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<div class="textbook-toc">


<br>

#### Data Programming for Social Scientists 




# The Data Science Toolkit

We will need three tools to manage your data science projects: a data programming language (R), a project management interfact (R Studio), and a way to create data-driven documents (R Markdown).

## Core R [ [CH-01](http://ds4ps.org/dp4ss-textbook/ch-010-core-r.html) ]
* What is R?  [ [video](https://player.vimeo.com/video/180644880) ]
* Packages 
  - [CRAN Views](https://cran.r-project.org/web/views/) 
  - [Awesome R](https://awesome-r.com/) 
  - [R Bloggers](https://www.r-bloggers.com/)

## R Studio [ [CH-02](http://ds4ps.org/dp4ss-textbook/ch-020-rstudio.html) ]
* Installing R and R Studio
* Tour of R Studio 

## Data-Driven Docs [ [CH-03](http://ds4ps.org/dp4ss-textbook/ch-030-data-driven-docs.html) ]
* Automation & Flexibility 
* [The Importance of Reproducibility](https://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970) 
* Formats [link](https://rmarkdown.rstudio.com/formats.html)
* Gallery [link](https://rmarkdown.rstudio.com/gallery.html)

## Markdown [ [CH-04](http://ds4ps.org/dp4ss-textbook/ch-031-markdown.html) ]
* R Markdown Formats [overview](https://rmarkdown.rstudio.com/lesson-1.html)
  * Headers and Chunks [link](https://ds4ps.org/docs/#how-do-data-driven-docs-work)
  * Knitting [link](https://ds4ps.org/docs/#knitting-r-markdown-files)
  * [Customization](https://holtzy.github.io/Pimp-my-rmd/)
 
 
# Getting Started

## R as a Calculator [ [CH-05](http://ds4ps.org/dp4ss-textbook/ch-033-calculator.html) ]
* Mathematical Operators 
* Assignment 
* Objects
 
## Functions [ [CH-06](http://ds4ps.org/dp4ss-textbook/ch-040-functions.html) ] 
* Input-Output Devices 
* Arguments 
* Values 
* Returns 


## The Learning Curve [ [CH-07](http://ds4ps.org/dp4ss-textbook/ch-032-learning_r.html) ]
* Vocabular and verbs 
* Learning to Learn R


## Getting Help [ [CH-08](http://ds4ps.org/dp4ss-textbook/ch-021-help.html) ]
* Help files 
* Error messages 
* Discussion boards 


## Starting to Code
* Scripts 
* Comments 
* [Style Guides](https://jef.works/R-style-guide/) 
* [RMD File Styles and Knitting Tips](https://ds4ps.org/cpp-526-fall-2019/labs/r-markdown-files.html)




# One-Dimensional Datasets

## Intro to Vectors  [ [CH-09](http://ds4ps.org/dp4ss-textbook/ch-050-vectors.html) ]
* Observations vs Variables (rows vs columns) 
* Vector Types
  - Numeric
  - Character
  - Factors (ordered vs unordered) 
  - Logical (true/false) 
* Checking Vector Types

## Working with Vectors
* Built-In Vectors: e.g. LETTERS
* Generating Vectors
* Missing Values and Non-Numbers
* Empty vectors: NULL


## Altering Vectors
* Defining factors, relevel()
* Recoding Values 
* Find and replace
* Variable Transformations
  - Vectorized addition
  - Defining new vector as function of others:  ifelse(), gsub(), [] <- 
* Casting 
* Implicit Casting (coercion)


## Identifying Groups within Data [ [CH-10](http://ds4ps.org/dp4ss-textbook/p-050-business-logic.html) ]
* Set theory as categories and membership 
* Logical Operators 
  - equal
  - not equal 
  - greater than or less than 
  - opposite of
* Compound Statements:  AND and OR
* Casting logical vectors 
* Algebra with logical vectors 
* Defining groups 
  - from categorical variables
  - from numeric variables
  - missing values as a group



