r; ggplot2; kernel-density; density-plot; Share. width instead. x, 10) ). . Additional distributional statistics can be computed, including the mean (), median (), variance (), and. Optional character vector of parameter names. In an earlier post, we learned how to make rain cloud plots with half violinplot, kind of from scratch. . g. These are wrappers for stats::dt, etc. ggdist-deprecated: Deprecated functions and arguments in ggdist; ggdist-ggproto: Base ggproto classes for ggdist; ggdist-package: Visualizations of Distributions and Uncertainty; guide_rampbar: Continuous colour ramp guide; lkjcorr_marginal: Marginal distribution of a single correlation from an LKJ. Make ggplot interactive. colour_ramp: (or color_ramp) A secondary scale that modifies the color scale to "ramp" to another color. 3. width and level computed variables can now be used in slab / dots sub-geometries. Aesthetics. bw: The bandwidth. We can use the raincloudplots package to create raincloud plots, or they can be built using the ggdist. New features and enhancements: The stat_sample_. g. This is a flexible sub-family of stats and geoms designed to make plotting dotplots straightforward. value. Here are the links to get set up. R-Tips Weekly. dist_wrapped_categorical is_dist_like distr_is_missing distr_is_constant. The default output (and sometimes input) data formats of popular modeling functions like JAGS and Stan often don’t quite conform to the ideal of tidy data. I tried plotting rnorm (100000) and on my laptop X11 cairo plot took 2. For compatibility with the base ggplot naming scheme for orientation, "x" can be used as an alias for "vertical" and "y" as an alias for "horizontal" (ggdist had an orientation parameter before base ggplot did, hence the discrepancy). First method: combine both variables with interaction(). We would like to show you a description here but the site won’t allow us. They also ensure dots do not overlap, and allow the. A nma_summary object. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as. While geom_lineribbon() is intended for use on data frames that have already been summarized using a point_interval() function, stat_lineribbon() is intended for use. Instantly share code, notes, and snippets. Details. Extra coordinate systems, geoms & stats. Author(s) Matthew Kay See Also. g. Provides 'ggplot2' themes and scales that replicate the look of plots by Edward Tufte, Stephen Few, 'Fivethirtyeight', 'The Economist', 'Stata', 'Excel', and 'The Wall Street Journal', among others. The philosophy of tidybayes is to tidy whatever format is output by a model, so in keeping with that philosophy, when applied to ordinal and multinomial brms models, add_epred_draws () adds an additional column called and a separate row containing the variable for each category is output for every draw and predictor. ggdist is an R package that provides a flexible set of ggplot2 is an R package that provides a flexible set of ggplot2ggdist 3. 2. 44 get_variables. What do the bars in ggdist::stat_halfeye () mean? I am trying to understand what the black point, thicker horizontal bar, and thinner horizontal bar mean when I use the stat_halfeye () function. Density, distribution function, quantile function and random generation for the generalised t distribution with df degrees of freedom, using location and scale, or mean and sd. . 1/0. See the third model below:This vignette describes how to use the tidybayes and ggdist packages to extract and visualize tidy data frames of draws from posterior distributions of model variables, means, and predictions from brms::brm. ggdist, an extension to the popular ggplot2 grammar of graphics toolkit, is an attempt to rectify this situation. Sorted by: 3. Raincloud plots are a combination of density graph, a box plot, and a beeswarm (or jitter) plot, and are used to compare distributions of quantitative/numerical variables across the levels of a categorical (or discrete) grouping variable. ggdist is an R package that provides a flexible set of ggplot2 geoms and stats designed especially for visualizing distributions and uncertainty. Value. A string giving the suffix of a function name that starts with "density_" ; e. It is designed for both frequentist and Bayesian uncertainty visualization, taking the view that uncertainty visualization can be unified through the perspective of distribution visualization: for. This format is also compatible with stats::density() . ggdist provides. the theme_gray theme of the ggplot2 package: ggp <- ggplot ( data, aes ( x, y, col = group)) + # Draw default ggplot2 plot geom_point () ggp. Breaking changes: The following changes, mostly due to new default density estimators, may cause some plots on sample data to change. "bounded" for [density_bounded()] , "unbounded" for [density_unbounded()] , or. While geom_lineribbon() is intended for use on data frames that have already been summarized using a point_interval() function, stat_lineribbon() is intended for use directly on data frames of draws or of