plotly · kojisposts · Aug 15, 2019 · Aug 2, 2019 · Aug 5, 2019 · Aug 6, 2019
diff --git a/.gitignore b/.gitignore
@@ -23,4 +23,4 @@ _posts/python/fundamentals/static-image/images
 _config_personal.yml
 _posts/python/html
 .Rproj.user
-documentation.Rproj
+documentation.Rproj
diff --git a/_posts/ggplot2/2016-11-29-geom_boxplot.Rmd b/_posts/ggplot2/2016-11-29-geom_boxplot.Rmd
@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: statistical
-order: 1
+order: 2
 output:
   html_document:
     keep_md: true

diff --git a/_posts/ggplot2/2016-11-29-geom_boxplot.md b/_posts/ggplot2/2016-11-29-geom_boxplot.md
@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: statistical
-order: 1
+order: 2
 output:
   html_document:
     keep_md: true

diff --git a/_posts/ggplot2/2016-11-29-geom_ribbon.Rmd b/_posts/ggplot2/2016-11-29-geom_ribbon.Rmd
@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: statistical
-order: 4
+order: 5
 output:
   html_document:
     keep_md: true

diff --git a/_posts/ggplot2/2016-11-29-geom_ribbon.md b/_posts/ggplot2/2016-11-29-geom_ribbon.md
@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: statistical
-order: 4
+order: 5
 output:
   html_document:
     keep_md: true

diff --git a/_posts/ggplot2/2016-11-29-geom_smooth.Rmd b/_posts/ggplot2/2016-11-29-geom_smooth.Rmd
@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: statistical
-order: 5
+order: 6
 output:
   html_document:
     keep_md: true

diff --git a/_posts/ggplot2/2016-11-29-geom_smooth.md b/_posts/ggplot2/2016-11-29-geom_smooth.md
@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: statistical
-order: 5
+order: 6
 output:
   html_document:
     keep_md: true

diff --git a/_posts/ggplot2/2016-11-29-stat_smooth.Rmd b/_posts/ggplot2/2016-11-29-stat_smooth.Rmd
@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: statistical
-order: 5
+order: 7
 output:
   html_document:
     keep_md: true

diff --git a/_posts/ggplot2/2016-11-29-stat_smooth.md b/_posts/ggplot2/2016-11-29-stat_smooth.md
@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: statistical
-order: 5
+order: 7
 output:
   html_document:
     keep_md: true

diff --git a/_posts/ggplot2/2017-04-21-geom_quantile.Rmd b/_posts/ggplot2/2017-04-21-geom_quantile.Rmd
@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: statistical
-order: 6
+order: 4
 output:
   html_document:
     keep_md: true

diff --git a/_posts/ggplot2/2017-04-21-geom_quantile.md b/_posts/ggplot2/2017-04-21-geom_quantile.md
@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: statistical
-order: 6
+order: 4
 output:
   html_document:
     keep_md: true

diff --git a/_posts/ggplot2/2019-07-12-geom_bin2d.Rmd b/_posts/ggplot2/2019-07-12-geom_bin2d.Rmd
@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: statistical
-order: 2
+order: 1
 output:
   html_document:
     keep_md: true

diff --git a/_posts/ggplot2/2019-07-12-geom_bin2d.md b/_posts/ggplot2/2019-07-12-geom_bin2d.md
@@ -9,7 +9,7 @@ language: ggplot2
 page_type: example_index
 has_thumbnail: true
 display_as: statistical
-order: 2
+order: 1
 output:
   html_document:
     keep_md: true

