Complete Guide to R: Wrangling, Visualizing, and Modeling Data
Focused View
Barton Poulson
8:15:38
24 View
01 - Make your data make sense.mp4
00:55
02 - Using the exercise files.mp4
00:55
01 - R in context.mp4
06:46
02 - Data science with R A case study.mp4
11:46
01 - Installing R.mp4
01:49
02 - Environments for R.mp4
04:27
03 - Installing RStudio.mp4
01:33
04 - Navigating the RStudio environment.mp4
06:04
05 - Entering data.mp4
07:05
06 - Data types and structures.mp4
12:24
07 - Comments and headers.mp4
04:59
08 - Packages for R.mp4
04:46
09 - The tidyverse.mp4
03:04
10 - Piping commands with %%.mp4
05:44
01 - Rs built-in datasets.mp4
04:58
02 - Exploring sample datasets with pacman.mp4
06:41
03 - Importing data from a spreadsheet.mp4
05:39
04 - Importing XML data.mp4
05:32
05 - Importing JSON data.mp4
05:39
06 - Saving data in native R formats.mp4
06:50
01 - Introduction to ggplot2.mp4
04:39
02 - Using colors in R.mp4
05:03
03 - Using color palettes.mp4
08:05
04 - Creating bar charts.mp4
09:22
05 - Creating histograms.mp4
05:30
06 - Creating box plots.mp4
05:24
07 - Creating scatterplots.mp4
05:58
08 - Creating multiple graphs.mp4
04:06
09 - Creating cluster charts.mp4
08:34
01 - Creating tidy data.mp4
10:12
02 - Using tibbles.mp4
04:51
03 - Using data.table.mp4
04:57
04 - Converting data from wide to tall and from tall to wide.mp4
04:13
05 - Converting data from tables to rows.mp4
05:02
06 - Working with dates and times.mp4
06:21
07 - Working with list data.mp4
05:14
08 - Working with XML data.mp4
05:22
09 - Working with categorical variables.mp4
06:29
10 - Filtering cases and subgroups.mp4
07:32
01 - Recoding categorical data.mp4
09:46
02 - Recoding quantitative data.mp4
07:10
03 - Transforming outliers.mp4
08:49
04 - Creating scale scores by counting.mp4
05:35
05 - Creating scale scores by averaging.mp4
03:26
01 - Data science with R A case study.mp4
18:43
01 - Computing frequencies.mp4
04:55
02 - Computing descriptive statistics.mp4
09:42
03 - Computing correlations.mp4
06:32
04 - Creating contingency tables.mp4
05:35
05 - Conducting a principal component analysis.mp4
13:00
06 - Conducting an item analysis.mp4
17:23
07 - Conducting a confirmatory factor analysis.mp4
05:50
01 - Comparing proportions.mp4
08:03
02 - Comparing one mean to a population One-sample t-test.mp4
06:20
03 - Comparing paired means Paired samples t-test.mp4
09:53
04 - Comparing two means Independent samples t-test.mp4
08:30
05 - Comparing multiple means One-factor analysis of variance.mp4
11:16
06 - Comparing means with multiple categorical predictors Factorial analysis of variance.mp4
08:47
01 - Predicting outcomes with linear regression.mp4
08:49
02 - Predicting outcomes with lasso regression.mp4
07:48
03 - Predicting outcomes with quantile regression.mp4
06:27
04 - Predicting outcomes with logistic regression.mp4
12:49
05 - Predicting outcomes with Poisson or log-linear regression.mp4
03:43
06 - Assessing predictions with blocked-entry models.mp4
10:35
01 - Grouping cases with hierarchical clustering.mp4
10:58
02 - Grouping cases with k-means clustering.mp4
07:54
03 - Classifying cases with k-nearest neighbors.mp4
11:57
04 - Classifying cases with decision tree analysis.mp4
09:13
05 - Creating ensemble models with random forest classification.mp4
09:20
01 - Next steps.mp4
02:20
Description
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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Barton Poulson
Instructor's CoursesFounder of datalab.cc, author for LinkedIn Learning, associate professor of psychology at Utah Valley University. I teach people how to use data to find practical solutions to real-life problems. #DataIsForDoing
Linkedin Learning
View courses Linkedin LearningLinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications.
It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.
- language english
- Training sessions 70
- duration 8:15:38
- English subtitles has
- Release Date 2024/05/01
