R for Data Science: Lunch Break Lessons

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Mark Niemann-Ross

13:41:47

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01 - New This Week

01 - Scale().mp4

03:09

02 - Introduction

01 - Welcome.mp4

00:50

02 - Exercise files.mp4

00:36

03 - R for Data Science Lessons (Jan-Mar 2018)

01 - R built-in data sets.mp4

05:21

02 - Vector math.mp4

05:57

03 - Subsetting.mp4

07:17

04 - R data types Basic types.mp4

07:34

05 - R data types Vector.mp4

05:16

06 - R data types List.mp4

05:27

07 - R data types Factor.mp4

05:15

08 - R data types Matrix.mp4

08:48

09 - R data types Array.mp4

03:50

10 - R data types Data frame.mp4

06:44

11 - Data frames Order and merge.mp4

08:10

12 - Data frames Read and update.mp4

04:44

04 - R for Data Science Lessons (Apr-Jun 2018)

01 - Data frames rbind.mp4

03:04

02 - Dataframes cbind.mp4

03:11

03 - apply and lapply.mp4

03:21

04 - mapply.mp4

02:21

05 - plot.mp4

02:45

06 - Brackets and double-brackets.mp4

02:50

07 - mean, rowMeans, and colMeans.mp4

01:49

08 - RSQLite.mp4

03:44

09 - sqldf.mp4

02:09

10 - Aggregate.mp4

03:17

11 - Random numbers.mp4

04:26

12 - Pipeline.mp4

04:42

13 - Working with clipboards.mp4

02:45

05 - R for Data Science Lessons (Jul-Sep 2018)

01 - Style guides.mp4

03:00

02 - cut.mp4

03:17

03 - split.mp4

03:44

04 - askYesNo.mp4

03:46

05 - cdplot.mp4

05:30

06 - Fun.mp4

03:09

07 - boxplot.mp4

02:28

08 - Histogram.mp4

03:30

09 - Plot to file.mp4

05:55

10 - coplot.mp4

03:57

11 - cowsay.mp4

02:34

12 - table.mp4

02:46

13 - Look inside.mp4

03:11

06 - R for Data Science Lessons (Oct-Dec 2018)

01 - barplot.mp4

02:31

02 - Pie chart.mp4

02:11

03 - unlist.mp4

03:24

04 - Joins Inner and full.mp4

03:03

05 - Joins Left and right.mp4

02:13

06 - Sets Union, intersect, and difference.mp4

02:10

07 - Sets Equal and in.mp4

02:14

08 - colors.mp4

02:25

09 - ifelse.mp4

03:05

10 - spineplot.mp4

02:36

11 - browser.mp4

03:37

12 - debugonce.mp4

02:25

13 - Default mirror.mp4

02:31

07 - R for Data Science Lessons (Jan-Mar 2019)

01 - Dealing with NA.mp4

06:01

02 - Using with().mp4

02:55

03 - Simple string matching.mp4

04:35

04 - grep.mp4

02:53

05 - dotchart.mp4

03:54

06 - fourfoldplot.mp4

03:34

07 - matplot.mp4

03:50

08 - dimnames.mp4

05:04

09 - mosaicplot.mp4

04:23

10 - stemplot.mp4

02:57

11 - stripchart.mp4

03:10

12 - sunflower.mp4

02:57

13 - Switch.mp4

02:16

08 - R for Data Science Lessons (Apr-Jun 2019)

01 - Switch on factors.mp4

02:18

02 - Anyall.mp4

04:13

03 - sub, gsub, regex, and backreferences.mp4

04:52

04 - agrep and fuzzy matching.mp4

04:44

05 - combn finds combinations.mp4

02:33

06 - edit, fix, and dataentry.mp4

04:57

07 - zeallot.mp4

05:30

08 - menu.mp4

02:58

09 - person.mp4

03:16

10 - txtProgressBar.mp4

03:13

11 - zip and tar.mp4

03:50

12 - bitwise.mp4

04:11

13 - by is like tapply.mp4

04:15

14 - Update your R.mp4

04:01

09 - R for Data Science Lessons (Jul-Sep 2019)

