R: Introduction and Review

---

# Econometrics

.it.slab[Recall:] Applied econometrics, data science, analytics require:

1. Intuition for the __theory__ behind statistics/econometrics (assumptions, results, strengths, weaknesses).

1. Practical knowledge of how to __apply theoretical methods__ to data.

1. Efficient methods for __working with data__ (cleaning, aggregating, joining, visualizing).

__This course__ aims to deepen your knowledge in each of these three areas.

- 1: As before.
- 2–3: __R__

---
class: inverse, middle
# R

---
layout: true
# R

---

## What is R?

To quote the [R project website](https://www.r-project.org):

> R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.

What does that mean?

- R was created for the statistical and graphical work required by econometrics.

- R has a vibrant, thriving online community. ([stack overflow](https://stackoverflow.com/questions/tagged/r))

- Plus it's __free__ and __open source__.

---

## Why are we using R?

1\. R is __free__ and __open source__—saving both you and the university 💰💵💰.

2\. _Related:_ Outside of a small group of economists, private- and public-sector __employers favor R__ over .mono[Stata] and most competing softwares.

3\. R is very __flexible and powerful__—adaptable to nearly any task, _e.g._, 'metrics, spatial data analysis, machine learning, web scraping, data cleaning, website building, teaching (these slides).

---

## Why are we using R?

4\. _Related:_ R imposes __no limitations__ on your amount of observations, variables, memory, or processing power. (I'm looking at __you__, .mono[Stata].)

5\. If you put in the work,† you will come away with a __valuable and marketable__ tool.

6\. I 💖 __R__

7\. R is a nice gateway to (and plays well with) other programming languages (.it[e.g.], Python, SQL, C++, JavaScript).

---
<img src="slides_files/figure-html/statistical languages-1.svg" style="display: block; margin: auto;" />

---

---

# R + Regression

``` r
# A simple regression
*fit = lm(dist ~ 1 + speed, data = cars)
# Show the coefficients
coef(summary(fit))
```

```
#>               Estimate Std. Error   t value     Pr(>|t|)
#> (Intercept) -17.579095  6.7584402 -2.601058 1.231882e-02
#> speed         3.932409  0.4155128  9.463990 1.489836e-12
```

``` r
# A nice, clear table
library(broom)
tidy(fit)
```

```
#> # A tibble: 2 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -17.6 6.76 -2.60 1.23e- 2
#> 2 speed 3.93 0.416 9.46 1.49e-12
```

---

# R + Plotting (w/ .mono[plot])

---

# R + Plotting (w/ .mono[plot])

``` r
# Load packages with dataset
library(gapminder)

# Create dataset
plot(
  x = gapminder$gdpPercap, y = gapminder$lifeExp,
  xlab = "GDP per capita", ylab = "Life Expectancy"
)
```

---

# R + Plotting (w/ .mono[ggplot2])

---

# R + Plotting (w/ .mono[ggplot2])

``` r
# Load packages
library(gapminder); library(dplyr)

# Create dataset
ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
geom_point(alpha = 0.75) +
scale_x_continuous("GDP per capita", label = scales::comma) +
ylab("Life Expectancy") +
theme_pander(base_size = 16)
```

---

# R + More plotting (w/ .mono[ggplot2])

---

# R + More plotting (w/ .mono[ggplot2])

``` r
# Load packages
library(gapminder); library(dplyr)

# Create dataset
ggplot(
  data = filter(gapminder, year %in% c(1952, 2002)),
  aes(x = gdpPercap, y = lifeExp, color = continent, group = country)
) +
geom_path(alpha = 0.25) +
geom_point(aes(shape = as.character(year), size = pop), alpha = 0.75) +
scale_x_log10("GDP per capita", label = scales::comma) +
ylab("Life Expectancy") +
scale_shape_manual("Year", values = c(1, 17)) +
scale_color_viridis("Continent", discrete = T, end = 0.95) +
guides(size = F) +
theme_pander(base_size = 16)
```

