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Overview of the functionality provided by the dynutils package

Robrecht Cannoodt & Wouter Saelens

Table of contents

Manipulation of lists

add_class: Add a class to an object

l <- list(important_number = 42) %>% add_class("my_list")
l
#> $important_number
#> [1] 42
#> 
#> attr(,"class")
#> [1] "my_list" "list"

extend_with: Extend list with more data

l %>% extend_with(
  .class_name = "improved_list", 
  url = "https://github.com/dynverse/dynverse"
)
#> $important_number
#> [1] 42
#> 
#> $url
#> [1] "https://github.com/dynverse/dynverse"
#> 
#> attr(,"class")
#> [1] "improved_list" "my_list"       "list"

Calculations

calculate_distance: Compute pairwise distances between two matrices

See ?calculate_distance for the list of currently supported distances.

x <- matrix(runif(30), ncol = 10)
y <- matrix(runif(50), ncol = 10)
calculate_distance(x, y, method = "euclidean")
#>          [,1]      [,2]      [,3]     [,4]      [,5]
#> [1,] 1.184305 0.9571034 1.1284052 1.022205 1.5531565
#> [2,] 1.078353 1.1378581 0.7076092 1.072164 0.8846294
#> [3,] 1.335627 1.4116406 1.3526085 1.239853 1.6282805

For euclidean distances, this is similar to calculating:

as.matrix(dist(rbind(x, y)))[1:3, -1:-3]
#>          4         5         6        7         8
#> 1 1.184305 0.9571034 1.1284052 1.022205 1.5531565
#> 2 1.078353 1.1378581 0.7076092 1.072164 0.8846294
#> 3 1.335627 1.4116406 1.3526085 1.239853 1.6282805

project_to_segments: Project a set of points to to set of segments

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)

ggplot() +
  geom_segment(aes(x = x[,1], xend = fit$x_proj[,1], y = x[,2], yend = fit$x_proj[,2], colour = "projection"), linetype = "dashed") +
  geom_point(aes(x[,1], x[,2], colour = "point")) +
  geom_segment(aes(x = segfrom[,1], xend = segto[,1], y = segfrom[,2], yend = segto[,2], colour = "segment")) +
  scale_colour_brewer(palette = "Dark2") +
  scale_x_continuous(name = NULL, breaks = NULL) +
  scale_y_continuous(name = NULL, breaks = NULL) +
  labs(colour = "Object type") +
  theme_classic()


str(fit)
#> List of 4
#>  $ x_proj     : num [1:25, 1:2] 0 0 0 0.278 0 ...
#>  $ distance   : num [1:25] 0.0068 0.01605 0.12144 0.00587 0.12604 ...
#>  $ segment    : int [1:25] 1 2 1 3 1 4 2 3 2 2 ...
#>  $ progression: num [1:25] 1 0.108 0.297 0.802 0 ...

calculate_mean: Calculate a (weighted) mean between vectors or a list of vectors; supports the arithmetic, geometric and harmonic mean

calculate_arithmetic_mean(0.1, 0.5, 0.9)
#> [1] 0.5
calculate_geometric_mean(0.1, 0.5, 0.9)
#> [1] 0.3556893
calculate_harmonic_mean(0.1, 0.5, 0.9)
#> [1] 0.2288136
calculate_mean(.1, .5, .9, method = "harmonic")
#> [1] 0.2288136

# example with multiple vectors
calculate_arithmetic_mean(c(0.1, 0.9), c(0.2, 1))
#> [1] 0.15 0.95

# example with a list of vectors
vectors <- list(c(0.1, 0.2), c(0.4, 0.5))
calculate_geometric_mean(vectors)
#> [1] 0.2000000 0.3162278

# example of weighted means
calculate_geometric_mean(c(0.1, 10), c(0.9, 20), c(0.5, 2), weights = c(1, 2, 5))
#> [1] 0.4736057 4.3491186

Manipulation of matrices

expand_matrix: Add rows and columns to a matrix

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)
#>   A B C         D E         F G         H         I J
#> a 0 0 0 0.2937302 0 0.5033395 0 0.7581031 0.5476466 0
#> b 0 0 0 0.0000000 0 0.0000000 0 0.0000000 0.0000000 0
#> c 0 0 0 0.1912601 0 0.8770575 0 0.7244989 0.7117439 0
#> d 0 0 0 0.8864509 0 0.1891936 0 0.9437248 0.3889051 0
#> e 0 0 0 0.0000000 0 0.0000000 0 0.0000000 0.0000000 0

Scaling of matrices and vectors

scale_uniform: Rescale data to have a certain center and max range

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)

Check the ranges and verify that the scaling is correct.

ranges <- apply(x_scaled, 2, range)
ranges                   # should all lie between -.5 and .5
#>            [,1] [,2]
#> [1,] -0.4061179 -0.5
#> [2,]  0.4061179  0.5
colMeans(ranges)         # should all be equal to 0
#> [1] 0 0
apply(ranges, 2, diff)   # max should be 1
#> [1] 0.8122358 1.0000000

scale_minmax: Rescale data to a [0, 1] range

x_scaled2 <- scale_minmax(x)

Check the ranges and verify that the scaling is correct.

apply(x_scaled2, 2, range)  # each column should be [0, 1]
#>      [,1] [,2]
#> [1,]    0    0
#> [2,]    1    1

scale_quantile: Cut off outer quantiles and rescale to a [0, 1] range

x_scaled3 <- scale_quantile(x, .05)

Check the ranges and verify that the scaling is correct.

apply(x_scaled3, 2, range)   # each column should be [0, 1]
#>      [,1] [,2]
#> [1,]    0    0
#> [2,]    1    1
qplot(x_scaled2[,1], x_scaled3[,1]) + theme_bw()
#> Warning: `qplot()` was deprecated in ggplot2 3.4.0.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.

