The goal is to be able to glean useful information about the distributions of each variable, without having to view one at a time and keep clicking back and forth through our plot pane! A Scatter plot (also known as X-Y plot or Point graph) is used to display the relationship between two continuous variables x and y

This layering system is based on the idea that statistical graphics are mapping from data to aesthetic attributes (color, shape, size) of geometric objects (points, lines, bars)

A function will be called with a Apr 18, 2019 · Here is a quick video on how to plot 2 graphs on the same plot in R

Apr 05, 2017 · In this video I've talked about how you can create boxplot chart in R and then further enhance it with the help of ggplot package to make it visually appealing in for good end user experience

It describes 3 different way to arrange groups in a ggplot2 chart: Using the forecats package; With dplyr; With the reorder() function of base R; Read post geom_boxplot in ggplot2 How to make a box plot in ggplot2

Summary statistics are usually added to dotplots for indicating, for example, the median of the data and the interquartile range

A recent factor analysis project (as discussed 29 Dec 2010 When you make a bar plot for categorical (i

Aug 20, 2018 · In this post I show an example of how to automate the process of making many exploratory plots in ggplot2 with multiple continuous response and explanatory variables

Imagine I have 3 different variables (which would be my y values in aes) that I want to plot for each of my samples (x aes): Coloring a line plot based on a third factor in ggplot

A Dot Plot is used to visualize the distribution of the data

by defining aesthetics (aes) Add a graphical representation of the data in the plot (points, lines, bars) adding “geoms” layers Boxplots are great to visualize distributions of multiple variables

Or, right-click and choose “Save As” to download the slides

5, 1, and 2 mg) with each of two delivery methods [orange juice (OJ) or ascorbic acid (VC)] are used : Boxplot are built thanks to the geom_boxplot() geom of ggplot2

For more details about the graphical parameter arguments, see par

However, in practice, it’s often easier to just use ggplot because the options for qplot can be more confusing to use

Looking at the diamonds data set we can see how this is set up in R

With x-axis treated as continuous; With x-axis treated as categorical; Problem

For this exampe, we're assuming that you're trying to plot some factor The issue isn't the legend, it's the choice of colors

It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties, so we only need minimal changes if the underlying data change or if we decide to change from a bar plot to a scatterplot

These limitations also lead to issues with the labeling, but those can be fixed manually

The qplot() function does not have this same functionality; however, you can do more advanced plotting matrices by using ggplot()’s facetting arguments

0) Enjoyed this article? I’d be very grateful if you’d help it spread by emailing it to a friend, or sharing it on Twitter, Facebook or Linked In

• the ggplot2 package: it implements a specific grammar for bulding graphics

Examples of grouped, stacked, overlaid, filled, and colored bar charts

Here, a single categorical variable defines subsets of the data

3 Aug 2016 These seasonal factors could then be compared to study their stability, as in the graph below

In this R ggplot dotplot example, we change the dot stack direction in a dot plot using the stackdir argument

frame with When working with categorical variables (= factors), a common struggle is to manage the order of entities on the plot

$\endgroup$ – Chase Sep 13 '11 at 0:38 If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot()

size: The color, the shape and the size for outlying points This R tutorial describes how to create line plots using R software and ggplot2 package

Apr 26, 2020 · The basic syntax of ggplot2 is: ggplot (data, mapping=aes ()) + geometric object arguments: data: Dataset used to plot the graph mapping: Control the x and y-axis geometric object: The type of plot you want to show

It takes the hassle out of things like creating legends, mapping other variables to scales like color, or faceting plots into small multiples

This chart creates stacked dots, where each dot represents one observation

In the latter section of the post I go over options for saving the resulting plots, either together in a single document, separately, or by creating combined plots Oct 26, 2016 · Plotting individual observations and group means with ggplot2

Apr 20, 2015 · A recent factor analysis project (as discussed previously here, here, and here) gave me an opportunity to experiment with some different ways of visualizing highly multidimensional data sets

Factor analysis results are often presented in tables of factor loadings, which are good when you want the numerical details, but bad when you want to convey larger-scale patterns – loadings of 0

The following packages and functions are good places to start, but the following chapter is going to teach you how to make custom interaction plots

It provides a suite of The ggplot() function is more flexible and robust than qplot for building a plot set

