1.2 — Meet R

# 1.2 — Meet R
## ECON 480 • Econometrics • Fall 2020
### Ryan Safner Assistant Professor of Economics <a href="mailto:safner@hood.edu">safner@hood.edu</a> <a href="https://github.com/ryansafner/metricsF20">ryansafner/metricsF20</a> <a href="https://metricsF20.classes.ryansafner.com"> metricsF20.classes.ryansafner.com</a>

---

# Outline

## [Meet R and R Studio](#9)

## [Ways to Use R](#16)

## [Coding Basics](#22)

## [Types of R Objects](#40)

## [Data Structures](#45)

## [Working with Objects](#49)

## [Working with Data](#66)

---

# Data Science

- .hi[You go into data analysis with the tools you know, not the tools you need]

- The next 2-3 weeks are all about giving you the tools you need 
    - Admittedly, a bit before you know what you need them *for*

- We will extend them as we learn specific models

---

# Why Not Excel? I

---

# Why Not Excel? II

---

# Why Use R?

- **Free** and **open source**

- A very large community
    - Written by statisticians for statistics
    - Most packages are written for `R` first
    
- Can handle virtually any data format

- Makes replication easy

- Can integrate into documents (with `R markdown`)

- R is a *language* so it can do *everything*
    - A good stepping stone to learning other languages like *Python*

]

]

---

# Excel and Stata Can't Do This (In Slides)

```r
library("gapminder")

ggplot(data = gapminder, 
       aes(x = gdpPercap,
           y = lifeExp,
           color = continent))+
  geom_point(alpha=0.3)+
  geom_smooth(method = "lm")+
	scale_x_log10(breaks=c(1000,10000, 100000),
	              label=scales::dollar)+
	labs(x = "GDP/Capita",
	     y = "Life Expectancy (Years)")+
  facet_wrap(~continent)+
  guides(color = F)+
  theme_light()
```
]

.right-plot[
.center[
<img src="1.2-slides_files/figure-html/unnamed-chunk-1-1.png" width="72.5%" />
]
]

---

# Or This: Execute R Code Inside Your Documents

```r
  library(gapminder)
  ```

The average GDP per capita is $`` `r
round(mean(gapminder$gdpPercap),2)` `` with a standard deviation of $`` `r
round(sd(gapminder$gdpPercap),2)` ``.  
]

The average GDP per capita is $7215.33 with a standard deviation of $9857.45.

]

---

---

# *R* and *R Studio* I

- .hi-purple[R Studio] is an integrated development environment (IDE) that makes your coding life a lot easier
  - Write code in scripts
  - Execute individual commands or entire scripts
  - Auto-complete, highlight syntax
  - View data, objects, and plots
  - Get help and documentation on commands and functions
  - Integrate code into documents with `R Markdown`
]
]

---

# *R* and *R Studio* II

- .hi[R] is like your car's engine, .hi-purple[R Studio] is the dashboard

- You will do everything in .hi-purple[R Studio]

- .hi[R] itself is just a command language (you could run it in your computer's shell/terminal/command prompt)
]

---

# *R* and *R Studio* III

R Studio has 4 window panes:

1. .hi-purple[Source].magenta[†]: a text editor for documents, R scripts, etc.
2. .hi-purple[Console]: type in commands to run
3. .hi-purple[Browser]: view files, plots, help, etc
4. .hi-purple[Environment]: view created objects, command history, version control

]

---

# Learning...

- .hi[You don't “*learn R*”, you learn *how to do things in R*]

- In order to do learn this, you need to learn *how to search for what you want to do*

]

<blockquote class="twitter-tweet">My <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> learning path: 1. Install R 2. Install RStudio 3. Google &quot;How do I [THING I WANT TO DO] in R?&quot; Repeat step 3 ad infinitum.&mdash; Jesse Mostipak (@kierisi) <a href="https://twitter.com/kierisi/status/898534740051062785?ref_src=twsrc%5Etfw">August 18, 2017</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

