# R scripts

Here goes a little bit of my late experiences with R scripts. Comments, suggestions and/or opinions are welcome.

Usefulness of R scripts

Besides being an amazing interactive tool for data analysis, R software commands can also be executed as scripts. This is useful for example when we need to work in large projects where different parts of the project needs to be implemented using different languages that are later glued together to form the final product.

In addition, it is extremely useful to be able to take advantage of pipeline capabilities of the form

cat file.txt | preProcessInPython.py | runRmodel.R | formatOutput.sh > output.txt


Write programs that do one thing and do it well. Write programs to work together. Write programs to handle text streams, because that is a universal interface. — Doug McIlroy

Basic R script

A basic template for an R script is given by

#! /usr/bin/env Rscript

# R commands here


To start with a simple example, create a file myscript.R and include the following code on it:

#! /usr/bin/env Rscript

x <- 5
print(x)


Now go to your terminal and type chmod +x myscript.R to give the file execution permission. Then, execute your first script by typing ./myscript.R on the terminal. You should see

[1] 5


displayed on your terminal since the result is by default directed to stdout. We could have written the output of x to a file instead, of course. In order to do this just replace the print(x) statement by some writing command, as for example

output <- file("output_file.txt", "w")
write(x, file = output)
close(output)


which will write 5 to output_file.txt.

Processing command-line arguments

There are different ways to process command-line arguments in R scripts. My favorite so far is to use the getopt package from Allen Day and Trevor L. Davis. Type

require(devtools)
devtools::install_github("getopt", "trevorld")


in an R environment to install it on your machine. To use getopt in your R script you need to specify a 4 column matrix with information about the command-line arguments that you want to allow users to specify. Each row in this matrix represent one command-line option. For example, the following script allows the user to specify the output variable using the short flag -x or the long flag --xValue.

#! /usr/bin/env Rscript
require("getopt", quietly=TRUE)

spec = matrix(c(
"xValue"   , "x", 1, "double"
), byrow=TRUE, ncol=4)

opt = getopt(spec);

if (is.null(opt$xValue)) { x <- 5 } else { x <- opt$xValue
}

print(x)


As you can see above the spec matrix has four columns. The first defines the long flag name xValue, the second defines the short flag name x, the third defines the type of argument that should follow the flag (0 = no argument, 1 = required argument, 2 = optional argument.), the fourth defines the data type to which the flag argument shall be cast (logical, integer, double, complex, character) and there is a possible 5th column (not used here) that allow you to add a brief description of the purpose of the option. Now our myscript.R accepts command line arguments:

./myscript.R
[1] 5
myscript.R -x 7
[1] 7
myscript.R --xValue 9
[1] 9


Verbose mode and stderr

We can also create a verbose flag and direct all verbose comments to stderr instead of stdout, so that we don’t mix what is the output of the script with what is informative messages from the verbose option. Following is an illustration of a verbose flag implementation.

#! /usr/bin/env Rscript
require("getopt", quietly=TRUE)

spec = matrix(c(
"xValue" , "x", 1, "double",
"verbose", "v", 0, "logical"
), byrow=TRUE, ncol=4)

opt = getopt(spec);

if (is.null(opt$xValue)) { x <- 5 } else { x <- opt$xValue
}

if (is.null(opt$verbose)) { verbose <- FALSE } else { verbose <- opt$verbose
}

if (verbose) {
write("Verbose going to stderr instead of stdout",
stderr())
}

write(x, file = stdout())


We have now two possible flags to specify in our myscript.R:

./myscript.R
5
./myscript.R -x 7
7
./myscript.R -x 7 -v
Verbose going to stderr instead of stdout
7


The main difference of directing verbose messages to stderr instead of stdout appear when we pipe the output to a file. In the code below the verbose message appears on the terminal and the value of x goes to the output_file.txt, as desired.

