This tutorial makes use of the following R package(s): ggplot2.


Datasets do not always follow a nice symmetrical distribution nor do their spreads behave systematically across different levels (e.g. medians). Such distributions do not lend themselves well to visual exploration since they can mask simple patterns. They can also be a problem when testing hypotheses using traditional statistical procedures. A solution to this problem is non-linear re-expression (aka transformation) of the values. In univariate analysis, we often seek to symmetrize the distribution and/or equalize the spread. In multivariate analysis, the objective is to usually linearize the relationship between variables and/or to normalize the residual in a regression model.

One popular form of re-expression is the log (natural or base 10). The other is the family of power transformations (of which the log is a special case) implemented using either the Tukey transformation or the Box-Cox transformation.

The log transformation

One of the most popular transformations used in data analysis is the logarithm. The log, \(y\), of a value \(x\) is the power to which the base must be raised to produce \(x\). This requires that the log function be defined by a base, \(b\), such as 10, 2 or exp(1) (the latter defining the natural log).

\[ y = log_b(x) \Leftrightarrow x=b^y \]

In R, the base is defined by passing the parameter base= to the log() function as in log(x , base=10).

Re-expressing with the log is particularly useful when the change in one value as a function of another is multiplicative and not additive. An example of such a dataset is the compounding interest. Let’s assume that we start off with $1000 in an investment account that yields 10% interest each year. We can calculate the size of our investment for the next 50 years as follows:

rate <- 0.1               # Rate is stored as a fraction
y    <- vector(length=50) # Create an empty vector that can hold 50 values
y[1] <- 1000              # Start 1st year with $1000

# Next, compute the investment ammount for years 2, 3, ..., 50.
# Each iteration of the loop computes the new amount for year i based 
# on the previous year's amount (i-1).
for(i in 2:length(y)){
  y[i] <- y[i-1] + (y[i-1] * rate)  # Or y[i-1] * (1 + rate)

The vector y gives us the amount of our investment for each year over the course of 50 years.

 [1]   1000.000   1100.000   1210.000   1331.000   1464.100   1610.510   1771.561   1948.717   2143.589   2357.948
[11]   2593.742   2853.117   3138.428   3452.271   3797.498   4177.248   4594.973   5054.470   5559.917   6115.909
[21]   6727.500   7400.250   8140.275   8954.302   9849.733  10834.706  11918.177  13109.994  14420.994  15863.093
[31]  17449.402  19194.342  21113.777  23225.154  25547.670  28102.437  30912.681  34003.949  37404.343  41144.778
[41]  45259.256  49785.181  54763.699  60240.069  66264.076  72890.484  80179.532  88197.485  97017.234 106718.957

We can plot the values as follows:

# Note that a scatter plot is created from 2 variables, however, if only one
# is passed to the plot() function, R will assume that the x variable is
# an equally spaced index.
plot(y, pch=20)

The change in difference between values from year to year is not additive, in other words, the difference between years 48 and 49 is different than that for years 3 and 4.

Years Difference
y[49] - y[48] 8819.75
y[4] - y[3] 121

However, the ratios between the pairs of years are identical:

Years Ratio
y[49] / y[48] 1.1
y[4] / y[3] 1.1

We say that the change in value is multiplicative across the years. In other words, the value amount 6 years out is \(value(6) = (yearly\_increase)^{6} \times 1000\) or 1.1^6 * 1000 = 1771.561 which matches value y[7].

When we expect a variable to change multiplicatively as a function of another variable, it is usually best to transform the variable using the logarithm. To see why, plot the log of y.

plot(log(y), pch=20)

Note the change from a curved line to a perfectly straight line. The logarithm will produce a straight line if the rate of change for y is constant over the range of x. This is a nifty property since it makes it so much easier to see if and where the rate of change differs. For example, let’s look at the population growth rate of the US from 1850 to 2013.

dat <- read.csv("", header=TRUE)
plot(US ~ Year, dat, type="l") 

The population count for the US follows a slightly curved (convex) pattern. It’s difficult to see from this plot if the rate of growth is consistent across the years (though there is an obvious jump in population count around the 1950’s). Let’s log the population count.

plot(log(US) ~ Year, dat, type="l")  

It’s clear from the log plot that the rate of growth for the US has not been consistent over the years (had it been consistent, the line would have been straight). In fact, there seems to be a gradual decrease in growth rate over the 150 year period (though a more thorough analysis would be needed to see where and when the growth rates changed).

