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The course titled ‘R Programming: Advanced Analytics In R For Data Science‘ is more focused on the data science applications that can be performed with R. The R is one of the languages that are best suited for analytics-based tasks. This course is aimed at those who are strong with the basics of R and is strictly not for beginners. The course is aimed to focus on the areas that need analytics-based approach using R to solve some of the data science problems.
The course would like the candidates those who are not okay with basics to complete the course titled, “R Programming A-Z™: R for Data Science with Real Exercises!” First and then move on to this course. There are ample benefits that one can derive from this course that could fetch them some of the best jobs in the data science industry.
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The following are some of the salient features of the R Programming: Advanced Analytics In R For Data Science course:
Who can take up this R course?
The role played by Data Science here:
The small amount of data can be handled by DBMS frameworks and using the spreadsheets like Excel. The world had moved on from the times when humans used paper and pen to record the activities of the day. The paper and pen work was the prime tools that were used to maintain records. The records maintenance is an integral part of anything in this world.
The Excel is used to perform some basic operations on the data and to show the results in the form of graphs. Moreover, the excel cannot generate accurate results using any data. The analysis of the data was done by those who could perform calculations on the data that would yield valuable outcomes. The outcomes on which the organization could check the working conditions of the organization.
Topics that are covered in this course with their explanation
The R Programming: Advanced Analytics In R For Data Science course runs for 6 hours, and the following topics are covered in this course:
Introduction to R:
Keeping in mind that already the users who have registered for this course, there is a small introduction that has been given in this course.
Concepts for analysis of data
The project for data science needs the preparation of data; the prepared data must be tested and then it should be reported.
Out of all these steps, the preparation of data consumes a lot of time. To reduce the time that is taken to prepare the data, a model is created and then it is reported.
The data preparation takes the data that has been provided, and the model can be prepared then and there. The complete.cases () is a function that is used to check for rows that have no missing values.
If there are no missing values, then the function returns a logical vector. The logical vector is used to indicate that there are no missing values.
Descriptive statistics for data preparation
First-hand tools give firsthand information like
Graphical as well as analytical tools are used.
Graphical tools:
Graphical tools are used to plot various types of plots
Pie diagram:
A pie chart is a circle partitioned into segments where each of the segments represents a category. The size of each segment depends upon the relative frequency and is determined by the angle (frequency X 3600). Pie charts are used to visualize the absolute and relative frequencies.
Histogram:
The histogram is based on the idea to categorize the data into different groups and plot the bars for each category with height. The area of the bars (= height X width) is proportional to the
relative frequency. So, the widths of the bars need not necessarily to be the same.
Lists
Vectors, matrices, and arrays are that each of these types of objects may only contain one type of data. The lists can have different types of data that can be stored. The elements of a list can also be a matrix, and the elements can be represented by its elements. The list is created using list () function.
To access the elements of the list and accessing List Elements:
Elements of the list can be accessed by the index of the element in the list. In case of named lists, it can also be accessed using the names. For example, a vector may contain all numeric data or all character data.
A list is a special type of object that can contain data of multiple types. Lists are characterized by the fact that their elements do not need to be the same object types. Lists can contain elements of different types so that the list elements may have different modes. Lists can even contain other structured objects, such as lists and Lists can extract sublists. List elements can have names.
Difference between a vector and list:
String vector
The elements of a vector can be named.
To define a factor, we start with a vector of values, a second vector that gives the collection of possible values, a third vector that gives labels to the possible values.
The factor function encodes the vector of discrete values into a factor and factor(x) where x is a vector of strings or integers.
If the vector contains only a subset of possible values and not the entire values, then include a second argument that gives the possible levels of the factor. To define a factor, we start with a vector of values, a second vector that gives the collection of possible values, a third vector that gives labels to the possible values.
The factor function encodes the vector of discrete values into a factor: factor(x) where x is a vector of strings or integers. If the vector contains only a subset of possible values and not the entire values, then include a second argument that gives the possible levels of the factor.
factor (x = character (), levels, labels = levels, exclude = NA, …)
levels: Determines the categories of the factor variable.
Default is the sorted list of all the distinct values of x.
labels: (Optional) Vector of values that will be the labels of the categories in the levels argument.
exclude: (Optional) It defines which levels will be classified as NA in any output using the factor variable.
Operations with Strings
We need formatting and display of strings to obtain the results of specific operations in the required format.
RStudio team
tidyverse
ggplot 2
Data Frames
Data frames are generic data objects of R, used to store tabular data. Data frames which allow creating recursive data structures.
A dataframe can also be created by reading data from a file using the following command
newDF = read.table(path=“Path of the file”)
In the path, please use ‘/’ instead of ‘\.’
Example: “C:/Users/hii/Documents/R/R-Workspace/”
A separator can also be used to distinguish between
entries. Default separator is space, ”
newDF = read.table(file=”path of the file”, sep)
To access the rows and columns in data frame
A separator can also be used to distinguish between entries. Default separator is space, ‘’
newDF = read.table(file=”path of the file”, sep)
A separator can also be used to distinguish between entries. Default separator is space, ‘’
newDF = read.table(file=“path of the file”, sep)
a subset () which extracts a subset of data based on conditions
Editing data frames
Data frames can be edited by direct assignment.
A data frame can also be edited using the edit () command.
Create an instance of the data frame and use edit command to open a table editor, changes can be manually made.
The commands c, cbind, vector and matrix functions combine data. Another option is the data frame.In a data frame, we can combine variables of equal length, with each row in the data frame containing observations on the same unit. Hence, it is similar to the matrix or cbind functions. The advantage is that one can make changes to the data without affecting the original data.
Steps to Install R
One has to follow the following steps to install R.
Step 1: Go to the cran.r-project.org to install R
Please make sure that you select the package compatible with your operating system
Step 2: Installation of RStudio
One should go to the rstudio.com to download RStudio
One should select the package that is very compatible with one’s operating system
Step 3: Installation of packages
install.packages(<comma_separated package names>, dependencies = TRUE) in the R console
For example: To install packages plyr and dplyr, run the following command:
install.packages(“plyr”, “dplyr”, dependencies = TRUE)
Thus, these are some of the details about the course and why one should enroll in this course.
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