Fast Subsetting Matrix-Like Objects

fsubset returns subsets of vectors, matrices or data frames which meet conditions. It is programmed very efficiently and uses C source code from the data.table package. The methods also provide enhanced functionality compared to subset. The function ss provides an (internal generic) programmers alternative to [ that does not drop dimensions and is significantly faster than [ for data frames.

Usage

fsubset(.x, ...)
sbt(.x, ...)     # Shorthand for fsubset

# S3 method for default
fsubset(.x, subset, ...)

# S3 method for matrix
fsubset(.x, subset, ..., drop = FALSE)

# S3 method for data.frame
fsubset(.x, subset, ...)

# Methods for indexed data / compatibility with plm:

# S3 method for pseries
fsubset(.x, subset, ..., drop.index.levels = "id")

# S3 method for pdata.frame
fsubset(.x, subset, ..., drop.index.levels = "id")


# Fast subsetting (replaces `[` with drop = FALSE, programmers choice)
ss(x, i, j, check = TRUE)

Arguments

.x: object to be subsetted according to different methods.
x: a data frame / list, matrix or vector/array (only i).
subset: logical expression indicating elements or rows to keep: missing values are taken as FALSE. The default, matrix and pseries methods only support logical vectors or row-indices (or a character vector of rownames if the matrix has rownames).
...: For the matrix or data frame method: multiple comma-separated expressions indicating columns to select. Otherwise: further arguments to be passed to or from other methods.
drop: passed on to [ indexing operator. Only available for the matrix method.
i: positive or negative row-indices or a logical vector to subset the rows of x.
j: a vector of column names, positive or negative indices or a suitable logical vector to subset the columns of x. Note: Negative indices are converted to positive ones using j <- seq_along(x)[j].
check: logical. FALSE skips checks on i and j, e.g. whether indices are negative. This offers a speedup to programmers, but can terminate R if zero or negative indices are passed.
drop.index.levels: character. Either "id", "time", "all" or "none". See indexing.

Details

fsubset is a generic function, with methods supplied for vectors, matrices, and data frames (including lists). It represents an improvement over subset in terms of both speed and functionality. The function ss is an improvement of [ to subset (vectors) matrices and data frames without dropping dimensions. It is significantly faster than [.data.frame.

For ordinary vectors, subset can be integer or logical, subsetting is done in C and more efficient than [ for large vectors.

For matrices the implementation is all base-R but slightly more efficient and more versatile than subset.matrix. Thus it is possible to subset matrix rows using logical or integer vectors, or character vectors matching rownames. The drop argument is passed on to the [ method for matrices.

For both matrices and data frames, the ... argument can be used to subset columns, and is evaluated in a non-standard way. Thus it can support vectors of column names, indices or logical vectors, but also multiple comma separated column names passed without quotes, each of which may also be replaced by a sequence of columns i.e. col1:coln, and new column names may be assigned e.g. fsubset(data, col1 > 20, newname = col2, col3:col6) (see examples).

For data frames, the subset argument is also evaluated in a non-standard way. Thus next to vector of row-indices or logical vectors, it supports logical expressions of the form col2 > 5 & col2 < col3 etc. (see examples). The data frame method is implemented in C, hence it is significantly faster than subset.data.frame. If fast data frame subsetting is required but no non-standard evaluation, the function ss is slightly simpler and faster.

Factors may have empty levels after subsetting; unused levels are not automatically removed. See fdroplevels to drop all unused levels from a data frame.

Value

An object similar to .x/x containing just the selected elements (for a vector), rows and columns (for a matrix or data frame).

Note

ss offers no support for indexed data. Use fsubset with indices instead.

No replacement method fsubset<- or ss<- is offered in collapse. For efficient subset replacement (without copying) use data.table::set, which can also be used with data frames and tibbles. To search and replace certain elements without copying, and to efficiently copy elements / rows from an equally sized vector / data frame, see setv.

For subsetting columns alone, please also see selecting and replacing columns.

Note that the use of %==% can yield significant performance gains on large data.

Examples

fsubset(airquality, Temp > 90, Ozone, Temp)
#>    Ozone Temp
#> 1     NA   93
#> 2     NA   92
#> 3     97   92
#> 4     97   92
#> 5     NA   91
#> 6     NA   92
#> 7     76   97
#> 8    118   94
#> 9     84   96
#> 10    85   94
#> 11    96   91
#> 12    78   92
#> 13    73   93
#> 14    91   93
fsubset(airquality, Temp > 90, OZ = Ozone, Temp) # With renaming
#>     OZ Temp
#> 1   NA   93
#> 2   NA   92
#> 3   97   92
#> 4   97   92
#> 5   NA   91
#> 6   NA   92
#> 7   76   97
#> 8  118   94
#> 9   84   96
#> 10  85   94
#> 11  96   91
#> 12  78   92
#> 13  73   93
#> 14  91   93
fsubset(airquality, Day == 1, -Temp)
#>   Ozone Solar.R Wind Month Day
#> 1    41     190  7.4     5   1
#> 2    NA     286  8.6     6   1
#> 3   135     269  4.1     7   1
#> 4    39      83  6.9     8   1
#> 5    96     167  6.9     9   1
fsubset(airquality, Day == 1, -(Day:Temp))
#>   Ozone Solar.R Wind
#> 1    41     190  7.4
#> 2    NA     286  8.6
#> 3   135     269  4.1
#> 4    39      83  6.9
#> 5    96     167  6.9
fsubset(airquality, Day == 1, Ozone:Wind)
#>   Ozone Solar.R Wind
#> 1    41     190  7.4
#> 2    NA     286  8.6
#> 3   135     269  4.1
#> 4    39      83  6.9
#> 5    96     167  6.9
fsubset(airquality, Day == 1 & !is.na(Ozone), Ozone:Wind, Month)
#>   Ozone Solar.R Wind Month
#> 1    41     190  7.4     5
#> 2   135     269  4.1     7
#> 3    39      83  6.9     8
#> 4    96     167  6.9     9
fsubset(airquality, Day %==% 1, -Temp)  # Faster for big data, as %==% directly returns indices
#>   Ozone Solar.R Wind Month Day
#> 1    41     190  7.4     5   1
#> 2    NA     286  8.6     6   1
#> 3   135     269  4.1     7   1
#> 4    39      83  6.9     8   1
#> 5    96     167  6.9     9   1

ss(airquality, 1:10, 2:3)         # Significantly faster than airquality[1:10, 2:3]
#>    Solar.R Wind
#> 1      190  7.4
#> 2      118  8.0
#> 3      149 12.6
#> 4      313 11.5
#> 5       NA 14.3
#> 6       NA 14.9
#> 7      299  8.6
#> 8       99 13.8
#> 9       19 20.1
#> 10     194  8.6
fsubset(airquality, 1:10, 2:3)    # This is possible but not advised
#>    Solar.R Wind
#> 1      190  7.4
#> 2      118  8.0
#> 3      149 12.6
#> 4      313 11.5
#> 5       NA 14.3
#> 6       NA 14.9
#> 7      299  8.6
#> 8       99 13.8
#> 9       19 20.1
#> 10     194  8.6