Data Visualisation with ggplot2 – Data Carpentry: From Data Wrangling to Data Visualisation

```{r setup, include=FALSE} source("data/download_data.R") ``` :::: instructor - This lesson is a broad overview of ggplot2 and focuses on (1) getting familiar with the layering system of ggplot2, (2) using the argument `group` in the `aes()` function, (3) basic customization of the plots. :::::::::::: ::::::::::::::::::::::::::::::::::::::: objectives - Produce scatter plots, boxplots, and barplots using ggplot. - Set universal plot settings. - Describe what faceting is and apply faceting in ggplot. - Modify the aesthetics of an existing ggplot plot (including axis labels and colour). - Build complex and customized plots from data in a data frame. :::::::::::::::::::::::::::::::::::::::::::::::::: :::::::::::::::::::::::::::::::::::::::: questions - What are the components of a ggplot? - How do I create scatterplots, boxplots, and barplots? - How can I change the aesthetics (ex. colour, transparency) of my plot? - How can I create multiple plots at once? :::::::::::::::::::::::::::::::::::::::::::::::::: We start by loading the required package. **`ggplot2`** is also included in the **`tidyverse`** package. ```{r load-package, message=FALSE, purl=FALSE} library(tidyverse) ``` If not still in the workspace, load the data we saved in the previous lesson. ```{r load-data, purl=FALSE} interviews_plotting <- read_csv("data_output/interviews_plotting.csv") ``` If you were unable to complete the previous lesson or did not save the data, then you can create it now. ```{r, purl=FALSE, eval=FALSE} ## Not run, but can be used to load in data from previous lesson! interviews_plotting <- interviews %>% ## pivot wider by items_owned separate_rows(items_owned, sep = ";") %>% ## if there were no items listed, changing NA to no_listed_items replace_na(list(items_owned = "no_listed_items")) %>% mutate(items_owned_logical = TRUE) %>% pivot_wider(names_from = items_owned, values_from = items_owned_logical, values_fill = list(items_owned_logical = FALSE)) %>% ## pivot wider by months_lack_food separate_rows(months_lack_food, sep = ";") %>% mutate(months_lack_food_logical = TRUE) %>% pivot_wider(names_from = months_lack_food, values_from = months_lack_food_logical, values_fill = list(months_lack_food_logical = FALSE)) %>% ## add some summary columns mutate(number_months_lack_food = rowSums(select(., Jan:May))) %>% mutate(number_items = rowSums(select(., bicycle:car))) ``` ## Plotting with **`ggplot2`** **`ggplot2`** is a plotting package that makes it simple to create complex plots from data stored in a data frame. It provides a programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties. Therefore, we only need minimal changes if the underlying data change or if we decide to change from a bar plot to a scatterplot. This helps in creating publication quality plots with minimal amounts of adjustments and tweaking. **`ggplot2`** functions work best with data in the 'long' format, i.e., a column for every dimension, and a row for every observation. Well-structured data will save you lots of time when making figures with **`ggplot2`** ggplot graphics are built step by step by adding new elements. Adding layers in this fashion allows for extensive flexibility and customization of plots. Each chart built with ggplot2 must include the following - Data - Aesthetic mapping (aes) - Describes how variables are mapped onto graphical attributes - Visual attribute of data including x-y axes, color, fill, shape, and alpha - Geometric objects (geom) - Determines how values are rendered graphically, as bars (`geom_bar`), scatterplot (`geom_point`), line (`geom_line`), etc. Thus, the template for graphic in ggplot2 is: ``` %>% ggplot(aes()) + () ``` Remember from the last lesson that the pipe operator `%>%` places the result of the previous line(s) into the first argument of the function. **`ggplot`** is a function that expects a data frame to be the first argument. This allows for us to change from specifying the `data =` argument within the `ggplot` function and instead pipe the data into the function. - use the `ggplot()` function and bind the plot to a specific data frame. ```{r ggplot-steps-1, eval=FALSE, purl=FALSE} interviews_plotting %>% ggplot() ``` - define a mapping (using the aesthetic (`aes`) function), by selecting the variables to be plotted and specifying how to present them in the graph, e.g. as x/y positions or characteristics such as