analytical distributions, and will perform the summarization using a. n: The sample size of the x input argument. This includes retail locations and customer service 1-800 phone lines. Speed, accuracy and happy customers are our top. "bounded" for [density_bounded()] , "unbounded" for [density_unbounded()] , or. . The numerical arguments other than n are recycled to the length of the result. I hope the below is sufficiently different to merit a new answer. Stack Overflow Public questions & answers; Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Talent Build your employer brand ; Advertising Reach developers & technologists worldwide; Labs The future of collective knowledge sharing; About the companyposition_dodgejust {ggdist} R Documentation: Dodge overlapping objects side-to-side, preserving justification Description. When plotting in R using ggplot, I've noticed that sometimes if you don't specify any limitations on the y-axis by default the plot will not have any "0" mark at the bottom of the y axis (it is assumed the bottom corner represents 0). Warehousing & order fulfillment. x: x position of the geometry . One of: A function which takes a numeric vector and returns a list with elements x (giving grid points for the density estimator) and y (the corresponding densities). total () applies gdist () to any number of line segments. Parametric takes on either "Yes" or "No". ggstance. The benefit of this is that it automatically works with group_by and facet and you don't need to manually add geoms for each group. It supports various types of confidence, bootstrap, probability, and prior distributions, as well as point, interval, dot, line, and eye plots. 1. Provide details and share your research! But avoid. 9). Details ggdist is an R package that provides a flexible set of ggplot2 geoms and stats designed espe-cially for visualizing distributions and uncertainty. com @CedScherer @Z3tt {ggtext} element_markdown() → formatted text elements,Log [a/ (a + b)] = β 0 + β 1X1 +. All objects will be fortified to produce a data frame. Changes should usually be small, and generally should result in more accurate density estimation. ggdist is an R package that aims to make it easy to integrate popular Bayesian modeling methods into a tidy data + ggplot workflow. with boxplot + dotplot. If you want perfect smooth line for these distribution curves, you may consider directly draw the density function using stat_function(). This topic was automatically closed 21 days after the last reply. Horizontal versions of ggplot2 geoms. In this tutorial, we use several geometries to. y: The estimated density values. In this post, I will continue exploring R packages that make ggplot2 more powerful. It gets the name because of the Convex Hull shape. Warehousing & order fulfillment. There are three options:A lot of time can be spent on polishing plots for presentations and publications. Default ignores several meta-data column names used in ggdist and tidybayes. ggdist provides a family of functions following this format, including density_unbounded() and density_bounded(). . 本期. Default aesthetic mappings are applied if the . com ggdist unifies a variety of uncertainty visualization types through the lens of distributional visualization, allowing functions of distributions to be mapped to directly to visual channels (aesthetics), making it straightforward to express a variety of (sometimes weird!) uncertainty visualization types. g. frame (x = c (-4, 10)), aes (x = x)) + stat_function (fun = dt, args = list (df = 1. counterparts, which now understand the dist, args, and arg1. The default output (and sometimes input) data formats of popular modeling functions like JAGS and Stan often don’t quite conform to the ideal of tidy data. This is a flexible family of stats and geoms designed to make plotting distributions (such as priors and posteriors in Bayesian models, or even sampling distributions from other models) straightforward, and support a range of useful plots, including intervals, eye plots (densities + intervals), CCDF bar plots. ggplot2可视化经典案例 (4) 之云雨图. We illustrate the features of RStan through an example in Gelman et al. This geometry consists of a "spike" (vertical/horizontal line segment) and a "point" (at the end of the line segment). This vignette describes the slab+interval geoms and stats in ggdist. So they're not "the same" necessarily, but one is a special case of the other. frame, and will be used as the layer data. Details. ggalt. This geom wraps geom_slabinterval() with defaults designed to produce point + multiple-interval plots. Research in uncertainty visualization has developed a rich variety of improved uncertainty visualizations, most of which are difficult to create in existing grammar of graphics implementations. 3. 