<blockquote class="twitter-tweet">A surprisingly large part of having expertise in a topic is not so much knowing everything about it but learning the language and sources well enough to be extremely efficient in google searches.&mdash; Katie Mack (@AstroKatie) <a href="https://twitter.com/AstroKatie/status/1071442842873159681?ref_src=twsrc%5Etfw">December 8, 2018</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

]

---

# ...and Sucking

---

---

# 1. Using the Console

- Type individual commands into the console window

- Great for testing individual commands to see what happens

- Not saved! Not reproducible! Not recommended!
]

```r
2+2
```

```
## [1] 4
```

```r
summary(mpg$hwy)
```

```
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   12.00   18.00   24.00   23.44   27.00   44.00
```
]

---

# 1. Using the Console

- Type individual commands into the console window

- Great for testing individual commands to see what happens

- Not saved! Not reproducible! Not recommended!
]

---

# 2. Writing an R Script

- Source pane is a text-editor

- Make `.R` files: all input commands in a single script

- Comment with `#`

- Can run any or all of script at once

- Can save, reproduce, and send to others!

]

---

# 3. Using Markdown

- A later lecture: `R Markdown`, a simple markup language to write documents in
    - **Optional**, but many students have enjoyed it and use it well beyond this class!
    
- Can integrate text, `R` code, figures, citations & bibliographies in a *single* plain-text file & output into a variety of formats: PDF, webpage, slides, Word doc, etc.

---

# For Today

- Practicing typing at the Command line/Console

- Learning different commands and objects relevant for data analysis

- Saving and running `.R` scripts

- Later: `R markdown`, literate programming, workflow management

- .hi-purple[Today may seem a bit overwhelming]
    - You don't need to know or internalize all of this today
    - Use this as a reference to come back to over the semester

---

---

# Getting to Know Your Computer

- R assumes a default (often inconvenient) .hi["working directory"] on your computer
    - The first place it looks to `open` or `save` files

- Find out where R this is with `getwd()`

- Change it with `setwd(path/to/folder)`.magenta[†]

- Soon I'll show you better ways where you won't ever have to worry about this

.footnote[.magenta[†] Note the path is OS-specific. For Windows it might be `C:/Documents/`. For Mac it is often your username folder.]

---

# Coding

Hadley Wickham

Chief Scientist, R Studio
]
]

> "There’s an implied contract between you and R: it will do the tedious computation for you, but in return, you must be completely precise in your instructions. Typos matter. Case matters." - [R for Data Science, Ch. 4](https://r4ds.had.co.nz/workflow-basics.html)
]

---

# Say Hello to My Little Friend

---

# Say Hello to My Better Friend

---

# R Is Helpful Too!

- type `help(function_name)` or `?(function_name)` to get documentation on a function
.center[

From Kieran Healy's excellent (free online!) [book on Data Visualization](http://socviz.co/).]

![:scale 50%](http://socviz.co/assets/ch-09-read-a-help-page.png)

]
---

# Tips for Writing Code

- Comment, comment, comment!
- The hashtag `#` starts a comment, R will ignore everything on the rest of that line

```r
# Run regression of y on x, save as reg1 
reg1<-lm(y~x, data=data) #runs regression 
summary(reg1$coefficients) #prints coefficients
```

- Save often!
  - Write scripts that save the commands that did what you wanted (and comment them!)
  - Better yet, use a version control system like Git (I hope to cover this later)

---

# Style and Naming

.smallest[
- Once we start writing longer blocks of code, it helps to have a consistent (and human-readable!) style
- I follow [this style guide](https://adv-r.had.co.nz/Style.html) (you are not required to).magenta[†]

- Naming objects and files will become important.magenta[‡]
 - DO NOT USE SPACES! You've seen seen webpages intended to be called `my webpage in html` turned into `http://my%20webpage%20in%20html.html`

```r
i_use_underscores
some.people.use.snake.case
othersUseCamelCase
```
]

.footnote[.magenta[†] Also described in [today's course notes page](http://metricsF20.classes.ryansafner.com/class/1.2-class/) and the course [reference page](http://metricsF20.classes.ryansafner.com/reference).

