hypercompetent

rcau <- function() {
  angle <- 2 * pi * runif(1)
  radius <- 1 * (runif(1) ^ (1/2))
  
  x <- radius * cos(angle)
  y <- radius * sin(angle)
  
  c(x, y)
}

hypercompetent

library(reshape2)
library(ggplot2)

df <- melt(volcano[20:44,10:34])

names(df) <- c("x","y","val")

rect <- data.frame(st = 5, en = 20)

ggplot() +

hypercompetent

pearson_residuals <- function(counts, theta = 100) {
  counts_sum0 <- as(matrix(Matrix::colSums(counts),nrow=1),"dgCMatrix")
  counts_sum1 <- as(matrix(Matrix::rowSums(counts),ncol=1),"dgCMatrix")
  counts_sum <- sum(counts@x)
  
  #get residuals
  mu <- (counts_sum1 %*% counts_sum0) / counts_sum
  z <- (counts - mu) / sqrt(mu + mu^2/theta)
  
  #clip to sqrt(n)

hypercompetent

library(rgl)

sl  <- shapelist3d(cube3d(alpha = 1, 
                          color = 'grey90', 
                          specular = 'black', 
                          lit = TRUE), 
                   plot = FALSE)

create_next_level <- function(sl, level) {
  

hypercompetent

hypercompetent

hypercompetent

# Tidyeval notes
library(dplyr)
library(rlang)
library(tasic2016data)

# one character element:
test <- "primary_type_id"

expr_test <- parse_expr(test)

hypercompetent

plot_logo <- function() {
  
  library(ggplot2)
  
  core_rect <- data.frame(xmin = 0,
                          xmax = 4,
                          ymin = 0,
                          ymax = 0.6,
                          color = "")
  

hypercompetent

library(cocoframer)
library(purrr)
library(rgl)

structures <- c("root","CA")
mesh_list <- map(structures, ccf_2017_mesh)

names(mesh_list) <- structures

plot_ccf_meshes(mesh_list,

hypercompetent

library(purrr)
library(magrittr)
library(microbenchmark)

y_test <- sample(0:9, 2000, replace = TRUE)
pred1 <- sample(0:9, 2000, replace= TRUE)
pred2 <- sample(0:9, 2000, replace= TRUE)
preds <- list(pred1, pred2)

# want to apply the following to preds