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‎_posts/ggplot2/2019-08-02-geom_violin.Rmd

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@@ -72,6 +72,9 @@ With geom\_violin(), the y-axis must always be the continuous variable, and the
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```{r, results='hide'}
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library(plotly)
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district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv", stringsAsFactors = FALSE)
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district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
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district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))
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p <- ggplot(district_density,aes(x=cluster, y=dem_margin, fill=cluster)) +
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geom_violin(colour=NA) +
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```{r, results='hide'}
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library(plotly)
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district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv", stringsAsFactors = FALSE)
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district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
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district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))
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p <- ggplot(district_density,aes(x=cluster, y=dem_margin, fill=cluster)) +
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geom_violin(colour=NA) +
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```{r, results='hide'}
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library(plotly)
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district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv", stringsAsFactors = FALSE)
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district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
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district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))
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p <- ggplot(district_density,aes(x=cluster, y=dem_margin, fill=cluster)) +
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geom_violin(colour=NA) +
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```{r, results='hide'}
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library(plotly)
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district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv", stringsAsFactors = FALSE)
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district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
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district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))
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p <- ggplot(district_density,aes(x=cluster, y=dem_margin, fill=cluster)) +
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geom_violin(colour=NA) +

‎_posts/ggplot2/2019-08-02-geom_violin.md

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```r
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library(plotly)
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district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv", stringsAsFactors = FALSE)
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district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
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district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))
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p <- ggplot(district_density,aes(x=cluster, y=dem_margin, fill=cluster)) +
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geom_violin(colour=NA) +
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```r
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library(plotly)
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district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv", stringsAsFactors = FALSE)
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district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
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district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))
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p <- ggplot(district_density,aes(x=cluster, y=dem_margin, fill=cluster)) +
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geom_violin(colour=NA) +
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```r
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library(plotly)
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district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv", stringsAsFactors = FALSE)
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district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
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district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))
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p <- ggplot(district_density,aes(x=cluster, y=dem_margin, fill=cluster)) +
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geom_violin(colour=NA) +
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```r
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library(plotly)
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district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv", stringsAsFactors = FALSE)
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district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
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district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))
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p <- ggplot(district_density,aes(x=cluster, y=dem_margin, fill=cluster)) +
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geom_violin(colour=NA) +

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