and R, work through the questions
below. You can also get more help with [this
video](https://www.youtube.com/watch?v=shs95EH4EhY&list=PLmnVhyQ-20EUFRmsjpYTyB5--zyolr6-o&index=9&t=24s){target="_blank"}

### Practice in R

#### 1

Let x be defined by

```{r}
x <- 5:15
```

Try executing this chunk (in R studio, not the webview) by clicking the
*Run* button within the chunk or by placing your cursor inside it and
pressing *Ctrl+Shift+Enter*.

This will run the code in the Console. You may need to switch to Console
(from Rmarkdown) in the lower right window area to see this. The
executed code is also displayed in your processed file (hit **Knit**
again to see this!).

Note running this chunk has added an object named `x` to the
**Environment** tab area (top right area of screen). But nothing was
"returned" in the console. You prove this by typing `x` in the console.
What does it return?

Determine what the ":" does! Complete the following sentence:

The : means FILL THIS IN.

#### 2

Now try to guess the output of these commands

-   length(x)
-   max(x)
-   x\[x \< 10\]
-   x\^2
-   x\[ x \< 12 & x \> 7\]

INSERT AN R CHUNK HERE AND RUN EACH OF THESE COMMANDS. Add a new chunk
by clicking the *Insert Chunk* button on the toolbar or by pressing
*Ctrl+Alt+I*. Then state what each of these does.

#### 3

Is `-1:2` the same as `(-1):2` or `-(1:2)`? INSERT AN R CHUNK HERE AND
RUN EACH OF THESE COMMANDS. Then state what each of these does.

### Data input, plotting, and tests

You can read in a dataset from the internet following this protocol.

```{r}
sleep <- read.csv("http://raw.githubusercontent.com/jsgosnell/CUNY-BioStats/master/datasets/sleep.csv", stringsAsFactors = T)
```

Run this chunk and note it has added an object named `sleep` to the
environment.

Info on the dataset is viewable \@
<http://www.statsci.org/data/general/sleep.html>.

#### 4

How many rows does the **sleep** data set have (hint: `?dim`)? What kind
of data is stored in each variable?

ENTER ANSWERS HERE. ADD ANY R CHUNKS YOU USED TO FIND THE ANSWER.

#### 5

Change the column named *BodyWt* to *Body_weight*"\* in the sleep
dataset.

ADD ANY R CHUNKS YOU USED TO COMPLETE THE TASK.

#### 6

Produce a plot of how *TotalSleep* differs between primates and other
species. What is this plot showing?

Note, as of early 2020 R no longer reads in strings as factors! This
means the Primate column, which is full of "Yes"s and "No"s, reads in as
words and R doesn't know how to plot them. There are many ways to handle
this. You can modify the read.csv command (add stringsAsFactors = T
option), eg

```{r, eval=F}
sleep <- read.csv("http://raw.githubusercontent.com/jsgosnell/CUNY-BioStats/master/datasets/sleep.csv", stringsAsFactors = T)
```

If you do this, you'll need to rechange anything you previously updated
to the object (like renaming the BodyWt column).

You can also modify a single column for the actual object

```{r, eval = F}
sleep$Primate <- factor (sleep$Primate)
```

or for a single command, eg (plot not actually shown!)

```{r, eval = F}
plot(BodyWt ~ factor(Primate), data = sleep)
```

NOTE YOU CAN ADD A PLOT TO THE DOCUMENT TOO! AMEND THE BELOW AS NEEDED.

```{r}
plot(cars)
```

#### 7

The **sleep** dataset begs to have a linear model fit for it. Let's
consider. First plot how *TotalSleep* is explained by *BrainWt*. Are
there any issues with the data? Exclude any outlier and fit a linear
model to obtain the p-value for the model (hint: summary()). What does
this imply?

ENTER ANSWERS HERE. ADD ANY R CHUNKS YOU USED TO FIND THE ANSWER.

### EXTRA QUESTIONS

*not required*

![alt
text](https://upload.wikimedia.org/wikipedia/commons/9/94/Puffin_Mrkoww.jpg)
**Dow Puffin** *Matthew Zalewski / CC BY
(<https://creativecommons.org/licenses/by/3.0>)*

#### 8

Sometimes data doesn't have headers (column names),so you have to add
them. Download a dataset on alcids (birds like puffins and auklets) from
<https://raw.githubusercontent.com/jsgosnell/CUNY-BioStats/master/datasets/alcids55.csv>.\
You'll need to modify the read.csv function by specifying
`header = False`, then use the `names` function to name the columns
\["year", "a1_abund", "NAO", "a2_abund", "a3_abund", "a4_abund",
"a5_abund", "a6_abund"\]. Try it and check your input using the `head`
command.

ENTER ANSWERS HERE. ADD ANY R CHUNKS YOU USED TO FIND THE ANSWER.

#### 9

Here's a sample dataset:

| Date       | greenness | Richness | habitat   |
|------------|-----------|----------|-----------|
| 12-25-2009 | 13766     | 46       | forest    |
| 01-01-2010 | 50513     | 60       | forest    |
| 01-15-2010 | 25084     | 60       | grassland |

Enter it into R (manually or via a .csv). (Hint: you have a piece of
this in the code already). Check your input using the head() command.

ENTER ANSWERS HERE. ADD ANY R CHUNKS YOU USED TO FIND THE ANSWER.