---

# Coding Basics

- You'll have to get used to the fact that you are coding in commands to execute

- Start with the easiest: simple math operators and calculations:

```r
> 2+2
```

```
## [1] 4
```

- Note that R will ask for **input** with `>` and give you **output** starting with `## [1]`

---

# Coding Basics II

- We can start using more fancy commands

```r
2^3
```

```
## [1] 8
```

```r
sqrt(25)
```

```
## [1] 5
```

```r
log(6)
```

```
## [1] 1.791759
```

```r
pi/2
```

```
## [1] 1.570796
```

---

# Packages

.pull-left[
.smallest[
- Since R is open source, users contribute **packages**
 - Really it's just users writing custom functions and saving them for others to use
- Load packages with `library()`
 - e.g. `library("package_name")`
- If you don't have a package, you must first `install.packages()`.magenta[†] 
 - e.g. `install.packages("package_name")`
]
.footnote[.magenta[†] Yes, note the plural, even if it's just for one package!]

]
.pull-right[

]

---

# R: Objects and Functions

1. creating `objects`
 - assign values to an object with `=` (or `<-`)

2. running `functions` on `objects`
    - syntax: `function_name(object_name)`
]

```r
# make an object
my_object = -c(1,2,3,4,5)

# look at it 
my_object
```

```
## [1] -1 -2 -3 -4 -5
```

```r
# find the sum
sum(my_object)
```

```
## [1] -15
```

```r
# find the mean 
mean(my_object)
```

```
## [1] -3
```
]
---

# R: Objects and Functions II

- Some functions may have multiple inputs

- The argument of a function can be another function!

]

```r
# find the sd
sd(my_object)
```

```
## [1] 1.581139
```

```r
# round everything in my object to two decimals
round(my_object,2)
```

```
## [1] -1 -2 -3 -4 -5
```

```r
# round the sd to two decimals
round(sd(my_object),2)
```

```
## [1] 1.58
```
]

---

# Types of R Objects

---

# Numeric

- Can be mathematically manipulated
]

```r
x = 2 
y = 3
x+y
```

```
## [1] 5
```

```r
x*y
```

```
## [1] 6
```
]
.footnote[.magenta[†] If you want to get technical, R may call these `integer` or `double` if there are decimal values.]

---

# Character

- Can contain spaces, so long as contained within quote marks
]

```r
name = "Ryan Safner"
address = "Washington D.C."

name
```

```
## [1] "Ryan Safner"
```

```r
address
```

```
## [1] "Washington D.C."
```
]

---

# Logical

.pull-left[
.smallest[
- .hi[Logical] objects are **binary** `TRUE` or `FALSE` indicators
- Used a lot to evaluate *conditionals*:
 - `>`, `<`: greater than, less than
 - `>=`, `<=`: greater than or equal to, less than or equal to
 - `==`, `!=`: is equal to, is not equal to.magenta[†]
 - `&in&` : is a member of the set of `$(\in)$`
 - `&`: "AND"
 - `|`: "OR" 
]

Two `==` *evaluate* a conditional statement.]

]

```r
z = 10 # set z equal to 10

z==10 # is z equal to 10?
```

```
## [1] TRUE
```

```r
"red"=="blue" # is red equal to blue?
```

```
## [1] FALSE
```

```r
z > 1 & z < 12 # is z > 1 AND < 12?
```

```
## [1] TRUE
```

```r
z <= 1 | z==10 # is z >= 1 OR equal to 10?
```

```
## [1] TRUE
```

]

---

# Factor

- .hi[Factor] objects contain **categorical** data - membership in mutually exclusive groups

- Look like strings, behave more like logicals, but with more than two options

```
##  [1] senior    junior    freshman  junior    freshman  sophomore junior   
##  [8] freshman  senior    junior   
## Levels: freshman sophomore junior senior
```

- We'll make much more extensive use of them later

```
## [1] senior junior freshman junior freshman sophomore junior 
## [8] freshman senior junior 
## Levels: freshman < sophomore < junior < senior
```

---

# Data Structures

---

# Vectors

- `Vector`: the simplest type of object, just a collection of objects

- Make a vector using the combine `c()` function
]

```r
# create a vector called vec
vec = c(1,"orange", 83.5, pi)

# look at vec
vec
```

```
## [1] "1"                "orange"           "83.5"             "3.14159265358979"
```
]
]
---

