Agenda

• Why you should know R
• What are the ways you can write R
• How you can help R users improve their code
• How R users can consume your code
• How you can get R code into production

Me

• Data Scientist
• Microsoft AI MVP
• Microsoft Machine Learning MCT
• Author: geni.us/rfundamentals
• Locke Data
• t: @theStephLocke

Locke Data

• Data Science Consultancy
• Focussed on helping orgs start
• Site: itsalocke.com
• t: @lockedata

This talk

• Get the slides at itsalocke.com/talks
• Grab a sticker and/or a business card
• Book giveaway for good questions
• Limited supply of tshirts available

Why

• Data Science
• Data wrangling
• Cross-platform capability
• Reproducibility
• Efficeincy
• Flexibility
• Cash

Why not Python?

It isn’t one or the other

There’s plenty of ways for these two languages to interoperate and co-exist. Pick the right hammer for a given nail!

Data profiling

DataExplorer::create_report(iris)

Nice webscraping

library(tidyverse)
library(rvest)

"https://en.wikipedia.org/wiki/List_of_superhero_debuts" %>%
  read_html() %>%
  html_nodes(xpath = "//*[@id='mw-content-text']/div/table") %>%
  map(html_table, fill = TRUE) %>%
  map_df( ~ mutate(., `Year Debuted` = as.character(`Year Debuted`))) %>%
  mutate(`Char_Team` = coalesce(`Character / Team / Series`, `Character / Team`)) %>%
  select(Char_Team, Year_Debut = `Year Debuted`) %>%
  mutate(Year = str_extract(Year_Debut, "[0-9]{4}")) %>%
  skimr::skim()

## Skim summary statistics
##  n obs: 376 
##  n variables: 3 
## 
## -- Variable type:character -----------------------------------------------------
##    variable missing complete   n min max empty n_unique
##   Char_Team       0      376 376   0  92     1      376
##        Year       1      375 376   4   4     0       92
##  Year_Debut       1      375 376   4  38     0      248

Great dataviz

library(leaflet)
data(quakes)

# Show first 20 rows from the `quakes` dataset
leaflet(data = quakes[1:20, ]) %>% addTiles() %>% addMarkers(~long, ~lat, popup = ~as.character(mag), 
    label = ~as.character(mag))

Super apps

Cost of Coffee

Data driven content

• R
• Python
• JS
• Bash
• SQL

Variables, comments, etc

# Define a variable
a <- 25

# Call a variable
a

## [1] 25

# Do something to it
a + 1

## [1] 26

Structures

# Vector
a = c(25, 30)

# Matrix
matrix(a)

##      [,1]
## [1,]   25
## [2,]   30

# Data frame
data.frame(a, b = a/5, c = LETTERS[1:2])

##    a b c
## 1 25 5 A
## 2 30 6 B

# List
list(vector = a, matrix = matrix(a))

## $vector
## [1] 25 30
## 
## $matrix
##      [,1]
## [1,]   25
## [2,]   30

Functions

# Define a function
showAsPercent <- function(x) {
    paste0(round(x * 100, 0), "%")
}

# Call a function
showAsPercent(0.1)

## [1] "10%"

Extending R

# Get a package
install.packages("tibble")

# Activate a package
library(tibble)

# Use the package as a namespace
tibble::as.tibble(a)

Base (similar to pandas)

df <- data.frame(a = 1:10, b = LETTERS[1:5])  # setup
df[1:2, ]  # row numbers

##   a b
## 1 1 A
## 2 2 B

df[df$a < 2, ]  # value filters

##   a b
## 1 1 A

Base (similar to pandas)

df[df$a < 3, 1]  # column filter

## [1] 1 2

df[df$a < 3, 1, drop = FALSE]  # column filter (keep data.frame)

##   a
## 1 1
## 2 2

OO

# Orig OO (s3): cyclismo.org/tutorial/R/s3Classes.html
library(R6)
Loan <- R6Class("Loan", public = list(term = NA, initialize = function(term) {
    if (!missing(term)) {
        self$term <- term
    }
}, extendBy = function(ext) {
    self$term <- self$term + ext
}))

