\n ", "If you are an experienced hiker, you might want to go", "to the North Cascades since there are a lot of tracks,", "higher elevations and total length to overcome.", sep = " \n " ), caption = " \n\n Data Visualisation by Tobias Stalder \n \n Source: TidyX Crew (Ellis Hughes, Patrick Ward) \n Link to Data: /rfordatascience/tidytuesday/blob/master/data/2020//readme.md") + # Customize general theme theme( # Set default color and font family for the text text = element_text( color = "gray12", family = "Bell MT"), # Customize the text in the title, subtitle, and caption plot.title = element_text( face = "bold", size = 25, hjust = 0.05), plot.subtitle = element_text( size = 14, hjust = 0.05), plot.caption = element_text( size = 10, hjust =. Plt <- plt + # Add labels labs( title = " \n Hiking Locations in Washington", subtitle = paste( " \n This Visualisation shows the cummulative length of tracks,", "the amount of tracks and the mean gain in elevation per location. 5 ) ) + theme( # Remove axis ticks and text axis.title = element_blank(), axis.ticks = element_blank(), = element_blank(), # Use gray text for the region names = element_text( color = "gray12", size = 12), # Move the legend to the bottom legend.position = "bottom", ) plt 5, title.position = "top", title.hjust =. Plt <- plt + # Annotate the bars and the lollipops so the reader understands the scaling annotate( x = 11, y = 1300, label = "Mean Elevation Gain \n ", geom = "text", angle = - 67.5, color = "gray12", size = 2.5, family = "Bell MT" ) + annotate( x = 11, y = 3150, label = "Cummulative Length ", geom = "text", angle = 23, color = "gray12", size = 2.5, family = "Bell MT" ) + # Annotate custom scale inside plot annotate( x = 11.7, y = 1100, label = "1000", geom = "text", color = "gray12", family = "Bell MT" ) + annotate( x = 11.7, y = 2100, label = "2000", geom = "text", color = "gray12", family = "Bell MT" ) + annotate( x = 11.7, y = 3100, label = "3000", geom = "text", color = "gray12", family = "Bell MT" ) + # Scale y axis so bars don't start in the center scale_y_continuous( limits = c( - 1500, 3500), expand = c( 0, 0), breaks = c( 0, 1000, 2000, 3000) ) + # New fill and legend title for number of tracks per region scale_fill_gradientn( "Amount of Tracks", colours = c( "#6C5B7B", "#C06C84", "#F67280", "#F8B195") ) + # Make the guide for the fill discrete guides( fill = guide_colorsteps( barwidth = 15, barheight =. But the take-home message so far must be about how easy it is to convert one regular plot into a circular plot by using coord_polar(). Not too bad! It’s clear the plot isn’t ready for publication yet. 9 ) + # Add dots to represent the mean gain geom_point( aes( x = reorder( str_wrap(region, 5),sum_length), y = mean_gain ), size = 3, color = "gray12" ) + # Lollipop shaft for mean gain per region geom_segment( aes( x = reorder( str_wrap(region, 5), sum_length), y = 0, xend = reorder( str_wrap(region, 5), sum_length), yend = 3000 ), linetype = "dashed", color = "gray12" ) + # Make it circular! coord_polar() plt Plt <- ggplot(plot_df) + # Make custom panel grid geom_hline( aes( yintercept = y), ame( y = c( 0 : 3) * 1000), color = "lightgrey" ) + # Add bars to represent the cumulative track lengths # str_wrap(region, 5) wraps the text so each line has at most 5 characters # (but it doesn't break long words!) geom_col( aes( x = reorder( str_wrap(region, 5), sum_length), y = sum_length, fill = n ), position = "dodge2", show.legend = TRUE, alpha =.
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