# Data Frames I

- `Data frame`: what we'll be using almost always

- Think like a "spreadsheet"

- Each .hi[column] is a vector (variable)

- Each .hi-purple[row] is an observation (pair of values for all variables)
]

```r
library("ggplot2")

diamonds
```

```
## # A tibble: 53,940 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 10 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39
## # … with 53,930 more rows
```

]

]
---

# Data Frames II

- Dataframes are really just combinations of (column) vectors

- You can make data frames by combinining named vectors with `data.frame()` or creating each column/vector in each argument 
]

```r
# make two vectors
fruits = c("apple","orange","pear","kiwi","pineapple")
numbers = c(3.3,2.0,6.1,7.5,4.2)

# combine into dataframe
df = data.frame(fruits,numbers)

# do it all in one step (note the = instead of <-)
df = data.frame(fruits=c("apple","orange","pear","kiwi","pineapple"),
 numbers=c(3.3,2.0,6.1,7.5,4.2))

# look at it
df
```

```
##      fruits numbers
## 1     apple     3.3
## 2    orange     2.0
## 3      pear     6.1
## 4      kiwi     7.5
## 5 pineapple     4.2
```

]

---

---

# Objects: Storing, Viewing, and Overwriting

- We want to store things in objects to run functions on them later
- Recall, any object is created with the assignment operator `=` or `<-`

```r
my_vector = c(1,2,3,4,5)
```

- R will not give any output after an assignment

---

# Objects: Storing, Viewing, and Overwriting

- *View* an object (and list its contents) by typing its name

```r
my_vector
```

```
## [1] 1 2 3 4 5
```

- objects maintain their values until they are assigned different values that will *overwrite* the object

```r
my_vector = c(2,7,9,1,5)
my_vector
```

```
## [1] 2 7 9 1 5
```

---

# Objects: Checking and Changing Classes

- Check what type of object something is with `class()`

```r
class("six")
```

```
## [1] "character"
```

```r
class(6)
```

```
## [1] "numeric"
```

- Can also use logical tests of `is.()`

```r
is.numeric("six")
```

```
## [1] FALSE
```

```r
is.character("six")
```

```
## [1] TRUE
```

---

# Objects: Checking and Changing Classes

- Convert objects from one class to another with `as.object_class()`
    - Pay attention: you can't convert non-numbers to `numeric`, etc!

```r
as.character(6)
```

```
## [1] "6"
```

```r
as.numeric("six")
```

```
## [1] NA
```

---

# Objects: Different Classes and Coercion I

- Different types of objects have different rules about mixing classes
- Vectors can *not* contain different types of data
    - Different types of data will be "**coerced**" into the lowest-common denominator type of object

```r
mixed_vector = c(pi, 12, "apple", 6.32)
class(mixed_vector)
```

```
## [1] "character"
```

```r
mixed_vector
```

```
## [1] "3.14159265358979" "12"               "apple"            "6.32"
```

---

# Objects: Different Classes and Coercion II

- Data frames can have columns with different types of data, so long as all the elements in each column are the same class.magenta[†]

```r
df
```

```
##      fruits numbers
## 1     apple     3.3
## 2    orange     2.0
## 3      pear     6.1
## 4      kiwi     7.5
## 5 pineapple     4.2
```
]

```r
class(df$fruits)
```

```
## [1] "character"
```

```r
class(df$numbers)
```

```
## [1] "numeric"
```
]

---

# More on Data Frames I

- Learn more about a data frame with the `str()` command to view its structure

```r
class(df) 
```

```
## [1] "data.frame"
```

```r
str(df)
```

```
## 'data.frame':	5 obs. of  2 variables:
##  $ fruits : chr  "apple" "orange" "pear" "kiwi" ...
##  $ numbers: num  3.3 2 6.1 7.5 4.2
```

