Bar Plots in ggplot2

How to make Bar Plots plots ggplot2 with Plotly.

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geom_bar is designed to make it easy to create bar charts that show counts (or sums of weights).

Default bar plot

library(plotly)
g <- ggplot(mpg, aes(class))  
p <-  g + geom_bar()

ggplotly(p)

library(plotly)
g <- ggplot(mpg, aes(class))  
p <-  g + geom_bar(aes(weight = displ))

ggplotly(p)

Add colour

library(plotly)

dat <- data.frame(
    time = factor(c("Lunch","Dinner"), levels=c("Lunch","Dinner")),
    total_bill = c(14.89, 17.23)
)

p <- ggplot(data=dat, aes(x=time, y=total_bill, fill=time)) +
    geom_bar(stat="identity")

fig <- ggplotly(p)

fig

Setting custom colours:

library(plotly)

dat1 <- data.frame(
    sex = factor(c("Female","Female","Male","Male")),
    time = factor(c("Lunch","Dinner","Lunch","Dinner"), levels=c("Lunch","Dinner")),
    total_bill = c(13.53, 16.81, 16.24, 17.42)
)

p <- ggplot(data=dat1, aes(x=time, y=total_bill, fill=sex)) +
    geom_bar(stat="identity", position=position_dodge(), colour="black") +
    scale_fill_manual(values=c("#999999", "#E69F00"))

fig <- ggplotly(p)

fig

Stacking bar plots

Bar plots are automatically stacked when multiple bars are at the same location. The order of the fill is designed to match the legend.

library(plotly)
g <- ggplot(mpg, aes(class))  
p <-  g + geom_bar(aes(fill = drv))

ggplotly(p)

Showing mean

library(plotly)
df <- data.frame(trt = c("a", "b", "c"), outcome = c(2.3, 1.9, 3.2))  
p <-    
 ggplot(df, aes(trt, outcome)) +
  geom_col()

ggplotly(p)

geom_point() displays exactly the same information and doesn't require the y-axis to touch zero.

library(plotly)
df <- data.frame(trt = c("a", "b", "c"), outcome = c(2.3, 1.9, 3.2))  
p <-    
 ggplot(df, aes(trt, outcome)) +
  geom_point()

ggplotly(p)

You can also use geom_bar() with continuous data, in which case it will show counts at unique locations.

library(plotly)
df <- data.frame(x = rep(c(2.9, 3.1, 4.5), c(5, 10, 4)))  
p <-  ggplot(df, aes(x)) + geom_bar()

ggplotly(p)

Using binwidth

library(plotly)
df <- data.frame(x = rep(c(2.9, 3.1, 4.5), c(5, 10, 4)))  
p <-  ggplot(df, aes(x)) + geom_histogram(binwidth = 0.5)

ggplotly(p)

Error Bars

barplot with error bars

library(plotly)
library(dplyr)
set.seed(123)

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

df.summ <- df %>% group_by(cut) %>% summarize(Mean = mean(table), Min = min(table), Max = max(table))

p <- ggplot(df.summ, aes(x = cut, y = Mean, ymin = Min, ymax = Max, fill = cut)) + 
  geom_bar(stat = "identity") + 
  geom_errorbar() + 
  ggtitle("Bar chart with Error Bars")

ggplotly(p)

Ordered Bar Chart

ordering variable in geom_bar

library(plotly)
library(plyr)

dane<-data.frame(x=1:10,y=seq(-5,4),g=rep(c('A','B'),each=5))
dane$x<-as.factor(dane$x)

p <- ggplot(data=dane,aes(x=x,y=y,fill=g)) +
    geom_bar(stat="identity")

ggplotly(p)

Precentages

using geom_bar to show percentages

library(plotly)
set.seed(123)

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

p <- ggplot(df, aes(x = color)) + 
  geom_bar(aes(y = ..count../sum(..count..), fill = cut)) + 
  scale_fill_brewer(palette = "Set3") + 
  ylab("Percent") + 
  ggtitle("Show precentages in bar chart")

ggplotly(p)

Set manual colors using geom_bar to manually specify colors.

library(plotly)
library(RColorBrewer)
set.seed(123)

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

# Simply use fill = a vector of colors
p <- ggplot(df, aes(x = color)) +
  geom_bar(fill = brewer.pal(length(unique(df$color)), "Set3")) + 
  ylab("Count") + 
  ggtitle("Specify manual colors in a bar chart")

ggplotly(p)

Reordered Bar Chart

Re-ordering bars shown using geom_bar.

library(plotly)

df <- data.frame(x = as.factor(LETTERS[1:5]), 
                 y = sample(10:20, size = 5))

# First change factor levels
df$x <- factor(df$x, levels = c("C", "B", "A", "D", "E"))

# Plot
p <- ggplot(df, aes(x, y, fill = x)) + 
  geom_bar(stat = "identity") +
  ggtitle("Bar Chart with changed factor levels")

ggplotly(p)

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)