A 2d density plot is useful to study the relationship between 2 numeric variables if you have a huge number of points. To avoid overlapping as in the scatterplot besideit divides the plot area in a multitude of small fragment and represents the number of points in this fragment.

There are several types of 2d density plots. Each has its proper ggplot2 function. This post describes all of them. For 2d histogram, the plot area is divided in a multitude of squares. It is a 2d version of the classic histogram. This function offers a bins argument that controls the number of bins you want to display. This function provides the bins argument as well, to control the number of division per axis.

As you can plot a density chart instead of a histogramit is possible to compute a 2d density and represent it. Several possibilities are offered by ggplot2 : you can show the contour of the distribution, or the area, or use the raster function:. Whatever you use a 2d histogram, a hexbin chart or a 2d distribution, you can and should custom the colour of your chart.

You can see other methods in the ggplot2 section of the gallery. This document is a work by Yan Holtz. Any feedback is highly encouraged.

You can fill an issue on Githubdrop me a message on Twitteror send an email pasting yan. Related chart types. Contact This document is a work by Yan Holtz. Github Twitter.The aim of this ggplot2 tutorial is to show you step by step, how to make and customize a density plot using ggplot2. This function can also be used to personalize the different graphical parameters including main titleaxis labelslegendbackground and colors.

An R script is available in the next section to install the package. The data must be a numeric vector or a data. Different point shapes and line types can be used in the plot. By default, ggplot2 uses solid line type and circle shape. For more details follow this link : ggplot2.

You can also use other color scales, such as ones taken from the RColorBrewer package. The different color systems available in R have been described in detail here. To change density plot color according to the group, you have to specify the name of the data column containing the groups using the argument groupName. Use the argument groupColorsto specify colors by hexadecimal code or by name. In this case, the length of groupColors should be the same as the number of the groups. Use the argument brewerPaletteto specify colors using RColorBrewer palette.

It is also possible to position the legend inside the plotting area. You have to indicate the x, y coordinates of legend box. The facet approach splits a plot into a matrix of panels. Each panel shows a different subset of the data. As you can see in the above plot, y axis have different scales in the different panels. The other arguments which can be used are described at this link : ggplot2 customize.

They are used to customize the plot axis, title, background, color, legend, …. Note that an eBook is available on easyGgplot2 package here. Contact : Alboukadel Kassambara alboukadel. This analysis was performed using R ver. Introduction Install and load easyGgplot2 package Data format Basic density plot Change the line type of the density plot Density plot with multiple groups Customize your density plot Parameters Main title and axis labels Axis ticks Background and colors Change density plot background and fill colors Change density plot color according to the group Legend Legend position Legend background color, title and text font styles Axis scales Create a customized plots with few R code Facet : split a plot into a matrix of pannels Facet with one variable Faceting with two variables Facet scales Facet label apperance ggplot2.

Introduction ggplot2. At the end of this tutorial you will be able to draw, with few R code, the following plots: ggplot2. Install and load easyGgplot2 package easyGgplot2 R package can be installed as follow : install.Historams are constructed by binning the data and counting the number of observations in each bin.

Using a binwidth of 0. Eruptions were sometimes classified as short or long ; these were coded as 2 and 4 minutes. It would matter if we wanted to estimate means and standard deviation of the durations of the long eruptions. It would be very useful to be able to change this parameter interactively. A histogram can be used to compare the data distribution to a theoretical model, such as a normal distribution. The Galton data frame in the UsingR package is one of several data sets used by Galton to study the heights of parents and their children.

Using the base graphics hist function we can compare the data distribution of parent heights to a normal distribution with mean and standard deviation corresponding to the data:.

Create the histogram with a density scale using the computed varlable. The smoothness is controlled by a bandwidth parameter that is analogous to the histogram binwidth. Most density plots use a kernel density estimatebut there are other possible strategies; qualitatively the particular strategy rarely matters. Using base graphics, a density plot of the geyser duration variable with default bandwidth:.

The lattice densityplot function by default adds a jittered strip plot of the data to the bottom:. Density estimates are generally computed at a grid of points and interpolated. Defaults in R vary from 50 to points. Computational effort for a density estimate at a point is proportional to the number of observations. Storage needed for an image is proportional to the number of point where the density is estimated.

