Kmisc introduces a bunch of utility functions to make the R coding experience a bit easier. Some of the most useful functions in the package are shown here.
set.seed(123)
suppressPackageStartupMessages( library(Kmisc) )
dat <- data.frame( x=letters[1:4], y=1:4, z=LETTERS[1:4] )
without: This function is used to remove columns from a list / data.frame.
## let's remove columns 'x' and 'z' from dat.
dat[ !(names(dat) %in% c('x', 'z')) ]
## y
## 1 1
## 2 2
## 3 3
## 4 4
## I always find that syntax awkward. Let's use Kmisc::without instead.
without( dat, x, z )
## y
## 1 1
## 2 2
## 3 3
## 4 4
## what if there is a variable 'x' in the global environment?
x <- "a"
without( dat, x, z )
## y
## 1 1
## 2 2
## 3 3
## 4 4
## finally, we can also index by $ (note: NOT done by partial matching):
without( dat, dat$x, dat$z )
## y
## 1 1
## 2 2
## 3 3
## 4 4
## this could be handy for vectors with very long names, if using an IDE with
## auto-complete
extract: Extract vectors from a data.frame or list. Although there is already
a good subsetting syntax for lists and vectors, I wanted a complementary
function for without.
extract( dat, x, y)
## x y
## 1 a 1
## 2 b 2
## 3 c 3
## 4 d 4
without.re, extract.re: Extract variables whose names don't match / do match
a regular expression pattern.
extract.re( dat, "[xy]")
## x y
## 1 a 1
## 2 b 2
## 3 c 3
## 4 d 4
without.re( dat, "[xy]")
## z
## 1 A
## 2 B
## 3 C
## 4 D
swap: Replace elements in a vector.
tDat <- dat ## make a temporary copy of dat
## Replace some elements in tDat$y
tDat$y <- swap( tDat$y, from=c(2, 4), to=c(20, 40) )
cbind( dat$y, tDat$y )
## [,1] [,2]
## [1,] 1 1
## [2,] 2 20
## [3,] 3 3
## [4,] 4 40
factor_to_char, char_to_factor: A set of functions that recurse through
a list / data.frame and set all elements that are characters to factors,
and vice versa.
bDat <- data.frame( x=rnorm(10), y=sample(letters,10), z=sample(letters,10) )
str( bDat )
## 'data.frame': 10 obs. of 3 variables:
## $ x: num -0.5605 -0.2302 1.5587 0.0705 0.1293 ...
## $ y: Factor w/ 10 levels "c","f","k","l",..: 10 7 6 9 5 8 3 4 2 1
## $ z: Factor w/ 10 levels "a","d","e","f",..: 10 9 7 8 1 5 6 3 4 2
str( factor_to_char(bDat) )
## 'data.frame': 10 obs. of 3 variables:
## $ x: num -0.5605 -0.2302 1.5587 0.0705 0.1293 ...
## $ y: chr "x" "r" "p" "w" ...
## $ z: chr "z" "w" "q" "s" ...
dapply: The data.frame version of the l/sapply series of functions.
Why have this function when sapply still does much the same? I always get
frustrated with the fact that either an array or a list is returned
by sapply, but never a data.frame.
dat <- data.frame( x = rnorm(100), y = rnorm(100), z = rnorm(100) )
dapply( dat, summary )
## x y z
## Min. -2.31000 -2.0500 -2.0100
## 1st Qu. -0.62600 -0.7280 -0.5740
## Median -0.05870 -0.2060 0.0535
## Mean -0.00979 -0.0238 0.1010
## 3rd Qu. 0.56200 0.5720 0.7790
## Max. 2.19000 3.2400 2.2900
kMerge: Left joins, aka. merge( all.x=TRUE, ... ) without any mangling
of the order.
dat1 <- data.frame( id=5:1, x=c("a","a","b","b","b"), y=rnorm(5) )
dat2 <- data.frame( id=c(1, 2, 4), z=rnorm(3) )
## default merge changes id order
merge( dat1, dat2, by="id", all.x=TRUE )
## id x y z
## 1 1 b -0.5229 0.68375
## 2 2 b 0.6608 -0.06082
## 3 3 b -1.3388 NA
## 4 4 a 1.2181 0.63296
## 5 5 a 0.2374 NA
## even the sort parameter can't save you
merge( dat1, dat2, by="id", all.x=TRUE, sort=TRUE )
## id x y z
## 1 1 b -0.5229 0.68375
## 2 2 b 0.6608 -0.06082
## 3 3 b -1.3388 NA
## 4 4 a 1.2181 0.63296
## 5 5 a 0.2374 NA
# kMerge keeps it as is
kMerge( dat1, dat2, by="id" )
## id x y z
## 1 5 a 0.2374 NA
## 2 4 a 1.2181 0.63296
## 3 3 b -1.3388 NA
## 4 2 b 0.6608 -0.06082
## 5 1 b -0.5229 0.68375
in_interval: A fast C implementation for determing which elements of a
vector x lie within an interval [lo, hi).
x <- runif(10); lo <- 0.5; hi <- 1
print( data.frame( x=x, inner=in_interval(x, lo, hi) ) )
## x inner
## 1 0.90915 TRUE
## 2 0.05638 FALSE
## 3 0.50291 TRUE
## 4 0.35054 FALSE
## 5 0.84556 TRUE
## 6 0.80644 TRUE
## 7 0.11733 FALSE
## 8 0.71269 TRUE
## 9 0.23527 FALSE
## 10 0.07496 FALSE
stack_list: Use this to stack data.frames in a list. This can be useful if
e.g. you've run some kind of bootstrap procedure and have all your results
stored in as a list of data.frames – even do.call( rbind, dfs ) can be slow.
