# ggfortify : Extension to ggplot2 to handle some popular packages - R software and data visualization

**ggfortify** extends **ggplot2** for plotting some popular **R** packages using a standardized approach, included in the function **autoplot**().

This article describes how to draw:

- a
**matrix**, - a
**scatter plot**, **diagnostic plots**for**linear model**,**time series**,- the results of
**principal component analysis**, - the results of
**clustering analysis**, - and
**survival curves**

The following R packages and functions are covered in the package **ggfortify**:

Package name | Functions |
---|---|

base | matrix and table |

cluster | clara, fanny and pam |

changepoint | cpt |

dlm | dlmFilter and dlmSmooth |

fGarch | fGARCH |

forecast | bats, forecast, ets and nnetar |

fracdiff | fracdiff |

glmnet | glmnet |

KFAS | KFS and signal |

lfda | klfda and self |

MASS | isoMDS and sammon |

stats | acf, ar, Arima, smdscale, decomposed.ts, density, fractanal, glm, HoltWinters, kmeans, lm, prcomp, princomp, spec, stepfun, stl and ts |

survival | survfit and survfit.cox |

strucchange | breakpoints and breakpointsfull |

timeSeries | timeSeries |

tseries | irts |

vars | varprd |

xts | xts |

zoo | zooreg |

# Installation

**ggfortify** can be installed from GitHub or CRAN:

```
# Github
if(!require(devtools)) install.packages("devtools")
devtools::install_github("sinhrks/ggfortify")
```

```
# CRAN
install.packages("ggfortify")
```

# Loading ggfortify

`library("ggfortify")`

# Plotting matrix

The function **autoplot.matrix**() is used:

`autoplot(object, geom = "tile")`

**object**: an object of class**matrix****geom**: allowed values are “tile” (for heatmap) or “point” (for scatter plot)

The **mtcars** data set is used in the example below.

```
df <- mtcars[, c("mpg", "disp", "hp", "drat", "wt")]
df <- as.matrix(df)
```

**Plot a heatmap**:

```
# Heatmap
autoplot(scale(df))
```

**Plot a scatter plot**: The data should be a matrix with 2 columns named **V1** and **V2**. The R code below plots **mpg** by **wt**. We start by renaming column names.

```
# Extract the data
df2 <- df[, c("wt", "mpg")]
colnames(df2) <- c("V1", "V2")
# Scatter plot
autoplot(df2, geom = 'point') +
labs(x = "mpg", y = "wt")
```

# Plotting diagnostics for LM and GLM

The function **autoplot.lm**() is used to draw diagnostic plots for **LM** and **GLM** [in **stats** package].

`autoplot(object, which = c(1:3, 5))`

**object**:**stats::lm**instance**which**: If a subset of the plots is required, specify a subset of the numbers 1:6.**ncol**and**nrow**allows you to specify the number of subplot columns and rows.

## Diagnostic plots for Linear Models (LM)

*iris* data set is used for computing the linear model

```
# Compute a linear model
m <- lm(Petal.Width ~ Petal.Length, data = iris)
# Create the plot
autoplot(m, which = 1:6, ncol = 2, label.size = 3)
```

```
# Change the color by groups (species)
autoplot(m, which = 1:6, label.size = 3, data = iris,
colour = 'Species')
```

## Diagnostic plots with Generalized Linear Models (GLM)

*USArrests* data set is used.

```
# Compute a generalized linear model
m <- glm(Murder ~ Assault + UrbanPop + Rape,
family = gaussian, data = USArrests)
# Create the plot
# Change the theme and colour
autoplot(m, which = 1:6, ncol = 2, label.size = 3,
colour = "steelblue") + theme_bw()
```

# Plotting time series

## Plotting ts objects

- Data set:
**AirPassengers** - R Function:
**autoplot.ts**()

`autoplot(AirPassengers)`

The function **autoplot**() can handle also other time-series-likes packages, including:

**zoo::zooreg**()**xts::xts**()**timeSeries::timSeries**()**tseries::irts**()**forecast::forecast**()**vars:vars**()

## Plotting with changepoint package

The **changepoint** package provides a simple approach for identifying shifts in mean and/or variance in a **time series**.

**ggfortify** supports **cpt** object in **changepoint** package.

```
library(changepoint)
autoplot(cpt.meanvar(AirPassengers))
```

## Plotting with strucchange package

**strucchange** is an R package for detecting jumps in data.

**Data set**: Nile

```
library(strucchange)
autoplot(breakpoints(Nile ~ 1))
```

# Plotting PCA (Principal Component Analysis)

- Data set:
**iris** - Function:
**autoplot.prcomp**()

```
# Prepare the data
df <- iris[, -5]
# Principal component analysis
pca <- prcomp(df, scale. = TRUE)
# Plot
autoplot(pca, loadings = TRUE, loadings.label = TRUE,
data = iris, colour = 'Species')
```

# Plotting K-means

- Data set:
**USArrests** - Function:
**autoplot.kmeans**()

The original data is required as **kmeans** object doesn’t store original data. Samples will be colored by groups (clusters).

```
autoplot(kmeans(USArrests, 3), data = USArrests,
label = TRUE, label.size = 3, frame = TRUE)
```

# Plotting cluster package

**ggfortify** supports cluster::**clara**, cluster::**fanny** and cluster::**pam** classes. These functions return object containing original data, so there is no need to pass original data explicitly.

The R code below shows an example for **pam**() function:

```
library(cluster)
autoplot(pam(iris[-5], 3), frame = TRUE, frame.type = 'norm')
```

# Plotting Local Fisher Discriminant Analysis

```
library(lfda)
# Local Fisher Discriminant Analysis (LFDA)
model <- lfda(iris[,-5], iris[, 5], 4, metric="plain")
autoplot(model, data = iris, frame = TRUE, frame.colour = 'Species')
```

# Plotting survival curves

```
library(survival)
fit <- survfit(Surv(time, status) ~ sex, data = lung)
autoplot(fit)
```

# Learn more

Read more on ggfortify.

# Infos

This analysis has been performed using **R software** (ver. 3.2.1) and **ggplot2** (ver. 1.0.1)

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