fix

Author: kojisposts

_posts/ggplot2/2019-08-06-geom_density_2d.Rmd

@@ -42,6 +42,7 @@ packageVersion('plotly')
 Source: [Brett Carpenter from Data.World](https://data.world/brettcarpenter/craft-beer-data)
 
 ```{r, results='hide'}
+library(plotly)
 beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
 
 p <- ggplot(beers, aes(x=abv, y=ibu)) +
@@ -65,6 +66,9 @@ chart_link
 Since each of the lines (in the above graph) shows a different "level", setting "fill = stat(level)" allows for a filled graph.
 
 ```{r, results='hide'}
+library(plotly)
+beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
+
 p <- ggplot(beers, aes(x=abv, y=ibu)) +
   stat_density_2d(aes(fill = stat(level)), geom="polygon") +
   labs(y = "bitterness (IBU)",
@@ -84,7 +88,11 @@ chart_link
 
 ### Preset Colourscale
 ["Viridis" colourscales](https://ggplot2.tidyverse.org/reference/scale_viridis.html) are designed to still be perceptible in black-and-white, as well as for those with colourblindness. It comes with five colourscales, selected using the option= parameter: "magma" (or "A"), "inferno" (or "B"), "plasma" (or "C"), "viridis" (or "D", the default), and "cividis" (or "E").
+
 ```{r, results='hide'}
+library(plotly)
+beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
+
 p <- ggplot(beers, aes(x=abv, y=ibu)) +
   stat_density_2d(aes(fill = stat(level)), geom="polygon") +
   scale_fill_viridis_c(option = "plasma") +
@@ -107,6 +115,9 @@ chart_link
 ### Customized Colourscale
 You can also set your own colour gradients by defining a high and low point.
 ```{r, results='hide'}
+library(plotly)
+beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
+
 p <- ggplot(beers, aes(x=abv, y=ibu)) +
   stat_density_2d(aes(fill = stat(level)), geom="polygon") +
   scale_fill_gradient(low = "lightskyblue1", high = "darkred") +
@@ -130,7 +141,9 @@ chart_link
 I use variable "style2" to filter out the six most common beer styles. This way, we can see that the cluster of beers in the top right (i.e. more bitter and higher alcohol content) are IPAs - perhaps unsurprisingly.
 
 ```{r, results='hide'}
+library(plotly)
 library(dplyr)
+beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
 
 p <- ggplot(beers, aes(x=abv, y=ibu)) +
   geom_density_2d(alpha=0.5) +

_posts/ggplot2/2019-08-06-geom_density_2d.md

@@ -44,6 +44,7 @@ Source: [Brett Carpenter from Data.World](https://data.world/brettcarpenter/craf
 
 
 ```r
+library(plotly)
 beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
 
 p <- ggplot(beers, aes(x=abv, y=ibu)) +
@@ -66,6 +67,9 @@ Since each of the lines (in the above graph) shows a different "level", setting
 
 
 ```r
+library(plotly)
+beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
+
 p <- ggplot(beers, aes(x=abv, y=ibu)) +
   stat_density_2d(aes(fill = stat(level)), geom="polygon") +
   labs(y = "bitterness (IBU)",
@@ -85,6 +89,9 @@ chart_link
 ["Viridis" colourscales](https://ggplot2.tidyverse.org/reference/scale_viridis.html) are designed to still be perceptible in black-and-white, as well as for those with colourblindness. It comes with five colourscales, selected using the option= parameter: "magma" (or "A"), "inferno" (or "B"), "plasma" (or "C"), "viridis" (or "D", the default), and "cividis" (or "E").
 
 ```r
+library(plotly)
+beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
+
 p <- ggplot(beers, aes(x=abv, y=ibu)) +
   stat_density_2d(aes(fill = stat(level)), geom="polygon") +
   scale_fill_viridis_c(option = "plasma") +
@@ -106,6 +113,9 @@ chart_link
 You can also set your own colour gradients by defining a high and low point.
 
 ```r
+library(plotly)
+beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
+
 p <- ggplot(beers, aes(x=abv, y=ibu)) +
   stat_density_2d(aes(fill = stat(level)), geom="polygon") +
   scale_fill_gradient(low = "lightskyblue1", high = "darkred") +
@@ -128,7 +138,9 @@ I use variable "style2" to filter out the six most common beer styles. This way,
 
 
 ```r
+library(plotly)
 library(dplyr)
+beers <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/beers.csv", stringsAsFactors = FALSE)
 
 p <- ggplot(beers, aes(x=abv, y=ibu)) +
   geom_density_2d(alpha=0.5) +