See Chapter 12 for more on choosing colors. No lines on the left and right of the graph 4. Making a Proportional Stacked Area Graph Problem You want to make a stacked area graph with the overall height scaled to a constant value. Solution First, calculate the proportions. See Also For more on summarizing data by groups, see Recipe Adding a Confidence Region Problem You want to add a confidence region to a graph. A line graph with a shaded confidence region The shaded region is actually a very dark grey, but it is mostly transparent.
If the reverse order were used, the shaded region could obscure the line. In the area graphs in Recipe 4. Here, it goes from ymin to ymax. In a scatter plot, each observation in a data set is represented by a point.
Often, a scatter plot will also have a line showing the predicted values based on some statistical model. With large data sets, it can be problematic to plot every single observation because the points will be overplotted, obscuring one another. A basic scatter plot Discussion To use different shapes in a scatter plot, set shape.
The default value of size is 2. An alternative is to use shape 19, which is also a solid circle, but comes out smooth in more cases see Figure Left: scatter plot with hollow circles shape 21 ; right: with smaller points Figure Point shapes 16 and 19, as they appear with some bitmap output devices 5. Grouping points by a variable mapped to colour left , and to shape right Discussion The grouping variable must be categorical—in other words, a factor or character vector. If it is stored as a vector of numeric values, it should be converted to a factor before it is used as a grouping variable.
Left: mapping to both shape and colour; right: with manually set shapes and colors See Also To use different shapes, see Recipe 5. For more on using different colors, see Chapter Using Different Point Shapes Problem You want to use point shapes that are different from the defaults. Some of the point shapes 1—14 have just an outline, some 15—20 are solid, and some 21—25 have an outline and fill that can be controlled separately.
You can also use characters for points. For shapes 21—25, the outline is controlled by colour and the fill is controlled by fill. This is done a little indirectly, by choosing shapes that have both colour and fill, and a color palette that includes NA and another color the NA will result in a hollow shape.
A variable mapped to shape and another mapped to fill See Also For more on using different colors, see Chapter For more information about recoding a continuous variable to a categorical one, see Recipe Mapping a Continuous Variable to Color or Size Problem You want to represent a third continuous variable using color or size. Solution Map the continuous variable to size or colour.
To represent a third continuous variable, weightLb, we must map it to another aesthetic property. We can easily perceive small differences in spatial position, so we can interpret the variables mapped to x and y coordinates with high accuracy. When you map a variable to one of these properties, it should be one where accuracy is not very important for interpretation. When a variable is mapped to size, the results can be perceptually misleading.
The largest dots in Figure have about 36 times the area of the smallest ones, but they 5. Left: outlined points with a continuous variable mapped to fill; right: with a discrete legend instead of continuous colorbar represent only about 3. If it is important for the sizes to proportionally represent the quantities, you can change the range of sizes. By default the sizes of points go from 1 to 6 mm.
See Recipe 5. When it comes to color, there are actually two aesthetic attributes that can be used: colour and fill. For most point shapes, you use colour. However, shapes 21—25 have an outline with a solid region in the middle where the color is controlled by fill. These outlined shapes can be useful when using a color scale with light colors, as in Figure , because the outline sets them off from the background. This is because it is difficult to compare the sizes of different shapes; for example, a size 4 triangle could appear larger than a size 3.
Also, some of the shapes really are different sizes: shapes 16 and 19 are both circles, but at any given numeric size, shape 19 circles are visually larger than shape 16 circles.
See Also To use different colors from the default, see Recipe Dealing with Overplotting Problem You have many points and they obscure each other. Solution With large data sets, the points in a scatter plot may obscure each other and prevent the viewer from accurately assessing the distribution of the data. If the amount of overplotting is low, you may be able to alleviate it by using smaller points, or by using a different shape like shape 1, a hollow circle through which other points can be seen.
