# Discrete Colors in R

How to use and configure discrete color sequences, also known as categorical or qualitative color scales in R.

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### Discrete vs Continuous Color

In the same way as the X or Y position of a mark in cartesian coordinates can be used to represent continuous values (i.e. amounts or moments in time) or categories (i.e. labels), color can be used to represent continuous or discrete data. This page is about using color to represent categorical data using discrete colors, but Plotly can also represent continuous values with color.

### Discrete Color Concepts

This document explains the following discrete-color-related concepts:

• color sequences are lists of colors to be mapped onto discrete data values. No interpolation occurs when using color sequences, unlike with continuous color scales, and each color is used as-is. Color sequence defaults depend on the colors attribute and can be explicitly specified using a vector of colors as argument.
• legends are visible representations of the mapping between colors and data values. Legend markers also change shape when used with various kinds of traces, such as symbols or lines for scatter-like traces. Legends are configurable under the layout.legend attribute. Legends are the discrete equivalent of continuous color bars

### Discrete Color with Plotly

Most Plotly functions accept a color argument which automatically assigns data values to discrete colors if the data is non-numeric. If the data is numeric, the color will automatically be considered continuous. This means that numeric strings must be parsed to be used for continuous color, and conversely, numbers used as category codes must be converted to strings.

For example, in the tips dataset, the smoker column contains strings:

library(plotly)
library(reshape)
data("tips")

fig <- plot_ly(tips, x = ~total_bill, y = ~tip, split = ~smoker, type = 'scatter', mode = 'markers') %>%
layout(legend=list(title=list(text='smoker')), title = "String 'smoker' values mean discrete colors",
plot_bgcolor='#e5ecf6',
xaxis = list(
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff') )
fig


The size column, however, contains numbers:

library(plotly)
library(reshape)
data("tips")

fig <- plot_ly(tips, x = ~total_bill, y = ~tip, color = ~size, type = 'scatter', mode = 'markers') %>%
layout(title = "Numeric 'size' values mean continuous color",
plot_bgcolor='#e5ecf6',
xaxis = list(
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff') )
fig


Converting this column to strings is very straightforward, but note that the ordering in the legend may not be sequential by default, but here it produces sequentially (see below for how to control discrete order):

library(plotly)
library(reshape)
data("tips")

tips$size = as.character(tips$size) #convert to string

fig <- plot_ly(tips, x = ~total_bill, y = ~tip, split = ~size, type = 'scatter', mode = 'markers') %>%
layout(title = "String 'size' values mean discrete colors",
plot_bgcolor='#e5ecf6',
xaxis = list(
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff') )
fig


Converting a string column to a numeric one is also quite straightforward:

library(plotly)
library(rWind)
data(wind)

fig <- plot_ly(wind, r = ~r, theta = ~t, type="barpolar", color = ~nms,
marker =list(colorscale = 'Accent')) %>%
layout(title = 'Part of a continuous color scale used as a discrete sequence',  legend=list(title=list(text='strength')),
plot_bgcolor='#e5ecf6',
xaxis = list(
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
xaxis = list(
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff') , polar = list(angularaxis = list(
rotation = 90,
direction = 'clockwise'
)), margin = 0.01)
fig


### Discrete Colors in 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.

library(dash)
library(dashCoreComponents)
library(dashHtmlComponents)
library(plotly)

app <- Dash$new() app$layout(
htmlDiv(
list(
dccGraph(id = 'graph'),
htmlLabel("Color mode:"),
options = list(list(label = "discrete", value = "d"),
list(label = "continuous", value = "c")),
value = 'Secondary'
)
)
)
)
app$callback( output(id = 'graph', property='figure'), params=list(input(id='radio', property='value')), function(value) { if(value == 'd'){ library(plotly) library(reshape) data("tips") fig <- plot_ly(tips, x = ~total_bill, y = ~tip, split = ~size, type = 'scatter', mode = 'markers') %>% layout(title = "'size' values mean discrete colors") return(fig) } else{ library(plotly) library(reshape) data("tips") fig <- plot_ly(tips, x = ~total_bill, y = ~tip, color = ~size, type = 'scatter', mode = 'markers') %>% layout(title = "'size' values mean continuous color") return(fig) } })  Use app$run_server() to run the dash app.