size, shape, color, etc. ```{r ggplot-steps-2, eval=FALSE, purl=FALSE} interviews_plotting %>% ggplot(aes(x = no_membrs, y = number_items)) ``` - add 'geoms' – graphical representations of the data in the plot (points, lines, bars). **`ggplot2`** offers many different geoms; we will use some common ones today, including: - `geom_point()` for scatter plots, dot plots, etc. - `geom_boxplot()` for, well, boxplots! - `geom_line()` for trend lines, time series, etc. To add a geom to the plot use the `+` operator. Because we have two continuous variables, let's use `geom_point()` first: ```{r first-ggplot, purl=FALSE} interviews_plotting %>% ggplot(aes(x = no_membrs, y = number_items)) + geom_point() ``` The `+` in the **`ggplot2`** package is particularly useful because it allows you to modify existing `ggplot` objects. This means you can easily set up plot templates and conveniently explore different types of plots, so the above plot can also be generated with code like this, similar to the "intermediate steps" approach in the previous lesson: ```{r first-ggplot-with-plus, fig.alt="Scatter plot of number of items owned versus number of household members.", eval=FALSE, purl=FALSE} # Assign plot to a variable interviews_plot <- interviews_plotting %>% ggplot(aes(x = no_membrs, y = number_items)) # Draw the plot as a dot plot interviews_plot + geom_point() ``` ::::::::::::::::::::::::::::::::::::::::: callout ## Notes - Anything you put in the `ggplot()` function can be seen by any geom layers that you add (i.e., these are universal plot settings). This includes the x- and y-axis mapping you set up in `aes()`. - You can also specify mappings for a given geom independently of the mapping defined globally in the `ggplot()` function. - The `+` sign used to add new layers must be placed at the end of the line containing the *previous* layer. If, instead, the `+` sign is added at the beginning of the line containing the new layer, **`ggplot2`** will not add the new layer and will return an error message. :::::::::::::::::::::::::::::::::::::::::::::::::: ```{r ggplot-with-plus-position, eval=FALSE, purl=FALSE} ## This is the correct syntax for adding layers interviews_plot + geom_point() ## This will not add the new layer and will return an error message interviews_plot + geom_point() ``` ## Building your plots iteratively Building plots with **`ggplot2`** is typically an iterative process. We start by defining the dataset we'll use, lay out the axes, and choose a geom: ```{r create-ggplot-object, fig.alt="Scatter plot of number of items owned versus number of household members.", purl=FALSE} interviews_plotting %>% ggplot(aes(x = no_membrs, y = number_items)) + geom_point() ``` Then, we start modifying this plot to extract more information from it. For instance, when inspecting the plot we notice that points only appear at the intersection of whole numbers of `no_membrs` and `number_items`. Also, from a rough estimate, it looks like there are far fewer dots on the plot than there rows in our dataframe. This should lead us to believe that there may be multiple observations plotted on top of each other (e.g. three observations where `no_membrs` is 3 and `number_items` is 1). There are two main ways to alleviate overplotting issues: 1. changing the transparency of the points 2. jittering the location of the points Let's first explore option 1, changing the transparency of the points. What we mean when we say "transparency" we mean the opacity of point, or your ability to see through the point. We can control the transparency of the points with the `alpha` argument to `geom_point`. Values of `alpha` range from 0 to 1, with lower values corresponding to more transparent colors (an `alpha` of 1 is the default value). Specifically, an alpha of 0.1, would make a point one-tenth as opaque as a normal point. Stated differently ten points stacked on top of each other would correspond to a normal point. Here, we change the `alpha` to 0.5, in an attempt to help fix the overplotting. While the overplotting isn't solved, adding transparency begins to address this problem, as the points where there are overlapping observations are darker (as opposed to lighter gray): ```{r adding-transparency, fig.alt="Scatter plot of number of items owned versus number of household members, with transparency added to points.", purl=FALSE} interviews_plotting %>% ggplot(aes(x = no_membrs, y = number_items)) + geom_point(alpha = 0.3) ``` That only helped a little bit with the