---

# More on Data Frames II

- Take a look at the first 5 (or `n`) rows with `head()`

```r
head(df)
```

```
##      fruits numbers
## 1     apple     3.3
## 2    orange     2.0
## 3      pear     6.1
## 4      kiwi     7.5
## 5 pineapple     4.2
```

```r
head(df, n=2)
```

```
##   fruits numbers
## 1  apple     3.3
## 2 orange     2.0
```

---

# More on Data Frames III

- Get summary statistics.magenta[†] by column (variable) with `summary()`

```r
summary(df)
```

```
##     fruits             numbers    
##  Length:5           Min.   :2.00  
##  Class :character   1st Qu.:3.30  
##  Mode  :character   Median :4.20  
##                     Mean   :4.62  
##                     3rd Qu.:6.10  
##                     Max.   :7.50
```

.footnote[.magenta[†] For `numeric` data only; a frequency table is displayed for `character` or `factor` data]
---

# More on Data Frames IV

- Note, once you save an object, it shows up in the **Environment Pane** in the upper right window 
- Click the blue arrow button in front of the object for some more information

# More on Data Frames V

- `data.frame` objects can be viewed in their own panel by clicking on the name of the object
- Note you cannot edit anything in this pane, it is for viewing only

# Functions Again I

- Functions in R are vectorized, meaning running a function on a vector applies it to *each* element

```r
my_vector = c(2,4,5,10)
my_vector+4 # add 4 to all elements
```

```
## [1]  6  8  9 14
```

```r
my_vector^2 # square all elements
```

```
## [1]   4  16  25 100
```

---

# Functions Again II

- But often we want to run functions on vectors that *aggregate* to a result (e.g. a statistic):

```r
length(my_vector) # how many elements
```

```
## [1] 4
```

```r
sum(my_vector) # add all elements
```

```
## [1] 21
```

```r
max(my_vector) # find largest element
```

```
## [1] 10
```

```r
min(my_vector) # find smallest element
```

```
## [1] 2
```

```r
mean(my_vector) # mean of all elements
```

```
## [1] 5.25
```

```r
median(my_vector) # median of all elements
```

```
## [1] 4.5
```

```r
sd(my_vector) # standard deviation
```

```
## [1] 3.40343
```

---

# Common Errors

- If you make a coding error (e.g. forget to close a parenthesis), R might show a `+` sign waiting for you to finish the command

```r
> 2+(2*3
+
```

- Either finish the command-- e.g. add `)`--or hit `Esc` to cancel

---

# Working with Data

---

# Indexing and Subsetting I

```r
mtcars
```

```
##                    mpg cyl  disp  hp drat    wt  qsec
## Mazda RX4         21.0   6 160.0 110 3.90 2.620 16.46
## Mazda RX4 Wag     21.0   6 160.0 110 3.90 2.875 17.02
## Datsun 710        22.8   4 108.0  93 3.85 2.320 18.61
## Hornet 4 Drive    21.4   6 258.0 110 3.08 3.215 19.44
## Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02
## Valiant           18.1   6 225.0 105 2.76 3.460 20.22
## Duster 360        14.3   8 360.0 245 3.21 3.570 15.84
## Merc 240D         24.4   4 146.7  62 3.69 3.190 20.00
## Merc 230          22.8   4 140.8  95 3.92 3.150 22.90
## Merc 280          19.2   6 167.6 123 3.92 3.440 18.30
## Merc 280C         17.8   6 167.6 123 3.92 3.440 18.90
## Merc 450SE        16.4   8 275.8 180 3.07 4.070 17.40
```
]
]

.pull-right[
.smallest[
- Each element in a data frame is .h1[indexed] by referring to its row and column: `df[r,c]`
- To select elements by row and column (".h1[subset]"), type in the row(s) and/or column(s) to select
 - Leaving `r` or `c` blank selects *all* rows or columns
 - Select multiple values with `c()`1
 - Select a range of values with `:`
 - Don't forget the comma between `r` and `c`! 
]

.footnote[1 You can also "negate" values, selecting everything *except* for values with a `-` in front of them.]
]