OO

acc <- Loan$new(36)
acc$extendBy(6)
acc$term

## [1] 42

Functional

library(magrittr)
iris %>% {
    tail(., nrow(.)/5)
} %>% pairs()

Functional

library(tidyverse)
iris %>% filter(Petal.Width < 2) %>% group_by(Species) %>% summarise_all(mean)

## # A tibble: 3 x 5
##   Species    Sepal.Length Sepal.Width Petal.Length Petal.Width
##   <fct>             <dbl>       <dbl>        <dbl>       <dbl>
## 1 setosa             5.01        3.43         1.46       0.246
## 2 versicolor         5.94        2.77         4.26       1.33 
## 3 virginica          6.34        2.79         5.32       1.76

Best practices

• Make sure to declare packages using library() at the top of the script
• Label sections of work using # ---- SectionName ---- to allow you to pick up the code into a LaTeX or markdown doc later
• Never delete <- always make new objects with modifications
• If comfortable with coding, or have a lot of data to process use data.table over dplyr
• Try to do all data manipulation at the top of the script
• Reuse your code by writing functions for anything you do frequently

Documentation

Document as you go!

• Use markdown to use a light syntax for integrating your code and commentary
• Use LaTeX for finer level of control on output layout
• Use these instead of Excel or Word so that a change in assumption means updating the code and re-running the doc
• Load your main R file up and use the labels from # ---- SectionName ---- to save repetition
• Present in HTML, slide decks, PDF, Word at the click of a button

Workflow best practices

• Focus on working code & documentation
• Use source control (github)
• Write tests (assertive, assertthat, testthat)
• Regularly visualise (ggplot2)
• Do code reviews
• Modularise
• Use Rstudio
• Consider structuring as a package

Next steps

• geni.us/rfundamentals
• notebooks.azure.com/stephlocke/libraries

• rstats

• datacamp.com
• r-bloggers.com

Building packages

github.com/lockedata/pRojects geni.us/rpkgs

devtools::install_github("lockedata/projects")
pRojects::createPackageProject()

Writing tests

• testthat
• stubthat
• mockr

library(testthat)
context("Some thing I want to test")
test_that("things make sense", expect_equal(1, 1))

Code coverage

library(covr)
`?`(package_coverage)
`?`(codecov)

Continuous integration

• Travis CI

JavaScript

library(V8)
ct <- v8()
ct$eval("var foo = 123")
ct$eval("var bar = 456")
ct$eval("foo + bar")

## [1] "579"

ct$source(system.file("js/underscore.js", package = "V8"))
ct$call("_.filter", mtcars, JS("function(x){return x.mpg < 15}"))

##                      mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Duster 360          14.3   8  360 245 3.21 3.570 15.84  0  0    3    4
## Cadillac Fleetwood  10.4   8  472 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8  460 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8  440 230 3.23 5.345 17.42  0  0    3    4
## Camaro Z28          13.3   8  350 245 3.73 3.840 15.41  0  0    3    4

Python

library(reticulate)
main <- import_main()
builtins <- import_builtins()
builtins$print("foo")
py_run_string("x = 10")
# access the python main module via the 'py' object
py$x

## [1] 10

APIs

library(httr)
"https://foaas.com/version" %>% GET() %>% content()

## $message
## [1] "Version 2.0.0"
## 
## $subtitle
## [1] "FOAAS"

plumber

rplumber.io

#' Echo the parameter that was sent in
#' @param msg The message to echo back.
#' @get /echo
function(msg = "") {
    list(msg = paste0("The message is: '", msg, "'"))
}

opencpu

• opencpu.org
• jsfiddle.net/user/opencpu/fiddles/

docker

• containerit
• rize
• liftr

Hadoop(ish)

• sparklyr
• ML Server
• Jupyter

Microsoft

• SQL Server sp_execute_external_script()
• ML server
• Azure ML Studio
• Power BI

Shiny

• shinyserver
• shinyapps.io
• shinyproxy.io

Covered

• Why you should know R
• What are the ways you can write R
• How you can help R users improve their code
• How R users can consume your code
• How you can get R code into production

Follow up

• itsalocke.com/talks
• geni.us/rfundamentals
• t: @theStephLocke
• t: @LockeData