The difference is even more prominent when used on very large lists.
dfs <- replicate(1E3,
data.frame(x=rnorm(10), y=sample(letters,10), z=sample(LETTERS,10)),
simplify=FALSE
)
str( stack_list(dfs) )
## 'data.frame': 10000 obs. of 4 variables:
## $ x : num 1.519 0.377 -2.052 -1.364 -0.201 ...
## $ y : chr "n" "e" "l" "f" ...
## $ z : chr "E" "C" "K" "I" ...
## $ list_index: int 1 1 1 1 1 1 1 1 1 1 ...
system.time( stack_list(dfs) )
## user system elapsed
## 0.01 0.00 0.01
system.time( do.call(rbind, dfs) )
## user system elapsed
## 0.471 0.008 0.482
For whatever reason, R is missing some nice builtin 'string' functions. I've introduced a few functions, implemented in C for speed, for two common string operations.
str_rev: Reverses a character vector; ie, a vector of strings.
str_rev2 is there if you need to reverse a potentially unicode string.
str_rev( c("ABC", "DEF", NA, paste(LETTERS, collapse="") ) )
## [1] "CBA" "FED"
## [3] NA "ZYXWVUTSRQPONMLKJIHGFEDCBA"
str_slice: Slices a vector of strings at consecutive indices n.
str_slice2 exists for potentially unicode strings.
str_slice( c("ABCDEF", "GHIJKL", "MNOP", "QR"), 2 )
## [[1]]
## [1] "AB" "CD" "EF"
##
## [[2]]
## [1] "GH" "IJ" "KL"
##
## [[3]]
## [1] "MN" "OP"
##
## [[4]]
## [1] "QR"
str_sort: sort a string.
str_sort("asnoighewgypfuiweb")
## [1] "abeefgghiinopsuwwy"
The real point of releasing this package is due to the HTML helper functions I've made. I've found them especially useful in laying out R Markdown documents, and also controlling more finely CSS styling and such.
makeHTMLTable: Converts a data.frame or matrix into an HTML table.
Best seen with an example. Note that we require results='asis' in the chunk
options.
dat <- data.frame(apple = c("a", "b", "c"), banana = c(1, 2, 3))
makeHTMLTable(dat, use.col.names = TRUE)
| apple | banana |
| a | 1 |
| b | 2 |
| c | 3 |
kTable: Generated 1D and 2D (contingency) tables, which are then typically
passed through makeHTMLtable for 1D tables, or pxt for 2D tables.
x <- factor( rbinom(100, 2, 0.2) )
y <- factor( rbinom(100, 3, 0.3) )
p1t( kTable( x, top.left.cell="foo" ) )
| foo | Count (%) |
| 0 | 61 (61.0%) |
| 1 | 36 (36.0%) |
| 2 | 3 (3.00%) |
| Total | 100 |
pxt( kTable(x, y,
top.left.cell="foo",
top.label="bar",
left.label="baz"
) )
| foo | bar | |||||
| 0 | 1 | 2 | 3 | Total | ||
| baz | 0 | 17 (58.6%) | 25 (59.5%) | 17 (65.3%) | 2 (66.6%) | 61 |
| 1 | 10 (34.4%) | 16 (38.0%) | 9 (34.6%) | 1 (33.3%) | 36 | |
| 2 | 2 (6.89%) | 1 (2.38%) | 0 (0.00%) | 0 (0.00%) | 3 | |
| Total | 29 | 42 | 26 | 3 | 100 | |
kTable with google=TRUE: Generate a 1D table output with the googleVis
package.
suppressPackageStartupMessages( library(googleVis) )
tmp <- gvisTable( kTable(x, google=TRUE) )
cat( tmp$html$chart )
hImg, hSvg: These utility functions do the work of simultaneously writing a
plot to file, and then producing the HTML code needed to embed that plot in
the page. Once again, we require results='asis' in the chunk options. I
also show off the nice little gradient function.
x <- rnorm(100); y <- x + runif(100)
## note that the default resolution is high for PNGs
hImg( file="test.png", width=400, height=300, dpi=300,
xyplot( y ~ x, col=gradient(x+y) )
)
hSvg( file="test.svg", width=400, height=300,
xyplot( y ~ x, col=gradient(x+y) )
)
kCoef, kAnova: These are functions that produce 'nicer' coefficient output
from a model fit, especially when factors are used. However, they don't work
quite right for models with interaction effects yet.
x <- rnorm(100); y <- x + runif(100)
z <- factor( rep(c('a','b','c','d'), each=25) )
myFit <- lm( y ~ x + z )
## the vanilla way
coef( summary( myFit ) )
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.55452 0.05728 9.6815 7.920e-16
## x 1.03122 0.02855 36.1191 2.714e-57
## zb -0.21696 0.07961 -2.7255 7.645e-03
## zc 0.02635 0.07999 0.3294 7.426e-01
## zd 0.01673 0.07935 0.2109 8.335e-01
## nicer parsing of variable names
kCoef( myFit )
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.55452 0.05728 9.6815 7.920e-16
## x 1.03122 0.02855 36.1191 2.714e-57
## z: a -> b -0.21696 0.07961 -2.7255 7.645e-03
## z: a -> c 0.02635 0.07999 0.3294 7.426e-01
## z: a -> d 0.01673 0.07935 0.2109 8.335e-01
html: Custom HTML in an R Markdown document.
html(
table( class="my-favourite-table",
tr(
td("Apples"),
td("Bananas")
),
tr(
td("20"),
td("30")
)
)
)
| Apples | Bananas |
| 20 | 30 |