diff --git a/03_data_science_practice.Rmd b/03_data_science_practice.Rmd
index c6db7c2..c36d6e2 100644
--- a/03_data_science_practice.Rmd
+++ b/03_data_science_practice.Rmd
@@ -78,7 +78,7 @@ Ray Li (2016) ["7 habits of highly effective data analysis"](http://dataconomy.c
Noble, William Stafford (2009-07-31) [A Quick Guide to Organizing Computational Biology Projects](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000424), _PLoS Comput Biol_ 5(7): e1000424.
-### Version Control with Git & GitHub
+## Version Control with Git & GitHub
Jenny Bryan, the STAT 545 TAs, Jim Hester [Happy Git and GitHub for the useR](https://happygitwithr.com/)
@@ -112,9 +112,9 @@ Emily Robinson, [Red Flags in Data Science Interviews](http://hookedondata.org/R
-### R packages supporting robust workflow
+## R packages supporting robust workflow
-#### {janitor}
+### {janitor} -
[{janitor}](sfirke.github.io/janitor/index.html) -- "has simple functions for examining and cleaning dirty data. It was built with beginning and intermediate R users in mind and is optimized for user-friendliness. Advanced R users can already do everything covered here, but with janitor they can do it faster and save their thinking for the fun stuff."
@@ -123,7 +123,7 @@ CRAN: [janitor: Simple Tools for Examining and Cleaning Dirty Data](https://cran
GitHub: [sfirke/janitor](https://github.com/sfirke/janitor)
-#### {packrat}
+### {packrat} -
[{Packrat} is a dependency management system for R](http://rstudio.github.io/packrat/)
@@ -137,6 +137,12 @@ GitHub: [rstudio/packrat](https://github.com/rstudio/packrat)
Miles McBain (2019-04-09) [A workflow for lightweight R dependency management](https://milesmcbain.xyz/packrat-lite/)
+### {usethis} -
+
+"usethis is a workflow package: it automates repetitive tasks that arise during project setup and development, both for R packages and non-package projects"
+
+[usethis 1.5.0](https://www.tidyverse.org/articles/2019/04/usethis-1.5.0/) (April 2019)
+
***
diff --git a/04_using_R.Rmd b/04_using_R.Rmd
index 11756e1..903d36a 100644
--- a/04_using_R.Rmd
+++ b/04_using_R.Rmd
@@ -67,113 +67,6 @@ Jennifer Bryan and Jim Hester, ["Debugging R code"](https://whattheyforgot.org/d
-## Data wrangling
-
-(See {The tidyverse}, below)
-
-#### {datapasta}
-
-Vignette: [How to Datapasta](https://cran.r-project.org/web/packages/datapasta/vignettes/how-to-datapasta.html)
-
-***
-
-## The tidyverse
-
-All too often, data are messy. There are rows with no contents, colour-coded cells, and inconsistent values.
-
-One important way that data can be cleaned is to ensure that the structure is tidy. What do we mean by tidy data?
-
-> There are three interrelated rules which make a dataset tidy:
-> * Each variable must have its own column.
-> * Each observation must have its own row.
-> * Each value must have its own cell.
-
-And
-
-> Why ensure that your data is tidy? There are two main advantages:
->
-> 1. There’s a general advantage to picking one consistent way of storing data. If you have a consistent data structure, it’s easier to learn the tools that work with it because they have an underlying uniformity.
->
-> 2. There’s a specific advantage to placing variables in columns because it allows R’s vectorised nature to shine. As you learned in mutate and summary functions, most built-in R functions work with vectors of values. That makes transforming tidy data feel particularly natural.
-
-(from [@Wickham_Grolemund2016])
-
-
-This won't solve things like inconsistent values and colour-coded cells, but it will solve some other messiness.
