# Overview of the functionality provided by the dynutils package

#### Robrecht Cannoodt & Wouter Saelens

## Table of contents

- Manipulation of lists: `add_class`, `extend_with`
- Calculations: `calculate_distance`, `project_to_segments`, `calculate_mean`
- Manipulation of matrices: `expand_matrix`
- Scaling of matrices and vectors: `scale_uniform`, `scale_minmax`, `scale_quantile`
- Manipulation of functions: `inherit_default_params`
- Manipulation of packages: `check_packages`, `install_packages`
- Manipulation of vectors: `random_time_string`
- Tibble helpers: `list_as_tibble`, `tibble_as_list`, `extract_row_to_list`, `mapdf`
- File helpers: `safe_tempdir`
- Assertion helpers: `%all_in%`, `%has_names%`, `is_single_numeric`, `is_bounded`

## 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()

## 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
    #>            FALSE             TRUE            FALSE
    check_packages(c("princurve", "mlr", "tidyverse"))
    #> princurve       mlr tidyverse
    #>     FALSE     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] "20221011_123239__test__syYeN626jT"

    random_time_string("test")
    #> [1] "20221011_123239__test__e6MCnXcJM7"

    random_time_string("test")
    #> [1] "20221011_123239__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/Rtmp2aL2UF/filef21d031cfe904/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)