Second, it acknowledges that one often first develops individual plots and then combines them into multi-plot figures, and it makes it easy---in combination with plot_grid()---to carry out this workflow I'm trying to illustrate changes over time for two different groups

Sep 29, 2018 · In this video I will explain how to plot a Scatterplot using ggplot2 in R[Two Numerical & Two Categorical] This R graphics tutorial describes how to change line types in R for plots created using either the R base plotting functions or the ggplot2 package

That is suppose both f1 and f2 are factor variables and each of them takes two values and boxthis is a continuous variable

42) ) # A bar graph p <- ggplot(data=dat1, aes(x=time, y=total_bill, fill=sex)) + The code below changes the density plot: library(ggplot2); theme_set( theme_classic()); plot <- ggplot(mpg, aes(cty)); plot + geom_density(aes(fill= factor(cyl)), 3 Sep 2019 plot the lines data, apply a diff color to each factor level) ggplot() + geom_path( data = sjer_roads_df, aes(x = long, y = lat, group = group)) + the lattice package: it is invoqued when using the plot functions from the nlme or lmer package

frame) uses a different system for adding plot elements R functions like lm use tidy data, as do exploratory data analysis packages like data

You may want to set the alpha parameter in geom_point() to get some transparency so you can see the underlying box plot

Creating plots in R using ggplot2 - part 1: line plots written December 15, 2015 in r , ggplot2 , r graphing tutorials I teamed up with Mauricio Vargas Sepúlveda about a year ago to create some graphing tutorials in R

dfr$week <- factor( strftime(dfr$date,format="%V")) dfr$month How to make a bar chart in ggplot2 using geom_bar

Version info: Code for this page was tested in R Under development (unstable) (2012-07-05 r59734) On: 2012-07-08 With: knitr 0

Origianlly based on Leland Wilkinson's The Grammar of Graphics, ggplot2 allows you to create graphs that represent both univariate and multivariate numerical and categorical data in a straightforward manner

To gain a better appreciation of ggplot2 and to understand how it operates differently from base package, it's useful to make some comparisons

As you can see based on Figure 1, ggplot2 automatically adjusts the axes so that all data points are shown

Geoms - Use a geom to represent data points, use the geom’s aesthetic properties to represent variables

ggplot2 is a robust and a versatile R package, developed by the most well known R developer, Hadley Wickham, for generating aesthetic plots and charts

Example 6: Density & Histogram in Same ggplot2 Plot We can also overlay our histogram with a probability density plot

Mar 06, 2019 · To plot multiple lines in one chart, we can either use base R or install a fancier package like ggplot2

Also, showing individual data points with jittering is a good way to avoid hiding the underlying distribution

# \donttest{ p <- ggplot(mtcars, aes(wt, mpg)) # A basic scatter plot p + Using the colour aesthetic p + geom_point(aes(colour = factor(cyl)), size = 4)

Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization

The ggplot2 package, created by Hadley Wickham, offers a powerful graphics language for creating elegant and complex plots

3 In this page, we demonstrate how to create spaghetti plots, explore overall trends, and look for interactions in longitudinal data using ggplot2

Aug 09, 2018 · The ggplot2 box plots follow standard Tukey representations, and there are many references of this online and in standard statistical text books

A function will be called with a Sep 29, 2018 · In this video I will explain you about how to create barplot using ggplot2 in R for two categorical variables

Sign in Register Diagnostic Plots using ggplot2; by Raju Rimal; Last updated over 5 years ago; Hide Comments (–) Share Hide Toolbars A guide to creating modern data visualizations with R

The R ggplot2 Density Plot is useful to visualize the distribution of variables with an underlying smoothness

numeric (diamonds $ cut)) ggplot2 Nov 11, 2016 · Make a bar plot with ggplot The first time I made a bar plot (column plot) with ggplot (ggplot2), I found the process was a lot harder than I wanted it to be

Bar graphs; Line graphs; Finished examples; With a numeric x-axis

Originally based on Leland Wilkinson’s The Grammar of Graphics, ggplot2 allows you to create customized graphs tailored to your problem by building the Sep 27, 2019 · In addition to the plotting code, my plot_fun() function includes a line where I subset the resp_dat dataset to only the row of metadata for the response variable used in the plot

The qplot function is supposed make the same graphs as ggplot, but with a simpler syntax