-
-For more about the principles of tidy data, see Hadley Wickham's article "Tidy data", in _The Journal of Statistical Software_ [@tidydata]
-
- + [alternate link:](http://vita.had.co.nz/papers/tidy-data.html)
- + [informal and code-heavy version](https://cran.r-project.org/package=tidyr/vignettes/tidy-data.html)
-
-
-### Other tidyverse references
-
-Karl Broman and Kara Woo, ["Data organization in spreadsheets"](https://github.com/kbroman/Paper_DataOrg) (github page with source manuscript) -- application of tidy principles to spreadsheets.
-
-* see also Karl Broman's tutorial, ["Data organization: organizing data in spreadsheets)
-
-
-Bruno Rodriguez, [Modern R with the tidyverse](https://b-rodrigues.github.io/modern_R/)
-
-
-Jesse Sadler, [Excel vs R: A Brief Introduction to R (With examples using dplyr and ggplot](http://kbroman.org/dataorg/](https://www.jessesadler.com/post/excel-vs-r/) (2017-10-02)
-
-
-#### Categorical data
-
-Emily Robinson, _Categorical data in the tidyverse_ {link to DataCamp course removed}
-
-
-#### Tidy text
-
-If you're going to undertake text mining and natural language processing, your text (i.e. your data) needs to be tidy. Fortunately, there's an R package for that: `tidytext`.
-
-See the companion chapter on the topics of [Text Analysis and Text Mining].
-
-
-
-
-### tidyverse R packages
-
-[The tidyverse: ](http://tidyverse.org/)
-
-[The tidyverse R packages on github](https://github.com/hadley/tidyverse)
-
-#### {broom}
-
-
-#### {dplyr}
-
-`dplyr` now gets its own page, labelled [**Data Wrangling**](DataWrangling.md)
-
-
-#### {purrr}
-
-* [A purrr tutorial](https://github.com/Cascadia-R/purrr-tutorial) -- Cascadia-R, 2017-06-03
-
-* Charlotte Wickham, [purr tutorial](https://github.com/cwickham/purrr-tutorial) -- github
-
-
-##### {usethis}
-
-[usethis 1.5.0](https://www.tidyverse.org/articles/2019/04/usethis-1.5.0/) (April 2019)
-
-
-***
-
-### more about tidy data
-
-* Hadley Wickham & Garrett Grolemund, [_R for Data Science_](http://r4ds.had.co.nz/)
-
-* Hadley Wickham
- + [Tidy data and tidy tools (video of presentation, December 2011)](https://vimeo.com/33727555)
-
-* Garrett Grolemund
- + [Data Tidying](http://garrettgman.github.io/tidying/) (part of [Data Science with R](http://garrettgman.github.io/))
-
-* Chester Ismay and Ted Laderas, [A gRadual-intRoduction to the tidyverse](https://github.com/Cascadia-R/gRadual-intRoduction-tidyverse?utm_content=buffer98896&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer)
-
***
diff --git a/20_data_wrangling.Rmd b/20_data_wrangling.Rmd
index 00f351f..2da2c47 100644
--- a/20_data_wrangling.Rmd
+++ b/20_data_wrangling.Rmd
@@ -1,11 +1,52 @@
-# Data Wrangling (emphasis on **dplyr**) {#datawrangling}
-
+# Data Wrangling (emphasis on tidy data) {#datawrangling}
## Introduction
Data is rarely in condition to use it...there's invariably something amiss. Data wrangling (a.k.a. data carpentry) is the process of getting it ready for analysis.
+And all too often, data are messy. There are rows with no contents, colour-coded cells, and inconsistent values.
+
+One important way that data can be cleaned is to ensure that the structure is tidy. What do we mean by tidy data?
+
+> There are three interrelated rules which make a dataset tidy:
+> * Each variable must have its own column.
+> * Each observation must have its own row.
+> * Each value must have its own cell.
+
+And
+
+> Why ensure that your data is tidy? There are two main advantages:
+>
+> 1. There’s a general advantage to picking one consistent way of storing data. If you have a consistent data structure, it’s easier to learn the tools that work with it because they have an underlying uniformity.
