Building R Packages

class: center, middle, inverse, title-slide

# Building R Packages
## Lessons Learned and Recommendations
### David Ranzolin
### November 7, 2019

---

# About Me
.pull-left[
- Senior Analyst at SFSU
- R Partisan
- D3.js Dilettante
]
.pull-right[
- GIS Enthusiast
- Crazy Cat Lady
- Washed-up Point Guard
]

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---

# Today's Presentation

.pull-left[
* R packages
    * Why build one?
    * Anatomy
* `rsfsu` usage
    * Code
    * Rmd templates
    * Project template
* What do you need to know?
    * Recommendations
    * Resources
* Questions?
]
.pull-right[
![rfirst](images/r_first_then.png)
]
---

# What is an R Package?

> *A package bundles together code, data, documentation, and tests, and is easy to share with others. The huge variety of packages is one of the reasons that R is so successful: **the chances are that someone has already solved a problem that you’re working on, and you can benefit from their work by downloading their package**.*

<div style="text-align: right;"> -Hadley Wickham in <i>R Packages</i> </div>

---

# Benefits of Building R Packages

* Save time!
    * Perform complex calculations
    * Reduce clicks
    * Shortcuts to insight
    * Collaboration
    * Apply styles and branding 
    * Organize your project files

---

# Your IR Context: Where does R fit in?

> *Anything that can be automated, should be automated*

.pull-left[
#### Consider your 'data pipeline':
* Where is the most time spent?
    * Extraction and cleaning?
    * Visualization and analysis?
    * Design and formatting?
* How many applications?
    * RDBMS?
    * SPSS/SAS/Stata?
    * Excel?
    * Tableau/WebFocus/Power BI?
    * Adobe Illustrator?
    * Powerpoint?
* $?
]
.pull-right[
![rfirst](images/cats_cash.PNG)
]

---

# Package Anatomy: Tour of {rsfsu}

[The rsfsu GitHub repository](https://github.com/ir-sfsu/rsfsu)

![rsfsu](images/rsfsu.PNG)

---

# Getting Data 1: The Data Warehouse

```r
library(rsfsu)

# Use SQL
library(DBI)
con <- connect_to_datamart()
sql <- "SELECT STU_LEVEL_DESC1, 
               AVG(TERM_GPA) AS avg_term_gpa
        FROM IRDMSTG.DM_ENR
        WHERE FULL_PART LIKE 'Full%' 
        AND STU_LEVEL_DESC3 LIKE '%Undergrad%'
        GROUP BY STU_LEVEL_DESC1"
res <- dbGetQuery(con, sql)

# Use dplyr
library(dplyr)
res <- tbl_IR("DM_ENR") %>% 
  filter(FULL_PART == "Full-time",
         STU_LEVEL_DESC3 %like% "%Undergrad%") %>% 
  group_by(STU_LEVEL_DESC1) %>% 
  summarize(avg_term_gpa = mean(TERM_GPA, na.rm = TRUE))
```

---

# Getting Data 2: ERS Files

```r
# Fall 18 ERSS
f18 <- get_erss('184')

# Fall 13 ERSA
f13a <- get_ersa('134', replace_labels = FALSE)

# 2007 ERSG
g07 <- get_ersg('07')

# Get last 3 years of ersg
ersg1518 <- purrr::map_dfr(15:18, get_ersg)
```

---

# Getting Data 3: DoE / CSU / Box / Etc

```r
# Data from CA DoE: https://www.cde.ca.gov/ds/
enr1718 <- get_school_enr_data(year = '2017-18')
susp <- get_school_discipline_data(year = '1718',category = 'susp')
ucgrads <- get_school_grad_data('2016-17', 'UCGradEth')

# CSU Open Data
inst <- get_csu_open_data("Institution of Origin All Data")
hegis <- get_csu_open_data("Hegis CIP Crossover")

# Box files
library(boxr) 
eop <- box_read_excel(box_search("EOP Students"))

# Shortcuts to file server
cc <- campus_climate_tidy()

ca_counties_shp <- file.path(ir_data(), "Shapefiles/ca_counties")
ca_counties <- sf::st_read(ca_counties_shp)
```

---

# Analyzing Data 1: Grad/Retention Rates

```r
# Calc grad rates by cohort, sex
grad_rates <- calc_rates(tbl_IR("DM_RETN_SID"),
                         cohort_year = "20124",
                         years_out = 6,
                         subject = "graduation",
                         COHORT_TYPE_DESC1, SEX)
visualize_rates(grad_rates)
```

![viz_rates](images/viz_rates1.png)

