Easy Bar Plots in R Ggplot One X One Y Variable
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 ) 
Source: https://plotly.com/ggplot2/bar-charts/
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