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visualization_function_pres.Rmd
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visualization_function_pres.Rmd
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---
title: "Data Visualization Intro"
author: Austin Delany
date: '2023-05-16'
output: html_document
---
# Creating Visuals With R Functions
## FlareVis GitHub Repository
Repo for FLARE visualizations can be found at <https://github.com/FLARE-forecast/flareVis>
## Forking the branch and cloning locally
- Go to <https://github.com/FLARE-forecast/flareVis> and fork the repository (create a copy on your github account)
- Click the green "code" button and copy the HTTPS link
- Go to your local Rstudio and create a new project (Version Control, Git, URL)
## Create A visualization function
```{r}
example_plot <- function(data, depths, tzone, ylims){
p <- ggplot(data, aes(x=datetime,y=prediction, group = parameter)) +
geom_line() +
ylab('Temperature (deg C)') +
xlab('Date')
ggtitle('FLARE Forecast Members') +
ylim(ylims) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
return(p)
}
```
#Read in forecast data -- try to do as much filtering as possible before the collect() statement
```{r, echo=FALSE}
forecast_s3 <- arrow::s3_bucket('forecasts/parquet', endpoint_override = 's3.flare-forecast.org', anonymous = TRUE)
sunp_data <- arrow::open_dataset(forecast_s3) |>
dplyr::filter(site_id == 'sunp',
reference_datetime == '2023-05-01 00:00:00',
variable == 'temperature',
depth == 1.0) |>
collect()
```
#Call function with data and display plot
```{r}
forecast_fig <- example_plot(sunp_data, depths = NULL, tzone = NULL, ylims = c(0,30))
forecast_fig
```