overplotting problem, so let's try option two. We can jitter the points on the plot, so that we can see each point in the locations where there are overlapping points. Jittering introduces a little bit of randomness into the position of our points. You can think of this process as taking the overplotted graph and giving it a tiny shake. The points will move a little bit side-to-side and up-and-down, but their position from the original plot won't dramatically change. We can jitter our points using the `geom_jitter()` function instead of the `geom_point()` function, as seen below: ```{r adding-jitter, fig.alt="Scatter plot of number of items owned versus number of household members, showing jitter.", purl=FALSE} interviews_plotting %>% ggplot(aes(x = no_membrs, y = number_items)) + geom_jitter() ``` The `geom_jitter()` function allows for us to specify the amount of random motion in the jitter, using the `width` and `height` arguments. When we don't specify values for `width` and `height`, `geom_jitter()` defaults to 40% of the resolution of the data (the smallest change that can be measured). Hence, if we would like *less* spread in our jitter than was default, we should pick values between 0.1 and 0.4. Experiment with the values to see how your plot changes. ```{r adding-width-height, fig.alt="Scatter plot of number of items owned versus number of household members, with jitter and transparency.", purl=FALSE} interviews_plotting %>% ggplot(aes(x = no_membrs, y = number_items)) + geom_jitter(alpha = 0.3, width = 0.2, height = 0.2) ``` For our final change, we can also add colours for all the points by specifying a `color` argument inside the `geom_jitter()` function: ```{r adding-colors, fig.alt="Scatter plot of number of items owned versus number of household members, showing points as blue.", purl=FALSE} interviews_plotting %>% ggplot(aes(x = no_membrs, y = number_items)) + geom_jitter(alpha = 0.3, color = "blue", width = 0.2, height = 0.2) ``` To colour each village in the plot differently, you could use a vector as an input to the argument **`color`**. However, because we are now mapping features of the data to a colour, instead of setting one colour for all points, the colour of the points now needs to be set inside a call to the **`aes`** function. When we map a variable in our data to the colour of the points, **`ggplot2`** will provide a different colour corresponding to the different values of the variable. We will continue to specify the value of **`alpha`**, **`width`**, and **`height`** outside of the **`aes`** function because we are using the same value for every point. ggplot2 understands both the Commonwealth English and American English spellings for colour, i.e., you can use either `color` or `colour`. Here is an example where we color points by the **`village`** of the observation: ```{r color-by-species, purl=FALSE} interviews_plotting %>% ggplot(aes(x = no_membrs, y = number_items)) + geom_jitter(aes(color = village), alpha = 0.3, width = 0.2, height = 0.2) ``` There appears to be a positive trend between number of household members and number of items owned (from the list provided). Additionally, this trend does not appear to be different by village. ::::::::::::::::::::::::::::::::::::::::: callout ## Notes As you will learn, there are multiple ways to plot the a relationship between variables. Another way to plot data with overlapping points is to use the `geom_count` plotting function. The `geom_count()` function makes the size of each point representative of the number of data items of that type and the legend gives point sizes associated to particular numbers of items. ```{r color-by-species-notes, fig.alt="Previous plot with dots colored by village.", purl=FALSE} interviews_plotting %>% ggplot(aes(x = no_membrs, y = number_items, color = village)) + geom_count() ``` :::::::::::::::::::::::::::::::::::::::::::::::::: ::::::::::::::::::::::::::::::::::::::: challenge ## Exercise Use what you just learned to create a scatter plot of `rooms` by `village` with the `respondent_wall_type` showing in different colours. Does this seem like a good way to display the relationship between these variables? What other kinds of plots might you use to show this type of data? ::::::::::::::: solution ## Solution ```{r scatter-challenge, fig.alt="Scatter plot showing positive trend between number of household members and number of items owned.", answer=TRUE, purl=FALSE} interviews_plotting %>% ggplot(aes(x = village, y = rooms)) + geom_jitter(aes(color = respondent_wall_type), alpha = 0.3, width = 0.2, height = 0.2) ``` This is not a great way to show this type of data because it is difficult to distinguish between villages. What other plot types could help you visualize this relationship better? ::::::::::::::::::::::::: :::::::::::::::::::::::::::::::::::::::::::::::::: ## Boxplot We can use boxplots to visualize the distribution of rooms for each wall type: ```{r boxplot, fig.alt="Box plot of number of rooms by wall type.", purl=FALSE} interviews_plotting %>% ggplot(aes(x = respondent_wall_type, y = rooms)) + geom_boxplot() ``` By adding points to a boxplot, we can have a better idea of the number of measurements and of their distribution: ```{r boxplot-with-jitter, fig.alt="Previous plot with dot plot added as additional layer to show individual values. Boxplot layer is transparent.", purl=FALSE} interviews_plotting %>% ggplot(aes(x = respondent_wall_type, y = rooms)) + geom_boxplot(alpha = 0) + geom_jitter(alpha = 0.3, color = "tomato", width = 0.2, height = 0.2) ``` We can see that muddaub houses and sunbrick houses tend to be smaller than burntbrick houses. Notice how the boxplot layer is behind the jitter layer? What do you need to change in the code to put the boxplot layer in front of the jitter layer? ::::::::::::::::::::::::::::::::::::::: challenge ## Exercise Boxplots are useful summaries, but hide the *shape* of the distribution. For example, if the distribution is bimodal, we would not see it in a boxplot. An alternative to the boxplot is the violin plot, where the shape (of the density of points) is drawn. - Replace the box plot with a violin plot; see `geom_violin()`. ::::::::::::::: solution ## Solution ```{r violin-plot} interviews_plotting %>% ggplot(aes(x = respondent_wall_type, y = rooms)) + geom_violin(alpha = 0) + geom_jitter(alpha = 0.5, color = "tomato") ``` ::::::::::::::::::::::::: So far, we've looked at the distribution of room number within wall type. Try making a new plot to explore the distribution of another variable within wall type. - Create a boxplot for `liv_count` for each wall type. Overlay the boxplot layer on a jitter layer to show actual measurements. ::::::::::::::: solution ## Solution ```{r boxplot-exercise, fig.alt="Box plot of number of livestock owned by wall type, with dot plot added as additional layer to show individual values."} interviews_plotting %>% ggplot(aes(x = respondent_wall_type, y = liv_count)) + geom_boxplot(alpha = 0) + geom_jitter(alpha = 0.5, width = 0.2, height = 0.2) ``` ::::::::::::::::::::::::: - Add colour to the data points on your boxplot according to whether the respondent is a member of an irrigation association (`memb_assoc`). ::::::::::::::: solution ## Solution ```{r boxplot-exercise-factor, fig.alt="Previous plot with dots colored based on whether respondent was a member of an irrigation association."} interviews_plotting %>% ggplot(aes(x = respondent_wall_type, y = liv_count)) + geom_boxplot(alpha = 0) + geom_jitter(aes(color = memb_assoc), alpha = 0.5, width = 0.2, height = 0.2) ``` ::::::::::::::::::::::::: :::::::::::::::::::::::::::::::::::::::::::::::::: ## Barplots Barplots are also useful for visualizing categorical data. By default, `geom_bar` accepts a variable for x, and plots the number of instances each value of x (in this case, wall type) appears in the dataset. ```{r barplot-1, fig.alt="Bar plot showing counts of respondent wall types."} interviews_plotting %>% ggplot(aes(x = respondent_wall_type)) + geom_bar() ``` We can use the `fill` aesthetic for the `geom_bar()` geom to colour bars by the portion of each count that is from each village. ```{r barplot-stack, fig.alt="Stacked bar plot of wall types showing each village as a different color."} interviews_plotting %>% ggplot(aes(x = respondent_wall_type)) + geom_bar(aes(fill = village)) ``` This creates a stacked bar chart. These are generally more difficult to read than side-by-side bars. We can separate the portions of the stacked bar that correspond to each village and put them side-by-side by using the `position` argument for `geom_bar()` and setting it to "dodge". ```{r barplot-dodge, fig.alt="Bar plot of respondent wall types with each village as a separate bar."