---

# R + Animated plots (w/ .mono[gganimate])

---

# R + Animated plots (w/ .mono[gganimate])

``` r
# The package for animating ggplot2
library(gganimate)
# As before
ggplot(
  data = gapminder %>% filter(continent != "Oceania"),
  aes(gdpPercap, lifeExp, size = pop, color = country)
) +
geom_point(alpha = 0.7, show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
scale_x_log10("GDP per capita", label = scales::comma) +
facet_wrap(~continent) +
theme_pander(base_size = 16) +
theme(panel.border = element_rect(color = "grey90", fill = NA)) +
# Here comes the gganimate-specific bits
labs(title = "Year: {frame_time}") +
ylab("Life Expectancy") +
transition_time(year) +
ease_aes("linear")
```

---

# R + Interactive plots (w/ .mono[plotly])

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Population: 127,467,972","Country: Jordan GDP per capita: $4,519 Life Expectancy: 73 Population: 6,053,193","Country: Korea, Dem. Rep. GDP per capita: $1,593 Life Expectancy: 67 Population: 23,301,725","Country: Korea, Rep. GDP per capita: $23,348 Life Expectancy: 79 Population: 49,044,790","Country: Kuwait GDP per capita: $47,307 Life Expectancy: 78 Population: 2,505,559","Country: Lebanon GDP per capita: $10,461 Life Expectancy: 72 Population: 3,921,278","Country: Malaysia GDP per capita: $12,452 Life Expectancy: 74 Population: 24,821,286","Country: Mongolia GDP per capita: $3,096 Life Expectancy: 67 Population: 2,874,127","Country: Myanmar GDP per capita: $944 Life Expectancy: 62 Population: 47,761,980","Country: Nepal GDP per capita: $1,091 Life Expectancy: 64 Population: 28,901,790","Country: Oman GDP per capita: $22,316 Life Expectancy: 76 Population: 3,204,897","Country: Pakistan GDP per capita: $2,606 Life Expectancy: 65 Population: 169,270,617","Country: Philippines GDP per capita: $3,190 Life Expectancy: 72 Population: 91,077,287","Country: Saudi Arabia GDP per capita: $21,655 Life Expectancy: 73 Population: 27,601,038","Country: Singapore GDP per capita: $47,143 Life Expectancy: 80 Population: 4,553,009","Country: Sri Lanka GDP per capita: $3,970 Life Expectancy: 72 Population: 20,378,239","Country: Syria GDP per capita: $4,185 Life Expectancy: 74 Population: 19,314,747","Country: Taiwan GDP per capita: $28,718 Life Expectancy: 78 Population: 23,174,294","Country: Thailand GDP per capita: $7,458 Life Expectancy: 71 Population: 65,068,149","Country: Vietnam GDP per capita: $2,442 Life Expectancy: 74 Population: 85,262,356","Country: West Bank and Gaza GDP per capita: $3,025 Life Expectancy: 73 Population: 4,018,332","Country: Yemen, Rep. GDP per capita: $2,281 Life Expectancy: 63 Population: 22,211,743"],"hoverinfo":["text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text"],"type":"scatter","name":"Asia","marker":{"color":"rgba(195,61,128,1)","size":[7.2833677032611703,5.0366742772105511,15.825794950002397,6.0038551775084494,100,5.4885757968865079,84.992423174145756,21.092734358392438,9.9899214212171294,6.967036805208692,5.4486779640188709,14.170002642513126,5.4217673735241698,6.6645667857825792,8.5194183204946352,5.1661515651696996,5.268157622329988,6.7740549160008952,5.1927078015947616,8.4269886856689471,7.0680653264473081,5.2165405985883098,17.181986655810427,11.547963739162922,6.9743429261239678,5.3136754041044263,6.4539223852883403,6.3772951550671531,6.6553850669033432,9.673940986400666,11.128983571510208,5.2751506031910447,6.5860308855116312],"sizemode":"area","line":{"color":"rgba(195,61,128,1)"}},"textfont":{"color":