![]() ![]() The principal components of every plot can be defined as follow: The gg in ggplot2 means Grammar of Graphics, a graphic concept which describes plots by using a “grammar”.Īccording to ggplot2 concept, a plot can be divided into different fundamental parts : Plot = data + Aesthetics + Geometry. ![]() The data set and coordinate system can be defined using the ggplot function.Īdditional layers, including geoms, are added using the + operator.īoxplots are useful for visualizing the distribution of a continuous variable.īarplot are useful for visualizing categorical data.įaceting allows you to generate multiple plots based on a categorical variable.Ggplot2 is a powerful and a flexible R package, implemented by Hadley Wickham, for producing elegant graphics. ![]() Ggplot2 is a flexible and useful tool for creating plots in R. Note: The parameters width and height also determine the font size in the saved plot. My_plot <- ggplot ( percent_items, 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 ( = element_text ( colour = "grey20", size = 12, angle = 45, hjust = 0.5, vjust = 0.5 ), = element_text ( colour = "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 ) Make sure you have the fig_output/ folder in your working directory. Instead, use the ggsave() function, which allows you easily change the dimension and resolution of your plot by adjusting the appropriate arguments ( width, height and dpi). 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. Try using a different color palette (seeĪfter creating your plot, you can save it to a file in your favorite format.See if you can make the bars white with black outline.With all of this information in hand, please take another five minutes toĮither improve one of the plots generated in this exercise or create aīeautiful graph of your own. Grey_theme <- theme ( = element_text ( colour = "grey20", size = 12, angle = 45, hjust = 0.5, vjust = 0.5 ), = element_text ( colour = "grey20", size = 12 ), text = element_text ( size = 16 ), plot.title = element_text ( hjust = 0.5 )) ggplot ( percent_items, 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 Now, let’s change names of axes to something more informative than ‘village’ and Which do you like best? CustomizationĪnd think of ways you could improve the plot. Of packages that extend the capabilities of ggplot2, including additionalĮxperiment with at least two different themes. Ggplot2 extensions website provides a list Package provides a wide variety of options (including an Excel 2003 theme). Point to create a new hand-crafted theme. Theme_light() are popular, and theme_void() can be useful as a starting Ggplot2 comes with several other themes which can be useful to quicklyĬhange the look of your visualization. In addition to theme_bw(), which changes the plot background to white, Ggplot ( percent_items, aes ( x = village, y = percent )) + geom_bar ( stat = "identity", position = "dodge" ) + facet_wrap ( ~ items ) + theme_bw () + theme ( id = element_blank ())
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