splice_df <- function(x, ...) {
expr <- rlang::enquo(x)
cols <- lapply(rlang::ensyms(..., .named = TRUE), as.character)
lapply(cols, function(col_name) {
rlang::quo(`[[`(!!expr, !!col_name))
})
}
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# based on a article from here https://dirkschumacher.github.io/ompr/articles/problem-graph-coloring.html | |
library(maptools) | |
library(dplyr) | |
# devtools::install_github("dirkschumacher/ompr@milp") | |
# CC by | |
map_data <- rgdal::readOGR("https://raw.githubusercontent.com/nvkelso/natural-earth-vector/master/geojson/ne_50m_admin_0_countries.geojson", "OGRGeoJSON") |
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# LICENSE MIT | |
# Data from RKI with special Terms | |
library(dplyr) | |
library(readr) | |
library(tidyr) | |
# go to https://survstat.rki.de/Content/Query/Create.aspx | |
# Selected calendar weeks for rows and diseases for columns. | |
# had to manually edit, because not a valid csv |
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# this is just a script to test the rhxl package, I just quickly looked at the data | |
# Ethiopia Who is doing What Where - 3W December 2017 | |
# source: https://data.humdata.org/dataset/3w-december-2017 | |
url <- "https://data.humdata.org/dataset/615416d2-457b-461a-8155-090f0ced0bf8/resource/f71bf111-8706-42f4-ba46-4ce3c8c949dc/download/3w_hxl.xlsx" | |
# load the rhxl package | |
# https://github.com/dirkschumacher/rhxl | |
library(rhxl) | |
download.file(url, "file.xlsx") |
a <- Matrix::sparseVector(1:2, i = 1:2, length = 2)
b <- Matrix::sparseVector(1:2, i = 1:2, length = 2)
class(a * b)
#> [1] "dsparseVector"
#> attr(,"package")
#> [1] "Matrix"
class(a / b) # bug? numeric instead of sparseVector
#> [1] "numeric"
library(armacmp)
# taken from https://gallery.rcpp.org/articles/black-scholes-three-ways/
put_option_pricer_arma <- armacmp(function(s = type_colvec(),
k = type_scalar_numeric(),
r = type_scalar_numeric(),
y = type_scalar_numeric(),
t = type_scalar_numeric(),
sigma = type_scalar_numeric()) {
library(armacmp)
# Arnold, T., Kane, M., & Lewis, B. W. (2019). A Computational Approach to Statistical Learning. CRC Press.
# logistic regression using the Newton-Raphson
log_reg <- armacmp(function(X, y) {
beta <- rep.int(0, ncol(X))
for (i in seq_len(25)) {
b_old <- beta
alpha <- X %*% beta
p <- 1 / (1 + exp(-alpha))
library(armacmp)
# code from https://nextjournal.com/wolfv/how-fast-is-r-with-fastr-pythran
# which in turn comes in part from http://www.tylermw.com/throwing-shade/
# Author: Tyler Morgan-Wall
# first the R version
faster_bilinear <- function (Z, x0, y0){
i = floor(x0)
library(armacmp)
# some of julia's microbenchmarks translated to C++
# https://github.com/JuliaLang/Microbenchmarks/blob/master/perf.R
fib_cpp <- armacmp(function(n = type_scalar_int()) {
fib_rec <- function(nr = type_scalar_int()) {
if (nr < 2) {
return(nr, type = type_scalar_int())
`?` <- function(lhs, rhs) {
if (missing(rhs)) {
return(eval(bquote(utils::`?`(.(substitute(lhs))))))
}
rhs <- substitute(rhs)
envir <- parent.frame()
split_colon <- strsplit(deparse(rhs), ":")
stopifnot(length(split_colon) == 1L, length(split_colon[[1L]]) == 2L)
rhs_chr <- split_colon[[1L]]