292 p. ISBN 978--691-18161-5. The best seats are near the front, facing sideways. orange_pal ← RColorBrewer::brewer.pal(n = 6, name = "Oranges") orange_pal ## [1] "#FEEDDE" "#FDD0A2" "#FDAE6B" "#FD8D3C" "#E6550D" ## [6] "#A63603" orange_rev ← rev(orange_pal) orange_rev ## [1] "#A63603" "#E6550D" "#FD8D3C" "#FDAE6B" "#FDD0A2" ## [6] "#FEEDDE" The brewer.pal() function produces evenly spaced color schemes to order from any one of several named palettes. It does this by defining a function called autoplot(). (Both these sorts of books already exist: see the references in the appendix.) This open access book provides a concise explanation of the fundamentals and background of the surround sound recording and playback technology Ambisonics. The subset() function takes the by_country object and selects only the cases where gdp_mean is over 25,000, with the result that only those points are labeled in the plot. Remember, group_by() groups your data from left to right, with the rightmost or innermost group being the level calculations will be done at; mutate() adds a column at the current level of grouping; and summarize() aggregates to the next level up. Even if viewers understand all these things, they must still perform the visual task of interpreting the graph. To clean up the summary a little, we convert it to a tibble, then use prefix_strip() and prefix_replace() to • 159 160 • Chapter 6 Race: Black Liberal Figure 6.10: Average marginal effects plot. New Riders. A study on dual-scale data charts. Index numbers can have complications of their own, but here they allow us to use one axis instead of two, and also to calculate a sensible difference between the two series and plot that as well, in a panel below. 9777 The educational categories previously spread over the columns have been gathered into two new columns. As noted earlier, I strongly encourage you go through this exercise manually, typing (rather than copying and pasting) the code yourself. In the main text, references to objects or other things that exist in the R language or in your R project—tables of data, variables, functions, and so on—will also appear in a monospaced or “typewriter” typeface. To begin with we will let ggplot use its defaults for many of these elements. However, data visualization is not simply a matter of competing standards of good taste. Sold by Inospire-au and ships from Amazon Fulfillment. I am also grateful to those in the R community who helped me while I wrote this book, whether directly, through comments and suggestions; indirectly, by independently solving problems I ran into myself; or unwittingly, via the excellent example of their own open and generous style of work. This is one reason pie charts are usually a bad idea. We’ll use the gapminder data to make our first plots. ## 13 Presumed Italy 11.1 4.28 21554. An alternative is to use a line graph to join up the time observations, faceting on educational categories Sometimes it may be preferable to show that the underlying variable is categorical, as a bar chart makes clear, and not continuous, as a line graph suggests. But if you ggplot2-exts.org r-graph-gallery.com policyviz.com • 231 232 • Chapter 8 analyze data, visualization can help you uncover features in it. Doing this doesn’t produce any output, Preface however. It is often helpful to be able to see that output and its partial results. From there we will briefly examine some of what we know about the perception of shapes, colors, and relationships between objects. We use the interaction() function to do this. To make the exposition clearer, we have periodically repeated chunks of code • 249 250 • Appendix that differ only in the dependent or independent variable being plotted. If you follow the text and examples in this book, then by the end you will • • • • understand the basic principles behind effective data visualization; have a practical sense for why some graphs and figures work well, while others may fail to inform or actively mislead; know how to create a wide range of plots in R using ggplot2; and know how to refine plots for effective presentation. In the legend for the first figure, shown in figure 3.18 on the left, we see several visual elements. The result will be a Cleveland dotplot, a simple and extremely effective method of presenting data that is usually better than either a bar chart or a table. Unlike the gapminder data, some observations are missing. Indeed, the literature on chartjunk suggests that the two may have some qualities in common. First, we introduced some new geom_ functions that allowed us to draw new kinds of plots. Instead of using color to distinguish the debt categories, we put their values on the y-axis instead. Imagine individual-level data with arbitrarily precise information on personal characteristics, time, and location of death. At this point it makes sense to use some intermediate objects to build things up as we go. Consider the following data: 60000 N 40000 head(bad_date) 20000 /11 /11 9/9 /11 9/8 /11 9/7 /11 9/6 /11 9/5 /11 9/4 9/3 1 /11 9/2 0/1 9/1 9/1 /11 ## # A tibble: 