# R version of “An exploratory technique for visualizing the distributions of 100 variables:”

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Rick Wicklin (@RickWicklin) made a

recent post to the SAS blog on

An exploratory technique for visualizing the distributions of 100

variables. It’s a very succinct tutorial on both the power of

boxplots and how to make them in SAS (of course). I’m not one to let R

be “out-boxed”, so I threw together a quick re-creation of his example,

mostly as tutorial for any nascent R folks that come across it. (As an

aside, I catch Rick’s and other cool, non-R stuff via the Stats

Blogs blog aggregator.)

The R implementation (syntax notwithstanding) is extremely similar.

First, we’ll need some packages to assist with data reshaping and pretty

plotting:

library(reshape2) library(ggplot2) |

Then, we setup a list so we can pick from the same four distributions

and set the random seed to make this example reproducible:

dists <- c(rnorm, rexp, rlnorm, runif) set.seed(1492) |

Now, we generate a data frame of the `100`

variables with `1,000`

observations, normalized from `0`

-`1`

:

many_vars <- data.frame(sapply(1:100, function(x) { # generate 1,000 random samples tmp <- sample(dists, 1)[[1]](1000) # normalize them to be between 0 & 1 (tmp - min(tmp)) / (max(tmp) - min(tmp)) })) |

The `sapply`

iterates over the numbers `1`

through `100`

, passing each

number into a function. Each iteration samples an object from the

`dists`

list (which are actual R functions) and then calls the function,

telling it to generate `1,000`

samples and normalize the result to be

values between `0`

& `1`

. By default, R will generate column names that

begin with `X`

:

str(many_vars[1:5]) # show the structure of the first 5 cols ## 'data.frame': 1000 obs. of 5 variables: ## $ X1: num 0.1768 0.4173 0.5111 0.0319 0.0644 ... ## $ X2: num 0.217 0.275 0.596 0.785 0.825 ... ## $ X3: num 0.458 0.637 0.115 0.468 0.469 ... ## $ X4: num 0.5186 0.0358 0.5927 0.1138 0.1514 ... ## $ X5: num 0.2855 0.0786 0.2193 0.433 0.9634 ... |

We’re going to plot the boxplots, sorted by the third quantile (just

like in Rick’s example), so we’ll calculate their rank and then use

those ranks (shortly) to order a factor varible:

ranks <- names(sort(rank(sapply(colnames(many_vars), function(x) { as.numeric(quantile(many_vars[,x], 0.75)) })))) |

There’s alot going on in there. We pass the column names from the

`many_vars`

data frame to a function that will return the quantile we

want. Since `sapply`

preserves the names we passed in as well as the

values, we extract them (via `names`

) after we rank and sort the named

vector, giving us a character vector in the order we’ll need:

str(ranks) ## chr [1:100] "X29" "X8" "X92" "X43" "X11" "X52" "X34" ... |

Just like in the SAS post, we’ll need to reshape the data into long

format from wide

format,

which we can do with `melt`

:

many_vars_m <- melt(as.matrix(many_vars)) str(many_vars_m) ## 'data.frame': 100000 obs. of 3 variables: ## $ Var1 : int 1 2 3 4 5 6 7 8 9 10 ... ## $ Var2 : Factor w/ 100 levels "X1","X2","X3",..: 1 1 1 1 1 1 1 1 1 1 ... ## $ value: num 0.1768 0.4173 0.5111 0.0319 0.0644 ... |

And, now we’ll use our ordered column names to ensure that our boxplots

will be presented in the right order (it would be in alpha order if

not). Factor variables in R are space-efficient and allow for handy

manipulations like this (amongst other things). By default,

`many_vars_m$Var2`

was in alpha order and this call just re-orders that

factor.

many_vars_m$Var2 <- factor(many_vars_m$Var2, ranks) str(many_vars_m) ## 'data.frame': 100000 obs. of 3 variables: ## $ Var1 : int 1 2 3 4 5 6 7 8 9 10 ... ## $ Var2 : Factor w/ 100 levels "X29","X8","X92",..: 24 24 24 24 24 24 24 24 24 24 ... ## $ value: num 0.1768 0.4173 0.5111 0.0319 0.0644 ... |

Lastly, we plot all our hard work (click/touch for larger version):

gg <- ggplot(many_vars_m, aes(x=Var2, y=value)) gg <- gg + geom_boxplot(fill="#BDD7E7", notch=TRUE, outlier.size=1) gg <- gg + labs(x="") gg <- gg + theme_bw() gg <- gg + theme(panel.grid=element_blank()) gg <- gg + theme(axis.text.x=element_text(angle=-45, hjust=0.001, size=5)) gg |

Here’s the program in it’s entirety:

library(reshape2) library(ggplot2) dists <- c(rnorm, rexp, rlnorm, runif) set.seed(1) many_vars <- data.frame(sapply(1:100, function(x) { tmp <- sample(dists, 1)[[1]](1000) (tmp - min(tmp)) / (max(tmp) - min(tmp)) })) ranks <- names(sort(rank(sapply(colnames(many_vars), function(x) { as.numeric(quantile(many_vars[,x], 0.75)) })))) many_vars_m <- melt(as.matrix(many_vars)) many_vars_m$Var2 <- factor(many_vars_m$Var2, ranks) gg <- ggplot(many_vars_m, aes(x=Var2, y=value)) gg <- gg + geom_boxplot(fill="steelblue", notch=TRUE, outlier.size=1) gg <- gg + labs(x="") gg <- gg + theme_bw() gg <- gg + theme(panel.grid=element_blank()) gg <- gg + theme(axis.text.x=element_text(angle=-45, hjust=0.001)) gg |

I tweaked the boxplot, using a

notch

and making the outliers take up a fewer pixels.

I’m definitely in agreement with Rick that this is an excellent way to

compare many distributions.

Bonus points for the commenter who shows code to color the bars by which distribution generated them!

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