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/tidyr.png), url(img/dplyr.png), url(img/purrr.png)
background-size: 15%
background-position: 32% 65%, 50% 65%, 68% 65%
Part 2
Tidy data reshaping & summaries
class: middle, inverse
Tidy data reshaping & summaries
- pivoting data with tidyr (~25 min)
- grouped summaries with dplyr (~25 min)
- working with nested data using purrr (~25 min)
class: dark, center
background-image: url(img/tidyr.png)
background-size: 15%
background-position: 50% 65%
tidyr
pivoting / altering data shape
background-image: url(img/tidyr.png)
background-size: 8%
background-position: 95% 5%
tidyr
The goal of tidyr is to help you create tidy data.
Tidy data is data where:
- Every column is variable.
- Every row is an observation.
- Every cell is a single value.
Tidy data describes a standard way of storing data that is used wherever possible throughout the tidyverse.
If you ensure that your data is tidy, you’ll spend less time fighting with the tools and more time working on your analysis. Learn more about tidy data in vignette("tidy-data").
background-image: url(img/tidyr.png)
background-size: 8%
background-position: 95% 5%
Tall/long vs. wide data
-
Tall (or long) data are considered "tidy", in that they adhere to the three tidy-data principles
-
Wide data are not necessarily "messy", but have a shape less ideal for easy handling in the tidyverse
Example in longitudinal data design:
- wide data: each participant has a single row of data, with all longitudinal observations in separate columns
- tall data: a participant has as many rows as longitudinal time points, with measures in separate columns
background-image: url(img/tidyr.png)
background-size: 8%
background-position: 95% 5%
tidyr
.pull-left[
pivoting
pivot_longer() - wide to long
pivot_wider() - long to wide
Transforms data shape
]
--
.pull-right[
knitr::include_graphics("gifs/tall_wide.gif")
]
background-image: url(img/tidyr.png)
background-size: 8%
background-position: 95% 5%
Pivoting longer
takes tidy-select column arguments, so it is easy to grab all the columns you are after.
penguins |>
pivot_longer(contains("_"))
background-image: url(img/tidyr.png)
background-size: 8%
background-position: 95% 5%
Why pivot longer?
.pull-left[
Can be convenient for easy sub-plots with ggplot
penguins |>
pivot_longer(contains("_")) |>
ggplot(aes(x = value, fill = species)) +
geom_density() +
facet_wrap(~ name, scales = "free") +
scale_fill_viridis_d(alpha = .5) +
theme(legend.position = "bottom")
]
.pull-right[
]
background-image: url(img/tidyr.png)
background-size: 8%
background-position: 95% 5%
pivoting wider
penguins_long <- penguins |>
mutate(id = row_number()) |>
pivot_longer(contains("_"),
names_to = c("body_part",
"measure",
"unit"),
names_sep = "_")
penguins_long |>
pivot_wider(names_from = c("body_part", "measure", "unit"), # pivot these columns
values_from = "value", # take the values from here
names_sep = "_") # separate names_from with this character
class: inverse, middle, center
Go to RStudio
live demo
class: inverse, middle, center
Go to subsetting exercises
learnr::run_tutorial("005-pivoting", "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, center
background-image: url(img/dplyr.png), url(img/tidyr.png)
background-size: 15%
background-position: 41% 65%, 59% 65%
dplyr + tidyr
data summaries
background-image: url(img/dplyr.png)
background-size: 8%
background-position: 95% 5%
dplyr - comparison to base-R
tidy
penguins |>
summarise(mean(bill_length_mm, na.rm = TRUE))
base
mean(penguins$bill_length_mm, na.rm = TRUE)
class: inverse, middle, center
Go to RStudio
live demo
class: inverse, middle, center
Go to subsetting exercises
learnr::run_tutorial("006-summarising", "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), url(img/tidyr.png), url(img/purrr.png)
background-size: 15%
background-position: 32% 65%, 50% 65%, 68% 65%
dplyr + tidyr + purrr
Working with nested data - avoiding loops
background-image: url(img/dplyr.png), url(img/tidyr.png), url(img/purrr.png)
background-size: 8%
background-position: 93% 5%, 97.5% 19%, 84.5% 5%
comparison to base-R
tidy
penguins |>
nest_by(species, island) |>
mutate(lm_model = list(
lm(bill_length_mm ~ bill_depth_mm, data = data)
))
base
penguins$groups <- interaction(penguins$species, penguins$island)
models <- list()
for(i in 1:length(unique(penguins$groups))){
tmp <- penguins[penguins$groups == groups[i],]
models[[i]] <- lm(bill_length_mm ~ bill_depth_mm, data = data)
}
# or
lapply(unique(penguins$groups), function(x) lm(bill_length_mm ~ bill_depth_mm,
data = penguins[penguins$groups == x,]))
class: inverse, middle, center
Go to RStudio
live demo
class: inverse, middle, center
Go to subsetting exercises
learnr::run_tutorial("006-nesting", "tidyquintro")
countdown(minutes = 8,
left = 0, right = 0, top = "50%",
play_sound = TRUE,
warn_when = 30)
class: dark, middle, center
End of part 2
Athanasiamo/tidyquintro documentation built on Oct. 11, 2022, 7:15 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
layout: true
knitr::opts_chunk$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/tidyr.png), url(img/dplyr.png), url(img/purrr.png) background-size: 15% background-position: 32% 65%, 50% 65%, 68% 65%
Part 2
Tidy data reshaping & summaries
class: middle, inverse
Tidy data reshaping & summaries
-
- pivoting data with tidyr (~25 min)
- grouped summaries with dplyr (~25 min)
- Every column is variable.
