class: center, middle, inverse, title-slide # Refining your plots ### Daniel Anderson ### Week 6, Class 1 --- layout: true <script> feather.replace() </script> <div class="slides-footer"> <span> <a class = "footer-icon-link" href = "https://github.com/uo-datasci-specialization/c2-dataviz-2021/raw/main/static/slides/w6p1.pdf"> <i class = "footer-icon" data-feather="download"></i> </a> <a class = "footer-icon-link" href = "https://dataviz-2021.netlify.app/slides/w6p1.html"> <i class = "footer-icon" data-feather="link"></i> </a> <a class = "footer-icon-link" href = "https://github.com/uo-datasci-specialization/c2-dataviz-2021"> <i class = "footer-icon" data-feather="github"></i> </a> </span> </div> --- class: inverse-red middle # Reviewing Lab 2 --- class: inverse-blue # Data viz in Wild Raleigh Maggie ### Ann-Marie and Murat on deck --- # Agenda * Axes and aspect ratios * Annotations * Themes (a little bit) -- ### What we won't get to Each of the following are pretty fundamental to good data viz, but we won't have time to go over them today. Please make sure to read the corresponding chapters: * Handling high data density (lots of overlapping points) * Compound figures + See [{patchwork}](https://github.com/thomasp85/patchwork) and [{cowplot}](https://github.com/wilkelab/cowplot) * Exporting figures --- # Learning Objectives * Understand how to make a wide variety of tweaks to ggplot to essentially make it look however you want it to. * Understand common modifications to plots to make them more clear and reduce cognitive load --- # Axes * Cartesian coordinates - what we generally use <!-- --> --- # Different units <!-- --> --- # Aspect ratio <!-- --> --- background-image: url("http://socviz.co/dataviz-pdfl_files/figure-html4/ch-01-perception-curves-1.png") background-size: contain --- # Same scales Use `coord_fixed()` <!-- --> --- # Changing aspect ratio * Explore how your plot will look in its final size * No hard/fast rules (if on different scales) * Not even really rules of thumb * Keep visual perception in mind * Try your best to be truthful - show the trend/relation, but don't exaggerate/hide it --- # Handy function (from an apparently deleted tweet from [@tjmahr](https://twitter.com/tjmahr)) <blockquote class="twitter-tweet tw-align-center" data-lang="en"><p lang="en" dir="ltr">here&#39;s my favorite helper <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> function. preview ggsave() output<br><br>ggpreview &lt;- function (..., device = &quot;png&quot;) {<br> fname &lt;- tempfile(fileext = paste0(&quot;.&quot;, device))<br> ggplot2::ggsave(filename = fname, device = device, ...)<br> system2(&quot;open&quot;, fname)<br> invisible(NULL)<br>}</p>&mdash; tj mahr 🍕🍍 (@tjmahr) </blockquote> --- # Gist (side note: gists are a good way to share things) * See the full code/example [here](https://gist.github.com/tjmahr/1dd36d78ecb3cff10baf01817a56e895) * Let's take 5 minutes to play around: + Create a plot (could even be the example in the gist) + Try different aspect ratios by changing the width/length <div class="countdown" id="timer_603ec00d" style="right:0;bottom:0;" data-warnwhen="0"> <code class="countdown-time"><span class="countdown-digits minutes">05</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code> </div> --- # Scale transformations ### Raw scale ```r library(gapminder) ggplot(gapminder, aes(year, gdpPercap)) + geom_line(aes(group = country), color = "gray70") ``` <!-- --> --- ### Log10 scale ```r ggplot(gapminder, aes(year, gdpPercap)) + geom_line(aes(group = country), color = "gray70") + * scale_y_log10(labels = scales::dollar) ``` <!