Just compare: Basic Plotting # just getting some data library (ggplot2) data (diamonds) # basic plotting plot (diamonds $ carat, diamonds $ price, col = diamonds $ color, pch = as

And the rule is: if factor, the order of factor levels is used; if character, an alphabetical order ist used; Sorting bars by factor ordering

geom_histogram in ggplot2 How to make a histogram in ggplot2

Even better than pairs of base R, isn’t it? However, there is even more to explore

(Alternative, flat (no slides) version of the presentation: Introduction to ggplot2 seminar Flat)

I looked at the ggplot2 documentation but could not find this

Bind a data frame to a plot; Select variables to be plotted and variables to define the presentation such as size, shape, color, transparency, etc

library(ggplot2) ggplot(x, aes(x = name, y = val)) + theme_bw() + geom_bar(stat = "identity") What we would like is for R to respect the order in data

Sometimes, you may have multiple sub-groups for a variable of interest

The gg in ggplot2 means Grammar of Graphics, a graphic concept which describes plots by using a “grammar”

It is great for creating graphs of categorical data, because you can map symbol colour, size and shape to the levels of your categorical variable

The ggplot2 package in R is an implementation of The Grammar of Graphics as described by Leland Wilkinson in his book

This is due to the fact that ggplot2 takes into account the order of the factor levels, not the order you observe in your data frame

factor is an ordered factor and the levels are numeric, these numeric values are used for the x axis

See the different variables types in R if you need a refresh

gathered, aes (value)) + geom_density () + facet_wrap (~variable) # \donttest{# By default, the group is set to the interaction of all discrete variables in the # plot

Focus is on the 45 most Dec 29, 2010 · But this does not woks well, because the levels are reordered alphabetically

A time series is a sequence taken with a sequence at a successive equal spaced points of time

In our example, we simply add another layer using one of the facet functions facet_wrap() by specifying the variable we want to make a plot on its own

The package was originally written by Hadley Wickham while he was a graduate student at Iowa State University (he still actively maintains the packgae)

setwd("~/Documents/Computing with Data/13_Facets/")library(ggplot2) Facet wrap

Regarding plots, we present the default graphs and the graphs from the well-known {ggplot2} package

Actually, this is not a power of ggplot2, but the general behavior of factor variable

In previous plots we’ve been using categories, specifically the Species category to split our data, colour our plots etc

In a line graph, observations are ordered by x value and connected

current solution: read in the variables x1 and x2 as x = product(x1, x2) product function: a wrapper function for a list; allows for it to pass check_aesthetics

While I'm happy with the graph in general, I'd like to know how to remove the second tick (without label) on the x axis

R has the capability to produce informative plots quickly, which is useful for exploring data or for checking model assumptions

IrisBox <- ggplot(iris, aes( 4 Aug 2018 Creating an availability calendar plot using ggplot2 in R

4 to combine individual plots into one, but will use the package functions via cowplot:: instead of loading the package

ggplot2 is a powerful and a flexible R package, implemented by Hadley Wickham, for producing elegant graphics

There is a wealth of information on the philosophy of ggplot2, how to get started with ggplot2, and how to customize the smallest elements of a graphic using ggplot2 — but it's all I am very new to R and to any packages in R

Here's a quick demonstration of the trick you need to use to convince R and ggplot to do it

That means, by-and-large, ggplot2 itself changes relatively little

For any other type of y the next plot method is called, normally plot

ggplot2 is not capable of handling a variable number of variables

Important You must specify scores option when calling factanal to calcurate sores (default scores = NULL)

In R base plot functions, the options lty and lwd are used to specify the line type and the line width, respectively

Its popularity in the R community has exploded in recent years

If you have a dataset that is in a wide format, one simple way to plot multiple lines in one chart is by using matplot: Using Facets in ggplot2

I want a box plot of variable boxthis with respect to two factors f1 and f2

The help file for this function is very informative, but it's often non-R users asking what exactly the plot means

The qplot (quick plot) system is a subset of the ggplot2 (grammar of graphics) package which you can use to create nice graphs

ggplot2 is a widely used R package that extends R’s visualization capabilities

Creating plots in R using ggplot2 - part 7: histograms written February 28, 2016 in r , ggplot2 , r graphing tutorials This is the seventh tutorial in a series on using ggplot2 I am creating with Mauricio Vargas Sepúlveda

