# Learning to code

Here is some tip and advice on how to learn coding.

# Big picture

A well respected resource that introduces thinking about coding for data analysis is Code and Data for the Social Sciences: A Practitioner’s Guide by Matthew Gentzkow and Jesse M. Shapiro. They talk about issues like replication, organization of a project, or version control.

# Learning to code for data analysis

## R

There are two popular *toungues* (beyond base) in R, called *data.table* and *tidyverse*. We use *tidyverse*.

There are great many resources, to learn R for data analysis. Here are some ideas:

- To learn tidyverse, you may start with the wonderful book by Hadley Wickham and Garrett Grolemund R for Data Science.
- A wonderful intro, with a focus on starting R and data wrangling, is by Jenny Briant’s Data wrangling, exploration, and analysis with R course, aka STAT545.
- U Cincinatti has a very nice guide with discussions on basics, workflow, manipulation in R Programming Guide.
- At CMU, Alexandra Chouldechova has a nice programming in R materials.
- A great online course is by Roger Peng, Jeff Leek and Brian Caffo R programming onCoursera
- At Data Carpentry, François Michonneau and Auriel Fournier has a fantastic content –Data Analysis and Visualization in R for Ecologists.
- Grant McDermott has a more advanced lecture series with amazing content Data Science for Economists.
- Working with
**time series**is hard. A great resource by Hansjörg Neth: Data Science for Psychologists Chapter 10 Dates and times. - What They Forgot to Teach You About R, awesome material by Jennifer Bryan and Jim Hester. workshop material.

## Stata

There are many great materials, here is some we like:

- UCLA extensive material at UCLA IDRE Stats.
- Amazing two part series by Kurt Schmidheiny Part 1 Part 2
- At Data Carpentry, CEU’s Miklós Koren and Arieda Muco are developing a Stata course for Economist.
- Plus, a Stata cheatsheat.

## Python

Python is a general purpose language, used for many applications beyond data science/statistics. There are great many resources, to learn Python for data analysis. Here are some ideas:

- Very nice courses are available widely, for instance on Datacamp, and Codeacademy.
- A set of very nice lessons at Python for Everybody.
- NYU has a great group also offering a Python cours: QuanEcon.

# Learning a second language

Some people have experience using one language but would now learn a second one. Some ideas we found useful:

## R for Stata users

In Economics and many other social sciences, we use Stata for research, and learnt R or Python as a second language. Here are some links and tutorials we found useful.

- Matthieu Gomez has a wonderful intro to R for Stata users . For instance the bit on regressions is pretty useful, I come back to it regularly.
- John Ricco has a short intro to basics of data wrangling

## R to/from Python

For this textbook, Stata and R code were developed early on, and we started to work on Python code set only after the proof was ready. Some ideas we (and our RAs) found useful

## Python for Stata users

TBA

## Othe useful sites

### Get datasets for exercises, projects

Social Science Data Sources & Statistical Methods

### tools

Great list of data tools by the UC Berkeley Library and Research IT run Research Data Management (RDM) Program

## Help us expand this bit

So if you are here, you have scrolled through. Maybe you thought, why don’t you have X. Well, please share ideas with HERE. Cheers.