Discuss ggplot2 ”; Previous Next ggplot2 is an R package used for statistical computing and data representation using data visualization. It follows underlying graphics called Grammar of Graphics which includes certain rules and independent components which can be used to represent data in various formats. This tutorial includes all the basic entities and functions which are primarily used in R. Print Page Previous Next Advertisements ”;
ggplot2 – Themes ”; Previous Next In this chapter, we will focus on using customized theme which is used for changing the look and feel of workspace. We will use “ggthemes” package to understand the concept of theme management in workspace of R. Let us implement following steps to use the required theme within mentioned dataset. GGTHEMES Install “ggthemes” package with the required package in R workspace. > install.packages(“ggthemes”) > Library(ggthemes) Implement new theme to generate legends of manufacturers with year of production and displacement. > library(ggthemes) > ggplot(mpg, aes(year, displ, color=factor(manufacturer)))+ + geom_point()+ggtitle(“This plot looks a lot different from the default”)+ + theme_economist()+scale_colour_economist() It can be observed that the default size of the tick text, legends and other elements are little small with previous theme management. It is incredibly easy to change the size of all the text elements at once. This can be done on creating a custom theme which we can observe in below step that the sizes of all the elements are relative (rel()) to the base_size. > theme_set(theme_gray(base_size = 30)) > ggplot(mpg, aes(x=year, y=class))+geom_point(color=”red”) Print Page Previous Next Advertisements ”;
ggplot2 – Useful Resources ”; Previous Next The following resources contain additional information on ggplot2. Please use them to get more in-depth knowledge on this. Useful Video Courses Learning R through an Example 13 Lectures 1.5 hours PARTHA MAJUMDAR More Detail Predictive Modeling with R For Beginners 36 Lectures 2.5 hours Ermin Dedic, Aditya Sharma More Detail Artificial Intelligence Projects: Project-Based Learning Best Seller 139 Lectures 18.5 hours Learnkart Technology Pvt Ltd More Detail How to Visualize Data with R 15 Lectures 1 hours Packt Publishing More Detail Programación práctica en R desde cero 73 Lectures 4.5 hours Julian Cuero More Detail Graphics in R: Data Visualization and Data Analysis with R 160 Lectures 13 hours Nkosingimele Ngcobo More Detail Print Page Previous Next Advertisements ”;
ggplot2 – Multi Panel Plots ”; Previous Next Multi panel plots mean plot creation of multiple graphs together in a single plot. We will use par() function to put multiple graphs in a single plot by passing graphical parameters mfrow and mfcol. Here we will use “AirQuality” dataset to implement multi panel plots. Let us understand the dataset first to have a look on creation of multi panel plots. This dataset includes Contains the responses of a gas multi-sensor device deployed on the field in an Italian city. Hourly responses averages are recorded along with gas concentrations references from a certified analyzer. Insight of par() function Understand the par() function to create a dimension of required multi panel plots. > par(mfrow=c(1,2)) > # set the plotting area into a 1*2 array This creates a blank plot with dimension of 1*2. Now create the bar plot and pie chart of the mentioned dataset using following command. This same phenomenon can be achieved with the graphical parameter mfcol. Creating Multi Panel Plots The only difference between the two is that, mfrow fills in the subplot region row wise while mfcol fills it column wise. > Temperature <- airquality$Temp > Ozone <- airquality$Ozone > par(mfrow=c(2,2)) > hist(Temperature) > boxplot(Temperature, horizontal=TRUE) > hist(Ozone) > boxplot(Ozone, horizontal=TRUE) The boxplots and barplots are created in single window basically creating a multi panel plots. Same plot with a change of dimensions in par function would look as follows − par(mfcol = c(2, 2)) Print Page Previous Next Advertisements ”;
ggplot2 – Diverging Charts ”; Previous Next In the previous chapters, we had a look on various types of charts which can be created using “ggplot2” package. We will now focus on the variation of same like diverging bar charts, lollipop charts and many more. To begin with, we will start with creating diverging bar charts and the steps to be followed are mentioned below − Understanding dataset Load the required package and create a new column called ‘car name’ within mpg dataset. #Load ggplot > library(ggplot2) > # create new column for car names > mtcars$`car name` <- rownames(mtcars) > # compute normalized mpg > mtcars$mpg_z <- round((mtcars$mpg – mean(mtcars$mpg))/sd(mtcars$mpg), 2) > # above / below avg flag > mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, “below”, “above”) > # sort > mtcars <- mtcars[order(mtcars$mpg_z), ] The above computation involves creating a new column for car names, computing the normalized dataset with the help of round function. We can also use above and below avg flag to get the values of “type” functionality. Later, we sort the values to create the required dataset. The output received is as follows − Convert the values to factor to retain the sorted order in a particular plot as mentioned below − > # convert to factor to retain sorted order in plot. > mtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`) The