+>
+> 2. There’s a specific advantage to placing variables in columns because it allows R’s vectorised nature to shine. As you learned in mutate and summary functions, most built-in R functions work with vectors of values. That makes transforming tidy data feel particularly natural.
+
+(from [@Wickham_Grolemund2016])
+
+
+This won't solve things like inconsistent values and colour-coded cells, but it will solve some other messiness.
+
+For more about the principles of tidy data, see Hadley Wickham's article "Tidy data", in _The Journal of Statistical Software_ [@tidydata]
+
+ + [alternate link:](http://vita.had.co.nz/papers/tidy-data.html)
+ + [informal and code-heavy version](https://cran.r-project.org/package=tidyr/vignettes/tidy-data.html)
+
+
+### Other tidyverse references
+
+Karl Broman and Kara Woo, ["Data organization in spreadsheets"](https://github.com/kbroman/Paper_DataOrg) (github page with source manuscript) -- application of tidy principles to spreadsheets.
+
+* see also Karl Broman's tutorial, ["Data organization: organizing data in spreadsheets)
+
+
+Bruno Rodriguez, [Modern R with the tidyverse](https://b-rodrigues.github.io/modern_R/)
+
+
+Jesse Sadler, [Excel vs R: A Brief Introduction to R (With examples using dplyr and ggplot](http://kbroman.org/dataorg/](https://www.jessesadler.com/post/excel-vs-r/) (2017-10-02)
+
+
+
## Theory and methods
@@ -13,28 +54,30 @@ Data is rarely in condition to use it...there's invariably something amiss. Dat
[Stat 545: Data wrangling, exploration, and analysis with R](http://stat545.com/index.html) -- course materials associated with the University of British Columbia's Statistics 545 course. Prepared in large part by Dr. Jenny Bryan.
-### Tidy evaluation
-* programming with **dplyr**
+***
-Edwin Thoen, 2017-08-25 [Tidy evaluation, most common actions](https://edwinth.github.io/blog/dplyr-recipes/)
+## Tools
-### Reading messy files
-Luis D. Verde, 2018-12-14, [Tidyeval meets PDF table hell](http://luisdva.github.io/rstats/Tidyeval-pdf-hell/) -- great solution to the common problem of broken rows ("values that are broken up into two lines for whatever reason (often to optimize space on a page in a table in a typeset pdf)").
+### {datapasta} -
+Vignette: [How to Datapasta](https://cran.r-project.org/web/packages/datapasta/vignettes/how-to-datapasta.html)
-### Working with dates
-
-
+### {janitor} -
+
+***
+
+## The tidyverse
+
+[The tidyverse: ](http://tidyverse.org/)
-## R
+[The tidyverse R packages on github](https://github.com/hadley/tidyverse)
-Arranged by package
-### **dplyr**
+### {dplyr} -
**package**
@@ -51,7 +94,8 @@ github: [hadley/dplyr](https://github.com/hadley/dplyr)
-### **forcats**
+
+### {forcats} -
[reference page](https://forcats.tidyverse.org/)
@@ -60,9 +104,12 @@ Working with factors
[Be the boss of your factors](https://stat545.com/block029_factors.html#change-order-of-the-levels-because-i-said-so)
+Emily Robinson, _Categorical data in the tidyverse_ {link to DataCamp course removed}
+
-### **purrr**
+### {purrr} -
+
[reference page](https://purrr.tidyverse.org/)
@@ -84,4 +131,54 @@ Emorie D Beck, [Intro to purrr](https://emoriebeck.github.io/R-tutorials/purrr/)
Sharon Machlis, [R Tip: Access nested list items with purrr](https://www.infoworld.com/video/90327/r-tip-access-nested-list-items-with-purrr) {video}
