mirror of
https://github.com/agdamsbo/FreesearchR.git
synced 2025-09-12 09:59:39 +02:00
20 KiB
20 KiB
getfun works
Code
getfun("stats::lm")
Output
function (formula, data, subset, weights, na.action, method = "qr",
model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE,
contrasts = NULL, offset, ...)
{
ret.x <- x
ret.y <- y
cl <- match.call()
mf <- match.call(expand.dots = FALSE)
m <- match(c("formula", "data", "subset", "weights", "na.action",
"offset"), names(mf), 0L)
mf <- mf[c(1L, m)]
mf$drop.unused.levels <- TRUE
mf[[1L]] <- quote(stats::model.frame)
mf <- eval(mf, parent.frame())
if (method == "model.frame")
return(mf)
else if (method != "qr")
warning(gettextf("method = '%s' is not supported. Using 'qr'",
method), domain = NA)
mt <- attr(mf, "terms")
y <- model.response(mf, "numeric")
w <- as.vector(model.weights(mf))
if (!is.null(w) && !is.numeric(w))
stop("'weights' must be a numeric vector")
offset <- model.offset(mf)
mlm <- is.matrix(y)
ny <- if (mlm)
nrow(y)
else length(y)
if (!is.null(offset)) {
if (!mlm)
offset <- as.vector(offset)
if (NROW(offset) != ny)
stop(gettextf("number of offsets is %d, should equal %d (number of observations)",
NROW(offset), ny), domain = NA)
}
if (is.empty.model(mt)) {
x <- NULL
z <- list(coefficients = if (mlm) matrix(NA_real_, 0,
ncol(y)) else numeric(), residuals = y, fitted.values = 0 *
y, weights = w, rank = 0L, df.residual = if (!is.null(w)) sum(w !=
0) else ny)
if (!is.null(offset)) {
z$fitted.values <- offset
z$residuals <- y - offset
}
}
else {
x <- model.matrix(mt, mf, contrasts)
z <- if (is.null(w))
lm.fit(x, y, offset = offset, singular.ok = singular.ok,
...)
else lm.wfit(x, y, w, offset = offset, singular.ok = singular.ok,
...)
}
class(z) <- c(if (mlm) "mlm", "lm")
z$na.action <- attr(mf, "na.action")
z$offset <- offset
z$contrasts <- attr(x, "contrasts")
z$xlevels <- .getXlevels(mt, mf)
z$call <- cl
z$terms <- mt
if (model)
z$model <- mf
if (ret.x)
z$x <- x
if (ret.y)
z$y <- y
if (!qr)
z$qr <- NULL
z
}
<bytecode: 0x12c7f2dd8>
<environment: namespace:stats>
argsstring2list works
Code
argsstring2list("A=1:5,b=2:4")
Output
$A
[1] 1 2 3 4 5
$b
[1] 2 3 4
factorize works
Code
factorize(mtcars, names(mtcars))
Output
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4
Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1
Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3
Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4
Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.9 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2
AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2
Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1
Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2
Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8
Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2
default_parsing works
Code
default_parsing(mtcars)
Output
# A tibble: 32 x 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <fct> <fct>
1 21 6 160 110 3.9 2.62 16.5 FALSE TRUE 4 4
2 21 6 160 110 3.9 2.88 17.0 FALSE TRUE 4 4
3 22.8 4 108 93 3.85 2.32 18.6 TRUE TRUE 4 1
4 21.4 6 258 110 3.08 3.22 19.4 TRUE FALSE 3 1
5 18.7 8 360 175 3.15 3.44 17.0 FALSE FALSE 3 2
6 18.1 6 225 105 2.76 3.46 20.2 TRUE FALSE 3 1
7 14.3 8 360 245 3.21 3.57 15.8 FALSE FALSE 3 4
8 24.4 4 147. 62 3.69 3.19 20 TRUE FALSE 4 2
9 22.8 4 141. 95 3.92 3.15 22.9 TRUE FALSE 4 2
10 19.2 6 168. 123 3.92 3.44 18.3 TRUE FALSE 4 4
# i 22 more rows
remove_empty_attr works
Code
remove_empty_attr(ds)
Output
$mpg
[1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
[31] 15.0 21.4
$cyl
[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
$disp
[1] 160.0 160.0 108.0 258.0 360.0 225.0 360.0 146.7 140.8 167.6 167.6 275.8
[13] 275.8 275.8 472.0 460.0 440.0 78.7 75.7 71.1 120.1 318.0 304.0 350.0
[25] 400.0 79.0 120.3 95.1 351.0 145.0 301.0 121.0
$hp
[1] 110 110 93 110 175 105 245 62 95 123 123 180 180 180 205 215 230 66 52
[20] 65 97 150 150 245 175 66 91 113 264 175 335 109