./myscript.R -x 7 -v > output_file.txt
Verbose going to stderr instead of stdout

cat output_file.txt
7


stdin in a non-interactive mode

The take fully advantage of the pipeline capabilities that I have mentioned at the beginning of this post, it is useful to accept input from stdin. For example, a template of a script that reads one line at a time from stdin could be

input_con  <- file("stdin")
open(input_con)
while (length(oneLine <- readLines(con = input_con,
n = 1,
warn = FALSE)) > 0) {
# do something one line at a time ...
}
close(input_con)


Note that when we are running our R scripts from the terminal we are in a non-interactive mode, which means that

input_con <- stdin()


would not work as expected on the template above. As described on the help page for stdin():

stdin() refers to the ‘console’ and not to the C-level ‘stdin’ of the process. The distinction matters in GUI consoles (which may not have an active ‘stdin’, and if they do it may not be connected to console input), and also in embedded applications. If you want access to the C-level file stream ‘stdin’, use file(“stdin”).

And that is the reason I used

input_con <- file("stdin")
open(input_con)


instead. Naturally, we could allow the data to be inputted from stdin by default while making a flag available in case the user wants to provide a file path containing the data to be read. Below is a template for this:

spec = matrix(c(
"data"       , "d" , 1, "character"
), byrow=TRUE, ncol=4);

opt = getopt(spec);

if (is.null(opt$data)) { data_file <- "stdin" } else { data_file <- opt$data
}

if (data_file == "stdin"){
input_con  <- file("stdin")
open(input_con)
sep = "\t", stringsAsFactors = FALSE)
close(input_con)
} else {
sep = "\t", stringsAsFactors = FALSE)
}


References:

[1] Relevant help pages, as ?Rscript for example.
[2] Reference manual of the R package getopt.

# Near-zero variance predictors. Should we remove them?

Datasets come sometimes with predictors that take an unique value across samples. Such uninformative predictor is more common than you might think. This kind of predictor is not only non-informative, it can break some models you may want to fit to your data (see example below). Even more common is the presence of predictors that are almost constant across samples. One quick and dirty solution is to remove all predictors that satisfy some threshold criterion related to their variance.

Here I discuss this quick solution but point out that this might not be the best approach to use depending on your problem. That is, throwing data away should be avoided, if possible.

It would be nice to know how you deal with this problem.

Zero and near-zero predictors

Constant and almost constant predictors across samples (called zero and near-zero variance predictors in [1], respectively) happens quite often. One reason is because we usually break a categorical variable with many categories into several dummy variables. Hence, when one of the categories have zero observations, it becomes a dummy variable full of zeroes.

To illustrate this, take a look at what happens when we want to apply Linear Discriminant Analysis (LDA) to the German Credit Data.

require(caret)
data(GermanCredit)

require(MASS)
r = lda(formula = Class ~ ., data = GermanCredit)

Error in lda.default(x, grouping, ...) :
variables 26 44 appear to be constant within groups


If we take a closer look at those predictors indicated as problematic by lda we see what is the problem. Note that I have added +1 to the index since lda does not count the target variable when informing you where the problem is.

colnames(GermanCredit)[26 + 1]
[1] "Purpose.Vacation"

table(GermanCredit[, 26 + 1])

0
1000

colnames(GermanCredit)[44 + 1]
[1] "Personal.Female.Single"

table(GermanCredit[, 44 + 1])

0
1000


Quick and dirty solution: throw data away

As we can see above no loan was taken to pay for a vacation and there is no single female in our dataset. A natural first choice is to remove predictors like those. And this is exactly what the function nearZeroVar from the caret package does. It not only removes predictors that have one unique value across samples (zero variance predictors), but also removes predictors that have both 1) few unique values relative to the number of samples and 2) large ratio of the frequency of the most common value to the frequency of the second most common value (near-zero variance predictors).

x = nearZeroVar(GermanCredit, saveMetrics = TRUE)

str(x, vec.len=2)

'data.frame':  62 obs. of  4 variables:
$freqRatio : num 1.03 1 ...$ percentUnique: num  3.3 92.1 0.4 0.4 5.3 ...
$zeroVar : logi FALSE FALSE FALSE ...$ nzv          : logi  FALSE FALSE FALSE ...


