Statistical Analysis with R
R is an open-source programming language that provides a wide variety of statistical and graphical techniques.
Getting started with SPSS
Overview of our R Software Training Programs
R has “become the de-facto standard for writing statistical software among statisticians. This Training on Statistical Data Analysis using R 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.
The overall courses provide the participants with a practical application of the statistical component of RStudio Statistics.
Participants will review several statistical techniques, gain an understanding of when and why to use these various techniques as well as how to apply them with confidence, interpret their output, and graphically display the results.
This program is provided by Statistician, Biostatistician, Epidemiologists, and Researchers.
Tailor-Made Course
We can prepare R software trainings as tailor-made course to meet individual or organization-wide needs. A training needs assessment will be done on the training participants to collect data on the existing skills, knowledge gaps, training expectations, and tailor-made needs.
Explore Traning Programs
Getting started with R
Handling statistical data is an essential part of many research. However, many people find the idea of using statistics, and especially statistical software packages, extremely daunting.
This course, Getting started with R, takes a step-by-step approach to statistics software through seven interactive activities.
Target Participants
This training on Statistical Data Analysis using R is intended for Data Scientists, Data Analysts, Business Intelligence Analysts and any other professional who want to explore the vast range of analytical and graphical capabilities of R.
Course learning outcomes
After studying this course, you should be able to:
- An introduction to R, basic data types, and R/RStudio installation
- Overview of base R concepts and specific data wrangling packages in R
- Connecting to databases, executing database queries in R
- How to use R for graphical summary
- R programming
- How to carry out a range of analyses using R
Course content
1. Introduction to Statistical Analysis
- 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
2. Introduction to R software for statistical computing
- 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)
3. Data Wrangling and Cleaning in R
- 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
4. Explanatory Data Analysis (EDA) in R
- Creating tables of frequencies and proportions
- Cross tabulations of categorical variables
- Descriptive statistics for continuous variables
5. Data Visualization using R base package
- 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
6. Mean Comparison Tests in R
- One Sample T Test
- Independent Samples T Test
- Paired Samples T Test
- One-way analysis of variance (ANOVA)
7. Tests of Associations in R
- Chi-Square test of independence
- Pearson’s Correlation
- Spearman’s Rank-Order Correlation
8. Predictive Regression Models using R
- Linear Regression
- Multiple Linear Regression
- Binary Logistic Regression
- Ordinal Logistic Regression
Training Approach
This training on Statistical Data Analysis using R is delivered by our seasoned trainers who have vast experience as expert professionals using R programming language. The course is taught through a mix of practical activities, theory, group works and case studies.
Training manuals and additional reference materials are provided to the participants.
Prerequisites
Basic knowledge of Statistics ideal.
Certification
Upon successful completion of this course, participants will be issued with a certificate.
Statistical Analysis with R for Public Health Specialization
Master Statistics for Public Health and Learn R. Develop your statistical thinking skills and learn key data analysis methods through R.
Statistics are everywhere. The probability it will rain today. Trends over time in unemployment rates.
The odds that India will win the next cricket world cup. In sports like football, they started out as a bit
of fun but have grown into big business. Statistical analysis also has a key role in medicine, not least
in the broad and core discipline of public health.
In this specialisation, you’ll take a peek at what medical research is and how – and indeed why – you
turn a vague notion into a scientifically testable hypothesis. You’ll learn about key statistical
concepts like sampling, uncertainty, variation, missing values and distributions.
Then you’ll get your hands dirty with analysing data sets covering some big public
health challenges – fruit and vegetable consumption and cancer, risk factors for diabetes,
and predictors of death following heart failure hospitalisation – using R, one of the most
widely used and versatile free software packages around.
Target Participants
This training is intended for public health professionals.
What you'll learn
You'll learn:
- Recognise the key components of statistical thinking in order to defend the critical role of statistics in modern public health research and practice.
- Apply appropriate methods in order to formulate and examine statistical associations between variables within a data set in R.
- Describe a given data set from scratch using descriptive statistics and graphical methods as a first step for more advanced analysis using R software.
- Interpret the output from your analysis and appraise the role of chance and bias as explanations for your results.
Course learning outcomes
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from Parkland College
Course content
Module 1: Introduction to Statistics & Data Analysis in Public Health
- Defend the critical role of statistics in modern public health research and practice
- Describe a data set from scratch, including data item features and data quality issues, using descriptive statistics and graphical methods in R
- Select and apply appropriate methods to formulate and examine statistical associations between variables within a data set in R
- Interpret the output from your analysis and appraise the role of chance and bias
Module 2: Linear Regression in R for Public Health
- Describe when a linear regression model is appropriate to use
- Read in and check a data set's variables using the software R prior to undertaking a model analysis
- Fit a multiple linear regression model with interactions, check model assumptions and interpret the output
Module 3: Logistic Regression in R for Public Health
- Describe a data set from scratch using descriptive statistics and simple graphical methods as a first step for advanced analysis using R software
- Interpret the output from your analysis and appraise the role of chance and bias as potential explanations
- Run multiple logistic regression analysis in R and interpret the output
- Evaluate the model assumptions for multiple logistic regression in R
Module 4: Survival Analysis in R for Public Health
- Run Kaplan-Meier plots and Cox regression in R and interpret the output
- Describe a data set from scratch, using descriptive statistics and simple graphical methods as a necessary first step for more advanced analysis
- Describe and compare some common ways to choose a multiple regression model
Training Approach
This training on Statistical Analysis with R is delivered by our seasoned trainers who have vast experience as expert professionals using R programming language. The course is taught through a mix of practical activities, theory, group works and case studies.
Training manuals and additional reference materials are provided to the participants.
Prerequisites
Basic knowledge of Statistics ideal.
Certification
Upon successful completion of this course, participants will be issued with a certificate.
Frequently Asked Questions
When can I start this course?
- This course is open enrollment, so you can register and start the course whenever you are ready.
What happens when I complete the course?
- You will automatically get a certificate of completion as soon as you complete the course and pass the graded quizzes and project.