Loved by learners at thousands of companies
If you have experience with SAS and want to learn R, this is the course for you. R is FREE (cost) and OPEN (license) and is one of the fastest growing software languages for statistics and data science. This course is a gentle introduction to the R language with every chapter providing a detailed mapping of R functions to SAS procedures highlighting similarities and differences. You will orient yourself in the R environment and discover how to wrangle, visualize, and model data plus customize your output for final presentation. Throughout the course, you will follow a consistent workflow of data quality checking and cleaning, exploring relationships, modeling, and presenting results. You will leave this course with coded examples that provide a template to use immediately with a dataset of your own.
Getting Started with RFree
This first chapter will get you oriented into the R programming environment. You'll learn how to get help, load a dataset, and increase functionality by adding packages. You'll begin working with the abalone dataset (through the dplyr package workflow) to get descriptive statistics and create helpful visualizations (using the ggplot2 package).Get help and load data in R50 xpGetting help50 xpLoad dataset and get details100 xpAdd functionality with packages100 xpDataset contents and descriptive statistics50 xpLoad external dataset100 xpDataset contents100 xpDescriptive statistics100 xpGraphical visualizations50 xpHistograms100 xpBoxplots and violin plots100 xpScatterplots100 xp
Now that you are oriented in the R environment, this chapter will advance your understanding of R's versatility working with data objects. You'll learn how to create and modify variables in the abalone data set. Using your ggplot2 visualization skills, you will discover the data errors in the abalone data and then create a final cleaned data set ready for analysis and modeling.Objects - the building blocks of R50 xpCreate data objects in R100 xpCreate composite object types100 xpSelecting elements from objects50 xpDetermine variable types50 xpSelect elements in objects100 xpManipulating datasets and data objects50 xpCreate new variables100 xpRecode variables100 xpObject type conversion100 xpData quality and cleaning50 xpVariables inspection100 xpIllogical weights100 xpCheck dimension measurements100 xpCheck final dataset100 xp
Once your data set has been cleaned, the next step is exploration. In chapter 3 you will learn how to compute descriptive statistics, explore associations (e.g., correlations) among the variables, and perform bi-variate statistical tests (e.g., t-tests and chi-square tests). You will also create graphical visualizations which illustrate the bi-variate associations and group comparison tests.Exploratory data analysis50 xpDescriptive statistics and function masking100 xpSpecific statistics for one or more variables100 xpSummary statistics by group100 xpCorrelations and t-tests50 xpBivariate correlations100 xpScatterplots100 xpCorrelations by sex50 xpTests for two groups100 xpCategorical data: analyze and visualize50 xpChi-square tests100 xpMosaic plots100 xpAge categories by shellweight categories100 xp
Models and Presentation
In this final chapter, you will learn how to work with one of the most versatile data object types in R called a list. These skills will enable you to save and manipulate your output from descriptive statistics, associations, and group comparison computations. You will also learn how to perform ANOVA (analysis of variance) and linear regression in R. All your skills are put to use in the final exercises to create the best models for predicting abalone ages from their sex, size, and weight measurements.Working with output objects50 xpDescriptive statistics output100 xpSummarise output100 xpGroup_by output100 xpWorking with lists50 xpHmisc describe output100 xpCorrelations output100 xpt-tests output100 xpChi-square tests output100 xpANOVA and linear models50 xpANOVA100 xpLinear regression100 xpFinal models evaluation50 xpAbalone age predictors100 xpBest model by sex100 xpCourse summary and recommendations50 xp
Research Professor, Senior Biostatistician
Melinda Higgins, PhD @mhiggins2000 is an avid R user, advocate and instructor. She originally learned S and then transitioned to R in the mid 1990's. She has over 25 years experience as a research scientist and statistician. She has a PhD in Chemometrics (applied pattern recognition in Chemistry) and a MS in Statistics (with a focus in experimental design). She enjoys teaching courses in biostatistics and leading workshops on statistical programming using SAS, SPSS, R, the RStudio IDE and Rmarkdown. She is also the co-founder of R-Ladies Atlanta, GA.
What do other learners have to say?
I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.
Devon Edwards Joseph
Lloyds Banking Group
DataCamp is the top resource I recommend for learning data science.
Harvard Business School
DataCamp is by far my favorite website to learn from.
Decision Science Analytics, USAA