Figure in Recipe 5. See Chapter 14 for more information. Another solution is to bin the points into rectangles and map the density of the points to the fill color of the rectangles, as shown in Figure With the binned visualization, the vertical bands are barely visible. The density of points in the lower-left corner is much greater, which tells us that the vast majority of diamonds are small and inexpensive. This is because the range of the color scale starts not from zero, but from the smallest nonzero quantity in a bin—probably 1, in this case.
To use it, you must first install the hexbin package, with install. This will convey a different story than a standard scatter plot because it will obscure the number of data points at each location on the discrete axis. This may be problematic in some cases, but desirable in others. To do this, see Recipe 6. This instructs it to fit the data with the lm linear model function. This can be change by setting colour.
As with any other line, the attributes linetype and size can also be set. Another common type of model fit is a logistic regression. In this data set, there are nine different measured attributes of breast cancer biopsies, as well as the class of the tumor, which is either benign or malignant. To prepare the data for logistic regression, we must convert the factor class, with the levels be nign and malignant, to a vector with numeric values of 0 and 1.
There are two reasons for this. The second is that even if it extrapolates, the loess function only offers prediction within the x range of the data. Adding Fitted Lines from an Existing Model Problem You have already created a fitted regression model object for a data set, and you want to plot the lines for that model.
Sometimes, however, you may want to create the model yourself and then add it to your graph. For example, lm has predict. Adding lines from a model can be simplified by using the function predictvals , defined next. If a vector with two numbers, use those as the min and max of the prediction range. Different ways of extracting the x range, depending on model type if is. This is because the default behavior is to return predicted values in the scale of the linear predictors, instead of in the scale of the response y variable.
As we did in Recipe 5. A fitted logistic model 5. Adding Fitted Lines from Multiple Existing Models Problem You have already created a fitted regression model object for a data set, and you want to plot the lines for that model. Solution Use the predictvals function from the previous recipe along with dlply and ldply from the plyr package.
If you pass it a data frame, it simply returns an lm object. With the preceding code, the x range of the predicted values for each group spans the x range of each group, and no further; for the males, the prediction line stops at the oldest male, while for females, the prediction line continues further right, to the oldest female. Predictions for each group extend to the full x range of all groups together 5.
Adding Annotations with Model Coefficients Problem You want to add numerical information about a model to a plot. Solution To add simple text to a plot, simply add an annotation. If you use a math expression, the syntax must be correct for it to be a valid R expression object. You can test validity by wrapping it in expression and seeing if it throws an error make sure not to use quotes around the expression. See Recipe 7.
Scatter plot with automatically generated expression 5. Marginal rug added to a scatter plot In this particular data set, the marginal rug is not as informative as it could be. The resolution of the waiting variable is in whole minutes, and because of this, the rug lines have a lot of overplotting. To reduce the overplotting, we can jitter the line positions and make them slightly thinner by specifying size Figure United Kingdom GBR To manually add annotations, use annotate , and specify the coordinates and label Figure , left.
As was the case with vjust, the labels will still slightly overlap with the points. Doing so will shift the labels a distance proportional to the length of the label, making longer labels move further than shorter ones. Switzerland CHE Scatter plot with selected labels and expanded x range If any individual position adjustments are needed, you have a couple of options. One option is to copy the columns used for the x and y coordinates and modify the numbers for the individual items to move the text around.
Make sure to use the original numbers for the coordinates of the points, of course! See Also For more on controlling the appearance of the text, see Recipe 9. Creating a Balloon Plot Problem You want to make a balloon plot, where the area of the dots is proportional to their numerical value. Left: balloon plot with value mapped to radius; right: with value mapped to area Discussion The example here is a scatter plot, but that is not the only way to use balloon plots.
Next, we wanted to set the y coordinate so that it is just underneath the bottom of each circle. This requires a little arithmetic: take the numeric value of Hair and subtract a small value from it, where the value depends in some way on count. This actually requires taking the square root of count, since the radius has a linear relationship with the square root of count.
The number that this value divided by 22 in this case is found by trial and error; it depends on the particular data values, radius, and text size. The text under the circles is in a shade of grey. Balloon plot with categorical axes and text labels See Also To add labels to the circles, see Recipes 5. Solution A scatter plot matrix is an excellent way of visualizing the pairwise relationships among several variables. Zambia This will also show higher correlations in a larger font.
