Below are some of the common uses of R.
1. Data Science and Analytics:
R is highly used for data analysis, particularly in research and business intelligence.
Analysts use R for data cleaning and statistical analysis; it generates visual reports and dashboards.
2. Statistical Research:
R is used by a statistician for hypothesis testing, sampling, regression analysis, and statistical modeling.
3. Machine Learning and AI:
So many libraries for machine learning are available in R that it is an ideal package for the predictive modeling, classification, clustering and much more.
Popular libraries in the process include randomForest, caret, and xgboost in building a machine learning model.
4. Bioinformatics:
R is extremely popular in genomics, epidemiology and other life science fields. Specialized tools that can be used to analyze biological data are available from the Bioconductor repository.
5. Econometrics & Finance:
The main field where R is used is for time series analysis, econometrics, and other financial modeling domains including stock market analysis, optimization of portfolios, risk management, etc.
6. Data Visualization and Reporting:
Amongst the most popular usage of R is in visualizing datasets, including bar charts, histograms, scatter plots, heatmaps, just to name a few and then moving into more complex visualizations.
It is possible to obtain sophisticated graphics that are tailored to presentation goals through the employment of tools such as ggplot2, and Shiny can be used to develop interactive web-based visualizations.
1. Data Science and Analytics:
R is highly used for data analysis, particularly in research and business intelligence.
Analysts use R for data cleaning and statistical analysis; it generates visual reports and dashboards.
2. Statistical Research:
R is used by a statistician for hypothesis testing, sampling, regression analysis, and statistical modeling.
3. Machine Learning and AI:
So many libraries for machine learning are available in R that it is an ideal package for the predictive modeling, classification, clustering and much more.
Popular libraries in the process include randomForest, caret, and xgboost in building a machine learning model.
4. Bioinformatics:
R is extremely popular in genomics, epidemiology and other life science fields. Specialized tools that can be used to analyze biological data are available from the Bioconductor repository.
5. Econometrics & Finance:
The main field where R is used is for time series analysis, econometrics, and other financial modeling domains including stock market analysis, optimization of portfolios, risk management, etc.
6. Data Visualization and Reporting:
Amongst the most popular usage of R is in visualizing datasets, including bar charts, histograms, scatter plots, heatmaps, just to name a few and then moving into more complex visualizations.
It is possible to obtain sophisticated graphics that are tailored to presentation goals through the employment of tools such as ggplot2, and Shiny can be used to develop interactive web-based visualizations.