minor updates

00-preface.Rmd

@@ -13,17 +13,17 @@ reproduce the teaching contents more easily.
 ### What you will learn from this course/book {-}
 In part I, you will find a general introduction to data science (by Dr YH Huang):
 
+2) Basic programming and visualisation skills: R scripts for the quantitative 
+   methods and data visualisation.
 1) Quantitative methods: t-test, correlation analysis, clustering, linear 
    regression, linear classification.
-2) Basic programming and visualisation skills: R scripts for the above methods 
-   and data visualisation.
 3) Gain familiarity with common databases in the biomedical domain. 
 4) Introduce ethical, legal, social and technological issues related to 
    biomedical data sciences.
 5) Introduce good practice in managing a data science project and communicate 
    results to key stakeholders.
 
-In part II, you will experience data types in five different biomedical topics,
+In part II, you will experience data types in four different biomedical topics,
 which will be illustrated with both introduction and cases that are suitable for
 problem-based learning format:
 
@@ -41,15 +41,18 @@ readers
 
 ## Other reference books {-}
 
-Besides this online book as a collection of R materials for the teaching contents, we also recommend the following online books as reference:
+Besides this online book as a collection of R materials for the teaching 
+contents, we also recommend the following online books as reference:
 
 1. [Introduction to Data Science: Data Wrangling and Visualization with R](http://rafalab.dfci.harvard.edu/dsbook-part-1/) by Rafeal A. Irizarry
 2. [Advanced Data Science: Statistics and Prediction Algorithms Through Case Studies](http://rafalab.dfci.harvard.edu/dsbook-part-2/) by Rafeal A. Irizarry
 
 
 ## Acknowledgements {-}
 
-We thank all teachers contributing to this course from all years, including Dr Lequan Yu (2022-23) and Dr Carlos Wong (2022-23) and student helpers serving as demonstrators, including Mr Mingze Gao and Ms Fangxin Cai.
+We thank all teachers contributing to this course across all years, including 
+Dr Lequan Yu (2022-23) and Dr Carlos Wong (2022-23) and student helpers serving 
+as demonstrators, including Mr Mingze Gao and Ms Fangxin Cai.
 
 <!-- ## Last notes {-} -->
 

README.md

@@ -18,5 +18,6 @@ To build this book from the source files, use the following script in R:
 bookdown::render_book("index.Rmd", "bookdown::gitbook")
 ```
 
-To release the build, you can simply copy the whole contents in the `_book` 
-folder to the `docs` folder and push it to this github repository.
+To release the build, you can simply push the `docs` folder to this github 
+repository. Note, the website only shows the `docs` folder while other files are
+source codes for generating the `docs` folder.

notebooks/chapter1-R/01-introR.Rmd

@@ -6,6 +6,16 @@ Introduction to R (1 hour teaching). You can get this Rmd file on Moodle or
 [here](https://github.com/StatBiomed/BMDatSci-book/blob/main/notebooks/chapter1-R/01-introR.Rmd)
 (right-click and "save link as" to download).
 
+R is a programming language, particularly popular for its power in statistical
+computing, elegant graphics, and also genomic data analysis. It is a free and
+open-source software, with active support and development from the community.
+Additionally, R is relatively easy to get started for scientific computing.
+
+> __Note__ To learn and practice R programming, you need to install `R` and `RStudio` on 
+> your computer. You can follow the instructions for installation in the 
+> [Appendix A](https://statbiomed.github.io/BMDS-book/install.html) chapter.
+
+
 ## Data types
 In R language setting (similar to some other programming languages), there are 
 a few commonly used data types that are predefined in the built-in environment.
@@ -320,7 +330,7 @@ help("read.table")
 ?read.table
 ```
 
-Here, let read an example file. Data is available on Moodle and on the 
+Here, let's read an example file. Data is available on Moodle and on the 
 [github repository](https://github.com/StatBiomed/BMDatSci-book/blob/main/notebooks/chapter1-R)
 
 ```{r}
@@ -547,6 +557,7 @@ summary(fit) # show results
 ```
 
 This means the fitted regression is: 
+
 `Sepal.Length ~ 1.856 + 0.65*Sepal.Width + 0.709*Petal.Length - 0.556*Petal.Width`
 
 We can check how good the regression is by plotting it out