# Two-Dimensional Datasets

## Dataframes
* Creating data frames from vectors
* the $ operator
* Checking and changing class types
* Filter rows and select columns 
* Reorder rows or columns 
* CSV vs RDS formats

## Matrices and Lists
* Matrix
* Lists
* Building data objects: 
- data.frame() vs cbind() and rbind()
* Transformations of Datasets 



# Data IO

## Getting Data into R [ [tutorial](https://www.datacamp.com/community/tutorials/r-data-import-tutorial) ]
* Read options   
* Copy and paste from Excel 
* Using rdata format 
* Read from csv or tsv
* Read text files
* Import from Excel
* Import from common format (foreign package)
* Import from the web (RCurl)
* Import from GitHub
* Import from DropBox
* APIs
  - Census
  - Socrata
  - Twitter

## Saving Data [ [tutorial](https://thomasleeper.com/Rcourse/Tutorials/savingdata.html) ]
* Write options 
  - CSV 
  - R Data Sets (RDS)
  - CSV vs RDS
  - Tables 
  - RData Format
  - SPSS or Stata 
* Copy to Clipboard 
* Copy to Excel 

## Navigation 
* Navigating R (directories, paths, object lists)
* Built-In Datasets

# Data Wrangling (dplyr)

Data wrangling is the process of preparing data for analysis, which includes reading data into R from a variety of formats, cleaning data, tidying datasets, creating subsets and filters, transforming variables, grouping data, and joining multiple datasets. 

The goal of data wrangling is to create a **rodeo dataset** (clean and well-structured) that is ready for the big show (modeling and visualization)! 

## Slicing Datasets – Base R and dplyr
* Subset operator
* By index, including order / match
* By logical
* Recycling
* Subset by row  -- dplyr::filter()
* Indices
* Selector Vectors
* Subset by column  ---  dplyr::select()

## Combining Datasets
* merge and match
* join in dplyr
* inner, outer, right, left

# Explore and Describe

## Summarizing Vectors
* Counting things:  sum( logical statement )
* Categorical data:  tables
* Missing values
* prop.table() and margin.table()
* Numeric data:  min, max, mean, summary / quantile
* Missing values
* All at once:  summary + data.frame / matrix
* Creating tables of descriptives: factors vs numeric

## Summarizing Groups of Vectors
* Table ( f1, f2 ), ftable( row.vars=c(“f1”,”f2”), col.vars=”f3” )
* Function over groups:  tapply( v1, f1 )  or dplyr:: group_by() + summarise()
* Functions over levels of numeric data:  tapply( v1, cut(v2) )
* tapply( v1, INDEX=list(f1,f2)  or dplyr:: group_by() + summarise()
* aggregate( dat, FUN, by=f1 )
*  https://cran.r-project.org/web/packages/DescTools/vignettes/DescToolsCompanion.pdf


# Visualize 

## Principles of Visual Communication
* Ground, figure, narrative (context, subject, action)
* Tufte’s rules 
* Visual tragedies 

## Core Graphics Engine
* Defining a canvas: xlim, ylim
* Adding data
* Type (point, line, both)
* Symbols
* Color
* Size
* Adding grids
* Adding axes
* Adding titles / axes labels
* Adding data labels:  text()
* Margins

## Advanced Graphics
* Colors and color functions
* Custom fonts / math symbols
* Multiple Plots (core graphics)
  - Incorrect:  https://en.wikipedia.org/wiki/File:Smallmult.png#/media/File:Smallmult.png
* Custom graph layouts

## ggplot2
* Grammar of graphics concept
* ggplot overview


# Make Dynamic

## R shiny [ [tutorial](http://rmarkdown.rstudio.com/authoring_shiny.html) ]
* What makes documents dynamic? 
* Widgets 
  - input objects 
* Render functions 
* reactive 

## flexdashboards [ [overview](http://rmarkdown.rstudio.com/flexdashboard/) ]
* Principles of good dashboard design 
* Layouts 
* Sidebars 
* Value boxes 
* CSS basics 



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