In EvaMaeRey/ggxmean: Statistical Geoms
class: inverse
From source .Rmd:
title: "Extending ggplot2 statistical geometries"
subtitle: "MAA Metro New York"
author: "Gina Reynolds"
date: 'Saturday May 1, 2021, 11AM'
output:
xaringan::moon_reader:
lib_dir: libs
seal: false
nature:
ratio: 16:10
highlightStyle: github
highlightLines: true
countIncrementalSlides: false
beforeInit: "https://platform.twitter.com/widgets.js"
class: inverse
From source .Rmd:
class: inverse
background-image: url(https://images.unsplash.com/photo-1482685945432-29a7abf2f466?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=1532&q=80)
background-size: cover
# Extending ggplot2 statistical geometries
## MAA Metro New York
### Dr. Evangeline 'Gina' Reynolds
### Saturday May 1, 2021, 11AM
#### Photo Credit: Mike Lewis HeadSmart Media
```r
knitr::opts_chunk$set(echo = T, comment = "", message = F,
warning = F, cache = T, fig.retina = 3)
library(tidyverse)
library(flipbookr)
library(xaringanthemer)
xaringanthemer::mono_light(
base_color = "#02075D",
# header_font_google = google_font("Josefin Sans"),
# text_font_google = google_font("Montserrat", "200", "200i"),
# code_font_google = google_font("Droid Mono"),
text_font_size = ".85cm",
code_font_size = ".15cm")
theme_set(theme_gray(base_size = 20))
```
class: inverse
background-image: url(https://images.unsplash.com/photo-1482685945432-29a7abf2f466?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=1532&q=80)
background-size: cover
Extending ggplot2 statistical geometries
MAA Metro New York
Dr. Evangeline 'Gina' Reynolds
Saturday May 1, 2021, 11AM
Photo Credit: Mike Lewis HeadSmart Media
knitr::opts_chunk$set(echo = T, comment = "", message = F,
warning = F, cache = T, fig.retina = 3)
library(tidyverse)
library(flipbookr)
library(xaringanthemer)
xaringanthemer::mono_light(
base_color = "#02075D",
# header_font_google = google_font("Josefin Sans"),
# text_font_google = google_font("Montserrat", "200", "200i"),
# code_font_google = google_font("Droid Mono"),
text_font_size = ".85cm",
code_font_size = ".15cm")
theme_set(theme_gray(base_size = 20))
class: inverse
From source .Rmd:
class: inverse
From source .Rmd:
class: inverse, center middle
# If visualization packages used this approach they would be able to "draw *every* statistical graphic".
???
The grammar of graphics framework, proposed by Leland Wilkinson in 1999, that identified 'seven orthogonal components' in the creation of data visualizations.\
Wilkinson asserted that if data visualization packages were created using a separation of concerns approach -- dividing decision making surrounding these components --- the packages would be able to "draw every statistical graphic". The grammar of graphic principles were incredibly powerful and gave rise to a number of visualization platforms including Tableau, vega-lite, and ggplot2.
class: inverse, center middle
If visualization packages used this approach they would be able to "draw every statistical graphic".
???
The grammar of graphics framework, proposed by Leland Wilkinson in 1999, that identified 'seven orthogonal components' in the creation of data visualizations.\
Wilkinson asserted that if data visualization packages were created using a separation of concerns approach -- dividing decision making surrounding these components --- the packages would be able to "draw every statistical graphic". The grammar of graphic principles were incredibly powerful and gave rise to a number of visualization platforms including Tableau, vega-lite, and ggplot2.
class: inverse
From source .Rmd:
# Creators enjoy freedom, fine control
--
# Grammar of graphics inspired:
--
#- Tableau
#- vega-lite
#- ggplot2
Creators enjoy freedom, fine control
--
Grammar of graphics inspired:
--
- Tableau
- vega-lite
- ggplot2
class: inverse
From source .Rmd:
### Use ggplot2 in the classroom because R, and ...
--
> The transferrable skills from ggplot2 are not the idiosyncracies of plotting syntax, but a **powerful way of thinking about visualisation**, as a way of mapping between variables and the visual properties of geometric objects that you can perceive.
???
