![]() 3.13 Example 11: Remove Legend from Pie Chart.3.12 Example 10: Changing Legend Position.3.11 Example 9: Adding Custom Legend Title.3.9 Example 7: Adding Labels to Pie Chart using geom_label().3.8 Example 6: Using RColorBrewer Color Pallete with scale_fill_brewer().3.7 Example 5: Using Minimal Theme with theme_minimal().3.6 Example 4: Applying Gray Scale to Pie Chart using scale_fill_grey().3.5 Example 3: Coloring Pie Chart Using scale_fill_manual().3.4 Example 2: Adding Labels to Pie Chart in ggplot2 with geom_text().3.3 Example 1: Basic Pie Chart in ggplot2.3 Examples of Pie Chart in R using ggplot2.(See here for an animated version of this “Less is more” philosophy). library ( ggthemes ) d %>% mutate ( Task = reorder ( Task, Percentage, function ( e ) e )) %>% ggplot ( aes ( Hours, Percentage )) + geom_bar ( stat = "identity" ) + facet_wrap ( ~ Task ) + geom_text ( aes ( label = paste0 ( Percentage, "%" ), y = Percentage ), vjust = 1.4, size = 5, color = "white" ) + theme_tufte () + theme ( = element_text ( angle = 90, hjust = 1 ), axis.ticks = element_blank (), = element_blank ()) + ylab ( "" ) Some people take this philosophy even further, and drop the y-axis altogether (since we do already have those percentages annotated on the bars). This can be achieved with theme_tufte: library ( ggthemes ) d %>% mutate ( Task = reorder ( Task, Percentage, function ( e ) e )) %>% ggplot ( aes ( Hours, Percentage )) + geom_bar ( stat = "identity" ) + facet_wrap ( ~ Task ) + geom_text ( aes ( label = paste0 ( Percentage, "%" ), y = Percentage ), vjust = 1.4, size = 5, color = "white" ) + theme_tufte () + theme ( = element_text ( angle = 90, hjust = 1 )) But some prefer Edward Tufte’s approach of maximizing the “Data/Ink Ratio”- that is, dropping borders, grids, and axis lines. I don’t have terribly strong opinions about these choices (I’m pretty happy with ggplot2’s theme_bw()). A simple proxy for this is to order by “% who spend % mutate ( Task = reorder ( Task, Percentage, function ( e ) e )) %>% ggplot ( aes ( Hours, Percentage )) + geom_bar ( stat = "identity" ) + facet_wrap ( ~ Task ) + geom_text ( aes ( label = paste0 ( Percentage, "%" ), y = Percentage ), vjust = 1.4, size = 5, color = "white" ) + theme ( = element_text ( angle = 90, hjust = 1 )) + xlab ( "Hours spent per week" )įrom here, the last step would be to adjust the colors, fonts, and other “design” choices. I like to give them an order that makes them easier to browse- something along the lines of. The ordering of task facets is arbitrary (alphabetical in this plot). ggplot ( d, aes ( Hours, Percentage )) + geom_bar ( stat = "identity" ) + facet_wrap ( ~ Task ) + geom_text ( aes ( label = paste0 ( Percentage, "%" ), y = Percentage ), vjust = 1.4, size = 5, color = "white" ) readr::read_csv is useful for constructing a table on the fly: library ( readr ) d 4 a dayīasic exploratory data analysis,11,32,46,12Įxtract/transform/load,43,32,20,5" ) # reorganize library ( tidyr ) d 4 hours a day on it!”) So I add a geom_text layer. I start by transcribing the data directly from the plot into R. (I’d note that this post is appropriate for Pi Day, but I’m more of a Tau Day observer anyway). ![]() This also serves as an example of the thought process I go through in creating a data visualization. So here I’ll show how I would have created a different graph (using R and ggplot2) to communicate the same information. The problem with a lot of pie-chart bashing (and most “chart-shaming,” in fact) is that people don’t follow up with a better alternative. But at a glance, do you have any idea whether more time is spent “Presenting Analysis” or “Data cleaning”? We’re meant to compare and contrast these six tasks. But this is an especially unfortunate example. ![]() Pie charts have a bad reputation among statisticians and data scientists, with good reason ( see here for more). But I was disappointed that in an article about data scientists (!) they would include a chart this terrible: Narasimhan gave insightful and well-communicated answers, and I both recognized familiar opinions and learned new perspectives. I wasn’t disappointed in the interview: General Electric’s Dr. The title intrigued me immediately, partly because I find myself explaining that same topic somewhat regularly. Yesterday a family member forwarded me a Wall Street Journal interview titled What Data Scientists Do All Day At Work.
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