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(require '[clara.rules :as r])

;;;; Define 3 rules, where the "priority" order is r1, r2, r3, where the highest priority is first
;;;; and the rest is in descending order of priority.

;;;; :type :rule/result "syntetic" fact is used to hold the final changes that can be queried out
;;;; from a session after `r/fire-rules` via `r/query` on the `find-results` query.
;;;; A namespace qualified keyword is used to avoid collision with externally given :type of
;;;; "real" facts.

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#' ---
#' title: "Beer example"
#' author: "Matt Pettis ([email protected])"
#' date: "`r Sys.Date()`"
#' output:
#'   html_document:
#'     toc: true
#'     toc_depth: 3
#'     code_folding: show
#' editor_options:

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library(lubridate)
library(tidyverse)

seq(
    from=as.POSIXct("2012-1-1 0:00", tz="UTC"),
    to=as.POSIXct("2012-1-3 23:00", tz="UTC"),
    by="hour"
) ->
    dt

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library(tidyverse)
library(broom)

US <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us.csv") %>%
  mutate(new_deaths = deaths - lag(deaths)) %>%
  filter(date >= "2020-02-26")

models <- tibble(degrees = 2:4) %>%
  mutate(model = map(degrees, ~ lm(log(new_deaths + 1) ~ poly(date, .), data = US)))

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## Using run-length-encoding, create groups of identical values and put that
## common grouping identifier into a `grp` column.
library(tidyverse)

set.seed(42)

df <- tibble(x = sample(c(0,1), size=20, replace=TRUE, prob = c(0.2, 0.8)))

df %>%
    mutate(grp = rle(x)$lengths %>% {rep(seq(length(.)), .)})

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---
title: "spread-group-percent"
author: "Matt Pettis"
date: "March 22, 2019"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

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library(geosphere)
library(tidyverse)

# Simple dataframe and use case:
ref_ll <- c(-95.0, 45.0)

df__ <- tribble(
   ~lon,  ~lat,
   -95.0, 45.0,
   -96.0, 45.0,

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################################################################################
# Make a function that does what I want
# Take a dataframe, identify the target column with runs, ID the index column,
# which is the column that I want to capture the first and last values of for
# runs.


#' rle_as_df
#' 
#' Turn run-length encoding into a dataframe so it can be used with ggplot/geom_area.

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# Dealing with NULLs as list values

library(tidyverse)

# As advertised on the tin:
is.null(NULL)
#> [1] TRUE

# is.null() is not vectorized?
is.null(list(NULL))

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``` r
## 'or'ing together same-length vectors of booleans.
## use case is to set up sets of booleans that have the same length and should
## be all 'or'ed together to form a condition.

# Base R way:
lstt <- list(
    a = c(T, F, F, F)
    , b = c(F, T, F, F)
    , c = c(F, F, T, F)