| quick-conversion {collapse} | R Documentation |
Fast, flexible and precise conversion of common data objects, without method dispatch and extensive checks:
qDF, qDT and qTBL convert vectors, matrices, higher-dimensional arrays and suitable lists to data frame, data.table and tibble, respectively.
qM converts vectors, higher-dimensional arrays, data frames and suitable lists to matrix.
mctl and mrtl column- or row-wise convert a matrix to list, data frame or data.table. They are used internally by qDF and qDT, dapply, BY, etc...
qF converts atomic vectors to factor (documented on a separate page).
as_numeric_factor and as_character_factor convert factors, or all factor columns in a data frame / list, to numeric or character (by converting the levels).
# Converting between matrices, data frames / tables / tibbles
qDF(X, row.names.col = FALSE, keep.attr = FALSE, class = "data.frame")
qDT(X, row.names.col = FALSE, keep.attr = FALSE, class = c("data.table", "data.frame"))
qTBL(X, row.names.col = FALSE, keep.attr = FALSE, class = c("tbl_df","tbl","data.frame"))
qM(X, keep.attr = FALSE, class = NULL)
# Programmer functions: matrix rows or columns to list / DF / DT - fully in C++
mctl(X, names = FALSE, return = "list")
mrtl(X, names = FALSE, return = "list")
# Converting factors or factor columns
as_numeric_factor(X, keep.attr = TRUE)
as_character_factor(X, keep.attr = TRUE)
X |
a vector, factor, matrix, higher-dimensional array, data frame or list. | |||||||||||||||||||||
row.names.col |
should a column capturing names or row.names be added? i.e. when converting atomic objects to data frame or data frame to data.table. Can be logical | |||||||||||||||||||||
keep.attr |
logical. | |||||||||||||||||||||
class |
if a vector of classes is passed here, the converted object will be assigned these classes. If | |||||||||||||||||||||
names |
logical. Should the list be named using row/column names from the matrix? | |||||||||||||||||||||
return |
an integer or string specifying what to return. The options are:
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Object conversions using these functions are maximally efficient and involve 3 consecutive steps: (1) Converting the storage mode / dimensions / data of the object, (2) converting / modifying the attributes and (3) modifying the class of the object:
(1) is determined by the choice of function and the optional row.names.col argument to qDF and qDT. Higher-dimensional arrays are converted by expanding the second dimension (adding columns, same as as.matrix, as.data.frame, as.data.table).
(2) is determined by the keep.attr argument: keep.attr = TRUE seeks to preserve the attributes of the object. It's effect is like copying attributes(converted) <- attributes(original), and then modifying the "dim", "dimnames", "names", "row.names" and "levels" attributes as necessitated by the conversion task. keep.attr = FALSE only converts / assigns / removes these attributes and drops all others.
(3) is determined by the class argument: Setting class = "myclass" will yield a converted object of class "myclass", with any other / prior classes being removed by this replacement. Setting class = NULL does NOT mean that a class NULL is assigned (which would remove the class attribute), but rather that the default classes are assigned: qM assigns no class, qDF a class "data.frame", and qDT a class c("data.table", "data.frame"). At this point there is an interaction with keep.attr: If keep.attr = TRUE and class = NULL and the object converted already inherits the respective default classes, then any other inherited classes will also be preserved (with qM(x, keep.attr = TRUE, class = NULL) any class will be preserved if is.matrix(x) evaluated to TRUE.)
The default keep.attr = FALSE ensures hard conversions so that all unnecessary attributes are dropped. Furthermore in qDF and qDT the default classes were explicitly assigned, thus any other classes (like 'tbl_df', 'tbl', 'pdata.frame', 'sf', 'tsibble' etc.) will be removed when these objects are passed, regardless of the keep.attr setting. This is to ensure that the default methods for 'data.frame' and 'data.table' can be assumed to work, even if the user chooses to preserve further attributes. For qM a more lenient default setup was chosen to enable the full preservation of time series matrices with keep.attr = TRUE. If the user wants to keep attributes attached to a matrix but make sure that all default methods work properly, either one of qM(x, keep.attr = TRUE, class = "matrix") or unclass(qM(x, keep.attr = TRUE)) should be employed.
qDF - returns a data.frame
qDT - returns a data.table
qTBL - returns a tibble
qM - returns a matrix
mctl, mrtl - return a list, data frame or data.table
qF - returns a factor
as_numeric_factor - returns X with factors converted to numeric variables
as_character_factor - returns X with factors converted to character variables
## Basic Examples mtcarsM <- qM(mtcars) # Matrix from data.frame mtcarsDT <- qDT(mtcarsM) # data.table from matrix columns mtcarsTBL <- qTBL(mtcarsM) # tibble from matrix columns head(mrtl(mtcarsM, TRUE, "data.frame")) # data.frame from matrix rows, etc.. head(qDF(mtcarsM, "cars")) # Adding a row.names column when converting from matrix head(qDT(mtcars, "cars")) # Saving row.names when converting data frame to data.table cylF <- qF(mtcars$cyl) # Factor from atomic vector cylF # Factor to numeric conversions identical(mtcars, as_numeric_factor(dapply(mtcars, qF)))