# D Reporting with `R`

and `R Commander`

A nice feature of `R Commander`

is that integrates seamless with `R Markdown`

, which is able to create `.html`

, `.pdf`

and `.docx`

reports directly from the outputs of `R`

. Depending on the kind of report that we want, we will need the following auxiliary software^{35}:

`.html`

. No extra software is required.`.docx`

and`.rtf`

. You must install`Pandoc`

, a document converter software. Download it here.`.pdf`

(only recommended for experts). An installation of LaTeX, additionally to`Pandoc`

, is needed. Download LaTeX here.

The workflow is simple. Once you have done some statistical analysis, either by using `R Commander`

’s menus or `R`

code directly, you will end up with an `R`

script, on the `'R Script'`

tab, that contains all the commands you have run so far. Switch then to the `'R Markdown'`

tab and you will see the commands you have entered in a different layout, which essentially encapsulates the code into chunks delimited by ````{r}`

and `````

. This will generate a report once you click in the `'Generate report'`

button.

Let’s illustrate this process through an example. Suppose we were analyzing the `Boston`

dataset, as we did in Section 3.1.2. *Ideally*^{36} our final script would be something like this:

```
# A simple and non-exhaustive analysis for the price of the houses in the Boston
# dataset. The purpose is to quantify, by means of a multiple linear model,
# the effect of 14 variables in the price of a house in the suburbs of Boston.
# Import data
library(MASS)
data(Boston)
# Make a multiple linear regression od medv in the rest of variables
mod <- lm(medv ~ ., data = Boston)
summary(mod)
# Check the linearty assumption
plot(mod, 1) # Clear non-linearity
# Let's consider the transformations given in Harrison and Rubinfeld (1978)
modTransf <- lm(I(log(medv * 1000)) ~ I(rm^2) + age + log(dis) +
log(rad) + tax + ptratio + I(black / 1000) +
I(log(lstat / 100)) + crim + zn + indus + chas +
I((10 * nox)^2), data = Boston)
summary(modTransf)
# The non-linearity is more subtle now
plot(modTransf, 1)
# Look for the best model in terms of the BIC
modTransfBIC <- stepwise(modTransf)
summary(modTransfBIC)
# Let's explore the most significant variables, to see if the model can be
# reduced drastically in complexity
mod3D <- lm(I(log(medv * 1000)) ~ I(log(lstat / 100)) + crim, data = Boston)
summary(mod3D)
# With only 2 variables, we explain the 72% of variability.
# Compared with the 80% with 10 variables, it is an important improvement
# in terms of simplicity.
# Let's add these variables to the dataset, so we can call scatterplotMatrix
# and scatter3d through R Commander's menu
Boston$logMedv <- log(Boston$medv * 1000)
Boston$logLstat <- log(Boston$lstat / 100)
# Visualize the pair-by-pair relations of the response and two predictors
scatterplotMatrix(~ crim + logLstat + logMedv, reg.line = lm, smooth = FALSE,
spread = FALSE, span = 0.5, ellipse = FALSE,
levels = c(.5, .9), id.n = 0, diagonal = 'histogram',
data = Boston)
# Visualize the full relation between the response and the two predictors
scatter3d(logMedv ~ crim + logLstat, data = Boston, fit = "linear",
residuals = TRUE, bg = "white", axis.scales = TRUE, grid = TRUE,
ellipsoid = FALSE)
```

This contains all the major points in the analysis, that now can be expanded and detailed. You can download the script here, open it through `'File' -> 'Open script file...'`

and run it by yourself in `R Commander`

. If you so, and then switch to the `R Markdown`

tab, you will see this:

```
---
title: "Replace with Main Title"
author: "Your Name"
date: "AUTOMATIC"
---
```{r echo=FALSE, message=FALSE}
# include this code chunk as-is to set options
knitr::opts_chunk$set(comment=NA, prompt=TRUE)
library(Rcmdr)
library(car)
library(RcmdrMisc)
```
```{r echo=FALSE}
# include this code chunk as-is to enable 3D graphs
library(rgl)
knitr::knit_hooks$set(webgl = hook_webgl)
```
```{r}
# A simple and non-exhaustive analysis for the price of the houses in the Boston
```
```{r}
# dataset. The purpose is to quantify, by means of a multiple linear model,
```
```{r}
# the effect of 14 variables in the price of a house in the suburbs of Boston.
```
```{r}
# Import data
```
```{r}
library(MASS)
```
```{r}
data(Boston)
```
```{r}
# Make a multiple linear regression of medv in the rest of variables
```
```{r}
mod <- lm(medv ~ ., data = Boston)
```
```{r}
summary(mod)
```
[More outputs - omitted]
```
```

The complete, lengthy, file can be downloaded here. This is an `R Markdown`

file, which has extension `.Rmd`

. As you can see, by default, `R Commander`

will generate a *code chunk* like