$drat
[1] 3.90 3.90 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 3.92 3.07 3.07 3.07 2.93
[16] 3.00 3.23 4.08 4.93 4.22 3.70 2.76 3.15 3.73 3.08 4.08 4.43 3.77 4.22 3.62
[31] 3.54 4.11
$wt
[1] 2.620 2.875 2.320 3.215 3.440 3.460 3.570 3.190 3.150 3.440 3.440 4.070
[13] 3.730 3.780 5.250 5.424 5.345 2.200 1.615 1.835 2.465 3.520 3.435 3.840
[25] 3.845 1.935 2.140 1.513 3.170 2.770 3.570 2.780
$qsec
[1] 16.46 17.02 18.61 19.44 17.02 20.22 15.84 20.00 22.90 18.30 18.90 17.40
[13] 17.60 18.00 17.98 17.82 17.42 19.47 18.52 19.90 20.01 16.87 17.30 15.41
[25] 17.05 18.90 16.70 16.90 14.50 15.50 14.60 18.60
$vs
[1] 0 0 1 1 0 1 0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0 0 0 1
$am
[1] 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1
$gear
[1] 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4
$carb
[1] 4 4 1 1 2 1 4 2 2 4 4 3 3 3 4 4 4 1 2 1 1 2 2 4 2 1 2 2 4 6 8 2
Code
remove_empty_attr(dplyr::bind_cols(ds))
Output
# A tibble: 32 x 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
# i 22 more rows
Code
remove_empty_attr(ds[[1]])
Output
[1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
[31] 15.0 21.4
remove_empty_cols works
Code
remove_empty_cols(data.frame(a = 1:10, b = NA, c = c(2, NA)), cutoff = 0.5)
Output
a c
1 1 2
2 2 NA
3 3 2
4 4 NA
5 5 2
6 6 NA
7 7 2
8 8 NA
9 9 2
10 10 NA
append_list works
Code
append_list(data.frame(letters[1:20], 1:20), ls_d, "letters")
Output
$letters
letters.1.20. X1.20
1 a 1
2 b 2
3 c 3
4 d 4
5 e 5
6 f 6
7 g 7
8 h 8
9 i 9
10 j 10
11 k 11
12 l 12
13 m 13
14 n 14
15 o 15
16 p 16
17 q 17
18 r 18
19 s 19
20 t 20
Code
append_list(letters[1:20], ls_d, "letters")
Output
$letters
[1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
[20] "t"
missing_fraction works
Code
missing_fraction(c(NA, 1:10, rep(NA, 3)))
Output
[1] 0.2857143
data_description works
Code
data_description(data.frame(sample(1:8, 20, TRUE), sample(c(1:8, NA), 20, TRUE)),
data_text = "This data")
Output
[1] "This data has 20 observations and 2 variables, with 16 (80%) complete cases."
Data type filter works
Code
data_type_filter(default_parsing(mtcars), type = c("categorical", "continuous"))
Output
# A tibble: 32 x 9
mpg cyl disp hp drat wt qsec gear carb
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <fct>
1 21 6 160 110 3.9 2.62 16.5 4 4
2 21 6 160 110 3.9 2.88 17.0 4 4
3 22.8 4 108 93 3.85 2.32 18.6 4 1
4 21.4 6 258 110 3.08 3.22 19.4 3 1
5 18.7 8 360 175 3.15 3.44 17.0 3 2
6 18.1 6 225 105 2.76 3.46 20.2 3 1
7 14.3 8 360 245 3.21 3.57 15.8 3 4
8 24.4 4 147. 62 3.69 3.19 20 4 2
9 22.8 4 141. 95 3.92 3.15 22.9 4 2
10 19.2 6 168. 123 3.92 3.44 18.3 4 4
# i 22 more rows
Code
data_type_filter(default_parsing(mtcars), type = NULL)
Output
# A tibble: 32 x 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <fct> <fct>
1 21 6 160 110 3.9 2.62 16.5 FALSE TRUE 4 4
2 21 6 160 110 3.9 2.88 17.0 FALSE TRUE 4 4
3 22.8 4 108 93 3.85 2.32 18.6 TRUE TRUE 4 1
4 21.4 6 258 110 3.08 3.22 19.4 TRUE FALSE 3 1
5 18.7 8 360 175 3.15 3.44 17.0 FALSE FALSE 3 2
6 18.1 6 225 105 2.76 3.46 20.2 TRUE FALSE 3 1
7 14.3 8 360 245 3.21 3.57 15.8 FALSE FALSE 3 4
8 24.4 4 147. 62 3.69 3.19 20 TRUE FALSE 4 2
9 22.8 4 141. 95 3.92 3.15 22.9 TRUE FALSE 4 2
10 19.2 6 168. 123 3.92 3.44 18.3 TRUE FALSE 4 4
# i 22 more rows
sort_by works
Code
sort_by(c("Multivariable", "Univariable"), c("Univariable", "Minimal",
"Multivariable"))
Output
[1] "Univariable" NA "Multivariable"
if_not_missing works
Code
if_not_missing(NULL, "new")
Output
[1] "new"
Code
if_not_missing(c(2, "a", NA))
Output
[1] "2" "a"
Code
if_not_missing("See")
Output
[1] "See"
merge_expression, expression_string and pipe_string works
Code
merge_expression(list(rlang::call2(.fn = "select", !!!list(c("cyl", "disp")),
.ns = "dplyr"), rlang::call2(.fn = "default_parsing", .ns = "FreesearchR")))
Output
dplyr::select(c("cyl", "disp")) %>% FreesearchR::default_parsing()
Code
expression_string(pipe_string(lapply(list("mtcars", rlang::call2(.fn = "select",
