| Type: | Package |
| Title: | Common Functionality for the 'dynverse' Packages |
| Version: | 1.0.11 |
| Description: | Provides common functionality for the 'dynverse' packages. 'dynverse' is created to support the development, execution, and benchmarking of trajectory inference methods. For more information, check out https://dynverse.org. |
| License: | MIT + file LICENSE |
| URL: | https://github.com/dynverse/dynutils |
| BugReports: | https://github.com/dynverse/dynutils/issues |
| RoxygenNote: | 7.2.1 |
| Depends: | R (≥ 3.0.0) |
| Imports: | assertthat, crayon, desc, dplyr, magrittr, Matrix, methods, proxyC (≥ 0.3.3), purrr, Rcpp, remotes, stringr, tibble |
| Suggests: | covr, ggplot2, hdf5r (≥ 1.3.4), knitr, rmarkdown, testthat |
| LinkingTo: | Rcpp |
| Encoding: | UTF-8 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | yes |
| Packaged: | 2022-10-11 10:32:40 UTC; rcannood |
| Author: | Robrecht Cannoodt 
[aut, cre] (github: rcannood),
Wouter Saelens
[aut] (github: zouter) |
| Maintainer: | Robrecht Cannoodt <rcannood@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2022-10-11 11:02:32 UTC |
Add class to object whilst keeping the old classes
Description
Add class to object whilst keeping the old classes
Usage
add_class(x, class)
Arguments
x |
a R object |
class |
A character vector naming classes |
Examples
library(purrr)
l <- list(important_number = 42) %>% add_class("my_list")
Check whether a vector are all elements of another vector
Description
Check whether a vector are all elements of another vector
Usage
all_in(x, table)
x %all_in% table
Arguments
x |
The values to be matched. |
table |
The values to be matched against. |
Examples
## Not run:
library(assertthat)
assert_that(c(1, 2) %all_in% c(0, 1, 2, 3, 4))
# TRUE
assert_that("a" %all_in% letters)
# TRUE
assert_that("A" %all_in% letters)
# Error: "A" is missing 1 element from letters: "A"
assert_that(1:10 %all_in% letters)
# Error: 1:10 is missing 10 elements from letters: 1L, 2L, 3L, ...
## End(Not run)
Apply a nubnax scale.
Description
Anything outside the range of [0, 1] will be set to 0 or 1.
Usage
apply_minmax_scale(x, addend, multiplier)
Arguments
x |
A numeric vector, matrix or data frame. |
addend |
A minimum vector for each column |
multiplier |
A scaling vector for each column |
Value
The scaled matrix or verctor. The numeric centering and scalings used are returned as attributes.
Apply a quantile scale.
Description
Anything outside the range of [0, 1] will be set to 0 or 1.
Usage
apply_quantile_scale(x, addend, multiplier)
Arguments
x |
A numeric vector, matrix or data frame. |
addend |
A minimum vector for each column |
multiplier |
A scaling vector for each column |
Value
The scaled matrix or vector. The numeric centering and scalings used are returned as attributes.
Apply a uniform scale
Description
Apply a uniform scale
Usage
apply_uniform_scale(x, addend, multiplier)
Arguments
x |
A numeric vector, matrix or data frame. |
addend |
A centering vector for each column |
multiplier |
A scaling vector for each column |
Value
The centered, scaled matrix. The numeric centering and scalings used are returned as attributes.
Calculate (column-wise) distances/similarity between two matrices
Description
These matrices can be dense or sparse.
Usage
calculate_distance(
x,
y = NULL,
method = c("pearson", "spearman", "cosine", "euclidean", "chisquared", "hamming",
"kullback", "manhattan", "maximum", "canberra", "minkowski"),
margin = 1,
diag = FALSE,
drop0 = FALSE
)
list_distance_methods()
calculate_similarity(
x,
y = NULL,
margin = 1,
method = c("spearman", "pearson", "cosine"),
diag = FALSE,
drop0 = FALSE
)
list_similarity_methods()
Arguments
x |
A numeric matrix, dense or sparse. |
y |
(Optional) a numeric matrix, dense or sparse, with |
method |
Which distance method to use. Options are: |
margin |
integer indicating margin of similarity/distance computation. 1 indicates rows or 2 indicates columns. |
diag |
if |
drop0 |
if |
Examples
## Generate two matrices with 50 and 100 samples
library(Matrix)
x <- Matrix::rsparsematrix(50, 1000, .01)
y <- Matrix::rsparsematrix(100, 1000, .01)
dist_euclidean <- calculate_distance(x, y, method = "euclidean")
dist_manhattan <- calculate_distance(x, y, method = "manhattan")
dist_spearman <- calculate_distance(x, y, method = "spearman")
dist_pearson <- calculate_distance(x, y, method = "pearson")
dist_angular <- calculate_distance(x, y, method = "cosine")
Calculate a (weighted) mean between vectors or a list of vectors
Description
This function supports the arithmetic, geometric and harmonic mean.