6 x 2 Date Figure A.3: A bad date. Each of these elements is represented in the legend: the point color, the line color, and the ribbon fill. Geographers call this the Modifiable Areal Unit Problem, or MAUP (Openshaw 1983). When elements are not aligned but still share a scale, comparison is a little harder but still pretty good. Hide other formats and editions. It multiplies the vstrat variable by the year variable to get a vector of stratum information for each year. This makes it easy to be misled by them, as when (for example) we overestimate the size of a contrast between two adjacent shaded areas on a map or grid simply because they share a boundary. Similarly, a color or a fill mapping • 127 128 • Chapter 5 Figure 5.23: Every mapped variable has a scale. How can you tell which one you need? 0.1 ' ' 1 ## ## (Dispersion parameter for binomial family taken to be 1) ## ## Null deviance: 2247.9 on 1697 degrees of freedom ## Residual deviance: 1345.9 on 1686 degrees of freedom ## (1169 observations deleted due to missingness) ## AIC: 1370 ## ## Number of Fisher Scoring iterations: 6 The summary reports the coefficients and other information. They can look impressive, but they are also harder to grasp. Its existence is worth any amount of hand waving about connection and collaboration. We used the guides() function to remove the legends for a color mapping and a label mapping. 50 45 Lat 40 35 30 25 –120 –100 Long Figure 7.5: Improving the projection. It is worth trying to minimize the scope of the inevitable final scramble. We can either load the whole package with library(scales) or, more conveniently, just grab the specific formatter we want from that library. ## 4 Informed Germany 13.0 80255. https://CRAN.R -project.org/package=RColorBrewer. We did not have to have any strong idea of the differences between these methods. Data Visualization is brimming with insights into how quantitative analysts can use visualization as a tool for understanding and communication. library(maptools) library(mapproj) library(rgeos) library(rgdal) us_counties ← readOGR(dsn="data/geojson/gz_2010_us_050_00_5m.json", layer="OGRGeoJSON") us_counties_aea ← spTransform(us_counties, CRS("+proj=laea +lat_0=45 +lon_0=-100 \ +x_0=0 +y_0=0 +a=6370997 +b=6370997 \ +units=m +no_defs")) [email protected]$id ← rownames([email protected]) With the file imported, we then extract, rotate, shrink, and move Alaska, resetting the projection in the process. The rbind() function does this for us. A scatterplot is a visual representation of data, not a way to magically transmit pure understanding. There are a variety of scale transformations that you can use in just this way. In general we want to identify groupings, classifications, or entities than can be treated as the same thing or part of the same thing: • • • • • • • Proximity: Things that are spatially near to one another seem to be related. The • 5 6 • Chapter 1 Figure 1.4: A chart with a considerable amount of junk in it. If not, try it and see what happens. url ← "https://cdn.rawgit.com/kjhealy/viz-organdata/master/organdonation.csv" organs ← read_csv(file = url) ## Parsed with column specification: ## cols( ## .default = col_character(), ## year = col_integer(), ## donors = col_double(), ## pop = col_integer(), ## pop.dens = col_double(), ## gdp = col_integer(), ## gdp.lag = col_integer(), ## health = col_double(), ## health.lag = col_double(), ## pubhealth = col_double(), ## roads = col_double(), ## cerebvas = col_integer(), ## assault = col_integer(), ## external = col_integer(), ## txp.pop = col_double() ## ) ## See spec(...) for full column specifications. We index data frames, tibbles, and other arrays by row first, and then by column. ## 2 Afghanistan Asia 1957 30.3 9240934 821. https:// byjustinfox.com/2014/12/14/the-rise-of-the-y-axis-zero-fundamentalists/. The wedges of these two pie charts are ordered (clockwise, from the top), but it’s not so easy to follow them. MatrixModels: Modelling with sparse and dense matrices. ———. We also know more Show the Right Numbers about selecting the right sort of computed statistic to show on the graph, if that’s what’s needed, and how to facet our core plot by one or more variables. Free tools for coding have been around for a long time, but in recent years what we might call the “ecology of assistance” has gotten better. We’re going to be rich! Summary count of religious preferences by census region religion Protestant Protestant None None Catholic Protestant Protestant Other Christian Protestant bigregion Northeast Northeast Northeast Northeast Northeast Northeast Figure 5.1: How we want to transform the individual-level data. Princeton University Press, 2019. Again, for faceted plots where both variables are continuous, we generally do not want the scales to be free, because it allows the x- or y-axis for each panel to vary with the range of the data inside that panel only, instead of the range across the whole dataset. (2018a). ## 5 55> Female 1947 11810 7.60 coll4 343. The graph was widely circulated on social media. Encoding numbers as lengths (absent a scale) works too, but not as effectively. Zero is the FIPS code for the entire United States, and thus the data in this row are for the whole country. As we learn about new geoms, we will also get more adventurous and depart from some of ggplot’s default arguments and settings. 