} interviews_plotting %>% ggplot(aes(x = respondent_wall_type)) + geom_bar(aes(fill = village), position = "dodge") ``` This is a nicer graphic, but we're more likely to be interested in the proportion of each housing type in each village than in the actual count of number of houses of each type (because we might have sampled different numbers of households in each village). To compare proportions, we will first create a new data frame (`percent_wall_type`) with a new column named "percent" representing the percent of each house type in each village. We will remove houses with cement walls, as there was only one in the dataset. ```{r wall-type-data} percent_wall_type <- interviews_plotting %>% filter(respondent_wall_type != "cement") %>% count(village, respondent_wall_type) %>% group_by(village) %>% mutate(percent = (n / sum(n)) * 100) %>% ungroup() ``` Now we can use this new data frame to create our plot showing the percentage of each house type in each village. ```{r barplot-wall-type, fig.alt="Side by side bar plot showing percent of respondents in each village with each wall type."} percent_wall_type %>% ggplot(aes(x = village, y = percent, fill = respondent_wall_type)) + geom_bar(stat = "identity", position = "dodge") ``` ::::::::::::::::::::::::::::::::::::::: challenge ## Exercise Create a bar plot showing the proportion of respondents in each village who are or are not part of an irrigation association (`memb_assoc`). Include only respondents who answered that question in the calculations and plot. Which village had the lowest proportion of respondents in an irrigation association? ::::::::::::::: solution ## Solution ```{r barplot-memb-assoc, fig.alt="Bar plot showing percent of respondents in each village who were part of association."} percent_memb_assoc <- interviews_plotting %>% filter(!is.na(memb_assoc)) %>% count(village, memb_assoc) %>% group_by(village) %>% mutate(percent = (n / sum(n)) * 100) %>% ungroup() percent_memb_assoc %>% ggplot(aes(x = village, y = percent, fill = memb_assoc)) + geom_bar(stat = "identity", position = "dodge") ``` Ruaca had the lowest proportion of members in an irrigation association. ::::::::::::::::::::::::: :::::::::::::::::::::::::::::::::::::::::::::::::: ## Adding Labels and Titles By default, the axes labels on a plot are determined by the name of the variable being plotted. However, **`ggplot2`** offers lots of customization options, like specifying the axes labels, and adding a title to the plot with relatively few lines of code. We will add more informative x-and y-axis labels to our plot, a more explanatory label to the legend, and a plot title. The `labs` function takes the following arguments: - `title` -- to produce a plot title - `subtitle` -- to produce a plot subtitle (smaller text placed beneath the title) - `caption` -- a caption for the plot - `...` -- any pair of name and value for aesthetics used in the plot (e.g., `x`, `y`, `fill`, `color`, `size`) ```{r barplot-wall-types-labeled, fig.alt="Previous plot with plot title and labells added."} percent_wall_type %>% ggplot(aes(x = village, y = percent, fill = respondent_wall_type)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Proportion of wall type by village", fill = "Type of Wall in Home", x = "Village", y = "Percent") ``` ## Faceting Rather than creating a single plot with side-by-side bars for each village, we may want to create multiple plot, where each plot shows the data for a single village. This would be especially useful if we had a large number of villages that we had sampled, as a large number of side-by-side bars will become more difficult to read. **`ggplot2`** has a special technique called *faceting* that allows the user to split one plot into multiple plots based on a factor included in the dataset. We will use it to split our barplot of housing type proportion by village so that each village has its own panel in a multi-panel plot: ```{r barplot-faceting, fig.alt="Bar plot showing percent of each wall type in each village."} percent_wall_type %>% ggplot(aes(x = respondent_wall_type, y = percent)) + geom_bar(stat = "identity", position = "dodge") + labs(title="Proportion of wall type by village", x="Wall Type", y="Percent") + facet_wrap(~ village) ``` Click the "Zoom" button in your RStudio plots pane to view a larger version of this plot. Usually plots with white background look more readable when printed. We can set the background to white using the function `theme_bw()`. Additionally, you can remove the grid: ```{r barplot-theme-bw, fig.alt="Bar plot showing percent of each wall type in each village, with black and white theme applied.", purl=FALSE} percent_wall_type %>% ggplot(aes(x = respondent_wall_type, y = percent)) + geom_bar(stat = "identity", position = "dodge") + labs(title="Proportion of wall type by village", x="Wall Type", y="Percent") + facet_wrap(~ village) + theme_bw() + theme(panel.grid = element_blank()) ``` What if we wanted to see the proportion of respondents in each village who owned a particular item? We can calculate the percent of people in each village who own each item and then create a faceted series of bar plots where each plot is a particular item. First we need to calculate the percentage of people in each village who own each item: ```{r percent-items-data} percent_items <- interviews_plotting %>% group_by(village) %>% summarize(across(bicycle:no_listed_items, ~ sum(.x) / n() * 100)) %>% pivot_longer(bicycle:no_listed_items, names_to = "items", values_to = "percent") ``` To calculate this percentage data frame, we needed to use the `across()` function within a `summarize()` operation. Unlike the previous example with a single wall type variable, where each response was exactly one of the types specified, people can (and do) own more than one item. So there are multiple columns of data (one for each item), and the percentage calculation needs to be repeated for each column. Combining `summarize()` with `across()` allows us to specify first, the columns to be summarized (`bicycle:no_listed_items`) and then the calculation. Because our calculation is a bit more complex than is available in a built-in function, we define a new formula: - `~` indicates that we are defining a formula, - `sum(.x)` gives the number of people owning that item by counting the number of `TRUE` values (`.x` is shorthand for the column being operated on), - and `n()` gives the current group size. After the `summarize()` operation, we have a table of percentages with each item in its own column, so a `pivot_longer()` is required to transform the table into an easier format for plotting. Using this data frame, we can now create a multi-paneled bar plot. ```{r percent-items-barplot, fig.alt="Multi-panel bar chart showing percent of respondents in each village and who owned each item, with no grids behid bars."} percent_items %>% ggplot(aes(x = village, y = percent)) + geom_bar(stat = "identity", position = "dodge") + facet_wrap(~ items) + theme_bw() + theme(panel.grid = element_blank()) ``` ## **`ggplot2`** themes In addition to `theme_bw()`, which changes the plot background to white, **`ggplot2`** comes with several other themes which can be useful to quickly change the look of your visualization. The complete list of themes is available at [https://ggplot2.tidyverse.org/reference/ggtheme.html](https://ggplot2.tidyverse.org/reference/ggtheme.html). `theme_minimal()` and `theme_light()` are popular, and `theme_void()` can be useful as a starting point to create a new hand-crafted theme. The [ggthemes](https://jrnold.github.io/ggthemes/reference/index.html) package provides a wide variety of options (including an Excel 2003 theme). The [**`ggplot2`** extensions website](https://exts.ggplot2.tidyverse.org/) provides a list of packages that extend the capabilities of **`ggplot2`**, including additional themes. ::::::::::::::::::::::::::::::::::::::: challenge ## Exercise Experiment with at least two different themes. Build the previous plot using each of those themes. Which do you like best? :::::::::::::::::::::::::::::::::::::::::::::::::: ## Customization Take a look at the [**`ggplot2`** cheat sheet](https://raw.githubusercontent.com/rstudio/cheatsheets/main/data-visualization.pdf), and think of ways you could improve the plot. Now, let's change names of axes to something more informative than 'village' and 'percent' and add a title to the figure: ```{r ggplot-customization, purl=FALSE} percent_items %>% ggplot(aes(x = village, y = percent)) + geom_bar(stat = "identity", position = "dodge") + facet_wrap(~ items) + labs(title = "Percent of respondents in each village who owned each item", x = "Village", y = "Percent of Respondents") + theme_bw() ``` The axes have more informative names, but their readability can be improved by increasing the font size: ```{r ggplot-customization-font-size, purl=FALSE} percent_items %>% ggplot(aes(x = village, y = percent)) + geom_bar(stat = "identity", position = "dodge") + facet_wrap(~ items) + labs(title = "Percent of respondents in each village who owned each item", x = "Village", y = "Percent of Respondents") + theme_bw() + theme(text = element_text(size = 16)) ``` Note that it is also