Complete Guide to R: Wrangling, Visualizing, and Modeling Data

سرفصل های دوره

Trying to locate meaning and direction in big data is difficult. R can help you find your way. R is a statistical programming language to analyze and visualize the relationships between large amounts of data. This course with data analytics expert Barton Poulson provides a thorough introduction to R, with detailed instruction for installing and navigating R and RStudio and hands-on examples, from exploratory graphics to neural networks. Barton shows how to get R and popular R packages up and running and start importing, cleaning, and converting data for analysis. He also shows how to create visualizations such as bar charts, histograms, and scatterplots and transform categorical, qualitative, and outlier data to best meet your research questions and the requirements of your algorithms.

01 - Introduction

01 - Make your data make sense

02 - Using the exercise files

02 - 1. What Is R

01 - R in context

02 - Data science with R A case study

03 - 2. Getting Started

01 - Installing R

02 - Environments for R

03 - Installing RStudio

04 - Navigating the RStudio environment

05 - Entering data

06 - Data types and structures

07 - Comments and headers

08 - Packages for R

09 - The tidyverse

10 - Piping commands with %%

04 - 3. Importing Data

01 - Rs built-in datasets

02 - Exploring sample datasets with pacman

03 - Importing data from a spreadsheet

04 - Importing XML data

05 - Importing JSON data

06 - Saving data in native R formats

05 - 4. Visualizing Data with ggplot2

01 - Introduction to ggplot2

02 - Using colors in R

03 - Using color palettes

04 - Creating bar charts

05 - Creating histograms

06 - Creating box plots

07 - Creating scatterplots

08 - Creating multiple graphs

09 - Creating cluster charts

06 - 5. Wrangling Data

01 - Creating tidy data

02 - Using tibbles

03 - Using data.table

04 - Converting data from wide to tall and from tall to wide

05 - Converting data from tables to rows

06 - Working with dates and times

07 - Working with list data

08 - Working with XML data

09 - Working with categorical variables

10 - Filtering cases and subgroups

07 - 6. Recoding Data

01 - Recoding categorical data

02 - Recoding quantitative data

03 - Transforming outliers

04 - Creating scale scores by counting

05 - Creating scale scores by averaging

08 - 7. An R for Data Science Case Study

01 - Data science with R A case study

09 - 8. Exploring Data

01 - Computing frequencies

02 - Computing descriptive statistics

03 - Computing correlations

04 - Creating contingency tables

05 - Conducting a principal component analysis

06 - Conducting an item analysis

07 - Conducting a confirmatory factor analysis

10 - 9. Analyzing Data

01 - Comparing proportions

02 - Comparing one mean to a population One-sample t-test

03 - Comparing paired means Paired samples t-test

04 - Comparing two means Independent samples t-test

05 - Comparing multiple means One-factor analysis of variance

06 - Comparing means with multiple categorical predictors Factorial analysis of variance

11 - 10. Predicting Outcomes

01 - Predicting outcomes with linear regression

02 - Predicting outcomes with lasso regression

03 - Predicting outcomes with quantile regression

04 - Predicting outcomes with logistic regression

05 - Predicting outcomes with Poisson or log-linear regression

06 - Assessing predictions with blocked-entry models

12 - 11. Clustering and Classifying Cases

01 - Grouping cases with hierarchical clustering

02 - Grouping cases with k-means clustering

03 - Classifying cases with k-nearest neighbors

04 - Classifying cases with decision tree analysis

05 - Creating ensemble models with random forest classification

13 - Conclusion

01 - Next steps

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تولید کننده: LinkedIn Learning (Lynda)