### Color Sequences in Plotly

By default, Plotly will use the color sequence from the colors attribute, and the default active template is plotly which uses the plotly color sequence. You can choose any of the following built-in qualitative color sequences however, or define your own.

library("RColorBrewer")
display.brewer.all(type = 'qual')


Here is an example that creates a scatter plot using Plotly , with points colored using the built-in qualitative accent color sequence.

library(plotly)
library(gapminder)
data("gapminder")

fig <- plot_ly(gapminder, x = ~year, y = ~lifeExp, type = 'scatter', mode = 'lines', color = ~continent,
line =list(colorscale = 'Accent')) %>%
layout(title = 'Built-in Accent color sequence', legend=list(title=list(text='continent')),
plot_bgcolor='#e5ecf6',
xaxis = list(
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff') )
fig


### Explicitly Constructing a Color Sequence

The Plotlycolors argument accepts explicitly-constructed color sequences as well, as lists of CSS colors:

library(plotly)
library(gapminder)
data("gapminder")

data <- gapminder[gapminder$year == 2007, ] fig <- plot_ly()%>% add_bars(data = data, x = ~pop, y = ~continent, width = 1, color = ~continent, orientation = 'h', text = ~ country, hovertemplate = paste('<b>%{text}</b>', '<br><b>Continent</b>: %{y}<br>', '<i>pop</i>:$%{x}'),
colors = c("red", "green", "blue", "goldenrod", "magenta")
) %>%
layout( title = "Explicit color sequence", legend=list(title=list(text='continent')),
plot_bgcolor='#e5ecf6',
xaxis = list(
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'))

fig


Warning: If your color sequence has fewer colors than the number of unique values in the column you are mapping to color, the given colors will be mapped for some values and random colors will be mapped for other values :

library(plotly)
data("tips")

fig <- plot_ly(tips, x = ~total_bill, y = ~tip, type = 'scatter', mode = 'markers', color = ~day,
colors = c("red", "blue"))
fig <- fig %>%
layout( title = "Ambiguous! given colors mapped to some values only", legend=list(title=list(text='day')),
plot_bgcolor='#e5ecf6',
xaxis = list(
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'))

fig


### Directly Mapping Colors to Data Values

The example above assigned colors to data values on a first-come-first-served basis, but you can directly map colors to data values if this is important to your application with colors. Note that this does not change the order in which values appear in the figure or legend, as can be controlled below:

library(plotly)
library(gapminder)
data("gapminder")

pal <- c("red", "green", "blue", "goldenrod", "magenta")
pal <- setNames(pal, c("Europe", "Asia", "Americas", "Oceania", "Africa"))

fig <- plot_ly()%>%
add_bars(data = data, x = ~pop, y = ~continent, width = 1, color = ~continent, orientation = 'h',
text = ~ country,
hovertemplate = paste('<b>%{text}</b>',
'<br><b>Continent</b>: %{y}<br>',
'<i>pop</i>: $%{x}'), colors = pal) %>% layout( title = "Explicit color mapping", legend=list(title=list(text='continent')), plot_bgcolor='#e5ecf6', xaxis = list( zerolinecolor = '#ffff', zerolinewidth = 2, gridcolor = 'ffff'), yaxis = list( zerolinecolor = '#ffff', zerolinewidth = 2, gridcolor = 'ffff')) fig  If your data set already contains valid CSS colors which you wish to use directly, you can pass the special value "identity" to colors, in which case the legend is hidden by default, and the color does not appear in the hover label: library(plotly) library(gapminder) data(gapminder) fig <- plot_ly(x = c("a","b","c"), y = c(1,3,2), type = 'bar', marker = list(color = c('red', 'goldenrod', '#00D'))) fig <- fig %>% layout(plot_bgcolor='#e5ecf6', xaxis = list( title = 'x', zerolinecolor = '#ffff', zerolinewidth = 2, gridcolor = 'ffff'), yaxis = list( title = 'y', zerolinecolor = '#ffff', zerolinewidth = 2, gridcolor = 'ffff' )) fig  ### Controlling Discrete Color Order Plotly lets you specify an ordering over categorical variables with categoryorder, which will apply to colors and legends as well as symbols and axes. This can be used with either colors or color. library(plotly) library(gapminder) data("gapminder") data <- gapminder[gapminder$year == 2007, ]

