part1.Rmd

---
title: "Part 1: Introduction"
output:
  html_document:
    df_print: paged
editor_options:
  chunk_output_type: console
---

This file has notes for the morning session of Day 1 of the workshop.

# Welcome

* Introduction

* The morning sessions are being recorded.  Videos off and muted.  Use chat for questions.
  * I will stop if you should type or try something.  (leave sound on so you know when to come back)
* Between sessions: independent work.  Use discussion boards on Canvas for questions (please do!)
  * Materials from people other than me to read and work through.
  * Exercises in the github repository.  
    * Some exercises have refreshers or mini-lessons at the top.  If you think there's something in an exercise though that wasn't covered in the day's material, please do let me know so I can fix that for the future.
* The afternoon sessions will not be recorded.  Can unmute to ask questions or use chat.  Think: office hours, so bring your questions.

# RStudio Tour Notes

* Can run R without R Studio
* Open project files
* RStudio Panes
  * Console
  * Lower right:
    * Files: exercise files
    * More > Go to Working Directory
    * Plots, Viewer output
    * Packages: install button, check boxes next to names, installing
      * Is tidyverse installed?
      * Install praise
      * **EXERCISE:** Open R Studio and install the praise package
    * Help
  * Upper right:
    * Environment
    * History
  * New File
    * File Types

* Type in Console
    * working directory: getwd()
    * Type commands @ prompt, get output
    * Getting stuck with + - esc (twice), Control-C
    * up arrow for previous commands
    * R as a calculator
      * () for grouping `(3 + 1)^2 * 5`
    * Call a function: 
      * Functions have a name
      * They optionally take one or more input values (arguments)
      * They optionally return a value (or more complicated object) or do something
      * `log(16)`, `round(2.1)`, `round(pi, 3)` - pi is built in value
      * Can nest function calls: `round(log(16))`, `round(log(16), 2)`
    * R is a programming language
    * assign a value: `x <- 3` >> shows up in environment
      * right hand side evaluated first: `x <- x + 1`
    * case sensitive
    * Spaces: mostly don't matter (matter in operators, names): `x < - 4`
    * **EXERCISE**: 
      * Create a variable x with a numerical value of your choice
      * Multiply x by 3 and save the value back to x
      * Take the log of x
      * Subtract 4 from the log of x
      * Square the above
    * Comparing Values: > == <= !
* Create a code file
    * comment character
    * library commands at top
* Send code from file to console (MAC: command + return, PC: Ctrl + return)
    * Cursor
      * Send a comment line and it sends the line below
      * Sends a full command, even over multiple lines
      * Cursor advances
    * Highlighted region
* **EXERCISE**: 
  * Open a new R Script file
  * Type a comment at the top
  * load the praise library
  * Call the praise() function
  * Run the code in your file
  * Save the file


# Vectors

Vectors hold multiple values (all of the same type).  Create a vector with the `c()` function:
```{r}
x <- c(1, 4, 6, 3, 4, 2, 3, 4)
x
```

Access elements of the vector using an index for the position.  First element is at position 1.
```{r}
x[1]
```

Range of values:
```{r}
x[1:3]
```

Some functions take vectors as input and operate on each element.
```{r}
log(x)
```

Functions don't (generally) change their input values:
```{r}
x  # still the same as before
```

Other functions take vectors as input and do something with the whole vector.
```{r}
sum(x)
```

```{r}
table(x)
```

With a table, the top line is the unique values, and the bottom line is the counts of each value.

We can use vectors in comparsions

```{r}
x > 3
```

TRUE and FALSE are important, because we can use them to select values from vectors

```{r}
x[x > 3]
```

You'll learn more about vectors in your independent work and exercises.

**EXERCISE**: In the console or an open R Script file, make a vector called fav_numbers and put 5 numbers in it.  Then write an expression to select just the 4th value from that vector.  Hint: remember the `c()` function.

```{r}

```


# Missing Values

`NA` is the symbol for missing data; it can be used with any data type
```{r}
NA  
y <- c("dog", "cat", NA, NA, "bird")
x <- c(NA, 2, NA, 4, NA, 6, NA, 6, 6, 4)
is.na(x)  
```

```{r}
sum(c(TRUE, FALSE))
sum(is.na(x))  
```


# Coming Up

You'll learn more about working with vectors.  You'll also learn about factors today, which is how R stores and helps you work with categorical variables.  Factors can be a bit tricky, so I expect there will be some questions about them in the afternoon session.  

Remember: ask questions on the Canvas discussion board, and bring questions about any of the material to the afternoon session.  We'll start the afternoon session with a short quiz and then open it up for questions or topics that you'd like me to review.  We can also talk through exercise answers.