2. Then you supply the arguments to the appropriate scale function. We are still not quite where we originally wanted to be. Inside both mutate() and summarize(), we are able to create new variables in a way that we have not seen before. They are also usually put on their guard by overly elaborate presentation of simple trends, as when a three-dimensional ribbon is used to display a simple line. Data Visualization with R and ggplot. By analogy, think of the %>% operator as allowing us to start with a data frame and perform a sequence or pipeline of operations to turn it into another, usually smaller and more aggregated, table. Your code manipulates the data and creates all the objects and outputs you need. In the upper right, the three groups are still salient but the row of blue circles is now seen as a grouped entity. We need two data frames, one containing the map data, and the other containing the fill variables we want plotted. In particular, it is easy for a table of results to get detached from the sequence of steps that produced it. An exercise by Jan Vanhove (2016) demonstrates the usefulness of looking at model fits and data at the same time. A map is, after all, just a set of lines drawn in the right order on a grid. We are not restricted to selecting contiguous elements either. ## 9 Afghanistan Asia 1992 41.7 16317921 649. This is partly a matter of the mapping being correct in strictly numerical terms. If you think you have completed typing your code but instead of seeing the > command prompt at the console you see the + character, that may mean R thinks you haven’t written a complete expression yet. The lines we draw still represent states. ## 2 Informed Canada 14.0 0.751 23711. Second, I want you to understand why the code is written the way it is, such that when you look at data of your own you can feel confident about your ability to get from a rough picture in your head to a high-quality graphic on your screen or page. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data ... This also means that, as Look at Data you learn ggplot, it is very important to grasp the core steps first, before worrying about adjustments and polishing. This is the course website for SOCIOL 880: Data Management (Spring 2019). Data Visualization: A Practical Introduction Paperback - 18 December 2018 by Kieran Healy (Author) › Visit Amazon's Kieran Healy Page. In chapter 6 we will learn how to calculate frequencies and other statistics from data with a complex or weighted survey design. Multiple types of observational units are stored in the same table. A different sort of problem is shown in figure 1.12. I want to make sure you are not left with the “How to Draw an Owl in Three Steps” problem common to many tutorials. Each, especially bins, will make a big difference in how the resulting figure looks. 0.618 0.495 0.0145 ## 7 1848 Zachary Taylor Whig 0.562 0.473 0.0479 ## 8 1852 Franklin Pierce Dem. p3 ← p2 + geom_text_repel(data = subset(opiates, year == max(year) & abbr !="DC"), mapping = aes(x = year, y = adjusted, label = abbr), size = 1.8, segment.color = NA, nudge_x = 30) + coord_cartesian(c(min(opiates$year), max(opiates$year))) • 195 196 • Chapter 7 By default, geom_text_repel will add little line segments that indicate what the labels refer to. Let’s focus on getting across the relationship between employee numbers and revenue, as that seems to be the motivation for it in the first place. The fill mapping is useful but also redundant. plot_section ← function(section="Culture", x = "Year", y = "Members", data = asasec, smooth=FALSE){ require(ggplot2) require(splines) # Note use of aes_string() rather than aes() p ← ggplot(subset(data, Sname==section), mapping = aes_string(x=x, y=y)) if(smooth == TRUE) { p0 ← p + geom_smooth(color = "#999999", size = 1.2, method = "lm", formula = y ~ ns(x, 3)) + scale_x_continuous(breaks = c(seq(2005, 2015, 4))) + labs(title = section) } else { p0 ← p + geom_line(color= "#E69F00", size=1.2) + scale_x_continuous(breaks = c(seq(2005, 2015, 4))) + labs(title = section) } print(p0) } This function is not very general. Retrieved from https://CRAN.R-project.org/package=nlme. Instead of sending the result to the console, we can instead assign it to an object we create: my_numbers ← c(1, 2, 3, 1, 3, 5, 25) your_numbers ← c(5, 31, 71, 1, 3, 21, 6) To see what you made, type the name of the object and hit return: my_numbers ## [1] If you learn only one keyboard shortcut in RStudio, make it this one! Meanwhile, just over 10 percent of those saying they were Protestant live in the Northeast. Doherty, M. E., Anderson, R. B., Angott, A. M., & Klopfer, D. S. (2007). Are there any variables in our data that can sensibly be mapped to the color aesthetic? (2018). Spa Spa Spa Spa Donors Spa Spa Spa Spa 20 Ita Ita Ita Ita