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Today's Presentation

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.

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

Package Anatomy: Tour of {rsfsu}

The rsfsu GitHub repository

Getting Data 1: The Data Warehouse

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

# 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

# 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

# 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)

Analyzing Data 2: Time-to-Degree

# 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

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

Reporting Data 1: Plot Theme

Reporting Data 2: Table Theme

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")

Reporting Data 3: Reports as Functions

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

Other Helpers

sfsu_colors()

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

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 - Generate View() tabs for each step in a pipe chain.

• rcanvas - R client for the Canvas LMS. Get data, copy courses, grade students, manage your online classroom, etc.

• d3rain - 'Raindrop' visualizations in R with d3.js.

• inferregex - Infer the regular expression of a string.

Connect with me on...

• Twitter: @daranzolin
• LinkedIn: dranzolin
• GitHub: daranzolin
• Blog: daranzolin.github.io
• Email: daranzolin@sfsu.edu

Photo Cred

• Allison Horst

• cashcats.biz