A collection of utility functions that facilitate looking up vector values from a lookup table, annotate values in at table for clearer viewing, and support a safer approach to vector sampling, sequence generation, and aggregation.
You can install the released version of zmisc from CRAN with:
install.packages("zmisc")You can use pak to install the development version of
zmisc from GitHub with:
pak::pak("torfason/zmisc")In order to use the package, you generally want to attach it first:
library(zmisc)The functions lookup()
and lookuper()
are used to look up values from a lookup table, which can be supplied as
a vector, a list, or a
data.frame. The functions are in some ways similar to the
Excel function VLOOKUP(), but are designed to work smoothly
in an R workflow, in particular within pipes.
The lookup() function implements lookup of certain strings (such as variable names) from a lookup table which maps keys onto values (such as variable labels or descriptions).
The lookup table can be in the form of a two-column
data.frame, in the form of a named vector, or
in the form of a list. If the table is in the form of a
data.frame, the lookup columns should be named
name (for the key) and value (for the value).
If the lookup table is in the form of a named vector or
list, the name is used for the key, and the returned value
is taken from the values in the vector or list.
Original values are returned if they are not found in the lookup
table. Alternatively, a default can be specified for values
that are not found. Note that an NA in x will never be
found and will be replaced with the default value. To specify different
defaults for values that are not found and for NA values in
x, the default must be crafted manually to
achieve this.
Any names in x are not included in the result.
fruit_lookup_vector <- c(a="Apple", b="Banana", c="Cherry")
lookup(letters[1:5], fruit_lookup_vector)
lookup(letters[1:5], fruit_lookup_vector, default = NA)
mtcars_lookup_data_frame <- data.frame(
name = c("mpg", "hp", "wt"),
value = c("Miles/(US) gallon", "Gross horsepower", "Weight (1000 lbs)"))
lookup(names(mtcars), mtcars_lookup_data_frame)The lookuper() function returns a function equivalent to the lookup() function, except that instead of taking a lookup table as an argument, the lookup table is embedded in the function itself.
This can be very useful, in particular when using the lookup function
as an argument to other functions that expect a function which maps
character->character but do not offer a
good way to pass additional arguments to that function.
lookup_fruits <- lookuper(list(a="Apple", b="Banana", c="Cherry"))
lookup_fruits(letters[1:5])The functions zample(), zeq(), and zingle() are intended to make your code less likely to break in mysterious ways when you encounter unexpected boundary conditions. The zample() and zeq() are almost identical to the sample() and seq() functions, but a bit safer.
The zample()
function duplicates the functionality of sample(), with the
exception that it does not attempt the (sometimes dangerous)
user-friendliness of switching the interpretation of the first element
to a number if the length of the vector is 1. zample()
always treats its first argument as a vector containing
elements that should be sampled, so your code won’t break in unexpected
ways when the input vector happens to be of length 1.
# For vectors of length 2 or more, zample() and sample() are identical
set.seed(42); zample(7:11)
set.seed(42); sample(7:11)
# For vectors of length 1, zample() will still sample from the vector,
# whereas sample() will "magically" switch to interpreting the input
# as a number n, and sampling from the vector 1:n.
set.seed(42); zample(7)
set.seed(42); sample(7)
# The other arguments work in the same way as for sample()
set.seed(42); zample(7:11, size=13, replace=TRUE, prob=(5:1)^3)
set.seed(42); sample(7:11, size=13, replace=TRUE, prob=(5:1)^3)
# Of course, sampling more than the available elements without
# setting replace=TRUE will result in an error
set.seed(42); tryCatch(zample(7, size=2), error=wrap_error)The zeq() function creates an increasing integer sequence, but differs from the standard one in that it will not silently generate a decreasing sequence when the second argument is smaller than the first. If the second argument is one smaller than the first it will generate an empty sequence, if the difference is greater, the function will throw an error.
# For increasing sequences, zeq() and seq() are identical
zeq(11,15)
zeq(11,11)
# If second argument equals first-1, an empty sequence is returned
zeq(11,10)
# If second argument is less than first-1, the function throws an error
tryCatch(zeq(11,9), error=wrap_error)The zingle()
function returns the first element in a vector, but only if all the
other elements are identical to the first one (the vector only has a
zingle value). If the elements are not all identical, it
throws an error. The vector must contain at least one
non-NA value, or the function errors out as well. This is
especially useful in aggregations, when all values in a given group
should be identical, but you want to make sure.
# If all elements are identical, all is good.
# The value of the element is returned.
zingle(c("Alpha", "Alpha", "Alpha"))
# If any elements differ, an error is thrown
tryCatch(zingle(c("Alpha", "Beta", "Alpha")), error=wrap_error)
if (require("dplyr", quietly=TRUE, warn.conflicts=FALSE)) {
d <- tibble::tribble(
~id, ~name, ~fouls,
1, "James", 3,
2, "Jack", 2,
1, "James", 4
)
# If the data is of the correct format, all is good
d %>%
dplyr::group_by(id) %>%
dplyr::summarise(name=zingle(name), total_fouls=sum(fouls))
}
if (require("dplyr", quietly=TRUE, warn.conflicts=FALSE)) {
# If a name does not match its ID, we should get an error
d[1,"name"] <- "Jammes"
tryCatch({
d %>%
dplyr::group_by(id) %>%
dplyr::summarise(name=zingle(name), total_fouls=sum(fouls))
}, error=wrap_error)
}The notate()
function adds annotations to factor and
labelled variables that make it easier to see both values
and labels/levels when using the View() function
This function adds level/label information as an annotation to either
factors or labelled variables. This function is called
notate() rather than annotate() to avoid
conflict with ggplot2::annotate(). It is a generic that can
operate either on individual vectors or on a
data.frame.
When printing labelled variables from a
tibble in a console, both the numeric value and the text
label are shown, but no variable labels. When using the
View() function, only variable labels are shown but no
value labels. For factors, there is no way to view the integer levels
and values at the same time.
In order to allow the viewing of both variable and value labels at
the same time, this function converts both factor and
labelled variables to character, including
both numeric levels (labelled values) and character values
(labelled labels) in the output.
d <- data.frame(
chr = letters[1:4],
fct = factor(c("alpha", "bravo", "chrly", "delta")),
lbl = ll_labelled(c(1, 2, 3, NA),
labels = c(one=1, two=2),
label = "A labelled vector")
)
dn <- notate(d)
dn
# View(dn)