The last line of this version of the panel. Scatter plot with correlations in the upper triangle, smoothing lines in the lower triangle, and histograms on the diagonal It may be more desirable to use linear regression lines instead of LOWESS lines.
The panel. Scatter plot matrix with smaller points and linear fit lines The size of the points can also be controlled using the cex parameter. The default value for cex is 1; make it smaller for smaller points and larger for larger points. See Also To create a correlation matrix, see Recipe The ggpairs function from the GGally package can also make scatter plot matrices. Making a Basic Histogram Problem You want to make a histogram.
This may be too fine or too coarse for your data. You can change the size of the bins by using binwidth, or you can divide the range of the data into a specific number of bins. Different appearance of histograms with the origin at 31 and 35 The results look quite different, even though they have the same bin size. The faith ful data set is not particularly small, with observations; with smaller data sets, this is even more of an issue. They are closed on the lower bound and open on the upper bound.
If you have bin boundaries at 1, 2, 3, etc. In other words, the first bin contains 1 but not 2, and the second bin contains 2 but not 3. See Also Frequency polygons provide a better way of visualizing multiple distributions without the bars interfering with each other. See Recipe 6. For this example, we used the birthwt data set.
It contains data about birth weights and a number of risk factors for low birth weight: birthwt low age lwt race smoke ptl ht ui ftv bwt 0 19 2 0 0 0 1 0 0 33 3 0 0 0 0 3 0 20 1 1 0 0 0 1 To change the labels, we need to change the names of the factor levels.
With facets, the axes have the same y scaling in each facet. If your groups have different sizes, it might be hard to compare the shapes of the distributions of each one. The grouping variable must be a factor or character vector. Without it, ggplot will stack the histogram bars on top of each other vertically, making it much more difficult to see the distribution of each group. Making a Density Curve Problem You want to make a kernel density curve.
To show more of the curve, set the x limits Figure Or it could be because you have a small data set. Density curves with adjust set to. Since the y values for the density curve are small the area under the curve always sums to 1 , it would be barely visible if you overlaid it on a histogram without any transformation.
Density curve overlaid on a histogram See Also See Recipe 6. Left: different line colors for each group; right: different semitransparent fill colors for each group Discussion To make these plots, the data must all be in one data frame, with one column containing a categorical variable used for grouping.
We looked at the relationship between smoke smoking and bwt birth weight in grams. To make it so ggplot knows to treat smoke as categorical, we can either convert that column of the data frame to a factor, or tell ggplot to treat it as a factor by using factor smoke inside of the aes statement. For these examples, we converted it to a factor in the data. Another method for visualizing the distributions is to use facets, as shown in Figure We can align the facets vertically or horizontally.
If you want to see the histograms along with the density curves, the best option is to use facets, since other methods of visualizing both histograms in a single graph can be difficult to interpret. Density curves overlaid on histograms 6. Making a Frequency Polygon Problem You want to make a frequency polygon. That is, like a histogram, it shows what is in the data, whereas a kernel density estimate is just that—an estimate—and requires you to pick some value for the bandwidth.
In Figure , the data is divided into groups by race, and we visualize the distributions of bwt for each group. To make this work, we can modify the data frame by converting race to a factor, or tell ggplot to treat it as a factor by using factor race inside of the aes statement. The whiskers start from the edge of the box and extend to the furthest data point that is within 1. If there are any data points that are past the ends of the whiskers, they are considered outliers and displayed with dots.
Left: box plot with narrower boxes; right: with smaller, hollow outlier points If there are many outliers and there is overplotting, you can change the size and shape of the outlier points with outlier. The default size is 2 and the default shape is Box plot of a single group The calculation of quantiles works slightly differently from the box plot function in base R. This can sometimes be noticeable for small sample sizes.
Adding Notches to a Box Plot Problem You want to add notches to a box plot to assess whether the medians are different. A notched box plot Discussion Notches are used in box plots to help visually assess whether the medians of distributions differ. If the notches do not overlap, this is evidence that the medians are different.
