noamross

library(mgcv)
library(spdep)
library(sf)
library(ggplot2)

# Columbus crime data from mgcv/spdep
example(columbus)

# Make a neighborhood object compatible with mgcv's MRF
nb <- poly2nb(columbus)

noamross

library(ggplot2)

dat <- data.frame(a = letters[1:10], b = exp(rnorm(10)))
ggplot(dat, aes(x = a, y = b)) + geom_col() + scale_y_log10() # Basic log scale transform
trans = scales::trans_new("logshift", \(x) log10(x*10), \(x) (10^x)/10) # Custom transformation
ggplot(dat, aes(x = a, y = b)) + geom_col() + scale_y_continuous(trans = trans) # Use custom transformation
ggplot(dat, aes(x = a, y = b)) + geom_col() + scale_y_continuous(trans = trans, breaks = c(0.25, 0.5, 1, 2, 4, 8)) # Add breaks

ggplot(dat, aes(x = a, y = b)) + 
  geom_col() + 

noamross

library(sys)
library(RMariaDB)
library(DBI)
library(withr)

# Clean up from previous session
unlink("doltdb", recursive = TRUE)
try(dbDisconnect(conn), silent = TRUE)

noamross

library(leaflet)
library(tidyverse)

outline <- quakes[chull(quakes$long, quakes$lat),]

map <- leaflet(quakes) %>%
  addTiles(group = "OSM (default)") %>%
  # Overlay groups
  addCircles(~long, ~lat, ~10^mag/5, stroke = F, group = "Quakes") %>%
  addPolygons(data = outline, lng = ~long, lat = ~lat,

noamross

library(ggplot)
library(sf)
library(ggspatial)
library(snapbox)
library(raster)

worldclim <- raster::getData('worldclim', var='prec', res=2.5, lon = 142.61, lat = -23.64)

area <- st_bbox(
  c(xmin = 101.03, ymin = -44.82, xmax = 168.23, ymax = -9.09),

noamross

#!/bin/bash

funzip $1|  # uncompress first file in zip
tr -d '\000' | #remove null characters
sed "/^\s*$/d; s/ \{1,\}\t/\t/g; s/\t \{1,\}/\t/g; s/\r//" |  #removes empty lines, whitespace around tabs, extra newlines
cut -s -f 1,3,4,5,6,8,12,13,14,15,16,17,18,19,20,21,23,24,25,26,34,35,36,38,40,42,44,45,46,85,86,87,88,89 #| #only select certain columns
pv -N Process -c  |
gzip -9 | 
pv -N Compress -c > $1.gz

noamross

#' Create a random effect basis with integers rather than factors
#' @import mgcv
#' @export
smooth.construct.re2.smooth.spec <- function (object, data, knots) {
  if (!is.null(object$id))
    stop("random effects don't work with ids.")
  if(any(sapply(data, is.numeric))) data <- lapply(data, as.factor)   ## <-- All I did was this (and below)
  form <- as.formula(paste("~", paste(object$term, collapse = ":"),
                           "-1"))
  object$X <- model.matrix(form, data)

noamross

#' Function to partition unequal sized groups into shards of similar size.
#'
#' Based on the greedy algorithm described in [The Wikipedia article on the
#' the partitioning problem](https://en.wikipedia.org/wiki/Partition_problem#The_greedy_algorithm)
#'
#' @param groups_vector An integer vector of group ids, such as a group ID
#'   column in a data frame
#' @param n_shards The number of shards to split groups up into
#' @examples
#' n_groups <- 200

noamross

Keybase proof

I hereby claim:

• I am noamross on github.
• I am noamross (https://keybase.io/noamross) on keybase.
• I have a public key ASB-BxfY2ry-bqcNtfdHGtsrS2Xwb0kBX2i_fgE1znBsSQo

To claim this, I am signing this object:

noamross

---
title: "test"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
runtime: shiny
---

```{r setup, include = FALSE}
library(shiny)