Overview
R PROGRAMMING
R programming is one of the first choice for people for the sole purpose of data analysis due to its high flexibility and ease of use. It is a language that will help its learners sustain in the data world.
R is also preferred by Data Science professionals because of its open-source nature and as an additional bonus, R is updated continuously with new features released immediately.
In this world of information overload and data explosion, there is a dire need to leverage this data and make sense of it all. R is rapidly becoming the leading programming language for effective data analysis and statistics. It is the tool of choice for many data science professionals in every industry.
Mentor Institute of Technologies has designed this special course in an effort to make the students more capable to deal with statistical analysis and Big Data. This course is Mandatory for individuals who are seeking to step toward their career in Data Science.
Why choose R programming?
- R is free and open source .
- R has new features and updates instantly.
- R has great ability to visualize data compared to other software like SAS.
- Availability for advanced options like machine learning.
- R integrates with Big Data better.
Benefits of R programming
- Learn to implement R programming in data science and advanced data analytics.
- Recognized globally as the standard for Data analysis.
- A necessary skill for data science aspirants.
In this course, you will learn about:
- The basics of statistical computing and data analysis
- How to use R for analytical programming
- How to implement data structure in R
- R loop functions and debugging tools
- Object-oriented programming concepts in R
- Data visualization in R
- How to perform error handling
- Writing custom R functions
Course Content
- Interface of R and RStudio
- Data types and indexing (assignments, objects, vectors, matrices, data frames, lists)
- R built in functions and syntax
- Working directory
- Packages
- Importing data from CSV, Excel, .txt, SPSS, Stata files etc…
- Exporting data as CSV
- Preparing data for analysis (renaming variables, variable types, missing values)
- Computing, transforming and recoding variables
- Subset datasets by row and by columns
- Descriptive statistics, frequency tables, cross tabulation tables
- Graphics (boxplot, histogram, scatterplot, partitioning window)
- Basic analyses (t-test, correlation, ANOVA, regression, chi-square)
- Sub-setting datasets
- Sorting datasets
- Reshaping datasets
- Merging and appending datasets
- Aggregating datasets (statistics by group, using the suite of apply functions)
- User written functions (syntax, if/else statements)
- Loops (for loop, while loop, repeat loop)
- Graphs for quantitative data (boxplots, scatterplots, bar graphs, histograms etc…)
- Changing graph elements: titles, point size, colors
- Extensive use of ggplot2
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