Statistical educators that introduce students to one of these tools arguably are doing more than constructing one-off plots to discuss statistical principles with students: they are introducing students to 'a powerful way of thinking about data visualization'.
Statistical educators often use ggplot2 as their grammar-of-graphics-based data visualization tool as students can learn it along side the rich statistical ecosystem of the R programming language. The R programming language thus may serve as a one-stop-shop for statistical tooling; with recent developments in packages and IDEs writing code is becoming more accessible and welcoming to newcomers.
Use ggplot2 in the classroom because R, and ...
--
The transferrable skills from ggplot2 are not the idiosyncracies of plotting syntax, but a powerful way of thinking about visualisation, as a way of mapping between variables and the visual properties of geometric objects that you can perceive.
???
Statistical educators that introduce students to one of these tools arguably are doing more than constructing one-off plots to discuss statistical principles with students: they are introducing students to 'a powerful way of thinking about data visualization'.
Statistical educators often use ggplot2 as their grammar-of-graphics-based data visualization tool as students can learn it along side the rich statistical ecosystem of the R programming language. The R programming language thus may serve as a one-stop-shop for statistical tooling; with recent developments in packages and IDEs writing code is becoming more accessible and welcoming to newcomers.
class: inverse
From source .Rmd:
???
Still, using ggplot2 for statistical education can be a challenge at times. When it is used to discuss statistical concepts, sometimes it feels as though getting something done with the plotting library derails the focus on discussion of statistical concepts and material.
???
Still, using ggplot2 for statistical education can be a challenge at times. When it is used to discuss statistical concepts, sometimes it feels as though getting something done with the plotting library derails the focus on discussion of statistical concepts and material.
class: inverse
From source .Rmd:
class: inverse, center, middle
# The problem
--
## sometimes ggplot2 is hard
class: inverse, center, middle
The problem
--
sometimes ggplot2 is hard
class: inverse
From source .Rmd:
## The status quo: adding the mean
```r
library(ggplot2)
ggplot(airquality) +
aes(x = Ozone) +
geom_histogram() +
ggxmean::geom_x_mean()
```
???
Consider for example, a the seemingly simple enterprise of adding a vertical line at the mean of x, perhaps atop a histogram or density plot.
The status quo: adding the mean
library(ggplot2)
ggplot(airquality) +
aes(x = Ozone) +
geom_histogram() +
ggxmean::geom_x_mean()
???
Consider for example, a the seemingly simple enterprise of adding a vertical line at the mean of x, perhaps atop a histogram or density plot.
class: inverse
From source .Rmd:
`r chunk_reveal("basic", title = "### Adding the mean at x w/ base ggplot2")`
```r
ggplot(data = airquality) +
aes(x = Ozone) +
geom_histogram() +
geom_vline(
xintercept =
mean(airquality$Ozone,
na.rm = T)
)
```
???
Creating this plot requires greater focus on ggplot2 *syntax*, likely detracting from discussion of *the mean* that statistical instructors desire.
It may require a discussion about dollar sign syntax and how geom_vline is actually a special geom -- an annotation -- rather than being mapped to the data. None of this is relevant to the point you as an instructor aim to make: maybe that the the mean is the balancing point of the data or maybe a comment about skewness.
r chunk_reveal("basic", title = "### Adding the mean at x w/ base ggplot2")
ggplot(data = airquality) +
aes(x = Ozone) +
geom_histogram() +
geom_vline(
xintercept =
mean(airquality$Ozone,
na.rm = T)
)
???
Creating this plot requires greater focus on ggplot2 syntax, likely detracting from discussion of the mean that statistical instructors desire.
It may require a discussion about dollar sign syntax and how geom_vline is actually a special geom -- an annotation -- rather than being mapped to the data. None of this is relevant to the point you as an instructor aim to make: maybe that the the mean is the balancing point of the data or maybe a comment about skewness.
class: inverse
From source .Rmd:
### Adding the conditional means
```r
airquality %>%
group_by(Month) %>%
summarise(
Ozone_mean =
mean(Ozone, na.rm = T)
) ->
airquality_by_month
ggplot(airquality) +
aes(x = Ozone) +
geom_histogram() +
facet_grid(rows = vars(Month)) +
geom_vline(data = airquality_by_month,
aes(xintercept =
Ozone_mean))
```
???
