Merge pull request #373 from UBC-DSCI/dev

merge dev into master

Author: trevorcampbell

README.md

@@ -142,13 +142,16 @@ bookdown::gitbook:
 #### Figures
 - make sure all figures get (capitalized) labels ("Figure \\@ref(blah)", not "figure below" or "figure above")
 - make sure all figures get captions
-- specify image widths in terms of linewidth percent (e.g. `out.width="70%"`)
+- specify image widths of pngs and jpegs in terms of linewidth percent 
+(e.g. `out.width="70%"`),
+for plots we create in R use `fig.width` and `fig.height`.
 - center align all images via `fig.align = "center"`
 - make sure we have permission for every figure/logo that we use
 - Make sure all figures follow the visualization principles in Chapter 4
 - Make sure axes are set appropriately to not inflate/deflate differences artificially *where it does not compromise clarity* (e.g. in the classification
   chapter there are a few examples where zoomed-in accuracy axes are better than using the full range 0 to 1)
-- 
+- Fig size for bar charts should be: `fig.width=5, fig.height=3` (an exception are figs 1.7 & 1.8 so that we can read the axis labels)
+- cropping width for syntax diagrams is 1625 (done using `image_crop`)
 
 #### Tables
 - make sure all tables get capitalized labels ("Table \\@ref(blah)", not "table below" or "table above")

_bookdown.yml

@@ -3,4 +3,4 @@ language:
   ui:
     chapter_name: "Chapter "
 delete_merged_file: true
-rmd_files: ["index.Rmd", "intro.Rmd", "reading.Rmd", "wrangling.Rmd", "viz.Rmd", "classification1.Rmd", "classification2.Rmd", "regression1.Rmd", "regression2.Rmd", "clustering.Rmd", "inference.Rmd", "jupyter.Rmd", "version-control.Rmd", "setup.Rmd", "appendixA.Rmd", "references.Rmd"]
+rmd_files: ["index.Rmd", "authors.Rmd", "intro.Rmd", "reading.Rmd", "wrangling.Rmd", "viz.Rmd", "classification1.Rmd", "classification2.Rmd", "regression1.Rmd", "regression2.Rmd", "clustering.Rmd", "inference.Rmd", "jupyter.Rmd", "version-control.Rmd", "setup.Rmd", "appendixA.Rmd", "references.Rmd"]

acknowledgements.Rmd

@@ -1,2 +1,23 @@
 # Acknowledgments {-}
 
+We'd like to thank everyone that has contributed to the development of 
+[*Data Science: A First Introduction*](https://ubc-dsci.github.io/introduction-to-datascience/). 
+This is an open source textbook that began as a collection of course readings
+for DSCI 100, a new introductory data science course 
+at the University of British Columbia (UBC).
+Several faculty members in the UBC Department of Statistics 
+were pivotal in shaping the direction of that course, 
+and as such contributed greatly to the broad structure and 
+list of topics in this book. We would especially like to thank Matías
+Salibían-Barrera for his mentorship during the initial development and roll-out
+of both DSCI 100 and this book. His door was always open when
+we needed to chat about how to
+best introduce and teach data science our first year students.
+
+We also owe a debt of gratitude to all of the students of DSCI 100 over the past
+few years. They provided invaluable feedback on the book and worksheets; 
+they found bugs for us (and stood by very patiently in class while 
+we frantically fixed those bugs); and they brought a level of enthusiasm to the class
+that sustained us during the hard work of creating a new course and writing a textbook.
+Our interactions with them taught us how to teach data science, and that learning
+is reflected in the content of this book. 

authors.Rmd

@@ -1,9 +1,28 @@
 # About the authors {-}
 
-Tiffany Timbers is an Assistant Professor of Teaching in the Department of Statistics and Co-Director for the Master of Data Science program (Vancouver Option) at the University of British Columbia. In these roles she teaches and develops curriculum around the responsible application of Data Science to solve real-world problems. One of her favorite courses she teaches is a graduate course on collaborative software development, which focuses on teaching how to create R and Python packages using modern tools and workflows.
+Tiffany Timbers is an Assistant Professor of Teaching in the Department of
+Statistics and Co-Director for the Master of Data Science program (Vancouver
+Option) at the University of British Columbia. In these roles she teaches and
+develops curriculum around the responsible application of Data Science to solve
+real-world problems. One of her favorite courses she teaches is a graduate
+course on collaborative software development, which focuses on teaching how to
+create R and Python packages using modern tools and workflows.
 
