# Learn about API authentication here: https://plot.ly/ggplot2/getting-started
# Find your api_key here: https://plot.ly/settings/api
library(reshape2)
library(plotly)
sp <- ggplot(tips, aes(x=total_bill, y=tip/total_bill)) + geom_point(shape=1)
# Divide by levels of "sex", in the vertical direction
sp + facet_grid(sex ~ .)
ggplotly()
Write feedback@plot.ly with questions or submit an issue. See the ggplot2 → plotly test tables for ggplot2 conversion coverage. Docs inspired by Cookbook for R.
# Learn about API authentication here: https://plot.ly/ggplot2/getting-started
# Find your api_key here: https://plot.ly/settings/api
library(reshape2)
library(plotly)
sp <- ggplot(tips, aes(x=total_bill, y=tip/total_bill)) + geom_point(shape=1)
# Divide by levels of "sex", in the horizontal direction
sp + facet_grid(. ~ sex)
ggplotly()
Inspired by Cookbook for R.
# Learn about API authentication here: https://plot.ly/ggplot2/getting-started
# Find your api_key here: https://plot.ly/settings/api
library(reshape2)
library(plotly)
sp <- ggplot(tips, aes(x=total_bill, y=tip/total_bill)) + geom_point(shape=1)
# Divide by day, going horizontally and wrapping with 2 columns
sp + facet_wrap( ~ day, ncol=2)
ggplotly()
Inspired by Cookbook for R.
# Learn about API authentication here: https://plot.ly/ggplot2/getting-started
# Find your api_key here: https://plot.ly/settings/api
library(reshape2)
library(plotly)
# A histogram of bill sizes
hp <- ggplot(tips, aes(x=total_bill)) + geom_histogram(binwidth=2,colour="white")
ggplotly()
# Learn about API authentication here: https://plot.ly/ggplot2/getting-started
# Find your api_key here: https://plot.ly/settings/api
library(reshape2)
library(plotly)
hp <- ggplot(tips, aes(x=total_bill)) + geom_histogram(binwidth=2,colour="white")
# Histogram of total_bill, divided by sex and smoker
hp + facet_grid(sex ~ smoker)
ggplotly()
Inspired by Cookbook for R.
# Learn about API authentication here: https://plot.ly/ggplot2/getting-started
# Find your api_key here: https://plot.ly/settings/api
library(reshape2)
library(plotly)
hp <- ggplot(tips, aes(x=total_bill)) + geom_histogram(binwidth=2,colour="white")
# Same as above, with scales="free_y"
hp + facet_grid(sex ~ smoker, scales="free_y")
ggplotly()
Inspired by Cookbook for R.
# Learn about API authentication here: https://plot.ly/ggplot2/getting-started
# Find your api_key here: https://plot.ly/settings/api
library(reshape2)
library(plotly)
hp <- ggplot(tips, aes(x=total_bill)) + geom_histogram(binwidth=2,colour="white")
# With panels that have the same scaling, but different range (and therefore different physical sizes)
hp + facet_grid(sex ~ smoker, scales="free", space="free")
ggplotly()
Inspired by Cookbook for R.
# Learn about API authentication here: https://plot.ly/ggplot2/getting-started
# Find your api_key here: https://plot.ly/settings/api
library(plotly)
## read in data set (tolerance data from the ALDA book)
tolerance <- read.table("http://www.ats.ucla.edu/stat/r/examples/alda/data/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"))
})
tolerance$id <- with(tolerance, reorder(id, tolerance[ifelse(time == 0, TRUE, NA)], FUN = mean, na.rm = TRUE))
ggplot(data = tolerance, aes(x = time, y = tolerance)) + geom_point() +
stat_smooth(method = "lm", se = FALSE) + facet_wrap(~id)
ggplotly()
Inspired by the IDRE at UCLA.
# Learn about API authentication here: https://plot.ly/ggplot2/getting-started
# Find your api_key here: https://plot.ly/settings/api
library(plotly)
set.seed(42)
n <- 100
df <- data.frame(location = rep(LETTERS[1:4], n),
score = sample(45:80, 4*n, replace = TRUE))
df$p <- inv.logit(0.075 * df$score + rep(c(-4.5, -5, -6, -2.8), n))
df$pass <- sapply(df$p, function(x){rbinom(1, 1, x)})
preds <- predict(g, newdata = new.data, type = 'response',se = TRUE)
new.data$pred.full <- preds$fit
new.data$ymin <- new.data$pred.full - 2*preds$se.fit
new.data$ymax <- new.data$pred.full + 2*preds$se.fit
ggplot(df,aes(x = score, y = pass)) +
facet_wrap(~location) +
geom_point(size=3) +
geom_ribbon(data = new.data,aes(y = pred.full, ymin = ymin, ymax = ymax),alpha = 0.25) +
geom_line(data = new.data,aes(y = pred.full),colour = "blue")
ggplotly()
Inspired by Stack Overflow.
# Learn about API authentication here: https://plot.ly/ggplot2/getting-started
# Find your api_key here: https://plot.ly/settings/api
library(plotly)
ggplot(mpg, aes(displ, hwy))+
geom_point()+
stat_smooth()+
facet_wrap(~year)
ggplotly()
Inspired by R Study Group.
# Learn about API authentication here: https://plot.ly/ggplot2/getting-started
# Find your api_key here: https://plot.ly/settings/api
library(plotly)
ggplot(mpg, aes(displ, hwy))+
geom_point()+
facet_wrap(~manufacturer)
ggplotly()
Inspired by R Study Group.