ClaytonJY

# x is assumed to be a numeric vector
# output will be TRUE if both that value and the previous value are strictly below the threshold, FALSE otherwise
f <- function(x, threshold) {
  less_than <- (x < threshold)
  
  less_than & c(FALSE, !diff(less_than))
}


#### usage ####

ClaytonJY

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ClaytonJY

library(rlang)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 

ClaytonJY

# test function
f <- function(x) {
  if (missing(x)) {
    stop("error, Will Robinson")
  } else {
    x
  }
}

f(1)  # 1

ClaytonJY

library(dplyr)
library(purrr)
library(rsample)

# suppose we want to keep cylinder-groups together
# we'll vfold those instead of the whole thing
initial_fold <- mtcars %>%
  distinct(cyl) %>%
  vfold_cv(v = 3)

ClaytonJY

diag(1.1)          
#>      [,1]
#> [1,]    1
diag(0.9)          
#> <0 x 0 matrix>
diag(0.9, nrow = 1)
#>      [,1]
#> [1,]  0.9
diag(-1) 

ClaytonJY

library(dplyr)
library(purrr)


#### setup ####

tbl <- tibble(
  id = 1:25
)

ClaytonJY

library(dplyr)
library(purrr)

# helper to make examples
new_tbl <- function(n_groups, row_range = 5:10) {

  1:n_groups %>%
    map_df(~tibble(
      group_id = .x,
      value    = rnorm(sample.int(row_range, 1L))

ClaytonJY

library(tidyverse)

mt_tbl <- as_tibble(mtcars)

# types need to match those in mtcars (all doubles)
# careful; `na_if` doesn't work with multiple values
missing_codes <- list(
  cyl = 6.0,
  disp = 160.0,
  carb = 1.0

ClaytonJY

library(ggplot2)
library(rlang)

f <- function(df, x, y, facet) {
  
  x <- enquo(x)
  y <- enquo(y)
  facet = enquo(facet)
  
  g <- ggplot(df, aes(x = !!x, y = !!y)) +