Description Usage Arguments Details Value Author(s) Examples. View source: R/biplot.R. Draw a bi-plot, comparing 2 selected principal components.

1. How To Read Biplot
2. R Biplot Example Csv Files
3. R Pca Biplot
4. R Biplot Example Csv Pdf

FactoMineR is a quick and easy R package for generating biplots, such as the following plot showing the columns as arrows with the rows to be added later as points. As you might recall from a previous post, a biplot maps a data matrix by plotting both the rows and columns in the same figure. Here the columns (variables) are arrows and the rows (individuals) will be points. By default, FactoMineR avoids cluttered maps by separating the variables and individuals factor maps into two plots. The variables factor map appears below, and the individuals factor map will be shown later in this post.

The dataset comes from David Wishart’s book Whiskey Classified, Choosing Single Malts by Flavor. Some 86 whiskies from different regions of Scotland were rated on 12 aromas and flavors from “not present” (a rating of 0) to “pronounced” (a rating of 4). Luba Gloukhov ran a cluster analysis with this data and plotted the location where each whisky was distilled on a map of Scotland. The dataset can be retrieved as a csv file using the R function read.csv(“clipboard’). All you need to do is go to the web site, select and copy the header and the data, and run the R function read.csv pointing to the clipboard. All the R code is presented at the end of this post.

Each arrow in the above plot represents one of the 12 ratings. FactoMineR takes the 86 x 12 matrix and performs a principal component analysis. The first principal component is labeled as Dim 1 and accounts for almost 27% of the total variation. Dim 2 is the second principal component with an additional 16% of the variation. One can read the component loadings for any rating by noting the perpendicular projection of the arrow head onto each dimension. Thus, Medicinal and Smoky have high loadings on the first principal component with Sweetness, Floral and Fruity anchoring the negative end. One could continue in the same manner with the second principal component, however, at some point we might notice the semi-circle that runs from Floral, Sweetness and Fruity through Nutty, Winey and Spicy to Smoky, Tobacco and Medicinal. That is, the features sweep out a one-dimensional arc, not unlike a multidimensional scaling of color perceptions (see Figure 1).

The modeling process remains same, as explained for R users above. Import numpy as np from sklearn.decomposition import PCA import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import scale%matplotlib inline. #Load data set data = pd.readcsv('BigMartPCA.csv') #convert it to numpy arrays X=data.values. For example – A marketing company can categorise their customers based on their economic background, age and several other factors to sell their products, in a better way. Get a deep insight into Descriptive Statistics in R. Applications of Clustering in R. Applications of R clustering are as follows. In R, we can read data from files stored outside the R environment. We can also write data into files that will be stored and accessed by the operating system. R can read and write into various file formats like csv, excel, txt,rds, xml, json, etc.

Now, we will add the 86 points representing the different whiskies. But first we will run a cluster analysis so that when we plot the whiskies, different colors will indicate cluster membership. I have included the R code to run both a finite mixture model using the R package mclust and a k-means. Both procedures yield four-cluster solutions that classify over 90% of the whiskies into the same clusters. Luba Gloukhov also extracted four clusters by looking for an “elbow” in the plot of the within-cluster sum-of-squares from two through nine clusters. By default, Mclust will test one through nine clusters and select the best model using the BIC as the selection criteria. The cluster profiles from mclust are presented below.

