sharlagelfand

library(glue)
library(RPostgreSQL)
library(DBI)

drv <- dbDriver("PostgreSQL")
con <- dbConnect(drv, 
                 host=Sys.getenv("JDBCURL"),
                 port = Sys.getenv("PORT"),
                 dbname = Sys.getenv("DBNAME"),
                 user = Sys.getenv("USER"),

sharlagelfand

---
title: "Plotly and floating TOC"
output: 
  html_document:
    toc: true
    toc_float: true
---

# h1

sharlagelfand

``` r
# tidyeval for multiple arguments that can take multiple variables

library(dplyr)

# in the case where the verbs' arguments are ... e.g. group_by(), select(), use !!! within the function to expand the variables back out
# no enquo() or enquos() is needed because you will pass in a vars() call

group_select <- function(df, group_vars, select_vars){
  df %>%

sharlagelfand

not_equal <- function(a, b){
  ifelse(a != b | is.na(a) | is.na(b),
            ifelse(is.na(a) & is.na(b),
                   NA,
                   TRUE),
            FALSE)
}

not_equal("A", "A")
#> [1] FALSE

sharlagelfand

options(shiny.reactlog = TRUE)
library(shiny)
library(reactlog)

mod_iris_ui <- function(id) {
  ns <- NS(id)

  tagList(
    fluidRow(
      column(

sharlagelfand

# Deriving the "overall working status" of someone's employment positions 
# in Ontario, where it is the highest of Full Time > Part Time > Casual.
# e.g. if someone has a full time and a part time position, their overall
# working status is full time. If they have two part time positions, it's part time.
library(dplyr)

sample_df <- tibble::tribble(
  ~id, ~working_status, ~province,
  1, "Full Time", "Ontario",
  1, "Casual", "Ontario",

sharlagelfand

library(tibble)
library(tidyr)

x <- tibble(a = 1,
            b = 1:2,
            c = 3:4)

x
#> # A tibble: 2 x 3
#>       a     b     c

sharlagelfand

library(httr)
library(dplyr)

resp <- GET("https://summary-api.datamermaid.org/v1/sites/")
content <- jsonlite::fromJSON(content(resp, "text", encoding = "UTF-8"), simplifyDataFrame = TRUE)
as_tibble(content[["features"]])

sharlagelfand

library(ggplot2)

plot_diamonds <- function() {
  p <- ggplot(head(diamonds), aes(x = cut, y = price)) +
    geom_jitter()
  res <- list(
    name = "diamonds",
    p = p
  )
  class(res) <- "diamonds"

sharlagelfand

sharla_diff <- function(df, expected_df) {
  data_as_expected <- dplyr::all_equal(expected_df, df)

  if (!isTRUE(data_as_expected)) {
    data_diffs <- janitor::compare_df_cols(expected_df, df)

    cols_mismatch <- dplyr::filter(data_diffs, is.na(expected_df) | is.na(df))

    extra_cols <- cols_mismatch %>%
      dplyr::filter(is.na(expected_df)) %>%