Usage
calculate_mean(..., method, weights = NULL)
calculate_harmonic_mean(..., weights = NULL)
calculate_geometric_mean(..., weights = NULL)
calculate_arithmetic_mean(..., weights = NULL)
Arguments
... |
Can be:
|
method |
The aggregation function. Must be one of |
weights |
Weights with the same length as |
Examples
calculate_arithmetic_mean(0.1, 0.5, 0.9)
calculate_geometric_mean(0.1, 0.5, 0.9)
calculate_harmonic_mean(0.1, 0.5, 0.9)
calculate_mean(.1, .5, .9, method = "harmonic")
# example with multiple vectors
calculate_arithmetic_mean(c(0.1, 0.9), c(0.2, 1))
# example with a list of vectors
vectors <- list(c(0.1, 0.2), c(0.4, 0.5))
calculate_geometric_mean(vectors)
# example of weighted means
calculate_geometric_mean(c(0.1, 10), c(0.9, 20), c(0.5, 2), weights = c(1, 2, 5))
Check which packages are installed
Description
Check which packages are installed
Usage
check_packages(...)
Arguments
... |
A set of package names |
Examples
check_packages("SCORPIUS", "dynutils")
check_packages(c("princurve", "mlr", "tidyverse"))
Common functionality for the dynverse packages
Description
Provides common functionality for the dynverse packages. dynverse is created to support the development, execution, and benchmarking of trajectory inference methods. For more information, check out dynverse.org.
Manipulation of lists
-
add_class(): Add a class to an object -
extend_with(): Extend list with more data
Calculations
-
calculate_distance(): Calculate pairwise distances between two (sparse) matrices -
calculate_similarity(): Calculate pairwise similarities between two (sparse) matrices -
calculate_mean(): Calculate a (weighted) mean between vectors or a list of vectors; supports the arithmetic, geometric and harmonic mean -
project_to_segments(): Project a set of points to to set of segments
Manipulation of matrices
-
expand_matrix(): Add rows and columns to a matrix
Scaling of matrices and vectors
-
scale_uniform(): Rescale data to have a certain center and max range -
scale_minmax(): Rescale data to a [0, 1] range -
scale_quantile(): Cut off outer quantiles and rescale to a [0, 1] range
Manipulation of functions
-
inherit_default_params(): Have one function inherit the default parameters from other functions
Manipulation of packages
-
check_packages(): Easily checking whether certain packages are installed -
install_packages(): Install packages taking into account the remotes of another
Manipulation of vectors
-
random_time_string(): Generates a string very likely to be unique
Tibble helpers
-
list_as_tibble(): Convert a list of lists to a tibble whilst retaining class information -
tibble_as_list(): Convert a tibble back to a list of lists whilst retaining class information -
extract_row_to_list(): Extracts one row from a tibble and converts it to a list -
mapdf(): Apply a function to each row of a data frame
File helpers
-
safe_tempdir(): Create an empty temporary directory and return its path
Assertion helpers
-
%all_in%(): Check whether a vector are all elements of another vector -
%has_names%(): Check whether an object has certain names -
is_single_numeric(): Check whether a value is a single numeric -
is_bounded(): Check whether a value within a certain interval
Package helpers
-
recent_news(): Print the most recent news (assumes NEWS.md file as specified bynews())
These functions will be removed soon
Description
Use calculate_distance() instead.
Usage
euclidean_distance(x, y = NULL)
correlation_distance(x, y = NULL)
Arguments
x |
A numeric matrix, dense or sparse. |
y |
(Optional) a numeric matrix, dense or sparse, with |
Expand a matrix with given rownames and colnames
Description
Expand a matrix with given rownames and colnames
Usage
expand_matrix(mat, rownames = NULL, colnames = NULL, fill = 0)
Arguments
mat |
The matrix to expand |
rownames |
The desired rownames |
colnames |
The desired colnames |
fill |
With what to fill missing data |
Examples
x <- matrix(runif(12), ncol = 4, dimnames = list(c("a", "c", "d"), c("D", "F", "H", "I")))
expand_matrix(x, letters[1:5], LETTERS[1:10], fill = 0)
Extend an object
Description
Extend an object
Usage
extend_with(object, .class_name, ...)
