How these slides were made

Required Installations

• tidyverse
• magrittr

install.packages(c("tidyverse", "magrittr"))

library(tidyverse)
library(magrittr)

## ── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 2.2.1     ✔ purrr   0.2.4
## ✔ tibble  1.3.4     ✔ dplyr   0.7.4
## ✔ tidyr   0.7.2     ✔ stringr 1.2.0
## ✔ readr   1.1.1     ✔ forcats 0.2.0

## ── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

## 
## Attaching package: 'magrittr'

## The following object is masked from 'package:purrr':
## 
##     set_names

## The following object is masked from 'package:tidyr':
## 
##     extract

The tidyverse

• What is it?
• What’s included?
  ADD MORE!!

Pre-installed datasets

data()

The starwars dataset

starwars

## # A tibble: 87 x 13
##                  name height  mass    hair_color  skin_color eye_color
##                 <chr>  <int> <dbl>         <chr>       <chr>     <chr>
##  1     Luke Skywalker    172    77         blond        fair      blue
##  2              C-3PO    167    75          <NA>        gold    yellow
##  3              R2-D2     96    32          <NA> white, blue       red
##  4        Darth Vader    202   136          none       white    yellow
##  5        Leia Organa    150    49         brown       light     brown
##  6          Owen Lars    178   120   brown, grey       light      blue
##  7 Beru Whitesun lars    165    75         brown       light      blue
##  8              R5-D4     97    32          <NA>  white, red       red
##  9  Biggs Darklighter    183    84         black       light     brown
## 10     Obi-Wan Kenobi    182    77 auburn, white        fair blue-gray
## # ... with 77 more rows, and 7 more variables: birth_year <dbl>,
## #   gender <chr>, homeworld <chr>, species <chr>, films <list>,
## #   vehicles <list>, starships <list>

Viewing datasets

> starwars
> print(starwars)
> str(starwars)
> head(starwars) # first 5 rows

glimpse(starwars) 

## Observations: 87
## Variables: 13
## $ name       <chr> "Luke Skywalker", "C-3PO", "R2-D2", "Darth Vader", ...
## $ height     <int> 172, 167, 96, 202, 150, 178, 165, 97, 183, 182, 188...
## $ mass       <dbl> 77.0, 75.0, 32.0, 136.0, 49.0, 120.0, 75.0, 32.0, 8...
## $ hair_color <chr> "blond", NA, NA, "none", "brown", "brown, grey", "b...
## $ skin_color <chr> "fair", "gold", "white, blue", "white", "light", "l...
## $ eye_color  <chr> "blue", "yellow", "red", "yellow", "brown", "blue",...
## $ birth_year <dbl> 19.0, 112.0, 33.0, 41.9, 19.0, 52.0, 47.0, NA, 24.0...
## $ gender     <chr> "male", NA, NA, "male", "female", "male", "female",...
## $ homeworld  <chr> "Tatooine", "Tatooine", "Naboo", "Tatooine", "Alder...
## $ species    <chr> "Human", "Droid", "Droid", "Human", "Human", "Human...
## $ films      <list> [<"Revenge of the Sith", "Return of the Jedi", "Th...
## $ vehicles   <list> [<"Snowspeeder", "Imperial Speeder Bike">, <>, <>,...
## $ starships  <list> [<"X-wing", "Imperial shuttle">, <>, <>, "TIE Adva...

Viewing datasets in RStudio

Click on the variable name in the Environments pane

Will execute this code:

View(mtcars)

What is the tidyverse?

• Designed (originally) by Hadley Wickham
  
• A suite of tools that emphasize consistency and transparency
• Smart defaults
• Open source and constantly expanded/improved
• Packages for importing, formatting, analyzing, modeling and plotting
• All of these can be connected(!)

The pipe %>%

sum(1:8) %>%
  sqrt()

## [1] 6

Principles of tidy-ness

• Each type of observation gets its own dataframe
• Variables make up the columns
• Observations make up the rows
• Values go into cells

Making data tidy

• Human-readable data

• Tidying up
  

  

table %>% 
  gather(`1999`, `2000`, key = "year", value = "cases")

Selecting data

filter(starwars, height < 90)

## # A tibble: 3 x 13
##                    name height  mass hair_color skin_color eye_color
##                   <chr>  <int> <dbl>      <chr>      <chr>     <chr>
## 1                  Yoda     66    17      white      green     brown
## 2 Wicket Systri Warrick     88    20      brown      brown     brown
## 3         Ratts Tyerell     79    15       none grey, blue   unknown
## # ... with 7 more variables: birth_year <dbl>, gender <chr>,
## #   homeworld <chr>, species <chr>, films <list>, vehicles <list>,
## #   starships <list>

filter(starwars, height < 90) %>%
  select(name, gender, species, height)

