Small (Helper) Functions
small-helpers.RdConvenience functions in the collapse package that help to deal with object attributes such as variable names and labels, object checking, metaprogramming, and that improve the workflow.
Usage
.c(...) # Non-standard concatenation i.e. .c(a, b) == c("a", "b")
nam %=% values
massign(nam, values, # can also assign to different environment.
envir = parent.frame())
vlabels(X, attrn = "label", # Get labels of variables in X, in attr(X[[i]], attrn)
use.names = TRUE)
vlabels(X, attrn = "label") <- value
setLabels(X, value = NULL, # Set labels of variables in X (by reference) and return X
attrn = "label", cols = NULL)
vclasses(X, use.names = TRUE) # Get classes of variables in X
namlab(X, class = FALSE, # Return data frame of names and labels,
attrn = "label", N = FALSE, # and (optionally) classes, number of observations
Ndistinct = FALSE) # and number of non-missing distinct values
add_stub(X, stub, pre = TRUE, # Add a stub (i.e. prefix or postfix) to column names
cols = NULL)
rm_stub(X, stub, pre = TRUE, # Remove stub from column names, also supports general
regex = FALSE, # regex matching and removing of characters
cols = NULL, ...)
all_identical(...) # Check exact equality of multiple objects or list-elements
all_obj_equal(...) # Check near equality of multiple objects or list-elements
all_funs(expr) # Find all functions called in an R language expression
setRownames(object, # Set rownames of object and return object
nm = if(is.atomic(object)) seq_row(object) else NULL)
setColnames(object, nm) # Set colnames of object and return object
setDimnames(object, dn, # Set dimension names of object and return object
which = NULL)
unattrib(object) # Remove all attributes from object
setAttrib(object, a) # Replace all attributes with list of attributes 'a'
setattrib(object, a) # Same thing by reference, returning object invisibly
copyAttrib(to, from) # Copy all attributes from object 'from' to object 'to'
copyMostAttrib(to, from) # Copy most attributes from object 'from' to object 'to'
is_categorical(x) # The opposite of is.numeric
is_date(x) # Check if object is of class "Date", "POSIXlt" or "POSIXct"Arguments
- X
a matrix or data frame (some functions also support vectors and arrays although that is less common).
- x
a (atomic) vector.
- expr
an expression of type "language" e.g.
quote(x / sum(x)).- object, to, from
a suitable R object.
- a
a suitable list of attributes.
- attrn
character. Name of attribute to store labels or retrieve labels from.
- N, Ndistinct
logical. Options to display the number of observations or number of distinct non-missing values.
- value
for
whichvandalloc: a single value of any vector type. Forvlabels<-andsetLabels: a matching character vector or list of variable labels.- use.names
logical. Preserve names if
Xis a list.- cols
integer. (optional) indices of columns to apply the operation to. Note that for these small functions this needs to be integer, whereas for other functions in the package this argument is more flexible.
- class
logical. Also show the classes of variables in X in a column?
- stub
a single character stub, i.e. "log.", which by default will be pre-applied to all variables or column names in X.
- pre
logical.
FALSEwill post-applystub.- regex
logical. Match pattern anywhere in names using a regular expression and remove it with
gsub.- nm
a suitable vector of row- or column-names.
- dn
a suitable vector or list of names for dimension(s).
- which
integer. If
NULL,dnhas to be a list fully specifying the dimension names of the object. Alternatively, a vector or list of names for dimensionswhichcan be supplied. See Examples.- nam
character. A vector of object names.
- values
a matching atomic vector or list of objects.
- envir
the environment to assign into.
- ...
for
.c: Comma-separated expressions. Forall_identical / all_obj_equal: Either multiple comma-separated objects or a single list of objects in which all elements will be checked for exact / numeric equality. Forrm_stub: further arguments passed togsub.
Details
all_funs is the opposite of all.vars, to return the functions called rather than the variables in an expression. See Examples.
copyAttrib and copyMostAttrib take a shallow copy of the attribute list, i.e. they don't duplicate in memory the attributes themselves. They also, along with setAttrib, take a shallow copy of lists passed to the to argument, so that lists are not modified by reference. Atomic to arguments are however modified by reference. The function setattrib, added in v1.8.9, modifies the object by reference i.e. no shallow copies are taken.
copyMostAttrib copies all attributes except for "names", "dim" and "dimnames" (like the corresponding C-API function), and further only copies the "row.names" attribute of data frames if known to be valid. Thus it is a suitable choice if objects should be of the same type but are not of equal dimensions.
