mpettis

#' ---
#' title: "Graph_Explorations.R"
#' author: "Matt Pettis (Matthew.Pettis@gmail.com)"
#' date: "`r Sys.Date()`"
#' output:
#'   html_document:
#'     toc: true
#'     toc_depth: 3
#'     code_folding: hide
#' editor_options:

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#;; Transform an input into shape fit for updating
#;; Here, each row has 3 observations, a modelnumber, serialnumber, and
#;; salesordernumber for each object.
library(readxl)
library(janitor)
#> 
#> Attaching package: 'janitor'
#> The following objects are masked from 'package:stats':
#> 
#>     chisq.test, fisher.test

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#;; Find the local midnight time in UTC for a given timezone

library(tidyverse)
library(lubridate)
library(glue)

# tgtTimezone <- "America/Chicago"
# tgtTimezone <- "America/Sao_Paulo"
# tgtYear <- 2021
# 

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# https://pypi.org/project/scoping/
# See also: https://github.com/bskinn/tempvars
from scoping import scoping

aofl = [{"a":1, "b":2}, {"a":3, "b":4}]

with scoping():
    for d in aofl:
        x = d
        scoping.keep('x')

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# Plot date ranges with color scheme, using scale_fill_manual()
library(tidyverse)
plot_df <-
    tribble(
        ~start_date, ~end_date, ~cat,
        "2021-01-01","2021-01-02","a",
        "2021-01-03","2021-01-05","b"
        ) %>%
    mutate(cat = factor(cat)) %>%
    mutate(start_date = ymd(start_date)) %>%

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``` r
library(httr)
library(httr2curl)

cat(
    h2c(
        httr::POST("https://api.tis-dev.trane.com/tis-security-context/securityContext",
                   httr::add_headers(.headers=c("Content-Type"="application/json", "Accept"="application/json")),
                   body = '{"username":"myusername","password":"mypassword"}',
                   encode = "json")

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# See: https://gist.github.com/drsimonj/2038ff9f9c67063f384f10fac95de566
# See: https://adv-r.hadley.nz/meta-big-picture.html
# See: https://dplyr.tidyverse.org/reference/tidyeval.html
# See: https://dplyr.tidyverse.org/articles/programming.html


library(dplyr)

# Set up lags
lags <- c(1:30)

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``` r
# Load libraries
library(lubridate)
#> Warning: package 'lubridate' was built under R version 3.6.3
#> 
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#> 
#>     date, intersect, setdiff, union
library(dplyr)

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### Let's say you get timestamps that do not have their tzone info as part of the timestamp string,
### but stored in a separate field.  Further, assume your system has read them in using your local
### tzone.  How do use knowledge of our local timezone and that of the the intended timezone to
### correct the timestamp to be in the intended timezone?
###
### In this exercise, we have the following:
###     - The timestamp, with no explicit timezone which was read into system using default initial
###       timezone of the first user.
###     - A second system that has timezone different from the first timezone.
###     - The intended timezone of the timestamp, which is what the timestamp should be in.

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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.