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R Data Visualization: Learn To Add Text Annotations To Plots

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R Data Visualization: Learn To Add Text Annotations To Plots

  1. 1. www.r-squared.in/git-hub dataCrunch Data Visualization With R Learn To Add Text Annotations To Plots
  2. 2. dataCrunch Text Annotations Slide 2
  3. 3. dataCrunchText Annotations: Objectives Slide 3 In this section, we will learn to: Add text annotations to the plots using ● text() function ● mtext() function
  4. 4. dataCrunchText Annotations: Introduction Slide 4 The text() and the mtext() functions allow the user to add text annotations to the plots. While the text() function places the text inside the plot, the mtext() function places the text on the margins of the plot. Below is the syntax for both the functions: # the text function text(x, y = NULL, labels = seq_along(x), adj = NULL, pos = NULL, offset = 0.5, vfont = NULL, cex = 1, col = NULL, font = NULL, ...) # the mtext function mtext(text, side = 3, line = 0, outer = FALSE, at = NA, adj = NA, padj = NA, cex = NA, col = NA, font = NA, ...) Let us explore each function and its arguments one by one:
  5. 5. dataCrunchText Annotations: text() Slide 5 To add text annotations using the text() function, the following 3 arguments must be supplied: ● x: x axis coordinate ● y: y axis coordinate ● text: the text to be added to the plot Below is a simple example: # create a basic plot plot(mtcars$disp, mtcars$mpg) # add text text(340, 30, "Sample Text") The text appears at the coordinates (340, 30) on the plot. Ensure that the text is enclosed in double quotes and the coordinates provided are within the range of the X and Y axis variable.
  6. 6. dataCrunchtext(): Color Slide 6 # create a basic plot plot(mtcars$disp, mtcars$mpg) # modify the color of the text text(340, 30, "Sample Text", col = "red") Description The color of the text can be modified using the col argument in the text() function. Code
  7. 7. dataCrunchtext(): Color Slide 7 The below plot depicts the appearance of the text when different options for col are applied:
  8. 8. dataCrunchtext(): Font Slide 8 # create a basic plot plot(mtcars$disp, mtcars$mpg) # modify the font of the text text(340, 30, "Sample Text", font = 2) Description The font of the text can be modified using the font argument in the text() function. Code
  9. 9. dataCrunchtext(): Font Slide 9 The below plot depicts the appearance of the text when different options for font are applied:
  10. 10. dataCrunchtext(): Font Family Slide 10 # create a basic plot plot(mtcars$disp, mtcars$mpg) # modify the font family of the text text(340, 30, "Sample Text", family = mono) Description The font family of the text can be modified using the family argument in the text() function. Code
  11. 11. dataCrunchtext(): Font Family Slide 11 The below plot depicts the appearance of the text when different options for font family are applied:
  12. 12. dataCrunchtext(): Font Size Slide 12 # create a basic plot plot(mtcars$disp, mtcars$mpg) # modify the size of the text text(340, 30, "Sample Text", cex = 1.5) Description The size of the text can be modified using the cex argument in the text() function. Code
  13. 13. dataCrunchtext(): Font Size Slide 13 The below plot depicts the appearance of the text when different options for font size are applied:
  14. 14. dataCrunchmtext(): Introduction Slide 14 The mtext() function places text annotations on the margins of the plot instead of placing them inside the plot. It allows the user to modify the location of the text in multiple ways and we will explore them one by one. Below is a simple example: # create a basic plot plot(mtcars$disp, mtcars$mpg) # add text mtext("Sample Text") As you can see, the text is placed on the margin of the plot and not inside the plot. Next, we will learn to specify the margin where the text should be placed.
  15. 15. dataCrunchmtext(): Margin Slide 15 # create a basic plot plot(mtcars$disp, mtcars$mpg) # specify the margin on which the text should appear mtext("Sample Text", side = 1) Description The margin on which we want to place the text can be specified using the side argument. It takes 4 values from 1-4 each representing one side of the plot. Code
  16. 16. dataCrunchmtext(): Margin Options Slide 16 The side argument can be used to specify the margin on which the text should be placed. side Margin 1 Bottom 2 Left 3 Top 4 Right
  17. 17. dataCrunchmtext(): Margin Slide 17 The below plot depicts the appearance of the text when different options for side are applied:
  18. 18. dataCrunchmtext(): Line Slide 18 # create a basic plot plot(mtcars$disp, mtcars$mpg) # place the text away from the margin mtext("Sample Text", line = 1) Description The line argument places the text at the specified distance from the margin. The default value is 0 and as the value increases the text is placed farther from the margin and outside the plot, and as the value decreases the text is placed inside the plot and farther from the margin. Code
  19. 19. dataCrunchmtext(): Line Slide 19 # create a basic plot plot(mtcars$disp, mtcars$mpg) # place the text away from the plot mtext("Sample Text", line = -1) Description The line argument places the text inside the plot when the values is less than zero. Code
  20. 20. dataCrunchmtext(): Line Slide 20 The below plot depicts the appearance of the text when different options for line are applied:
  21. 21. dataCrunchmtext(): adj Slide 21 # create a basic plot plot(mtcars$disp, mtcars$mpg) # align the text to the left mtext("Sample Text", adj= 0) Description The adj argument is used for horizontal alignment of the text. If set to 0, the text will be left aligned and at 1, it will be right aligned. Code
  22. 22. dataCrunchmtext(): adj Slide 22 # create a basic plot plot(mtcars$disp, mtcars$mpg) # align the text to the right mtext("Sample Text", adj= 1) Description When the value is set to 1, the text will be right aligned. Code
  23. 23. dataCrunchmtext(): adj Slide 23 The below plot depicts the appearance of the text when different options for text() are applied:
  24. 24. dataCrunch Slide 24 Visit dataCrunch for tutorials on: → R Programming → Business Analytics → Data Visualization → Web Applications → Package Development → Git & GitHub

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