Black	Red	Green	Blue	Total
27	36	6	17	86
31%	42%	7%	20%	100%
Body	2.7	1.4	3.7	1.9	2.1
Sweetness	2.4	2.5	1.5	2.1	2.3
Smoky	1.5	1.0	3.7	1.9	1.5
Medicinal	0.0	0.2	3.3	1.0	0.5
Tobacco	0.0	0.0	0.7	0.3	0.1
Honey	1.9	1.1	0.2	1.0	1.3
Spicy	1.6	1.1	1.7	1.6	1.4
Winey	1.9	0.5	0.5	0.8	1.0
Nutty	1.9	1.3	1.2	1.4	1.5
Malty	2.1	1.7	1.3	1.7	1.8
Fruity	2.1	1.9	1.2	1.3	1.8
Floral	1.6	2.1	0.2	1.4	1.7

Finally, we are ready to look at the biplot with the rows represented as points and the color of each point indicating cluster membership, as shown below in what FactoMineR calls the individuals factor map. To begin, we can see clear separation by color suggesting that differences among the cluster reside in the first two dimensions of this biplot. It is important to remember that the cluster analysis does not use the principal component scores. There is no data reduction prior to the clustering.

The Green cluster contains only 6 whiskies and falls toward the right of the biplot. This is the same direction as the arrows for Medicinal, Tobacco and Smoky. Moreover, the Green cluster received the highest scores on these features. Although the arrow for Body does not point in that direction, you should be able to see that the perpendicular projection of the Green points will be higher than that for any other cluster. The arrow for Body is pointed upward because a second and larger cluster, the Black, also receives a relatively high rating. This is not the case for other three ratings. Green is the only cluster with high ratings on Smoky or Medicinal. Similarly, though none of the whiskies score high on Tobacco, the six Green whiskies do get the highest ratings.

You can test your ability to interpret biplots by asking on what features the Red cluster should score the highest. Look back up to the vector map, and identify the arrows pointing in the same direction as the Red cluster or pointing in a direction so that the Red points will project toward the high end of the arrow. Do you see at least Floral and Sweetness? The process continues in the same manner for the Black cluster, but the Blue cluster, like its points, fall in the middle without any distinguishing features.

Hopefully, you have not been troubled by my relaxed and anthropomorphic writing style. Vectors do not reposition themselves so that all the whiskies earning high scores will project themselves toward its high end, and points do not move around looking for that one location that best reproduces all their ratings. However, principal component analysis does use a singular value decomposition to factor data matrices into row and column components that reproduce the original data as closely as possible. Thus, there is some justification for such talk. Nevertheless, it helps with the interpretation to let these vectors and points come alive and have their own intentions.

What Did We Do and Why Did We Do It?

We began trying to understand a cluster analysis derived from a data matrix containing the ratings for 86 whiskies across 12 aroma and taste features. Although not a large data matrix, one still has some difficulty uncovering any underlying structure by looking one variable/column at a time. The biplot helps by creating a low-dimensional graphic display with ratings as vectors and whiskies as points. The ratings appeared to be arrayed along an arc from floral to medicinal, and the 86 whiskies were located as points in this same space.

Now, we are ready to project the cluster solution onto this biplot. By using separate ratings, the finite mixture model worked in the 12-dimensional rating space and not in the two-dimensional world of the biplot. Yet, we see relatively coherent clusters occupying different regions of the map. In fact, except for the Blue cluster falling in the middle, the clusters move along the arc from a Red floral to a Black malty/honey/nutty/winey to a Green medicinal. The relationships among the four clusters are revealed by their color coding on the biplot. They are no longer four qualitatively distinct entries, but a continuum of locally adjacent groupings arrayed along a nonlinear dimension from floral to medicinal.

R code needed to run all the analysis in this post.

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In thistutorial you will learn how to read a csv file in R Programming with'read.csv' and 'read.csv2' functions. You will learn to import data inR from your computer or from a source on internet using url for readingcsv data.

Common methods for importingCSV data in R

1. Read a file from currentworking directory - using setwd.

2. Read a filefrom any location on your computer using file path.

3. Usefile.choose() method to select a csv file to load in R.

4. Use fullurl to read a csv file from internet.

CSV files

CSV standsfor Comma Seperated Values. A CSV file is used to storedata. It is a plain text file with .csv extension. In these type offiles values are seperated by ',' (comma) or ';' (semi-colon)

If you want to learn R efficiently, Step by Step for Data Analysis or Data Science with Practical Examples, 1 on 1 live from a professional R Tutor please check this R Tutoring Online with Exercises and Projects.