This means that the confidence region the notch went past the bounds or hinges of one of the boxes. Mean markers on a box plot Discussion The horizontal line in the middle of a box plot displays the median, not the mean. For data that is normally distributed, the median and mean will be about the same, but for skewed data these values will differ. Making a Violin Plot Problem You want to make a violin plot to compare density estimates of different groups. A violin plot Discussion Violin plots are a way of comparing multiple data distributions.
With ordinary density curves, it is difficult to compare more than just a few distributions because the lines visually interfere with each other. A violin plot is a kernel density estimate, mirrored so that it forms a symmetrical shape.
Traditionally, they also have narrow box plots overlaid, with a white dot at the median, as shown in Figure Additionally, the box plot outliers are not displayed, which we do by setting outlier.
The default range goes from the minimum to maximum data values; the flat ends of the violins are at the extremes of the data. A violin plot with box plot overlaid on it Figure The default value is 1; use larger values for more smoothing and smaller values for less smoothing Figure : 6.
Left: violin plot with more smoothing; right: with less smoothing See Also To create a traditional density curve, see Recipe 6. To use different point shapes, see Recipe 4.
A dot plot Discussion This kind of dot plot is sometimes called a Wilkinson dot plot. In these dot plots, the placement of the bins depends on the data, and the width of each dot corresponds to the maximum width of each bin.
Dot plot with no y labels, max bin size of. With the default dotdensity binning algorithm, the position of each stack is centered above the set of data points that it represents. Dot plot with histodot fixed-width binning The dots can also be stacked centered, or centered in such a way that stacks with even and odd quantities stay aligned. This requires using a bit of a hack, by treating the x variable as a numeric variable and subtracting or adding a small quantity to shift the box plots and dot plots left and right.
Dot plot of multiple groups, binning along the y-axis Figure Dot plot next to box plot When the x variable is treated as numeric you must also specify the group, or else the data will be treated as a single group, with just one box plot and dot plot.
This makes a 2D kernel density estimate from the data. Left: points and density contour; right: with.. The main difference is that the raster geom renders more efficiently than the tile geom. In theory they should appear the same, but in practice they often do not. Marginal rug added to a scatter plot As with the one-dimensional density estimate, you can control the bandwidth of the estimate.
To do this, pass a vector for the x and y bandwidths to h. This argument gets passed on to the function that actually generates the density estimate, kde2d. The density curve is an estimate of the distribution under certain assumptions, while the binned visualization represents the observed data directly.
If you want to use a different color palette, see Recipe In addition to the standard repertoire of axis labels, tick marks, and legends, you can also add individual graphical or text elements to your plot.
These can be used to add extra contextual information, highlight an area of the plot, or add some descriptive text about the data. Adding Text Annotations Problem You want to add a text annotation to a plot.
The overplotting can lead to output with aliased jagged edges when outputting to a bitmap. If the axes are continuous, you can use the special values Inf and -Inf to place text annotations at the edge of the plotting area, as shown in Figure You will also need to adjust the position of the text relative to the corner using hjust and vjust—if you leave them at their default values, the text will be centered on the edge.
It may take a little experimentation with these values to get the text positioned to your liking: Chapter 7: Annotations 7. Modified text properties Figure Text positioned at the edge of the plotting area See Also See Recipe 5. For more on controlling the appearance of the text, see Recipe 9. Using Mathematical Expressions in Annotations Problem You want to add a text annotation with mathematical notation.
To mix regular text with expressions, use single quotes within double quotes or vice versa to mark the plain-text parts. Each block of text enclosed by the inner quotes is treated as a variable in a mathematical expression.
For using other fonts in mathematical expressions, see Recipe Mathematical expression with regular text 7. Adding Lines Problem You want to add lines to a plot. Left: horizontal and vertical lines; right: angled line Discussion The previous examples demonstrate setting the positions of the lines manually, resulting in one line drawn for each geom added.