Further, for the case of adding a vertical line at the mean for different subsets of the data, a different approach is required. This enterprise may take instructor/analyst/student on an even larger detour -- possibly googling, and maybe landing on the following stack overflow page where 11,000 analytics souls (some repeats to be sure) have landed:
<https://stackoverflow.com/questions/1644661/add-a-vertical-line-with-different-intercept-for-each-panel-in-ggplot2>
The solutions to this problem involve data manipulation *prior* to plotting the data. The solution disrupts the forward flow ggplot build. One must take a pause, which may involve toggling back and forth between stack overflow solutions, disrupting momentum you are working on to talk about the pooled mean and the conditional mean.
Adding the conditional means
airquality %>%
group_by(Month) %>%
summarise(
Ozone_mean =
mean(Ozone, na.rm = T)
) ->
airquality_by_month
ggplot(airquality) +
aes(x = Ozone) +
geom_histogram() +
facet_grid(rows = vars(Month)) +
geom_vline(data = airquality_by_month,
aes(xintercept =
Ozone_mean))
???
Further, for the case of adding a vertical line at the mean for different subsets of the data, a different approach is required. This enterprise may take instructor/analyst/student on an even larger detour -- possibly googling, and maybe landing on the following stack overflow page where 11,000 analytics souls (some repeats to be sure) have landed:
The solutions to this problem involve data manipulation prior to plotting the data. The solution disrupts the forward flow ggplot build. One must take a pause, which may involve toggling back and forth between stack overflow solutions, disrupting momentum you are working on to talk about the pooled mean and the conditional mean.
class: inverse
From source .Rmd:
`r chunk_reveal("cond_means_hard", title = "#### Conditional means (may require a trip to stackoverflow!)")`
r chunk_reveal("cond_means_hard", title = "#### Conditional means (may require a trip to stackoverflow!)")
class: inverse
From source .Rmd:
# "...mastery of advanced programming skills should not be allowed to crowd out data analysis skills or statistical thinking." -- GAISE report
--
"...mastery of advanced programming skills should not be allowed to crowd out data analysis skills or statistical thinking." -- GAISE report
--
class: inverse
From source .Rmd:
class: inverse, center, bottom
background-image: url(https://images.unsplash.com/photo-1482685945432-29a7abf2f466?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=1532&q=80)
background-size: cover
# But other statistical geometries in ggplot2 flow!
--
... ggplot is known for being able to 'speak your plot into existence'.
class: inverse, center, bottom
background-image: url(https://images.unsplash.com/photo-1482685945432-29a7abf2f466?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=1532&q=80)
background-size: cover
But other statistical geometries in ggplot2 flow!
--
... ggplot is known for being able to 'speak your plot into existence'.
class: inverse
From source .Rmd:
# Declarative
--
## "Let there be a a box plot..."
--
## and there was a boxplot.
--
## "Let there be LOESS smoothing..."
--
## and there was loess smoothing
Declarative
--
"Let there be a a box plot..."
--
and there was a boxplot.
--
"Let there be LOESS smoothing..."
--
and there was loess smoothing
class: inverse
From source .Rmd:
`r chunk_reveal("base_gg_flow")`
```r
ggplot(cars) +
aes(x = speed) +
aes(y = dist) +
geom_point() +
geom_smooth() ->
g1
library(palmerpenguins)
ggplot(penguins) +
aes(x = species) +
aes(y = bill_length_mm) +
geom_point() +
geom_boxplot()
```
r chunk_reveal("base_gg_flow")
ggplot(cars) +
aes(x = speed) +
aes(y = dist) +
geom_point() +
geom_smooth() ->
g1
library(palmerpenguins)
ggplot(penguins) +
aes(x = species) +
aes(y = bill_length_mm) +
geom_point() +
geom_boxplot()
class: inverse
From source .Rmd:
# But, infrastructure to *extend* ggplot2 ...
--
--
But, infrastructure to extend ggplot2 ...
--
--
class: inverse
From source .Rmd:
## Build new statistical geometries that deliver the *flow* experience!
--
- # for students
--
- # for instructors
--
- # for analysts
Build new statistical geometries that deliver the flow experience!