A logarithm is defined by a base. Some of the most common bases are 10, 2 and exp(1) with the latter being the natural log. The bases can be defined in the call to log() by adding a second parameter to that function. For example, to apply the log base 2 to the 5th value of the vector y, type log( y[5], 2). To apply the natural log to that same value, simply type log( y[5], exp(1)). If you don’t specify a base, R will default to the natural log.

The choice of a log base will not impact the shape of the logged values in the plot, only in its absolute value. So unless interpretation of the logged value is of concern, any base will do. Generally, you want to avoid difficult to interpret logged values. For example, if you apply log base 10 to the investment dataset, you will end up with a smaller range of values thus more decimal places to work with whereas a base 2 logarithm will generate a wider range of values and thus fewer decimal places to work with.

A rule of thumb is to use log base 10 when the range of values to be logged covers 3 or more powers of ten, \(\geq 10^3\) (for example, a range of 5 to 50,000); if the range of values covers 2 or fewer powers of ten, \(\leq 10^2\)(for example, a range of 5 to 500) then a natural log or a log base 2 log is best.

The Tukey transformation

The Tukey family of transformations offers a broader range of re-expression options (which includes the log). The values are re-expressed using the algorithm:

\[ \begin{equation} T_{Tukey} = \begin{cases} x^p , & p > 0 \\ log(x), & p = 0 \\ -x^p, & p < 0 \end{cases} \end{equation} \] The objective is to find a value for \(p\) from a “ladder” of powers (e.g. -2, -1, -1/2, 0, 1/2, 1, 2) that does a good job in re-expressing the batch of values. Technically, \(p\) can take on any value. But in practice, we normally pick a value for \(p\) that may be “interpretable” in the context of our analysis. For example, a log transformation (p=0) may make sense if the process we are studying has a steady growth rate. A cube root transformation (p = 1/3) may make sense if the entity being measured is a volume (e.g. rain fall measurements). But sometimes, the choice of \(p\) may not be directly interpretable or may not be of concern to the analyst.

A nifty solution to finding an appropriate \(p\) is to create a function whose input is the vector (that we want to re-express) and a \(p\) parameter we want to explore.

RE <- function(x, p = 0) {
  if(p > 0) {
    z <- x^p
  } else if(p < 0) {
    z <- -x^p
  } else{
    z <- log(x)

To use the custom function RE simply pass two vectors: the batch of numbers being re-expressed and the \(p\) parameter.

# Create a skewed distribution of 50 random values
a <- rgamma(50, shape=1)

# Let's look at the skewed distribution
boxplot(a, horizontal = TRUE)

The batch is strongly skewed to the right. Let’s first try a square-root transformation (p=1/2) <- RE(a, p = 1/2)   
boxplot(, horizontal = TRUE)

That certainly helps minimize the skew, but the distribution still lacks symmetry. Let’s try a log transformation (p=0): <- RE(a, p = 0)   
boxplot(, horizontal = TRUE)

That’s a little too much over-correction; we don’t want to substitute a right skew for a left skew. Let’s try a power in between (i.e. p=1/4): <- RE(a, p = 1/4)   
boxplot(, horizontal = TRUE)

That’s much better. The distribution is now nicely balanced about its median.

