10-data.Rmd

# Example datasets 


## Edgar Anderson's Iris Data

In R:

```{r}
data(iris)
```
From the `iris` manual page:

> This famous (Fisher's or Anderson's) iris data set gives the
> measurements in centimeters of the variables sepal length and width
> and petal length and width, respectively, for 50 flowers from each
> of 3 species of iris.  The species are *Iris setosa*, *versicolor*,
> and *virginica*.

![Iris setosa  (credit Wikipedia)](https://upload.wikimedia.org/wikipedia/commons/thumb/5/56/Kosaciec_szczecinkowaty_Iris_setosa.jpg/220px-Kosaciec_szczecinkowaty_Iris_setosa.jpg)
![Iris versicolor  (credit Wikipedia)](https://upload.wikimedia.org/wikipedia/commons/thumb/4/41/Iris_versicolor_3.jpg/220px-Iris_versicolor_3.jpg)
![Iris virginica  (credit Wikipedia)](https://upload.wikimedia.org/wikipedia/commons/thumb/9/9f/Iris_virginica.jpg/220px-Iris_virginica.jpg)


```{r dtiris}
datatable(iris)
```

For more details, see `?iris`.

## Motor Trend Car Road Tests

In R

```{r}
data(mtcars)
```

From the `?mtcars` manual page:

> The data was extracted from the 1974 *Motor Trend* US magazine, and
> comprises fuel consumption and 10 aspects of automobile design and
> performance for 32 automobiles (1973-74 models).



```{r dtmtcars, fig.cap=""}
datatable(mtcars)
```

For more details, see `?mtcars`.

## Sub-cellular localisation

The `hyperLOPIT2015` data is used to demonstrate t-distributed stochastic neighbor embedding (t-SNE) and its
comparison to principal component analysis (PCA). These data provide sub-cellular localisation of
proteins in Mouse E14TG2a embryonic stem cells, as published
in [Christoforou et al. (2016)](https://doi.org/10.1038/ncomms9992).

The data comes as an `MSnSet` object from the `Biocpkg("MSnbase")`
package, specifically developed for such quantitative proteomics
data. Alternatively, comma-separated files containing a somehow
simplified version of the data can also be
found [here](https://github.com/lgatto/hyperLOPIT-csvs/). 

These data are only used to illustrate some concepts and are not
loaded and used directly to avoid installing numerous dependencies. 

They are available through the Bioconductor project and can be
installed with

```{r prolocinstall, eval=FALSE}
source("http://www.bioconductor.org/biocLite.R")
biocLite(c("MSnbsase", "pRoloc")) ## software
biocLite("pRolocdata") ## date
```

## The diamonds data 

The `diamonds` data ships with the `r CRANpkg("ggplot2")` package and
predict the price (in US dollars) of about 54000 round cut diamonds.


In R:

```{r}
library("ggplot2")
data(diamonds)
```

```{r dtdiamonds, fig.cap=""}
datatable(diamonds)
```

See also `?diamonds`.

## The Sonar data

The `Sonar` data from the `r CRANpkg("mlbench")` package can be used
to train a classifer to recognise mines from rocks using sonar
data. The data is composed to 60 features representing the energy
within a particular frequency band.

In R:

```{r}
library("mlbench")
data(Sonar)
```

```{r dtsonar, fig.cap=""}
datatable(Sonar)
```

See also `?Sonar`.

## Housing Values in Suburbs of Boston

The `Boston` data from the `r CRANpkg("MASS")` provides the median
value of owner-occupied homes (`medv`) in $1000s as well as 13 other
features for 506 homes in Boston.

In R:

```{r, message=FALSE}
library("MASS")
data(Boston)
```


```{r dtboston, fig.cap=""}
datatable(Boston)
```

See also `?Boston`.

## Customer churn

This data from the `r CRANpkg("C50")` package and distributes a
training set with 3333 samples and a test set containing 1667 samples
of customer attrition.

In R:

```{r}
library("modeldata")
data(mlc_churn, package = "modeldata")
churnTrain <- mlc_churn[1:3333, ]
churnTest <- mlc_churn[3334:5000, ]
```


```{r dtchurn, fig.cap=""}
datatable(churnTrain)
```