Recap III

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# Recap III
### The R Bootcamp <a href='https://therbootcamp.github.io'>www.therbootcamp.com</a> <a href='https://twitter.com/therbootcamp'>@therbootcamp</a>
### July 2018

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<a href="https://therbootcamp.github.io/">Basel, July 2018</a>
&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;
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<a href="https://therbootcamp.github.io/">www.therbootcamp.com</a>
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.pull-left35[
 
> ### "As good as R is for statistics, it's as good if not better for data visualisation."
> ### Nathaniel D. Phillips
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.pull-right65[
 
<img src="https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/ggplotgallery.png" width="100%" style="display: block; margin: auto;" />

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# Beuatiful plot in 8 lines of code!

```r
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, col = class)) +
  geom_point() + 
  geom_smooth(col = "blue", method = "lm")+
  labs(x = "Engine Displacement in Liters", y = "Highway miles per gallon", 
       title = "MPG data", caption = "Source: mpg data in ggplot2", 
       subtitle = "Cars with higher engine displacement tend to have lower highway mpg") + 
  theme_bw()
```

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# Caret

.pull-left55[

<high>Caret</high> stands for <high>C</high>lassification <high>A</high>nd <high>RE</high>gression <high>T</high>raining.

`caret` is a 'wrapper' packages that automates the machine learning process.

Evaluate and provides <high>hundreds of different ML algorithms</high> by changing one `character` string (not line!).

`caret` knows each model's tuning parameters and <high>chooses the best ones</high> for your data.

```r
library(caret)

train(..., method = "lm") # Regression!
train(..., method = "rf") # Random forests!
train(..., method = "ada") # Boosted trees
```
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.pull-right4[
<div class="figure" style="text-align: center">
<img src="https://3qeqpr26caki16dnhd19sv6by6v-wpengine.netdna-ssl.com/wp-content/uploads/2014/09/Caret-package-in-R.png" alt="The almighty Caret!" width="90%" />
The almighty Caret!
</div>

]

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.pull-left5[

# ML steps with `caret`

Step 0: Create training and test data (if necessary)

```r
train_v <- createDataPartition(y, times, p)

data_train <- data %>% slice(train_v)
data_test <- data %>% slice(-train_v)
```

Step 1: Define control parameters

```r
ctl <- trainControl(method = "repeatedcv",
 number = 10, 
 repeats = 2) 
```

Step 2: Train model

```r
rpart_train <- train(form = income ~ ., 
 data = data_train,
 method = "rpart",
 trControl = ctl)
```

]

.pull-right45[
 
Step 3: Explore

```r
rpart_train            # Print object
varImp(rpart_train)    # Var importance
rpart_train$finalModel # Final model
```

Step 4: Predict

```r
my_pred <- predict(object = rpart_train, 
 newdata = data_test)
```

Step 4: Evaluate

```r
postResample(pred = bas_pred, 
             obs = baselers_test$income)
```

]

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# Interactive plots with Shiny

<img src=https://github.com/therbootcamp/therbootcamp.github.io/blob/master/_sessions/_image/shiny_definition.png?raw=true" height="450px"></img> 
 <a href="http://shiny.rstudio.com/https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/shiny-cheatsheet.pdf">RStudio cheat sheet</a>

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# You built this app (...or you could have if you had more time)

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# Interactive plots with Shiny

<img src="https://github.com/therbootcamp/therbootcamp.github.io/blob/master/_sessions/_image/shiny_definition.png?raw=true" height="450px"></img> 
 <a href="http://shiny.rstudio.com/https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/shiny-cheatsheet.pdf">RStudio cheat sheet</a>

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# The Stark family: A Game of Thrones example

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# Tools & Methods

.pull-left5[

### Basics
[Tokenizing](https://en.wikipedia.org/wiki/Word_segmentation) 
[Stemming](https://en.wikipedia.org/wiki/Stemming) 
[Part-of-speech tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging) 
[Parsing](https://en.wikipedia.org/wiki/Parsing) 
etc.

### Semantics
[Lexical semantics](https://en.wikipedia.org/wiki/Word_segmentation) 
[Machine Translation](https://en.wikipedia.org/wiki/Machine_translation) 
[Relationship extraction](https://en.wikipedia.org/wiki/Relationship_extraction) 
[Sentiment analysis](https://en.wikipedia.org/wiki/Sentiment_analysis) 
[Topic analysis](https://en.wikipedia.org/wiki/Topic_segmentation) 
etc.

]

.pull-right5[

### Discourse
[Automatic summarization](https://en.wikipedia.org/wiki/Automatic_summarization) 
[Discourse analysis](https://en.wikipedia.org/wiki/Discourse_analysis) 
etc.

### Speech
[Speech recognition](https://en.wikipedia.org/wiki/Speech_recognition) 
[Speech segmentation](https://en.wikipedia.org/wiki/Speech_segmentation) 
[Relationship extraction](https://en.wikipedia.org/wiki/Relationship_extraction) 
[Text-to-speech](https://en.wikipedia.org/wiki/Text-to-speech) 
etc.

]

from <a href="https://en.wikipedia.org/wiki/Natural-language_processing">Wikipedia</a>

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# Text analysis

.pull-left45[
Analysis of <high>Game of Thrones word frequencies</high> displayed as a word cloud.

]

.pull_right45[
 
Analysis of <high>Game of Thrones episode sentiment</high> as a function episode's index within a season.

]

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# Today

<a href="https://therbootcamp.github.io/BaselRBootcamp_2018July/schedule">Schedule</a>