End-to-end visualization using ggplot2

2.1. Boxplots

First, I’ll use boxplots, but ggplot2-style. I really like the look of a ggplot2() boxplot. It also allows me to seamlessly have multiple plots in a grid, as well as tinker around with the plotting parameters more flexibly than in a classical boxplot() approach, and end up with a nice-looking plot. We can see below how some inputs clearly vary across the 2 target categories, and others don’t.

df <- subset(d, select=c(glu, bp, skin, bmi, ped, age, type))  

library(gridExtra)
library(ggplot2)
p <- list()

for (j in colnames(df)[1:6]) {
  p[[j]] <- ggplot(data=df, aes_string(x="type", y=j)) + # Specify dataset, input or grouping col name and Y
            geom_boxplot(aes(fill=factor(type))) + guides(fill=FALSE) + # Boxplot by which factor + color guide
            theme(axis.title.y = element_text(face="bold", size=14))  # Make the Y-axis labels bigger/bolder
}

do.call(grid.arrange, c(p, ncol=3))

2.2. Density plots

I have used various overlay-density packages in the past, sm.density.compare() for example. I find the overlay-density rendering in ggplot2() to be more visually pleasing, with little plotting parameter tuning. E.g., it’s clear in the plot below that diabetic patients are associated with more number of pregnancies. I really like the alpha parameter.

df$npreg <- d$npreg
g <- ggplot(df, aes(npreg))
g + geom_density(aes(fill=factor(type)), alpha=0.8) + 
    labs(title="Density plot", 
         subtitle="# Pregnancies Grouped by Diabetes Type",
         x="# Pregnancies",
         fill="Diabetes Type")

3.1. Data preparation

Facet-wrapping and gridding is a must-have tool for deeper data views, but the process is a multi-step one. Not too complicated though - very intuitive under ggplot2. We start with creating some new categorical columns using the continuous ones. Note that this can be done in different ways: appending new columns directly to the data frame, or using the more sleeker dplyr() in combination with magrittr(), which I absolutely love. This integrates a number of operations into a single chunk, making it quite seamless. I am also loading up plyr(), since I will be using it later.

library(magrittr)
library(plyr)
library(dplyr)
df_grid <- d %>% 
          mutate(Skin = ifelse(d$skin <= 29, "low skin fold", "high skin fold"),
                  BMI = ifelse(d$bmi <= 33, "low BMI", "high BMI"),
                  Ped = ifelse(d$ped <= 0.31, "low pedigree",
                ifelse(d$ped > 0.3134 & d$ped <= 0.5844, "medium pedigree", "high pedigree"))) %>% 
  
            mutate(Ped = factor(Ped, levels = c("low pedigree", "medium pedigree", "high pedigree")))

3.2. Reshaping the data

Next, we need to prepare the data a little more before throwing it into the facet_grid() mix. Most importantly, we need to “reshape” it, i.e., while our data is a “wide”-form data frame, we need to convert this to a “long”-form to enable facet_grid() to easily pick up what it needs to “facet” the plot by. We will also add a “size” column - this will allow us to make more granular adjustments in our plot. I will also rename columns in order to enable easier axis labeling when plotting. Again, notice that instead of using reshape2(), which I have used for many years, we’re using gather() from tidyr(), all sewn together with the pipe in magrittr().

library(tidyr)
DF <- df_grid %>% 
    subset(select=c(type, Skin, BMI, Ped)) %>% 
    gather(variable, value, -c(Skin, Ped, BMI))

colnames(DF)[5] <- "Diabetes_Value"
DF$size <- rep(1.5, nrow(DF))
s <- 1.5

3.3. Facet Grid

We’ll try the basic facet_grid() plot, after which we’ll go in and make some adjustments. For now, our goal is the following: to see a “matrix” or “grid” of the BMI distribution across diabetes type, as a 2x2 table of pedigree/skin fold combinations. In other words, for low pedigree/low skin fold, how does BMI distribute across diabetes type? You can see the amount of information you can pack into just one plot. I have found this to be useful when presenting to an end-user or customer. It becomes all the more useful since its a very clear representation of this slice/dice, with little room for ambiguity.

# Simple
library(ggplot2)
ggplot(data=DF, aes(x=Diabetes_Value, fill=BMI)) + geom_bar() +  # Barplot
  facet_grid(Skin ~ Ped)   # wrap up everything to showcase by multiple cols

This looks nice, but I would like to add more of a “pop”. I am going to outline each box, and bolden the fonts. Note that you can also color the “grid strips”, but I won’t do that right now.

