ha0ye

library(tidyverse)
library(lubridate)

# modified from https://stackoverflow.com/questions/48026462/writing-r-function-using-lubridate-package-that-takes-dateex-august-14-and
zodiac_sign <- function(input)
{
    zodiac_sign_df <- data.frame(Month = c("March", "April", "May", "June", "July", "August", "September", "October", "November", "December", "January", "February"),
                                 Zodiac_Separation_Dt = c(21, 20, 21, 21, 23, 23, 23, 23, 22, 22, 20, 19),
                                 LowerSideZS = c("Pisces", "Aries","Taurus","Gemini","Cancer","Leo","Virgo","Libra","Scorpio","Sagittarius","Capricorn","Aquarius"),
                                 UpperSideZS = c("Aries","Taurus","Gemini","Cancer","Leo","Virgo","Libra","Scorpio","Sagittarius","Capricorn","Aquarius", "Pisces"),

ha0ye

f <- function(x)
{
    n <- sum(x)
    v <- numeric(n)
    prev <- NULL
    for (i in 1:n)
    {
        idx <- x > floor(sum(x)/2) # how many counts are > n/2
        if (sum(idx) >= 2) # error if more than 2 (impossible?)
        {

ha0ye

library(rEDM)

# generate time series
set.seed(42)
ts_length <- 50
x <- rnorm(ts_length)

# pre-compute distance matrix
x_dists <- as.matrix(dist(x))
x_dists <- x_dists[, -ts_length] # last point can't be a nearest neighbor

ha0ye

calc3 <- function(sets)
{
  sets <- check_sets(sets)
  set_lengths <- vapply(sets, length, 0)
  set_order <- order(set_lengths)
  sets <- sets[set_order]
  set_lengths <- set_lengths[set_order]
  
  n_sets <- length(sets)
  set_names <- names(sets)

ha0ye

library(rEDM)

#### Example 1: a single forward prediction

# Assume we have already done the analysis, finding that the best embedding 
#   dimension is 2. Our objective is to make multiple 1-step ahead forecasts 
#   using simplex and E = 2.
dat <- data.frame(yr = as.numeric(time(sunspot.year)), 
                  sunspot_count = as.numeric(sunspot.year))
E <- 3

ha0ye

full_text <- readLines(file, warn = FALSE)
start_par_line <- grep("^\\$par", full_text)
start_objective_line <- grep("^\\$objective", full_text)
param_lines <- seq(from = start_par_line, 
                   to = start_objective_line - 3, 
                   by = 2)

params <- purrr::map_dfc(param_lines, function(line_idx)
{
    readr::read_delim(file, delim = " ", 

ha0ye

library(dplyr)
library(microbenchmark)

set.seed(42)
n <- 10000
df <- data.frame(a = rep(seq(n), 2), 
                 x = rnorm(2 * n))

group_summarize_mutate <- function(df)
{

ha0ye

library(likert)
data(pisaitems)

##### Item 24: Reading Attitudes
items24 <- pisaitems[,substr(names(pisaitems), 1,5) == 'ST24Q']
head(items24); ncol(items24)

names(items24) <- c(
    ST24Q01="I read only if I have to.",
    ST24Q02="Reading is one of my favorite hobbies.",

ha0ye

library(drake)
library(furrr)

future::plan(future::multiprocess) 

tictoc::tic()
params = data.frame(t = c(1, 2, 3, 1, 2, 3))
out = purrr::pmap(params, Sys.sleep)
tictoc::toc()

ha0ye

library(lavaan)
library(visNetwork)

model <- '
  # measurement model
    ind60 =~ x1 + x2 + x3
    dem60 =~ y1 + y2 + y3 + y4
    dem65 =~ y5 + y6 + y7 + y8
  # regressions
    dem60 ~ ind60