---
title: "Plotting dlfs"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Plotting dlfs}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
# nolint start: indentation-linter
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
  )
# nolint end
```

```{r setup}
library(daisytools)
```
There is a general plot function `plot_dlf` that handles both single files and list of files as returned by `read_dlf`.


## Plotting data from a directory of dlf files
To get started we will read some dlf files with `read_dlf`
```{r}
data_dir <- system.file("extdata", package="daisytools")
dlfs <- read_dlf(file.path(data_dir, "annual"))
names(dlfs)
```
`plot_dlf` can only plot multiple dlf files with the same columns. The data we just read contains data from two different log types and multiple scenarios

```{r}
colnames(dlfs[[1]]@data)
colnames(dlfs[[8]]@data)
```
So we need to select a subset of the files that have the same columns.

```{r}
dlfs <- dlfs[c(1, 2, 3, 4)]
names(dlfs)
dlfs <- drop_dir_from_names(dlfs)
dlfs <- strip_common_prefix_from_names(dlfs)
names(dlfs)
```
These dlfs contain annually logged variables related to field nitrogen
```{r}
colnames(dlfs[[1]]@data)
```

We can plot all the variables, or a subset. Here we plot "Surface_Loss" and "Denitrification".
```{r}
y_vars <- c("Surface_Loss", "Denitrification")
plot_dlf(dlfs, "year", y_vars, type="bar")
```
Notice how `plot_dlf` plots each variable in a separate sub plot, and each scenario (AKA dlf) with a different color.


We can also make a line or points plot, or combine the plots types
```{r}
plot_dlf(dlfs, "year", y_vars, "lines")
plot_dlf(dlfs, "year", y_vars, "points")
plot_dlf(dlfs, "year", y_vars, "lines-points-bar")
```

The lines and points plot types are usually better suited when we have more data points. To see this we will load another dlf file using `read_dlf`
```{r}
path <- file.path(data_dir, "hourly/P2D-Daily-Soil_Chemical_110cm.dlf")
dlf <- read_dlf(path)
colnames(dlf@data)
```
```{r}
y_vars <- c("Leak_Matrix", "Transform")
plot_dlf(dlf, "time", y_vars, "points")
```

If we only want to plot a part of the time period we can use `subset_dlf`
```{r}
summer95 <- subset_dlf(dlf, "1995-06-01", "1995-08-31")
plot_dlf(summer95, "time", y_vars, "lines", title_suffix=" - Summer of '95")
```


## Plotting Daisy spawn output

To get started we will read some dlf files with `read_dlf`
```{r}
data_dir <- system.file("extdata", package="daisytools")
path <- file.path(data_dir, "daisy-spawn-like")
dlfs <- read_dlf(path)
names(dlfs)
```

As before we need to ensure that we only plot dlfs with the same columns. In this case `read_dlf` has done most of the work for us and we can directly index the list of dlfs
```{r}
colnames(dlfs$Harvest@data)
plot_dlf(dlfs$Harvest, "time", "leaf_DM", "lines")
```

We can of course also plot the other log type
```{r}
colnames(dlfs$`FWater200-Y`@data)
plot_dlf(dlfs$`FWater200-Y`, "time", "Irrigation", "bar")
```

## Plotting depth distributed output
To get started we will read a dlf file with `read_dlf`
```{r}
data_dir <- system.file("extdata", package="daisytools")
path <- file.path(data_dir, "daily/DailyP/DailyP-Daily-WaterFlux.dlf")
dlf <- read_dlf(path)
head(dlf)
```

`plot_dlf` does not yet support plotting depth data. Instead you have to use `plot_dlf_depth`
```{r}
plot_dlf_depth(dlf, "q")
```
By default `plot_dlf_depth` selects four time points at random. You can also pass the desired time points
```{r}
plot_dlf_depth(dlf, "q", c("1990-04-23", "1990-04-27"))
```

Depth distributed data is often better visualized by animating it. This can be done with `animate_dlf`. Try running `example(animate_dlf)`.