In Athanasiamo/tidyquintro: Quick Intro to Tidyverse

layout: true

---

knitr::opts_chunk$set(echo = TRUE, 
                      message = FALSE, 
                      error = FALSE, 
                      warning = FALSE)

# These are the defaults
xaringanExtra::use_extra_styles(
  hover_code_line = TRUE,         #<<
  mute_unhighlighted_code = TRUE  #<<
)

library(knitr)
library(tidyquintro)
library(countdown)

class: dark, center background-image: url(img/ggplot2.png), url(img/dplyr.png), url(img/pipe.png) background-size: 15% background-position: 32% 65%, 50% 65%, 68% 65%

Part 1

Tidy data wrangling

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class: middle, inverse

Tidy data wrangling

- plotting data with ggplot2 (~25 min) - sub-setting data with dplyr (~25 min)
- chaining commands with the pipe |> (~10 min)
- adding and altering variables with dplyr (~25 min)

---

background-image: url(img/ggplot2.png) background-size: 8% background-position: 95% 5% name: ggplot

ggplot2

grammar of graphics

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background-image: url(img/ggplot2.png) background-size: 8% background-position: 95% 5%

ggplot2 setting

.pull-left[

ggplot(data = penguins,
       mapping = aes(x = bill_length_mm)) +
  geom_histogram(
    fill = "forestgreen" #<<
  )

]

.pull-right[

]

---

background-image: url(img/ggplot2.png) background-size: 8% background-position: 95% 5%

ggplot2 mapping

.pull-left[

ggplot(data = penguins,
       mapping = aes(x = bill_length_mm,
                     fill = species)) + #<<
  geom_histogram( )

]

.pull-right[

]

---

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Go to RStudio

live demo

---

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Go to plotting exercises

learnr::run_tutorial("001-plotting", "tidyquintro")

countdown(minutes = 8, 
          left = 0, right = 0, top = "50%", 
          padding = "50px", 
          margin = "10%", 
          font_size = "4em", 
          color_running_background  = "transparent",
          color_finished_background = "#ffffff",
          play_sound = TRUE,
          warn_when = 30)

---

class: dark, middle, center background-image: url(img/dplyr.png) background-size: 15% background-position: 50% 95% name: dplyr-subset

dplyr

data subsetting

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background-image: url(img/dplyr.png) background-size: 8% background-position: 95% 5%

dplyr

grammar of data manipulation

provides a consistent set of verbs that help you solve the most common data manipulation challenges:

`select()` picks variables based on their names. `filter()` picks cases based on their values.

mutate() - adds or alters variables that are functions of existing variables
summarise() reduces multiple values down to a single summary.
arrange() changes the ordering of the rows.

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background-image: url(img/dplyr.png) background-size: 8% background-position: 95% 5% name: filter

dplyr

.pull-left[

filter() - subsetting rows

Reducing the number of rows in a data sat based on some logic.

• filter() evaluates a statement to be logical (TRUE or FALSE)
  ]

--

.pull-right[

knitr::include_graphics("gifs/filtering.gif")

]

---

background-image: url(img/dplyr.png) background-size: 8% background-position: 95% 5%

dplyr - comparison to base-R

tidy

filter(penguins, bill_length_mm > 40)

base

penguins[penguins$bill_length_mm > 40, ]

# or
subset(penguins, bill_length_mm > 40)

https://dplyr.tidyverse.org/articles/base.html

---

class: inverse, middle, center

Go to RStudio

live demo

---

background-image: url(img/dplyr.png) background-size: 8% background-position: 95% 5% name: select

dplyr

.pull-left[

select() - reduce columns

Reducing the number of columns (or rearranging columns) Can be used with column names, index integer, or tidyselect-functions

tidy-select helpers - ends_with("string") - column names ending with "string"
- starts_with("string") - column names starting with "string"
- contains("string") - column names containing "string"
]

--

.pull-right[

knitr::include_graphics("gifs/selecting.gif")