--
- # for students
--
- # for instructors
-
for analysts
class: inverse
From source .Rmd:
class: center, inverse, middle
# Introducing {ggxmean}
--
https://github.com/EvaMaeRey/ggxmean
class: center, inverse, middle
Introducing {ggxmean}
--
https://github.com/EvaMaeRey/ggxmean
class: inverse
From source .Rmd:
`r chunk_reveal("geom_x_mean", title = "geom_x_mean()")`
```r
library(ggplot2)
library(ggxmean)
ggplot(airquality) +
aes(x = Ozone) +
geom_histogram() +
geom_x_mean() +
facet_grid(rows =
vars(Month))
```
r chunk_reveal("geom_x_mean", title = "geom_x_mean()")
library(ggplot2)
library(ggxmean)
ggplot(airquality) +
aes(x = Ozone) +
geom_histogram() +
geom_x_mean() +
facet_grid(rows =
vars(Month))
class: inverse
From source .Rmd:
`r chunk_reveal("univariate", title = "### Other univariate markers") `
```r
ggplot(airquality) +
aes(x = Ozone) +
geom_histogram() +
geom_x_median() +
geom_x_quantile(
quantile = .25,
linetype = "dashed"
) +
geom_x_percentile(
percentile = 100,
linetype = "dotted"
)
```
r chunk_reveal("univariate", title = "### Other univariate markers")
ggplot(airquality) +
aes(x = Ozone) +
geom_histogram() +
geom_x_median() +
geom_x_quantile(
quantile = .25,
linetype = "dashed"
) +
geom_x_percentile(
percentile = 100,
linetype = "dotted"
)
class: inverse
From source .Rmd:
`r chunk_reveal("lm_sequence", title = "### Relevant to OLS", widths = c(1,1)) `
```r
ggplot(data = cars) +
aes(speed, dist) +
geom_point() + #BREAK
ggxmean::geom_lm() + #BREAK
ggxmean::geom_lm_fitted(color = "blue",
size = 3) + #BREAK
ggxmean::geom_lm_residuals() + #BREAK
ggxmean::geom_lm_conf_int() + #BREAK
ggxmean::geom_lm_intercept(color = "red",
size = 5) + #BREAK
ggxmean::geom_lm_formula(size = 10) #BREAK
```
r chunk_reveal("lm_sequence", title = "### Relevant to OLS", widths = c(1,1))
ggplot(data = cars) +
aes(speed, dist) +
geom_point() + #BREAK
ggxmean::geom_lm() + #BREAK
ggxmean::geom_lm_fitted(color = "blue",
size = 3) + #BREAK
ggxmean::geom_lm_residuals() + #BREAK
ggxmean::geom_lm_conf_int() + #BREAK
ggxmean::geom_lm_intercept(color = "red",
size = 5) + #BREAK
ggxmean::geom_lm_formula(size = 10) #BREAK
class: inverse
From source .Rmd:
`r chunk_reveal("geom_normal_dist", title = "### fitting distributions", widths = c(1,1)) `
```r
ggplot(data = faithful) +
aes(waiting) +
geom_rug() +
geom_histogram(
aes(y = ..density..)) + #BREAK
geom_normal_dist(fill = "blue") + #BREAK
facet_grid(rows =
vars(eruptions > 3)) #BREAK
```
r chunk_reveal("geom_normal_dist", title = "### fitting distributions", widths = c(1,1))
ggplot(data = faithful) +
aes(waiting) +
geom_rug() +
geom_histogram(
aes(y = ..density..)) + #BREAK
geom_normal_dist(fill = "blue") + #BREAK
facet_grid(rows =
vars(eruptions > 3)) #BREAK
class: inverse
From source .Rmd:
class: inverse
From source .Rmd:
class: inverse, center, bottom
background-image: url(https://images.unsplash.com/photo-1482685945432-29a7abf2f466?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=1532&q=80)
background-size: cover
# Thank you!
--
## {ggxmean} development package: https://evamaerey.github.io/ggxmean/
--
# Feedback welcome!
class: inverse, center, bottom
background-image: url(https://images.unsplash.com/photo-1482685945432-29a7abf2f466?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=1532&q=80)
background-size: cover
Thank you!
