Other Helper Functions
formatfunc
formatfunc() is a function that returns a function,
which itself helps format numbers using the format()
function, in the same spirit as the label_ functions in the
scales package. It is largely used for the numformat
argument of sumtable().
formatfunc() for the most part takes the same arguments
as format(), and so help(format) can be a
guide for using it. However, there are some differences.
Some defaults are changed. By default,
scientific = FALSE, trim = TRUE.
There are four new arguments as well. percent = TRUE
will format the number as a percentage by multiplying it by 100 and
adding a % at the end. You can instead set percent equal to
some number, and that number will instead be taken as 100%, instead of
1. So percent = 100, for example, will just add a % at the
end without doing any multiplying.
prefix and suffix will, naturally, add
prefixes or suffixes to the formatted number. So
prefix = '$', suffix = 'M', for example, will produce a
function that will turn 3 into $3M.
scale will multiply the number by scale before
formatting it. So
prefix = '$', suffix = 'M', scale = 1/1000000 will turn
3000000 into $3M.
library(vtable)
my_formatter_func <- formatfunc(percent = TRUE, digits = 3, nsmall = 2, big.mark = ',')
my_formatter_func(523.2355987)## [1] "52,323.56%"pctile
pctile(x) is short for
quantile(x,1:100/100). So in one sense this is another
shortcut function. But this inherently lets you interact with
percentiles a bit differently.
While quantile() has you specify which percentile you
want in the function call, pctile() returns an object with
all integer percentiles, and you can pull out which ones you want
afterwards. pctile(x)[50] is the 50th percentile, etc..
This can be convenient in several applications, an obvious one being in
sumtable.
library(vtable)
#Some random normal data, and its percentiles
d <- rnorm(1000)
pc <- pctile(d)
#25th, 50th, 75th percentile
pc[c(25,50,75)]## 25% 50% 75%
## -0.67444356 0.02647806 0.69934796
weighted.sd
weighted.sd(x, w) is a function to calculate a weighted
standard deviation of x using w as weights,
much like the base weighted.mean() does for means. It is
mostly used as a helper function for sumtable() when
group.weights is specified. However, you can use it on its
own if you like. Unlike weighted.mean(), setting
na.rm = TRUE will account for missings both in
x and w.
The weighted standard deviation is calculated as
\[ \frac{\sum_i(w_i*(x_i-\bar{x}_w)^2)}{\frac{N_w-1}{N_w}\sum_iw_i} \]
Where \(\bar{x}_w\) is the weighted mean of \(x\), and \(N_w\) is the number of observations with a nonzero weight.
## [1] 67## [1] 29.01149## [1] 23.80476