The {oeli} package offers a collection of handy
functions that I found useful while developing R packages. Perhaps
you’ll find them helpful too!
The released package version can be installed from CRAN via:
install.packages("oeli")The package includes helpers for various tasks and objects. Some demos are shown below. Click the headings for reference pages with documentation on all available helpers in each category.
The package has density and sampling functions for distributions not in base R, such as Dirichlet, multivariate normal, truncated normal, and Wishart.
ddirichlet(x = c(0.2, 0.3, 0.5), concentration = 1:3)
#> [1] 4.5
rdirichlet(concentration = 1:3)
#> [1] 0.1273171 0.5269401 0.3457428For faster computation, Rcpp implementations are also available:
microbenchmark::microbenchmark(
"R" = rmvnorm(mean = c(0, 0, 0), Sigma = diag(3)),
"Rcpp" = rmvnorm_cpp(mean = c(0, 0, 0), Sigma = diag(3))
)
#> Unit: microseconds
#> expr min lq mean median uq max neval
#> R 200.5 208.25 263.396 217.10 234.35 2154.7 100
#> Rcpp 2.7 2.90 5.386 4.05 4.40 72.0 100Retrieving default arguments of a function:
f <- function(a, b = 1, c = "", ...) { }
function_defaults(f)
#> $b
#> [1] 1
#>
#> $c
#> [1] ""Create all possible permutations of vector elements:
permutations(LETTERS[1:3])
#> [[1]]
#> [1] "A" "B" "C"
#>
#> [[2]]
#> [1] "A" "C" "B"
#>
#> [[3]]
#> [1] "B" "A" "C"
#>
#> [[4]]
#> [1] "B" "C" "A"
#>
#> [[5]]
#> [1] "C" "A" "B"
#>
#> [[6]]
#> [1] "C" "B" "A"Quickly have a basic logo for your new package:
package_logo("my_package", brackets = TRUE, use_logo = FALSE)
How to print a matrix without filling up the entire
console?
x <- matrix(rnorm(10000), ncol = 100, nrow = 100)
print_matrix(x, rowdots = 4, coldots = 4, digits = 2, label = "what a big matrix")
#> what a big matrix : 100 x 100 matrix of doubles
#> [,1] [,2] [,3] ... [,100]
#> [1,] 2.39 0.3 -0.48 ... 0.56
#> [2,] -1.33 0.62 0.37 ... -1.21
#> [3,] -0.03 -0.43 1.71 ... 0.07
#> ... ... ... ... ... ...
#> [100,] 0.14 -0.16 2.49 ... -1.58And what about a data.frame?
x <- data.frame(x = rnorm(1000), y = LETTERS[1:10])
print_data.frame(x, rows = 7, digits = 0)
#> x y
#> 1 0 A
#> 2 -1 B
#> 3 0 C
#> 4 -1 D
#> < 993 rows hidden >
#>
#> 998 -1 H
#> 999 -1 I
#> 1000 0 JLet’s simulate a Markov chain:
Gamma <- sample_transition_probability_matrix(dim = 3)
simulate_markov_chain(Gamma = Gamma, T = 20)
#> [1] 2 1 1 3 1 1 2 2 3 2 2 2 2 2 1 1 1 1 1 3The group_data.frame() function groups a given
data.frame based on the values in a specified column:
df <- data.frame("label" = c("A", "B"), "number" = 1:10)
group_data.frame(df = df, by = "label")
#> $A
#> label number
#> 1 A 1
#> 3 A 3
#> 5 A 5
#> 7 A 7
#> 9 A 9
#>
#> $B
#> label number
#> 2 B 2
#> 4 B 4
#> 6 B 6
#> 8 B 8
#> 10 B 10Is my matrix a proper transition probability matrix?
matrix <- diag(4)
matrix[1, 2] <- 1
check_transition_probability_matrix(matrix)
#> [1] "Must have row sums equal to 1"