1. I am trying to plot the density curve of a t-distribution with mean = 3 and df = 1. For compatibility with the base ggplot naming scheme for orientation, "x" can be used as an alias for "vertical" and "y" as an alias for "horizontal" (ggdist had an orientation parameter before base ggplot did, hence the discrepancy). My code is below. Geopolitical forecasting tournaments have stimulated the development of methods for improving probability judgments of real-world events. Additional arguments passed on to the underlying ggdist plot stat, see Details. na. Improved support for discrete distributions. Thus, a/ (a + b) is the probability of success (e. ggdist: Visualizations of Distributions and Uncertainty. It acts as a meta-geom for many other ggdist geoms that are wrappers around this geom, including eye plots, half-eye plots, CCDF barplots, and point+multiple interval plots, and supports both horizontal and vertical orientations, dodging (via the position argument), and relative justification of slabs with their corresponding intervals. geom_swarm () and geom_weave (): dotplots on raw data with defaults intended to create "beeswarm" plots. tidybayes is an R package that aims to make it easy to integrate popular Bayesian modeling methods into a tidy data + ggplot workflow. Converting YEAR to a factor is not necessary. 1 Answer. To do that, you. tidy() summarizes information about model components such as coefficients of a. In order to remove gridlines, we are going to focus on position scales. Before use ggplot (. Set a ggplot color by groups (i. Important: All of the data and code shown can be accessed through our Business Science R-Tips Project. We use a network of warehouses so you can sit back while we send your products out for you. cut_cdf_qi: Categorize values from a CDF into quantile intervals density_auto: Automatic density. as quasirandom distribution. R-Tips Weekly. You must supply mapping if there is no plot mapping. Raincloud Plots with ggdist. Major changes include: Support for slabs with true gradients with varying alpha or fill in R 4. automatic-partial-functions: Automatic partial function application in ggdist. This format is also compatible with stats::density() . Hmm, this could probably happen somewhere in the point_interval() family. Accelarating ggplot2A combination of stat_sample_slabinterval() and geom_slabinterval() with sensible defaults. Details. I co-direct the Midwest Uncertainty. How can I permit ggdist::stat_halfeye() to skip groups with 1 obs. The following vignette describes the geom_lineribbon () family of stats and geoms in ggdist, a family of stats and geoms for creating line+ribbon plots: for example, plots with a fit line and one or more uncertainty bands. A string giving the suffix of a function name that starts with "density_" ; e. This vignette describes the slab+interval geoms and stats in ggdist. width, was removed in ggdist 3. Deprecated arguments. ggdist provides a family of functions following this format, including density_unbounded() and density_bounded(). #> #> This message will be. Introduction. stat. payload":{"allShortcutsEnabled":false,"fileTree":{"figures-source":{"items":[{"name":"cheat_sheet-slabinterval. All stat_dist_. It provides methods which are minimal wrappers to the standard d, p, q, and r distribution functions which are applied to each distribution in the vector. Details. 💡 Step 1: Load the Libraries and Data First, run this. na. 1 (R Core Team, 2021). In particular, it supports a selection of useful layouts (including the. Beretta. pdf","path":"figures-source/cheat_sheet-slabinterval. Warehousing & order fulfillment. This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks. New features and enhancements: Several computed variables in stat_slabinterval() can now be shared across sub-geometries: . "bounded" for [density_bounded()]. rm. Step 2: Then Click the “CS” hyperlink to “ggplot2”. 26th 2023. This format is also compatible with stats::density() . ggdist provides a family of functions following this format, including density_unbounded() and density_bounded(). 10K views 2 years ago R Tips. It provides a range of new functionality that can be added to the plot object in order to customize how it should change with time. . Dots + point + interval plot (shortcut stat) Description. edu> Description Provides primitiThe problem with @jlhoward's solution is that you need to manually add goem_ribbon for each group you have. A string giving the suffix of a function name that starts with "density_" ; e. Please refer to the end of. The limits_function argument: this was a parameter for determining the function to compute limits of the slab in stat_slabinterval () and its derived stats. This format is also compatible with stats::density() . ggdist provides a family of functions following this format, including density_unbounded() and density_bounded(). ggdensity Tutorial. As a next step, we can plot our data with default theme specifications, i. g. width column generated by the point_interval () family of functions, making them often more convenient than a vanilla geom_ribbon () + geom_line (). Many people are familiar with the idea that reformatting a probability as a frequency can sometimes help people better reason with it (such as on classic. Default ignores several meta-data column names used in ggdist and tidybayes. datatype: When using composite geoms directly without a stat (e. "bounded" for [density_bounded()] , "unbounded" for [density_unbounded()] , or. families of stats have been merged (#83). Geoms and stats based on geom_dotsinterval() create dotplots that automatically determine a bin width that ensures the plot fits within the available space. Package ‘ggdist’ May 13, 2023 Title Visualizations of Distributions and Uncertainty Version 3. ggdist source: R/geom_lineribbon. "bounded" for [density_bounded()] , "unbounded" for [density_unbounded()] , or. Load the packages and write the codes as shown below. 