Difference between revisions of "Lesson 6"
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| − | <font size="5">''' | + | <font size="5">'''Programming Data Visualisation with R: Methods, Approaches and Best Practices'''</font> |
| + | |||
| + | =Content= | ||
| + | * An overview of R | ||
| + | ** Introducing R and R Studio | ||
| + | ** Doing data science with tidyverse | ||
| + | * Data Visualisation with R | ||
| + | ** A layered grammar of graphics and ggplot2 | ||
| + | ** ggplot2 extensions | ||
| + | ** interactive and dynamic graphics with plotly R | ||
| + | * Reproducible Analytics with R | ||
| + | ** Introducing R Markdown | ||
| + | * Democratizing Visually Driven Analytics on the web | ||
| + | ** Introducing Shiny framework | ||
| + | |||
| + | =Core Readings= | ||
| + | |||
| + | * [https://rviews.rstudio.com/2017/06/08/what-is-the-tidyverse/ What is Tidyverse?] | ||
| + | * [https://www.jstatsoft.org/article/view/v059i10 Tidy Data] | ||
| + | * [https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html Introduction to dplyr] | ||
| + | * Wickham, Hadley (March 2010). "[https://www-jstor-org.libproxy.smu.edu.sg/stable/25651297?seq=1#metadata_info_tab_contents A Layered Grammar of Graphics]". ''Journal of Computational and Graphical Statistics''. 19 (1): 3–28. | ||
| + | * [https://vimeo.com/178485416 What is R Markdown?] | ||
| + | * Chapter [https://r4ds.had.co.nz/pipes.html 18 Pipes] in [https://r4ds.had.co.nz/ R for Data Science]. | ||
| + | * Chapter [https://r4ds.had.co.nz/tibbles.html 10 Tibbles] in [https://r4ds.had.co.nz/ R for Data Science]. | ||
| + | |||
| + | =Optional Readings= | ||
| + | |||
| + | * [https://garrettgman.github.io/tidying/ Data Tidying] | ||
| + | * Wickham, Hadley (2009). '''ggplot2: Elegant Graphics for Data Analysis'''. Springer. | ||
| + | * Healy, Kieran (2019) '''[http://socviz.co/ Data Visualization: A practical introduction]''' (online version) | ||
| + | * [https://r4ds.had.co.nz/r-markdown.html Chapter 27 R Markdown] of [https://r4ds.had.co.nz/ R for Data Science]. | ||
| + | |||
| + | =Self-learning Courses from DataCamp= | ||
| + | |||
| + | * Data Visualization with ggplot2 (Part 1) | ||
| + | * Data Visualization with ggplot2 (Part 2) | ||
| + | * Data Visualization with ggplot2 (Part 3) | ||
Latest revision as of 23:33, 23 September 2019
IS428 Visual Analytics for Business Intelligence
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Programming Data Visualisation with R: Methods, Approaches and Best Practices
Content
- An overview of R
- Introducing R and R Studio
- Doing data science with tidyverse
- Data Visualisation with R
- A layered grammar of graphics and ggplot2
- ggplot2 extensions
- interactive and dynamic graphics with plotly R
- Reproducible Analytics with R
- Introducing R Markdown
- Democratizing Visually Driven Analytics on the web
- Introducing Shiny framework
Core Readings
- What is Tidyverse?
- Tidy Data
- Introduction to dplyr
- Wickham, Hadley (March 2010). "A Layered Grammar of Graphics". Journal of Computational and Graphical Statistics. 19 (1): 3–28.
- What is R Markdown?
- Chapter 18 Pipes in R for Data Science.
- Chapter 10 Tibbles in R for Data Science.
Optional Readings
- Data Tidying
- Wickham, Hadley (2009). ggplot2: Elegant Graphics for Data Analysis. Springer.
- Healy, Kieran (2019) Data Visualization: A practical introduction (online version)
- Chapter 27 R Markdown of R for Data Science.
Self-learning Courses from DataCamp
- Data Visualization with ggplot2 (Part 1)
- Data Visualization with ggplot2 (Part 2)
- Data Visualization with ggplot2 (Part 3)