Ita Ita 10 Ita Ita Ita Ita 50 123 Spa Spa Ita • 100 Ita 150 200 Roads than 1998. https://CRAN.R-project .org/package=here. Instead of having separate bars distinguished by heights, we can array the percentages for each distribution proportionally within a single bar. Like gapminder, it has a country-year structure. Normal x, - skewed residuals 250 500 750 4000 1000 7. Make sure you complete your expressions. We can tell geom_bar() not to do any work on the variable before plotting it. If there are a large number of warnings, R will collect them all and invite you to view them with the warnings() function. But when the datasets are visualized as a scatterplot, with the x variables plotted on the horizontal axis and the y variables on the vertical, the differences between them are readily apparent. This is the intensity or vividness of the color. Or rather, each approach draws attention to features of the data in slightly different ways. At the beginning of the plotting code, we set up an object called f_labs, which is in effect a tiny data frame that associates new labels with the values of the type variable in studebt. They are now stacked up on top of each other in the rows. Checkershadow illusion. It seems that pop-out on the • 19 Color & shape, N = 100 20 • Chapter 1 13 13 12 12 11 11 Biscuits Biscuits color channel is stronger than it is on the shape channel. ———. We can select elements using them, too. Try it with and without a geom_point() layer. The backslash is an “escape” character. 2 Common Problems Reading in Data Date formats Date formats can be annoying. It won’t be evaluated and it won’t trigger a syntax error. If it is, then summarize_if() will apply the summary function or functions we want to organdata. Each of these maps shows data for the same event, but the impressions they convey are very different. In chapter 4 we learned how calculate and then plot frequency tables of categorical variables, using some data from the General Social Survey. Sebastopol, CA: O’Reilly Media. You should also take care to name your saved figures in a sensible way. Readings. Second, the group_by() function sets up how the grouped or nested data will be processed within the summarize() step. The elements of graphing data. Six variables are plotted: the size of the army, its location on a two-dimensional surface, direction of the army’s movement, and temperature on various dates during the retreat from Moscow.” It is worth noting how far removed Minard’s image is from most contemporary statistical graphics. 28 • Chapter 1 Position in space Color hue Motion Shape Figure 1.25: Channels for mapping unordered categorical data, arranged top-to-bottom from more to less effective, after Munzer (2014, 102). ———. In this chapter, we will begin by looking briefly at how ggplot can use various modeling techniques directly within geoms. (2015). The key for the second figure, shown on the right, has only a dot for each continent, with no shaded background or line. 2000 Each facet is labeled at the top. We can make life easier for ourselves by using RStudio. Things get more complex when, as is often the case in the social sciences, some or all variables are categorical or otherwise limited in the range of values they can take. It hands the plotting duties to geom_text(), which means that we can use all of that geom’s arguments in the annotate() call. It cannot force you to be honest with yourself, your data, and your audience. That is, write this: ggplot(data = mpg, aes(x = displ, y = hwy)) + geom_point() and not this: ggplot(data = mpg, aes(x = displ, y = hwy)) +geom_point() R Studio will do its best to help you with the task of writing your code. Meanwhile, a low-population county just under that threshold would be coded as being in the lowest (lightest) bin. In any case, figure 8.21 reproduces a typical slide from the deck. 6015. If is a named object that already exists in your workspace, like a vector of numbers or a table of data, then you provide it unquoted, as in mean(my_numbers). Log in, Book Review: Fundamentals of Data Visualization, Data Visualization with R, A New Online Book, Data Science From Scratch 2nd Edition: Book Review, Book Review: Python Data Science Handbook, Data Visualization: A Practical Introduction. This suggests that maybe there are other methods that geom_smooth() understands, and which we might tell it to use instead. 