R Statistical software for data visualization and data analysis used by researchers, statisticians, & data miners for quantitative data analysis. Statistical Data Management and Analysis using R course provides an insight into quantitative data management and analysis (exploring, summarizing, statistical analyzing, visualizing). R is an open source software with many features for quantitative data management and analysis. Making Sense of Data is an important skill. Our modern world is a complicated place, and we are bombarded by data at every turn. The first step to understanding and making sense of data is being able to summarize data. R is a programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical data analysis. You will start with the most basic importing techniques and advanced ways to handle even the most difficult datasets to import. R has “become the de-facto standard for writing statistical software among statisticians. This course will give you a solid foundation in creating statistical analysis solutions using the R language, and how to carry out a range of commonly used analytical processes. Finally, you will learn to implementation and data analysis.
Introduce the visual representation of variables in scatter graphs, bar charts, and histograms
Import and export data in various formats in R
Perform advanced statistical data analysis
Enhance your data analysis skills and learn to handle even the most complex datasets
Learn how to handle vector and raster data in R
Explain the basic steps of the research process
Explain differences between populations and samples
Explain differences between experimental and non-experimental research designs
Explain differences between independent and dependent variables
Overview of the R Studio IDE
Installing, loading and updating R packages
Creating objects in R
Data types
Data structures
Sorting vectors and data frames
Directory management commands
Direct data entry in R (for small data sets)
Importing data from other software
Decision structures (if, if-else, if-else if-else)
Repetitive structures (for and while loops)
Other important programming functions (break, next, warn, stop)
Working with variables
Transform continuous variables to categorical variables
Add new variables to data frames
Handling missing values
Sub-setting data frames
Appending and merging data frames
Spit data frames
Stack and unstack data frames
Creating tables of frequencies and proportions
Cross tabulations of categorical variables
Descriptive statistics for continuous variables
Introduction to graphs and charts in R
Customizing graph attributes (titles, axes, text, legends)
Graphs for categorical variables
Graphs for continuous variables
Graphs to investigate relationship between variables
One Sample T Test
Independent Samples T Test
Paired Samples T Test
One-way analysis of variance (ANOVA)
Chi-Square test of independence
Pearson's Correlation
Spearman's Rank-Order Correlation
Linear Regression
Multiple Linear Regression
Binary Logistic Regression
Ordinal Logistic Regression
Introduce participants to R as a quantitative data analysis tool
Enable learner master R software and R-studio as a user interface
Enable learner import data from various sources
Introduce basic statistics for exploratory data analysis including methods for describing and summarizing variable distributions
Provide essential skills for data manipulation including selecting subsets and recoding
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