CSV fileshave many benefits, as they are simple text files consisting of linesand each line of data is represented by a line in csv file which helpsfor storing tabular data. Most applications support reading and writingcsv format.

An exampleof csv file is

Name,Age,Salary
Peter,35,3000
John,25,4000
Sarah,29,2900
David,54,7000
Create a new folder 'csvfiles' on your C: drive. Open any text editorlike notepad, copy this data into it and save it as 'testfile.csv' incsvfiles folder. Now you are good to go.

Reading csv file with read.csvfunction

The function read.csv() isused to import data from a csv file. This function can take manyarguments, but the most important is file which is thename of file to be read. This function reads the data as a dataframe.If the values are seperated by a comma use read.csv() and if the valuesare seperated by ; (a semi-colon) use read.csv2() function. Otherwisethere is no difference between these two functions.

Read csv from working directory

In case youhave a folder with many csv files and want to read from this folderquite often then it is better to first set that folder as your currentworking directory so that you can easily read files of this folder. Forthat purpose first you will need to use getwd() function and then usesetwd() function. Lets say we want to make csvfiles folder onC: drive as our current working directory. We find our current workingdirectory

>getwd()

[1] 'd:/ProgramFiles/RStudio'

Then we setour working directory to csvfiles folder on c: drive

>setwd('c:/csvfiles')

Checkingagain for working directory

>getwd()

[1]'c:/csvfiles'

Now its timeto read the file testfile.csv

>data <- read.csv('testfile.csv')

How To Read Biplot

Analysisof csv file

Here data isa new variable or object which will store values read from csv file.read.csv is the name of function and we are providing only one argumentto this function which is the file name with extension. After importingdata in R you can check and see it with some common functions.

1. View():This function will show you the values of csv file in a table format.

2. nrow():This function returns the total number of rows in your dataframe.

3.ncol(): Returnsthe total number of columns in your dataframe.

4.colnames(): This function returns the column headers or columnnames.

5.str(): Returns the structure of your dataframe. Column names with datatypes and factors.

RstudioOutput:

Readcsv with file path

If you have to read a singlecsv file or you don't want to change your working directory theninstead of using getwd and setwd for setting it, simply use file pathfor reading that file. Lets suppose your current working directory is 'd:/ProgramFiles/RStudio'. Andyou simply want to read csv file without changing it. First you willcreate a new variable file and assign the complete path of file withits name and extension to this variable. And then use it to import datain R.

>file <- 'c:/csvfiles/testfile.csv'

> data <-read.csv(file)

R Biplot Example Csv Files

Readcsv with file.choose()

In case youdon't exactly know the file location or even not sure about name offile you may simply use file.choose option in read.csv function. Thiswill open a file dialog box to select the file you want to open in R.

R Pca Biplot

>data <- read.csv(file.choose())

Rread csv from internet source

To read a csvfile from a web resource for data analysis the same function i.eread.csv() will be used. In this case you need to have a complete urlor internet location of csv file. Lets support we want to read a filenamed advertising.csv from a website with this url'http://faculty.marshall.usc.edu/gareth-james/ISL/Advertising.csv'

R Biplot Example Csv Pdf

This is asample file which contains four columns and about 200 rows. You canimport it in R and use the analysis methods describe earlier to have aview of this file contents. Then you may simply download this file onyour computer and use the earlier methods to open it as a practice forreinforcing what you learnt in this tutorial.

To read filefrom that location R code will be

> data <-read.csv('http://faculty.marshall.usc.edu/gareth-james/ISL/Advertising.csv')

or you canuse the file variable for storing url and then using it to import filein R

> file <-'http://faculty.marshall.usc.edu/gareth-james/ISL/Advertising.csv'

> data <-read.csv(file)