It is also possible to map values from the data to xintercept, yintercept, and so on, and even draw them from another data frame. If the axis represents a factor, the first level has a numeric value of 1, the second level has a value of 2, and so on.
You can specify the numerical intercept manually, or calculate the numerical value using which levels Multiple lines, drawn at the mean of each group Figure Lines with a discrete axis You may have noticed that adding lines differs from adding other annotations. The line geoms had code to handle the special cases where they were used to add a single line, and changing it would break backward compatibility. Lines are often used to indicate summarized information about data.
Solution Use annotate "segment". Line segments with arrow heads The default angle is 30, and the default length of the arrowhead lines is 0. If one or both axes are discrete, the x and y positions are such that the categorical items have coordinate values 1, 2, 3, and so on.
See Also For more information about the parameters for drawing arrows, load the grid package and see? Adding a Shaded Rectangle Problem You want to add a shaded region.
Any geom can be used with annotate , as long as you pass in the proper parameters. Highlighting an Item Problem You want to change the color of an item to make it stand out.
Solution To highlight one or more items, create a new column in the data and map it to the color. Highlighting one item Discussion If you have a small number of items, as in this example, instead of creating a new column you could use the original one and specify the colors for every level of that variable. For example, the following code will use the group column from PlantGrowth and manually set the colors for each of the three levels.
For more information about removing the legend, see Recipe Adding Error Bars Problem You want to add error bars to a graph. See Recipe 3. For line graphs, if the error bars are a different color than the lines and points, you should draw the error bars first, so that they are underneath the points and lines.
We also made sure the Cultivar was used as a grouping variable by mapping it to group. But by setting the colour of the error bars, we made it so that the variable for colour was not used for grouping, and we needed some other way to inform ggplot that the two data entries at each x were in different groups so that they would be dodged. See Also See Recipe 3. See Recipe 4. Solution Create a new data frame with the faceting variable s , and a value to use in each facet.
Top: different annotations in each facet; bottom: the same annotation in each facet Discussion This method can be used to display information about the data in each facet, as shown in Figure For example, in each facet we can show linear regression lines, the formula for each line, and the r2 value.
Annotations in each facet with information about the data We needed to write our own function here because generating the linear model and extracting the coefficients requires operating on each subset data frame directly. Any geom can be used, as long as the input data is structured correctly. See Also See Recipe 7.
But not all the geoms in ggplot2 treat the x- and y-axes equally. For example, box plots summarize the data along the y-axis, the lines in line graphs move in only one direction along the x-axis, error bars have a single x value and a range of y values, and so on.
Left: a box plot with regular axes; right: with swapped axes Sometimes when the axes are swapped, the order of items will be the reverse of what you want. On a graph with standard x- and y-axes, the x items start at the left and go to the right, which corresponds to the normal way of reading, from left to right. When you swap the axes, the items still go from the origin outward, which in this case will be from bottom to top—but this conflicts with the normal way of reading, from top to bottom.
Solution You can use xlim or ylim to set the minimum and maximum values of a continuous axis. Left: box plot with default range; right: with manually set range The latter example sets the y range from 0 to the maximum value of the weight column, though a constant value like 10 could instead be used as the maximum.
The first way is to modify the scale, and the second is to apply a coordinate transform. When you modify the limits of the x or y scale, any data outside of the limits is removed—that is, the out-of-range data is not only not displayed, it is removed from consideration entirely. With the box plots in these examples, if you restrict the y range so that some of the original data is clipped, the box plot statistics will be computed based on clipped data, and the shape of the box plots will change.
With a coordinate transform, the data is not clipped; in essence, it zooms in or out to the specified range.
Reversing a Continuous Axis Problem You want to reverse the direction of a continuous axis. The direction of an axis can also be reversed by specifying the limits in reversed order, with the maximum first, then the minimum: 8. The same is true for the x-axis properties. Changing the Order of Items on a Categorical Axis Problem You want to change the order of items on a categorical axis. To manually set the order of items on the axis, specify limits with a vector of the levels in the desired order.
