slides1.Rpres

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Text Analysis in R
========================================================
author: Wouter van Atteveldt
date: Session 1: Managing data in R

Motivational Example
========================================================


```{r,echo=F, eval=F}
load("~/learningr/api_auth.rda")
twitteR::setup_twitter_oauth(tw_consumer_key, tw_consumer_secret, tw_token, tw_token_secret)
tweets = searchTwitteR("#bigdata", resultType="recent", n = 100)
saveRDS(tweets, file="ex_tweets.rds")
```
```{r, echo=F}
tweets = readRDS("ex_tweets.rds")
```

```{r, eval=F}
library(twitteR)
tweets = searchTwitteR("#bigdata", resultType="recent", n = 100)
tweets = plyr::ldply(tweets, as.data.frame)
```

```{r}
kable(head(tweets[c("id", "created", "text")]))
```

Motivational Example
======

```{r}
library(RTextTools)
library(corpustools)
dtm = create_matrix(tweets$text)
dtm.wordcloud(dtm, freq.fun = sqrt)
```


Course Overview
===
type:section 

Thursday: Introduction to R
  + *Intro & Organizing data*
  + Transforming data
  + Accessing APIs from R

Friday: Corpus Analysis & Topic Modeling

Saturday: Machine Learning & Sentiment Analysis

Sunday: Semantic Networks & Grammatical Analysis

Introduction
===

+ Please introduce yourself
  + Background
  + What do you want to learn?
  + Experience with R / text / programming


Course Components
===

+ Each 3h session: 
+ Lecture & Interactive sessions
  + Please interrupt me!
+ Break
+ Hands-on sessions
+ http://vanatteveldt.com
  + Slides, hand-outs, data

What is R?
===

+ Programming language
+ Statistics Toolkit
+ Open Source
+ Community driven
  + Packages/libraries
  + Including many text analysis libraries
  
Cathedral and Bazar
===

<img src="cath_bazar.jpg">
  
The R Ecosystem
===

+ R
+ RStudio
  + RMarkdown / RPresentation
+ Packages
  + CRAN
  + Github


Interactive 1a: What is R?
====
type: section

Installing and using packages
===

```{r, eval=F}
install.packages("plyr")
library(plyr)
plyr::rename

devtools::install_github("amcat/amcat-r")
```

Data types: vectors
===

```{r}
x = 12
class(x)
x = c(1, 2, 3)
class(x)
x = "a text"
class(x)
```

Data Frames
===

```{r}
df = data.frame(id=1:3, age=c(14, 18, 24), 
          name=c("Mary", "John", "Luke"))
df
class(df)
```

Selecting a column
===

```{r}
df$age
df[["age"]]
class(df$age)
class(df$name)
```

Useful functions
===

Data frames:

```{r, eval=F}
colnames(df)
head(df)
tail(df)
nrow(df)
ncol(df)
summary(df)
```

Vectors:

```{r, eval=F}
mean(df$age)
length(df$age)
```


Other data types
===

+ Data frame:
  + Rectangular data frame
  + Columns vectors of same length
    + (vetor always has one type)
+ List:
  + Contain anything (inc data frames, lists)
  + Elements arbitrary type
+ Matrix:
  + Rectangular
  + All cells same (primitive) type
  
  
Finding help (and packages)
===

+ Built-in documentation
  + CRAN package vignettes
+ Task views
+ Google (sorry...)
  + r mailing list
  + stack exchange
  
Organizing Data in R
===
type: section
  

Subsetting

Recoding & Renaming columns

Ordering



Subsetting
===

```{r}
df[1:2, 1:2]
df[df$id %% 2 == 1, ]
df[, c("id", "name")]
```

Subsetting: `subset` function
===
```{r}
subset(df, id == 1)
subset(df, id >1 & age < 20)
```

Recoding columns
===
  
```{r}
df2 = df
df2$age2 = df2$age + df2$id
df2$age[df2$id == 1] = NA
df2$id = NULL
df2$old = df2$age > 20
df2$agecat = 
  ifelse(df2$age > 20, "Old", "Young")
df2
```

Text columns
===

+ `character` vs `factor`

```{r}
df2=df
df2$name = as.character(df2$name)
df2$name[df2$id != 1] = 
    paste("Mr.", df2$name[df2$id != 1])
df2$name = toupper(df2$name)
df2$name = gsub("\\.\\s*", "_", df2$name)
df2[grepl("mr", df2$name, ignore.case = T), ]
```

Renaming columns
===

```{r}
df2 = df
colnames(df2) = c("ID", "AGE", "NAME")
colnames(df2)[2] = "leeftijd"
df2 = plyr::rename(df2, c("NAME"="naam"))
df2
```
  
Ordering
====

```{r}
df[order(df$age), ]
plyr::arrange(df, -age)
```

Accessing elements
====

+ Data frame
  + Select one column: `df$col`, ` df[["col"]]`, 
  + Select columns: `df[c("col1" ,"col2")]`
  + Subset: `df[rows, columns]`
+ List:
  + Select one element: `l$el`, ` l[["el"]]`, `l[[1]]` 
  + Select columns: `l[[1:3]]`
+ Matrix:
  + All cells same type
  + Subset: `m[rows, columns]`

Interactive 1b
====
type: section

Organizing Data

Hands-on 1
====
type: section

Break

Hand-outs:
+ Playing with data
+ Organizing data
+ Play with your own data!