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Introduction to Data Visualization with ggplot2
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    Introduction to Data Visualization with ggplot2

    # Make the points 40% opaque
    ggplot(diamonds, aes(carat, price, color = clarity)) + # or size can be used
      geom_point(alpha = 0.4) +
      geom_smooth() # fitted line

    Attributes

    Labels and size are only applicable to categorical variables.

    A common adjustment is the position. Position specifies how ggplot will adjust for overlapping bars or points on a single layer. For example, we have identity, dodge, stack, fill, jitter, jitterdodge, and nudge

    Geometries

    The jitter geom is a convenient shortcut for geom_point(position = "jitter"). It adds a small amount of random variation to the location of each point, and is a useful way of handling overplotting caused by discreteness in smaller datasets.

    p + theme(axis.line = element_line(color = "red", linetype = "dashed"))
    
    # Similarly, element_rect() changes rectangles and element_text() changes text. You can remove a plot element using element_blank()
    
    plt_prop_unemployed_over_time +
      theme(
        rect = element_rect(fill = "grey92"),
        legend.key = element_rect(color = NA),
        axis.ticks = element_blank(),
        panel.grid = element_blank(),
        panel.grid.major.y = element_line(
          color = "white",
          size = 0.5,
          linetype = "dotted"
        ),
    	
    	# Set the axis text color to grey25
    	
       axis.text = element_text(color = "grey25"),
    	
        # Set the plot title font face to italic and font size to 16
    	
       plot.title = element_text(size = 16, face = "italic")
      )
      
      theme_*()
      library(ggthemes)
      # Acces built in templates
      theme_set()
      # Change all to a certain theme
    p <- ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
      # Use a jitter position function with width 0.1
      geom_point(alpha = 0.5, position=position_jitter(width=0.1))
    
    # The brewer scales provide sequential, diverging and qualitative colour schemes from ColorBrewer. These are particularly well suited to display discrete values on a map.
    scale_fill_brewer 
    
    

    p + theme(legend.position = new_value) Here, the new value can be

    "top", "bottom", "left", or "right'": place it at that side of the plot. "none": don't draw it. c(x, y): c(0, 0) means the bottom-left and c(1, 1) means the top-right.

    Whitespace means all the non-visible margins and spacing in the plot.

    To set a single whitespace value, use unit(x, unit), where x is the amount and unit is the unit of measure.

    Borders require you to set 4 positions, so use margin(top, right, bottom, left, unit)

    Create a nice plot

    # Set the color scale
    palette <- brewer.pal(5, "RdYlBu")[-(2:4)]
    
    # Add a title and caption
    ggplot(gm2007, aes(x = lifeExp, y = country, color = lifeExp)) +
      geom_point(size = 4) +
      geom_segment(aes(xend = 30, yend = country), size = 2) +
      geom_text(aes(label = round(lifeExp,1)), color = "white", size = 1.5) +
      scale_x_continuous("", expand = c(0,0), limits = c(30,90), position = "top") +
      scale_color_gradientn(colors = palette) +
      labs(title = "Highest and lowest life expectancies, 2007", caption = "Source: gapminder")