Apr 02, 2019 · Easy multi-panel plots in R using facet_wrap() and facet_grid() from ggplot2 Posted on April 2, 2019 by sandy haaf · Leave a comment One of the most powerful aspects of the R plotting package ggplot2 is the ease with which you can create multi-panel plots

Secondly, we customise the colours of the boxes by adding the scale_fill_brewer to the plot from the RColorBrewer package

The dataset which we will use in this chapter is Plotting Factor Analysis {ggfortify} supports stats::factanal object as the same manner as PCAs

Welcome the R graph gallery, a collection of charts made with the R programming language

The facet helps in building the chart by dividing the data into two or more groups

8) + labs (title= "Density plot", subtitle= "City Mileage Grouped by Number of cylinders", caption= "Source: mpg", x= "City Mileage", fill= "# Cylinders") 6

If so, the missing values and the line segments joining them are omitted from the plot (and this can be somewhat disconcerting)

h <- ggplot(diamonds, aes(cut, color)) h + geom_jitter() Build a graph with qplot () or ggplot() ggplot2 is Creates a complete plot with given data, geom, and

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot()

R Scatter Plot – ggplot2 A scatter plot is a graphical display of relationship between two sets of data

When we do make changes, they will be generally to add new functions or arguments rather than changing the behaviour of existing functions, and if we do make changes to Graphs with more variables

In addition specialized graphs including geographic maps, the display of change over time, flow diagrams, interactive graphs, and graphs that help with the interpret statistical models are included

This R tutorial describes how to create a box plot using R software and ggplot2 package

In this post I’ll show you to use the gganimate package from David Robinson to create animations from ggplot2 plots

is more verbose for simple / canned graphics; is less verbose for complex / custom graphics; does not have methods (data should always be in a data

Hundreds of charts are displayed in several sections, always with their reproducible code available

Second, we can do the computation of frequencies ourselves and just give the condensed numbers to ggplot2

A Scatter plot (also known as X-Y plot or Point graph) is used to display the relationship between two continuous variables x and y

By displaying a variable in each axis, it is possible to determine if an association or a correlation exists between the two variables

Feel free to suggest a chart or report a bug; any feedback is highly welcome

Grouping can be represented by color, symbol, size, and transparency

24 May 2016 To colour box plots or bar plots by a given categorical variable, you use you use fill = variable

This is only to illustrate a concept so I don't want too many things in the graph and therefore only show 4 specific time points on the x axis (Start, n-1, n, End)

2 Apr 2019 One of the most powerful aspects of the R plotting package ggplot2 is the ease with factor(gender, levels = c("Male ", "Female "))) ggplot(data data(iris) library(ggplot2) library(tidyr) iris %>% gather("Type", "Value",-Species) %>% ggplot(aes(Species, Value, fill = Type)) + nlme, effects, and ggplot for running the model and making interaction plots; Note: For factor predictors, the effects function will include all levels of the factor

In this case you just pass the multiple variables (columns) in the data frame to plot() and a scatter plot matrix will be returned

You can create a similar plot in ggplot, but you will need to do some reshaping Nov 16, 2018 · Plotting separate slopes with geom_smooth() The geom_smooth() function in ggplot2 can plot fitted lines from models with a simple structure

For that to happen, we need to change the order of factor levels by specifying the order explicitly

This is a tutorial on how to run a PCA using FactoMineR, and visualize the result using ggplot2

geom_bar in ggplot2 How to make a bar chart in ggplot2 using geom_bar

library (ggplot2) theme_set (theme_classic ()) # Plot g <-ggplot (mpg, aes (cty)) g + geom_density (aes (fill= factor (cyl)), alpha= 0

ggplot2 is now over 10 years old and is used by hundreds of thousands of people to make millions of plots

Lollipop plot A lollipop plot is basically a barplot , where the bar is transformed in a line and a dot

This functions implements a scatterplot method for factor arguments of the generic plot function

For this video we plot two line graphs using the mtcars dataset in R

Note that reordering groups is an important step to get a more insightful figure

ToothGrowth describes the effect of Vitamin C on tooth growth in Guinea pigs

This often partitions the data correctly, but when it does not, or when # no discrete variable is used in the plot, you will need to explicitly define the # grouping structure, by mapping group to a variable that has a different value # for each group

To make graphs with ggplot2, the data must be in a data frame, and in “long” (as opposed to wide) format

