README.Rmd

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output: github_document
---

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```{r, include = FALSE}
knitr::opts_chunk$set(
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  fig.path = "man/figures/README-",
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# cram

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The goal of cram is to process output from the Lynker CRAM model.


## Installation

You can install the development version of cram from [GitHub](https://github.com/) with:

``` r
# install.packages("remotes")
remotes::install_github("Lynker-Tech/cram")
```

## Example

This is a basic example which shows you how to solve a common problem:

```{r parse_example}
library(cram)

base_folder  <-  "D:/downloads/runs/2049 v0.469 0.13/"

## basic example code
models       <-  cram::parse_directory(base_folder)
models 
```

Now that we've located the model directory, we can use the `process_cram()` function to process the output sheets within the model directory
```{r proc_cram_ex}
cram1 <- cram::process_cram(
            model_directory = models[1, ],
            return_wide     = TRUE
          )

head(cram1, 15)
```

<br>

We can also build a lookup table for the output sheets in the model directory using the `lookup_table()` function
```{r lookup_ex}
lookup_df <- cram::lookup_table(
            model_directory = models[1, ]
          )

head(lookup_df, 15)
```

<br>

We can identify the variables in one of processed CRAM output sheets
```{r}
output_variables <- find_variables(
                        df   = cram1
                      )

head(output_variables, 15)
```

Identify available variables using `find_variables()`, and use `plot_cram()` to plot a set of variables for multiple model scenarios after processing CRAM output sheets using `process_cram() `
```{r}
# Plot specific variables for CRAM model runs
cram_plot <- plot_cram(
                  cram_df   = cram1,
                  plot_vars = output_variables$names[17:19],
                  start     = "2004-01-01",
                  end       = "2007-01-01",
                  wrap      = FALSE
                )

cram_plot
```