_targets.Rmd

---
title: "Target Markdown"
output: html_document
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

Target Markdown is a powerful R Markdown interface for reproducible analysis pipelines, and the chapter at https://books.ropensci.org/targets/markdown.html walks through it in detail. This R Markdown report the example from the chapter. Try it out in both interactive and non-interactive modes, either by running the code chunks in different ways or setting the `tar_interactive` chunk option.

# Packages

The example requires several R packages, and `targets` must be version 0.5.0.9000 or above. 

```{r, eval = FALSE}
install.packages(c("biglm", "dplyr", "ggplot2", "readr", "targets", "tidyr"))
```

# Setup

If you are using old versions of `targets` (<= 0.7.0) and/or `knitr` (<= 1.33), you will need to load the `targets` package in the R Markdown document in order for Target Markdown code chunks to work.

```{r}
library(targets)
```

Near the top of the document, you may also wish to remove the `_targets_r` directory previously written by non-interactive runs of the report. Otherwise, your pipeline may contain superfluous targets.

```{r}
library(targets)
tar_unscript()
```

# Globals

We first define some global options/functions common to all targets. The function below plots a histogram of ozone concentrations, and our histogram target will need it.

```{targets example-globals, tar_globals = TRUE}
options(tidyverse.quiet = TRUE)
tar_option_set(packages = c("biglm", "dplyr", "ggplot2", "readr", "tidyr"))
create_plot <- function(data) {
  ggplot(data) +
    geom_histogram(aes(x = Ozone), bins = 12) +
    theme_gray(24)
}
```

# Targets

Our first target borrows the `airquality` dataset built into base R.

```{targets raw-data}
tar_target(raw_data, airquality)
```

Our next targets preprocess the data, make a histogram, and fit a model.

```{targets downstream-targets}
list(
  tar_target(data, raw_data %>% filter(!is.na(Ozone))),
  tar_target(hist, create_plot(data))
)
```

Set the `tar_simple` chunk option to `TRUE` to define a single target with the command in the code chunk. The chunk below only contains `biglm(Ozone ~ Wind + Temp, data)` in the source, but because `tar_simple` is `TRUE`, it is shorthand for `tar_target(name = fit, command = biglm(Ozone ~ Wind + Temp, data))`. All other arguments to `tar_target()` are set to their default values (configurable with `tar_option_set()`).

```{targets fit, tar_simple = TRUE}
biglm(Ozone ~ Wind + Temp, data)
```

# Pipeline

If you ran all the `{targets}` chunks in non-interactive mode, then your R scripts are set up to run the pipeline.

```{r}
tar_make()
```

# Output

You can retrieve results from the `_targets/` data store using `tar_read()` or `tar_load()`.

```{r, message = FALSE}
library(biglm)
tar_read(fit)
```

```{r}
tar_read(hist)
```

The `targets` dependency graph helps your readers understand the steps of your pipeline at a high level.

```{r}
# some random EDA
if(F){
tar_load(data)
data %>%
  gather("variable","value",-x,-y) %>%
  filter(variable=="MODCF_interannualSD.tif") %>%
  ggplot(aes(x=x,y=y,fill=value))+
#  facet_wrap(~variable)+
  geom_raster()+
  geom_sf(data=region,inherit.aes = F,fill=NA,col="red")
}

```

At this point, you can go back and run `{targets}` chunks in interactive mode without interfering with the code or data of the non-interactive pipeline.