We can see above that if we call the nearZeroVar function with the argument saveMetrics = TRUE we have access to the frequency ratio and the percentage of unique values for each predictor, as well as flags that indicates if the variables are considered zero variance or near-zero variance predictors. By default, a predictor is classified as near-zero variance if the percentage of unique values in the samples is less than ${10\%}$ and when the frequency ratio mentioned above is greater than 19 (95/5). These default values can be changed by setting the arguments uniqueCut and freqCut.

We can explore which ones are the zero variance predictors

x[x[,"zeroVar"] > 0, ]

freqRatio percentUnique zeroVar  nzv
Purpose.Vacation               0           0.1    TRUE TRUE
Personal.Female.Single         0           0.1    TRUE TRUE


and which ones are the near-zero variance predictors

x[x[,"zeroVar"] + x[,"nzv"] > 0, ]

freqRatio percentUnique zeroVar  nzv
ForeignWorker                       26.02703           0.2   FALSE TRUE
CreditHistory.NoCredit.AllPaid      24.00000           0.2   FALSE TRUE
CreditHistory.ThisBank.AllPaid      19.40816           0.2   FALSE TRUE
Purpose.DomesticAppliance           82.33333           0.2   FALSE TRUE
Purpose.Repairs                     44.45455           0.2   FALSE TRUE
Purpose.Vacation                     0.00000           0.1    TRUE TRUE
Purpose.Retraining                 110.11111           0.2   FALSE TRUE
Purpose.Other                       82.33333           0.2   FALSE TRUE
SavingsAccountBonds.gt.1000         19.83333           0.2   FALSE TRUE
Personal.Female.Single               0.00000           0.1    TRUE TRUE
OtherDebtorsGuarantors.CoApplicant  23.39024           0.2   FALSE TRUE
OtherInstallmentPlans.Stores        20.27660           0.2   FALSE TRUE
Job.UnemployedUnskilled             44.45455           0.2   FALSE TRUE


Now, should we always remove our near-zero variance predictors? Well, I am not that comfortable with that.

Try not to throw your data away

Think for a moment, the solution above is easy and “solves the problem”, but we are assuming that all those predictors are non-informative, which is not necessarily true, specially for the near-zero variance ones. Those near-variance predictors can in fact turn out to be very informative.

For example, assume that a binary predictor in a classification problem has lots of zeroes and few ones (near-variance predictor). Every time this predictor is equal to one we know exactly what is the class of the target variable, while a value of zero for this predictor can be associated with either one the classes. This is a valuable predictor that would be thrown away by the method above.

This is somewhat related to the separation problem that can happen in logistic regression, where a predictor (or combination of predictors) can perfectly predicts (separate) the data. The common approach not long ago was to exclude those predictors from the analysis, but better solutions were discussed by [2], which proposed a penalized likelihood solution, and [3], that suggested the use of weekly informative priors for the regression coefficients of the logistic model.

Personally, I prefer to use a well designed bayesian model whenever possible, more like the solution provided by [3] for the separation problem mentioned above. One solution for the near-variance predictor is to collect more data, and although this is not always possible, there is a lot of applications where you know you will receive more data from time to time. It is then important to keep in mind that such well designed model would still give you sensible solutions while you still don’t have enough data but would naturally adapt as more data arrives for your application.

References:

[1] Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling. Springer.
[2] Zorn, C. (2005). A solution to separation in binary response models. Political Analysis, 13(2), 157-170.
[3] Gelman, A., Jakulin, A., Pittau, M.G. and Su, Y.S. (2008). A weakly informative default prior distribution for logistic and other regression models. The Annals of Applied Statistics, 1360-1383.

# Character strings in R

This post deals with the basics of character strings in R. My main reference has been Gaston Sanchez‘s ebook [1], which is excellent and you should read it if interested in manipulating text in R. I got the encoding’s section from [2], which is also a nice reference to have nearby. Text analysis will be one topic of interest to this Blog, so expect more posts about it in the near future.