possible to change the fonts of your plots. If you are on Windows, you may have to install the [**`extrafont`** package](https://github.com/wch/extrafont), and follow the instructions included in the README for this package. After our manipulations, you may notice that the values on the x-axis are still not properly readable. Let's change the orientation of the labels and adjust them vertically and horizontally so they don't overlap. You can use a 90-degree angle, or experiment to find the appropriate angle for diagonally oriented labels. With a larger font, the title also runs off. We can add "\\n" in the string for the title to insert a new line: ```{r ggplot-customization-label-orientation, fig.alt="Multi-panel bar charts showing percent of respondents in each village and who owned each item, with grids behind the bars.", purl=FALSE} percent_items %>% ggplot(aes(x = village, y = percent)) + geom_bar(stat = "identity", position = "dodge") + facet_wrap(~ items) + labs(title = "Percent of respondents in each village \n who owned each item", x = "Village", y = "Percent of Respondents") + theme_bw() + theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 45, hjust = 0.5, vjust = 0.5), axis.text.y = element_text(colour = "grey20", size = 12), text = element_text(size = 16)) ``` If you like the changes you created better than the default theme, you can save them as an object to be able to easily apply them to other plots you may create. We can also add `plot.title = element_text(hjust = 0.5)` to centre the title: ```{r ggplot-custom-themes, purl=FALSE} grey_theme <- theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 45, hjust = 0.5, vjust = 0.5), axis.text.y = element_text(colour = "grey20", size = 12), text = element_text(size = 16), plot.title = element_text(hjust = 0.5)) percent_items %>% ggplot(aes(x = village, y = percent)) + geom_bar(stat = "identity", position = "dodge") + facet_wrap(~ items) + labs(title = "Percent of respondents in each village \n who owned each item", x = "Village", y = "Percent of Respondents") + grey_theme ``` ::::::::::::::::::::::::::::::::::::::: challenge ## Exercise With all of this information in hand, please take another five minutes to either improve one of the plots generated in this exercise or create a beautiful graph of your own. Use the RStudio [**`ggplot2`** cheat sheet](https://raw.githubusercontent.com/rstudio/cheatsheets/main/data-visualization.pdf) for inspiration. Here are some ideas: - See if you can make the bars white with black outline. - Try using a different colour palette (see [http://www.cookbook-r.com/Graphs/Colors\_(ggplot2)/](https://www.cookbook-r.com/Graphs/Colors_\(ggplot2\)/)). :::::::::::::::::::::::::::::::::::::::::::::::::: After creating your plot, you can save it to a file in your favourite format. The Export tab in the **Plot** pane in RStudio will save your plots at low resolution, which will not be accepted by many journals and will not scale well for posters. Instead, use the `ggsave()` function, which allows you to easily change the dimension and resolution of your plot by adjusting the appropriate arguments (`width`, `height` and `dpi`). Make sure you have the `fig_output/` folder in your working directory. ```{r ggsave-example, eval=FALSE, purl=FALSE} my_plot <- percent_items %>% ggplot(aes(x = village, y = percent)) + geom_bar(stat = "identity", position = "dodge") + facet_wrap(~ items) + labs(title = "Percent of respondents in each village \n who owned each item", x = "Village", y = "Percent of Respondents") + theme_bw() + theme(axis.text.x = element_text(color = "grey20", size = 12, angle = 45, hjust = 0.5, vjust = 0.5), axis.text.y = element_text(color = "grey20", size = 12), text = element_text(size = 16), plot.title = element_text(hjust = 0.5)) ggsave("fig_output/name_of_file.png", my_plot, width = 15, height = 10) ``` Note: The parameters `width` and `height` also determine the font size in the saved plot. :::::::::::::::::::::::::::::::::::::::: keypoints - `ggplot2` is a flexible and useful tool for creating plots in R. - The data set and coordinate system can be defined using the `ggplot` function. - Additional layers, including geoms, are added using the `+` operator. - Boxplots are useful for visualizing the distribution of a continuous variable. - Barplots are useful for visualizing categorical data. - Faceting allows you to generate multiple plots based on a categorical variable. ::::::::::::::::::::::::::::::::::::::::::::::::::