1 Answer. . after_stat () replaces the old approaches of using either stat (), e. The ggbio package extends and specializes the grammar of graphics for biological data. For a given eta η and a K imes K K ×K correlation matrix R R : Each off-diagonal entry of R R, r_ {ij}: i e j rij: i =j, has the following marginal distribution (Lewandowski, Kurowicka, and Joe 2009):Noticed one lingering issue with position_dodge(). plot = TRUE. 26th 2023. 89), interval_size_range = c (1, 3)) To eliminate the giant point, you want to change the. This is a flexible sub-family of stats and geoms designed to make plotting dotplots straightforward. stat_dist_interval: Interval plots. They are useful to jointly model reaction time and a binary outcome, such as 2 different choices or accuracy (i. Accelarating ggplot2I'm making a complementary cumulative distribution function barplot with {ggdist}. Clearance. . p <- ggplot (mtcars, aes (factor (cyl), fill = factor (vs))) + geom_bar (position = "dodge2") plotly::ggplotly (p) Plot. 3. – nico. by has changed. This geom sets some default aesthetics equal to the . R. While geom_lineribbon() is intended for use on data frames that have already been summarized using a point_interval() function, stat_ribbon() is intended for use directly on. name: The. R","contentType":"file"},{"name":"abstract_stat. This vignette describes the dots+interval geoms and stats in ggdist. If TRUE, missing values are silently. The ggdist package is a ggplot2 extension that is made for visualizing distributions and uncertainty. For compatibility with the base ggplot naming scheme for orientation, "x" can be used as an alias for "vertical" and "y" as an alias for "horizontal" (ggdist had an orientation parameter before base ggplot did, hence the discrepancy). Warehousing & order fulfillment. The return value must be a data. Author(s) Matthew Kay See Also. A function can be created from a formula (e. Introduction. ggdist object is displayed correctly if adjusting xlim low value from 0 to 50. g. ggdist__wrapped_categorical density. R'' ``ggdist-geom_slabinterval. x: The grid of points at which the density was estimated. #> To restore the old behaviour of a single split violin, #> set split. Details. #> Separate violin plots are now plotted side-by-side. pdf","path":"figures-source/cheat_sheet-slabinterval. mapping: Set of aesthetic mappings created by aes(). I'm trying to plot predicted draws from a brms model using ggdist, specifically stat_slab, and having issues with coord_cartesian to zoom in. If object is a stanreg object, the default is to show all (or the first 10) regression coefficients (including the intercept). ggdist unifies a variety of uncertainty visualization types through the lens of distributional visualization, allowing functions of distributions to be mapped to directly to. width = c (0. ggdist-deprecated: Deprecated functions and arguments in ggdist; ggdist-ggproto: Base ggproto classes for ggdist; ggdist-package: Visualizations of Distributions and Uncertainty; guide_rampbar: Continuous colour ramp guide; lkjcorr_marginal: Marginal distribution of a single correlation from an LKJ. Speed, accuracy and happy customers are our top. Please read the cheat sheets. Polished raincloud plot using the Palmer penguins data · GitHub. 2, support for fill_type = "gradient" should be auto-detected based on the graphics device you are using. Specifically, we leverage Amazon’s infrastructure so we can often get same-day delivery in about a dozen cities. The . This geom wraps geom_slabinterval() with defaults designed to produce point + multiple-interval plots. 