Creating character strings

The class of an object that holds character strings in R is “character”. A string in R can be created using single quotes or double quotes.

chr = 'this is a string'
chr = "this is a string"

chr = "this 'is' valid"
chr = 'this "is" valid'


We can create an empty string with empty_str = "" or an empty character vector with empty_chr = character(0). Both have class “character” but the empty string has length equal to 1 while the empty character vector has length equal to zero.

empty_str = ""
empty_chr = character(0)

class(empty_str)
[1] "character"
class(empty_chr)
[1] "character"

length(empty_str)
[1] 1
length(empty_chr)
[1] 0


The function character() will create a character vector with as many empty strings as we want. We can add new components to the character vector just by assigning it to an index outside the current valid range. The index does not need to be consecutive, in which case R will auto-complete it with NA elements.

chr_vector = character(2) # create char vector
chr_vector
[1] "" ""

chr_vector[3] = "three" # add new element
chr_vector
[1] ""      ""      "three"

chr_vector[5] = "five" # do not need to
# be consecutive
chr_vector
[1] ""      ""      "three" NA      "five"


Auxiliary functions

The functions as.character() and is.character() can be used to convert non-character objects into character strings and to test if a object is of type “character”, respectively.

Strings and data objects

R has five main types of objects to store data: vector, factor, multi-dimensional array, data.frame and list. It is interesting to know how these objects behave when exposed to different types of data (e.g. character, numeric, logical).

• vector: Vectors must have their values all of the same mode. If we combine mixed types of data in vectors, strings will dominate.
• arrays: A matrix, which is a 2-dimensional array, have the same behavior found in vectors.
• data.frame: By default, a column that contains a character string in it is converted to factors. If we want to turn this default behavior off we can use the argument stringsAsFactors = FALSE when constructing the data.frame object.
• list: Each element on the list will maintain its corresponding mode.
# character dominates vector
c(1, 2, "text")
[1] "1"    "2"    "text"

# character dominates arrays
rbind(1:3, letters[1:3])
[,1] [,2] [,3]
[1,] "1"  "2"  "3"
[2,] "a"  "b"  "c"

# data.frame with stringsAsFactors = TRUE (default)
df1 = data.frame(numbers = 1:3, letters = letters[1:3])
df1
numbers letters
1       1       a
2       2       b
3       3       c

str(df1, vec.len=1)
'data.frame':  3 obs. of  2 variables:
$numbers: int 1 2 ...$ letters: Factor w/ 3 levels "a","b","c": 1 2 ...

# data.frame with stringsAsFactors = FALSE
df2 = data.frame(numbers = 1:3, letters = letters[1:3],
stringsAsFactors = FALSE)
df2
numbers letters
1       1       a
2       2       b
3       3       c

str(df2, vec.len=1)
'data.frame':  3 obs. of  2 variables:
$numbers: int 1 2 ...$ letters: chr  "a" ...

# Each element in a list has its own type
list(1:3, letters[1:3])
[[1]]
[1] 1 2 3

[[2]]
[1] "a" "b" "c"


Character encoding

R provides functions to deal with various set of encoding schemes. The Encoding() function returns the encoding of a string. iconv() converts the encoding.

chr = "lá lá"
Encoding(chr)
[1] "UTF-8"

chr = iconv(chr, from = "UTF-8",
to = "latin1")
Encoding(chr)
[1] "latin1"


References:

# Computing and visualizing LDA in R

As I have described before, Linear Discriminant Analysis (LDA) can be seen from two different angles. The first classify a given sample of predictors ${x}$ to the class ${C_l}$ with highest posterior probability ${\pi(y = C_l|x)}$. It minimizes the total probability of misclassification. To compute ${\pi(y = C_l|x)}$ it uses Bayes’ rule and assume that ${\pi(x|y = C_l)}$ follows a Gaussian distribution with class-specific mean ${\mu_l}$ and common covariance matrix ${\Sigma}$. The second tries to find a linear combination of the predictors that gives maximum separation between the centers of the data while at the same time minimizing the variation within each group of data.

The second approach [1] is usually preferred in practice due to its dimension-reduction property and is implemented in many R packages, as in the lda function of the MASS package for example. In what follows, I will show how to use the lda function and visually illustrate the difference between Principal Component Analysis (PCA) and LDA when applied to the same dataset.