Arguments
object |
A list |
.class_name |
A class name to add |
... |
Extra information in the list |
Examples
library(purrr)
l <- list(important_number = 42) %>% add_class("my_list")
l %>% extend_with(
.class_name = "improved_list",
url = "https://github.com/dynverse/dynverse"
)
l
Extracts one row from a tibble and converts it to a list
Description
Extracts one row from a tibble and converts it to a list
Usage
extract_row_to_list(tib, row_id)
Arguments
tib |
the tibble |
row_id |
the index of the row to be selected, or alternatively an expression which will be evaluated to such an index |
Value
the corresponding row from the tibble as a list
See Also
list_as_tibble tibble_as_list mapdf
Examples
library(tibble)
tib <- tibble(
a = c(1, 2),
b = list(log10, sqrt),
c = c("parrot", "quest"),
.object_class = list(c("myobject", "list"), c("yourobject", "list"))
)
extract_row_to_list(tib, 2)
extract_row_to_list(tib, which(a == 1))
Check whether an object has certain names
Description
Check whether an object has certain names
Usage
has_names(x, which)
x %has_names% which
Arguments
x |
object to test |
which |
name |
Examples
## Not run:
library(assertthat)
li <- list(a = 1, b = 2)
assert_that(li %has_names% "a")
# TRUE
assert_that(li %has_names% "c")
# Error: li is missing 1 name from "c": "c"
assert_that(li %has_names% letters)
# Error: li is missing 24 names from letters: "c", "d", "e", ...
## End(Not run)
Inherit default parameters from a list of super functions
Description
Inherit default parameters from a list of super functions
Usage
inherit_default_params(super_functions, fun)
Arguments
super_functions |
A list of super functions of which 'fun“ needs to inherit the default parameters |
fun |
The function whose default parameters need to be overridden |
Value
Function fun, but with the default parameters of the super_functions
Examples
fun1 <- function(a = 10, b = 7) runif(a, -b, b)
fun2 <- function(c = 9) 2^c
fun3 <- inherit_default_params(
super = list(fun1, fun2),
fun = function(a, b, c) {
list(x = fun1(a, b), y = fun2(c))
}
)
fun3
Check package availability
Description
If the session is interactive, prompt the user whether to install the packages.
Usage
install_packages(..., try_install = interactive())
Arguments
... |
The names of the packages to be checked |
try_install |
Whether running interactivly, which will prompt the user before installation |
Examples
## Not run:
install_packages("SCORPIUS")
## End(Not run)
Check whether a value within a certain interval
Description
Check whether a value within a certain interval
Usage
is_bounded(
x,
lower_bound = -Inf,
lower_closed = FALSE,
upper_bound = Inf,
upper_closed = FALSE
)
Arguments
x |
A value to be tested |
lower_bound |
The lower bound |
lower_closed |
Whether the lower bound is closed |
upper_bound |
The upper bound |
upper_closed |
Whether the upper bound is closed |
Examples
## Not run:
library(assertthat)
assert_that(is_bounded(10))
# TRUE
assert_that(is_bounded(10:30))
# TRUE
assert_that(is_bounded(Inf))
# Error: Inf is not bounded by (-Inf,Inf)
assert_that(is_bounded(10, lower_bound = 20))
# Error: 10 is not bounded by (20,Inf)
assert_that(is_bounded(
10,
lower_bound = 20,
lower_closed = TRUE,
upper_bound = 30,
upper_closed = FALSE
))
# Error: 10 is not bounded by [20,30)
## End(Not run)
Check whether a value is a single numeric
Description
Check whether a value is a single numeric
Usage
is_single_numeric(x)
Arguments
x |
A value to be tested |
Examples
## Not run:
library(assertthat)
assert_that(is_single_numeric(1))
# TRUE
assert_that(is_single_numeric(Inf))
# TRUE
assert_that(is_single_numeric(1.6))
# TRUE
assert_that(is_single_numeric(NA))
# Error: NA is not a single numeric value
assert_that(is_single_numeric(1:6))
# Error: 1:6 is not a single numeric value
assert_that(is_single_numeric("pie"))
# Error: "pie" is not a single numeric value
## End(Not run)
Check if an object is a sparse matrix
Description
Check if an object is a sparse matrix
Usage
is_sparse(x)
Arguments
x |
An object to test |
Examples
library(Matrix)
is_sparse(matrix(1:10)) # FALSE
is_sparse(Matrix::rsparsematrix(100, 200, .01)) # TRUE
Convert a list of lists to a tibble
Description
Convert a list of lists to a tibble
Usage
list_as_tibble(list_of_rows)
Arguments
list_of_rows |
The list to be converted to a tibble |
Value
A tibble with the same number of rows as there were elements in list_of_rows
See Also
tibble_as_list extract_row_to_list mapdf
Examples
library(purrr)
li <- list(
list(a = 1, b = log10, c = "parrot") %>% add_class("myobject"),
list(a = 2, b = sqrt, c = "quest") %>% add_class("yourobject")
)
tib <- list_as_tibble(li)
tib
Apply a function to each row of a data frame
Description
The mapdf functions transform their input by applying a function to each row of a data frame and returning a vector the same length as the input.