18) This package provides the visualization of bayesian network inferred from gene expression data. Similar. I think it would make most sense for {ggdist} to take this output and rearrange it into a long form - creating a new group from the column names. . It provides methods which are minimal wrappers to the standard d, p, q, and r distribution functions which are applied to each distribution in the vector. An alternative to jittering your raw data is the ggdist::stat_dots element. A string giving the suffix of a function name that starts with "density_" ; e. The ggdist package is a ggplot2 extension that is made for visualizing distributions and uncertainty. ggdist-deprecated: Deprecated functions and arguments in ggdist; ggdist-ggproto: Base ggproto classes for ggdist; ggdist-package: Visualizations of Distributions and Uncertainty; guide_rampbar: Continuous colour ramp guide; lkjcorr_marginal: Marginal distribution of a single correlation from an LKJ. Ensures the dotplot fits within available space by reducing the size of the dots automatically (may result in very small dots). width instead. A ggplot2::Geom representing a slab (ridge) geometry which can be added to a ggplot() object. Overlapping Raincloud plots. We would like to show you a description here but the site won’t allow us. If TRUE, missing values are silently. The following vignette describes the geom_lineribbon () family of stats and geoms in ggdist, a family of stats and geoms for creating line+ribbon plots: for example, plots with a fit line and one or more uncertainty bands. We use a network of warehouses so you can sit back while we send your products out for you. If FALSE, the default, missing values are removed with a warning. In this tutorial, I highlight the potential problem of box plots, illustrate why raincloud plots are great, and show numerous ways how to create such hybrid charts in R with {ggplot2}. For example, input formats might expect a list instead of a data frame, and. New features and enhancements: Several computed variables in stat_slabinterval() can now be shared across sub-geometries: The . R'' ``ggdist-geom_dotsinterval. theme_ggdist theme_tidybayes facet_title_horizontal axis_titles_bottom_left facet_title_left_horizontal facet_title_right_horizontal Value. . A string giving the suffix of a function name that starts with "density_" ; e. A named list in the format of ggplot2::theme() Details. The distributional package allows distributions to be used in a vectorised context. For example, input formats might expect a list instead of a data frame, and. Huge thanks for all your work on ggdist, it is really excellent!While annotate (geom = "text") will add a single text object to the plot, geom_text () will create many text objects based on the data, as discussed in Recipe 5. These objects are imported from other packages. If you wish to scale the areas according to the number of observations, you can set aes (thickness = stat (pdf*n)) in stat_halfeye (). Stan is a C++ library for Bayesian inference using the No-U-Turn sampler (a variant of Hamiltonian Monte Carlo) or frequentist inference via optimization. . This format is also compatible with stats::density() . prob argument, which is a long-deprecated alias for . They also ensure dots do not overlap, and allow the generation of quantile dotplots using the quantiles. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This is a flexible family of stats and geoms designed to make plotting distributions (such as priors and posteriors in Bayesian models, or even sampling distributions from other models) straightforward, and support a range of useful plots, including intervals, eye plots. Breaking changes: The following changes, mostly due to new default density estimators, may cause some plots on sample data to change. . This includes retail locations and customer service 1-800 phone lines. Set of aesthetic mappings created by aes(). The ggdist package is a ggplot2 extension that is made for visualizing distributions and uncertainty. errors and I want to use the stat_interval() function to show the 50%, 80%, 90%, and 95% confidence intervals of these samples. ggdist is an R package that provides a flexible set of ggplot2 geoms and stats for visualizing distributions and uncertainty in frequentist and Bayesian models. after_stat () replaces the old approaches of using either stat (), e. We’ll show see how ggdist can be used to make a raincloud plot. Compatibility with other packages. ggdist unifiesa variety of uncertainty visualization types through the lens of distributional visualization, allowing functions of distributions to be mapped to directly to visual channels (aesthetics), making itA function will be called with a single argument, the plot data. R. Improved support for discrete distributions. 3. e. This vignette shows how to combine the ggdist geoms with output from the broom package to enable visualization of uncertainty from frequentist models. A combination of stat_slabinterval() and geom_lineribbon() with sensible defaults for making line + multiple-ribbon plots. To address overplotting, stat_dots opts for stacking and resizing points. width and level computed variables can now be used in slab / dots sub-geometries. Use . 5 using ggplot2. scaled with mean=x, sd=u and df=df. When TRUE and only a single column / vector is to be summarized, use the name . g. edu> Description Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist. Customer Service. edu> Description Provides primitiValue. We use a network of warehouses so you can sit back while we send your products out for you. Note: In earlier versions of wiqid the scale argument to *t2 functions was incorrectly named sd; they are not the same. alpha: The opacity of the slab, interval, and point sub-geometries. This ensures that with a justification of 0 the bottom edge of the slab touches the interval and with a justification of. Add a comment | 1 Answer Sorted by: Reset to. . pstudent_t gives the cumulative distribution function (CDF) rstudent_t generates random draws. For a more general introduction to tidybayes and its use on general-purpose Bayesian modeling languages. 