- Every row is an observation.
- Every cell is a single value.
-
Tall (or long) data are considered "tidy", in that they adhere to the three tidy-data principles
-
Wide data are not necessarily "messy", but have a shape less ideal for easy handling in the tidyverse
- wide data: each participant has a single row of data, with all longitudinal observations in separate columns
- tall data: a participant has as many rows as longitudinal time points, with measures in separate columns
- working with nested data using purrr (~25 min)
class: dark, center background-image: url(img/tidyr.png) background-size: 15% background-position: 50% 65%
tidyr
pivoting / altering data shape
background-image: url(img/tidyr.png) background-size: 8% background-position: 95% 5%
tidyr
The goal of tidyr is to help you create tidy data.
Tidy data is data where:
Tidy data describes a standard way of storing data that is used wherever possible throughout the tidyverse.
If you ensure that your data is tidy, you’ll spend less time fighting with the tools and more time working on your analysis. Learn more about tidy data in vignette("tidy-data").
background-image: url(img/tidyr.png) background-size: 8% background-position: 95% 5%
Tall/long vs. wide data
Example in longitudinal data design:
background-image: url(img/tidyr.png) background-size: 8% background-position: 95% 5%
tidyr
.pull-left[
pivoting
pivot_longer() - wide to long
pivot_wider() - long to wide
Transforms data shape ]
--
.pull-right[
knitr::include_graphics("gifs/tall_wide.gif")
]
background-image: url(img/tidyr.png) background-size: 8% background-position: 95% 5%
Pivoting longer
takes tidy-select column arguments, so it is easy to grab all the columns you are after.
penguins |> pivot_longer(contains("_"))
background-image: url(img/tidyr.png) background-size: 8% background-position: 95% 5%
Why pivot longer?
.pull-left[ Can be convenient for easy sub-plots with ggplot
penguins |> pivot_longer(contains("_")) |> ggplot(aes(x = value, fill = species)) + geom_density() + facet_wrap(~ name, scales = "free") + scale_fill_viridis_d(alpha = .5) + theme(legend.position = "bottom")
]
.pull-right[
]
background-image: url(img/tidyr.png) background-size: 8% background-position: 95% 5%
pivoting wider
penguins_long <- penguins |> mutate(id = row_number()) |> pivot_longer(contains("_"), names_to = c("body_part", "measure", "unit"), names_sep = "_") penguins_long |> pivot_wider(names_from = c("body_part", "measure", "unit"), # pivot these columns values_from = "value", # take the values from here names_sep = "_") # separate names_from with this character
class: inverse, middle, center
Go to RStudio
live demo
class: inverse, middle, center
Go to subsetting exercises
learnr::run_tutorial("005-pivoting", "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, center background-image: url(img/dplyr.png), url(img/tidyr.png) background-size: 15% background-position: 41% 65%, 59% 65%
dplyr + tidyr
data summaries
background-image: url(img/dplyr.png) background-size: 8% background-position: 95% 5%
dplyr - comparison to base-R
tidy
penguins |> summarise(mean(bill_length_mm, na.rm = TRUE))
base
mean(penguins$bill_length_mm, na.rm = TRUE)
class: inverse, middle, center
Go to RStudio
live demo
class: inverse, middle, center
Go to subsetting exercises
learnr::run_tutorial("006-summarising", "tidyquintro")
countdown(minutes = 8, left = 0, right = 0, top = "50%", play_sound = TRUE, warn_when = 30)
class: dark, center background-image: url(img/dplyr.png), url(img/tidyr.png), url(img/purrr.png) background-size: 15% background-position: 32% 65%, 50% 65%, 68% 65%
dplyr + tidyr + purrr
Working with nested data - avoiding loops
background-image: url(img/dplyr.png), url(img/tidyr.png), url(img/purrr.png) background-size: 8% background-position: 93% 5%, 97.5% 19%, 84.5% 5%
comparison to base-R
tidy
penguins |> nest_by(species, island) |> mutate(lm_model = list( lm(bill_length_mm ~ bill_depth_mm, data = data) ))
base
penguins$groups <- interaction(penguins$species, penguins$island) models <- list() for(i in 1:length(unique(penguins$groups))){ tmp <- penguins[penguins$groups == groups[i],] models[[i]] <- lm(bill_length_mm ~ bill_depth_mm, data = data) } # or lapply(unique(penguins$groups), function(x) lm(bill_length_mm ~ bill_depth_mm, data = penguins[penguins$groups == x,]))
class: inverse, middle, center
Go to RStudio
live demo
class: inverse, middle, center
Go to subsetting exercises
learnr::run_tutorial("006-nesting", "tidyquintro")
countdown(minutes = 8, left = 0, right = 0, top = "50%", play_sound = TRUE, warn_when = 30)
class: dark, middle, center
End of part 2
R Package Documentation
Browse R Packages
We want your feedback!
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