Hypothesis testing: Conducting statistical tests (t-tests, ANOVA, chi-squar tests,etc.)
Regression analysis:
Modeling relationships between variables using linear, logistic, or other regression techniques.
Time series analysis: Analyzing time-dependent data (e.g., forecasting, trend analysis).
Data Visualization:
Creating plots: Generating various types of plots (scatter plots, histograms, bar charts, line charts,etc.) using packages like ggplot2, plotly,andggvis.
Customizing plots: Modifying aesthetics, adding annotations, and creating interactive visualizations.
Machine Learning:
Building models: Implementing Middle East Mobile Number List machine learning algorithms (e.g., decision trees, random forests, support vector machines, neural networks).
Model evaluation:
Assessing model performance WhatsApp Material using metrics like accuracy, precision, recall, and F1-score.
Model deployment: Integrating models into real-world applications.
Data Mining:
Pattern discovery: Identifying patterns, trends, and anomalies in large datasets.
Association rule mining: Finding relationships between items in a dataset.
Clustering: Grouping data points bas on similarity.
Popular R Packages for Job Functions:
Data manipulation: dplyr, tidyr, data.table
Statistical analysis:
stats, lmtest, car
Data visualization:
gplot2, plotly, ggvis
Machine learning: caret, randomForest, xgboost, keras
Data mining: arules, cluster
R’s versatility and extensive ecosystem make it a valuable tool for a wide range of data-relat job functions.