output obtained is mentioned below − Diverging Bar Chart Now create a diverging bar chart with the mentioned attributes which is taken as required co-ordinates. > # Diverging Barcharts > ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + + geom_bar(stat=”identity”, aes(fill=mpg_type), width=.5) + + scale_fill_manual(name=”Mileage”, + labels = c(“Above Average”, “Below Average”), + values = c(“above”=”#00ba38”, “below”=”#f8766d”)) + + labs(subtitle=”Normalised mileage from ”mtcars””, + title= “Diverging Bars”) + + coord_flip() Note − A diverging bar chart marks for some dimension members pointing to up or down direction with respect to mentioned values. The output of diverging bar chart is mentioned below where we use function geom_bar for creating a bar chart − Diverging Lollipop Chart Create a diverging lollipop chart with same attributes and co-ordinates with only change of function to be used, i.e. geom_segment() which helps in creating the lollipop charts. > ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + + geom_point(stat=”identity”, fill=”black”, size=6) + + geom_segment(aes(y = 0, + x = `car name`, + yend = mpg_z, + xend = `car name`), + color = “black”) + + geom_text(color=”white”, size=2) + + labs(title=”Diverging Lollipop Chart”, + subtitle=”Normalized mileage from ”mtcars”: Lollipop”) + + ylim(-2.5, 2.5) + + coord_flip() Diverging Dot Plot Create a diverging dot plot in similar manner where the dots represent the points in scattered plots in bigger dimension. > ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + + geom_point(stat=”identity”, aes(col=mpg_type), size=6) + + scale_color_manual(name=”Mileage”, + labels = c(“Above Average”, “Below Average”), + values = c(“above”=”#00ba38”, “below”=”#f8766d”)) + + geom_text(color=”white”, size=2) + + labs(title=”Diverging Dot Plot”, + subtitle=”Normalized mileage from ”mtcars”: Dotplot”) + + ylim(-2.5, 2.5) + + coord_flip() Here, the legends represent the values “Above Average” and “Below Average” with distinct colors of green and red. Dot plot convey static information. The principles are same as the one in Diverging bar chart, except that only point are used. Print Page Previous Next Advertisements ”;
ggplot2 – Marginal Plots ”; Previous Next In this chapter, we shall discuss about Marginal Plots. Understanding Marginal Plots Marginal plots are used to assess relationship between two variables and examine their distributions. When we speak about creating marginal plots, they are nothing but scatter plots that has histograms, box plots or dot plots in the margins of respective x and y axes. Following steps will be used to create marginal plot with R using package “ggExtra”. This package is designed to enhance the features of “ggplot2” package and includes various functions for creating successful marginal plots. Step 1 Install “ggExtra” package using following command for successful execution (if the package is not installed in your system). > install.packages(“ggExtra”) Step 2 Include the required libraries in the workspace to create marginal plots. > library(ggplot2) > library(ggExtra) Step 3 Reading the required dataset “mpg” which we have used in previous chapters. > data(mpg) > head(mpg) # A tibble: 6 x 11 manufacturer model displ year cyl trans drv cty hwy fl class <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr> 1 audi a4 1.8 1999 4 auto(l5) f 18 29 p compa~ 2 audi a4 1.8 1999 4 manual(m5) f 21 29 p compa~ 3 audi a4 2 2008 4 manual(m6) f 20 31 p compa~ 4 audi a4 2 2008 4 auto(av) f 21 30 p compa~ 5 audi a4 2.8 1999 6 auto(l5) f 16 26 p compa~ 6 audi a4 2.8 1999 6 manual(m5) f 18 26 p compa~ > Step 4 Now let us create a simple plot using “ggplot2” which will help us understand the concept of marginal plots. > #Plot > g <- ggplot(mpg, aes(cty, hwy)) + + geom_count() + + geom_smooth(method=”lm”, se=F) > g Relationship between Variables Now let us create the marginal plots using ggMarginal function which helps to generate relationship between two attributes “hwy” and “cty”. > ggMarginal(g, type = “histogram”, fill=”transparent”) > ggMarginal(g, type = “boxplot”, fill=”transparent”) The output for histogram marginal plots is mentioned below − The output for box marginal plots is mentioned below − Print Page Previous Next Advertisements ”;
ggplot2 – Pie Charts ”; Previous Next A pie chart is considered as a circular statistical graph, which is divided into slices to illustrate numerical proportion. In the mentioned pie chart, the arc length of each slice is proportional to the quantity it represents. The arc length represents the angle of pie chart. The total degrees of pie chart are 360 degrees. The semicircle or semi pie chart comprises of 180 degrees. Creating Pie Charts Load the package in the mentioned workspace as shown below − > # Load modules > library(ggplot2) > > # Source: Frequency table > df <- as.data.frame(table(mpg$class)) > colnames(df) <- c(“class”, “freq”) The sample chart can be created using the following command − > pie <- ggplot(df, aes(x = “”, y=freq, fill = factor(class))) + + geom_bar(width = 1, stat = “identity”) + + theme(axis.line = element_blank(), + plot.title = element_text(hjust=0.5)) + + labs(fill=”class”, + x=NULL, + y=NULL, + title=”Pie Chart of class”, + caption=”Source: mpg”) > pie If you observe the output, the diagram is not created in circular manner as mentioned below − Creating co-ordinates Let us execute the following command to create required pie chart as follows − > pie + coord_polar(theta = “y”, start=0) Print Page Previous Next Advertisements ”;