--
{ggxmean} development package: https://evamaerey.github.io/ggxmean/
--
Feedback welcome!
EvaMaeRey/ggxmean documentation built on April 10, 2024, 6:32 p.m.
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class: inverse
From source .Rmd:
title: "Extending ggplot2 statistical geometries"
subtitle: "MAA Metro New York"
author: "Gina Reynolds"
date: 'Saturday May 1, 2021, 11AM'
output:
xaringan::moon_reader:
lib_dir: libs
seal: false
nature:
ratio: 16:10
highlightStyle: github
highlightLines: true
countIncrementalSlides: false
beforeInit: "https://platform.twitter.com/widgets.js"
class: inverse
From source .Rmd:
class: inverse background-image: url(https://images.unsplash.com/photo-1482685945432-29a7abf2f466?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=1532&q=80) background-size: cover # Extending ggplot2 statistical geometries ## MAA Metro New York ### Dr. Evangeline 'Gina' Reynolds ### Saturday May 1, 2021, 11AM #### Photo Credit: Mike Lewis HeadSmart Media ```r knitr::opts_chunk$set(echo = T, comment = "", message = F, warning = F, cache = T, fig.retina = 3) library(tidyverse) library(flipbookr) library(xaringanthemer) xaringanthemer::mono_light( base_color = "#02075D", # header_font_google = google_font("Josefin Sans"), # text_font_google = google_font("Montserrat", "200", "200i"), # code_font_google = google_font("Droid Mono"), text_font_size = ".85cm", code_font_size = ".15cm") theme_set(theme_gray(base_size = 20)) ```
class: inverse background-image: url(https://images.unsplash.com/photo-1482685945432-29a7abf2f466?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=1532&q=80) background-size: cover
Extending ggplot2 statistical geometries
MAA Metro New York
Dr. Evangeline 'Gina' Reynolds
Saturday May 1, 2021, 11AM
Photo Credit: Mike Lewis HeadSmart Media
knitr::opts_chunk$set(echo = T, comment = "", message = F, warning = F, cache = T, fig.retina = 3) library(tidyverse) library(flipbookr) library(xaringanthemer) xaringanthemer::mono_light( base_color = "#02075D", # header_font_google = google_font("Josefin Sans"), # text_font_google = google_font("Montserrat", "200", "200i"), # code_font_google = google_font("Droid Mono"), text_font_size = ".85cm", code_font_size = ".15cm") theme_set(theme_gray(base_size = 20))
class: inverse
From source .Rmd:
class: inverse
From source .Rmd:
class: inverse, center middle # If visualization packages used this approach they would be able to "draw *every* statistical graphic". ??? The grammar of graphics framework, proposed by Leland Wilkinson in 1999, that identified 'seven orthogonal components' in the creation of data visualizations.\ Wilkinson asserted that if data visualization packages were created using a separation of concerns approach -- dividing decision making surrounding these components --- the packages would be able to "draw every statistical graphic". The grammar of graphic principles were incredibly powerful and gave rise to a number of visualization platforms including Tableau, vega-lite, and ggplot2.
class: inverse, center middle
If visualization packages used this approach they would be able to "draw every statistical graphic".
???
The grammar of graphics framework, proposed by Leland Wilkinson in 1999, that identified 'seven orthogonal components' in the creation of data visualizations.\ Wilkinson asserted that if data visualization packages were created using a separation of concerns approach -- dividing decision making surrounding these components --- the packages would be able to "draw every statistical graphic". The grammar of graphic principles were incredibly powerful and gave rise to a number of visualization platforms including Tableau, vega-lite, and ggplot2.
class: inverse
From source .Rmd:
# Creators enjoy freedom, fine control -- # Grammar of graphics inspired: -- #- Tableau #- vega-lite #- ggplot2
Creators enjoy freedom, fine control
--
Grammar of graphics inspired:
--
- Tableau
- vega-lite
- ggplot2
class: inverse
From source .Rmd:
### Use ggplot2 in the classroom because R, and ... --  > The transferrable skills from ggplot2 are not the idiosyncracies of plotting syntax, but a **powerful way of thinking about visualisation**, as a way of mapping between variables and the visual properties of geometric objects that you can perceive. ??? Statistical educators that introduce students to one of these tools arguably are doing more than constructing one-off plots to discuss statistical principles with students: they are introducing students to 'a powerful way of thinking about data visualization'. Statistical educators often use ggplot2 as their grammar-of-graphics-based data visualization tool as students can learn it along side the rich statistical ecosystem of the R programming language. The R programming language thus may serve as a one-stop-shop for statistical tooling; with recent developments in packages and IDEs writing code is becoming more accessible and welcoming to newcomers.
