Experimental functions for operations with default dataset
Workflow for these functions is rather simple. You should set up default
data.frame with default_dataset and then operate with it without any
reference to your data.frame. There are two kinds of operations. The first kind
modify default dataset, the second kind will be evaluated in the context of
the default dataset but doesn't modify it. It is not recommended to use one
of these functions in the scope of another of these functions. By now their
performance is not so high, especially .do_if/.modify_if can be
very slow.
.compute(expr) .do_if(cond, expr) .modify_if(cond, expr) .modify(expr) .calculate(expr, use_labels = FALSE) .calc(expr, use_labels = FALSE) .val_lab(...) .var_lab(...) .recode(x, ...) .fre(...) .cro(...) .cro_cases(...) .cro_cpct(...) .cro_rpct(...) .cro_tpct(...) .cro_mean(...) .cro_sum(...) .cro_median(...) .cro_mean_sd_n(...) .cro_fun(...) .cro_fun_df(...)
expr |
set of expressions in curly brackets which will be evaluated in the context of default dataset |
cond |
logical vector/expression |
use_labels |
logical. Experimental feature. If it equals to |
... |
further arguments |
x |
vector/data.frame - variable names in the scope of default dataset |
Functions which modify default dataset:
.modify Add and modify variables inside default data.frame. See
modify.
.compute Shortcut for .modify. Name is inspired by
SPSS COMPUTE operator. See modify.
.modify_if Add and modify variables inside subset of default
data.frame. See modify_if.
.do_if Shortcut for .modify_if. Name is inspired by
SPSS DO IF operator. See modify_if.
.where Leave subset of default data.frame which meet
condition. See where, subset.
.recode Change, rearrange or consolidate the values of an existing
variable inside default data.frame. See recode.
Other functions:
.var_lab Return variable label from default dataset. See
var_lab.
.val_lab Return value labels from default dataset. See
val_lab.
.fre Simple frequencies of variable in the default
data.frame. See fre.
.cro/.cro_cpct/.cro_rpct/.cro_tpct Simple
crosstabulations of variable in the default data.frame. See cro.
.cro_mean/.cro_sum/.cro_median/.cro_fun/.cro_fun_df
Simple crosstabulations of variable in the default data.frame. See
cro_fun.
.calculate Evaluate arbitrary expression in the context of
data.frame. See calculate.
data(mtcars)
default_dataset(mtcars) # set mtcars as default dataset
# calculate new variables
.compute({
mpg_by_am = ave(mpg, am, FUN = mean)
hi_low_mpg = ifs(mpg<mean(mpg) ~ 0, TRUE ~ 1)
})
# set labels
.apply_labels(
mpg = "Miles/(US) gallon",
cyl = "Number of cylinders",
disp = "Displacement (cu.in.)",
hp = "Gross horsepower",
mpg_by_am = "Average mpg for transimission type",
hi_low_mpg = "Miles per gallon",
hi_low_mpg = num_lab("
0 Low
1 High
"),
vs = "Engine",
vs = num_lab("
0 V-engine
1 Straight engine
"),
am = "Transmission",
am = num_lab("
0 Automatic
1 Manual
")
)
# calculate frequencies
.fre(hi_low_mpg)
.cro(cyl, hi_low_mpg)
.cro_mean(data.frame(mpg, disp, hp), vs)
# disable default dataset
default_dataset(NULL)
# Example of .recode
data(iris)
default_dataset(iris) # set iris as default dataset
.recode(Sepal.Length, lo %thru% median(Sepal.Length) ~ "small", other ~ "large")
.fre(Sepal.Length)
# example of .do_if
.do_if(Species == "setosa",{
Petal.Length = NA
Petal.Width = NA
})
.cro_mean(data.frame(Petal.Length, Petal.Width), Species)
# disable default dataset
default_dataset(NULL)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.