Introduction:
R is a powerful language used widely for data analysis and statistical computing. It was developed in early 90s.
Packages such as dplyr, tidyr, readr, data.table, SparkR, ggplot2 have made data manipulation, visualization and computation much faster.
But, what about Machine Learning ?
1. Basics of R Programming
Why learn R ?
1. The style of coding is quite easy.
2. It’s open source. No need to pay any subscription charges.
3. Availability of instant access to over 7800 packages customized for various computation tasks.
4. The community support is overwhelming. There are numerous forums to help you out.
5. Get high performance computing experience ( require packages)
6. One of highly sought skill by analytics and data science companies.
Let’s quickly understand the interface of R Studio:
1. R Console: This area shows the output of code you run. Also, you can directly write codes in console. Code entered directly in R console cannot be traced later. This is where R script comes to use.
2. R Script: As the name suggest, here you get space to write codes. To run those codes, simply select the line(s) of code and press Ctrl + Enter. Alternatively, you can click on little ‘Run’ button location at top right corner of R Script.
3. R environment: This space displays the set of external elements added. This includes data set, variables, vectors, functions etc. To check if data has been loaded properly in R, always look at this area.
4. Graphical Output: This space display the graphs created during exploratory data analysis. Not just graphs, you could select packages, seek help with embedded R’s official documentation.
How to install R Packages ?
The sheer power of R lies in its incredible packages. In R, most data handling tasks can be performed in 2 ways: Using R packages and R base functions. In this tutorial, I’ll also introduce you with the most handy and powerful R packages. To install a package, simply type:
install.packages("package name", dependencies = TRUE)
As a first time user, a pop might appear to select your CRAN mirror (country server), choose accordingly and press OK.
Note: You can type this either in console directly and press ‘Enter’ or in R script and click ‘Run’.
Basic Computations in R
Let’s begin with basics. To get familiar with R coding environment, start with some basic calculations. R console can be used as an interactive calculator too. Type the following in your console:
> 2 + 3
> 5
> 6 / 3
> 2
> (3*8)/(2*3)
> 4
>log(12)
> 1.07
>sqrt (121)
> 11
Similarly, you can experiment various combinations of calculations and get the results. In case, you want to obtain the previous calculation, this can be done in two ways. First, click in R console, and press ‘Up / Down Arrow’ key on your keyboard. This will activate the previously executed commands. Press Enter.
But, what if you have done too many calculations ? It would be too painful to scroll through every command and find it out. In such situations, creating variable is a helpful way.
In R, you can create a variable using <- or = sign. Let’s say I want to create a variable x to compute the sum of 7 and 8. I’ll write it as:
> x <- 8 + 7
> x
> 15
Once we create a variable, you no longer get the output directly (like calculator), unless you call the variable in the next line. Remember, variables can be alphabets, alphanumeric but not numeric. You can’t create numeric variables.
2. Essentials of R Programming
Understand and practice this section thoroughly. This is the building block of your R programming knowledge. If you get this right, you would face less trouble in debugging.
R has five basic or ‘atomic’ classes of objects. Wait, what is an object ?
Everything you see or create in R is an object. A vector, matrix, data frame, even a variable is an object. R treats it that way. So, R has 5 basic classes of objects. This includes:
1. Character
2. Numeric (Real Numbers)
3. Integer (Whole Numbers)
4. Complex
5. Logical (True / False)
Since these classes are self-explanatory by names, I wouldn’t elaborate on that. These classes have attributes. Think of attributes as their ‘identifier’, a name or number which aptly identifies them. An object can have following attributes:
1. names, dimension names
2. dimensions
3. class
4. length
Attributes of an object can be accessed using attributes() function. More on this coming in following section.
Let’s understand the concept of object and attributes practically. The most basic object in R is known as vector. You can create an empty vector using vector(). Remember, a vector contains object of same class.
For example: Let’s create vectors of different classes. We can create vector using c() or concatenate command also.
> a <- c(1.8, 4.5) #numeric
> b <- c(1 + 2i, 3 - 6i) #complex
> d <- c(23, 44) #integer
> e <- vector("logical", length = 5)
Similarly, you can create vector of various classes.
Data Types in R
R has various type of ‘data types’ which includes vector (numeric, integer etc), matrices, data frames and list. Let’s understand them one by one.
Vector: As mentioned above, a vector contains object of same class. But, you can mix objects of different classes too. When objects of different classes are mixed in a list, coercion occurs. This effect causes the objects of different types to ‘convert’ into one class. For example:
>qt<- c("Time", 24, "October", TRUE, 3.33) #character
> ab <- c(TRUE, 24) #numeric
> cd <- c(2.5, "May") #character
To check the class of any object, use class(“vector name”) function.