---

# Analyzing Data 2: Time-to-Degree

```r
# Calculate time-to-degree by cohort, college
calc_ttd(grad_years = "2017-18", ADM_BASE_DESC2, COLLEGE_LONG)
```

```
# A tibble: 19 x 4
   ADM_BASE_DESC2     COLLEGE_LONG                students avg_ttd
   <chr>              <chr>                          <int>   <dbl>
 1 1.Freshman Starter "Business                 "      608    4.85
 2 1.Freshman Starter "Education                "       27    4.03
 3 1.Freshman Starter "Ethnic Studies           "       25    4.95
 4 1.Freshman Starter "Health and Social Sci    "      539    4.53
 ```
---

# Analyzing Data 3: Grade Distributions

```r
# Visualize grade distribution for all 'GWAR' classes
tbl_IR("DM_CRS_EOT") %>% 
  filter(CRS_SUFFIX %like% "GW%") %>% 
  grade_dist()
```

![grade_dist](images/grade_dist.png)
---

# Reporting Data 1: Plot Theme

![scatter](images/scatter_plot2.PNG)
---

# Reporting Data 2: Table Theme

```r
transfers %>% 
  group_by(college) %>% 
  summarize(`Miles from Campus (Median)` = round(median(mfc), 1),
            Students = n()) %>% 
  arrange(desc(`Miles from Campus (Median)`)) %>% 
  gt_sfsu(rowname_col = "college")
```

![gt_sfsu](images/gt_sfsu2.PNG)
---

# Reporting Data 3: Reports as Functions

```r
generate_report("4th-week-summary", 
                "Spring 2019 4th Week Summary.html", 
                list(term = "20192"))
generate_report("campus_climate", 
                "English Dept CC Overview.html", 
                list(majors = "English", min_threshold = 20))

library(purrr)
terms <- paste0(2010:2018, 4)
out_files <- paste(terms, "report.html", sep = "-")
params <- map(terms, ~list(term = .x))
walk2(
  out_files, params, 
  ~generate_report("4th-Week-Summary", .x, .y)
  )
```

---

# Shiny Gadgets

![rgadget](images/generateReport.gif)

---

# Other Helpers

```r
sfsu_colors()
```

```
dark purple      purple   dark gold        gold        blue light green 
  "#231161"   "#463077"   "#C99700"   "#E8BF6A"   "#004F71"   "#A8AD00" 
     salmon         red        grey 
  "#B04A5A"   "#9A3324"   "#53565A" 
```

```r
seq_terms(20144, 20192)
```

```
 [1] "20144" "20152" "20154" "20162" "20164" "20172" "20174" "20182"
 [9] "20184" "20192"
```

* `reformat_terms`: convert SIS terms to academic years (“2123” -> “2011-12”)
* `clean_cols`: remove ordering prefixes (“2. Freshmen Starters” -> “Freshmen Starters”)
* `dm_join`: join two tables from different sources
* `conv_id`: convert between SID, EIN, and SSN
* `nice_term`: convert coded term ("20144" -> "Fall 2014")
* `percent_labels`: convert decimals to percentage labels (0.643 -> 64%)

---

# Templates

### Back to GitHub...

---

# What do you need to know?

1. Some R (not a ton!)
  * If you can write a function you can make a package
  
2. Some git (very little!)
  * `git init`
  * `git status`
  * `git add`
  * `git commit`
  * `git push`
  * `git pull`
  * `git clone`

---

# Recommendations

* Don't re-invent the wheel
    * Learn about your data
    * Learn from your coworkers
    * Learn about your office's history
    
* Break problems into smaller problems.
    * Could a function do that?
    * Could I turn this into a template?
    
* Use git for version control

* Use the devtools and usethis packages

* Learn a little HTML/CSS

---

# Resources

1. *R Packages* by Hadley Wickham (Free online!)
2. The usethis package
  * `create_package`
  * `use_readme_rmd`
  * `use_testthat`
  * `use_github`
3. The devtools package
  * `document`
  * `test`
  * `roxygen`
  * `install`
4. Twitter
5. The RStudio Community

---

# Thank you!

Some of my other packages:

- **[ViewPipeSteps](https://github.com/daranzolin/ViewPipeSteps)** - Generate View() tabs for each step in a pipe chain.

- **[rcanvas](https://github.com/daranzolin/rcanvas)** - R client for the Canvas LMS. Get data, copy courses, grade students, manage your online classroom, etc.

- **[d3rain](https://github.com/daranzolin/d3rain)** - 'Raindrop' visualizations in R with d3.js.

- **[inferregex](https://github.com/daranzolin/inferregex)** - Infer the regular expression of a string.

#### Connect with me on...
* [Twitter: @daranzolin](https://twitter.com/daranzolin)
* [LinkedIn: dranzolin](https://linkedin.com/in/dranzolin/)
* [GitHub: daranzolin](https://github.com/daranzolin)
* [Blog: daranzolin.github.io](https://daranzolin.github.io/)
* Email: daranzolin@sfsu.edu

---

# Photo Cred

- [Allison Horst](https://github.com/allisonhorst/stats-illustrations)

- [cashcats.biz](https://cashcats.biz/)