Use ggplot2 in the classroom because R, and ...
--
The transferrable skills from ggplot2 are not the idiosyncracies of plotting syntax, but a powerful way of thinking about visualisation, as a way of mapping between variables and the visual properties of geometric objects that you can perceive.
???
Statistical educators that introduce students to one of these tools arguably are doing more than constructing one-off plots to discuss statistical principles with students: they are introducing students to 'a powerful way of thinking about data visualization'.
Statistical educators often use ggplot2 as their grammar-of-graphics-based data visualization tool as students can learn it along side the rich statistical ecosystem of the R programming language. The R programming language thus may serve as a one-stop-shop for statistical tooling; with recent developments in packages and IDEs writing code is becoming more accessible and welcoming to newcomers.
class: inverse
From source .Rmd:
??? Still, using ggplot2 for statistical education can be a challenge at times. When it is used to discuss statistical concepts, sometimes it feels as though getting something done with the plotting library derails the focus on discussion of statistical concepts and material.
???
Still, using ggplot2 for statistical education can be a challenge at times. When it is used to discuss statistical concepts, sometimes it feels as though getting something done with the plotting library derails the focus on discussion of statistical concepts and material.
class: inverse
From source .Rmd:
class: inverse, center, middle # The problem -- ## sometimes ggplot2 is hard
class: inverse, center, middle
The problem
--
sometimes ggplot2 is hard
class: inverse
From source .Rmd:
## The status quo: adding the mean ```r library(ggplot2) ggplot(airquality) + aes(x = Ozone) + geom_histogram() + ggxmean::geom_x_mean() ``` ??? Consider for example, a the seemingly simple enterprise of adding a vertical line at the mean of x, perhaps atop a histogram or density plot.
The status quo: adding the mean
library(ggplot2) ggplot(airquality) + aes(x = Ozone) + geom_histogram() + ggxmean::geom_x_mean()
???
Consider for example, a the seemingly simple enterprise of adding a vertical line at the mean of x, perhaps atop a histogram or density plot.
class: inverse
From source .Rmd:
`r chunk_reveal("basic", title = "### Adding the mean at x w/ base ggplot2")`
```r
ggplot(data = airquality) +
aes(x = Ozone) +
geom_histogram() +
geom_vline(
xintercept =
mean(airquality$Ozone,
na.rm = T)
)
```
???
Creating this plot requires greater focus on ggplot2 *syntax*, likely detracting from discussion of *the mean* that statistical instructors desire.
It may require a discussion about dollar sign syntax and how geom_vline is actually a special geom -- an annotation -- rather than being mapped to the data. None of this is relevant to the point you as an instructor aim to make: maybe that the the mean is the balancing point of the data or maybe a comment about skewness.
r chunk_reveal("basic", title = "### Adding the mean at x w/ base ggplot2")
ggplot(data = airquality) + aes(x = Ozone) + geom_histogram() + geom_vline( xintercept = mean(airquality$Ozone, na.rm = T) )
???
Creating this plot requires greater focus on ggplot2 syntax, likely detracting from discussion of the mean that statistical instructors desire.
It may require a discussion about dollar sign syntax and how geom_vline is actually a special geom -- an annotation -- rather than being mapped to the data. None of this is relevant to the point you as an instructor aim to make: maybe that the the mean is the balancing point of the data or maybe a comment about skewness.
class: inverse
From source .Rmd:
### Adding the conditional means
```r
airquality %>%
group_by(Month) %>%
summarise(
Ozone_mean =
mean(Ozone, na.rm = T)
) ->
airquality_by_month
ggplot(airquality) +
aes(x = Ozone) +
geom_histogram() +
facet_grid(rows = vars(Month)) +
geom_vline(data = airquality_by_month,
aes(xintercept =
Ozone_mean))
```
???