-- --> --- <br/> <br/> <!-- --> --- <br/> <br/> <!-- --> --- # Scales ```r d <- tibble(x = c(1, 3.16, 10, 31.6, 100), log_x = log10(x)) ggplot(d, aes(x, 1)) + geom_point(color = "#0072B2") ggplot(d, aes(x, 1)) + geom_point(color = "#0072B2") + scale_x_log10() ggplot(d, aes(log_x, 1)) + geom_point(color = "#0072B2") ``` --- # Scales  --- # Don't transform twice <style type="text/css"> .code-bg-red .remark-code, .code-bg-red .remark-code * { background-color: #ffe0e0 !important; } </style> .code-bg-red[ ```r ggplot(d, aes(log_x, 1)) + geom_point(color = "#0072B2") + scale_x_log10() + xlim(-0.2, 2.5) ``` <!-- --> ] --- # Careful with labeling * Has the scale or the data been log transformed? * Specify the base ```r library(ggtext) ggplot(d, aes(log_x, 1)) + geom_point(color = "#0072B2") + * labs(x = "log<sub>10</sub>(x)") + * theme(axis.title.x = element_markdown()) ``` <!-- --> Labels should denote the data, not the scale of the axis --- ```r ggplot(d, aes(x, 1)) + geom_point(color = "#0072B2") + * scale_x_log10() ``` <!-- --> Labeling the above with `\(log_{10}(x)\)` would be ambiguous and confusing --- class: inverse-blue center middle # Labels and captions --- # Disclaimer * APA style requires the labels be made in specific ways * Much of the following discussion still applies * Our book (Wilke) uses a similar style throughout --- # Title ### What is the point of your figure? -- ### What are you trying to communicate -- * Figures should have only one title -- * Use integrated title/subtitles for sharing with a broad audience + Blog posts + Social media + Reports to stakeholders -- * Keep figures in subtext when there's a designated format you must adhere to -- * Make sure your figure has a title + Should not start with "This figure displays/shows..." --- # Caption Consider stating the data source Other details relevant to the figure but not important enough for a subtitle --- # Axis labels * The title for the axis * Critical for communication * **Never** use variable names (very common and very poor practice) * State the measure and the unit (if quantitative) + e.g., "Brain Mass (grams)", "Support for Measure (millions of people)", "Dollars spent" + Categorical variable likely will not need to the measurement unit --- # Omission * Consider omitting obvious or redundant labels + Use `labs(x = NULL)` or `labs(x = "")` + If already using `scale_x/y_*()` just supply the `name` argument <!-- --> --- # Omission * Do not omit axis titles that are not obvious <!-- --> --- # Don't overdo it <!-- --> --- class: inverse-red center middle # Annotations --- # Among the most effective * If possible, try to remove legends, and just include annotations --- # Building up a plot ```r remotes::install_github("clauswilke/dviz.supp") head(tech_stocks) ``` ``` ## # A tibble: 6 x 6 ## company ticker date price index_price price_indexed ## <chr> <chr> <date> <dbl> <dbl> <dbl> ## 1 Alphabet GOOG 2017-06-02 975.6 285.2 342.0757 ## 2 Alphabet GOOG 2017-06-01 966.95 285.2 339.0428 ## 3 Alphabet GOOG 2017-05-31 964.86 285.2 338.3100 ## 4 Alphabet GOOG 2017-05-30 975.88 285.2 342.1739 ## 5 Alphabet GOOG 2017-05-26 971.47 285.2 340.6276 ## 6 Alphabet GOOG 2017-05-25 969.54 285.2 339.9509 ``` --- ```r ggplot(tech_stocks, aes(date, price_indexed, color = ticker)) + geom_line() ``` <!-- --> --- ```r ggplot(tech_stocks, aes(date, price_indexed, color = ticker)) + geom_line() + * scale_color_OkabeIto() ``` <!