Slides and Tutorials
| R (Static Viz) | Radiant Tutorial | Datasets |
“Keep away from people who try to belittle your ambitions. Small people always do that, but the really great make you feel that you, too, can become great.”
— Mark Twain
Plot Theme
Show the Code
# https://stackoverflow.com/questions/74491138/ggplot-custom-fonts-not-working-in-quarto
# Chunk options
knitr::opts_chunk$set(
fig.width = 7,
fig.asp = 0.618, # Golden Ratio
# out.width = "80%",
fig.align = "center"
)
### Ggplot Theme
### https://rpubs.com/mclaire19/ggplot2-custom-themes
theme_custom <- function() {
font <- "Roboto Condensed" # assign font family up front
theme_classic(base_size = 14) %+replace% # replace elements we want to change
theme(
panel.grid.minor = element_blank(), # strip minor gridlines
text = element_text(family = font),
# text elements
plot.title = element_text( # title
family = font, # set font family
# size = 20, #set font size
face = "bold", # bold typeface
hjust = 0, # left align
# vjust = 2 #raise slightly
margin = margin(0, 0, 10, 0)
), plot.title.position = "plot",
plot.subtitle = element_text( # subtitle
family = font, # font family
# size = 14, #font size
hjust = 0,
margin = margin(2, 0, 5, 0)
),
plot.caption = element_text( # caption
family = font, # font family
size = 8, # font size
hjust = 1
), # right align
axis.title = element_text( # axis titles
family = font, # font family
size = 10 # font size
),
axis.text = element_text( # axis text
family = font, # axis family
size = 8
) # font size
)
}
# Set graph theme
theme_set(new = theme_custom())
#
| Variable #1 | Variable #2 | Chart Names | Chart Shape | |
|---|---|---|---|---|
| Quant | (Qual) | Violin Plot |
| No | Pronoun | Answer | Variable/Scale | Example | What Operations? |
|---|---|---|---|---|---|
| 1 | How Many / Much / Heavy? Few? Seldom? Often? When? | Quantities, with Scale and a Zero Value.Differences and Ratios /Products are meaningful. | Quantitative/Ratio | Length,Height,Temperature in Kelvin,Activity,Dose Amount,Reaction Rate,Flow Rate,Concentration,Pulse,Survival Rate | Correlation |
Which is the plots above is more evocative of the underlying data? The one which looks like a combo box-plot + density is probably giving us a greater sense of the spread of the data than the good old box plot.
Often one needs to view multiple densities at the same time. Ridge plots of course give us one option, where we get densities of a Quant variable split by a Qual variable. Another option is to generate a density plot facetted into small multiples using a Qual variable.
Yet another plot that allows comparison of multiple densities side by side is a violin plot. The violin plot combines the aspects of a boxplot(ranking of values, median, quantiles…) with a superimposed density plot. This allows us to look at medians, means, densities, and quantiles of a Quant variable with respect to another Qual variable. Let us see what this looks like!
In Figure 1, the plots show (very artificial!) distributions of a single Quant variable across levels of another Qual variable. At each level of the Qual variable along the X-axis, we have a violin plot showing the density.
diamonds dataset
## Set graph theme
theme_set(new = theme_custom())
##
diamonds %>%
gf_violin(price ~ cut,
fill = ~cut,
colour = ~cut,
alpha = 0.3, draw_quantiles = c(0, .25, .50, .75)
) %>%
gf_facet_wrap(vars(clarity)) %>%
gf_labs(title = "Plot D: Price by Cut facetted by Clarity") %>%
gf_theme(theme(axis.text.x = element_text(angle = 45, hjust = 1)))
## Set graph theme
theme_set(new = theme_custom())
##
diamonds %>% ggplot() +
geom_violin(aes(y = price, x = ""),
draw_quantiles = c(0, .25, .50, .75)
) + # note: y, not x
labs(title = "Plot A: violin for Diamond Prices")
###
diamonds %>% ggplot() +
geom_violin(aes(cut, price),
draw_quantiles = c(0, .25, .50, .75)
) +
labs(title = "Plot B: Price by Cut")
###
diamonds %>% ggplot() +
geom_violin(
aes(cut, price,
color = cut, fill = cut
),
draw_quantiles = c(0, .25, .50, .75),
alpha = 0.4
) +
labs(title = "Plot C: Price by Cut")
###
diamonds %>% ggplot() +
geom_violin(
aes(cut,
price,
color = cut, fill = cut
),
draw_quantiles = c(0, .25, .50, .75),
alpha = 0.4
) +
facet_wrap(vars(clarity)) +
labs(title = "Plot D: Price by Cut facetted by Clarity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
diamond Violin Plots
The distribution for price is clearly long-tailed (skewed). The distributions also vary considerably based on both cut and clarity. These Qual variables clearly have a large effect on the prices of individual diamonds.
- Box plots give us an idea of
medians,IQRranges, andoutliers. The shape of the density is not apparent from the box. - Densities give us shapes of distributions, but do not provide visual indication of other metrics like
meansormedians( at least not without some effort) - Violins help us do both!
- Violins can also be cut in half (since they are symmetric, like Buddhist Prayer Wheels), then placed horizontally, and combined with both a
boxplotand adot-plotto give usraincloud plotsthat look like this. (Yes, there is code over there, which you can reuse.)
- Histograms, Frequency Distributions, and Box Plots are used for Quantitative data variables
- Histograms “dwell upon” counts, ranges, means and standard deviations
- Frequency Density plots “dwell upon” probabilities and densities
- Box Plots “dwell upon” medians and Quartiles
- Qualitative data variables can be plotted as counts, using Bar Charts, or using Heat Maps
- Violin Plots help us to visualize multiple distributions at the same time, as when we split a Quant variable wrt to the levels of a Qual variable.
- Ridge Plots are density plots used for describing one Quant and one Qual variable (by inherent splitting)
- We can split all these plots on the basis of another Qualitative variable.(Ridge Plots are already split)
- Long tailed distributions need care in visualization and in inference making!
- Click on the Dataset Icon above, and unzip that archive. Try to make distribution plots with each of the three tools.
- A dataset from calmcode.io https://calmcode.io/datasets.html
inspect the dataset in each case and develop a set of Questions, that can be answered by appropriate stat measures, or by using a chart to show the distribution.
Winston Chang (2024). R Graphics Cookbook. https://r-graphics.org
See the scrolly animation for a histogram at this website: Exploring Histograms, an essay by Aran Lunzer and Amelia McNamara https://tinlizzie.org/histograms/?s=09
Minimal R using
mosaic.https://cran.r-project.org/web/packages/mosaic/vignettes/MinimalRgg.pdfSebastian Sauer, Plotting multiple plots using purrr::map and ggplot
Citation
@online{v.2022,
author = {V., Arvind},
title = {\textless Iconify-Icon
Icon=“material-Symbols:light-Group-Rounded” Width=“1.2em”
Height=“1.2em”\textgreater\textless/Iconify-Icon\textgreater{}
{Groups} and {Densities}},
date = {2022-11-15},
url = {https://av-quarto.netlify.app/content/courses/Analytics/Descriptive/Modules/28-Violins/},
langid = {en},
abstract = {Quant and Qual Variable Graphs and their Siblings}
}