Further, for the case of adding a vertical line at the mean for different subsets of the data, a different approach is required. This enterprise may take instructor/analyst/student on an even larger detour -- possibly googling, and maybe landing on the following stack overflow page where 11,000 analytics souls (some repeats to be sure) have landed:
<https://stackoverflow.com/questions/1644661/add-a-vertical-line-with-different-intercept-for-each-panel-in-ggplot2>
The solutions to this problem involve data manipulation *prior* to plotting the data. The solution disrupts the forward flow ggplot build. One must take a pause, which may involve toggling back and forth between stack overflow solutions, disrupting momentum you are working on to talk about the pooled mean and the conditional mean.
Adding the conditional means
airquality %>% group_by(Month) %>% summarise( Ozone_mean = mean(Ozone, na.rm = T) ) -> airquality_by_month ggplot(airquality) + aes(x = Ozone) + geom_histogram() + facet_grid(rows = vars(Month)) + geom_vline(data = airquality_by_month, aes(xintercept = Ozone_mean))
???
Further, for the case of adding a vertical line at the mean for different subsets of the data, a different approach is required. This enterprise may take instructor/analyst/student on an even larger detour -- possibly googling, and maybe landing on the following stack overflow page where 11,000 analytics souls (some repeats to be sure) have landed:
The solutions to this problem involve data manipulation prior to plotting the data. The solution disrupts the forward flow ggplot build. One must take a pause, which may involve toggling back and forth between stack overflow solutions, disrupting momentum you are working on to talk about the pooled mean and the conditional mean.
class: inverse
From source .Rmd:
`r chunk_reveal("cond_means_hard", title = "#### Conditional means (may require a trip to stackoverflow!)")`
r chunk_reveal("cond_means_hard", title = "#### Conditional means (may require a trip to stackoverflow!)")
class: inverse
From source .Rmd:
# "...mastery of advanced programming skills should not be allowed to crowd out data analysis skills or statistical thinking." -- GAISE report -- 
"...mastery of advanced programming skills should not be allowed to crowd out data analysis skills or statistical thinking." -- GAISE report
--
class: inverse
From source .Rmd:
class: inverse, center, bottom background-image: url(https://images.unsplash.com/photo-1482685945432-29a7abf2f466?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=1532&q=80) background-size: cover # But other statistical geometries in ggplot2 flow! -- ... ggplot is known for being able to 'speak your plot into existence'.
class: inverse, center, bottom background-image: url(https://images.unsplash.com/photo-1482685945432-29a7abf2f466?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=1532&q=80) background-size: cover
But other statistical geometries in ggplot2 flow!
-- ... ggplot is known for being able to 'speak your plot into existence'.
class: inverse
From source .Rmd:
# Declarative -- ## "Let there be a a box plot..." -- ## and there was a boxplot. -- ## "Let there be LOESS smoothing..." -- ## and there was loess smoothing
Declarative
--
"Let there be a a box plot..."
--
and there was a boxplot.
--
"Let there be LOESS smoothing..."
--
and there was loess smoothing
class: inverse
From source .Rmd:
`r chunk_reveal("base_gg_flow")`
```r
ggplot(cars) +
aes(x = speed) +
aes(y = dist) +
geom_point() +
geom_smooth() ->
g1
library(palmerpenguins)
ggplot(penguins) +
aes(x = species) +
aes(y = bill_length_mm) +
geom_point() +
geom_boxplot()
```
r chunk_reveal("base_gg_flow")
ggplot(cars) + aes(x = speed) + aes(y = dist) + geom_point() + geom_smooth() -> g1 library(palmerpenguins) ggplot(penguins) + aes(x = species) + aes(y = bill_length_mm) + geom_point() + geom_boxplot()
class: inverse
From source .Rmd:
# But, infrastructure to *extend* ggplot2 ... -- -- 
But, infrastructure to extend ggplot2 ...
--
--
class: inverse
From source .Rmd:
## Build new statistical geometries that deliver the *flow* experience! -- - # for students -- - # for instructors -- - # for analysts
Build new statistical geometries that deliver the flow experience!