"rgba(195,61,128,1)","size":[7.2833677032611703,5.0366742772105511,15.825794950002397,6.0038551775084494,100,5.4885757968865079,84.992423174145756,21.092734358392438,9.9899214212171294,6.967036805208692,5.4486779640188709,14.170002642513126,5.4217673735241698,6.6645667857825792,8.5194183204946352,5.1661515651696996,5.268157622329988,6.7740549160008952,5.1927078015947616,8.4269886856689471,7.0680653264473081,5.2165405985883098,17.181986655810427,11.547963739162922,6.9743429261239678,5.3136754041044263,6.4539223852883403,6.3772951550671531,6.6553850669033432,9.673940986400666,11.128983571510208,5.2751506031910447,6.5860308855116312]},"error_y":{"color":"rgba(195,61,128,1)","width":[]},"error_x":{"color":"rgba(195,61,128,1)","width":[]},"line":{"color":"rgba(195,61,128,1)"},"xaxis":"x","yaxis":"y","frame":null},{"x":[5937.0295260000003,36126.492700000003,33692.605080000001,7446.2988029999997,10680.792820000001,14619.22272,22833.308509999999,35278.418740000001,33207.0844,30470.0167,32170.37442,27538.41188,18008.944439999999,36180.789190000003,40675.996350000001,28569.719700000001,9253.896111,36797.933319999996,49357.190170000002,15389.92468,20509.64777,10808.47561,9786.5347139999994,18678.314350000001,25768.257590000001,28821.063699999999,33859.748350000002,37506.419070000004,8458.2763840000007,33203.261279999999],"y":[76.423000000000002,79.828999999999994,79.441000000000003,74.852000000000004,73.004999999999995,75.748000000000005,76.486000000000004,78.331999999999994,79.313000000000002,80.656999999999996,79.406000000000006,79.483000000000004,73.337999999999994,81.757000000000005,78.885000000000005,80.546000000000006,74.543000000000006,79.762,80.195999999999998,75.563000000000002,78.097999999999999,72.475999999999999,74.001999999999995,74.662999999999997,77.926000000000002,80.941000000000003,80.884,81.700999999999993,71.777000000000001,79.424999999999997],"mode":"markers","text":["Country: Albania GDP per capita: $5,937 Life Expectancy: 76 Population: 3,600,523","Country: Austria GDP per capita: $36,126 Life Expectancy: 80 Population: 8,199,783","Country: Belgium GDP per capita: $33,693 Life Expectancy: 79 Population: 10,392,226","Country: Bosnia and Herzegovina GDP per capita: $7,446 Life Expectancy: 75 Population: 4,552,198","Country: Bulgaria GDP per capita: $10,681 Life Expectancy: 73 Population: 7,322,858","Country: Croatia GDP per capita: $14,619 Life Expectancy: 76 Population: 4,493,312","Country: Czech Republic GDP per capita: $22,833 Life Expectancy: 76 Population: 10,228,744","Country: Denmark GDP per capita: $35,278 Life Expectancy: 78 Population: 5,468,120","Country: Finland GDP per capita: $33,207 Life Expectancy: 79 Population: 5,238,460","Country: France GDP per capita: $30,470 Life Expectancy: 81 Population: 61,083,916","Country: Germany GDP per capita: $32,170 Life Expectancy: 79 Population: 82,400,996","Country: Greece GDP per capita: $27,538 Life Expectancy: 79 Population: 10,706,290","Country: Hungary GDP per capita: $18,009 Life Expectancy: 73 Population: 9,956,108","Country: Iceland GDP per capita: $36,181 Life Expectancy: 82 Population: 301,931","Country: Ireland GDP per capita: $40,676 Life Expectancy: 79 Population: 4,109,086","Country: Italy GDP per capita: $28,570 Life Expectancy: 81 Population: 58,147,733","Country: Montenegro GDP per capita: $9,254 Life Expectancy: 75 Population: 684,736","Country: Netherlands GDP per capita: $36,798 Life Expectancy: 80 Population: 16,570,613","Country: Norway GDP per capita: $49,357 Life Expectancy: 80 Population: 4,627,926","Country: Poland GDP per capita: $15,390 Life Expectancy: 76 Population: 38,518,241","Country: Portugal GDP per capita: $20,510 Life Expectancy: 78 Population: 10,642,836","Country: Romania GDP per capita: $10,808 Life Expectancy: 72 Population: 22,276,056","Country: Serbia GDP per capita: $9,787 Life Expectancy: 74 Population: 10,150,265","Country: Slovak Republic GDP per capita: $18,678 Life Expectancy: 75 Population: 5,447,502","Country: Slovenia GDP per capita: $25,768 Life Expectancy: 78 Population: 2,009,245","Country: Spain GDP per capita: $28,821 Life Expectancy: 81 Population: 40,448,191","Country: Sweden GDP per capita: $33,860 Life Expectancy: 81 Population: 9,031,088","Country: Switzerland GDP per capita: $37,506 Life Expectancy: 82 Population: 7,554,661","Country: Turkey GDP per capita: $8,458 Life Expectancy: 72 Population: 71,158,647","Country: United Kingdom GDP per capita: $33,203 Life Expectancy: 79 Population: 60,776,238"],"hoverinfo":["text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text","text"],"type":"scatter","name":"Europe","marker":{"color":"rgba(241,131,76,1)","size":[5.245046430868654,5.5764344947817808,5.7344054381530807,5.3136169695475992,5.51324987857243,5.3093740875336248,5.7226261555152984,5.3796114161054014,5.3630638448095214,9.3868671397277694,10.922815503047355,5.7570345265074714,5.7029820570748928,5.007374714871009,5.2816896534626911,9.1753078889646815,5.034956762375665,6.1795735099811644,5.3190733593372634,7.7609544169978424,5.7524625087899377,6.5906647963047691,5.7169715493682585,5.3781258381859622,5.1303909133359307,7.9000120901776887,5.6363320771040026,5.5299518583211835,10.112776438296574,9.3646981784755976],"sizemode":"area","line":{"color":"rgba(241,131,76,1)"}},"textfont":{"color":"rgba(241,131,76,1)","size":[5.245046430868654,5.5764344947817808,5.7344054381530807,5.3136169695475992,5.51324987857243,5.3093740875336248,5.7226261555152984,5.3796114161054014,5.3630638448095214,9.3868671397277694,10.922815503047355,5.7570345265074714,5.7029820570748928,5.007374714871009,5.2816896534626911,9.1753078889646815,5.034956762375665,6.1795735099811644,5.3190733593372634,7.7609544169978424,5.7524625087899377,6.5906647963047691,5.7169715493682585,5.3781258381859622,5.1303909133359307,7.9000120901776887,5.6363320771040026,5.5299518583211835,10.112776438296574,9.3646981784755976]},"error_y":{"color":"rgba(241,131,76,1)","width":[]},"error_x":{"color":"rgba(241,131,76,1)","width":[]},"line":{"color":"rgba(241,131,76,1)"},"xaxis":"x","yaxis":"y","frame":null},{"x":[34435.367440000002,25185.009109999999],"y":[81.234999999999999,80.203999999999994],"mode":"markers","text":["Country: Australia GDP per capita: $34,435 Life Expectancy: 81 Population: 20,434,176","Country: New Zealand GDP per capita: $25,185 Life Expectancy: 80 Population: 4,115,771"],"hoverinfo":["text","text"],"type":"scatter","name":"Oceania","marker":{"color":"rgba(250,218,36,1)","size":[6.4579527845549851,5.2821713242547874],"sizemode":"area","line":{"color":"rgba(250,218,36,1)"}},"textfont":{"color":"rgba(250,218,36,1)","size":[6.4579527845549851,5.2821713242547874]},"error_y":{"color":"rgba(250,218,36,1)","width":[]},"error_x":{"color":"rgba(250,218,36,1)","width":[]},"line":{"color":"rgba(250,218,36,1)"},"xaxis":"x","yaxis":"y","frame":null}],"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.20000000000000001,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script>

