Powerpoint slides.Rmd

---
title: "Idaho Antibiotic Use"
author:
    - Jeremy Boyd
    - Karl Madaras-Kelly
date: "`r format(Sys.Date(), '%B %d, %Y')`"
output:
    powerpoint_presentation:
        reference_doc: blank_template.pptx
---

```{r setup, include=FALSE}

# Problems with creating a powerpoint from Rmd
# Doesn't allow you to change column widths in two-column slide.
# Doesn't allow you to change table styling to automatically get something that's not the template default.
# Feel like I if I did the powerpoint with Rmd it's be more of a pain because Karl wouldn't be able to edit it, and every time we did we'd have to redo a bunch of stuff that gets undone by re-knitting.

knitr::opts_chunk$set(echo = FALSE)

```


```{r, echo = FALSE, error = FALSE, message = FALSE, warning = FALSE}

# Read in provider type bank
p_types <- read_feather("Idaho provider type bank.feather") %>%
    select(Prscrbr_Type, Std_Provider_Type)

# Read in address-county info
address_county <- read_feather("Address-query-county bank.feather")

# By-provider dataset
p <- read_feather("Idaho prescribers by provider data.feather") %>%
    mutate(Bene_Prop_Fem = Bene_Feml_Cnt / Tot_Benes,
           Bene_Prop_White = Bene_Race_Wht_Cnt / Tot_Benes,
           Prscrbr_RUCA = floor(Prscrbr_RUCA),
           Prscrbr_RUCA_fct = factor(Prscrbr_RUCA),
           dataset_address = str_squish(
                paste(Prscrbr_St1,
                      Prscrbr_St2,
                      Prscrbr_City,
                      Prscrbr_State_Abrvtn,
                      Prscrbr_Zip5))) %>%
    
    # Standardized prescriber types
    left_join(p_types, by = c("Prscrbr_Type")) %>%
    
    # County info
    left_join(address_county %>%
                  select(dataset_address, county),
              by = "dataset_address") %>%
    select(Year = year,
           Prscrbr_NPI,
           Prscrbr_Type_Std = Std_Provider_Type,
           Prscrbr_RUCA,
           Prscrbr_RUCA_fct,
           Prscrbr_Gndr,
           Prscrbr_State_Abrvtn,
           Prscrbr_County = county,
           Antbtc_Tot_Clms,
           Tot_Benes,
           Bene_Avg_Age,
           Bene_Avg_Risk_Scre,
           Bene_Prop_Fem,
           Bene_Prop_White,
           dataset_address)

```

## Medicare Part D Datasets

- Both have 2013-2019 data.
- by-provider: one row per provider per year. Aggregate antibiotic claim counts.
- by provider & drug: one row per provider per drug per year. 


:::::: {.columns}
::: {.column}

By provider

<br>

- Bullet 1
- Bullet 2
- Bullet 3

:::

::: {.column}

By provider & drug

<br>

- Bullet 1
- Bullet 2
- Bullet 3

:::
::::::




## Example by-provider

```{r, echo = FALSE, error = FALSE, message = FALSE, warning = FALSE}

set.seed(1)
p %>%
    filter(Year == 2019,
           !is.na(Antbtc_Tot_Clms),
           Antbtc_Tot_Clms != 0) %>%
    sample_n(2) %>%
    select(`Prv NPI` = Prscrbr_NPI,
           `Prv Type` = Prscrbr_Type_Std,
           `Prv Rural` = Prscrbr_RUCA_fct,
           `Prv Gen` = Prscrbr_Gndr,
           `N Ant Clms` = Antbtc_Tot_Clms,
           `N Bens` = Tot_Benes,
           `Ben HCC` = Bene_Avg_Risk_Scre,
           `Ben Age` = Bene_Avg_Age,
           `Ben Fem %` = Bene_Prop_Fem,
           `Ben White %` = Bene_Prop_White) %>%
    mutate(across(matches("%"), ~ . * 100),
           across(matches("Age|Fem|White"), ~ round(., digits = 0)),
           `Ben HCC` = round(`Ben HCC`, digits = 2),
           across(everything(), ~ as.character(.))) %>%
    kable()

```


## Slide with R Output

```{r cars, echo = TRUE}
summary(cars)

```

## Slide with Plot

```{r pressure}
plot(pressure)
```

## Two Column Slide

:::::: {.columns}
::: {.column}

- Bullet 1
- Bullet 2
- Bullet 3

:::

::: {.column}

```{r pressure3}
plot(pressure)
```

:::
::::::