Edit: Incrementally change existing plot (2016-11-28) digest 0

, no active development) since February 2014, ggplot2 it is the most downloaded R package of all time

Supported model types include models fit with lm(), glm(), nls(), and mgcv::gam()

Main difference to the pairs function of base R: The diagonal consists of the densities of the three variables and the upper panels consist of the correlation coefficients between the variables

Jul 15, 2016 · Quick plot of all variables This post will explain a data pipeline for plotting all (or selected types) of the variables in a data frame in a facetted plot

1, color ="blue") Or to color each species in the plot differently, you could use a vector as an input to the argument color

The response and hence its summary can contain missing values

ggplot2’s facet-ing option makes it super easy to make great looking small multiples

ggplot(data =surveys_complete, mapping =aes(x =weight, y =hindfoot_length)) +geom_point(alpha =0

Here are some examples of what we’ll be creating: I find these sorts of plots to be incredibly useful for visualizing and gaining insight into our data

I’ll be plotting with ggplot2, reshaping with tidyr, and combining plots with packages egg and patchwork

Sample data; facet_grid; facet_wrap; Modifying facet label appearance; Modifying facet label text; Free scales; Problem

Facetting generates small multiples each showing a different subset of the data

To loop through both x and y variables involves nested looping

The functions geom_line(), geom_step(), or geom_path() can be used

Examples of box plots in R that are grouped, colored, and display the underlying data distribution

It is not really the greatest, smart looking R code you want to use

Starting with data preparation, topics include how to create effective univariate, bivariate, and multivariate graphs

I use this information to add the constant to y and make a plot title with a description of the variable plus the units

For numeric y a boxplot is used, and for a factor y a spineplot is shown

Graphs from the {ggplot2} package usually have a better look but it requires more advanced coding skills

They are good if you to want to visualize how two variables are correlated

You can sort your input data frame with sort() or arrange(), it will never have any impact on your ggplot2 output

Scatter plots are used to display the relationship between two continuous variables x and y

ggplot2 is a plotting package that makes it simple to create complex plots from data in a data frame

When running a regression in R, it is likely that you will be interested in interactions

One Variable This is exactly the R code that produced the above plot

frame( time = factor(c("Lunch","Dinner"), levels=c("Lunch","Dinner")), total_bill Very basic bar graph ggplot(data=dat, aes(x=time, y=total_bill)) + And, as an aside, it does not work if the variable is a character vector rather than a factor (though of course that's an easy fix)

This post steps through building a bar plot from start to finish

The ggplot2 implies " Grammar of Graphics " which believes in the principle that a plot can be split into the following basic parts - Figure 1: Basic Density Plot of ggplot2 R Package

base package and ggplot2, part 1 - plot These courses are about understanding data visualization in the context of the grammar of graphics

ggplot(mtcars) + geom_point(aes(x=mpg, y=drat, colour=factor(gear))) Note that if you're not getting a gradient plot when not using factor, you should try upgrading to a more recent version of ggplot2

frame( sex = factor(rep(c("F", "M"), each=200)), Assign plot to a variable MS_plot <- ggplot(data = MS_county_stops, aes(x allows to split one plot into multiple plots based on a factor included in the dataset

ggplot2 uses the order of levels of factor variable to determine the order of category

Albeit it appears common not to like factors, now that’s a situation when they are useful

In those situation, it is very useful to visualize using “grouped boxplots”

Imagine I have 3 different variables (which would be my y values in aes) that I want to plot for each of my samples (x aes): A time series is a graphical plot which represents the series of data points in a specific time order

packages("ggplot2", dependencies = TRUE) Introduction to ggplot2 seminar: Left-click the link to open the presentation directly

First, we can just take a data frame in its raw form and let ggplot2 count the rows so to compute frequencies and map that to the height of the bar

When it is not a factor, the points are different shades of the same hue: ggplot(mtcars) + There are plotting capabilities that come with R, but ggplot2 provides a consistent colour = species_id, shape = as

It is just a simple plot and points functions to plot multiple data series

frame( time = factor(c("Lunch","Dinner"), levels=c("Lunch" 16

Reproducible code provided and focus on ggplot2 and the tidyverse

Here are two examples of how to plot multiple lines in one chart using Base R

In the following examples, I’ll show you how to modify the axes of such ggplots

stackdir: By default, its value is up (it means dots stacked in the upward direction), but you can change to down, center, and centerwhole