These functions work a lot like purrr's map() functions.
Usage
mapdf(.x, .f, ...)
mapdf_lgl(.x, .f, ...)
mapdf_chr(.x, .f, ...)
mapdf_int(.x, .f, ...)
mapdf_dbl(.x, .f, ...)
mapdf_dfr(.x, .f, ...)
mapdf_dfc(.x, .f, ...)
mapdf_lat(.x, .f, ...)
walkdf(.x, .f, ...)
Arguments
.x |
A data.frame, data_frame, or tibble. |
.f |
A function or formula.
If a function, the first argument will be the row as a list.
If a formula, e.g. |
... |
Additional arguments passed on to the mapped function. |
Details
-
mapdf()always returns a list. -
mapdf_lgl(),mapdf_int(),mapdf_dbl()andmapdf_chr()return vectors of the corresponding type (or die trying). -
mapdf_dfr()andmapdf_dfc()return data frames created by row-binding and column-binding respectively. They require dplyr to be installed. -
mapdf_lat()returns a tibble by transforming outputted lists to a tibble using list_as_tibble. -
walkdf()calls .f for its side-effect and returns the input .x.
Examples
library(dplyr)
tib <- tibble(
a = c(1, 2),
b = list(log10, sqrt),
c = c("parrot", "quest"),
.object_class = list(c("myobject", "list"), c("yourobject", "list"))
)
# map over the rows using a function
tib %>% mapdf(class)
# or use an anonymous function
tib %>% mapdf(function(row) paste0(row$b(row$a), "_", row$c))
# or a formula
tib %>% mapdf(~ .$b)
# there are many more variations available
# see ?mapdf for more info
tib %>% mapdf_lgl(~ .$a > 1)
tib %>% mapdf_chr(~ paste0("~", .$c, "~"))
tib %>% mapdf_int(~ nchar(.$c))
tib %>% mapdf_dbl(~ .$a * 1.234)
Project a set of points to to set of segments
Description
Finds the projection index for a matrix of points x, when
projected onto a set of segments defined by segment_start and segment_end.
Usage
project_to_segments(x, segment_start, segment_end)
Arguments
x |
a matrix of data points. |
segment_start |
a matrix of segment start points. |
segment_end |
a matrix of segment end points. |
Value
A list with components
x_proj |
a matrix of projections of |
segment |
the index of the segment a point is projected on |
progression |
the progression of a projection along its segment |
distance |
the distance from each point in |
Examples
x <- matrix(rnorm(50, 0, .5), ncol = 2)
segfrom <- matrix(c(0, 1, 0, -1, 1, 0, -1, 0), ncol = 2, byrow = TRUE)
segto <- segfrom / 10
fit <- project_to_segments(x, segfrom, segto)
str(fit) # examine output
Generate random string
Description
Generate a random string with first the current time, together with a random number
Usage
random_time_string(name = NULL)
Arguments
name |
Optional string to be added in the random_time_string |
Examples
random_time_string("test")
Read/write R objects to a H5 file.
Description
Read/write R objects to a H5 file.
