An Introduction to Statistical Learning: with Applications in R by Gareth James et al.Hands-On Programming with R: Write Your Own Functions And Simulations by Garrett Grolemund & Hadley Wickham.Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce & Andrew Bruce.Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Géron.R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham & Garrett Grolemund.Inter-Rater Reliability Essentials: Practical Guide in R by A.Practical Statistics in R for Comparing Groups: Numerical Variables by A.Network Analysis and Visualization in R by A.GGPlot2 Essentials for Great Data Visualization in R by A.R Graphics Essentials for Great Data Visualization by A.Machine Learning Essentials: Practical Guide in R by A.Practical Guide To Principal Component Methods in R by A.Practical Guide to Cluster Analysis in R by A.Free Training - How to Build a 7-Figure Amazon FBA Business You Can Run 100% From Home and Build Your Dream Life! by ASM.Psychological First Aid by Johns Hopkins University.Excel Skills for Business by Macquarie University.Introduction to Psychology by Yale University.Business Foundations by University of Pennsylvania.IBM Data Science Professional Certificate by IBM.Python for Everybody by University of Michigan.Google IT Support Professional by Google.The Science of Well-Being by Yale University.AWS Fundamentals by Amazon Web Services.Epidemiology in Public Health Practice by Johns Hopkins University.Google IT Automation with Python by Google.Specialization: Genomic Data Science by Johns Hopkins University.Specialization: Software Development in R by Johns Hopkins University.Specialization: Statistics with R by Duke University.Specialization: Master Machine Learning Fundamentals by University of Washington.Courses: Build Skills for a Top Job in any Industry by Coursera.Specialization: Python for Everybody by University of Michigan.Specialization: Data Science by Johns Hopkins University.Course: Machine Learning: Master the Fundamentals by Stanford.# … with 2 more variables: Petal.Width_Mean, Petal.Width_SD Ĭoursera - Online Courses and Specialization Data science # Species Sepal.Length_Me… Sepal.Length_SD Sepal.Width_Mean Sepal.Width_SD Petal.Length_Me… Petal.Length_SD fns = list(Mean = mean, SD = sd), na.rm = TRUE, # 3 virginica 6.59 2.97 5.55 # Compute the mean and the sd of all numeric columns # Species Sepal.Length Sepal.Width Petal.Length Summarise(across(Sepal.Length:Petal.Length, mean, na.rm= TRUE)) # A tibble: 3 x 4 # 6 5.4 3.9 1.7 0.4 setosa # Compute the mean of multiple columns This can use " for the case where a list is used for. names: A glue specification that describes how to name the output columns. : Additional arguments for the function calls in. fns: Function or list of functions to apply to each column. You can pick columns by position, name, function of name, type, or any combination thereof using Boolean operators. We’ll use the function across() to make computation across multiple columns. This article describes how to compute summary statistics, such as mean, sd, quantiles, across multiple numeric columns.
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