Density section almost OK

135-stacked-density-graph.Rmd

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+---
+title: "Density chart with several groups"
+descriptionMeta: "How to build a density chart when several groups are available: multi density plot, stacked density plot and use of small multiples examples with ggplot2 code snippets."
+descriptionTop: "A [density plot](https://www.data-to-viz.com/graph/density.html) is a representation of the distribution of a numeric variable. Comparing the distribution of several variables with density charts is possible. Here are a few examples with their [ggplot2](ggplot2.html) implementation."
+sectionText: "Density Section"
+sectionLink: "density.html"
+DataToVizText: "Comparing distributions"
+DataToVizLink: "data-to-viz.com/story/OneNumOneCatSeveralObs.html"
+url: "density_mirror_ggplot2.html"
+output:
+  html_document:
+      self_contained: false    
+      mathjax: default
+      lib_dir: libs
+      template: template_rgg.html
+      css: style.css
+      toc: TRUE
+      toc_float: TRUE
+      toc_depth: 2
+      df_print: "paged"
+
+---
+
+
+```{r global options, include = FALSE}
+knitr::opts_chunk$set( warning=FALSE, message=FALSE)
+```
+
+
+<div class="container">
+
+
+# Multi density chart
+***
+
+<div class = "row">
+
+<div class = "col-md-5  col-sm-12 align-self-center">
+
+A multi density chart is a [density chart](density.html) where several groups are represented. It allows to compare their distribution. The issue with this kind of chart is that it gets easily <u>cluttered</u>: groups overlap each other and the figure gets unreadable.
+
+An easy workaround is to use <u>transparency</u>. However, it won't solve the issue completely and is is often better to consider the examples suggested further in this document.
+
+</div>
+
+<div class = "col-md-7  col-sm-12 align-self-center">
+
+```{r}
+# Libraries
+library(ggplot2)
+library(hrbrthemes)
+library(dplyr)
+library(tidyr)
+library(viridis)
+
+# The diamonds dataset is natively available with R.
+
+# Without transparency (left)
+p1 <- ggplot(data=diamonds, aes(x=price, group=cut, fill=cut)) + 
+    geom_density(adjust=1.5) +
+    theme_ipsum()
+#p1
+
+# With transparency (right)
+p2 <- ggplot(data=diamonds, aes(x=price, group=cut, fill=cut)) + 
+    geom_density(adjust=1.5, alpha=.4) +
+    theme_ipsum()
+#p2
+```
+
+</div>
+</div>
+
+```{r, out.width=c('50%', '50%'), fig.show='hold', echo=FALSE}
+p1
+p2
+```
+
+<br><br>
+Here is an example with [another dataset](https://www.data-to-viz.com/story/OneNumOneCatSeveralObs.html) where it works much better. Groups have very distinct distribution, it is easy to spot them even if on the same chart. Note that it is much better to add group name next to their distribution instead of having a legend beside the chart.
+
+```{r, out.width="80%", fig.align="center"}
+# Load dataset from github
+data <- read.table("https://raw.githubusercontent.com/zonination/perceptions/master/probly.csv", header=TRUE, sep=",")
+data <- data %>% 
+  gather(key="text", value="value") %>%
+  mutate(text = gsub("\\.", " ",text)) %>%
+  mutate(value = round(as.numeric(value),0))
+
+# A dataframe for annotations
+annot <- data.frame(
+  text = c("Almost No Chance", "About Even", "Probable", "Almost Certainly"),
+  x = c(5, 53, 65, 79),
+  y = c(0.15, 0.4, 0.06, 0.1)
+)
+
+# Plot
+data %>%
+  filter(text %in% c("Almost No Chance", "About Even", "Probable", "Almost Certainly")) %>%
+  ggplot( aes(x=value, color=text, fill=text)) +
+    geom_density(alpha=0.6) +
+    scale_fill_viridis(discrete=TRUE) +
+    scale_color_viridis(discrete=TRUE) +
+    geom_text( data=annot, aes(x=x, y=y, label=text, color=text), hjust=0, size=4.5) +
+    theme_ipsum() +
+    theme(
+      legend.position="none"
+    ) +
+    ylab("") +
+    xlab("Assigned Probability (%)")
+```
+
+
+
+
+
+
+
+
+# Small Multiple with `facet_wrap()` {#smallMulti}
+***
+Using small multiple is often the best option in my opinion. Distribution of each group gets easy to read, and comparing groups is still possible if they share the same X axis boundaries.
+```{r, out.width="80%", fig.align="center"}
+# Using Small multiple
+ggplot(data=diamonds, aes(x=price, group=cut, fill=cut)) + 
+    geom_density(adjust=1.5) +
+    theme_ipsum() +
+    facet_wrap(~cut) +
+    theme(
+      legend.position="none",
+      panel.spacing = unit(0.1, "lines"),
+      axis.ticks.x=element_blank()
+    )
+
+```
+
+
+
+# Stacked density chart {#stacked}
+***
+
+<div class = "row">
+
+<div class = "col-md-5  col-sm-12 align-self-center">
+
+Another solution is to <u>stack</u> the groups. This allows to see what group is the most frequent for a given value, but it makes it hard to understand the distribution of a group that is not on the bottom of the chart.
+
+Visit [data to viz](https://www.data-to-viz.com/caveat/stacking.html) for a complete explanation on this matter.
+
+
+```{r}
+# Stacked density plot:
+p <- ggplot(data=diamonds, aes(x=price, group=cut, fill=cut)) + 
+    geom_density(adjust=1.5, position="fill") +
+    theme_ipsum()
+#p
+```
+</div>
+
+<div class = "col-md-7  col-sm-12 align-self-center">
+
+```{r, echo=FALSE, fig.align='center', out.width="100%"}
+p
+```
+
+</div>
+</div>
+
+
+
+
+
+
+
+
+
+
+<!-- Close container -->
+</div>
+
+
+
+
+```{r, echo=FALSE}
+htmltools::includeHTML("htmlChunkRelatedDistribution.html")
+```
+
+
+
+
+
+
+