ggplot2 – Bar Plots & Histograms ”; Previous Next Bar plots represent the categorical data in rectangular manner. The bars can be plotted vertically and horizontally. The heights or lengths are proportional to the values represented in graphs. The x and y axes of bar plots specify the category which is included in specific data set. Histogram is a bar graph which represents the raw data with clear picture of distribution of mentioned data set. In this chapter, we will focus on creation of bar plots and histograms with the help of ggplot2. Understanding MPG Dataset Let us understand the dataset which will be used. Mpg dataset contains a subset of the fuel economy data that the EPA makes available in the below link − http://fueleconomy.gov It consists of models which had a new release every year between 1999 and 2008. This was used as a proxy for the popularity of the car. Following command is executed to understand the list of attributes which is needed for dataset. > library(ggplot2) The attaching package is ggplot2. The following object is masked _by_ .GlobalEnv − mpg Warning messages package arules was built under R version 3.5.1 package tuneR was built under R version 3.5.3 package ggplot2 was built under R version 3.5.3 Creating Bar Count Plot The Bar Count plot can be created with below mentioned plot − > # A bar count plot > p <- ggplot(mpg, aes(x=factor(cyl)))+ + geom_bar(stat=”count”) > p geom_bar() is the function which is used for creating bar plots. It takes the attribute of statistical value called count. Histogram The histogram count plot can be created with below mentioned plot − > # A historgram count plot > ggplot(data=mpg, aes(x=hwy)) + + geom_histogram( col=”red”, + fill=”green”, + alpha = .2, + binwidth = 5) geom_histogram() includes all the necessary attributes for creating a histogram. Here, it takes the attribute of hwy with respective count. The color is taken as per the requirements. Stacked Bar Chart The general plots of bar graphs and histogram can be created as below − > p <- ggplot(mpg, aes(class)) > p + geom_bar() > p + geom_bar() This plot includes all the categories defined in bar graphs with respective class. This plot is called stacked graph. Print Page Previous Next Advertisements ”;
ggplot2 – Time Series ”; Previous Next A time series is a graphical plot which represents the series of data points in a specific time order. A time series is a sequence taken with a sequence at a successive equal spaced points of time. Time series can be considered as discrete-time data. The dataset which we will use in this chapter is “economics” dataset which includes all the details of US economic time series. The dataframe includes following attributes which is mentioned below − Date Month of data collection Psavert Personal savings rate Pce Personal consumption expenditure Unemploy Number of unemployed in thousands Unempmed Median duration of unemployment Pop Total population in thousands Load the required packages and set the default theme to create a time series. > library(ggplot2) > theme_set(theme_minimal()) > # Demo dataset > head(economics) # A tibble: 6 x 6 date pce pop psavert uempmed unemploy <date> <dbl> <dbl> <dbl> <dbl> <dbl> 1 1967-07-01 507. 198712 12.6 4.5 2944 2 1967-08-01 510. 198911 12.6 4.7 2945 3 1967-09-01 516. 199113 11.9 4.6 2958 4 1967-10-01 512. 199311 12.9 4.9 3143 5 1967-11-01 517. 199498 12.8 4.7 3066 6 1967-12-01 525. 199657 11.8 4.8 3018 Create a basic line plots which creates a time series structure. > # Basic line plot > ggplot(data = economics, aes(x = date, y = pop))+ + geom_line(color = “#00AFBB”, size = 2) We can plot the subset of data using following command − > # Plot a subset of the data > ss <- subset(economics, date > as.Date(“2006-1-1”)) > ggplot(data = ss, aes(x = date, y = pop)) + + geom_line(color = “#FC4E07”, size = 2) Creating Time Series Here we will plot the variables psavert and uempmed by dates. Here we must reshape the data using the tidyr package. This can be achieved by collapsing psavert and uempmed values in the same column (new column). R function: gather()[tidyr]. The next step involves creating a grouping variable that with levels = psavert and uempmed. > library(tidyr) > library(dplyr) Attaching package: ‘dplyr’ The following object is masked from ‘package:ggplot2’: vars The following objects are masked from ‘package:stats’: filter, lag The following objects are masked from ‘package:base’: intersect, setdiff, setequal, union > df <- economics %>% + select(date, psavert, uempmed) %>% + gather(key = “variable”, value = “value”, -date) > head(df, 3) # A tibble: 3 x 3 date variable value <date> <chr> <dbl> 1 1967-07-01 psavert 12.6 2 1967-08-01 psavert 12.6 3 1967-09-01 psavert 11.9 Create a multiple line plots using following command to have a look on the relationship between “psavert” and “unempmed” − > ggplot(df, aes(x = date, y = value)) + + geom_line(aes(color = variable), size = 1) + + scale_color_manual(values = c(“#00AFBB”, “#E7B800″)) + + theme_minimal() Print Page Previous Next Advertisements ”;
