Lecture Template.Rmd

---
title: "Lecture 1: A World of Data"
author: "Nick Huntington-Klein"
date: "December 1, 2018"
output:   
  revealjs::revealjs_presentation:
    theme: solarized
    transition: slide
    self_contained: true
    smart: true
    fig_caption: true
    reveal_options:
      slideNumber: true
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
library(tidyverse)
theme_set(theme_gray(base_size = 15))
```

## A World of Data

It's cliche to say that the world focuses more on data than ever before, but that's just because it's true

Even moreso than understanding *statistics and probability*, in order to understand the world around us we need to understand *data*, *how data is used*, and *what it means*

Google and Facebook, among many others, have reams and reams of data on you and everybody else. What do they do with it? Why?

## Understanding the World

Increasingly, understanding the world is going to require the ability to understand data

And learning things about the world is going to require the ability to manipulate data 

## Data Scientist Pay

```{r, echo = FALSE}
#Read in data
salary <- read.csv(text="Experience,Salary
0.11508951406649626, 89130.43478260869
1.877024722932652, 92521.73913043478
4.128303495311169, 96956.52173913045
5.890238704177326, 100347.82608695651
8.044117647058828, 104000
10.051577152600172, 106869.5652173913
12.205882352941181, 110000
14.654731457800512, 112608.6956521739
16.956308610400683, 115478.26086956522
19.01300085251492, 118086.95652173912
19.99168797953965, 120173.91304347827
21.8994032395567, 125130.4347826087
23.758312020460362, 129826.08695652173
25.665387894288155, 135565.21739130435
27.425618073316286, 141043.47826086957
29.67455242966753, 148347.82608695654")
#Use ggplot to plot out data 
ggplot(salary,aes(x=Experience,y=Salary))+
  #with a smoothed line graph
  stat_smooth(method = lm, formula = y ~ poly(x, 10), se = FALSE)+
  #Have y-axis start at 50k
  expand_limits(y=50000)+
  #Add labels
  labs(title="Data Scientist Salary by Experience",subtitle="Data from Glassdoor; avg college grad starts $49,875")+
  xlab("Experience (Years)")
```

## Top Jobs for Economics Majors

Data from The Balance Careers

```{r, echo = FALSE}
#Read in data
topjobs <- read.csv(text="Job,Salary,UsesData
                    Market Research Analyst,71450,A Lot
                    Economic Consultant,112650,A Lot
                    Comp & Bfts Manager,130010,A Lot
                    Actuary,114850,A Lot
                    Credit Analyst,82900,A Little
                    Financial Analyst,99430,A Little
                    Policy Analyst,112030,A Lot
                    Lawyer,141890,Not a Lot
                    Management Consultant,93440,A Little
                    Business Reporter,67500,Not a Lot")
#Sort so it goes from lowest salary to highest
topjobs$Job <- reorder(topjobs$Job, topjobs$Salary)
#Reorder factor so it goes least to most
topjobs$UsesData <- factor(topjobs$UsesData,levels=c("Not a Lot","A Little","A Lot"))
#Plot out 
ggplot(topjobs,aes(x=Job,y=Salary/1000,fill=UsesData))+
  #With a bar graph
  geom_col()+
  #Label
  ylab("Avg. Salary (Thousands)")+xlab(element_blank())+
  labs(title="Do Top-Ten Econ Major Jobs Use Data?")+
  #Rotate job labels so they fit
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

## We Use Data to Understand the Economy

```{r, echo = FALSE}
#Read in data
gdp <- read.csv('GDP.csv')
#Get GDP in the first year of data using dplyr
gdp <- gdp %>%
  group_by(Country) %>%
  mutate(firstGDP=GDP[1]) %>%
  mutate(gdprel=GDP/firstGDP)
#Plot data
ggplot(gdp,aes(x=Year,y=gdprel,color=Country))+
  #Line graph
  geom_line()+
  #Label
  xlab("Year")+ylab("GDP Relative to 1960")+
  theme(legend.title=element_blank())
                      
```

## We Use Data to Understand Business

![Data from SkyHighNetworks](Lecture_01-cloud-market-revenue.PNG)

## We Use Data to Understand Politics


![Data from FiveThirtyEight](Lecture_01_Politics_Example.PNG)

## We Use Data to Understand the World

```{r, echo = FALSE}
#Read in data
data(co2)
#Plot, cex for bigger font
plot(co2,xlab="Year",ylab="Atmospheric CO2 Concentration",cex=1.75)
```

## This Class

In this class, we'll be accomplishing a few goals.

- Learning how to use the statistical programming language R
- Learning how to understand the data we see in the world
- Learning how to figure out *what data actually tells us*
- Learning about *causal inference* - the economist's comparative advantage!

## Why Programming?

Why do we have to learn to code? Why not just use Excel?

- Excel is great at being a spreadsheet. You should learn it. It's a pretty bad data analysis tool though
- Learning a programming language is a very important skill
- R is free, very flexible (heck, I wrote these slides in R), is growing in popularity, will be used in other econometrics courses, and easy to jump to something like Python if need be

## Don't Be Scared

- Programming isn't all that hard
- You're just telling the computer what to do
- The computer will do exactly as you say
- Just imagine it's like your bratty little sibling who would do what you said, *literally*

## Plus

- As mentioned, once you know one language it's much easier to learn others
- There will be plenty of resources and cheat sheets to help you
- Ever get curious and have a question? Now you can just *answer it*. How cool is that?

## Causal Inference?

What is causal inference?

- It's easy to get data to tell us what happened, but not **why**. "Correlation does not equal casuation"
- Economists have been looking at causation for longer than most other fields. We're good at it!
- Causal inference is often necessary to link data to *models* and actually *learn how the world works*
- We'll be taking a special approach to causal inference, one that lets us avoid complex mathematical models

## Lucky You!

This is a pretty unusual course. We're lucky enough to be able to start the econometrics sequence off this way.

In most places, you have to learn programming *while* learning advanced methods, and similarly for causal inference!

Here we have time to build these important skills and intuitions before sending you into the more mathematical world of other econometrics courses

## Structure of the Course

1. Programming and working with data
2. Causal Inference and learning from data
3. Onto the next course!

## Admin

- Syllabus
- Homework (due Sundays, including this coming Sunday)
- Short writing projects
- Attendance
- Midterms
- Final
- Extra Credit

## An Example

- Let's look at a real-world application of data to an important economic problem
- To look for: What data are they using?
- How do they tell a story with it?
- What can we learn from numbers alone?
- How do they interpret the data? Can we trust it?
- [Economic Lives of the Middle Class](https://www.nytimes.com/2018/11/03/upshot/how-the-economic-lives-of-the-middle-class-have-changed-since-2016-by-the-numbers.html)