Using lda from MASS R package

As usual, we are going to illustrate lda using the iris dataset. The data contains four continuous variables which correspond to physical measures of flowers and a categorical variable describing the flowers’ species.

require(MASS)

data(iris)

Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa


An usual call to lda contains formula, data and prior arguments [2].

r <- lda(formula = Species ~ .,
data = iris,
prior = c(1,1,1)/3)


The . in the formula argument means that we use all the remaining variables in data as covariates. The prior argument sets the prior probabilities of class membership. If unspecified, the class proportions for the training set are used. If present, the probabilities should be specified in the order of the factor levels.

> r$prior setosa versicolor virginica 0.3333333 0.3333333 0.3333333 > r$counts
setosa versicolor  virginica
50         50         50

> r$means Sepal.Length Sepal.Width Petal.Length Petal.Width setosa 5.006 3.428 1.462 0.246 versicolor 5.936 2.770 4.260 1.326 virginica 6.588 2.974 5.552 2.026 > r$scaling
LD1         LD2
Sepal.Length  0.8293776  0.02410215
Sepal.Width   1.5344731  2.16452123
Petal.Length -2.2012117 -0.93192121
Petal.Width  -2.8104603  2.83918785

> r$svd [1] 48.642644 4.579983  As we can see above, a call to lda returns the prior probability of each class, the counts for each class in the data, the class-specific means for each covariate, the linear combination coefficients (scaling) for each linear discriminant (remember that in this case with 3 classes we have at most two linear discriminants) and the singular values (svd) that gives the ratio of the between- and within-group standard deviations on the linear discriminant variables. prop = r$svd^2/sum(r$svd^2) > prop [1] 0.991212605 0.008787395  We can use the singular values to compute the amount of the between-group variance that is explained by each linear discriminant. In our example we see that the first linear discriminant explains more than ${99\%}$ of the between-group variance in the iris dataset. If we call lda with CV = TRUE it uses a leave-one-out cross-validation and returns a named list with components: • class: the Maximum a Posteriori Probability (MAP) classification (a factor) • posterior: posterior probabilities for the classes. r2 <- lda(formula = Species ~ ., data = iris, prior = c(1,1,1)/3, CV = TRUE) > head(r2$class)
[1] setosa setosa setosa setosa setosa setosa
Levels: setosa versicolor virginica

> head(r2$posterior, 3) setosa versicolor virginica 1 1 5.087494e-22 4.385241e-42 2 1 9.588256e-18 8.888069e-37 3 1 1.983745e-19 8.606982e-39  There is also a predict method implemented for lda objects. It returns the classification and the posterior probabilities of the new data based on the Linear Discriminant model. Below, I use half of the dataset to train the model and the other half is used for predictions. train <- sample(1:150, 75) r3 <- lda(Species ~ ., # training model iris, prior = c(1,1,1)/3, subset = train) plda = predict(object = r, # predictions newdata = iris[-train, ]) > head(plda$class) # classification result
[1] setosa setosa setosa setosa setosa setosa
Levels: setosa versicolor virginica

> head(plda$posterior, 3) # posterior prob. setosa versicolor virginica 3 1 1.463849e-19 4.675932e-39 4 1 1.268536e-16 3.566610e-35 5 1 1.637387e-22 1.082605e-42 > head(plda$x, 3) # LD projections
LD1        LD2
3 7.489828 -0.2653845
4 6.813201 -0.6706311
5 8.132309  0.5144625


Visualizing the difference between PCA and LDA

As I have mentioned at the end of my post about Reduced-rank DA, PCA is an unsupervised learning technique (don’t use class information) while LDA is a supervised technique (uses class information), but both provide the possibility of dimensionality reduction, which is very useful for visualization. Therefore we would expect (by definition) LDA to provide better data separation when compared to PCA, and this is exactly what we see at the Figure below when both LDA (upper panel) and PCA (lower panel) are applied to the iris dataset. The code to generate this Figure is available on github.