Manipulation of functions

inherit_default_params: Have one function inherit the default parameters from other functions

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
#> function (a = 10, b = 7, c = 9) 
#> {
#>     list(x = fun1(a, b), y = fun2(c))
#> }

Manipulation of packages

check_packages: Easily checking whether certain packages are installed

check_packages("SCORPIUS", "dynutils", "wubbalubbadubdub")
#>         SCORPIUS         dynutils wubbalubbadubdub 
#>             TRUE             TRUE            FALSE
check_packages(c("princurve", "mlr", "tidyverse"))
#> princurve       mlr tidyverse 
#>      TRUE     FALSE      TRUE

install_packages: Install packages taking into account the remotes of another

This is useful for installing suggested packages with GitHub remotes.

install_packages("SCORPIUS", package = "dynmethods", prompt = TRUE)
> install_packages("SCORPIUS", package = "dynmethods", prompt = TRUE)
Following packages have to be installed: SCORPIUS
Do you want to install these packages? (y/yes/1 or n/no/2): 1
Installing SCORPIUS
...
** testing if installed package can be loaded
* DONE (SCORPIUS)
Installed SCORPIUS
[1] "SCORPIUS"

Manipulation of vectors

random_time_string: Generates a string very likely to be unique

random_time_string("test")
#> [1] "20250902_172449__test__syYeN626jT"

random_time_string("test")
#> [1] "20250902_172449__test__e6MCnXcJM7"

random_time_string("test")
#> [1] "20250902_172449__test__9pFJ5mxS3R"

Tibble helpers

list_as_tibble: Convert a list of lists to a tibble whilst retaining class information

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
#> # A tibble: 2 × 4
#>       a b      c      .object_class
#>   <dbl> <list> <chr>  <list>       
#> 1     1 <fn>   parrot <chr [2]>    
#> 2     2 <fn>   quest  <chr [2]>

tibble_as_list: Convert a tibble back to a list of lists whilst retaining class information

li <- tibble_as_list(tib)

li
#> [[1]]
#> $a
#> [1] 1
#> 
#> $b
#> function (x)  .Primitive("log10")
#> 
#> $c
#> [1] "parrot"
#> 
#> attr(,"class")
#> [1] "myobject" "list"    
#> 
#> [[2]]
#> $a
#> [1] 2
#> 
#> $b
#> function (x)  .Primitive("sqrt")
#> 
#> $c
#> [1] "quest"
#> 
#> attr(,"class")
#> [1] "yourobject" "list"

extract_row_to_list: Extracts one row from a tibble and converts it to a list

extract_row_to_list(tib, 2)
#> $a
#> [1] 2
#> 
#> $b
#> function (x)  .Primitive("sqrt")
#> 
#> $c
#> [1] "quest"
#> 
#> attr(,"class")
#> [1] "yourobject" "list"

mapdf: Apply a function to each row of a data frame

The mapdf functions apply a function on each row of a data frame. They are based heavily on purrr’s map functions.

tib %>% mapdf(class)
#> [[1]]
#> [1] "myobject" "list"    
#> 
#> [[2]]
#> [1] "yourobject" "list"

Or use an anonymous function.

tib %>% mapdf(function(row) paste0(row$b(row$a), "_", row$c))
#> [[1]]
#> [1] "0_parrot"
#> 
#> [[2]]
#> [1] "1.4142135623731_quest"

Or even a formula.

tib %>% mapdf(~ .$b)
#> [[1]]
#> function (x)  .Primitive("log10")
#> 
#> [[2]]
#> function (x)  .Primitive("sqrt")

There are many more variations available. See ?mapdf for more info.

tib %>% mapdf_lgl(~ .$a > 1)
#> [1] FALSE  TRUE
tib %>% mapdf_chr(~ paste0("~", .$c, "~"))
#> [1] "~parrot~" "~quest~"
tib %>% mapdf_int(~ nchar(.$c))
#> [1] 6 5
tib %>% mapdf_dbl(~ .$a * 1.234)
#> [1] 1.234 2.468

File helpers

safe_tempdir: Create an empty temporary directory and return its path

safe_tempdir("samson")
#> [1] "/tmp/RtmpdDzygs/file1aa4f3e952c18/samson"

Assertion helpers

%all_in%: Check whether a vector are all elements of another vector

library(assertthat)
assert_that(c(1, 2) %all_in% c(0, 1, 2, 3, 4))
#> [1] TRUE
assert_that("a" %all_in% letters)
#> [1] 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, ...

%has_names%: Check whether an object has certain names

assert_that(li %has_names% "a")
#> Error: li is missing 1 name from "a": "a"
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 26 names from letters: "a", "b", "c", ...

is_single_numeric: Check whether a value is a single numeric

assert_that(is_single_numeric(1))
#> [1] TRUE
assert_that(is_single_numeric(Inf))
#> [1] TRUE
assert_that(is_single_numeric(1.6))
#> [1] 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

is_bounded: Check whether a value within a certain interval

assert_that(is_bounded(10))
#> [1] TRUE
assert_that(is_bounded(10:30))
#> [1] 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)

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