+[A purrr tutorial](https://github.com/Cascadia-R/purrr-tutorial) -- Cascadia-R, 2017-06-03
+
+Charlotte Wickham, [purr tutorial](https://github.com/cwickham/purrr-tutorial) -- github
+
+
+
+***
+
+### more about tidy data
+
+* Hadley Wickham & Garrett Grolemund, [_R for Data Science_](http://r4ds.had.co.nz/)
+
+* Hadley Wickham
+ + [Tidy data and tidy tools (video of presentation, December 2011)](https://vimeo.com/33727555)
+
+* Garrett Grolemund
+ + [Data Tidying](http://garrettgman.github.io/tidying/) (part of [Data Science with R](http://garrettgman.github.io/))
+
+* Chester Ismay and Ted Laderas, [A gRadual-intRoduction to the tidyverse](https://github.com/Cascadia-R/gRadual-intRoduction-tidyverse?utm_content=buffer98896&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer)
+
+
+
+
+## Working with dates
+
+
+
+
+
+{lubridate}
+
+
+
+
+### Tidy evaluation
+
+* programming with **dplyr**
+
+Edwin Thoen, 2017-08-25 [Tidy evaluation, most common actions](https://edwinth.github.io/blog/dplyr-recipes/)
+
+
+
+### Tidy text
+
+If you're going to undertake text mining and natural language processing, your text (i.e. your data) needs to be tidy. Fortunately, there's an R package for that: `tidytext`.
+
+See the companion chapter on the topics of [Text Analysis and Text Mining].
+
+
+
-30-
diff --git a/12_data_reading_fileformat.rmd b/21_data_reading_fileformat.rmd
similarity index 79%
rename from 12_data_reading_fileformat.rmd
rename to 21_data_reading_fileformat.rmd
index 0d8f009..e6d0fef 100644
--- a/12_data_reading_fileformat.rmd
+++ b/21_data_reading_fileformat.rmd
@@ -23,7 +23,7 @@ In particular, survey data
[R database interfaces](http://www.burns-stat.com/r-database-interfaces/)
-### **rio**
+### {rio} -
**package**
@@ -33,7 +33,7 @@ vignette: [Import, Export, and Convert Data Files](https://cran.r-project.org/pa
-### **googledrive**
+### {googledrive} -
**package**
@@ -43,7 +43,7 @@ tidyverse page: [`googledrive`](https://tidyverse.github.io/googledrive/)
-### **foreign**
+### {foreign} -
**package**
@@ -54,17 +54,30 @@ CRAN page: [foreign: Read Data Stored by Minitab, S, SAS, SPSS, Stata, Systat, W
* [How to open an SPSS file into R](http://www.milanor.net/blog/how-to-open-an-spss-file-into-r/), by Davide Massidda (2014-03-26)
+### {haven} -
-### Stata files
+**package**
+
+**articles**
+
+
+## PDF files
+
+Luis D. Verde, 2018-12-14, [Tidyeval meets PDF table hell](http://luisdva.github.io/rstats/Tidyeval-pdf-hell/) -- great solution to the common problem of broken rows ("values that are broken up into two lines for whatever reason (often to optimize space on a page in a table in a typeset pdf)").
-**`read.dta`**
+
+
+
+## Stata files
+
+### {read.dta} -
Reads a file in Stata version 5–12 binary format into a data frame.
CRAN page: [`read.dta`: Read Stata Binary Files](http://stat.ethz.ch/R-manual/R-devel/library/foreign/html/read.dta.html)
-**readstata13**
+### {readstata13} -
Function to read and write the 'Stata' file format.
@@ -72,9 +85,9 @@ CRAN Page: [readstata13: Import 'Stata' Data Files](readstata13: Import 'Stata'
+## Time series database files
-
-### **TSdbi** and related packages
+### {TSdbi} and related packages
**package**
@@ -90,3 +103,4 @@ Note: `TSdbi` has some related extension packages:
* CRAN page: [TSsdmx: 'TSdbi' Extension to Connect with 'SDMX'](https://cran.r-project.org/package=TSsdmx)
+-30-
\ No newline at end of file