Although we can see that this is an easy dataset to work with, it allow us to clearly see that the versicolor specie is well separated from the virginica one in the upper panel while there is still some overlap between them in the lower panel. This kind of difference is to be expected since PCA tries to retain most of the variability in the data while LDA tries to retain most of the between-class variance in the data. Note also that in this example the first LD explains more than ${99\%}$ of the between-group variance in the data while the first PC explains ${73\%}$ of the total variability in the data.

Closing remarks

Although I have not applied it on my illustrative example above, pre-processing [3] of the data is important for the application of LDA. Users should transform, center and scale the data prior to the application of LDA. It is also useful to remove near-zero variance predictors (almost constant predictors across units). Given that we need to invert the covariance matrix, it is necessary to have less predictors than samples. Attention is therefore needed when using cross-validation.

References:

[1] Venables, W. N. and Ripley, B. D. (2002). Modern applied statistics with S. Springer.
[2] lda (MASS) help file.
[3] Kuhn, M. and Johnson, K. (2013). Applied Predictive Modeling. Springer.

# Computing and visualizing PCA in R

Following my introduction to PCA, I will demonstrate how to apply and visualize PCA in R. There are many packages and functions that can apply PCA in R. In this post I will use the function prcomp from the stats package. I will also show how to visualize PCA in R using Base R graphics. However, my favorite visualization function for PCA is ggbiplot, which is implemented by Vince Q. Vu and available on github. Please, let me know if you have better ways to visualize PCA in R.

Computing the Principal Components (PC)

I will use the classical iris dataset for the demonstration. The data contain four continuous variables which corresponds to physical measures of flowers and a categorical variable describing the flowers’ species.

# Load data
data(iris)

Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa


We will apply PCA to the four continuous variables and use the categorical variable to visualize the PCs later. Notice that in the following code we apply a log transformation to the continuous variables as suggested by [1] and set center and scale. equal to TRUE in the call to prcomp to standardize the variables prior to the application of PCA:

# log transform
log.ir <- log(iris[, 1:4])
ir.species <- iris[, 5]

# apply PCA - scale. = TRUE is highly
# advisable, but default is FALSE.
ir.pca <- prcomp(log.ir,
center = TRUE,
scale. = TRUE)


Since skewness and the magnitude of the variables influence the resulting PCs, it is good practice to apply skewness transformation, center and scale the variables prior to the application of PCA. In the example above, we applied a log transformation to the variables but we could have been more general and applied a Box and Cox transformation [2]. See at the end of this post how to perform all those transformations and then apply PCA with only one call to the preProcess function of the caret package.

Analyzing the results

The prcomp function returns an object of class prcomp, which have some methods available. The print method returns the standard deviation of each of the four PCs, and their rotation (or loadings), which are the coefficients of the linear combinations of the continuous variables.

# print method
print(ir.pca)

Standard deviations:
[1] 1.7124583 0.9523797 0.3647029 0.1656840

Rotation:
PC1         PC2        PC3         PC4
Sepal.Length  0.5038236 -0.45499872  0.7088547  0.19147575
Sepal.Width  -0.3023682 -0.88914419 -0.3311628 -0.09125405
Petal.Length  0.5767881 -0.03378802 -0.2192793 -0.78618732
Petal.Width   0.5674952 -0.03545628 -0.5829003  0.58044745


The plot method returns a plot of the variances (y-axis) associated with the PCs (x-axis). The Figure below is useful to decide how many PCs to retain for further analysis. In this simple case with only 4 PCs this is not a hard task and we can see that the first two PCs explain most of the variability in the data.

# plot method
plot(ir.pca, type = "l")


The summary method describe the importance of the PCs. The first row describe again the standard deviation associated with each PC. The second row shows the proportion of the variance in the data explained by each component while the third row describe the cumulative proportion of explained variance. We can see there that the first two PCs accounts for more than ${95\%}$ of the variance of the data.

