Overview of the functionality provided by the dynutils package

add_class: Add a class to an object

#> $important_number
#> [1] 42
#> 
#> attr(,"class")
#> [1] "my_list" "list"

extend_with: Extend list with more data

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

calculate_distance: Compute pairwise distances between two matrices

See ?calculate_distance for the list of currently supported distances.

#>          [,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:

#>          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

#> 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 ...

expand_matrix: Add rows and columns to a matrix

#>   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

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).

Center the dataset at c(0, 0) with a minimum of c(-.5, -.5) and a maximum of c(.5, .5).

Check the ranges and verify that the scaling is correct.

#>            [,1] [,2]
#> [1,] -0.4061179 -0.5
#> [2,]  0.4061179  0.5
#> [1] 0 0
#> [1] 0.8122358 1.0000000

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

Check the ranges and verify that the scaling is correct.

#>      [,1] [,2]
#> [1,]    0    0
#> [2,]    1    1

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

Check the ranges and verify that the scaling is correct.

#>      [,1] [,2]
#> [1,]    0    0
#> [2,]    1    1

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

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

check_packages: Easily checking whether certain packages are installed

#>         SCORPIUS         dynutils wubbalubbadubdub 
#>             TRUE             TRUE            FALSE
#> princurve       mlr tidyverse 
#>      TRUE      TRUE      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)
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"

pritt: A friendly version of glue::glue

#> [1] "a: 10"
#> a: 10
#> [1] "glue"      "character"

random_time_string: Generates a string very likely to be unique

#> [1] "20180718_144154__test__mMIwnRLQgq"
#> [1] "20180718_144154__test__X7HCfj0o6f"
#> [1] "20180718_144154__test__F9Y32SIQAy"

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

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

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

#> [[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

#> $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.

#> [[1]]
#> [1] "myobject" "list"    
#> 
#> [[2]]
#> [1] "yourobject" "list"

Or use an anonymous function.

#> [[1]]
#> [1] "0_parrot"
#> 
#> [[2]]
#> [1] "1.4142135623731_quest"

Or even a formula.

#> [[1]]
#> function (x)  .Primitive("log10")
#> 
#> [[2]]
#> function (x)  .Primitive("sqrt")

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

#> [1] FALSE  TRUE
#> [1] "~parrot~" "~quest~"
#> [1] 6 5
#> [1] 1.234 2.468