Left: box plot with manually specified items on the x-axis; right: with only two items Figure Box plot with order reversed on the x-axis 8. In this case it might be useful to force the x- and y-axes to have the same scaling. With the marathon data, we might want the axis with half-marathon times stretched out to twice that of the axis with the marathon times Figure Left: box plot with automatic tick marks; right: with manually set tick marks Discussion The location of the tick marks defines where major grid lines are drawn.
If the axis represents a continuous variable, minor grid lines, which are fainter and unlabeled, will by default be drawn halfway between each major grid line. For discrete axes, you can change the order of items or remove them by specifying the limits see Recipe 8.
Setting breaks will change which of the levels are labeled, but will not remove them or change their order. For a discrete axis, setting limits reorders and removes items, and setting breaks controls which items have labels See Also To remove the tick marks and labels but not the data from the graph, see Recipe 8.
This will remove the tick marks on both axes. Discussion There are actually three related items that can be controlled: tick labels, tick marks, and the grid lines. For continuous axes, ggplot normally places a tick label, tick mark, and major grid line at each value of breaks. For categorical axes, these things go at each value of limits.
The tick labels on each axis can be controlled independently. However, the tick marks and grid lines must be controlled all together. To do this, we can define a formatter function, which takes in a value and returns the corresponding string. If you want to use these functions, you must first load the scales package, with li brary scales.
These commands control the appearance of only the tick labels, on only one axis. To control all of these at once, you can use the theming system, as discussed in Recipe 9. See Also See Recipe 9. X-axis tick labels with manually specified appearance 8. Removing Axis Labels Problem You want to remove the label on an axis.
Solution For the x-axis label, use theme axis. For the y-axis label, do the same with axis. X-axis label with a line break Figure In the example here, the x-axis represents group, but this should be obvious from the context. Another way to remove the axis label is to set it to an empty string. When you set the label to "", the name of the scale is changed and the empty text does display.
Solution To change the appearance of the x-axis label Figure , use axis. If you change any other properties of axis. Showing Lines Along the Axes Problem You want to display lines along the x- and y-axes, but not on the other sides of the graph. Solution Using themes, use axis. Using a Logarithmic Axis Problem You want to use a logarithmic axis for a graph. In contrast, with a linear axis, a given visual distance represents a constant quantity change; each centimeter might represent adding 10 to the quantity.
Some data sets are exponentially distributed on the x-axis, and others on the y-axis or both. Using a Logarithmic Axis Brachiosaurus Because of a few very large animals, the rest of the animals get squished into the lower-left corner—a mouse barely looks different from a triceratops! This is a case where the data is distributed exponentially on both axes.
In the example here, the automatically generated tick marks are spaced farther apart than is ideal. It is often useful to represent financial data this way, because it better represents proportional change. Plot with exponents in tick labels. Notice that different bases are used for the x and y axes. Top: a stock chart with a linear x-axis and log y-axis; bottom: with manual breaks 8. Adding Ticks for a Logarithmic Axis Problem You want to add tick marks with diminishing spacing for a logarithmic axis.
There is a long tick mark at each power of 10, and a mid-length tick mark at each 5. Log axes with ticks at each 5, and fixed coordinate ratio See Also For more on controlling the scaling ratio of the x- and y-axes, see Recipe 8. Making a Circular Graph Problem You want to make a circular graph. It contains samples of wind speed and direction for every 5 minutes throughout a day.
Polar plot It may also be useful to set the starting angle with the start argument, especially when using a discrete variable for theta. There are a few important things to keep in mind when using these geoms. First, by default, for the variable that is mapped to y or r , the smallest actual value gets mapped to the center; in other words, the smallest data value gets mapped to a visual radius value of 0.
Polar plot with different colors and breaks Next, when using a continuous x or theta , the smallest and largest data values are merged. Sometimes this is desirable, sometimes not. Finally, the theta values of the polar coordinates do not wrap around—it is presently not possible to have a geom that crosses over the starting angle usually vertical. Left: polar plot with line notice the data range of the radius ; right: with the radius representing a data range starting from zero The first problem is that the data values ranging from about to are mapped to the radius such that the smallest data value is at radius 0.
