Day 3 - PM

1 Psst…we need to talk



“The secret to writing better is reading more.”
-someone

Reading aloud

Let’s practice reading R scripts with a partner.

They’ll listen to what you say and turn it into R code.

It’s like a game of telephone, but for R.


Instructions

  1. Find a partner.
    • One of you gets to be Bert and one of you is Ernie.
  2. Bert will look at the first code block and tell Ernie what it does.
  3. Ernie will then write some code that accomplishes what Bert said.
  4. Then Bert can offer more clues to help.
    • Try not to say the exact names of functions, like “use filter” or “use write_csv”
    • Instead, say what you want to accomplish, like "Drop the rows where..." or "Save the data as..."


Example code to read aloud

Say aloud what this code does or tries to accomplish.

library(sandwich)
    
slices_bread <- 4

fillings <- read_csv("fillings.csv")

nrow(fillings)

names(fillings)

red_veggies <- filter(fillings, food_type == "vegetable", color = "red")



⚠️ Wait to click below until you decide who’s Bert.

1. Bert’s turn to read: BIG fish

No peaking Ernie!

We’ll start by turning some fishy code into plain language.

Bert’s s only! 
library(readr)

fishes <- read_csv("lake_superior_fish.csv")

names(big_fishes)

nrow(fishes)
 
big_fishes <- filter(fishes, length > 20)

nrow(big_fishes)



2. Ernie’s turn to read: Sleepy sheep 🐑🐑🐑

Ernie’s s only! 
library(ggplot2)

asleep <- FALSE

names <- c("Shaun", "Lambchop", "Dolly")

sheep_ids <- 1:3

my_sheep <- data.frame(sheep_name = names, 
                       sheep_id   = sheep_ids)

ggplot(my_sheep, aes(x = names, y = sheep_id)) +
    geom_point() + 
    labs(title = "Counting sheep backwards makes me Zzzz...")

asleep <- TRUE



3. Charades! Act it out

Instead of saying what the code does aloud, act out what the code does without speaking at ALL.

Ask Barbara very nicely to go first.

Barbara’s s only! 
cat_df <- read_excel("lost_cats.xlsx")

kittens <- filter(cat_df, age < 1)

catfood <- "SPAM"

hungry_kittens <- filter(kittens, hungry == TRUE)

fed_cats <- TRUE


2 Pipe things together: A %>% B


Wishing you could type the name of the data less often?

You can with the pipe!

Use the %>% to chain functions together and make our scripts more streamlined and easier to read. When reading code with the %>% you can read it as and then.

For example

puppy %>% runs_outside %>% rolls_in_mud %>% barks_joyfully(times = 3)

Can be read as

There is a puppy, it runs outside And then rolls in the mud And then barks joyfully 3 times.”

The %>% helps:

1. Eliminate nested parentheses

Let’s say you have 3 numbers. Your analysis requires that you take the sum of them, then take the log of that result, and then round the final outcome.

Without the pipe

round(log(sum(c(10, 20, 30, 50))))

The code above is dense and we need to read it backwards from right to left to understand the order of operations. The pipe on the other hand allows us to read the code from left to right.


With the pipe 

result <- c(10, 20, 30, 50) %>%
            sum() %>%
            log() %>%
            round()


2. Combine processing steps into a cohesive chunk

Without the pipe

scrap <- arrange(scrap, desc(price_per_pound))

scrap <- filter(scrap, origin != "All")

scrap <- mutate(scrap, 
                scrap_finder    = "BB8",
                measure_method  = "REM-24")

With the pipe

scrap <- scrap %>%
           arrange(desc(price_per_pound)) %>%
           filter(origin != "All") %>%
           mutate(scrap_finder    = "BB8",
                  measure_method  = "REM-24")


Explore!

Similar to above, use the %>% to combine the 3 analysis lines below.

# Group the scrap data by item
scrap <- group_by(scrap, item)

# Calculate each item's mean price 
scrap <- summarize(scrap, mean_price = mean(price_per_unit))

# Ungroup the data
scrap <- ungroup(scrap)

Let the %>% guide you.


3 Connect to databases

Databases and SQL

Prep

Let’s get our favorite starwars data.

