Customizing ggplot2 Legend Key with legend.key Ggsave("customize_panel_border_with_element_rect_ggplot2.png")Ĭustomize Panel Border with element_rect() 4. P + theme(panel.border = element_rect(color="purple", Customizing ggplot2 panel border with panel.border Ggsave("customize_panel_background_with_element_rect_ggplot2.png")Ĭustomize Panel Background with element_rect() 3. P + theme(panel.background = element_rect(fill = "linen", We can change the panel background, where the actual graph is displayed, to “linen” color. Customizing ggplot2 panel background with panel.background Ggsave("customize_plot_background_with_element_rect_ggplot2.png")Ĭustomize Plot Background with element_rect() 2. P + theme(plot.background = element_rect(fill = "skyblue")) Here we change the default plot background from white to “skyblue” using plot.background=element_rect(fill = “skyblue”). Note the default plot background, the area around the plot is white. Customizing ggplot2 plot background with plot.background
#Cplot change colors r margins how to#
And we will learn how to customize using the 7 rectangular theme elements by changing just one element at a time.ĭefault Scatterplot with Legend in ggplot2 1. Here is how the simple scatter plot with legends looks like. Let us start with making a simple scatter plot with default grey background theme. strip.background: to customize strip background in a facet plot.legend.background: to customize legend background.panel.border: to customize panel borderx.panel.background: to customize panel background.plot.background: to customize plot background color.
Here is the list of the elements that we can control using using element_lines(). In total there are 7 aspects of rectangular elements we can control the three broad aspects/elements of lines in a plot made with ggplot2. In this tutorial, we will mainly focus on changing color, size, line type and arrow using theme(). Here is element_rect() function with the available arguments and their default choices. Like other theme element functions, to control the color of the rectangle that defines the plot background, we will use “plot.background” element as argument to theme() function and use element_rect() to specify the fill color. For example, we can customize the line and fill colors of rectangles that define borders and backgrounds in a plot.
With element_rect(), we can customize all things that are rectangular in a plot. In this post, we will learn 7 tips to customize rectangular elements in ggplot2. In earlier posts, we looked at how to customize texts with element_text() and lines with element_line(). ggplot2’s theme system comes with multiple element_ functions, Ggplot2’s theme function helps us fully control the “non-data” elements of a plot made with ggplot2.
#Cplot change colors r margins series#
If (isTRUE( rug) & is.Continuing the blog post series on how to control theme elements in ggplot2, in this post we will look at how to customize rectangular elements in ggplot2 using element_rect() function. Se.lwd = se.lwd, se.fill = se.fill, se.col = se.col, se.lty = se.lty) )įactor.lty = factor.lty, factor.pch = factor.pch, factor.fill = factor.fill,įl = l, factor.cex = factor.cex, Scatter = scatter, scatter.pch = scatter.pch, l = l. Xlab = xlab, ylab = ylab, xaxs = xaxs, yaxs = yaxs, las = las, Xlim = xlim, ylim = ylim, x_is_factor = x_is_factor, Setup_cplot( plotdat = out, data = data, xvar = xvar, yvar = yvar, # optionally draw the plot if FALSE, just the data are returned Out <- setNames( out] = dx,, drop = FALSE], c( "xvals ", "yvals ", "upper ", "lower ", "factor ")) Marg <- margins( model = object, data = data, at = setNames( list( xvals), xvar), type = type, vcov = vcov) Stop( "Displaying effect of a factor variable with > 2 levels is not currently supported! ") Currently methods exist for \dQuote to simply generate the data structure for use elsewhere. #' Draw one or more conditional effects plots reflecting predictions or marginal effects from a model, conditional on a covariate.
#' Conditional predicted value and average marginal effect plots for models
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