Merge branch 'master' of github.com:rstudio-conf-2020/r-for-excel

collaborating.Rmd

@@ -5,7 +5,7 @@ TODO: swap out gh-pages screenshots
 
 ## Summary
 
-By being here at RStudio::conf and in this workshop, you are already part of the R community. We will discuss additional R communtities, how to engage, and how to ask for help. 
+Since the GitHub session (Chapter \@ref(github)), we have been practicing using GitHub with RStudio to collaborate with our most important collaborator: Future You. Here we will practice using GitHub with RStudio to collaborate with others now, with a mindset towards Future Us. We will also how to engage with the R community broadly and how to continue being a part of the #rstats community, including how to engage on Twitter, and how to ask for help. 
 
 *TODO: refine* Main purpose: work better with colleagues, help you help yourself. A big theme of this workshop has been how to more seamlessly collaborate with each other.
 

pivot-tables.Rmd

@@ -17,7 +17,7 @@ Pivot tables are powerful tools in Excel for summarizing data in different ways.
 - Citation: Reed D. 2019. SBC LTER: Reef: Abundance, size and fishing effort for California Spiny Lobster (Panulirus interruptus), ongoing since 2012. Environmental Data Initiative. [doi](https://doi.org/10.6073/pasta/a593a675d644fdefb736750b291579a0). 
 
 
-## Objectives
+### Objectives
 
 In R, we can use the `dplyr` package for pivot tables by using 2 functions `group_by` and `summarize` together with the pipe operator `%>%`. We will also continue to emphasize reproducibility in all our analyses.
 
@@ -26,7 +26,7 @@ In R, we can use the `dplyr` package for pivot tables by using 2 functions `grou
 - Learn `mutate()` and `select()` to work column-wise
 - Practice our reproducible workflow with RMarkdown and GitHub
 
-## Resources
+### Resources
 
 - [`dplyr` website: dplyr.tidyverse.org](https://dplyr.tidyverse.org/)
 - [R for Data Science: Transform Chapter](https://r4ds.had.co.nz/transform.html) by Hadley Wickham & Garrett Grolemund
@@ -67,6 +67,7 @@ library(tidyverse)
 library(readxl)
 library(here)
 library(skimr) # install.packages('skimr')
+library(kableExtra) # install.packages('kableExtra')
 ```
 
 We used the `read_excel()` before, which is the generic function that reads both .xls and .xlsx files. Since we know that this is a .xlsx file, we will demo using the `read_xlsx()` function. 
@@ -319,7 +320,19 @@ siteyear_summary <- lobsters %>%
 siteyear_summary 
 ```
 
-### R code in-line RMarkdown
+### Table formatting with `kable()`
+
+There are several options for formatting tables in RMarkdown; we'll show one here from the `kableExtra` package and learn more about it tomorrow. 
+
+It works nicely with the pipe operator, so we can build do this from our new object:
+
+```{r, eval=FALSE}
+## make a table with our new variable
+siteyear_summary %>%
+  kable()
+```
+
+### R code in-line in RMarkdown
 
 Before we let you try this on your own, let's go outside of our code chunk and write in Markdown. 
 

synthesis.Rmd

@@ -16,14 +16,14 @@ Source: Fisheries Statistics Division of the NOAA Fisheries
 
 *Note on the data:* "aggregate" here means "These names represent aggregations of more than one species. They are not inclusive, but rather represent landings where we do not have species-specific data. Selecting "Sharks", for example, will not return all sharks but only those where we do not have more specific information."
 
-## Objectives
+### Objectives
 
 - Synthesize data wrangling and visualization skills learned so far
 - Add a few new tools for data cleaning from `stringr`
 - Work collaboratively in an R Markdown file
 - Publish your collaborative work as a webpage
 
-## Resources
+### Resources
 
 - [Project oriented workflows](https://www.tidyverse.org/blog/2017/12/workflow-vs-script/) by Jenny Bryan
 

tidying.Rmd

@@ -12,7 +12,7 @@ Here, we'll continue by *reshaping* data frames (converting from long-to-wide, o
 
 ![](img/tidy_data.png)
 
-## Objectives
+### Objectives
 
 In this session we'll learn some tools to help make our data **tidy** and more coder-friendly. Those include:
 
@@ -21,7 +21,7 @@ In this session we'll learn some tools to help make our data **tidy** and more c
 - `tidyr::unite()` and `tidyr::separate()` to merge or separate information from different columns
 - Detect or replace a string with `stringr` functions
 
-## Resources
+### Resources
 
 -- [Ch. 12 *Tidy Data*, in R for Data Science](https://r4ds.had.co.nz/tidy-data.html) by Grolemund & Wickham
 - [`tidyr` documentation from tidyverse.org](https://tidyr.tidyverse.org/)