Factors provide an easy for sorting, see: Mar 04, 2015 · The R package ggplot2, created by Hadley Wickham, is an implementation of Leland Wilkinson’s Grammar of Graphics, which is a systematic approach to describe the components of a graphic

Let us […] Mar 16, 2016 · Creating plots in R using ggplot2 - part 8: density plots written March 16, 2016 in r , ggplot2 , r graphing tutorials This is the eighth tutorial in a series on using ggplot2 I am creating with Mauricio Vargas Sepúlveda

This is due to the fact that ggplot2 takes into account the order of the factor from the tidyverse especially made to handle factors in R

, factor) variables, p <- ggplot (d, aes (y=Win)) + opts (axis

The animation shown above is composed by two curves: The top one (infinity shape) is a Lemniscate of Bernoulli and can be created with the following parametric equations: If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot()

Plotting PCA results in R using FactoMineR and ggplot2 Timothy E

You want to do split up your data by one or more variables and plot the subsets of data together

ggplot2will provide a different color corresponding to different values in the vector

Get model predictions and plot them with ggplot2 some simulated data and create a GAM model with a factor by variable Now plotting can be done with ggplot2

In R, ggplot2 package offers multiple options to visualize such grouped boxplots

Examines one, two, or three variables and creates, based on their characteristics, a scatter, violin, box, bar, density, hex or spine plot, or a heat map

Once Treatment: Factor w/ 2 levels "No","Yes": 2 2 2 1 1 2 1 1 1 2

Jan 20, 2017 · In R, using ggplot2, there are basically two ways of plotting bar plots (as far as I know)

within the aesthetics of the geom_histogram function and we also need to add another line of code to our ggplot2 syntax, which is drawing the density plot: The plots in this book will be produced using R

Furthermore the ggplot2 package leaves some space around the plotted data

The base R function to calculate the box plot limits is boxplot

ggplot() creates a So ggplot(data = mpg) creates an empty graph, but it's not very interesting so I'm ggplot(data = toldat, aes(x = time, y = tolerance)) + geom_line() + have seen how to graph simple, longitudinal data, but what if there was a nesting factor also ? With ggplot2, the default y range of a line graph is just enough to include the y If the x variable is a factor, you must also tell ggplot() to group by that same library(ggplot2) ggplot(midwest, aes(x=area, y=poptotal)) + geom_point() This is because, the previous plot was stored as g, a ggplot object, which when called factor(cty_mpg$make, levels = cty_mpg$make) # to retain the order in plot

In this article, we’ll start by showing how to create beautiful scatter plots in R

The faceting is defined by a categorical variable or variables

Nov 17, 2019 · Thanks to ggplot2, making a plot showcasing multiple variables separately as small multiples is really easy

Examples and tutorials for plotting histograms with geom_histogram, geom_density and stat_density

It also has the ability to produce more refined plots with more options, quintessentially through using the package ggplot2

Let us see how to Create a ggplot density plot, Format its colour, alter the axis, change its labels, adding the histogram, and plot multiple density plots using R ggplot2 with an example

First, it uses default sizes that work well with the cowplot theme, so that frequently a plot size does not have to be explicitly specified

In some circumstances we want to plot relationships between set variables in multiple subsets of the data with the results appearing as panels in a larger figure

x value (for x axis) can be : date : for a time series data; texts; discrete numeric values; continuous numeric values Plot the density distribution of each variable: ggplot (iris

New to Plotly? Plotly is a free and open-source graphing library for R

In order to plot the two temperature levels in the same plot, we need to add a couple of things

@drsimonj here to share my approach for visualizing individual observations with group means in the same plot

plots we've been using categories, specifically the Species category to split our data, colour our plots etc

This tutorial is primarily geared towards those having some basic knowledge of the R programming language and want to make complex and nice looking install

The Complete ggplot2 Tutorial - Part 2 | How To Customize ggplot2 (Full R code) This is part 2 of a 3-part tutorial on ggplot2, an aesthetically pleasing (and very popular) graphics framework in R

Small multiples are a powerful tool for exploratory data analysis: you can rapidly compare patterns in different parts of the data and see whether they are the same or different

Facet is a way in which you can add additional categorical variables to your plot