```
```{r}
code line
```
```

for each `code line`

you run in `R Commander`

. You probably will want to modify this *crude* report manually by merging chunks of code, removing comments or adding more information in between chunks of code. To do so, go to `'Edit' -> 'Edit Markdown document'`

. Here you can also remove unnecessary chunks of code resulting from any mistake or irrelevant analyses.

The following file (download) could be a final report. Pay attention to the numerous changes with respect to the previous one:

```
---
title: "What makes a house valuable?"
subtitle: "A reproducible analysis in the Boston suburbs"
author: "Outstanding student 1, Awesome student 2 and Great student 3"
date: "31/11/16"
---
```{r echo=FALSE, message=FALSE, warning=FALSE}
# include this code chunk as-is to set options
knitr::opts_chunk$set(comment=NA, prompt=TRUE)
library(Rcmdr)
library(car)
library(RcmdrMisc)
```
```{r echo=FALSE, message=FALSE, warning=FALSE}
# include this code chunk as-is to enable 3D graphs
library(rgl)
knitr::knit_hooks$set(webgl = hook_webgl)
```
This short report shows a simple and non-exhaustive analysis for the price of
the houses in the `Boston` dataset. The purpose is to quantify, by means of a
multiple linear model, the effect of 14 variables in the price of a house in
the suburbs of Boston.
We start by importing the data into `R` and considering a multiple linear
regression of `medv` (median house value) in the rest of variables:
```{r}
# Import data
library(MASS)
data(Boston)
```
```{r}
mod <- lm(medv ~ ., data = Boston)
summary(mod)
```
The variables `indus` and `age` are non-significant in this model. Also,
although the adjusted R-squared is high, there seems to be a clear
non-linearity:
```{r}
plot(mod, 1)
```
In order to bypass the non-linearity, we are going to consider the
non-linear transformations given in Harrison and Rubinfeld (1978)
for both the response and the predictors:
```{r}
modTransf <- lm(I(log(medv * 1000)) ~ I(rm^2) + age + log(dis) +
log(rad) + tax + ptratio + I(black / 1000) +
I(log(lstat / 100)) + crim + zn + indus + chas +
I((10*nox)^2), data = Boston)
summary(modTransf)
```
The adjusted R-squared is now higher and, what is more important, the
non-linearity now is more subtle (it is still not linear but closer
than before):
```{r}
plot(modTransf, 1)
```
However, `modTransf` has more non-significant variables. Let\'s see if
we can improve over the previous model by removing some of the
non-significant variables? To see this, we look for the best model in
terms of the Bayesian Information Criterion (BIC) by `stepwise`:
```{r}
modTransfBIC <- stepwise(modTransf, trace = 0)
summary(modTransfBIC)
```
The resulting model has a slightly higher adjusted R-squared than `modTransf`
with all the variables significant.
We explore the most significant variables to see if the model can be reduced
drastically in complexity.
```{r}
mod3D <- lm(I(log(medv * 1000)) ~ I(log(lstat / 100)) + crim, data = Boston)
summary(mod3D)
```
It turns out that **with only 2 variables, we explain the 72% of variability**.
Compared with the 80% with 10 variables, it is an important improvement
in terms of simplicity: the logarithm of `lstat` (percent of lower status of
the population) and `crim` (crime rate) alone explain the 72% of the
variability in the house prices.
We add these variables to the dataset, so we can call `scatterplotMatrix` and
`scatter3d` through `R Commander`,
```{r}
Boston$logMedv <- log(Boston$medv * 1000)
Boston$logLstat <- log(Boston$lstat / 100)
```
and conclude with the visualization of:
1. the pair-by-pair relations of the response and the two predictors;
2. the full relation between the response and the two predictors.
```{r}
# 1
scatterplotMatrix(~ crim + logLstat + logMedv, reg.line = lm, smooth = FALSE,
spread = FALSE, span = 0.5, ellipse = FALSE,
levels = c(.5, .9), id.n = 0, diagonal = 'histogram',
data = Boston)
```
```{r webgl = TRUE}
# 2
scatter3d(logMedv ~ crim + logLstat, data = Boston, fit = "linear",
residuals = TRUE, bg = "white", axis.scales = TRUE, grid = TRUE,
ellipsoid = FALSE)
```
```

When we click on `'Generate report'`

for the above `R Markdown`

file, we should get the following output files:

`.html`

: visualize and download. Once it is produced, this file is difficult to modify, but very easy to distribute (anyone with a browser can see it).`.docx`

: visualize and download. Easy to modify in a document processor like Microsoft Office. Easy to distribute.`.rtf`

: download. Easy to modify in a document processor, not very elegant.`.pdf`

: visualize and download. Elegant and easy to distribute, but hard to modify once it is produced.

For advanced users, there is a lot of information on mastering `R Markdown`

here by using `RStudio`

, a more advanced framework than `R Commander`

.