shape = c( rep( 21, 5), NA) ) ) ) + # Minimal grid theme that only draws horizontal lines theme_minimal_hgrid( 12, rel_small = 1) + # Customize aspects of the legend theme( legend.position = "top", legend.justification = "right", legend.text = element_text( size = 9), legend.box. nrow = 1, override.aes = list( # 0 means no line, 1 is a solid line # The result is 5 keys with no line and 1 with a line linetype = c( rep( 0, 5), 1), # Now, 5 keys with the marker number 21 (the one used in the plot) # and 1 key without this marker. ) + # Add auto-positioned text geom_text_repel( aes( label = label), color = "black", size = 9 /.pt, # font size 9 pt point.padding = 0.1, box.padding = 0.6, = 0, max.overlaps = 1000, seed = 7654 # For reproducibility reasons ) + scale_color_manual( name = NULL, # it's one way to omit the legend title values = darken(region_cols, 0.3) # dot borders are a darker than the fill ) + scale_fill_manual( name = NULL, values = region_cols ) + # Add labels and customize axes scale_x_continuous( name = "Corruption Perceptions Index, 2015 (100 = least corrupt)", limits = c( 10, 95), breaks = c( 20, 40, 60, 80, 100), expand = c( 0, 0) # This removes the default padding on the ends of the axis ) + scale_y_continuous( name = "Human Development Index, 2015 \n (1.0 = most developed)", limits = c( 0.3, 1.05), breaks = c( 0.2, 0.4, 0.6, 0.8, 1.0), # Manually set axis breaks expand = c( 0, 0) ) + # Override default legend appearance guides( color = guide_legend( # All keys go in the same row. ![]() geom_smooth( aes( color = "y ~ log(x)", fill = "y ~ log(x)"), method = "lm", formula = y ~ log(x), se = FALSE, # Plot the line only (without confidence bands) fullrange = TRUE # The fit spans the full range of the horizontal axis ) + geom_point( aes( color = region, fill = region), size = 2.5, alpha = 0.5, shape = 21 # This is a dot with both border (color) and fill. Let’s plot these 12 cases as labels.# Okabe Ito colors # The last color is used for the regression fit. Apparently, there are twelve cases that suffice these conditions. You can read the the above code as follows: filter (or select) cases from the mpg-dataset with the condition that the case either has the maximum or minimum value on either displ or cty. # 1 chevrolet corv… 7 2008 8 manu… r 15 24 p 2sea… # manufacturer model displ year cyl trans drv cty hwy fl class Mpg_reduced % filter(displ = max(displ) | displ = min(displ) | cty = max(cty) | cty = min(cty)) 14.1.1 Recreating the graph with more manual labour.Let us see how to Create, Format its size, shape, and color. 13.3 Other ways to visualize two continuous variables An R ggplot2 Scatter Plot is useful for visualizing the relationship between any two data sets. ![]()
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