R 完整指南 - 整理、可视化和建模数据
Complete Guide to R - Wrangling, Visualizing, and Modeling Data
- 003 R in context
- 004 Data science with R A case study
- 005 Installing R
- 006 Environments for R
- 007 Installing RStudio
- 008 Navigating the RStudio environment
- 009 Entering data
- 010 Data types and structures
- 011 Comments and headers
- 012 Packages for R
- 013 The tidyverse
- 014 Piping commands with
- 015 R s built in datasets
- 016 Exploring sample datasets with pacman
- 017 Importing data from a spreadsheet
- 018 Importing XML data
- 019 Importing JSON data
- 020 Saving data in native R formats
- 021 Introduction to ggplot2
- 022 Using colors in R
- 023 Using color palettes
- 024 Creating bar charts
- 025 Creating histograms
- 026 Creating box plots
- 027 Creating scatterplots
- 028 Creating multiple graphs
- 029 Creating cluster charts
- 030 Creating tidy data
- 031 Using tibbles
- 032 Using data table
- 033 Converting data from wide to tall and from tall to wide
- 034 Converting data from tables to rows
- 035 Working with dates and times
- 036 Working with list data
- 037 Working with XML data
- 038 Working with categorical variables
- 039 Filtering cases and subgroups
- 040 Recoding categorical data
- 041 Recoding quantitative data
- 042 Transforming outliers
- 043 Creating scale scores by counting
- 044 Creating scale scores by averaging
- 045 Data science with R A case study
- 046 Computing frequencies
- 047 Computing descriptive statistics
- 048 Computing correlations
- 049 Creating contingency tables
- 050 Conducting a principal component analysis
- 051 Conducting an item analysis
- 052 Conducting a confirmatory factor analysis
- 053 Comparing proportions
- 054 Comparing one mean to a population One sample t test
- 055 Comparing paired means Paired samples t test
- 056 Comparing two means Independent samples t test
- 057 Comparing multiple means One factor analysis of variance
- 058 Comparing means with multiple categorical predictors Factorial analysis of variance
- 059 Predicting outcomes with linear regression
- 060 Predicting outcomes with lasso regression
- 061 Predicting outcomes with quantile regression
- 062 Predicting outcomes with logistic regression
- 063 Predicting outcomes with Poisson or log linear regression
- 064 Assessing predictions with blocked entry models
- 065 Grouping cases with hierarchical clustering
- 066 Grouping cases with k means clustering
- 067 Classifying cases with k nearest neighbors
- 068 Classifying cases with decision tree analysis
- 069 Creating ensemble models with random forest classification
- 070 Next steps