ggplot2 – Quick Guide ”; Previous Next ggplot2 – Introduction ggplot2 is an R package which is designed especially for data visualization and providing best exploratory data analysis. It provides beautiful, hassle-free plots that take care of minute details like drawing legends and representing them. The plots can be created iteratively and edited later. This package is designed to work in a layered fashion, starting with a layer showing the raw data collected during exploratory data analysis with R then adding layers of annotations and statistical summaries. Even the most experienced R users need help for creating elegant graphics. This library is a phenomenal tool for creating graphics in R but even after many years of near-daily use we still need to refer to our Cheat Sheet. This package works under deep grammar called as “Grammar of graphics” which is made up of a set of independent components that can be created in many ways. “Grammar of graphics” is the only sole reason which makes ggplot2 very powerful because the R developer is not limited to set of pre-specified graphics which is used in other packages. The grammar includes simple set of core rules and principles. In the year 2005, Wilkinson created or rather originated the concept of grammar of graphics to describe the deep features which is included between all statistical graphics. It focuses on the primary of layers which includes adapting features embedded with R. Relationship between “Grammar of Graphics” and R It tells the user or developer that a statistical graphic is used for mapping the data to aesthetic attributes such as color, shape, size of the concerned geometric objects like points, lines and bars. The plot may also contain various statistical transformations of the concerned data which is drawn on the mentioned coordinate system. It also includes a feature called as “Faceting” which is generally used to create the same plot for different subsets of the mentioned dataset. R includes various in-built datasets. The combination of these independent components totally comprises a particular graphic. Now let us focus on different types of plots which can be created with reference to the grammar − Data If user wants to visualize the given set of aesthetic mappings which describes how the required variables in the data are mapped together for creation of mapped aesthetic attributes. Layers It is made up of geometric elements and the required statistical transformation. Layers include geometric objects, geoms for short data which actually represent the plot with the help of points, lines, polygons and many more. The best demonstration is binning and counting the observations to create the specific histogram for summarizing the 2D relationship of a specific linear model. Scales Scales are used to map values in the data space which is used for creation of values whether it is color, size and shape. It helps to draw a legend or axes which is needed to provide an inverse mapping making it possible to read the original data values from the mentioned plot. Coordinate System It describes how the data coordinates are mapped together to the mentioned plane of the graphic. It also provides information of the axes and gridlines which is needed to read the graph. Normally it is used as a Cartesian coordinate system which includes polar coordinates and map projections. Faceting It includes specification on how to break up the data into required subsets and displaying the subsets as multiples of data. This is also called as conditioning or latticing process. Theme It controls the finer points of display like the font size and background color properties. To create an attractive plot, it is always better to consider the references. Now, it is also equally important to discuss the limitations or features which grammar doesn’t provide − It lacks the suggestion of which graphics should be used or a user is interested to do. It does not describe the interactivity as it includes only description of static graphics. For creation of dynamic graphics other alternative solution should be applied. The simple graph created with ggplot2 is mentioned below − ggplot2 – Installation of R R packages come with various capabilities like analyzing statistical information or getting in depth research of geospatial data or simple we can create basic reports. Packages of R can be defined as R functions, data and compiled code in a well-defined format. The folder or directory where the packages are stored is called the library. As visible in the above figure, libPaths() is the function which displays you the library which is located, and the function library shows the packages which are saved in the library. R includes number of functions which manipulates the packages. We will focus on three major functions which is primarily used, they are − Installing Package Loading a Package Learning about Package The syntax with function for installing a package in R is − Install.packages(“<package-name>”) The simple demonstration of installing a package is visible below. Consider we need to install package “ggplot2” which is data visualization library, the following syntax is used − Install.packages(“ggplot2”) To load the particular package, we need to follow the below mentioned syntax − Library(<package-name>) The same applies for ggplot2 as mentioned below − library(“ggplot2”) The output is depicted in snapshot below − To understand the need of required package and basic functionality, R provides help function which gives the complete detail of package which is installed. The complete syntax is mentioned below − help(ggplot2) ggplot2 – Default Plot in R In this chapter, we will focus on creating a simple plot with the help of ggplot2. We will use