To fix that, we need to modify our data frame by adding one row with a month of 0 that has the same value as the row with month In this case, it changed the default data frame for p from md to mdnew. See Recipe 8. Using Dates on an Axis Problem You want to use dates on an axis.
Solution Map a column of class Date to the x- or y-axis. Date "" , as. Table Months and days have different names in different languages the examples here are generated with a US locale. You can change the locale with Sys. For example, this will change the date formatting to use an Italian locale: Mac and Linux Sys.
UTF-8" Windows Sys. For example, the time of day can be stored as a number representing the hour. Time can also be stored as a number representing the number of minutes or seconds from some starting time.
Setting the Title of a Graph Problem You want to set the title of a graph. If you want to move the title inside the plotting area, you can use one of two methods, both of which are a little bit of a hack Figure The first method is to use ggti tle with a negative vjust value.
The drawback of this method is that it still reserves blank space above the plotting region for the title. Changing the Appearance of Text Problem You want to change the appearance of text in a plot. For example, axis. For theme elements, font size is in points. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
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Create, design, and build interactive dashboards using Shiny A highly practical guide to help you get to grips with the basics of data visualization techniques, and how you can implement them using R Who This Book Is For If you are looking to create custom data visualization solutions using the R programming language and are stuck somewhere in the process, this book will come to your rescue.
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Style and approach This book is packed with practical recipes, designed to provide you with all the guidance needed to get to grips with data visualization with R.
You will learn to leverage the power of R and ggplot2 to create highly customizable data visualizations of varying complexities. The readers will then learn how to create, design, and build interactive dashboards using Shiny.
It begins with a general discussion of the principles of effective graphics, ODS Graphics, and the SG procedures, then moves on to show examples of the procedures' many features. Author : Dan MacLean Publisher: ISBN: Category: Computers Page: View: Read Now » Over 60 recipes to model and handle real-life biological data using modern libraries from the R ecosystem Key Features Apply modern R packages to handle biological data using real-world examples Represent biological data with advanced visualizations suitable for research and publications Handle real-world problems in bioinformatics such as next-generation sequencing, metagenomics, and automating analyses Book Description Handling biological data effectively requires an in-depth knowledge of machine learning techniques and computational skills, along with an understanding of how to use tools such as edgeR and DESeq.
With the R Bioinformatics Cookbook, you'll explore all this and more, tackling common and not-so-common challenges in the bioinformatics domain using real-world examples. You will learn how to effectively analyze your data with the latest tools in Bioconductor, ggplot, and tidyverse. The book will guide you through the essential tools in Bioconductor to help you understand and carry out protocols in RNAseq, phylogenetics, genomics, and sequence analysis.
As you progress, you will get up to speed with how machine learning techniques can be used in the bioinformatics domain. You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse.
By the end of this book, you'll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data.
Working knowledge of R programming language and basic knowledge of bioinformatics are prerequisites. Since the birth of the rmarkdown package in early , R Markdown has grown substantially from a package that supports a few output formats such as HTML, PDF, and Word to an extensive and diverse ecosystem that enables the creation of books, blogs, scientific articles, websites, and more.
Due to its rapid success, this ecosystem is hard to learn completely meaning that R Markdown users, from novices to advanced users, likely do not know all that these packages have to offer. The R Markdown Cookbook confronts this gap by showcasing short, practical examples of wide-ranging tips and tricks to get the most out of these tools.
After reading this book, you will learn how to: Enhance your R Markdown content with diagrams, citations, and dynamically generated text Streamline your workflow with child documents, code chunk references, and caching Control the formatting and layout with Pandoc markdown syntax or by writing custom HTML and LaTeX templates Utilize chunk options and hooks to fine-tune how your code is processed Switch between different language engineers to seamlessly incorporate python, D3, and more into your analysis.
At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.
Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. Save my name, email, and website in this browser for the next time I comment. Notify me of follow-up comments by email. Notify me of new posts by email. This site uses Akismet to reduce spam. Learn how your comment data is processed.
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