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# facet_wrap in ggplot2

How to make subplots with facet_wrap in ggplot2 and R.

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### Basic Columns

library(reshape2)
library(plotly)

p <- ggplot(tips, aes(x=total_bill, y=tip/total_bill)) + geom_point(shape=1)

# Divide by day, going horizontally and wrapping with 2 columns
p <- p + facet_wrap( ~ day, ncol=2)

fig <- ggplotly(p)

fig


Inspired by Cookbook for R

### Add Unique Curves

library(plotly)

## read in data set (tolerance data from the ALDA book)
tolerance <- read.table("https://stats.idre.ucla.edu/wp-content/uploads/2016/02/tolerance1_pp.txt",
sep = ",", header = TRUE)

## change id and male to factor variables
tolerance <- within(tolerance, {
id <- factor(id)
male <- factor(male, levels = 0:1, labels = c("female", "male"))
})

p <- ggplot(data = tolerance, aes(x = time, y = tolerance)) + geom_point() +
stat_smooth(method = "lm", se = FALSE) + facet_wrap(~id)

fig <- ggplotly(p)

fig


Inspired by The IDRE at UCLA

### Add Stat_Smooth

library(plotly)

p <- ggplot(mpg, aes(displ, hwy))+
geom_point()+
stat_smooth()+
facet_wrap(~year)

fig <- ggplotly(p)

fig


Inspired by R Study Group

### Labels

library(plotly)
set.seed(123)

df <- diamonds[sample(1:nrow(diamonds), size = 1000), ]

# Create labels
labs <- c("Best","Second best","Third best","Average", "Average","Third Worst","Second Worst","Worst")
levels(df$clarity) <- rev(labs) p <- ggplot(df, aes(carat, price)) + geom_point() + facet_wrap(~ clarity) fig <- ggplotly(p) fig  Inspired by Stack Overflow ### Titles library(plotly) set.seed(123) df <- diamonds[sample(1:nrow(diamonds), size = 1000), ] # Create labels labs <- c("Best","Second best","Third best","Average", "Average","Third Worst","Second Worst","Worst") levels(df$clarity) <- rev(labs)

p <- ggplot(df, aes(carat, price)) +
geom_point() +
facet_wrap(~ clarity) +
ggtitle("Diamonds dataset facetted by clarity")

fig <- ggplotly(p)

fig


Inspired by ggplot2 Documentation

### Ordered Facets

library(plotly)
set.seed(123)

df <- diamonds[sample(1:nrow(diamonds), size = 1000), ]

# Reorer levels

levels(df$clarity) <- c("VS2", "VS1", "VVS2", "I1", "SI2", "IF", "VVS1", "SI1") p <- ggplot(df, aes(carat, price)) + geom_point() + facet_wrap(~ clarity) + ggtitle("Diamonds dataset facetted by clarity") fig <- ggplotly(p) fig  Inspired by Stack Overflow ### What About Dash? Dash for R is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library. Learn about how to install Dash for R at https://dashr.plot.ly/installation. Everywhere in this page that you see fig, you can display the same figure in a Dash for R application by passing it to the figure argument of the Graph component from the built-in dashCoreComponents package like this: library(plotly) fig <- plot_ly() # fig <- fig %>% add_trace( ... ) # fig <- fig %>% layout( ... ) library(dash) library(dashCoreComponents) library(dashHtmlComponents) app <- Dash$new()
app$layout( htmlDiv( list( dccGraph(figure=fig) ) ) ) app$run_server(debug=TRUE, dev_tools_hot_reload=FALSE) 