 
-Trevor Campbell is an Assistant Professor in the Department of Statistics at the University of British Columbia. His research focuses on automated, scalable Bayesian inference algorithms, Bayesian nonparametrics, streaming data, and Bayesian theory. He was previously a postdoctoral associate advised by Tamara Broderick in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute for Data, Systems, and Society (IDSS) at MIT, a Ph.D. candidate under Jonathan How in the Laboratory for Information and Decision Systems (LIDS) at MIT, and before that he was in the Engineering Science program at the University of Toronto.
+Trevor Campbell is an Assistant Professor in the Department of Statistics at
+the University of British Columbia. His research focuses on automated, scalable
+Bayesian inference algorithms, Bayesian nonparametrics, streaming data, and
+Bayesian theory. He was previously a postdoctoral associate advised by Tamara
+Broderick in the Computer Science and Artificial Intelligence Laboratory
+(CSAIL) and Institute for Data, Systems, and Society (IDSS) at MIT, a Ph.D.
+candidate under Jonathan How in the Laboratory for Information and Decision
+Systems (LIDS) at MIT, and before that he was in the Engineering Science
+program at the University of Toronto.
 
 
-Melissa Lee is an Assistant Professor of Teaching in the Department of Statistics at the University of British Columbia. She teaches and develops curriculum for undergraduate statistics and data science courses. Her work focuses on student-centered approaches to teaching, developing and assessing open educational resources, and promoting equity, diversity, and inclusion initiatives.
+Melissa Lee is an Assistant Professor of Teaching in the Department of
+Statistics at the University of British Columbia. She teaches and develops
+curriculum for undergraduate statistics and data science courses. Her work
+focuses on student-centered approaches to teaching, developing and assessing
+open educational resources, and promoting equity, diversity, and inclusion
+initiatives.

build_html.sh

@@ -1,2 +1,2 @@
 # Script to generate HTML book
-docker run --rm -m 5g -v $(pwd):/home/rstudio/introduction-to-datascience ubcdsci/intro-to-ds:v0.12.0 /bin/bash -c "cd /home/rstudio/introduction-to-datascience; Rscript _build_html.r"
+docker run --rm -m 5g -v $(pwd):/home/rstudio/introduction-to-datascience ubcdsci/intro-to-ds:v0.21.0 /bin/bash -c "cd /home/rstudio/introduction-to-datascience; Rscript _build_html.r"

build_pdf.sh

@@ -2,7 +2,9 @@
 
 # Copy files
 cp references.bib pdf/
+cp authors.Rmd pdf/
 cp preface-text.Rmd pdf/
+cp acknowledgements.Rmd pdf/
 cp intro.Rmd pdf/
 cp reading.Rmd pdf/
 cp wrangling.Rmd pdf/
@@ -22,11 +24,13 @@ cp -r data/ pdf/data
 cp -r img/ pdf/img
 
 # Build the book with bookdown
-docker run --rm -m 5g -v $(pwd):/home/rstudio/introduction-to-datascience ubcdsci/intro-to-ds:v0.12.0 /bin/bash -c "cd /home/rstudio/introduction-to-datascience/pdf; Rscript _build_pdf.r"
+docker run --rm -m 5g -v $(pwd):/home/rstudio/introduction-to-datascience ubcdsci/intro-to-ds:v0.21.0 /bin/bash -c "cd /home/rstudio/introduction-to-datascience/pdf; Rscript _build_pdf.r"
 
 # clean files in pdf dir
 rm -rf pdf/references.bib
-rm -rf pdf/preface-text.Rmd 
+rm -rf pdf/authors.Rmd
+rm -rf pdf/preface-text.Rmd
+rm -rf pdf/acknowledgements.Rmd
 rm -rf pdf/intro.Rmd 
 rm -rf pdf/reading.Rmd 
 rm -rf pdf/wrangling.Rmd