01 - Be careful with transpose.mp4

04:45

02 - Passwords.mp4

04:45

03 - heatmap.mp4

04:24

04 - combine.mp4

04:11

05 - stopifnot.mp4

02:44

06 - weighted.mean.mp4

02:16

07 - chartr.mp4

03:50

08 - file.choose.mp4

04:02

09 - duplicated and unique.mp4

02:52

10 - load and save.mp4

04:23

11 - floor, round, ceiling, and trunc.mp4

02:32

12 - expand.grid.mp4

02:55

13 - Professional groups.mp4

02:26

10 - R for Data Science Lessons (Oct-Dec 2019)

01 - Simplify with c.mp4

03:29

02 - Logical operators.mp4

05:56

03 - char.expand.mp4

03:57

04 - complete.cases.mp4

03:16

05 - swirl.mp4

02:08

06 - tryCatch.mp4

03:23

07 - Double colons.mp4

03:05

08 - for loop.mp4

04:54

09 - The 100th episode.mp4

04:12

10 - while loop.mp4

04:18

11 - repeat loop.mp4

04:14

12 - Create your own swirl lesson.mp4

04:04

13 - Logic and flow control.mp4

04:02

11 - R for Data Science Lessons (Jan-Mar 2020)

01 - matrix, row, and column.mp4

04:41

02 - cumsum, cumprod, cummax, an dcummin.mp4

04:11

03 - issymetric.mp4

03:14

04 - file.access.mp4

04:00

05 - file.info.mp4

04:01

06 - dput and dget.mp4

04:35

07 - Sort a data frame by multiple columns.mp4

04:12

08 - diag.mp4

02:52

09 - crossprod.mp4

03:13

10 - upper.tri and lower.tri.mp4

03:07

11 - strsplit() splits strings at matched characters.mp4

02:37

12 - Use setnames() to change the name of an object.mp4

05:03

13 - Change the structure of a vector with stack().mp4

04:44

12 - R for Data Science Lessons (Apr-Jun 2020)

01 - Use droplevels() to simplify factors.mp4

03:26

02 - Use .Rmd for documentation.mp4

07:03

03 - Use rep() to create long repetitive vectors.mp4

04:58

04 - Use format() to improve readability.mp4

04:53

05 - Use pmax() and pmin() to discover the scope of paired vectors.mp4

05:18

06 - Use print() for more than you do now.mp4

04:55

07 - Use range() and extendrange() to analyze and manipulate groups of numbers.mp4

03:42

08 - Evaluate the importance of a number with rank().mp4

04:51

09 - Use saveRDS() and readRDS() to serialize objects.mp4

03:26

10 - Use regular expressions with regexpr() and gregexpr().mp4

04:22

11 - message.mp4

05:21

12 - regexpr.mp4

05:45

13 - diff.mp4

04:50

13 - R for Data Science Lessons (Jul-Sep 2020)

01 - exists.mp4

01:58

02 - formulas.mp4

04:42

03 - RPres.mp4

05:26

04 - lattice Introduction.mp4

05:08

05 - lattice xyplot.mp4

05:37

06 - lattice cloud and wireframe.mp4

04:31

07 - lattice contourplot.mp4

04:08

08 - lattice barchart.mp4

04:57

09 - lattice splom charts.mp4

06:14

10 - lattice panels.mp4

04:50

11 - lattice stripplot.mp4

03:18

12 - whichmin and whichmax.mp4

02:52

13 - par font, size, color.mp4

05:10

14 - R for Data Science Lessons (Oct-Dec 2020)

01 - par margins.mp4

06:21

02 - par pch and points.mp4

03:17

03 - legend.mp4

05:26

04 - identical.mp4

03:29

05 - Matrix math Overview of functions.mp4

01:38

06 - Matrix math review.mp4

04:50

07 - matrix solve systems.mp4

04:11

08 - matrix solve inverse.mp4

03:32

09 - matrix backsolve and forwardsolve.mp4

05:24

10 - Matrix Determinant.mp4

03:00

11 - Arrays and outer.mp4

02:49

12 - Matrix Crossproduct.mp4

02:07

13 - Matrix SVD and QR decomposition.mp4

03:39

15 - R for Data Science Lessons (Jan-Mar 2021)