# summary method
summary(ir.pca)

Importance of components:
PC1    PC2     PC3     PC4
Standard deviation     1.7125 0.9524 0.36470 0.16568
Proportion of Variance 0.7331 0.2268 0.03325 0.00686
Cumulative Proportion  0.7331 0.9599 0.99314 1.00000


We can use the predict function if we observe new data and want to predict their PCs values. Just for illustration pretend the last two rows of the iris data has just arrived and we want to see what is their PCs values:

# Predict PCs
predict(ir.pca,
newdata=tail(log.ir, 2))

PC1         PC2        PC3         PC4
149 1.0809930 -1.01155751 -0.7082289 -0.06811063
150 0.9712116 -0.06158655 -0.5008674 -0.12411524


The Figure below is a biplot generated by the function ggbiplot of the ggbiplot package available on github.

The code to generate this Figure is given by

library(devtools)
install_github("ggbiplot", "vqv")

library(ggbiplot)
g <- ggbiplot(ir.pca, obs.scale = 1, var.scale = 1,
groups = ir.species, ellipse = TRUE,
circle = TRUE)
g <- g + scale_color_discrete(name = '')
g <- g + theme(legend.direction = 'horizontal',
legend.position = 'top')
print(g)


It projects the data on the first two PCs. Other PCs can be chosen through the argument choices of the function. It colors each point according to the flowers’ species and draws a Normal contour line with ellipse.prob probability (default to ${68\%}$) for each group. More info about ggbiplot can be obtained by the usual ?ggbiplot. I think you will agree that the plot produced by ggbiplot is much better than the one produced by biplot(ir.pca) (Figure below).

I also like to plot each variables coefficients inside a unit circle to get insight on a possible interpretation for PCs. Figure 4 was generated by this code available on gist.

PCA on caret package

As I mentioned before, it is possible to first apply a Box-Cox transformation to correct for skewness, center and scale each variable and then apply PCA in one call to the preProcess function of the caret package.

require(caret)
trans = preProcess(iris[,1:4],
method=c("BoxCox", "center",
"scale", "pca"))
PC = predict(trans, iris[,1:4])


By default, the function keeps only the PCs that are necessary to explain at least 95% of the variability in the data, but this can be changed through the argument thresh.

# Retained PCs

PC1        PC2
1 -2.303540 -0.4748260
2 -2.151310  0.6482903
3 -2.461341  0.3463921

trans\$rotation

PC1         PC2
Sepal.Length  0.5202351 -0.38632246
Sepal.Width  -0.2720448 -0.92031253
Petal.Length  0.5775402 -0.04885509
Petal.Width   0.5672693 -0.03732262


See Unsupervised data pre-processing for predictive modeling for an introduction of the preProcess function.

References:

[1] Venables, W. N., Brian D. R. Modern applied statistics with S-PLUS. Springer-verlag. (Section 11.1)
[2] Box, G. and Cox, D. (1964). An analysis of transformations. Journal of the Royal Statistical Society. Series B (Methodological) 211-252

# Plot matrix with the R package GGally

I am glad to have found the R package GGally. GGally is a convenient package built upon ggplot2 that contains templates for different plots to be combined into a plot matrix through the function ggpairs. It is a nice alternative to the more limited pairs function. The package has also functions to deal with parallel coordinate and network plots, none of which I have tried yet.

The following code shows how easy it is to create very informative plots like the one in Figure 1.

require(GGally)
data(tips, package="reshape")

ggpairs(data=tips, # data.frame with variables
columns=1:3, # columns to plot, default to all.
title="tips data", # title of the plot
colour = "sex") # aesthetics, ggplot2 style


Figure 1

Plots like the one above are very helpful, among others things, in the pre-processing stage of a classification problem, where you want to analyze your predictors given the class labels. It is particularly amazing that we can now use the arguments colour, shape, size and alpha provided by ggplot2.

Controlling plot types

We have some control over which type of plots to use. We can choose which type of graph will be used for continuous vs. continuous (continuous), continuous vs. discrete (combo) and discrete vs. discrete (discrete). We can also have different plots for the upper diagonal (upper) and for the lower diagonal (lower).