pal <- c("green", "blue", "magenta", "red", "goldenrod")
pal <- setNames(pal, c("Europe", "Asia", "Americas", "Oceania", "Africa"))

fig <- plot_ly()%>%
add_bars(data = data, x = ~pop, y = ~continent, width = 1, color = ~continent, orientation = 'h',
text = ~ country,
hovertemplate = paste('<b>%{text}</b>',
'<br><b>Continent</b>: %{y}<br>',
'<i>pop</i>: $%{x}'), colors = pal) %>% layout( title = "Explicit color sequence with explicit ordering", legend=list(title=list(text='continent')), plot_bgcolor='#e5ecf6', xaxis = list( zerolinecolor = '#ffff', zerolinewidth = 2, gridcolor = 'ffff'), yaxis = list( categoryorder = "array", categoryarray = c("Asia","Oceania", "Europe", "Africa", "Americas"), zerolinecolor = '#ffff', zerolinewidth = 2, gridcolor = 'ffff'), bargap = 1) fig  library(plotly) library(gapminder) data("gapminder") data <- gapminder[gapminder$year == 2007, ]

pal <- c("red", "green", "blue", "goldenrod", "magenta")
pal <- setNames(pal, c("Europe", "Asia", "Americas", "Oceania", "Africa"))

fig <- plot_ly()%>%
add_bars(data = data, x = ~pop, y = ~continent, width = 1, color = ~continent, orientation = 'h',
text = ~ country,
hovertemplate = paste('<b>%{text}</b>',
'<br><b>Continent</b>: %{y}<br>',
'<i>pop</i>: $%{x}'), colors = pal) %>% layout( title = "Explicit color mapping with explicit ordering", legend=list(title=list(text='continent')), plot_bgcolor='#e5ecf6', xaxis = list( zerolinecolor = '#ffff', zerolinewidth = 2, gridcolor = 'ffff'), yaxis = list( categoryorder = "array", categoryarray = c("Asia","Oceania", "Europe", "Africa", "Americas"), zerolinecolor = '#ffff', zerolinewidth = 2, gridcolor = 'ffff'), bargap = 1) fig  ### Using Sequential Scales as Discrete Sequences In most cases, discrete/qualitative/categorical data values have no meaningful natural ordering, such as in the continents example used above. In some cases, however, there is a meaningful order, and in this case it can be helpful and appealing to use part of a continuous scale as a discrete sequence, as in the following wind rose chart: library(plotly) library(rWind) data(wind) fig <- plot_ly(wind, r = ~r, theta = ~t, type="barpolar", color = ~nms, marker =list(colorscale = 'Accent')) %>% layout(title = list(text='Part of a continuous color scale used as a discrete sequence' ,font =list(size = 15)), legend=list(title=list(text='strength')), plot_bgcolor='#e5ecf6', xaxis = list( zerolinecolor = '#ffff', zerolinewidth = 2, gridcolor = 'ffff'), xaxis = list( zerolinecolor = '#ffff', zerolinewidth = 2, gridcolor = 'ffff') , polar = list(angularaxis = list( rotation = 90, direction = 'clockwise' )), margin = 0.01) 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)