iangow

library(readr)
library(dplyr, warn.conflicts = FALSE)
site_url <- "https://raw.githubusercontent.com/JarFraud/FraudDetection/master/"

final_df_filename <- "data_FraudDetection_JAR2020.csv"
final_df <- read_csv(paste0(site_url, final_df_filename),
                     col_types = "d") 

aaer_df_filename <- "AAER_firm_year.csv"

iangow

library(quantmod)
#> Loading required package: xts
#> Loading required package: zoo
#> 
#> Attaching package: 'zoo'
#> The following objects are masked from 'package:base':
#> 
#>     as.Date, as.Date.numeric
#> Loading required package: TTR

iangow

library(tidyverse)

scores <-
  tribble(
    ~value, ~count, 
    1, 0,
    2, 2,
    3, 7,
    4, 5,
    5, 6,

iangow

 library(tidyverse)
library(lubridate)

read_weather <- function(file, var) {
  read_csv(file,
           col_names = c("code", "station", "year", "month", "day", 
                         var, "days_accum", "quality"),
           col_types = "ciiiiddc",
           skip = 1) %>%
  filter(!is.na(year), !is.na(month), !is.na(day)) %>%

iangow

library(dplyr, warn.conflicts = FALSE)
library(dbplyr)
library(DBI)
library(ggplot2)
library(stargazer)
library(sandwich)
library(robustbase)
library(lfe)
library(purrr)
library(tidyr)

iangow

iangow

---
title: "Performance over time"
format: pdf
editor: visual
---

```{r, message=FALSE, include=FALSE}
library(DBI)
library(dplyr, warn.conflicts = FALSE)
library(dbplyr)

iangow

table_sql <- "SELECT n.nspname AS \"Name\",
    pg_catalog.pg_get_userbyid(n.nspowner) AS \"Owner\"
    FROM pg_catalog.pg_namespace n
    WHERE n.nspname !~ '^pg_' AND n.nspname <> 'information_schema'
    ORDER BY 1"

library(dplyr, warn.conflicts = FALSE)
library(DBI)

pg <- dbConnect(RPostgres::Postgres())

iangow

library(dplyr)

df <- tibble(test = c(rep(TRUE,  24), rep(FALSE,  11), 
                      rep(TRUE, 112), rep(FALSE, 104),
                      rep(TRUE, 113), rep(FALSE, 139),
                      rep(TRUE,  10), rep(FALSE,  21)),
             mask = c(rep("None", 24 + 11),
                      rep("Cloth", 112 + 104),
                      rep("Surgical", 113 + 139),
                      rep("N95", 10 + 21))) %>%

iangow

library(readxl)
library(dplyr, warn.conflicts = FALSE)

get_data <- function(date) {
  date <- as.Date(date)
  date_str <- gsub(" ", "", tolower(format(date, "%B-%e-%Y")))
  url <- paste0("https://www.mass.gov/doc/covid-19-raw-data-", date_str, 
                "/download")
  t <- tempfile()