!!!list(c("cyl", "disp")), .ns = "dplyr"), rlang::call2(.fn = "default_parsing",
.ns = "FreesearchR")), expression_string)), "data<-")
Output
[1] "data<-mtcars|>\ndplyr::select(c('cyl','disp'))|>\nFreesearchR::default_parsing()"
Code
expression_string(merge_expression(list(as.symbol(paste0("mtcars$", "mpg")),
rlang::call2(.fn = "select", !!!list(c("cyl", "disp")), .ns = "dplyr"), rlang::call2(
.fn = "default_parsing", .ns = "FreesearchR"))))
Output
[1] "mtcars$mpg|>\ndplyr::select(c('cyl','disp'))|>\nFreesearchR::default_parsing()"
remove_nested_list works
Code
remove_nested_list(dplyr::tibble(a = 1:10, b = rep(list("a"), 10)))
Output
# A tibble: 10 x 1
a
<int>
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
Code
remove_nested_list(as.data.frame(dplyr::tibble(a = 1:10, b = rep(list(c("a",
"b")), 10))))
Output
a
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
set_column_label works
Code
set_column_label(set_column_label(set_column_label(mtcars, ls), ls2), ls3)
Output
# A tibble: 32 x 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
# i 22 more rows
Code
expression_string(rlang::expr(FreesearchR::set_column_label(label = !!ls3)))
Output
[1] "FreesearchR::set_column_label(label=c(mpg='',cyl='',disp='',hp='Horses',drat='',wt='',qsec='',vs='',am='',gear='',carb=''))"
append_column works
Code
append_column(dplyr::mutate(mtcars, mpg_cut = mpg), mtcars$mpg, "mpg_cutter")
Output
mpg cyl disp hp drat wt qsec vs am gear carb mpg_cut
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 21.0
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 21.0
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 22.8
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 21.4
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 18.7
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 18.1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 14.3
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 24.4
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 22.8
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 19.2
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 17.8
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 16.4
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 17.3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 15.2
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 10.4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 10.4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 14.7
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 32.4
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 30.4
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 33.9
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 21.5
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 15.5
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 15.2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 13.3
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 19.2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 27.3
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 26.0
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 30.4
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 15.8
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 19.7
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 15.0
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 21.4
mpg_cutter
Mazda RX4 21.0
Mazda RX4 Wag 21.0
Datsun 710 22.8
Hornet 4 Drive 21.4
Hornet Sportabout 18.7
Valiant 18.1
Duster 360 14.3
Merc 240D 24.4
Merc 230 22.8
Merc 280 19.2
Merc 280C 17.8
Merc 450SE 16.4
Merc 450SL 17.3
Merc 450SLC 15.2
Cadillac Fleetwood 10.4
Lincoln Continental 10.4
Chrysler Imperial 14.7
Fiat 128 32.4
Honda Civic 30.4
Toyota Corolla 33.9
Toyota Corona 21.5
Dodge Challenger 15.5
AMC Javelin 15.2
Camaro Z28 13.3
Pontiac Firebird 19.2
Fiat X1-9 27.3
Porsche 914-2 26.0
Lotus Europa 30.4
Ford Pantera L 15.8
Ferrari Dino 19.7
Maserati Bora 15.0
Volvo 142E 21.4