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R Statistics Essential Training

R Statistics Essential Training

with Barton Poulson

Video: Welcome

Hi, I'm Bart Poulson and I'd like to welcome you to R Statistics Essential Training. In this course, I'll teach you to use charts such as histograms, bar charts, scatter plots and custom-layered plots to get the big picture of your data. Descriptive statistics such as means, standard deviations and correlations for a more precise depiction of your data. And inferential statistics like regression, t-tests, to help you determine the reliability of your results and how they can generalize to a broader population.
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  1. 1m 59s
    1. Welcome
      58s
    2. Using the exercise files
      20s
    3. Using the challenges
      41s
  2. 1h 24m
    1. Installing R on your computer
      3m 19s
    2. Using RStudio
      4m 36s
    3. Taking a first look at the interface
      10m 56s
    4. Installing and managing packages
      11m 17s
    5. Using built-in datasets in R
      5m 27s
    6. Entering data manually
      4m 37s
    7. Importing data
      8m 53s
    8. Converting tabular data to row data
      13m 6s
    9. Working with color in R
      10m 18s
    10. Exploring color with Colorbrewer
      6m 36s
    11. Challenge: Creating color palettes in R
      1m 56s
    12. Solution: Creating color palettes in R
      3m 54s
  3. 44m 33s
    1. Creating bar charts for categorical variables
      8m 32s
    2. Creating pie charts for categorical variables
      6m 47s
    3. Creating histograms for quantitative variables
      6m 2s
    4. Creating box plots for quantitative variables
      7m 12s
    5. Overlaying plots
      7m 25s
    6. Saving images
      5m 34s
    7. Challenge: Layering plots
      39s
    8. Solution: Layering plots
      2m 22s
  4. 33m 18s
    1. Calculating frequencies
      3m 33s
    2. Calculating descriptives
      5m 43s
    3. Using a single proportion: Hypothesis test and confidence interval
      4m 2s
    4. Using a single mean: Hypothesis test and confidence interval
      4m 9s
    5. Using a single categorical variable: One sample chi-square test
      5m 9s
    6. Examining robust statistics for univariate analyses
      7m 29s
    7. Challenge: Calculating descriptive statistics
      36s
    8. Solution: Calculating descriptive statistics
      2m 37s
  5. 32m 5s
    1. Examining outliers
      6m 42s
    2. Transforming variables
      9m 26s
    3. Computing composite variables
      6m 18s
    4. Coding missing data
      6m 4s
    5. Challenge: Transforming skewed data to pull in outliers
      42s
    6. Solution: Transforming skewed data to pull in outliers
      2m 53s
  6. 16m 4s
    1. Selecting cases
      5m 30s
    2. Analyzing by subgroup
      3m 14s
    3. Merging files
      5m 16s
    4. Challenge: Analyzing guinea pig data subgroups
      33s
    5. Solution: Analyzing guinea pig data subgroups
      1m 31s
  7. 19m 38s
    1. Creating bar charts of group means
      4m 33s
    2. Creating grouped box plots
      5m 4s
    3. Creating scatter plots
      5m 2s
    4. Challenge: Creating your own grouped box plots
      48s
    5. Solution: Creating your own grouped box plots
      4m 11s
  8. 55m 1s
    1. Calculating correlation
      3m 55s
    2. Computing a bivariate regression
      6m 13s
    3. Comparing means with the t-test
      7m 20s
    4. Comparing paired means: Paired t-test
      6m 35s
    5. Comparing means with a one-factor analysis of variance (ANOVA)
      8m 27s
    6. Comparing proportions
      3m 34s
    7. Creating cross tabs for categorical variables
      5m 10s
    8. Computing robust statistics for bivariate associations
      8m 39s
    9. Challenge: Comparing proportions across several different groups
      1m 1s
    10. Solution: Comparing proportions across several different groups
      4m 7s
  9. 23m 18s
    1. Creating clustered bar charts for means
      3m 43s
    2. Creating scatter plots for grouped data
      2m 49s
    3. Creating scatter plot matrices
      6m 10s
    4. Creating 3D scatter plots
      5m 13s
    5. Challenge: Creating your own scatter plot matrix
      42s
    6. Solution: Creating your own scatter plot matrix
      4m 41s
  10. 45m 14s
    1. Computing a multiple regression
      9m 26s
    2. Comparing means with a two-factor ANOVA
      5m 56s
    3. Conducting a cluster analysis
      14m 14s
    4. Conducting a principal components/factor analysis
      10m 31s
    5. Challenge: Creating a cluster analysis of states in the US
      39s
    6. Solution: Creating a cluster analysis of states in the US
      4m 28s
  11. 3m 40s
    1. Next steps
      3m 40s

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R Statistics Essential Training
5h 59m Intermediate Sep 26, 2013

Viewers: in countries Watching now:

R is the language of big data—a statistical programming language that helps describe, mine, and test relationships between large amounts of data. Author Barton Poulson shows how to use R to model statistical relationships using graphs, calculations, tests, and other analysis tools. Learn how to enter and modify data; create charts, scatter plots, and histograms; examine outliers; calculate correlations; and compute regressions, bivariate associations, and statistics for three or more variables. Challenge exercises with step-by-step solutions allow you to test your skills as you progress.

Topics include:
  • Installing R on your computer
  • Using the built-in datasets
  • Importing data
  • Creating bar and pie charts for categorical variables
  • Creating histograms and box plots for quantitative variables
  • Calculating frequencies and descriptives
  • Transforming variables
  • Coding missing data
  • Analyzing by subgroups
  • Creating charts for associations
  • Calculating correlations
  • Creating charts and statistics for three or more variables
  • Creating crosstabs for categorical variables
Subjects:
Developer Databases Programming Languages
Software:
R
Author:
Barton Poulson

Welcome

Hi, I'm Bart Poulson and I'd like to welcome you to R Statistics Essential Training. In this course, I'll teach you to use charts such as histograms, bar charts, scatter plots and custom-layered plots to get the big picture of your data. Descriptive statistics such as means, standard deviations and correlations for a more precise depiction of your data. And inferential statistics like regression, t-tests, to help you determine the reliability of your results and how they can generalize to a broader population.

I'll also show you how to read data in a variety of formats, to explore and manipulate your data and to create new variables and compute transformations. Finally, I'll demonstrate how you can create beautiful charts for publication and presentation and share your results with others. If you're ready to get going, let's get started with R Statistics Essential Training.

Find answers to the most frequently asked questions about R Statistics Essential Training.


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Q:  The R files within Chapters 01 to 10 don't appear to have any code in them. Where is the final code for each file?

A:  Look in the "Finished Versions" folder which contains final version of each file. These files contain the R code written by the author.

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