]

---

background-image: url(img/dplyr.png) background-size: 8% background-position: 95% 5%

dplyr - comparison to base-R

tidy

select(penguins, species, island, ends_with("mm"))

base

penguins[c(1, 2, grep("mm$", names(penguins)))]

# or
subset(penguins, select = c("species", "island", "bill_length_mm", "bill_depth_mm", "flipper_length_mm"))

https://dplyr.tidyverse.org/articles/base.html

---

class: inverse, middle, center

Go to RStudio

live demo

---

class: inverse, middle, center

Go to subsetting exercises

learnr::run_tutorial("002-subsetting", "tidyquintro")

countdown(minutes = 8,
          left = 0, right = 0, top = "50%", 
          play_sound = TRUE,
          warn_when = 30)

---

class: dark, center background-image: url(img/pipe.png) background-size: 15% background-position: 50% 65% name: pipe

magrittr

the pipe - chaining commands

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background-image: url(img/pipe.png) background-size: 8% background-position: 95% 5%

the pipe - chaining commands

• Common to many programming languages

• sending the output from one function, straight into another, without saving the intermediary

• Only really work when input is the first command to a function

• This is not the case for most base-R functions, but is always the case with tidyverse functions

• The common used pipe in R, |>, originally comes from the magrittr package, but also comes with dplyr

??? arguably, in tidyverse the chaining of commands is one of the things that makes it most powerful. Chaining commands is a common programming concept, where you send the output of one command directly into another, without saving the intermediary. This saves you from overcrowding your workspace with lots of new objects you will never use. It is commonly referred to as a "pipe" and in R the common pipe is |>

---

background-image: url(img/pipe.png) background-size: 8% background-position: 95% 5%

Use

.pull-left[

# standard
select(penguins, 
       species, island, ends_with("mm"))

# piped
penguins |> 
  select(species, island, ends_with("mm"))

]

.pull-right[

]

---

class: inverse, middle, center

Go to RStudio

live demo

---

class: inverse, middle, center

Go to chaining exercises

learnr::run_tutorial("003-chaining", "tidyquintro")

countdown(minutes = 8,
          left = 0, right = 0, top = "50%", 
          play_sound = TRUE,
          warn_when = 30)

---

class: dark, center background-image: url(img/dplyr.png) background-size: 15% background-position: 50% 65% name: dplyr-mutate

dplyr

data wrangling / manipulation

---

background-image: url(img/dplyr.png) background-size: 8% background-position: 95% 5%

dplyr

grammar of data manipulation

provides a consistent set of verbs that help you solve the most common data manipulation challenges:

select() picks variables based on their names.
filter() picks cases based on their values.

mutate() - adds or alters variables that are functions of existing variables

summarise() reduces multiple values down to a single summary.
arrange() changes the ordering of the rows.

---

background-image: url(img/dplyr.png) background-size: 8% background-position: 95% 5%

dplyr - comparison to base-R

tidy

penguins |> 
  mutate( 
    new_column = 1,
    bill_ld_ratio = bill_length_mm/bill_depth_mm
  )

base

penguins$new_column <- 1
penguins$bill_ld_ratio <- penguins$bill_length_mm/penguins$bill_depth_mm

https://dplyr.tidyverse.org/articles/base.html

---

class: inverse, middle, center

Go to RStudio

live demo

---

class: inverse, middle, center

Go to mutating exercises

learnr::run_tutorial("004-mutating", "tidyquintro")

countdown(minutes = 8,
          left = 0, right = 0, top = "50%", 
          play_sound = TRUE,
          warn_when = 30)

---

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End of part 1

30 minute lunch break

countdown(minutes = 30,
          left = 0, right = 0, top = "50%", 
          play_sound = TRUE,
          color_running_text = "white",
          warn_when = 5*60)




Athanasiamo/tidyquintro documentation built on Oct. 11, 2022, 7:15 p.m.