-- --> --- ```r ggplot(tech_stocks, aes(date, price_indexed, color = ticker)) + geom_line() + * scale_color_OkabeIto(name = "Company", * breaks = c("GOOG", "AAPL", "FB", "MSFT"), * labels = c("Alphabet", "Apple", "Facebook", "Microsoft")) ``` --- # Bad <!-- --> --- ```r ggplot(tech_stocks, aes(date, price_indexed, color = ticker)) + geom_line() + * scale_color_OkabeIto(name = "Company", * breaks = c("FB", "GOOG", "MSFT", "AAPL"), * labels = c("Facebook", "Alphabet", "Microsoft", "Apple")) ``` --- # Good <!-- --> --- ```r ggplot(tech_stocks, aes(date, price_indexed, color = ticker)) + geom_line() + scale_color_OkabeIto(name = "Company", breaks = c("FB", "GOOG", "MSFT", "AAPL"), labels = c("Facebook", "Alphabet", "Microsoft", "Apple")) + scale_x_date(name = "year", limits = c(ymd("2012-06-01"), ymd("2018-12-31")), expand = c(0,0)) + geom_text(data = filter(tech_stocks, date == "2017-06-02"), aes(y = price_indexed, label = company), * nudge_x = 280) ``` --- <!-- --> --- ```r ggplot(tech_stocks, aes(date, price_indexed, color = ticker)) + geom_line() + scale_color_OkabeIto(name = "Company", breaks = c("FB", "GOOG", "MSFT", "AAPL"), labels = c("Facebook", "Alphabet", "Microsoft", "Apple")) + scale_x_date(name = "year", limits = c(ymd("2012-06-01"), ymd("2018-12-31")), expand = c(0,0)) + geom_text(data = filter(tech_stocks, date == "2017-06-02"), aes(y = price_indexed, label = company), nudge_x = 280, * hjust = 0) ``` --- <!-- --> --- ```r ggplot(tech_stocks, aes(date, price_indexed, color = ticker)) + geom_line() + scale_color_OkabeIto(name = "Company", breaks = c("FB", "GOOG", "MSFT", "AAPL"), labels = c("Facebook", "Alphabet", "Microsoft", "Apple")) + scale_x_date(name = "year", limits = c(ymd("2012-06-01"), ymd("2018-10-31")), expand = c(0,0)) + geom_text(data = filter(tech_stocks, date == "2017-06-02"), aes(y = price_indexed, label = company), * color = "gray40", * nudge_x = 20, hjust = 0) + * guides(color = "none") ``` --- <!-- --> --- ```r ggplot(tech_stocks, aes(date, price_indexed, color = ticker)) + geom_line() + scale_color_OkabeIto(name = "Company", breaks = c("FB", "GOOG", "MSFT", "AAPL"), labels = c("Facebook", "Alphabet", "Microsoft", "Apple")) + * scale_x_date(name = "", limits = c(ymd("2012-06-01"), ymd("2018-10-31")), expand = c(0,0)) + * scale_y_continuous(name = "Stock Price, Indexed", * labels = scales::dollar) + geom_text(data = filter(tech_stocks, date == "2017-06-02"), aes(y = price_indexed, label = company), color = "gray40", nudge_x = 20, hjust = 0, size = 10) + guides(color = "none") + labs(title = "Tech growth over time", * caption = "Data from Wilke (2019): Fundamentals of Data Visualization") ``` --- <!-- --> --- # Labeling bars ```r avs <- tech_stocks %>% group_by(company) %>% summarize(stock_av = mean(price_indexed)) %>% ungroup() %>% mutate(share = stock_av / sum(stock_av)) avs ``` ``` ## # A tibble: 4 x 3 ## company stock_av share ## * <chr> <dbl> <dbl> ## 1 Alphabet 141.0205 0.2292441 ## 2 Apple 77.08241 0.1253058 ## 3 Facebook 274.7427 0.4466240 ## 4 Microsoft 122.3088 0.1988261 ``` --- # Bar plot ```r ggplot(avs, aes(fct_reorder(company, share), share)) + geom_col(fill = "#0072B2") ``` <!-- --> --- # Horizontal ```r ggplot(avs, aes(share, fct_reorder(company, share))) + geom_col(fill = "#0072B2", alpha = 0.9) ``` <!-- --> --- ```r ggplot(avs, aes(fct_reorder(company, share), share)) + geom_col(fill = "#0072B2", alpha = 0.9) + coord_flip() + * theme(panel.grid.major.y = element_blank(), * panel.grid.minor.x = element_blank(), * panel.grid.major.x = element_line(color = "gray80")) ``` <!