ggplot2 annotations cookbook

doc_ref/R_Graphics_Cookbook/7_Annotations.Rmd

@@ -10,6 +10,8 @@ output: html_document
 library(ggplot2)
 library(gcookbook) # For the data set 
 
+library(plyr)
+
 # Use annotate() and a text geom 
 p <- ggplot( faithful, aes( x = eruptions, y = waiting)) + 
   geom_point() 
@@ -44,5 +46,123 @@ p +
 
 ## 3. Adding Lines
 ```{r}
+p <- ggplot( heightweight, aes( x = ageYear, y = heightIn, colour = sex)) + 
+  geom_point() # Add horizontal and vertical lines 
+
+p + 
+  geom_hline( yintercept = 60) + 
+  geom_vline( xintercept = 14) # Add angled line
+
+p + 
+  geom_abline( intercept = 37.4, slope = 1.75)
+
+# Here we’ll take the average height for males and females and store it in a data frame, hw_means
+hw_means <- ddply( heightweight, "sex", summarise, heightIn = mean( heightIn)) 
+hw_means
+
+p + 
+  geom_hline( aes( yintercept = heightIn, colour = sex), data = hw_means, linetype ="dashed", size = 1)
+
+# You can specify the numerical intercept manually, or calculate the numerical value using which( levels(...))
+pg <- ggplot( PlantGrowth, aes( x = group, y = weight)) + 
+  geom_point() 
+pg + geom_vline( xintercept = 2) 
+
+pg + 
+  geom_vline( xintercept = which( levels( PlantGrowth$group) =="ctrl"))
+```
+
+## 4. Adding Line Segments and Arrows
+```{r}
+p <- ggplot( subset( climate, Source =="Berkeley"), aes( x = Year,y = Anomaly10y)) + 
+  geom_line() 
+p + annotate("segment", x = 1950, xend = 1980, y =-.25, yend =-.25)
+
+# It’s possible to add arrowheads or flat ends to the line segments, using arrow() from the grid package.
+library(grid) 
+p + 
+  annotate("segment", x = 1850, xend = 1820, y =-.8, yend =-.95, colour ="blue", size = 2, arrow = arrow()) + 
+  annotate("segment", x = 1950, xend = 1980, y =-.25, yend =-.25, 
+           arrow = arrow( ends ="both", angle = 90, length = unit(.2,"cm")))
+```
+
+## 5. Adding a Shaded Rectangle
+```{r}
+p <- ggplot( subset( climate, Source =="Berkeley"), aes( x = Year, y = Anomaly10y)) + 
+  geom_line() 
 
+p + annotate("rect", xmin = 1950, xmax = 1980, ymin =-1, ymax = 1, alpha =.1, fill ="blue") 
 ```
+
+## 6. Highlighting an Item
+```{r}
+pg <- PlantGrowth # Make a copy of the PlantGrowth data 
+pg$hl <- "no" # Set all to "no" 
+pg$hl[ pg$group =="trt2"] <- "yes" # If group is "trt2", set to "yes"
+
+# Then we’ll plot it with manually specified colors and with no legend
+ggplot( pg, aes( x = group, y = weight, fill = hl)) + 
+  geom_boxplot() + 
+  scale_fill_manual( values = c("grey85", "#FFDDCC"), guide = FALSE)
+```
+
+## 7. Adding Error Bars
+```{r}
+# Take a subset of the cabbage_exp data for this example 
+ce <- subset( cabbage_exp, Cultivar == "c39") 
+
+# With a line graph 
+ggplot( ce, aes( x = Date, y = Weight)) + 
+  geom_line( aes( group = 1)) + 
+  geom_point( size = 4) + geom_errorbar( aes( ymin = Weight-se, ymax = Weight + se), width =.2) 
+
+# Good: dodge width set to same as bar width (0.9) 
+ggplot( cabbage_exp, aes( x = Date, y = Weight, fill = Cultivar)) + 
+  geom_bar( position ="dodge" , stat ="identity") + 
+  geom_errorbar( aes( ymin = Weight-se, ymax = Weight + se), position = position_dodge( 0.9), width =.2)
+
+# For line graphs, if the error bars are a different color than the lines and points, you should draw the error bars first, so that they are underneath the points and lines.
+pd <- position_dodge(.3) 
+
+# Save the dodge spec because we use it repeatedly 
+ggplot( cabbage_exp, aes( x = Date, y = Weight, colour = Cultivar, group = Cultivar)) + 
+  geom_errorbar( aes( ymin = Weight-se, ymax = Weight + se), width =.2, size = 0.25, colour ="black", position = pd) +
+  geom_line( position = pd) + geom_point( position = pd, size = 2.5) 
+
+# Thinner error bar lines with size = 0.25, and larger points with size = 2.5
+```
+
+## 8. Adding Annotations to Individual Facets
+```{r}
+# The base plot 
+p <- ggplot( mpg, aes( x = displ, y = hwy)) + geom_point() + facet_grid(. ~ drv) 
+
+# A data frame with labels for each facet
+f_labels <- data.frame( drv = c("4", "f", "r"), label = c("4wd", "Front", "Rear")) 
+p + geom_text( x = 6, y = 40, aes( label = label), data = f_labels) 
+
+# If you use annotate(), the label will appear in all facets 
+p + 
+  annotate("text", x = 6, y = 42, label ="label text")
+
+# This function returns a data frame with strings representing the regression 
+# equation, and the r ^ 2 value # These strings will be treated as R math expressions 
+lm_labels <- function( dat) { 
+  mod <- lm( hwy ~ displ, data = dat) 
+  formula <- sprintf(" italic(y) == %. 2f %+. 2f * italic(x)", 
+                     round( coef( mod)[1], 2), 
+                     round( coef( mod)[2], 2)) 
+  r <- cor( dat$displ, dat$hwy) 
+  r2 <- sprintf(" italic( R ^ 2) == %. 2f", r ^ 2) 
+  data.frame( formula = formula, r2 = r2, stringsAsFactors = FALSE) 
+  } 
+# For the ddply() function 
+labels <- ddply( mpg, "drv", lm_labels) 
+labels
+
+# Plot with formula and R ^ 2 values 
+p + 
+  geom_smooth( method = lm, se = FALSE) + 
+  geom_text( x = 3, y = 40, aes( label = formula), data = labels, parse = TRUE, hjust = 0) + 
+  geom_text( x = 3, y = 35, aes( label = r2), data = labels, parse = TRUE, hjust = 0)  
+```

doc_ref/R_Graphics_Cookbook/Index.Rmd

@@ -11,6 +11,7 @@ output: html_document
 5. [5_Scatter_Plots](5_Scatter_Plots.html)
 6. [6_Summarized_Data_Distributions](6_Summarized_Data_Distributions.html)
 7. [7_Annotations](7_Annotations.html)
+8. [8_Axes](8_Axes.html)