# factoextra: Reduce overplotting of points and labels - R software and data mining

To reduce overplotting, the argument **jitter** is used in the functions **fviz_pca_xx()**, **fviz_ca_xx()** and **fviz_mca_xx()** available in the R package **factoextra**.

The argument **jitter** is a list containing the parameters *what*, *width* and *height* (i.e jitter = list(what, width, height)):

**what**: the element to be jittered. Possible values are “point” or “p”; “label” or “l”; “both” or “b”.**width**: degree of jitter in x direction**height**: degree of jitter in y direction

Some examples of usage are described in the next sections.

# Install required packages

**FactoMineR**: for computing PCA (Principal Component Analysis), CA (Correspondence Analysis) and MCA (Multiple Correspondence Analysis)**factoextra**: for the visualization of FactoMineR results

FactoMineR and factoextra R packages can be installed as follow :

```
install.packages("FactoMineR")
# install.packages("devtools")
devtools::install_github("kassambara/factoextra")
```

Note that, for factoextra a version >= 1.0.3 is required for using the argument **jitter**. If it’s already installed on your computer, you should re-install it to have the most updated version.

# Load FactoMineR and factoextra

```
library("FactoMineR")
library("factoextra")
```

# Multiple Correspondence Analysis (MCA)

```
# Load data
data(poison)
poison.active <- poison[1:55, 5:15]
# Compute MCA
res.mca <- MCA(poison.active, graph = FALSE)
# Default plot
fviz_mca_ind(res.mca)
```

```
# Use jitter to reduce overplotting.
# Only labels are jittered
fviz_mca_ind(res.mca, jitter = list(what = "label",
width = 0.1, height = 0.15))
```

```
# Jitter both points and labels
fviz_mca_ind(res.mca, jitter = list(what = "both",
width = 0.1, height = 0.15))
```

# Simple Correspondence Analysis (CA)

```
# Load data
data("housetasks")
# Compute CA
res.ca <- CA(housetasks, graph = FALSE)
# Default biplot
fviz_ca_biplot(res.ca)
```

```
# Jitter in y direction
fviz_ca_biplot(res.ca, jitter = list(what = "label",
width = 0.4, height = 0.3))
```

# Principal Componet Analysis (PCA)

```
# Load data
data(decathlon2)
decathlon2.active <- decathlon2[1:23, 1:10]
# Compute PCA
res.pca <- PCA(decathlon2.active, graph = FALSE)
# Default biplot
fviz_pca_ind(res.pca)
```

```
# Use jitter in x and y direction
fviz_pca_ind(res.pca, jitter = list(what = "label",
width = 0.6, height = 0.6))
```

# Infos

This analysis has been performed using **R software** (ver. 3.2.1), **FactoMineR** (ver. 1.30) and **factoextra** (ver. 1.0.2)

Show me some love with the like buttons below... Thank you and please don't forget to share and comment below!!

Montrez-moi un peu d'amour avec les like ci-dessous ... Merci et n'oubliez pas, s'il vous plaît, de partager et de commenter ci-dessous!

## Recommended for You!

## Recommended for you

This section contains best data science and self-development resources to help you on your path.

### Coursera - Online Courses and Specialization

#### Data science

- Course: Machine Learning: Master the Fundamentals by Standford
- Specialization: Data Science by Johns Hopkins University
- Specialization: Python for Everybody by University of Michigan
- Courses: Build Skills for a Top Job in any Industry by Coursera
- Specialization: Master Machine Learning Fundamentals by University of Washington
- Specialization: Statistics with R by Duke University
- Specialization: Software Development in R by Johns Hopkins University
- Specialization: Genomic Data Science by Johns Hopkins University

#### Popular Courses Launched in 2020

- Google IT Automation with Python by Google
- AI for Medicine by deeplearning.ai
- Epidemiology in Public Health Practice by Johns Hopkins University
- AWS Fundamentals by Amazon Web Services

#### Trending Courses

- The Science of Well-Being by Yale University
- Google IT Support Professional by Google
- Python for Everybody by University of Michigan
- IBM Data Science Professional Certificate by IBM
- Business Foundations by University of Pennsylvania
- Introduction to Psychology by Yale University
- Excel Skills for Business by Macquarie University
- Psychological First Aid by Johns Hopkins University
- Graphic Design by Cal Arts

### Books - Data Science

#### Our Books

- Practical Guide to Cluster Analysis in R by A. Kassambara (Datanovia)
- Practical Guide To Principal Component Methods in R by A. Kassambara (Datanovia)
- Machine Learning Essentials: Practical Guide in R by A. Kassambara (Datanovia)
- R Graphics Essentials for Great Data Visualization by A. Kassambara (Datanovia)
- GGPlot2 Essentials for Great Data Visualization in R by A. Kassambara (Datanovia)
- Network Analysis and Visualization in R by A. Kassambara (Datanovia)
- Practical Statistics in R for Comparing Groups: Numerical Variables by A. Kassambara (Datanovia)
- Inter-Rater Reliability Essentials: Practical Guide in R by A. Kassambara (Datanovia)

#### Others

- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham & Garrett Grolemund
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Géron
- Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce & Andrew Bruce
- Hands-On Programming with R: Write Your Own Functions And Simulations by Garrett Grolemund & Hadley Wickham
- An Introduction to Statistical Learning: with Applications in R by Gareth James et al.
- Deep Learning with R by François Chollet & J.J. Allaire
- Deep Learning with Python by François Chollet