5)) Is there a way to simply shift the distribution. , as generated by the point_interval() family of functions), making this geom often more convenient than vanilla ggplot2 geometries. 2 Answers. For a more general introduction to tidybayes and its use on general-purpose Bayesian modeling languages (like Stan and. geom_slabinterval () ), datatype is used to indicate which part of the geom a row in the data targets: rows with datatype = "slab" target the slab portion of the geometry and rows with datatype = "interval" target the interval portion of the geometry. Raincloud Plots with ggdist. . Feedstock license: BSD-3-Clause. We’ll show. Add interactivity to ggplot2. The main changes are: I have split tidybayes into two packages: tidybayes and ggdist; All geoms and stats now support automatic orientation detection; and. When FALSE and . Transitioning from Excel to R for data analysis enhances efficiency and enables more complex operations, and R's capability to convert Excel tables simplifies this transition. 67, 0. Using the gapminder::gapminder dataset as example data the following code plots and animates the density of worldwide life-expectancy over time. Get started with our course today. 1 Answer. We would like to show you a description here but the site won’t allow us. rm: If FALSE, the default, missing values are removed with a warning. This vignette shows how to combine the ggdist geoms with output from the broom package to enable visualization of uncertainty from frequentist models. While geom_dotsinterval () is intended for use on data frames that have already been summarized using a point_interval () function, stat_dots () is intended for use directly on data. The density ridgeline plot [ggridges package] is an alternative to the standard geom_density() [ggplot2 R package] function that can be useful for visualizing changes in distributions, of a continuous variable, over time or space. Stack Overflow Public questions & answers; Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Talent Build your employer brand ; Advertising Reach developers & technologists worldwide; Labs The future of collective knowledge sharing; About the companyggiraph. The general idea is to use xdist and ydist aesthetics supported by ggdist stats to visualize confidence distributions instead of visualizing posterior distributions as we might from a Bayesian. Details ggdist is an R. . In this tutorial, we use several geometries to make a custom Raincl. width column is present in the input data (e. This is a flexible family of stats and geoms designed to make plotting distributions (such as priors and posteriors in Bayesian models, or even sampling distributions from other models) straightforward, and support a range of useful plots, including intervals, eye plots. where a is the number of cases and b is the number of non-cases, and Xi the covariates. The resulting raw data looks more “drippy” than “rainy,” but I think the stacking ultimately makes the raw data more useful when trying to identify over/under-populated bins (e. This vignette also describes the curve_interval () function for calculating curvewise (joint) intervals for lineribbon plots. Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing. You don't need it. g. Revert to the old behavior by setting density = density_unbounded(bandwidth = "nrd0"). It builds on top of (and re-exports) several functions for visualizing uncertainty from its sister package, ggdist. Run the code above in your browser using DataCamp Workspace. Starting from your definition of df, you can do this in a few lines: library (ggplot2) cols = c (2,3,4,5) df1 = transform (df, mean=rowMeans (df [cols]), sd=apply (df [cols],1, sd)) # df1 looks like this # Gene count1 count2 count3 count4 Species mean sd #1 Gene1 12 4 36 12 A 16. g. g. g. In this tutorial, we use several geometries to make a custom Raincl. If FALSE, the default, missing values are removed with a warning. Think of it as the “caret of palettes”. ggdist provides a family of functions following this format, including density_unbounded() and density_bounded(). ggdist provides a family of functions following this format, including density_unbounded() and density_bounded(). Line + multiple-ribbon plot (shortcut stat) Description. There are more and often also more efficient ways to visualize your data than just line or bar charts! We show 4 great alternatives to standard graphs for data visualization with ggplot in R. g. An object of class "density", mimicking the output format of stats::density(), with the following components: .