>class(qt)
"character"
To convert the class of a vector, you can use as. command.
>bar <- 0:5
> class(bar)
> "integer"
>as.numeric(bar)
> class(bar)
> "numeric"
>as.character(bar)
> class(bar)
> "character"
Similarly, you can change the class of any vector. But, you should pay attention here. If you try to convert a “character” vector to “numeric” , NAs will be introduced. Hence, you should be careful to use this command.
List: A list is a special type of vector which contain elements of different data types. For example:
>my_list<- list(22, "ab", TRUE, 1 + 2i)
>my_list
[[1]]
[1] 22
[[2]]
[1] "ab"
[[3]]
[1] TRUE
[[4]]
[1] 1+2i
As you can see, the output of a list is different from a vector. This is because, all the objects are of different types. The double bracket [[1]] shows the index of first element and so on. Hence, you can easily extract the element of lists depending on their index. Like this:
>my_list[[3]]
> [1] TRUE
You can use [] single bracket too. But, that would return the list element with its index number, instead of the result above. Like this:
>my_list[3]
> [[1]]
[1] TRUE
Matrices: When a vector is introduced with row and column i.e. a dimension attribute, it becomes a matrix. A matrix is represented by set of rows and columns. It is a 2 dimensional data structure. It consist of elements of same class. Let’s create a matrix of 3 rows and 2 columns:
>my_matrix<- matrix(1:6, nrow=3, ncol=2)
>my_matrix
[,1] [,2]
[1,] 1 4
[2,] 2 5
[3,] 3 6
>dim(my_matrix)
[1] 3 2
>attributes(my_matrix)
$dim
[1] 3 2
As you can see, the dimensions of a matrix can be obtained using either dim() or attributes() command. To extract a particular element from a matrix, simply use the index shown above. For example(try this at your end):
>my_matrix[,2] #extracts second column
>my_matrix[,1] #extracts first column
>my_matrix[2,] #extracts second row
>my_matrix[1,] #extracts first row
As an interesting fact, you can also create a matrix from a vector. All you need to do is, assign dimension dim() later. Like this:
> age <- c(23, 44, 15, 12, 31, 16)
> age
[1] 23 44 15 12 31 16
> dim(age) <- c(2,3)
> age
[,1] [,2] [,3]
[1,] 23 15 31
[2,] 44 12 16
>class(age)
[1] "matrix"
You can also join two vectors using cbind() and rbind() functions. But, make sure that both vectors have same number of elements. If not, it will return NA values.
> x <- c(1, 2, 3, 4, 5, 6)
> y <- c(20, 30, 40, 50, 60)
>cbind(x, y)
>cbind(x, y)
x y
[1,] 1 20
[2,] 2 30
[3,] 3 40
[4,] 4 50
[5,] 5 60
[6,] 6 70
>class(cbind(x, y))
[1] “matrix”
Data Frame: This is the most commonly used member of data types family. It is used to store tabular data. It is different from matrix. In a matrix, every element must have same class. But, in a data frame, you can put list of vectors containing different classes. This means, every column of a data frame acts like a list. Every time you will read data in R, it will be stored in the form of a data frame. Hence, it is important to understand the majorly used commands on data frame:
>df<- data.frame(name = c("ash","jane","paul","mark"), score = c(67,56,87,91))
>df
name score
1 ash 67
2 jane 56
3 paul 87
4 mark 91
> dim(df)
[1] 4 2
>str(df)
'data.frame': 4 obs. of 2 variables:
$ name : Factor w/ 4 levels "ash","jane","mark",..: 1 2 4 3
$ score: num 67 56 87 91
>nrow(df)
[1] 4
>ncol(df)
[1] 2
Let’s understand the code above. df is the name of data frame. dim() returns the dimension of data frame as 4 rows and 2 columns. str() returns the structure of a data frame i.e. the list of variables stored in the data frame. nrow() and ncol() return the number of rows and number of columns in a data set respectively.
Here you see “name” is a factor variable and “score” is numeric. In data science, a variable can be categorized into two types: Continuous and Categorical.
Continuous variables are those which can take any form such as 1, 2, 3.5, 4.66 etc. Categorical variables are those which takes only discrete values such as 2, 5, 11, 15 etc. In R, categorical values are represented by factors. In df, name is a factor variable having 4 unique levels. Factor or categorical variable are specially treated in a data set. For more explanation, click here. Similarly, you can find techniques to deal with continuous variables here.