## # A tibble: 3 x 4
##                    name gender        species height
##                   <chr>  <chr>          <chr>  <int>
## 1                  Yoda   male Yoda's species     66
## 2 Wicket Systri Warrick   male           Ewok     88
## 3         Ratts Tyerell   male         Aleena     79

Ordering data

filter(starwars, height < 90) %>%
  select(name, gender, species, height) %>%
  arrange(height)

## # A tibble: 3 x 4
##                    name gender        species height
##                   <chr>  <chr>          <chr>  <int>
## 1                  Yoda   male Yoda's species     66
## 2         Ratts Tyerell   male         Aleena     79
## 3 Wicket Systri Warrick   male           Ewok     88

Summarizing data

starwars %>%
  na.omit() %>%
  group_by(species) %>%
  summarize(avg_mass = mean(mass))

## # A tibble: 11 x 2
##         species  avg_mass
##           <chr>     <dbl>
##  1       Cerean  82.00000
##  2         Ewok  20.00000
##  3       Gungan  66.00000
##  4        Human  81.01111
##  5      Kel Dor  80.00000
##  6     Mirialan  53.10000
##  7 Mon Calamari  83.00000
##  8   Trandoshan 113.00000
##  9      Twi'lek  55.00000
## 10      Wookiee 112.00000
## 11       Zabrak  80.00000

Adding summary data to our data frame

starwars %>%
  na.omit() %>%
  group_by(species, gender) %>%
  select(name, gender, species, mass) %>%
  mutate(avg_mass = mean(mass))

## # A tibble: 29 x 5
## # Groups:   species, gender [12]
##                  name gender species  mass  avg_mass
##                 <chr>  <chr>   <chr> <dbl>     <dbl>
##  1     Luke Skywalker   male   Human    77  85.94667
##  2        Darth Vader   male   Human   136  85.94667
##  3        Leia Organa female   Human    49  56.33333
##  4          Owen Lars   male   Human   120  85.94667
##  5 Beru Whitesun lars female   Human    75  56.33333
##  6  Biggs Darklighter   male   Human    84  85.94667
##  7     Obi-Wan Kenobi   male   Human    77  85.94667
##  8   Anakin Skywalker   male   Human    84  85.94667
##  9          Chewbacca   male Wookiee   112 112.00000
## 10           Han Solo   male   Human    80  85.94667
## # ... with 19 more rows

Summarizing data

starwars %>%
  count(species) %>%
  arrange(desc(n))

## # A tibble: 38 x 2
##     species     n
##       <chr> <int>
##  1    Human    35
##  2    Droid     5
##  3     <NA>     5
##  4   Gungan     3
##  5 Kaminoan     2
##  6 Mirialan     2
##  7  Twi'lek     2
##  8  Wookiee     2
##  9   Zabrak     2
## 10   Aleena     1
## # ... with 28 more rows

Basics of ggplot

• Creates a stardard grammar of graphics
• Seperates the data variables from the appearance variables
• Base R Example

plot(starwars$height, type='p', col='red', pch=16)

Different types of variables

Can convert between types

nums <- c(0.7, 1.2, 3.4)
factor_nums <- as.factor(nums)
levels(factor_nums)

## [1] "0.7" "1.2" "3.4"

nums + 1

## [1] 1.7 2.2 4.4

factor_nums + 1

## Warning in Ops.factor(factor_nums, 1): '+' not meaningful for factors

## [1] NA NA NA

Elements of a ggplot function

• First part is just the data

ggplot(data=starwars, aes(x=height, y=mass)) + ...

• Then we decide how the data will appear

... + geom_point(), + geom_boxplot(), etc.

Putting a plot together and highlighting factors

Plots can also summarize data

ggplot(subset(starwars, species %in% c('Droid', 'Human', "Gungan")),
       aes(x=species, y=height)) + 
  geom_boxplot()

Adding a trendline is often useful

ggplot(starwars, aes(x=height, y=mass)) + 
  geom_point(size=5) +
  stat_smooth(method='lm')

We can even pipe from dplyr to ggplot

starwars %>%
  filter(species == 'Human' & gender %in% c('male', 'female')) %>%
  ggplot(aes(mass)) +
  geom_histogram() +
  facet_grid(. ~ gender)

Takeaways

• The tidyverse has smart defaults
• The tidyverse can standardize workflow from data import to analysis/plotting
• The pipe (%>%) can link many functions
• Tidy data is useful for more complex statistics and modeling
• ggplot makes plotting more intuitive
  • Allows for focused control of each element (data, plot type, color, etc.)
  • Highlight the important relationships