| layout | title | permalink |
|---|---|---|
page |
Program |
/program/ |
The classes in the swirl course and the corresponding videos are grouped into thematic blocks that can be followed independently, depending on your preferences. Here you can see which lessons and videos belong into each block. At the bottom of the page you can find the order in which the course was first taught – if you're interested in going through all the content, this can be a useful model of how to do so. If you have no notions of R Programming, I recommend that you start with the swirl preloaded course "R Programming".
| Block | swirl lesson | videos | |
|---|---|---|---|
| 1 | Introduction to R | (not yet available) (logical expressions) | 1, Installing R and Rstudio, 1.1 Using R scripts |
| (not yet available) (vectors, lists, dataframes and Nas(1)) | 2, What is R? | ||
| (not yet available) (vectors, lists, dataframes and Nas(2)) | 7, Working with scripts and swirl at the same time | ||
| 2 | Cleaning and transforming text data | Lesson 1 (Text encoding) | 3, Text format & encoding |
| (not yet available) (Reading Excel) | 4, Tidy data | ||
| Lesson 2 (Cleaning text data I) | 5, Pipes | ||
| Lesson 3 (Cleaning text data II) | 11, Big data, linguistics and data science | ||
| Lesson 13 (Cleaning data and visualization III) | 12, Violin plots and dot plots | ||
| Lesson 17 (Case study I. Verifying and comparing data) | 13, Spreadsheet software | ||
| Lesson 18 (Case study II. Transforming Praat data) | |||
| 3 | Text mining | Lesson 4 (Tidy text. Unnest tokens) | 6, Main concepts of computational text analysis |
| Lesson 5 (Stopwords) | |||
| Lesson 6 (identifying chapters) | |||
| Lesson 15 (N-grams and collocations) | |||
| Lesson 11 (Language detection) | |||
| Lesson 16 (Co-occurrences and graphs) | |||
| 4 | Visualizations in ggplot2 | Lesson 7 (Histograms and boxplots in ggplot2) | 8, Data visualization with ggplot2 |
| Lesson 8 (Bar charts in ggplot2) | 19, Pie charts: how bad are they? | ||
| Lesson 13 (Cleaning data and visualization III) | |||
| 5 | Regular expressions | Lesson 9 (Regular expressions with stringr) | 9, Regular expressions |
| Lesson 10 (Regex in tibbles) | |||
| 6 | Maps in ggplot2 | Lesson 19 (Maps in ggplot2 I) | 18, Maps: coordinates and projections |
| Lesson 20 (Maps in ggplot2 II. Scatterpies) | 20, Shapefiles | ||
| Lesson 21 (Maps in ggplot2 III. Shapefiles) | 21, Voronoi diagrams | ||
| Lesson 22 (Maps in ggplot2 IV. Voronoi diagrams) | 22, Geographic and projected coordinates | ||
| 7 | Tagged corpora | Lesson 23 (Tagged corpora. Parsing XML files) | 23, XML |
| Lesson 24 (Analysing tagged corpora) | 24, Lemmatization and POS-tagging | ||
| Lesson 25 (Automatically tagging corpora) | 25, Tagging a corpus | ||
| 8 | Working with multiple objects | Lesson 12 (Working with several files) | 10, Language detection and working with several files. |
| Lesson 14 (Create your own functions) | |||
| 9 | Scraping data from the web | Accessing_Twitter(rtweet) | |
| Accessing_Twitter(streamR) | |||
| Accessing_Tumblr | |||
| Polite_web_scraping | |||
| 10 | Stylometry | (not yet available) | |
| (not yet available) | |||
| (not yet available) | |||
| (not yet available) |
Original course program
| Block | Session | Topic / Content | Videos | Swirl Lession |
|---|---|---|---|---|
| Introduction to R | 1 | Introduction and checking the installation. | 1, Installing R and Rstudio | Lesson 1 - Lesson 7 from Swirl - "R Programming" preloaded course |
| Basic notions of R programming language. | 2, What is R? | |||
| Cleaning and transforming text data | 2 | Text format and encoding. Tidy data. | 3, Text format & encoding | Lesson 1 (Text encoding) |
| 4, Tidy data | Lesson 2 (Cleaning text data I) | |||
| 5, Pipes (watch after swirl courses) | Lesson 3 (Cleaning text data II) | |||
| Text mining | 3 | Main concepts of computational text analysis. | 6, Main concepts of computational text analysis | Lesson 4 (Tidy text. Unnest tokens) |
| 7, Working with scripts and swirl at the same time | Lesson 5 (Stopwords) | |||
| Lesson 6 (identifying chapters) | ||||
| Visualizations in ggplot2 | 4 | Data visualization: intro to ggplot2 | 8, Data visualization with ggplot2 | Lesson 7 (Histograms and boxplots in ggplot2) |
| Lesson 8 (Bar charts in ggplot2) | ||||
| Regular expressions | 5 | Regular expressions | 9, Regular expressions | Lesson 9 (Regular expressions with stringr) |
| Lesson 10 (Regex in tibbles) | ||||
| Text mining | 6 | Language detection. | 10, Language detection and working with several files. | Lesson 11 (Language detection) |
| Reading several files into R. | Lesson 12 (Working with several files) | |||
| Cleaning and transforming text data | 7 | Cleaning data and visualization II. | 11, Big data, linguistics and data science | Lesson 13 (Cleaning data and visualization III) |
| 12, Violin plots and dot plots | ||||
| 13, Spreadsheet software | ||||
| Text mining | 8 | Creating a function. | video currently not available | Lesson 14 (Create your own functions) |
| N-gram analysis (collocations) | Lesson 15 (N-grams and collocations) | |||
| Text mining | 9 | N-gram analysis (cooccurrences). | video currently not available | Lesson 16 (Co-occurrences and graphs) |
| Cleaning and transforming text data | 10 | Case study: Praat data I and II. | video currently not available | Lesson 17 (Case study I. Verifying and comparing data) |
| Lesson 18 (Case study II. Transforming Praat data) | ||||
| Maps in ggplot2 | 11 | Maps in ggplot2 I: Basics and pie charts | 18, Maps: coordinates and projections | Lesson 19 (Maps in ggplot2 I) |
| 19, Pie charts: how bad are they? | Lesson 20 (Maps in ggplot2 II. Scatterpies) | |||
| Maps in ggplot2 | 12 | Maps in ggplot2 II: Shapefiles & Voronoi diagrams | 20, Shapefiles | Lesson 21 (Maps in ggplot2 III. Shapefiles) |
| 21, Voronoi diagrams | Lesson 22 (Maps in ggplot2 IV. Voronoi diagrams) | |||
| 22, Geographic and projected coordinates (watch after swirl courses) | ||||
| Tagged corpora | 13 | Working with a tagged corpus. | 23, XML | Lesson 23 (Tagged corpora. Parsing XML files) |
| 24, Lemmatization and POS-tagging | Lesson 24 (Analysing tagged corpora) | |||
| Tagged corpora | 14 | Automatically tagging a corpus. | 25, Tagging a corpus | Lesson 25 (Automatically tagging corpora) |