Examples
## Non-standard concatenation
.c(a, b, "c d", e == f)
#> [1] "a" "b" "c d" "e == f"
## Multiple assignment
.c(a, b) %=% list(1, 2)
.c(T, N) %=% dim(EuStockMarkets)
names(iris) %=% iris
list2env(iris) # Same thing
#> <environment: 0x13aefef18>
rm(list = c("a", "b", "T", "N", names(iris)))
## Variable labels
namlab(wlddev)
#> Variable
#> 1 country
#> 2 iso3c
#> 3 date
#> 4 year
#> 5 decade
#> 6 region
#> 7 income
#> 8 OECD
#> 9 PCGDP
#> 10 LIFEEX
#> 11 GINI
#> 12 ODA
#> 13 POP
#> Label
#> 1 Country Name
#> 2 Country Code
#> 3 Date Recorded (Fictitious)
#> 4 Year
#> 5 Decade
#> 6 Region
#> 7 Income Level
#> 8 Is OECD Member Country?
#> 9 GDP per capita (constant 2010 US$)
#> 10 Life expectancy at birth, total (years)
#> 11 Gini index (World Bank estimate)
#> 12 Net official development assistance and official aid received (constant 2018 US$)
#> 13 Population, total
namlab(wlddev, class = TRUE, N = TRUE, Ndistinct = TRUE)
#> Variable Class N Ndist
#> 1 country character 13176 216
#> 2 iso3c factor 13176 216
#> 3 date Date 13176 61
#> 4 year integer 13176 61
#> 5 decade integer 13176 7
#> 6 region factor 13176 7
#> 7 income factor 13176 4
#> 8 OECD logical 13176 2
#> 9 PCGDP numeric 9470 9470
#> 10 LIFEEX numeric 11670 10548
#> 11 GINI numeric 1744 368
#> 12 ODA numeric 8608 7832
#> 13 POP numeric 12919 12877
#> Label
#> 1 Country Name
#> 2 Country Code
#> 3 Date Recorded (Fictitious)
#> 4 Year
#> 5 Decade
#> 6 Region
#> 7 Income Level
#> 8 Is OECD Member Country?
#> 9 GDP per capita (constant 2010 US$)
#> 10 Life expectancy at birth, total (years)
#> 11 Gini index (World Bank estimate)
#> 12 Net official development assistance and official aid received (constant 2018 US$)
#> 13 Population, total
vlabels(wlddev)
#> country
#> "Country Name"
#> iso3c
#> "Country Code"
#> date
#> "Date Recorded (Fictitious)"
#> year
#> "Year"
#> decade
#> "Decade"
#> region
#> "Region"
#> income
#> "Income Level"
#> OECD
#> "Is OECD Member Country?"
#> PCGDP
#> "GDP per capita (constant 2010 US$)"
#> LIFEEX
#> "Life expectancy at birth, total (years)"
#> GINI
#> "Gini index (World Bank estimate)"
#> ODA
#> "Net official development assistance and official aid received (constant 2018 US$)"
#> POP
#> "Population, total"
vlabels(wlddev) <- vlabels(wlddev)
## Stub-renaming
log_mtc <- add_stub(log(mtcars), "log.")
head(log_mtc)
#> log.mpg log.cyl log.disp log.hp log.drat log.wt
#> Mazda RX4 3.044522 1.791759 5.075174 4.700480 1.360977 0.9631743
#> Mazda RX4 Wag 3.044522 1.791759 5.075174 4.700480 1.360977 1.0560527
#> Datsun 710 3.126761 1.386294 4.682131 4.532599 1.348073 0.8415672
#> Hornet 4 Drive 3.063391 1.791759 5.552960 4.700480 1.124930 1.1678274
#> Hornet Sportabout 2.928524 2.079442 5.886104 5.164786 1.147402 1.2354715
#> Valiant 2.895912 1.791759 5.416100 4.653960 1.015231 1.2412686
#> log.qsec log.vs log.am log.gear log.carb
#> Mazda RX4 2.800933 -Inf 0 1.386294 1.3862944
#> Mazda RX4 Wag 2.834389 -Inf 0 1.386294 1.3862944
#> Datsun 710 2.923699 0 0 1.386294 0.0000000
#> Hornet 4 Drive 2.967333 0 -Inf 1.098612 0.0000000
#> Hornet Sportabout 2.834389 -Inf -Inf 1.098612 0.6931472
#> Valiant 3.006672 0 -Inf 1.098612 0.0000000
head(rm_stub(log_mtc, "log."))
