Test.Rmd

---
title: "GPT - Self Documenting Code Test"
author: "Avery Dobbins"
date: "`r Sys.Date()`"
output: pdf_document
---

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

## R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.

When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

```{r cars}
summary(cars)
```

## Including Plots

You can also embed plots, for example:

```{r pressure, echo=FALSE}
plot(pressure)
```

Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.

## Run a data exploration script, and see if we cna get chatGPT to describe what it does for us.

```{r, echo=FALSE}
# load packages
require(tidyverse)
require(dplyr)
require(haven)
require(readxl)
```

```{r}
# load data ####
Corn <- read_dta("C:\\Users\\avery\\Downloads\\corn_kynetec_data.dta")
Soy <- read_dta("C:\\Users\\avery\\Downloads\\soy_kynetec_data.dta")
```

```{r}
# Data Cleaning for comparisons #####
# cut down the data to just what is needed to evaluate the recieved code.
# Add crop var, Add uid var
# remove all except:
# CompanyBrand, HybridVariety, year, CompanyParent

Corn_Cut <- Corn %>%
  transmute(
    uid = row_number(),
    crop = "Corn",
    year,
    HybridVariety,
    brandcompany = CompanyBrand, # Stata file will change this
    Origbrandcompany = CompanyBrand, # Our variable that will remain the same
    companyparent = CompanyParent, # Stata file will change this
    OrigcompanyparentLevels = zap_labels(CompanyParent), # preserves the # assignment
    OrigcompanyparentFactor = as_factor(CompanyParent) # Shows the company parent name
  ) %>%
  write_dta(path = "C:\\Users\\avery\\Downloads\\CUT_corn_kynetec_data.dta")

Soy_Cut <- Soy %>%
  transmute(
    uid = row_number(),
    crop = "Soy",
    year,
    HybridVariety,
    brandcompany = CompanyBrand, # Stata file will change this
    Origbrandcompany = CompanyBrand, # Our variable that will remain the same
    companyparent = CompanyParent, # Stata file will change this
    OrigcompanyparentLevels = zap_labels(CompanyParent), # preserves the # assignment
    OrigcompanyparentFactor = as_factor(CompanyParent) # Shows the company parent name
  ) %>%
  write_dta(path = "C:\\Users\\avery\\Downloads\\CUT_soy_kynetec_data.dta")
```

```{r}
# Load New Data from Ed.P #####
#Corn
Corn_Mask_EdP <- read_dta("C:\\Users\\avery\\Downloads\\Corn_Mask_EdP.dta") %>%
  filter(!is.na(acquired)) %>% #Step needed to replace acquired NAs with 1995/6
  transmute(
         crop,
         Origbrandcompany,
         OrigcompanyparentLevels = zap_labels(OrigcompanyparentFactor),
         OrigcompanyparentFactor = as_factor(OrigcompanyparentFactor),
         acquired = acquired+1, # this is for "previously purchased seeds"
         orig_comp
         ) %>%
  unique()

#Soy
Soy_Mask_EdP <- read_dta("C:\\Users\\avery\\Downloads\\Soy_Mask_EdP.dta") %>%
  filter(!is.na(acquired)) %>% #Step needed to replace acquired NAs with 1995/6
  transmute(
         crop,
         Origbrandcompany,
         OrigcompanyparentLevels = zap_labels(OrigcompanyparentFactor),
         OrigcompanyparentFactor = as_factor(OrigcompanyparentFactor),
         acquired = acquired+1, # this is for "previously purchased seeds",
         orig_comp
         ) %>%
  unique()
```

```{r}
# Load Phil Howard Data #####

PH_SalesData <- read_xls(
  path = "C:\\Users\\avery\\Desktop\\Research\\Herbicide_Resistance\\BrandWorkspace\\GlobalSeed2018.xls",
  sheet = "Sheet3",
  range = "A1:C400"
) %>%
  transmute(
    Owner = toupper(Firm),
    Owned = toupper(.[[2]]), # index instead of that awful var name.
    Year
  ) %>%
  drop_na() # Drops one obs on line 130 of the .xls

N <- nrow(PH_SalesData)
for (i in 1:N){
  a <- PH_SalesData[i,1]
  b <- PH_SalesData[i,2]
  
  for (j in i:N) {
    if(PH_SalesData[j,1] == b){
      PH_SalesData[j,1] <- a
    }
  }
}
```

```{r}

```