quick-conversion.RdFast, 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/qDT/qTBL, 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 character or numeric (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)a vector, factor, matrix, higher-dimensional array, data frame or list. mctl and mrtl only accept matrices, as_numeric_factor and as_character_factor only accept factors, data frames or lists.
should a column capturing names or row.names be added? e.g. when converting atomic objects to data frame or data frame to data.table. Can be logical TRUE, which will add a column "row.names" in front, or can supply a name for the column e.g. "variable".
logical. FALSE (default) yields a hard / thorough object conversion: All unnecessary attributes are removed from the object yielding a plain matrix / data.frame / data.table. FALSE yields a soft / minimal object conversion: Only the attributes 'names', 'row.names', 'dim', 'dimnames' and 'levels' are modified in the conversion. Other attributes are preserved. See also class.
if a vector of classes is passed here, the converted object will be assigned these classes. If NULL is passed, the default classes are assigned: qM assigns no class, qDF a class "data.frame", and qDT a class c("data.table", "data.frame"). If keep.attr = TRUE and class = NULL and the object already inherits the default classes, further inherited classes are preserved. See Details and the Example.
logical. Should the list be named using row/column names from the matrix?
an integer or string specifying what to return. The options are:
| Int. | String | Description | ||
| 1 | "list" | returns a plain list | ||
| 2 | "data.frame" | returns a plain data.frame | ||
| 3 | "data.table" | returns a plain data.table |
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. Its 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) evaluates to TRUE.)
The default keep.attr = FALSE ensures hard conversions so that all unnecessary attributes are dropped. Furthermore in qDF/qDT/qTBL the default classes were explicitly assigned. This is to ensure that the default methods apply, 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..
#> Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive Hornet Sportabout Valiant
#> mpg 21 21 22.8 21.4 18.7 18.1
#> cyl 6 6 4.0 6.0 8.0 6.0
#> Duster 360 Merc 240D Merc 230 Merc 280 Merc 280C Merc 450SE Merc 450SL
#> mpg 14.3 24.4 22.8 19.2 17.8 16.4 17.3
#> cyl 8.0 4.0 4.0 6.0 6.0 8.0 8.0
#> Merc 450SLC Cadillac Fleetwood Lincoln Continental Chrysler Imperial
#> mpg 15.2 10.4 10.4 14.7
#> cyl 8.0 8.0 8.0 8.0
#> Fiat 128 Honda Civic Toyota Corolla Toyota Corona Dodge Challenger
#> mpg 32.4 30.4 33.9 21.5 15.5
#> cyl 4.0 4.0 4.0 4.0 8.0
#> AMC Javelin Camaro Z28 Pontiac Firebird Fiat X1-9 Porsche 914-2
#> mpg 15.2 13.3 19.2 27.3 26
#> cyl 8.0 8.0 8.0 4.0 4
#> Lotus Europa Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
#> mpg 30.4 15.8 19.7 15 21.4
#> cyl 4.0 8.0 6.0 8 4.0
#> [ reached 'max' / getOption("max.print") -- omitted 4 rows ]
head(qDF(mtcarsM, "cars")) # Adding a row.names column when converting from matrix
#> cars mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#> 2 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#> 3 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#> 4 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> 5 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
#> [ reached 'max' / getOption("max.print") -- omitted 1 rows ]
head(qDT(mtcars, "cars")) # Saving row.names when converting data frame to data.table
#> cars mpg cyl disp hp drat wt qsec vs am gear carb
#> 1: Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#> 2: Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#> 3: Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#> 4: Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> 5: Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
#> [ reached getOption("max.print") -- omitted 1 row ]
cylF <- qF(mtcars$cyl) # Factor from atomic vector
cylF
#> [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4
#> Levels: 4 6 8
# Factor to numeric conversions
identical(mtcars, as_numeric_factor(dapply(mtcars, qF)))
#> [1] TRUE