# More color
p <- ggplot(data=DF, aes(x=Diabetes_Value, fill=BMI)) + geom_bar() +  # Barplot
  geom_rect(aes(fill=NA, size=size),xmin =-Inf,xmax=Inf,ymin=-Inf,ymax=Inf,alpha = 0.0002, colour="black",show.legend = F) +   # use box drawn around each location to cleanly separate facets + suppress guide
  scale_size(range=c(s,s), guide=FALSE) + # use line width/size feature for cleaner plotting
  facet_grid(Skin ~ Ped) +   # wrap up everything to showcase by multiple cols
  theme(strip.text.x = element_text(face="bold", size=12)) +
  theme(strip.text.y = element_text(face="bold", size=12)) 
  # optional changes in strip
#+ theme(strip.text.x = element_text(face="bold", size=12, colour="white")) +
#  theme(strip.text.y = element_text(face="bold", size=12, color="white")) +
#  theme(strip.background = element_rect(fill="black"))
plot(p)

Much better. Look how nicely this granular plot adjustment in ggplot2 allows each “block” in the matrix to pop out. Its very clear how BMI is distributed across diabetes type, and how that in turn is distributed across both pedigree function and skin fold. We see that (as expected): 1. A higher triceps skin fold thickness is associated with a higher BMI, as well as a higher count of diabetic people. 2. The above is more true for a higher diabetes pedigree function.

This kind of a grid plot presents a very powerful tool for such multi-dimensional data views.

4.1. Data preparation

As usual, we need to prep our data before pushing it into the ggplot2() function. We’ll reshape and scale the data first, all within the plyr(), dplyr(), and magrittr() framework. I’ll also specify some plotting parameters that I will call into my ggplot2() function. I’m going to rely on RColorBrewer() for these.

df_heat <- d[order(d$type),1:8]
DF_Heat <- df_heat %>%
          mutate(id = 1:nrow(df_heat)) %>%
          select(c(npreg:age, id))  %>%
          gather(variable, value, -id)  %>%
          ddply(.(variable), transform,
                    rescale = scale(value))  # Notice that this reorders by "variables"
          
# Color scale for heatmap
library(RColorBrewer)
colors <- brewer.pal(9, 'Reds')

# Lines to split patients into diabetic/non-diabetic
my.lines <- data.frame(x1 = 0.5, x2 = 7.5, y1 = 223.5, y2 = 223.5)

4.2. Rendering the heatmap

# Basic plot
p <- ggplot(DF_Heat, aes(as.factor(variable), as.factor(id), group=id)) + 
  geom_tile(aes(fill = rescale),colour = "white") +
  scale_fill_gradient(low="green", high="red")

# Make adjustments
base_size <- 9
p_adj <- p + theme_grey(base_size = base_size) + labs(x = "",y = "") + scale_x_discrete(expand = c(0, 0)) +
  scale_y_discrete(expand = c(0, 0)) +
  geom_segment(data=my.lines, aes(x = x1, y = y1, xend=x2, yend=y2), size=1, inherit.aes=F) +
  theme(axis.text.y = element_blank(), axis.ticks.y = element_blank()) 
plot(p_adj)

Note that I have suppressed the ticks on the Y-axis. We can clearly see regions of interest on the heatmap. It would be better for these to easily pop out at the viewer, to enable which, I am going to invoke geom_rect().

# Borders of rectangles to indicate areas of interest on heatmap
my.lines.rect.1 <- data.frame(xmin = 1.5, xmax = 2.5, ymin = 223.5, ymax = 255.5)
my.lines.rect.2 <- data.frame(xmin = 3.5, xmax = 4.5, ymin = 223.5, ymax = 332)
my.lines.rect.3 <- data.frame(xmin = 5.5, xmax = 6.5, ymin = 223.5, ymax = 280.5)

p_adj + geom_rect(data=my.lines.rect.1, aes(xmin = xmin, xmax = xmax, 
            ymin = ymin, ymax = ymax), fill = NA, col = "black", lty=2, inherit.aes = F) +
  geom_rect(data=my.lines.rect.2, aes(xmin = xmin, xmax = xmax, 
            ymin = ymin, ymax = ymax), fill = NA, col = "black", lty=5, inherit.aes = F) +
  geom_rect(data=my.lines.rect.3, aes(xmin = xmin, xmax = xmax, 
            ymin = ymin, ymax = ymax), fill = NA, col = "black", lty=4, inherit.aes = F)

Much better.