VISIALIZACION DE DATA.pdf
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ggplot2 is a grammar of graphics package for creating plots in R. It allows building graphs from data, a coordinate system, and geoms (visual marks). Geoms represent data points and their aesthetic properties like color, size, and position on the plot. Common geoms include points, lines, and bars. Scales map data values to visual properties. Coordinate systems define the space in which geoms are drawn.
VISIALIZACION DE DATA.pdf
- 1. Data Visualization with ggplot2 : : CHEAT SHEET ggplot2 is based on the grammar of graphics, the idea that you can build every graph from the same components: a data set, a coordinate system, and geoms—visual marks that represent data points. Basics GRAPHICAL PRIMITIVES a + geom_blank() (Useful for expanding limits) b + geom_curve(aes(yend = lat + 1, xend=long+1),curvature=1) - x, xend, y, yend, alpha, angle, color, curvature, linetype, size a + geom_path(lineend="butt", linejoin="round", linemitre=1) x, y, alpha, color, group, linetype, size a + geom_polygon(aes(group = group)) x, y, alpha, color, fill, group, linetype, size b + geom_rect(aes(xmin = long, ymin=lat, xmax= long + 1, ymax = lat + 1)) - xmax, xmin, ymax, ymin, alpha, color, fill, linetype, size a + geom_ribbon(aes(ymin=unemploy - 900, ymax=unemploy + 900)) - x, ymax, ymin, alpha, color, fill, group, linetype, size + = To display values, map variables in the data to visual properties of the geom (aesthetics) like size, color, and x and y locations. + = data geom x = F · y = A coordinate system plot data geom x = F · y = A color = F size = A coordinate system plot Complete the template below to build a graph. required ggplot(data = mpg, aes(x = cty, y = hwy)) Begins a plot that you finish by adding layers to. Add one geom function per layer. qplot(x = cty, y = hwy, data = mpg, geom = “point") Creates a complete plot with given data, geom, and mappings. Supplies many useful defaults. last_plot() Returns the last plot ggsave("plot.png", width = 5, height = 5) Saves last plot as 5’ x 5’ file named "plot.png" in working directory. Matches file type to file extension. F M A F M A aesthetic mappings data geom LINE SEGMENTS b + geom_abline(aes(intercept=0, slope=1)) b + geom_hline(aes(yintercept = lat)) b + geom_vline(aes(xintercept = long)) common aesthetics: x, y, alpha, color, linetype, size b + geom_segment(aes(yend=lat+1, xend=long+1)) b + geom_spoke(aes(angle = 1:1155, radius = 1)) a <- ggplot(economics, aes(date, unemploy)) b <- ggplot(seals, aes(x = long, y = lat)) ONE VARIABLE continuous c <- ggplot(mpg, aes(hwy)); c2 <- ggplot(mpg) c + geom_area(stat = "bin") x, y, alpha, color, fill, linetype, size c + geom_density(kernel = "gaussian") x, y, alpha, color, fill, group, linetype, size, weight c + geom_dotplot() x, y, alpha, color, fill c + geom_freqpoly() x, y, alpha, color, group, linetype, size c + geom_histogram(binwidth = 5) x, y, alpha, color, fill, linetype, size, weight c2 + geom_qq(aes(sample = hwy)) x, y, alpha, color, fill, linetype, size, weight discrete d <- ggplot(mpg, aes(fl)) d + geom_bar() x, alpha, color, fill, linetype, size, weight e + geom_label(aes(label = cty), nudge_x = 1, nudge_y = 1, check_overlap = TRUE) x, y, label, alpha, angle, color, family, fontface, hjust, lineheight, size, vjust e + geom_jitter(height = 2, width = 2) x, y, alpha, color, fill, shape, size e + geom_point(), x, y, alpha, color, fill, shape, size, stroke e + geom_quantile(), x, y, alpha, color, group, linetype, size, weight e + geom_rug(sides = "bl"), x, y, alpha, color, linetype, size e + geom_smooth(method = lm), x, y, alpha, color, fill, group, linetype, size, weight e + geom_text(aes(label = cty), nudge_x = 1, nudge_y = 1, check_overlap = TRUE), x, y, label, alpha, angle, color, family, fontface, hjust, lineheight, size, vjust discrete x , continuous y f <- ggplot(mpg, aes(class, hwy)) f + geom_col(), x, y, alpha, color, fill, group, linetype, size f + geom_boxplot(), x, y, lower, middle, upper, ymax, ymin, alpha, color, fill, group, linetype, shape, size, weight f + geom_dotplot(binaxis = "y", stackdir = "center"), x, y, alpha, color, fill, group f + geom_violin(scale = "area"), x, y, alpha, color, fill, group, linetype, size, weight discrete x , discrete y g <- ggplot(diamonds, aes(cut, color)) g + geom_count(), x, y, alpha, color, fill, shape, size, stroke THREE VARIABLES seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2)); l <- ggplot(seals, aes(long, lat)) l + geom_contour(aes(z = z)) x, y, z, alpha, colour, group, linetype, size, weight l + geom_raster(aes(fill = z), hjust=0.5, vjust=0.5, interpolate=FALSE) x, y, alpha, fill l + geom_tile(aes(fill = z)), x, y, alpha, color, fill, linetype, size, width h + geom_bin2d(binwidth = c(0.25, 500)) x, y, alpha, color, fill, linetype, size, weight h + geom_density2d() x, y, alpha, colour, group, linetype, size h + geom_hex() x, y, alpha, colour, fill, size i + geom_area() x, y, alpha, color, fill, linetype, size i + geom_line() x, y, alpha, color, group, linetype, size i + geom_step(direction = "hv") x, y, alpha, color, group, linetype, size j + geom_crossbar(fatten = 2) x, y, ymax, ymin, alpha, color, fill, group, linetype, size j + geom_errorbar(), x, ymax, ymin, alpha, color, group, linetype, size, width (also geom_errorbarh()) j + geom_linerange() x, ymin, ymax, alpha, color, group, linetype, size j + geom_pointrange() x, y, ymin, ymax, alpha, color, fill, group, linetype, shape, size continuous function i <- ggplot(economics, aes(date, unemploy)) visualizing error df <- data.frame(grp = c("A", "B"), fit = 4:5, se = 1:2) j <- ggplot(df, aes(grp, fit, ymin = fit-se, ymax = fit+se)) maps data <- data.frame(murder = USArrests$Murder, state = tolower(rownames(USArrests))) map <- map_data("state") k <- ggplot(data, aes(fill = murder)) k + geom_map(aes(map_id = state), map = map) + expand_limits(x = map$long, y = map$lat), map_id, alpha, color, fill, linetype, size Not required, sensible defaults supplied Geoms Use a geom function to represent data points, use the geom’s aesthetic properties to represent variables. Each function returns a layer. TWO VARIABLES continuous x , continuous y e <- ggplot(mpg, aes(cty, hwy)) continuous bivariate distribution h <- ggplot(diamonds, aes(carat, price)) RStudio® is a trademark of RStudio, Inc. • CC BY SA RStudio • [email protected] • 844-448-1212 • rstudio.com • Learn more at http://ggplot2.tidyverse.org • ggplot2 3.1.0 • Updated: 2018-12 ggplot (data = <DATA> ) + <GEOM_FUNCTION> (mapping = aes( <MAPPINGS> ), stat = <STAT> , position = <POSITION> ) + <COORDINATE_FUNCTION> + <FACET_FUNCTION> + <SCALE_FUNCTION> + <THEME_FUNCTION>
- 2. Scales Coordinate Systems A stat builds new variables to plot (e.g., count, prop). Stats An alternative way to build a layer + = data geom x = x · y = ..count.. coordinate system plot fl cty cyl x ..count.. stat Visualize a stat by changing the default stat of a geom function, geom_bar(stat="count") or by using a stat function, stat_count(geom="bar"), which calls a default geom to make a layer (equivalent to a geom function). Use ..name.. syntax to map stat variables to aesthetics. i + stat_density2d(aes(fill = ..level..), geom = "polygon") stat function geommappings variable created by stat geom to use c + stat_bin(binwidth = 1, origin = 10) x, y | ..count.., ..ncount.., ..density.., ..ndensity.. c + stat_count(width = 1) x, y, | ..count.., ..prop.. c + stat_density(adjust = 1, kernel = “gaussian") x, y, | ..count.., ..density.., ..scaled.. e + stat_bin_2d(bins = 30, drop = T) x, y, fill | ..count.., ..density.. e + stat_bin_hex(bins=30) x, y, fill | ..count.., ..density.. e + stat_density_2d(contour = TRUE, n = 100) x, y, color, size | ..level.. e + stat_ellipse(level = 0.95, segments = 51, type = "t") l + stat_contour(aes(z = z)) x, y, z, order | ..level.. l + stat_summary_hex(aes(z = z), bins = 30, fun = max) x, y, z, fill | ..value.. l + stat_summary_2d(aes(z = z), bins = 30, fun = mean) x, y, z, fill | ..value.. f + stat_boxplot(coef = 1.5) x, y | ..lower.., ..middle.., ..upper.., ..width.. , ..ymin.., ..ymax.. f + stat_ydensity(kernel = "gaussian", scale = “area") x, y | ..density.., ..scaled.., ..count.., ..n.., ..violinwidth.., ..width.. e + stat_ecdf(n = 40) x, y | ..x.., ..y.. e + stat_quantile(quantiles = c(0.1, 0.9), formula = y ~ log(x), method = "rq") x, y | ..quantile.. e + stat_smooth(method = "lm", formula = y ~ x, se=T, level=0.95) x, y | ..se.., ..x.., ..y.., ..ymin.., ..ymax.. ggplot() + stat_function(aes(x = -3:3), n = 99, fun = dnorm, args = list(sd=0.5)) x | ..x.., ..y.. e + stat_identity(na.rm = TRUE) ggplot() + stat_qq(aes(sample=1:100), dist = qt, dparam=list(df=5)) sample, x, y | ..sample.., ..theoretical.. e + stat_sum() x, y, size | ..n.., ..prop.. e + stat_summary(fun.data = "mean_cl_boot") h + stat_summary_bin(fun = "mean", geom = "bar") e + stat_unique() Scales map data values to the visual values of an aesthetic. To change a mapping, add a new scale. (n <- d + geom_bar(aes(fill = fl))) n + scale_fill_manual( values = c("skyblue", "royalblue", "blue", “navy"), limits = c("d", "e", "p", "r"), breaks =c("d", "e", "p", “r"), name = "fuel", labels = c("D", "E", "P", "R")) scale_ aesthetic to adjust prepackaged scale to use scale-specific arguments title to use in legend/axis labels to use in legend/axis breaks to use in legend/axis range of values to include in mapping GENERAL PURPOSE SCALES Use with most aesthetics scale_*_continuous() - map cont’ values to visual ones scale_*_discrete() - map discrete values to visual ones scale_*_identity() - use data values as visual ones scale_*_manual(values = c()) - map discrete values to manually chosen visual ones scale_*_date(date_labels = "%m/%d"), date_breaks = "2 weeks") - treat data values as dates. scale_*_datetime() - treat data x values as date times. Use same arguments as scale_x_date(). See ?strptime for label formats. X & Y LOCATION SCALES Use with x or y aesthetics (x shown here) scale_x_log10() - Plot x on log10 scale scale_x_reverse() - Reverse direction of x axis scale_x_sqrt() - Plot x on square root scale COLOR AND FILL SCALES (DISCRETE) n <- d + geom_bar(aes(fill = fl)) n + scale_fill_brewer(palette = "Blues") For palette choices: RColorBrewer::display.brewer.all() n + scale_fill_grey(start = 0.2, end = 0.8, na.value = "red") COLOR AND FILL SCALES (CONTINUOUS) o <- c + geom_dotplot(aes(fill = ..x..)) o + scale_fill_distiller(palette = "Blues") o + scale_fill_gradient(low="red", high="yellow") o + scale_fill_gradient2(low="red", high=“blue", mid = "white", midpoint = 25) o + scale_fill_gradientn(colours=topo.colors(6)) Also: rainbow(), heat.colors(), terrain.colors(), cm.colors(), RColorBrewer::brewer.pal() SHAPE AND SIZE SCALES p <- e + geom_point(aes(shape = fl, size = cyl)) p + scale_shape() + scale_size() p + scale_shape_manual(values = c(3:7)) p + scale_radius(range = c(1,6)) p + scale_size_area(max_size = 6) r <- d + geom_bar() r + coord_cartesian(xlim = c(0, 5)) xlim, ylim The default cartesian coordinate system r + coord_fixed(ratio = 1/2) ratio, xlim, ylim Cartesian coordinates with fixed aspect ratio between x and y units r + coord_flip() xlim, ylim Flipped Cartesian coordinates r + coord_polar(theta = "x", direction=1 ) theta, start, direction Polar coordinates r + coord_trans(ytrans = “sqrt") xtrans, ytrans, limx, limy Transformed cartesian coordinates. Set xtrans and ytrans to the name of a window function. π + coord_quickmap() π + coord_map(projection = "ortho", orientation=c(41, -74, 0))projection, xlim, ylim Map projections from the mapproj package (mercator (default), azequalarea, lagrange, etc.) Position Adjustments Position adjustments determine how to arrange geoms that would otherwise occupy the same space. s <- ggplot(mpg, aes(fl, fill = drv)) s + geom_bar(position = "dodge") Arrange elements side by side s + geom_bar(position = "fill") Stack elements on top of one another, normalize height e + geom_point(position = "jitter") Add random noise to X and Y position of each element to avoid overplotting e + geom_label(position = "nudge") Nudge labels away from points s + geom_bar(position = "stack") Stack elements on top of one another Each position adjustment can be recast as a function with manual width and height arguments s + geom_bar(position = position_dodge(width = 1)) A B Themes r + theme_bw() White background with grid lines r + theme_gray() Grey background (default theme) r + theme_dark() dark for contrast r + theme_classic() r + theme_light() r + theme_linedraw() r + theme_minimal() Minimal themes r + theme_void() Empty theme Faceting Facets divide a plot into subplots based on the values of one or more discrete variables. t <- ggplot(mpg, aes(cty, hwy)) + geom_point() t + facet_grid(cols = vars(fl)) facet into columns based on fl t + facet_grid(rows = vars(year)) facet into rows based on year t + facet_grid(rows = vars(year), cols = vars(fl)) facet into both rows and columns t + facet_wrap(vars(fl)) wrap facets into a rectangular layout Set scales to let axis limits vary across facets t + facet_grid(rows = vars(drv), cols = vars(fl), scales = "free") x and y axis limits adjust to individual facets "free_x" - x axis limits adjust "free_y" - y axis limits adjust Set labeller to adjust facet labels t + facet_grid(cols = vars(fl), labeller = label_both) t + facet_grid(rows = vars(fl), labeller = label_bquote(alpha ^ .(fl))) fl: c fl: d fl: e fl: p fl: r ↵c ↵d ↵e ↵p ↵r Labels t + labs( x = "New x axis label", y = "New y axis label", title ="Add a title above the plot", subtitle = "Add a subtitle below title", caption = "Add a caption below plot", <aes> = "New <aes> legend title") t + annotate(geom = "text", x = 8, y = 9, label = "A") Use scale functions to update legend labels <AES> geom to place manual values for geom’s aesthetics <AES> Legends n + theme(legend.position = "bottom") Place legend at "bottom", "top", "le ft ", or "right" n + guides(fill = "none") Set legend type for each aesthetic: colorbar, legend, or none (no legend) n + scale_fill_discrete(name = "Title", labels = c("A", "B", "C", "D", "E")) Set legend title and labels with a scale function. Zooming Without clipping (preferred) t + coord_cartesian( xlim = c(0, 100), ylim = c(10, 20)) With clipping (removes unseen data points) t + xlim(0, 100) + ylim(10, 20) t + scale_x_continuous(limits = c(0, 100)) + scale_y_continuous(limits = c(0, 100)) RStudio® is a trademark of RStudio, Inc. • CC BY SA RStudio • [email protected] • 844-448-1212 • rstudio.com • Learn more at http://ggplot2.tidyverse.org • ggplot2 3.1.0 • Updated: 2018-12 60 long lat