-- --> --- # Quick aside Let's actually make a bar plot theme ```r bp_theme <- function(...) { theme_minimal(...) + theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank(), panel.grid.major.x = element_line(color = "gray80"), plot.title.position = "plot") } ``` --- ```r ggplot(avs, aes(fct_reorder(company, share), share)) + geom_col(fill = "#0072B2", alpha = 0.9) + * geom_text(aes(company, share, label = round(share, 2)), * nudge_y = 0.02, * size = 8) + coord_flip() + bp_theme(base_size = 25) ``` <!-- --> --- ```r ggplot(avs, aes(fct_reorder(company, share), share)) + geom_col(fill = "#0072B2", alpha = 0.9) + * geom_text(aes(company, share, label = paste0(round(share*100), "%")), nudge_y = 0.02, size = 8) + coord_flip() + * scale_y_continuous("Market Share", labels = scales::percent) + labs(x = NULL, title = "Tech company market control", caption = "Data from Clause Wilke Book: Fundamentals of Data Visualizations") + bp_theme(base_size = 25) ``` --- <!-- --> --- ```r ggplot(avs, aes(fct_reorder(company, share), share)) + geom_col(fill = "#0072B2", alpha = 0.9) + * geom_text(aes(company, share, label = paste0(round(share*100), "%")), nudge_y = 0.02, size = 8) + coord_flip() + scale_y_continuous("Market Share", labels = scales::percent, expand = c(0, 0, 0.05, 0)) + labs(x = NULL, title = "Tech company market control", caption = "Data from Clause Wilke Book: Fundamentals of Data Visualizations") + bp_theme(base_size = 25) ``` --- <!-- --> --- # Last alternative ```r ggplot(avs, aes(fct_reorder(company, share), share)) + geom_col(fill = "#0072B2", alpha = 0.9) + geom_text(aes(company, share, label = paste0(round(share*100), "%")), * nudge_y = -0.02, size = 8, * color = "white") + coord_flip() + scale_y_continuous("Market Share", labels = scales::percent, expand = c(0, 0, 0.05, 0)) + labs(x = NULL, title = "Tech company market control", caption = "Data from Clause Wilke Book: Fundamentals of Data Visualizations") + bp_theme(base_size = 25) ``` --- <!-- --> --- # Distributions ```r ggplot(iris, aes(Sepal.Length, fill = Species)) + geom_density(alpha = 0.3, color = "white") ``` <!-- --> --- ```r ggplot(iris, aes(Sepal.Length, fill = Species)) + geom_density(alpha = 0.3, color = "white") + scale_fill_OkabeIto() ``` <!-- --> --- # Labeling ### One method ```r label_locs <- tibble(Sepal.Length = c(5.45, 6, 7), density = c(1, 0.8, 0.6), Species = c("setosa", "versicolor", "virginica")) ggplot(iris, aes(Sepal.Length, fill = Species)) + geom_density(alpha = 0.3, color = "white") + scale_fill_OkabeIto() + geom_text(aes(label = Species, y = density, color = Species), data = label_locs) ``` --- <!-- --> --- ```r ggplot(iris, aes(Sepal.Length, fill = Species)) + geom_density(alpha = 0.3, color = "white") + scale_fill_OkabeIto() + * scale_color_OkabeIto() + geom_text(aes(label = Species, y = density, color = Species), data = label_locs) + guides(color = "none", fill = "none") ``` --- <!-- --> --- ```r label_locs <- tibble(Sepal.Length = c(5.4, 6, 6.9), density = c(1, 0.75, 0.6), Species = c("setosa", "versicolor", "virginica")) ggplot(iris, aes(Sepal.Length, fill = Species)) + geom_density(alpha = 0.3, color = "white") + scale_fill_OkabeIto() + scale_color_OkabeIto() + geom_text(aes(label = Species, y = density), * color = "gray40", data = label_locs) + * guides(fill = "none") ``` --- <!