Both ggplot and lattice make it easy to show multiple densities for different subgroups in a single plot. In ggplot you can map the site variable to an aesthetic, such as color :. Often a more effective approach is to use the idea of small multiplescollections of charts designed to facilitate comparisons.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

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## geom_density

Learn more. Asked 5 years, 6 months ago. Active 5 years, 6 months ago. Viewed 14k times. Methexis Methexis 1, 4 4 gold badges 17 17 silver badges 28 28 bronze badges. Okay I am guessing instead of group I need fill Active Oldest Votes. Okay so the example worked for the above, here is the original data dl. I was not aware that ggplot2 has themes! Your data has identical values for 3 groups.

See explanation appended in my answer above. Thanks all for your help, I just reran the process to get the numbers to plot and it appears to have worked this time round! Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. The Overflow How many jobs can be done at home?

### Creating plots in R using ggplot2 - part 8: density plots

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Linked 0.A density plot shows the distribution of a numeric variable. A common task in dataviz is to compare the distribution of several groups. The graph provides a few guidelines on how to do so. Put 2 density charts face to face to compare the distribution of 2 numeric variables. Explains how to display several density charts on the same axis, and the potential associated caveats. It is a good practice to write group names next to shapes instead of adding a legend beside the chart.

If you have several groups, plotting them on the same axis often results in a cluttered and unreadable figure. Use small multiple to avoid that. Stack groups on of top of each other.

Allows to study the whole, but makes it hard to study each group. Add marginal distribution around your scatterplot with ggExtra and the ggMarginal function. Most basic The most basic density plot you can do with ggplot2.

Mirror density chart Put 2 density charts face to face to compare the distribution of 2 numeric variables. Multi density chart Explains how to display several density charts on the same axis, and the potential associated caveats.

Multi density with annotation It is a good practice to write group names next to shapes instead of adding a legend beside the chart.

Small multiple If you have several groups, plotting them on the same axis often results in a cluttered and unreadable figure. Stacked density chart Stack groups on of top of each other.

Marginal distribution Add marginal distribution around your scatterplot with ggExtra and the ggMarginal function. Related chart types.In this chapter, we will focus on creation of multiple plots which can be further used to create 3 dimensional plots.

This dataset provides fuel economy data from and for 38 popular models of cars. The dataset is shipped with ggplot2 package.

It is important to follow the below mentioned step to create different types of plots. A density plot is a graphic representation of the distribution of any numeric variable in mentioned dataset. It uses a kernel density estimate to show the probability density function of the variable. We can create the plot by renaming the x and y axes which maintains better clarity with inclusion of title and legends with different color combinations. Box plot also called as box and whisker plot represents the five-number summary of data.

The five number summaries include values like minimum, first quartile, median, third quartile and maximum. Dot plots are similar to scattered plots with only difference of dimension. In this section, we will be adding dot plot to the existing box plot to have better picture and clarity. Violin plot is also created in similar manner with only structure change of violins instead of box. Previous Page. Next Page. Previous Page Print Page.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

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**Introduction to ggplot in R**

From the graph, we can learn that the distribution of x is quite like gamma distribution, so we use fitdistr in package MASS to get the parameters of shape and rate of gamma distribution. Draw the actual point black dot and fitted graph red line in the same plot, and here is the question, please look the plot first. These two are nearly the same, but why the fitted graph don't fit the actual point well, there must be something wrong in the fitted graph, or the way I draw the fitted graph and actual points is totally wrong, what should I do?

After I get the parameter of the model I establish, in which way I evaluate the model, something like RSS residual square sum for linear model, or the p-value of shapiro.

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The way you calculate the density by hand seems wrong. There's no need for rounding the random numbers from the gamma distribution. As Pascal noted, you can use a histogram to plot the density of the points.

In the example below, I use the function density to estimate the density and plot it as points. I present the fit both with the points and with the histogram:. To assess the goodness of fit I recommend the package fitdistrplus. Here is how it can be used to fit two distributions and compare their fits graphically and numerically. These are mainly used to compare fits of different distributions in this case gamma versus Weibull.

More information can be found in my answer here :. NickCox rightfully advises that the QQ-Plot upper right panel is the best single graph for judging and comparing fits. Fitted densities are hard to compare. I include the other graphics as well for the sake of completeness.

Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. How to draw fitted graph and actual graph of gamma distribution in one plot?

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## Ggplot density plot