diff --git a/_posts/ggplot2/2019-08-02-geom_violin.Rmd b/_posts/ggplot2/2019-08-02-geom_violin.Rmd
@@ -0,0 +1,193 @@
+---
+title: geom_violin | Examples | Plotly
+name: geom_violin
+permalink: ggplot2/geom_violin/
+description: How to make a density map using geom_violin. Includes explanations on flipping axes and facetting.
+layout: base
+thumbnail: thumbnail/geom_violin.jpg
+language: ggplot2
+page_type: example_index
+has_thumbnail: true
+display_as: statistical
+order: 8
+output:
+  html_document:
+    keep_md: true
+---
+
+```{r, echo = FALSE, message=FALSE}
+knitr::opts_chunk$set(message = FALSE, warning=FALSE)
+Sys.setenv("plotly_username"="RPlotBot")
+Sys.setenv("plotly_api_key"="q0lz6r5efr")
+```
+
+### New to Plotly?
+
+Plotly's R library is free and open source!<br>
+[Get started](https://plot.ly/r/getting-started/) by downloading the client and [reading the primer](https://plot.ly/r/getting-started/).<br>
+You can set up Plotly to work in [online](https://plot.ly/r/getting-started/#hosting-graphs-in-your-online-plotly-account) or [offline](https://plot.ly/r/offline/) mode.<br>
+We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/r_cheat_sheet.pdf) (new!) to help you get started!
+
+### Version Check
+
+Version 4 of Plotly's R package is now [available](https://plot.ly/r/getting-started/#installation)!<br>
+Check out [this post](http://moderndata.plot.ly/upgrading-to-plotly-4-0-and-above/) for more information on breaking changes and new features available in this version.
+
+```{r}
+library(plotly)
+packageVersion('plotly')
+```
+
+### Basic violin plot
+A basic violin plot showing how Democratic vote share in the 2018 elections to the US House of Representatives varied by level of density. A horizontal bar is added, to divide candidates who lost from those who won.
+
+Source: [Dave Wassermann and Ally Flinn](https://docs.google.com/spreadsheets/d/1WxDaxD5az6kdOjJncmGph37z0BPNhV1fNAH_g7IkpC0/htmlview?sle=true#gid=0) for the election results and CityLab for its [Congressional Density Index](https://github.com/theatlantic/citylab-data/tree/master/citylab-congress). Regional classifications are according to the Census Bureau.
+
+```{r, results='hide'}
+library(plotly)
+district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv", stringsAsFactors = FALSE)
+district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
+district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))
+
+p <- ggplot(district_density,aes(x=cluster, y=dem_margin, fill=cluster)) +
+  geom_violin(colour=NA) +
+  geom_hline(yintercept=0, alpha=0.5) +
+  labs(title = "Democratic performance in the 2018 House elections, by region and density",
+       x = "Density Index\nfrom CityLab",
+       y = "Margin of Victory/Defeat")
+ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_violin/basic-graph")
+chart_link
+```
+
+```{r echo=FALSE}
+chart_link
+```
+
+### Flipping the Axes
+With geom\_violin(), the y-axis must always be the continuous variable, and the x-axis the categorical variable. To create horizontal violin graphs, keep the x- and y-variables as is and add coord\_flip().
+
+```{r, results='hide'}
+library(plotly)
+district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv", stringsAsFactors = FALSE)
+district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
+district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))
+
+p <- ggplot(district_density,aes(x=cluster, y=dem_margin, fill=cluster)) +
+  geom_violin(colour=NA) +
+  geom_hline(yintercept=0, alpha=0.5) +
+  labs(title = "Democratic performance in the 2018 House elections, by region and density",
+       x = "Density Index\nfrom CityLab",
+       y = "Margin of Victory/Defeat") +
+  coord_flip()
+ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_violin/flip-axes")
+chart_link
+```
+
+```{r echo=FALSE}
+chart_link
+```
+
+### Add facetting
+Including facetting by region.
+
+```{r, results='hide'}
+library(plotly)
+district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv", stringsAsFactors = FALSE)
+district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
+district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))
+
+p <- ggplot(district_density,aes(x=cluster, y=dem_margin, fill=cluster)) +
+  geom_violin(colour=NA) +
+  geom_hline(yintercept=0, alpha=0.5) +
+  facet_wrap(~region) +
+  labs(title = "Democratic performance in the 2018 House elections, by region and density",
+       x = "Density Index\nfrom CityLab",
+       y = "Margin of Victory/Defeat") +
+  coord_flip()
+ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_violin/add-facet")
+chart_link
+```
+
+```{r echo=FALSE}
+chart_link
+```
+
+### Customized Appearance
+Add colour to the facet titles, centre-align the title, rotate the y-axis title, change the font, and get rid of the unnecessary legend. Note that coord_flip() flips the axes for the variables and the titles, but does not flip theme() elements.
+
+```{r, results='hide'}
+library(plotly)
+district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv", stringsAsFactors = FALSE)
+district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
+district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))
+
+p <- ggplot(district_density,aes(x=cluster, y=dem_margin, fill=cluster)) +
+  geom_violin(colour=NA) +
+  geom_hline(yintercept=0, alpha=0.5) +
+  facet_wrap(~region) +
+  labs(title = "Democratic performance in the 2018 House elections, by region and density",
+       x = "Density Index\nfrom CityLab",
+       y = "Margin of Victory/Defeat") +
+  coord_flip() +
+  theme(axis.title.y = element_text(angle = 0, vjust=0.5),
+        plot.title = element_text(hjust = 0.5),
+        strip.background = element_rect(fill="lightblue"),
+        text = element_text(family = 'Fira Sans'),
+        legend.position = "none")
+ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_violin/customize-theme")
+chart_link
+```
+
+```{r echo=FALSE}
+chart_link
+```
+
+### Rotated Axis Text
+Rotated the x-axis text 45 degrees, and used facet\_grid to create a 4x1 facet (compared to facet\_wrap, which defaults to 2x2).
+
+```{r, results='hide'}
+library(plotly)
+district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv", stringsAsFactors = FALSE)
+district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
+district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))
+
+p <- ggplot(district_density,aes(x=cluster, y=dem_margin, fill=cluster)) +
+  geom_violin(colour=NA) +
+  geom_hline(yintercept=0, alpha=0.5) +
+  facet_grid(.~region) +
+  labs(title = "Democratic performance in the 2018 House elections, by region and density",
+       x = "Density Index\nfrom CityLab",
+       y = "Margin of Victory/Defeat") +
+  theme(axis.text.x = element_text(angle = -45),
+        plot.title = element_text(hjust = 0.5),
+        strip.background = element_rect(fill="lightblue"),
+        text = element_text(family = 'Fira Sans'),
+        legend.position = "none")
+ggplotly(p)
+
+# Create a shareable link to your chart
+# Set up API credentials: https://plot.ly/r/getting-started
+chart_link = api_create(p, filename="geom_violin/rotated-text")
+chart_link
+```
+
+```{r echo=FALSE}
+chart_link
+```
+