For example, the code below

pm = ggpairs(data=tips,
columns=1:3,
upper = list(continuous = "density"),
lower = list(combo = "facetdensity"),
title="tips data",
colour = "sex")
print(pm)


creates Figure 2, which uses the same data used in Figure 1, but with a density plot in the upper diagonal for continuous vs. continuous variables and a density plot faceted by a discrete variable in a continuous vs. discrete scenario.

Figure 2

The details section of the help file of the ggpairs function describes which plots are available for each scenario. Currently, the following are described there:

• continuous: exactly one of ‘points’, ‘smooth’, ‘density’, ‘cor’ or ‘blank’;
• combo: exactly one of ‘box’, ‘dot’, ‘facethist’, ‘facetdensity’, ‘denstrip’ or ‘blank’;
• discrete: exactly one of ‘facetbar’,’ratio’ or ‘blank’.

Auxiliary functions

We can insert a customized plot within a plot matrix created by ggpairs using the function putPlot. The following code creates a custom ggplot object cp and insert it in the second row and third column of the ggpairs object pm.

cp = ggplot(data.frame(x=1:10, y=1:10)) +
geom_point(aes(x, y))

putPlot(pm, cp, 2, 3)


We can also retrieve an specific ggplot object from a ggpairs object using the getPlot function, with the following syntax:

getPlot(plotMatrix, rowFromTop, columnFromLeft)


References:

# Unsupervised data pre-processing: individual predictors

I just got the excellent book Applied Predictive Modeling, by Max Kuhn and Kjell Johnson [1]. The book is designed for a broad audience and focus on the construction and application of predictive models. Besides going through the necessary theory in a not-so-technical way, the book provides R code at the end of each chapter. This enables the reader to replicate the techniques described in the book, which is nice. Most of such techniques can be applied through calls to functions from the caret package, which is a very convenient package to have around when doing predictive modeling.

Chapter 3 is about unsupervised techniques for pre-processing your data. The pre-processing step happens before you start building your model. Inadequate data pre-processing is pointed on the book as one of the common reasons on why some predictive models fail. Unsupervised means that the transformations you perform on your predictors (covariates) does not use information about the response variable.

Feature engineering

How your predictors are encoded can have a significant impact on model performance. For example, the ratio of two predictors may be more effective than using two independent predictors. This will depend on the model used as well as on the particularities of the phenomenon you want to predict. The manufacturing of predictors to improve prediction performance is called feature engineering. To succeed at this stage you should have a deep understanding of the problem you are trying to model.

Data transformations for individuals predictors

A good practice is to center, scale and apply skewness transformations for each of the individual predictors. This practice gives more stability for numerical algorithms used later in the fitting of different models as well as improve the predictive ability of some models. Box and Cox transformation [2], centering and scaling can be applied using the preProcess function from caret. Assume we have a predictors data frame with two predictors, x1 and x2, depicted in Figure 1.

Figure 1

Then the following code

set.seed(1)
predictors = data.frame(x1 = rnorm(1000,
mean = 5,
sd = 2),
x2 = rexp(1000,
rate=10))

require(caret)

trans = preProcess(predictors,
c("BoxCox", "center", "scale"))
predictorsTrans = data.frame(
trans = predict(trans, predictors))


will estimate the ${\lambda}$ of the Box and Cox transformation

$\displaystyle x^* = \bigg\{ \begin{array}{cc} \frac{x^{\lambda} - 1}{\lambda} & \text{ if }\lambda \neq 0 \\ \log(x) & \text{ if }\lambda = 0 \end{array}$

apply it to your predictors that take on positive values, and then center and scale each one of the predictors. The new data frame predictorsTrans with the transformed predictors is depicted in Figure 2.

Figure 2

I will write more about the book and the caret package at future posts. The complete code that I have used here to simulate the data, generate the pictures and transform the data can be found on gist.

References:

[1] Kuhn, M. and Johnson, K. (2013). Applied Predictive Modeling. Springer.
[2] Box, G. and Cox, D. (1964). An analysis of transformations. Journal of the Royal Statistical Society. Series B (Methodological) 211-252