Overview of the functionality provided by the dynutils package

knitr::opts_chunk$set(comment = "#>", collapse = TRUE)
library(dplyr)
library(readr)
library(purrr)
library(stringr)
library(ggplot2)
library(dynutils)
set.seed(1)

Table of contents

lines <- read_lines("functionality.Rmd")
headings <- c(which(grepl("^## ", lines)), length(lines))
subheadings <- which(grepl("^### .*`", lines))

strs <- map_chr(seq_len(length(headings) - 1), function(i) {
  head <- lines[[headings[[i]]]]
  subhead <- subheadings %>% keep(~ headings[[i]] < . & . < headings[[i+1]]) %>% lines[.]

  if (length(subhead) > 0) {
    fun_texts <- gsub("[^`]*(`[^`]*`).*", "\\1", subhead)
    fun_links <- subhead %>% 
      tolower() %>% 
      str_replace_all(" ", "-") %>%
      str_replace_all("[^a-z\\-_]", "") %>% 
      str_replace("^-*", "#")

    paste0(
      gsub("## ", "* ", head), ": \n",
      paste0("  [", fun_texts, "](", fun_links, ")", collapse = ",\n", sep = ""),
      "\n"
    )
  } else {
    ""
  }
})
cat(strs, sep = "")

Manipulation of lists

add_class: Add a class to an object

l <- list(important_number = 42) %>% add_class("my_list")
l

extend_with: Extend list with more data

l %>% extend_with(
  .class_name = "improved_list", 
  url = "https://github.com/dynverse/dynverse"
)

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")

For euclidean distances, this is similar to calculating:

as.matrix(dist(rbind(x, y)))[1:3, -1:-3]

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)

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)
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))

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)

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
colMeans(ranges)         # should all be equal to 0
apply(ranges, 2, diff)   # max should be 1

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]

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]
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

Manipulation of packages

check_packages: Easily checking whether certain packages are installed

check_packages("SCORPIUS", "dynutils", "wubbalubbadubdub")
check_packages(c("princurve", "mlr", "tidyverse"))

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")

random_time_string("test")

random_time_string("test")

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

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

li <- tibble_as_list(tib)

li

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

extract_row_to_list(tib, 2)

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)

Or use an anonymous function.

tib %>% mapdf(function(row) paste0(row$b(row$a), "_", row$c))

Or even 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)

File helpers

safe_tempdir: Create an empty temporary directory and return its path

```{R safe_tempdir} safe_tempdir("samson")

## Assertion helpers

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

```r
library(assertthat)
assert_that(c(1, 2) %all_in% c(0, 1, 2, 3, 4))
assert_that("a" %all_in% letters)
assert_that("A" %all_in% letters)
assert_that(1:10 %all_in% letters)

%has_names%: Check whether an object has certain names

assert_that(li %has_names% "a")
assert_that(li %has_names% "c")
assert_that(li %has_names% letters)

is_single_numeric: Check whether a value is a single numeric

assert_that(is_single_numeric(1))
assert_that(is_single_numeric(Inf))
assert_that(is_single_numeric(1.6))
assert_that(is_single_numeric(NA))
assert_that(is_single_numeric(1:6))
assert_that(is_single_numeric("pie"))

is_bounded: Check whether a value within a certain interval

assert_that(is_bounded(10))
assert_that(is_bounded(10:30))
assert_that(is_bounded(Inf))
assert_that(is_bounded(10, lower_bound = 20))
assert_that(is_bounded(
  10,
  lower_bound = 20,
  lower_closed = TRUE,
  upper_bound = 30,
  upper_closed = FALSE
))


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dynutils documentation built on March 22, 2021, 5:06 p.m.