-- --> --- # Other options * Rather than using a new data frame, you could use multiple calls to `annotate`. * One is not necessarily better than the other, but I prefer the data frame method * Keep in mind you can .bolder[always] use multiple data sources within a single plot + Each layer can have its own data source + Common in geographic data in particular --- # Annotate example ```r ggplot(iris, aes(Sepal.Length, fill = Species)) + geom_density(alpha = 0.3) + scale_fill_OkabeIto() + scale_color_OkabeIto() + * annotate("text", label = "setosa", x = 5.45, y = 1, color = "gray40") + * annotate("text", label = "versicolor", x = 6, y = 0.8, color = "gray40") + * annotate("text", label = "virginica", x = 7, y = 0.6, color = "gray40") + guides(fill = "none") ``` --- <!-- --> --- class: inverse-blue center middle # ggrepel --- # Plot text directly ```r cars <- rownames_to_column(mtcars) ggplot(cars, aes(hp, mpg)) + geom_text(aes(label = rowname)) ``` <!-- --> --- # Repel text ```r *library(ggrepel) ggplot(cars, aes(hp, mpg)) + * geom_text_repel(aes(label = rowname)) ``` <!-- --> --- # Slightly better ```r ggplot(cars, aes(hp, mpg)) + * geom_point(color = "gray70") + geom_text_repel(aes(label = rowname), * min.segment.length = 0) ``` <!-- --> --- # Common use cases * Label some sample data that makes some theoretical sense (we've seen this before) * Label outliers * Label points from a specific group (e.g., similar to highlighting - can be used in conjunction) --- # Some new data ```r remotes::install_github("kjhealy/socviz") library(socviz) ``` ```r by_country <- organdata %>% group_by(consent_law, country) %>% summarize(donors_mean= mean(donors, na.rm = TRUE), donors_sd = sd(donors, na.rm = TRUE), gdp_mean = mean(gdp, na.rm = TRUE), health_mean = mean(health, na.rm = TRUE), roads_mean = mean(roads, na.rm = TRUE), cerebvas_mean = mean(cerebvas, na.rm = TRUE)) ``` --- ```r by_country ``` ``` ## # A tibble: 17 x 8 ## # Groups: consent_law [2] ## consent_law country donors_mean donors_sd gdp_mean health_mean roads_mean ## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 Informed Australia 10.635 1.142808 22178.54 1957.5 104.8757 ## 2 Informed Canada 13.96667 0.7511607 23711.08 2271.929 109.2601 ## 3 Informed Denmark 13.09167 1.468121 23722.31 2054.071 101.6363 ## 4 Informed Germany 13.04167 0.6111960 22163.23 2348.75 112.7887 ## 5 Informed Ireland 19.79167 2.478437 20824.38 1479.929 117.7742 ## 6 Informed Netherlands 13.65833 1.551807 23013.15 1992.786 76.09357 ## # … with 11 more rows, and 1 more variable: cerebvas_mean <dbl> ``` --- # Scatterplot ```r ggplot(by_country, aes(gdp_mean, health_mean)) + geom_point() ``` <!-- --> --- # Outliers ```r ggplot(by_country, aes(gdp_mean, health_mean)) + geom_point() + * geom_text_repel(data = filter(by_country, * gdp_mean > 25000 | * gdp_mean < 20000), * aes(label = country)) ``` --- <!-- --> --- # Combine with highlighting ```r *library(gghighlight) ggplot(by_country, aes(gdp_mean, health_mean)) + geom_point() + * gghighlight(gdp_mean > 25000 | gdp_mean < 20000) + * geom_text_repel(aes(label = country)) ``` * Notice you only have to specify the points to highlight and `geom_text_repel` will then only label those points --- <!-- --> --- # Combine with highlighting Switch to make outliers grayed out and labeled ```r ggplot(by_country, aes(gdp_mean, health_mean)) + geom_point() + gghighlight(gdp_mean > 20000 & gdp_mean < 25000 ) + geom_text_repel(data = filter(by_country, gdp_mean > 25000 | gdp_mean < 20000), aes(label = country), color = "#BEBEBEB3") ``` Note I found the exact gray color by looking at the source code. Specifically, it is the output from `ggplot2::alpha("grey", 0.7)` --- <!-- --> --- # By group ```r ggplot(by_country, aes(gdp_mean, health_mean)) + geom_point() + geom_text_repel(data = filter(by_country, consent_law == "Presumed"), aes(label = country)) ``` <!