Linkedin - Complete Guide to R Wrangling Visualizing and Modeling Data

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Complete Guide to R Wrangling Visualizing and Modeling Data 1. Introduction
  • 1. Make your data make sense.mp4 (4.3 MB)
  • 2. Using the exercise files.mp4 (1.7 MB)
10. Analyzing Data
  • 1. Comparing proportions.mp4 (24.3 MB)
  • 2. Comparing one mean to a population- One-sample t-test.mp4 (16.1 MB)
  • 3. Comparing paired means- Paired samples t-test.mp4 (23.2 MB)
  • 4. Comparing two means- Independent samples t-test.mp4 (21.0 MB)
  • 5. Comparing multiple means- One-factor analysis of variance.mp4 (29.9 MB)
  • 6. Comparing means with multiple categorical predictors- Factorial analysis of variance.mp4 (21.8 MB)
11. Predicting Outcomes
  • 1. Predicting outcomes with linear regression.mp4 (27.7 MB)
  • 2. Predicting outcomes with lasso regression.mp4 (24.7 MB)
  • 3. Predicting outcomes with quantile regression.mp4 (18.3 MB)
  • 4. Predicting outcomes with logistic regression.mp4 (31.3 MB)
  • 5. Predicting outcomes with Poisson or log-linear regression.mp4 (9.3 MB)
  • 6. Assessing predictions with blocked-entry models.mp4 (30.6 MB)
12. Clustering and Classifying Cases
  • 1. Grouping cases with hierarchical clustering.mp4 (29.2 MB)
  • 2. Grouping cases with k-means clustering.mp4 (22.9 MB)
  • 3. Classifying cases with k-nearest neighbors.mp4 (30.6 MB)
  • 4. Classifying cases with decision tree analysis.mp4 (24.4 MB)
  • 5. Creating ensemble models with random forest classification.mp4 (24.0 MB)
13. Conclusion
  • 1. Next steps.mp4 (5.7 MB)
2. What Is R
  • 1. R in context.mp4 (13.5 MB)
  • 2. Data science with R- A case study.mp4 (30.6 MB)
3. Getting Started
  • 1. Installing R.mp4 (5.1 MB)
  • 10. Piping commands with %%.mp4 (7.9 MB)
  • 2. Environments for R.mp4 (11.2 MB)
  • 3. Installing RStudio.mp4 (3.3 MB)
  • 4. Navigating the RStudio environment.mp4 (17.4 MB)
  • 5. Entering data.mp4 (17.0 MB)
  • 6. Data types and structures.mp4 (28.7 MB)
  • 7. Comments and headers.mp4 (13.1 MB)
  • 8. Packages for R.mp4 (17.0 MB)
  • 9. The tidyverse.mp4 (9.3 MB)
4. Importing Data
  • 1. Rs built-in datasets.mp4 (16.3 MB)
  • 2. Exploring sample datasets with pacman.mp4 (21.3 MB)
  • 3. Importing data from a spreadsheet.mp4 (18.0 MB)
  • 4. Importing XML data.mp4 (19.3 MB)
  • 5. Importing JSON data.mp4 (21.3 MB)
  • 6. Saving data in native R formats.mp4 (19.8 MB)
5. Visualizing Data with ggplot2
  • 1. Introduction to ggplot2.mp4 (14.8 MB)
  • 2. Using colors in R.mp4 (16.0 MB)
  • 3. Using color palettes.mp4 (26.5 MB)
  • 4. Creating bar charts.mp4 (24.2 MB)
  • 5. Creating histograms.mp4 (13.1 MB)
  • 6. Creating box plots.mp4 (12.4 MB)
  • 7. Creating scatterplots.mp4 (14.1 MB)
  • 8. Creating multiple graphs.mp4 (10.0 MB)
  • 9. Creating cluster charts.mp4 (25.2 MB)
6. Wrangling Data
  • 1. Creating tidy data.mp4 (34.3 MB)
  • 10. Filtering cases and subgroups.mp4 (22.0 MB)
  • 2. Using tibbles.mp4 (13.3 MB)
  • 3. Using data.table.mp4 (15.8 MB)
  • 4. Converting data from wide to tall and from tall to wide.mp4 (11.4 MB)
  • 5. Converting data from tables to rows.mp4 (14.9 MB)
  • 6. Working with dates and times.mp4 (18.8 MB)
  • 7. Working with list data.mp4 (14.9 MB)
  • 8. Working with XML data.mp4 (17.2 MB)
  • 9. Working with categorical variables.mp4 (15.5 MB)
7. Recoding Data
  • 1. Recoding categorical data.mp4 (23.9 MB)
  • 2. Recoding quantitative data.mp4 (22.1 MB)
  • 3. Transforming outliers.mp4 (21.5 MB)
  • 4. Creating scale scores by counting.mp4 (14.4 MB)
  • 5. Creating scale scores by averaging.mp4 (8.5 MB)
8. An R for Data Science Case Study
  • 1. Data science with R- A case study.mp4 (57.4 MB)
9. Exploring Data
  • 1. Computing frequencies.mp4 (14.4 MB)
  • 2. Computing descriptive statistics.mp4 (25.3 MB)
  • 3. Computing correlations.mp4 (16.6 MB)
  • 4. Creating contingency tables.mp4 (16.8 MB)
  • 5. Conducting a principal component analysis.mp4 (37.8 MB)
  • 6. Conducting an item analysis.mp4 (52.1 MB)
  • 7. Conducting a confirmatory factor analysis.mp4 (18.1 MB)
  • Ex_Files_Complete_Guide_to_R.zip (6.2 MB)

Description

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Course details

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.



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Linkedin - Complete Guide to R Wrangling Visualizing and Modeling Data

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1.3 GB
seeders:37
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Linkedin - Complete Guide to R Wrangling Visualizing and Modeling Data

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