---

# Indexing and Subsetting II

```r
mtcars
```

### Subset by Row (Observations)
.code50[

```r
mtcars[1,] # first row
```

```
##           mpg cyl disp  hp drat   wt  qsec
## Mazda RX4  21   6  160 110  3.9 2.62 16.46
```

```r
mtcars[c(1,3,4),] # first, third, and fourth rows
```

```
##                 mpg cyl disp  hp drat    wt  qsec
## Mazda RX4      21.0   6  160 110 3.90 2.620 16.46
## Datsun 710     22.8   4  108  93 3.85 2.320 18.61
## Hornet 4 Drive 21.4   6  258 110 3.08 3.215 19.44
```

```r
mtcars[1:3,] # first three rows
```

```
##                mpg cyl disp  hp drat    wt  qsec
## Mazda RX4     21.0   6  160 110 3.90 2.620 16.46
## Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02
## Datsun 710    22.8   4  108  93 3.85 2.320 18.61
```
]
]

---

# Indexing and Subsetting III

```r
mtcars
```

### Subset by Column (Variable)

```r
mtcars[,6] # select column 6
```

```
##  [1] 2.620 2.875 2.320 3.215 3.440 3.460 3.570 3.190 3.150 3.440 3.440 4.070
```

```r
mtcars[,2:4] # select columns 2 through 4
```

```
##                   cyl  disp  hp
## Mazda RX4           6 160.0 110
## Mazda RX4 Wag       6 160.0 110
## Datsun 710          4 108.0  93
## Hornet 4 Drive      6 258.0 110
## Hornet Sportabout   8 360.0 175
## Valiant             6 225.0 105
## Duster 360          8 360.0 245
## Merc 240D           4 146.7  62
## Merc 230            4 140.8  95
## Merc 280            6 167.6 123
## Merc 280C           6 167.6 123
## Merc 450SE          8 275.8 180
```
]
]
---

# Indexing and Subsetting IV

```r
mtcars
```

### Subset by Column (Variable)

- Alternatively, double brackets `[[]]` selects a column by position

```r
mtcars[[6]]
```

```
##  [1] 2.620 2.875 2.320 3.215 3.440 3.460 3.570 3.190 3.150 3.440 3.440 4.070
```
]

- Can select columns .hi-turquoise[by name] with `$`

```r
mtcars$wt # same thing
```

```
##  [1] 2.620 2.875 2.320 3.215 3.440 3.460 3.570 3.190 3.150 3.440 3.440 4.070
```
]
]

---

# Indexing and Subsetting V

```r
mtcars
```

- Select observations (rows) that meet logical criteria

### Subset by Condition

```r
mtcars[mtcars$wt>4,] # select obs with wt>4
```

```
##             mpg cyl  disp  hp drat   wt qsec
## Merc 450SE 16.4   8 275.8 180 3.07 4.07 17.4
```

```r
mtcars[mtcars$cyl==6,] # select obs with exactly 6 cyl
```

```
##                 mpg cyl  disp  hp drat    wt  qsec
## Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46
## Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02
## Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44
## Valiant        18.1   6 225.0 105 2.76 3.460 20.22
## Merc 280       19.2   6 167.6 123 3.92 3.440 18.30
## Merc 280C      17.8   6 167.6 123 3.92 3.440 18.90
```

```r
mtcars[mtcars$wt<4 & mtcars$wt>2,] # select obs where 2<wt<4
```

```r
mtcars[mtcars$cyl==4 | mtcars$cyl==6,] # select obs with 4 OR 6 cyl
```

```
##                 mpg cyl  disp  hp drat    wt  qsec
## Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46
## Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02
## Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61
## Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44
## Valiant        18.1   6 225.0 105 2.76 3.460 20.22
## Merc 240D      24.4   4 146.7  62 3.69 3.190 20.00
## Merc 230       22.8   4 140.8  95 3.92 3.150 22.90
## Merc 280       19.2   6 167.6 123 3.92 3.440 18.30
## Merc 280C      17.8   6 167.6 123 3.92 3.440 18.90
```
]

]

---

# What's To Come

- Next class: data visualization with `ggplot2`

- And then: data wrangling with `tidyverse`

- And then: literate programming and workflow management with `R Markdown`, `R Projects`, maybe `git`

- Finally: onto statistics and econometric theory!