Let’s now understand the concept of missing values in R. This is one of the most painful yet crucial part of predictive modeling. You must be aware of all techniques to deal with them. The complete explanation on such techniques is provided here.
Missing values in R are represented by NA and NaN. Now we’ll check if a data set has missing values (using the same data frame df).
>df[1:2,2] <- NA #injecting NA at 1st, 2nd row and 2nd column of df
>df
name score
1 ash NA
2 jane NA
3 paul 87
4 mark 91
> is.na(df) #checks the entire data set for NAs and return logical output
name score
[1,] FALSE TRUE
[2,] FALSE TRUE
[3,] FALSE FALSE
[4,] FALSE FALSE
> table(is.na(df)) #returns a table of logical output
FALSE TRUE
6 2
>df[!complete.cases(df),] #returns the list of rows having missing values
name score
1 ash NA
2 jane NA
Missing values hinder normal calculations in a data set. For example, let’s say, we want to compute the mean of score. Since there are two missing values, it can’t be done directly. Let’s see:
mean(df$score)
[1] NA
> mean(df$score, na.rm = TRUE)
[1] 89
The use of na.rm = TRUE parameter tells R to ignore the NAs and compute the mean of remaining values in the selected column (score). To remove rows with NA values in a data frame, you can use na.omit:
>new_df<- na.omit(df)
>new_df
name score
3 paul 87
4 mark 91
Control Structures in R
As the name suggest, a control structure ‘controls’ the flow of code / commands written inside a function. A function is a set of multiple commands written to automate a repetitive coding task.
For example: You have 10 data sets. You want to find the mean of ‘Age’ column present in every data set. This can be done in 2 ways: either you write the code to compute mean 10 times or you simply create a function and pass the data set to it.
Let’s understand the control structures in R with simple examples:
if, else – This structure is used to test a condition. Below is the syntax:
if (<condition>){
##do something
} else {
##do something
}
Example
#initialize a variable
N <- 10
#check if this variable * 5 is > 40
if (N * 5 > 40){
print("This is easy!")
} else {
print ("It's not easy!")
}
[1] "This is easy!"
for – This structure is used when a loop is to be executed fixed number of times. It is commonly used for iterating over the elements of an object (list, vector). Below is the syntax:
for (<search condition>){
#do something
}
Example
#initialize a vector
y <- c(99,45,34,65,76,23)
#print the first 4 numbers of this vector
for(i in 1:4){
print (y[i])
}
[1] 99
[1] 45
[1] 34
[1] 65
while – It begins by testing a condition, and executes only if the condition is found to be true. Once the loop is executed, the condition is tested again. Hence, it’s necessary to alter the condition such that the loop doesn’t go infinity. Below is the syntax:
#initialize a condition
Age <- 12
#check if age is less than 17
while(Age < 17){
print(Age)
Age <- Age + 1 #Once the loop is executed, this code breaks the loop
}
[1] 12
[1] 13
[1] 14
[1] 15
[1] 16
There are other control structures as well but are less frequently used than explained above. Those structures are:
1. repeat – It executes an infinite loop
2. break – It breaks the execution of a loop
3. next – It allows to skip an iteration in a loop
4. return – It help to exit a function
Note: If you find the section ‘control structures’ difficult to understand, not to worry. R is supported by various packages to compliment the work done by control structures.
Useful R Packages:
Out of ~7800 packages listed on CRAN, I’ve listed some of the most powerful and commonly used packages in predictive modeling in this article. Since, I’ve already explained the method of installing packages, you can go ahead and install them now. Sooner or later you’ll need them.
Importing Data: R offers wide range of packages for importing data available in any format such as .txt, .csv, .json, .sql etc. To import large files of data quickly, it is advisable to install and use data.table, readr, RMySQL, sqldf, jsonlite.
Data Visualization: R has in built plotting commands as well. They are good to create simple graphs. But, becomes complex when it comes to creating advanced graphics. Hence, you should install ggplot2.
Data Manipulation: R has a fantastic collection of packages for data manipulation. These packages allows you to do basic & advanced computations quickly. These packages are dplyr, plyr, tidyr, lubridate, stringr. Check out this on data manipulation packages in R.
Modeling / Machine Learning: For modeling, caret package in R is powerful enough to cater to every need for creating machine learning model. However, you can install packages algorithms wise such as randomForest, rpart, gbm etc
Till here, you became familiar with the basic work style in R and its associated components.
I deeply appreciate your kindness in reading my blog posts.and will meet in next post with predictive modeling.
Thanks&Regards
NAGARJUNA MAHANTI
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