#> mpg cyl disp hp drat wt
#> Mazda RX4 3.044522 1.791759 5.075174 4.700480 1.360977 0.9631743
#> Mazda RX4 Wag 3.044522 1.791759 5.075174 4.700480 1.360977 1.0560527
#> Datsun 710 3.126761 1.386294 4.682131 4.532599 1.348073 0.8415672
#> Hornet 4 Drive 3.063391 1.791759 5.552960 4.700480 1.124930 1.1678274
#> Hornet Sportabout 2.928524 2.079442 5.886104 5.164786 1.147402 1.2354715
#> Valiant 2.895912 1.791759 5.416100 4.653960 1.015231 1.2412686
#> qsec vs am gear carb
#> Mazda RX4 2.800933 -Inf 0 1.386294 1.3862944
#> Mazda RX4 Wag 2.834389 -Inf 0 1.386294 1.3862944
#> Datsun 710 2.923699 0 0 1.386294 0.0000000
#> Hornet 4 Drive 2.967333 0 -Inf 1.098612 0.0000000
#> Hornet Sportabout 2.834389 -Inf -Inf 1.098612 0.6931472
#> Valiant 3.006672 0 -Inf 1.098612 0.0000000
rm(log_mtc)
## Setting dimension names of an object
head(setRownames(mtcars))
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#> 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
ar <- array(1:9, c(3,3,3))
setRownames(ar)
#> , , 1
#>
#> [,1] [,2] [,3]
#> 1 1 4 7
#> 2 2 5 8
#> 3 3 6 9
#>
#> , , 2
#>
#> [,1] [,2] [,3]
#> 1 1 4 7
#> 2 2 5 8
#> 3 3 6 9
#>
#> , , 3
#>
#> [,1] [,2] [,3]
#> 1 1 4 7
#> 2 2 5 8
#> 3 3 6 9
#>
setColnames(ar, c("a","b","c"))
#> , , 1
#>
#> a b c
#> [1,] 1 4 7
#> [2,] 2 5 8
#> [3,] 3 6 9
#>
#> , , 2
#>
#> a b c
#> [1,] 1 4 7
#> [2,] 2 5 8
#> [3,] 3 6 9
#>
#> , , 3
#>
#> a b c
#> [1,] 1 4 7
#> [2,] 2 5 8
#> [3,] 3 6 9
#>
setDimnames(ar, c("a","b","c"), which = 3)
#> , , a
#>
#> [,1] [,2] [,3]
#> [1,] 1 4 7
#> [2,] 2 5 8
#> [3,] 3 6 9
#>
#> , , b
#>
#> [,1] [,2] [,3]
#> [1,] 1 4 7
#> [2,] 2 5 8
#> [3,] 3 6 9
#>
#> , , c
#>
#> [,1] [,2] [,3]
#> [1,] 1 4 7
#> [2,] 2 5 8
#> [3,] 3 6 9
#>
setDimnames(ar, list(c("d","e","f"), c("a","b","c")), which = 2:3)
#> , , a
#>
#> d e f
#> [1,] 1 4 7
#> [2,] 2 5 8
#> [3,] 3 6 9
#>
#> , , b
#>
#> d e f
#> [1,] 1 4 7
#> [2,] 2 5 8
#> [3,] 3 6 9
#>
#> , , c
#>
#> d e f
#> [1,] 1 4 7
#> [2,] 2 5 8
#> [3,] 3 6 9
#>
setDimnames(ar, list(c("g","h","i"), c("d","e","f"), c("a","b","c")))
#> , , a
#>
#> d e f
#> g 1 4 7
#> h 2 5 8
#> i 3 6 9
#>
#> , , b
#>
#> d e f
#> g 1 4 7
#> h 2 5 8
#> i 3 6 9
#>
#> , , c
#>
#> d e f
#> g 1 4 7
#> h 2 5 8
#> i 3 6 9
#>
## Checking exact equality of multiple objects
all_identical(iris, iris, iris, iris)
#> [1] TRUE
l <- replicate(100, fmean(num_vars(iris), iris$Species), simplify = FALSE)
all_identical(l)
#> [1] TRUE
rm(l)
## Function names from expressions
ex = quote(sum(x) + mean(y) / z)
all.names(ex)
#> [1] "+" "sum" "x" "/" "mean" "y" "z"
all.vars(ex)
#> [1] "x" "y" "z"
all_funs(ex)
#> [1] "+" "sum" "/" "mean"
rm(ex)