Usage
read_h5(path)
read_h5_(file_h5)
write_h5(x, path)
write_h5_(x, file_h5, path)
Arguments
path |
Path to read from/write to. |
file_h5 |
A H5 file to read from/write to. |
x |
R object to write. |
Print the most recent news
Description
Print the most recent news
Usage
recent_news(path = NULL, package = detect_package_name(path = path), n = 2)
Arguments
path |
The path of the description in which the package resides |
package |
The package name |
n |
Number of recent news to print |
Create an empty temporary directory and return its path
Description
Create an empty temporary directory and return its path
Usage
safe_tempdir(subfolder)
Arguments
subfolder |
Name of a subfolder to be created |
Examples
## Not run:
safe_tempdir("samson")
# "/tmp/Rtmp8xCGJe/file339a13bec763/samson"
## End(Not run)
Rescale data to a [0, 1] range
Description
Rescale data to a [0, 1] range
Usage
scale_minmax(x)
Arguments
x |
A numeric vector, matrix or data frame. |
Value
The centered, scaled matrix or vector. The numeric centering and scalings used are returned as attributes.
Examples
## Generate a matrix from a normal distribution
## with a large standard deviation, centered at c(5, 5)
x <- matrix(rnorm(200*2, sd = 10, mean = 5), ncol = 2)
## Minmax scale the data
x_scaled <- scale_minmax(x)
## Plot rescaled data
plot(x_scaled)
## Show ranges of each column
apply(x_scaled, 2, range)
Cut off outer quantiles and rescale to a [0, 1] range
Description
Cut off outer quantiles and rescale to a [0, 1] range
Usage
scale_quantile(x, outlier_cutoff = 0.05)
Arguments
x |
A numeric vector, matrix or data frame. |
outlier_cutoff |
The quantile cutoff for outliers (default 0.05). |
Value
The centered, scaled matrix or vector. The numeric centering and scalings used are returned as attributes.
Examples
## Generate a matrix from a normal distribution
## with a large standard deviation, centered at c(5, 5)
x <- matrix(rnorm(200*2, sd = 10, mean = 5), ncol = 2)
## Scale the dataset between [0,1]
x_scaled <- scale_quantile(x)
## Plot rescaled data
plot(x_scaled)
## Show ranges of each column
apply(x_scaled, 2, range)
Rescale data to have a certain center and max range.
Description
scale_uniform uniformily scales a given matrix such that
the returned space is centered on center, and each column was scaled equally
such that the range of each column is at most max_range.
Usage
scale_uniform(x, center = 0, max_range = 1)
Arguments
x |
A numeric vector matrix or data frame. |
center |
The new center point of the data. |
max_range |
The maximum range of each column. |
Value
The centered, scaled matrix. The numeric centering and scalings used are returned as attributes.
Examples
## Generate a matrix from a normal distribution
## with a large standard deviation, centered at c(5, 5)
x <- matrix(rnorm(200*2, sd = 10, mean = 5), ncol = 2)
## Center the dataset at c(0, 0) with a minimum of c(-.5, -.5) and a maximum of c(.5, .5)
x_scaled <- scale_uniform(x, center = 0, max_range = 1)
## Plot rescaled data
plot(x_scaled)
## Show ranges of each column
apply(x_scaled, 2, range)
Switching of development stage within the dynverse
Description
Switching of development stage within the dynverse
Usage
switch_devel(file = "DESCRIPTION", desc = desc::desc(file = file))
switch_master(file = "DESCRIPTION", desc = desc::desc(file = file))
switch_cran(file = "DESCRIPTION", desc = desc::desc(file = file))
Arguments
file |
The description file, defaults to DESCRIPTION |
desc |
The read in description using the desc package |
Tests whether hdf5 is correctly installed and can load/write data
Description
Tests whether hdf5 is correctly installed and can load/write data
Usage
test_h5_installation(detailed = FALSE)
get_h5_test_data()
Arguments
detailed |
Whether top do a detailed check |
Convert a tibble to a list of lists
Description
Convert a tibble to a list of lists
Usage
tibble_as_list(tib)
Arguments
tib |
A tibble |
Value
A list with the same number of lists as there were rows in tib
See Also
list_as_tibble extract_row_to_list mapdf
Examples
library(tibble)
tib <- tibble(
a = c(1, 2),
b = list(log10, sqrt),
c = c("parrot", "quest"),
.object_class = list(c("myobject", "list"), c("yourobject", "list"))
)
li <- tibble_as_list(tib)
li