-- - # for students --
- # for instructors
-
for analysts
class: inverse
From source .Rmd:
class: center, inverse, middle # Introducing {ggxmean} -- https://github.com/EvaMaeRey/ggxmean
class: center, inverse, middle
Introducing {ggxmean}
--
https://github.com/EvaMaeRey/ggxmean
class: inverse
From source .Rmd:
`r chunk_reveal("geom_x_mean", title = "geom_x_mean()")`
```r
library(ggplot2)
library(ggxmean)
ggplot(airquality) +
aes(x = Ozone) +
geom_histogram() +
geom_x_mean() +
facet_grid(rows =
vars(Month))
```
r chunk_reveal("geom_x_mean", title = "geom_x_mean()")
library(ggplot2) library(ggxmean) ggplot(airquality) + aes(x = Ozone) + geom_histogram() + geom_x_mean() + facet_grid(rows = vars(Month))
class: inverse
From source .Rmd:
`r chunk_reveal("univariate", title = "### Other univariate markers") `
```r
ggplot(airquality) +
aes(x = Ozone) +
geom_histogram() +
geom_x_median() +
geom_x_quantile(
quantile = .25,
linetype = "dashed"
) +
geom_x_percentile(
percentile = 100,
linetype = "dotted"
)
```
r chunk_reveal("univariate", title = "### Other univariate markers")
ggplot(airquality) + aes(x = Ozone) + geom_histogram() + geom_x_median() + geom_x_quantile( quantile = .25, linetype = "dashed" ) + geom_x_percentile( percentile = 100, linetype = "dotted" )
class: inverse
From source .Rmd:
`r chunk_reveal("lm_sequence", title = "### Relevant to OLS", widths = c(1,1)) `
```r
ggplot(data = cars) +
aes(speed, dist) +
geom_point() + #BREAK
ggxmean::geom_lm() + #BREAK
ggxmean::geom_lm_fitted(color = "blue",
size = 3) + #BREAK
ggxmean::geom_lm_residuals() + #BREAK
ggxmean::geom_lm_conf_int() + #BREAK
ggxmean::geom_lm_intercept(color = "red",
size = 5) + #BREAK
ggxmean::geom_lm_formula(size = 10) #BREAK
```
r chunk_reveal("lm_sequence", title = "### Relevant to OLS", widths = c(1,1))
ggplot(data = cars) + aes(speed, dist) + geom_point() + #BREAK ggxmean::geom_lm() + #BREAK ggxmean::geom_lm_fitted(color = "blue", size = 3) + #BREAK ggxmean::geom_lm_residuals() + #BREAK ggxmean::geom_lm_conf_int() + #BREAK ggxmean::geom_lm_intercept(color = "red", size = 5) + #BREAK ggxmean::geom_lm_formula(size = 10) #BREAK
class: inverse
From source .Rmd:
`r chunk_reveal("geom_normal_dist", title = "### fitting distributions", widths = c(1,1)) `
```r
ggplot(data = faithful) +
aes(waiting) +
geom_rug() +
geom_histogram(
aes(y = ..density..)) + #BREAK
geom_normal_dist(fill = "blue") + #BREAK
facet_grid(rows =
vars(eruptions > 3)) #BREAK
```
r chunk_reveal("geom_normal_dist", title = "### fitting distributions", widths = c(1,1))
ggplot(data = faithful) + aes(waiting) + geom_rug() + geom_histogram( aes(y = ..density..)) + #BREAK geom_normal_dist(fill = "blue") + #BREAK facet_grid(rows = vars(eruptions > 3)) #BREAK
class: inverse
From source .Rmd:
class: inverse
From source .Rmd:
class: inverse, center, bottom background-image: url(https://images.unsplash.com/photo-1482685945432-29a7abf2f466?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=1532&q=80) background-size: cover # Thank you! -- ## {ggxmean} development package: https://evamaerey.github.io/ggxmean/ -- # Feedback welcome!
class: inverse, center, bottom background-image: url(https://images.unsplash.com/photo-1482685945432-29a7abf2f466?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=1532&q=80) background-size: cover
Thank you!
--
{ggxmean} development package: https://evamaerey.github.io/ggxmean/
--
Feedback welcome!
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