23. To avoid this, we will have the panels appear one on top of the other by saying we want to have only one column. (These last two are codes designating missing data and “Not a Number,” respectively.) The examples are discussed in terms of the potential pitfalls and . In addition to interacting with the console, you can also write your code in a text file and send that to R all at once. . In a similar way, the objects we create to make plots will have many parts and subparts, as the overall task of drawing a plot has many individual to-do items. Each chapter ends with a section suggesting where to go next (apart from continuing to read the book). Graph Tables, Add Labels, Make Notes ## 11 Presumed Finland 18.4 1.53 21019. When we see repeated actions like this in our code, we can ask whether there’s a better way to proceed. For example, when we were plotting the gdpPercap 90000 60000 30000 0 1950 1960 1970 1980 year 1990 2000 Figure 4.2: Plotting the data over time by country, again. Figure 8.15: Controlling various theme elements directly (and making several bad choices while doing so). A scatterplot shows Look at Data Correlations can run from -1 to 1, with zero meaning there is no association. While everyone knows that correlation is not causation, with time-series data we get this problem twice over. Her expert guidance and enthusiasm throughout the writing and production of the book made everything move a lot quicker than expected. Journal of the Royal Statistical Society Series A, 150, 192–229. p0 ← ggplot(data = us_states_elec, mapping = aes(x = long, y = lat, group = group, fill = d_points)) p1 ← p0 + geom_polygon(color = "gray90", size = 0.1) + coord_map(projection = "albers", lat0 = 39, lat1 = 45) p2 ← p1 + scale_fill_gradient2() + labs(title = "Winning margins") p2 + theme_map() + labs(fill = "Percent") p3 ← p1 + scale_fill_gradient2(low = "red", mid = scales::muted("purple"), high = "blue", breaks = c(-25, 0, 25, 50, 75)) + labs(title = "Winning margins") p3 + theme_map() + labs(fill = "Percent") If you look at the gradient scale for this first “purple America” map, in figure 7.9, you’ll see that it extends very high on the blue side. Similarly, the ghostly blobs in the Hermann grid effect can be thought of as a side-effect of the visual system being tuned for a different task. The assignment operator is ←. name treatment n Jane Doe a 4 Jane Doe b 1 John Smith a NA John Smith b 18 Mary Johnson a 6 Mary Johnson b 7 In this book we do not generally access data via [ or $. (He also circulated it publicly.) Nlme: Linear and nonlinear mixed effects models. Data Visualization: A Practical Introduction - Ebook written by Kieran Healy. Much of this book was written on the Robertson Scholars Bus, which goes back and forth between Duke and Chapel Hill on the half hour during term. You do the analysis, collect the output, and copy the relevant results into your paper, often manually reformatting them on the way. The ggrepel package has several other useful geoms and options to aid with effectively plotting labels along with points. We start with our table of data and then (%>%) group the countries by continent and year using the group_by() function. At the beginning, ggplot will do most of the work for you. Download Free PDF. 7.5 Is Your Data Really Spatial? New York: Springer. We used scale_x_log10(), scale_x_continuous(), and other scale_ functions to adjust axis labels. 1 Introduction. . Meanwhile in table 5.2 the numbers sum to a hundred across the rows, showing, for example, the distribution of religious affiliations within any particular region. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. We can change this by specifying different aesthetics for each geom. As a first attempt, we can use position = "dodge" to make the bars within each region of the country appear side by side. Feature analysis in early vision: Evidence from search asymmetries. Sketch a few bird-shaped ovals. p ← ggplot(data = county_full, mapping = aes(x = long, y = lat, fill = pct_black, group = group)) p1 ← p + geom_polygon(color = "gray90", size = 0.05) + coord_equal() p2 ← p1 + scale_fill_brewer(palette="Greens") Figure 7.12: Percent black population by county. Unformatted text preview: Introduction to Data Visualization BAN140 - Section Omar ALtrad, Ph.D., P.Eng., PMP SENECA BAN140 - Omar Altrad, Ph.D., P.Eng., PMP 1 Topics • Types of Visuals • Dimension Reduction Aggregation Examples • Data Aggregation in Tableau SENECA BAN140 - Omar Altrad, Ph.D., P.Eng., PMP 2 Type of Visuals Chapter Two: choosing an effective visual From Storytelling with . We will learn how to use some of its “action verbs” to select, group, summarize, and transform our data. EPUB & PDF Ebook Data Visualization: A Practical Introduction | EBOOK ONLINE DOWNLOAD. 76 • Chapter 4 points by continent, mapping color to continent was enough to get the right answer because continent is already a categorical variable, so the grouping is clear. It’s a big help in everyday life but is also easy to take for granted. It contains all the education categories that were previously given across the column headers, from zero to four years of elementary school to four or more years of college. Various labeled tick-marks orient the reader to the values on each axis. As a rule, dodged charts can be more cleanly expressed as faceted plots. (2014, December). PolicyViz, a site run by Jon Schwabish, covers a range of topics on data visualization. Underneath these terms there is a worked-out theory of the forms that tabular data can be stored in, but right now we don’t need to know those additional details. Members p ← ggplot(data = subset(asasec, Year == 2014), mapping = aes(x = Members, Figure 8.1: Back to basics.

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