-- --> --- # By group ```r ggplot(by_country, aes(gdp_mean, health_mean)) + * geom_point(color = "#DC5265") + * gghighlight(consent_law == "Presumed") + geom_text_repel(aes(label = country), min.segment.length = 0, * box.padding = 0.75) + labs(title = "GDP and Health", subtitle = "Countries with a presumed organ donation consent are highlighted", caption = "Data from the General Social Science Survey, Distributed through the socviz R package", x = "Mean GDP", y = "Mean Health") ``` --- <!-- --> --- class: inverse-blue middle # ggforce Quickly --- # Annotating groups of points Consider using any of the following from **ggforce** to annotate specific points * `geom_mark_rect()` * `geom_mark_circle()` * `geom_mark_ellipse()` * `geom_mark_hull()` --- # Examples ```r library(palmerpenguins) library(ggforce) penguins %>% drop_na() %>% # Can't take missing data ggplot(aes(bill_length_mm, bill_depth_mm)) + * geom_mark_ellipse(aes(group = species, label = species)) + geom_point(aes(color = species)) + * coord_cartesian(xlim = c(28, 62), ylim = c(13, 23)) + guides(color = "none") ``` --- class: middle <!-- --> --- # Limit to a single group ```r penguins %>% drop_na() %>% ggplot(aes(bill_length_mm, bill_depth_mm)) + geom_point(aes(color = species)) + geom_mark_ellipse(aes(group = species, label = species), * data = filter(drop_na(penguins), * species == "Gentoo")) + coord_cartesian(xlim = c(28, 62), ylim = c(13, 23)) ``` --- class: middle <!-- --> --- # Switch to hull Note - requires the **concaveman** package be installed ```r penguins %>% drop_na() %>% ggplot(aes(bill_length_mm, bill_depth_mm)) + geom_point(aes(color = species)) + * geom_mark_hull(aes(group = species, label = species), * data = filter(drop_na(penguins), * species == "Gentoo")) + coord_cartesian(xlim = c(28, 62), ylim = c(13, 23)) ``` --- class: middle <!-- --> --- # Change expand ```r penguins %>% drop_na() %>% ggplot(aes(bill_length_mm, bill_depth_mm)) + geom_point(aes(color = species)) + geom_mark_hull(aes(group = species, label = species), * expand = unit(1, "mm"), data = filter(drop_na(penguins), species == "Gentoo")) + coord_cartesian(xlim = c(28, 62), ylim = c(13, 23)) ``` --- class: middle <!-- --> --- # More in-depth annotations First create a description ```r penguins <- penguins %>% mutate(desc = ifelse(species != "Gentoo", "", "During deep dives, gentoo penguins reduce their heart rate from 80 to 100 beats per minute (bpm) down to 20 bpm. Gentoo penguins use nesting materials ranging from pebbles and molted feathers in Antarctica to vegetation on subantarctic islands. Gentoos are the third largest penguin, following the emperor and king.")) ``` --- # Now add as a description ```r penguins %>% drop_na() %>% ggplot(aes(bill_length_mm, bill_depth_mm)) + geom_point(aes(color = species)) + geom_mark_ellipse(aes(group = species, label = species, * description = desc), data = filter(drop_na(penguins), species == "Gentoo"), * label.fill = "#b3cfff") + coord_cartesian(xlim = c(28, 62), ylim = c(13, 23)) ``` --- class: middle <!-- --> --- # Similar We can also just add a textbox through **{ggtext}** ```r txtbox <- tibble( bill_length_mm = 23, bill_depth_mm = 16, lab = '"They may all waddle around in their tuxedolike feathers, but the penguins of the Antarctic Peninsula are not equal in their ability to adapt to a warming climate. While the populations of the Adélie and chinstrap penguin species are currently declining, the gentoo species is increasing. But this has not always been the case, according to a recent study published in the journal Scientific Reports." - Scientific American' ) ``` --- ```r penguins %>% drop_na() %>% ggplot(aes(bill_length_mm, bill_depth_mm)) + geom_point(aes(color = species)) + ggtext::geom_textbox(aes(label = lab), data = txtbox) + coord_cartesian(xlim = c(17, 62), ylim = c(13, 22)) ``` <!