#install.packages("RSQLite")
#install.packages("DBI")
library(RSQLite)
library(DBI)
library(dplyr)

people <- starwars

Create a local db

# Create a connection to a new database
# A .db file will appear in your project folder
conn <- dbConnect(RSQLite::SQLite(), "people.db")

Add our data

# Add the dataset to a table called "people_tbl"
## ERROR! This will throw an error about our data
try(dbWriteTable(conn, "people_tbl", people))

# Drop the list columns
people <- select(people, -films, -vehicles, -starships)

# Try again
try(dbWriteTable(conn, "people_tbl", people))

# List all the tables available in the database
dbListTables(conn)

Reading data

SQL

dbGetQuery(conn, "SELECT * FROM people_tbl")

# Filter the data
df <- dbGetQuery(conn, "SELECT * FROM people_tbl 
                        WHERE eye_color = 'brown'")

dplyr

tbl(conn, "people_tbl")
## # Source:   table<people_tbl> [?? x 11]
## # Database: sqlite 3.39.4 [/cloud/project/content/page/people.db]
##    name        height  mass hair_…¹ skin_…² eye_c…³ birth…⁴ sex   gender homew…⁵
##    <chr>        <int> <dbl> <chr>   <chr>   <chr>     <dbl> <chr> <chr>  <chr>  
##  1 Luke Skywa…    172    77 blond   fair    blue       19   male  mascu… Tatooi…
##  2 C-3PO          167    75 <NA>    gold    yellow    112   none  mascu… Tatooi…
##  3 R2-D2           96    32 <NA>    white,… red        33   none  mascu… Naboo  
##  4 Darth Vader    202   136 none    white   yellow     41.9 male  mascu… Tatooi…
##  5 Leia Organa    150    49 brown   light   brown      19   fema… femin… Aldera…
##  6 Owen Lars      178   120 brown,… light   blue       52   male  mascu… Tatooi…
##  7 Beru White…    165    75 brown   light   blue       47   fema… femin… Tatooi…
##  8 R5-D4           97    32 <NA>    white,… red        NA   none  mascu… Tatooi…
##  9 Biggs Dark…    183    84 black   light   brown      24   male  mascu… Tatooi…
## 10 Obi-Wan Ke…    182    77 auburn… fair    blue-g…    57   male  mascu… Stewjon
## # … with more rows, 1 more variable: species <chr>, and abbreviated variable
## #   names ¹​hair_color, ²​skin_color, ³​eye_color, ⁴​birth_year, ⁵​homeworld
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
# Filter the data
df <- tbl(conn, "people_tbl") %>% 
        filter(eye_color == "brown") 

# Show the SQL that *will* run, if you tell it to
show_query(df)
## <SQL>
## SELECT *
## FROM `people_tbl`
## WHERE (`eye_color` = 'brown')
# Run the SQL and return the data
df <- collect(df)

Database parameters for the real world

  • driver : Specific to the type of database: Oracle, PostgreSQL, Athena
  • url or host : The web or server location to connect to
  • port : A 4 digit number of the port to use for communicating with the db
  • db : The db name
  • user : Your username for the db
  • password : Your password for the db

Woah! That is a lot to keep track of. Luckily, there are helper packages to simplify connecting to various databases. Contact your Agency’s R user group for more info.

4 Password: Is it secret, is it safe?


Using passwords in R


Not secret, not safe

user <- "Me"
password <- "IloveR2D2!"

# Connect to db "Area51"

Yes secret, Yes safe

#install.packages("keyring")
library(keyring)

# This opens a popup to enter and store your password for the db
## Only need to do once
key_set("Area51", "Me")

# Then use this to safely add your password to your script
key_get("Area51", "Me")

Connecting to Agency databases

A live demo will begin shortly…


5 Your turn

Time to start putting what we’ve learned to use. Choose one of the paths below and use your new hard-earned R skills to explore data. When you feel ready, jump to the Grand Moff path and begin making a report for your own data.

ENSIGN
COMMODORE
GRAND MOFF

Explore the scrap and salvage economy on Jakku.

Media: solid waste
Planet: Jakku

Study the effects of jet-fuel on Porg reflexes.

Media: biological
Planet: Ahch-To

Travel back to Earth and explore your own data set.

Media: all
Planet: Earth


What’s next?

Join us!

  • R user groups: TidyTuesdays
  • R Teams in Teams

R heroes

Online courses

Web trainings

🐱 Concatulations!

You were an excellent Data Droid! Rey and BB8 absolutely couldn’t have done it without you.

You deserve an award.

    Meowwwwww R Users! 
      \
       \
        \
            |\___/|
          ==) ^Y^ (==
            \  ^  /
             )=*=(
            /     \
            |     |
           /| | | |\
           \| | |_|/\
      jgs  //_// ___/
               \_)


Return to Earth

You’re free! You can return to Earth now. Go ahead and robot frolic in the grass, enjoy the solar power, and jump in a lake. To help you fully acclimate, let’s look at some Earth data.


Study the housing habits of Earthlings. Create interactive maps showing the spatial clustering of different social characteristics of the human species.

Media: social-human
Planet: Earth


Return to Homebase