ggplot2 > Basic > geom_bar
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geom_bar in ggplot2

How to make a bar chart in ggplot2 using geom_bar. Examples of grouped, stacked, overlaid, filled, and colored bar charts.


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Bar Chart

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)) +
    geom_bar(stat="identity")

fig <- ggplotly(p)

fig

Colored Bar Chart

filled bar chart with geom_bar

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

No Legend

geom_bar with no legend

library(plotly)

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

# No legend, since the information is redundant
p <- ggplot(data=dat, aes(x=time, y=total_bill, fill=time)) +
    geom_bar(colour="black", stat="identity") +
    guides(fill=FALSE)

fig <- ggplotly(p)


fig

Position Dodge

geombar with positiondodge

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)
)

# Bar graph, time on x-axis, color fill grouped by sex -- use position_dodge()
p <- ggplot(data=dat1, aes(x=time, y=total_bill, fill=sex)) +
    geom_bar(stat="identity", position=position_dodge())

fig <- ggplotly(p)

fig

Custom Colors

geom_bar with manual colors

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

Styles & Themes

geom_bar with styles and theme

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)
)

# A bar graph
p <- ggplot(data=dat1, aes(x=time, y=total_bill, fill=sex)) +
    geom_bar(colour="black", stat="identity",
             position=position_dodge(),
             size=.3) +                        # Thinner lines
    xlab("Time of day") + ylab("Total bill") + # Set axis labels
    ggtitle("Average bill for 2 people") +     # Set title
    theme_bw()

fig <- ggplotly(p)


fig

Variable Comparison

using geom_bar for variable comparison

library(plotly)

DF <- read.table(text="Rank F1     F2     F3
1    500    250    50
2    400    100    30
3    300    155    100
4    200    90     10", header=TRUE)

library(reshape2)
DF1 <- melt(DF, id.var="Rank")

p <- ggplot(DF1, aes(x = Rank, y = value, fill = variable)) +
  geom_bar(stat = "identity")

fig <- ggplotly(p)

fig

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")

fig <- ggplotly(p)

fig

Stacked Bar Chart

geom_bar with stacked traces

library(plotly)
library(dplyr)

df <- structure(c(106487, 495681, 1597442, 2452577, 2065141, 2271925, 4735484, 3555352, 8056040, 4321887, 2463194, 347566, 621147, 1325727, 1123492, 800368, 761550, 1359737, 1073726, 36, 53, 141, 41538, 64759, 124160, 69942, 74862, 323543, 247236, 112059, 16595, 37028, 153249, 427642, 1588178, 2738157, 2795672, 2265696, 11951, 33424, 62469, 74720, 166607, 404044, 426967, 38972, 361888, 1143671, 1516716, 160037, 354804, 996944, 1716374, 1982735, 3615225, 4486806, 3037122, 17, 54, 55, 210, 312, 358, 857, 350, 7368, 8443, 6286, 1750, 7367, 14092, 28954, 80779, 176893, 354939, 446792, 33333, 69911, 53144, 29169, 18005, 11704, 13363, 18028, 46547, 14574, 8954, 2483, 14693, 25467, 25215, 41254, 46237, 98263, 185986), .Dim = c(19, 5), .Dimnames = list(c("1820-30", "1831-40", "1841-50", "1851-60", "1861-70", "1871-80", "1881-90", "1891-00", "1901-10", "1911-20", "1921-30", "1931-40", "1941-50", "1951-60", "1961-70", "1971-80", "1981-90", "1991-00", "2001-06"), c("Europe", "Asia", "Americas", "Africa", "Oceania")))
df.m <- melt(df)
df.m <- rename(df.m, Period = Var1, Region = Var2)

p <- ggplot(df.m, aes(x = Period, y = value/1e+06,fill = Region)) + ggtitle("Migration to the United States by Source Region (1820-2006), In Millions")
p <- p + geom_bar(stat = "identity", position = "stack")

fig <- ggplotly(p)

fig

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")

fig <- ggplotly(p)

fig

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")

fig <- ggplotly(p)

fig

Manuel 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")

fig <- ggplotly(p)

fig

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")

fig <- ggplotly(p)

fig

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)