-- --> --- # Last bit The **ggforce** package is well worth exploring more. See [here](https://z3tt.github.io/OutlierConf2021/) for a nice walkthrough that has good data viz and uses some of the **ggforce** functions (as well as illustrating a few other cool packages) --- class: inverse-red center middle # Themes (quickly) --- <!-- --> --- # ggthemes * Good place to start. All sorts of themes. * Includes color scales, etc., that align with themes * You can even conform with other software + fit into an economics conference with `theme_stata` See the themes [here](https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/) --- # BBC The BBC uses ggplot for most of its graphics. They've developed a package with a theme and some functions to help make it match their style more. See the repo [here](https://github.com/bbc/bbplot) Their [Journalism Cookbook](https://bbc.github.io/rcookbook/) is really nice too --- background-image: url(https://github.com/bbc/bbplot/raw/master/chart_examples/bbplot_example_plots.png) background-size: contain --- # ggthemeassist * Another great place to start with making major modifications/creating your own custom theme * Can't do everything, but can do a lot * See [here](https://github.com/calligross/ggthemeassist) [demo] --- # `theme()` for everything else * You can basically change your plot to look however you want through `theme` * Generally a bit more complicated * I've used ggplot for *years* and only really now gaining fluency with it --- # Quick example ### From Lab 3 ```r library(fivethirtyeight) g <- google_trends %>% pivot_longer(starts_with("hurricane"), names_to = "hurricane", values_to = "interest", names_pattern = "_(.+)_") landfall <- tibble(date = lubridate::mdy(c("August 25, 2017", "September 10, 2017", "September 20, 2017")), hurricane = c("Harvey Landfall", "Irma Landfall", "Maria Landfall")) ``` --- ```r p <- ggplot(g, aes(date, interest)) + geom_ribbon(aes(fill = hurricane, ymin = 0, ymax = interest), alpha = 0.6) + geom_vline(aes(xintercept = date), landfall, color = "gray80", lty = "dashed") + geom_text(aes(x = date, y = 80, label = hurricane), landfall, color = "gray80", nudge_x = 0.5, hjust = 0) + labs(x = "", y = "Google Trends", title = "Hurricane Google trends over time", caption = "Source: https://github.com/fivethirtyeight/data/tree/master/puerto-rico-media") + scale_fill_brewer("Hurricane", palette = "Set2") ``` --- <!-- --> --- ```r p + theme(panel.grid.major = element_line(colour = "gray30"), panel.grid.minor = element_line(colour = "gray30"), axis.text = element_text(colour = "gray80"), axis.text.x = element_text(colour = "gray80"), axis.text.y = element_text(colour = "gray80"), axis.title = element_text(colour = "gray80"), legend.text = element_text(colour = "gray80"), legend.title = element_text(colour = "gray80"), panel.background = element_rect(fill = "gray10"), plot.background = element_rect(fill = "gray10"), legend.background = element_rect(fill = NA, color = NA), legend.position = c(0.20, -0.1), legend.direction = "horizontal", plot.margin = margin(10, 10, b = 20, 10), plot.caption = element_text(colour = "gray80", vjust = 1), plot.title = element_text(colour = "gray80")) ``` --- <!-- --> --- class: inverse-green center middle # Next time ### Visualizing uncertainty Homework 2 is also posted currently, but is technically assigned Wednesday