01 - Matrix Eigenvalues and eigenvectors.mp4

01:38

02 - Locator.mp4

04:38

03 - on.exit.mp4

04:11

04 - missing.mp4

03:11

05 - nargs.mp4

02:28

06 - tidyverse.mp4

05:43

07 - gutenbergr.mp4

05:04

08 - Create and clean a natural language corpus.mp4

07:25

09 - Remove stopwords from an NLP corpus.mp4

05:16

10 - NLP and term-document matrix.mp4

05:53

16 - R for Data Science Lessons (April-June 2021)

01 - Analyze term-document matrix.mp4

05:38

02 - NLP packages Tidytext.mp4

05:07

03 - NLP packages Quanteda.mp4

07:40

04 - NLP packages Sentiment analysis.mp4

08:28

05 - Word clouds.mp4

03:10

06 - Hidden features of installr.mp4

04:01

07 - Use the Matrix package.mp4

05:29

08 - Create a sparse matrix.mp4

04:21

09 - Sparse matrices, triangles, and more.mp4

06:25

10 - Bootstrap analysis with R.mp4

06:08

11 - checkUsage.mp4

04:41

17 - R for Data Science Lessons (Jul-Sep 2021)

01 - Use R on the Raspberry Pi.mp4

07:32

02 - list2df().mp4

04:28

03 - Introduction to clustering.mp4

02:23

04 - Clustering with kmeans.mp4

06:57

05 - Clustering with pam and clara.mp4

06:23

06 - Understanding silhouette graphs.mp4

08:39

07 - Clustering with fanny.mp4

05:23

08 - Clustering with hclust.mp4

05:12

09 - Clustering with agnes.mp4

06:22

10 - Clustering with diana.mp4

04:20

11 - cutree and identify with hclust.mp4

04:15

12 - Clustering with mona.mp4

04:31

13 - Clustering dist vs. daisy.mp4

04:32

18 - R for Data Science Lessons (Oct-Dec 2021)

01 - Parameterized R markdown.mp4

03:42

02 - Run R on a schedule.mp4

02:53

03 - The new forward pipe operator.mp4

03:56

04 - Backslash lambda functions.mp4

05:24

05 - Dist() in depth.mp4

05:29

Exercise Files

Ex_Files_R_Data_Lunch_Break.zip

Ex_Files_R_for_Data_Sci_2021_Q3.zip

Ex_Files_R_for_Data_Sci_2021_Q4.zip

Description

Programming is learned in small bits. You build on basic concepts. You transfer the knowledge you already have to the next language. Lunch Break Lessons teaches R—one of the most popular programming languages for data analysis and reporting—in short lessons that expand on what existing programmers already know.

The five minutes you spend each week will provide you with a building block you can use in the next two hours at work. Review language basics, discover methods to improve existing R code, explore new and interesting features, and learn about useful development tools and libraries that will make your time programming with R that much more productive.

All series code samples can be downloaded at https://github.com/mnr/five-minutes-of-R.

Note: Because this is an ongoing series, viewers will not receive a certificate of completion.

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Data Science

R Programming

Mark Niemann-Ross

Instructor's Courses

I’m friends with technology. But I’m not blindly in love with it. I ask it questions. I explain it. I prioritize people over technology. Bigger, better, and faster features make shinier products, but that isn’t what people want. People don’t want widgets, they want elegance and relevance. I’m an educator, manager, evangelist, marketer, technologist, futurist, coder. I point to the future. I explain the present. I learn from the past. People speak different languages: marketing, sales, engineering, management, strategy. I’m fluent in all of those. Sometimes I’m abrupt. Sometimes I apologize. I’m passionate, but willing to believe I’m wrong. I try to inform myself. “Why” is my favorite question, followed by “When.” I write science fiction. Sometimes it’s about spaceships, sometimes it’s about products. The goal is the same; explain where we want to be, point out hazards, celebrate arrival. I’m engaging. Informed. Opinionated. Respectful. Your thoughts?

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