---
class: clear, middle

``` r
plot_ly(
 data = gapminder %>% filter(year == 2007),
 x = ~gdpPercap,
 y = ~lifeExp,
 type = "scatter",
 mode = "markers",
 size = ~pop,
 color = ~continent,
 colors = viridis::plasma(n = 5, end = .93),
 text = ~paste(
 "Country: ", country,
 " GDP per capita:", scales::dollar(gdpPercap, 1),
 " Life Expectancy:", scales::comma(lifeExp, 1),
 " Population:", scales::comma(pop)
 ),
 hoverinfo = "text",
 sizes = c(5, 100)
) %>%
 layout(
 title = "Gapminder data in 2007",
 xaxis = list(title = "GDP per capita (log scale)", type = "log"),
 yaxis = list(title = "Life Expectancy")
 )

---

# R + Maps
```

``` r
library(leaflet)
leaflet() %>%
  addTiles() %>%
  addMarkers(lng = -123.075, lat = 44.045, popup = "The University of Oregon")
```

<div class="leaflet html-widget html-fill-item" id="htmlwidget-d6f4e8b43989c4f7fef9" style="width:756px;height:360px;"></div>
<script type="application/json" data-for="htmlwidget-d6f4e8b43989c4f7fef9">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"https://openstreetmap.org/copyright/\">OpenStreetMap<\/a>, <a href=\"https://opendatacommons.org/licenses/odbl/\">ODbL<\/a>"}]},{"method":"addMarkers","args":[44.045,-123.075,null,null,null,{"interactive":true,"draggable":false,"keyboard":true,"title":"","alt":"","zIndexOffset":0,"opacity":1,"riseOnHover":false,"riseOffset":250},"The University of Oregon",null,null,null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]}],"limits":{"lat":[44.045,44.045],"lng":[-123.075,-123.075]}},"evals":[],"jsHooks":[]}</script>

---
class: inverse, middle
# Getting started with R

---
layout: true
# Starting R

---

## Installation

- Install [R](https://www.r-project.org/).

- Install [.mono[RStudio]](https://www.rstudio.com/products/rstudio/download/preview/).

- __Optional/Overkill:__ [Git](https://git-scm.com/downloads)
  - Create an account on [GitHub](https://github.com/)
  - Register for a student/educator [discount](https://education.github.com/discount_requests/new).
  - For installation guidance and troubleshooting, check out Jenny Bryan's [website](http://happygitwithr.com/).

- __Note:__ Many UO labs have R installed and ready (helpful in an pinch).

---

## Resources

### Free(-ish)

- Google (which inevitably leads to StackOverflow)
- Time
- ChatGPT, Copilot, and other AI assistants
- [Data services at the UO library](https://library.uoregon.edu/data-services)
- Your classmates
- Your GE and me
- R resources [here](http://edrub.in/ARE212/resources.html) and [here](https://education.rstudio.com/)
- [swirl](https://swirlstats.com/) and [learnr](https://cran.r-project.org/web/packages/learnr/index.html)

### Money

Short online courses, e.g., [DataCamp](https://www.datacamp.com)

---

## Some R basics

You will dive deeper into R in lab, but here six big points about R:

1. Everything is an __object__.

1. Every object has a __name__ and __value__.

1. You use __functions__ on these objects.

1. Functions come in __libraries__ (__packages__)

1. R will try to __help__ you.

1. R has its __quirks__.

]

`foo`

`foo = 2`

`mean(foo)`

`library(dplyr)`

`?dplyr`

`NA; error; warning`

]

---
exclude: true

## R _vs._ .mono[Stata]

Coming from .mono[Stata], here are a few important changes (benefits):

- Multiple objects and arrays (_e.g._, data frames) can exist in the same workspace (in memory). No more `keep`, `preserve`, `restore`, `snapshot` nonsense!

- (Base) R comes with lots of useful built-in functions—and provides all the tools necessary for you to build your own functions. However, many of the _best_ functions come from external libraries.

- You don't need to `tset` or `xtset` data (you can if you really want... `ts`).

---
layout: false
class: clear, middle

.it.slab[Next:] Metrics review(s)