The Box-Cox transformation

Another family of transformations is the Box-Cox transformation. The values are re-expressed using a modified version of the Tukey transformation:

\[ \begin{equation} T_{Box-Cox} = \begin{cases} \frac{x^p - 1}{p}, & p \neq 0 \\ log(x), & p = 0 \end{cases} \end{equation} \] Just as we can create a custom Tukey transformation function, we can create a Box-Cox transformation function too:

BC <- function(x, p = 0) {
  if(p == 0) {
    z <- log(x)
  } else {
    z <- (x^p - 1)/p

The Box-Cox method only differs in spread and location from the Tukey method. In other words, you will only see a difference in the re-expressed values but not a difference in the shape of the data as demonstrated in the following examples which pit the Box-Cox vs Tukey transformations for three different powers.

plot(BC(mtcars$mpg,-1/2), RE(mtcars$mpg,-1/2), main="P = -1/2")
plot(BC(mtcars$mpg, 0.0), RE(mtcars$mpg, 0.0), main="P = 0")
plot(BC(mtcars$mpg, 1/2), RE(mtcars$mpg, 1/2), main="P = 1/2")

In most cases, the choice between Box-Cox and Tukey will not matter. But it may be worth noting that a value of 1 will always be re-expressed as 0 in a Box-Cox transformation whereas a Tukey transformation will re-express 1 as 0, -1 or 1 depending of p’s value. Conversely, the Tukey method re-expresses 0 as 0 for all p values whereas the Box-Cox method does not. These differences may only matter if theory dictates it.

How quantile plots behave in the face of skewed data

It can be helpful to simulate distributions of difference skewness to see how a quantile or a normal quantile plot may behave.

Quantile plots

A beta distribution is used to simulate a range of distributions from right skew to left skew. The top row shows the different density distribution plots; the bottom row shows the quantile plots for each distribution (note that the x-axis maps the f-values).

Normal q-q plots

A beta distribution is used to simulate a range of distributions from right skew to left skew. The top row shows the different density distribution plots; the bottom row shows the normal q-q plots for each distribution.

Re-expressing to stabilize spread

A spread vs level plot not only tells us if there is a systematic relationship between spread and level, it can also suggest the power transformation to use. Note that the s-l method discussed here is not the one presented in Cleveland’s book (see the section titled “Spread-location plot” on the course website for an alternative version of the plot). We will make use of a custom function called sl available in the ES218.R script to help construct the s-l plot using .


df <- read.csv("")

# Create s-l table from custom sl() function <- sl(x=dimension, y=mean.length, df)

# Plot spread vs median
plot(sprd ~ med,, pch=16)

The plot suggests a monotonic relationship between spread and median. Next, we’ll fit a line to this scatter plot and compute its slope. We’ll use the lm() function, but note that any other line fitting strategies could be used as well.

plot(sprd ~ med,, pch=16)
# Run regression model
M <- lm(sprd ~ med,
abline(M, col="red")

The slope can be used to come up with the best power transformation to minimize the systematic increase in spread: \(p = 1 - slope\).

The slope can be extracted from the model M using the coef function:

(Intercept)         med 
  -2.317414    2.649166 

The second value in the output is the slope. So the power to use is 1 - 2.65 or -1.65. We will use the custom Tukey power transformation function RE to re-express the mean.length values. We’ll add the re-expressed values as a new column to df:

df$re.mean.length <- RE(df$mean.length, -1.65)

Let’s compare boxplots between the original values with the re-expressed values.

boxplot(mean.length ~ dimension, df, main = "Original data")
boxplot(re.mean.length ~ dimension, df, main = "Re-expressed data")

Recall that our goal here was to minimize any systematic relationship between spread and median. The re-expression seems to have equalized the spreads across the three groups.

We’ll check for a homogeneous spread across fitted medians using the original spread-level plot covered in last week’s lecture.

df1 <- df %>%
  group_by(dimension)                            %>%
  mutate( Median = median(re.mean.length),
          Residuals = sqrt( abs( re.mean.length - Median)))   

# Compute the coordinate values needed to connect residual medians
sl.line <- df1 %>%
  group_by(dimension)  %>%
  summarise(location = median(re.mean.length),
    = median(Residuals))

# Generate the s-l plot
ggplot(df1, aes(x=Median, y=Residuals)) + geom_jitter(alpha=0.4,width=0.01,height=0) +
  geom_line(data=sl.line,aes(x=location,, col="red") +
  ylab(expression(sqrt( abs( " Residuals ")))) +
  annotate("text", x=sl.line$location, y=1.75, label=sl.line$dimension)

The plot suggests that the re-expression does a great job in stabilizing the spread.