---
title: "First steps with the mipplot package"
author: "U-Tokyo mipplot team"
date: "`r Sys.Date()`"
output: rmarkdown::html_document
vignette: >
  %\VignetteIndexEntry{First steps with the mipplot package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

## Summary

The `mipplot` package is an `R` package for visualizing data in the IAMC template format.

This vignette introduces the basic features of the `mipplot` package.

## Data

This vignette uses a subset of the SR1.5 data.
This data is included in the `mipplot` package and does not need to be downloaded separately.

## load library

Load the `mipplot` and `tidyverse` packages.

```{r, message=FALSE, warning=FALSE}
library(mipplot)
library(tidyverse)
```

## load sample data

The SR1.5 subset data that comes with the `mipplot` package can be accessed under the name `mipplot::sr15_sample_data`. Let's take a look at what's inside.

```{r}
sr15_sample_data
```

The data is in `tibble` format. It contains the following columns: `model`, `scenario`, `region`, `variable`, `unit`, `period`, and `value`.

In addition, to read another data from a file, do the following.

```{r, eval=FALSE}
iamc_data <- mipplot_read_iamc("path_to_data.csv")
```

Check the list of models included in the data.

```{r}
sr15_sample_data$model %>% levels
```

Check the list of scenarios.

```{r}
sr15_sample_data$scenario %>% levels
```

Check the list of regions.

```{r}
sr15_sample_data$region %>% levels
```

Let's check the list of variables.

```{r}
sr15_sample_data %>% select(variable, unit) %>% distinct
```

## Data Filtering

This section explains how to filter data, using the example of extracting only models that contain `SSP2` scenarios.

```{r}
sr15_sample_data %>% 
  filter(str_detect(scenario, "^SSP2")) %>%
  select(model, scenario) %>% distinct
```

The `"^SSP2"` given to the `str_detect` function is called a regular expression.
The `"^"` stands for the beginning of a sentence, and the regular expression "^SSP2"" means to check if the string starts with "SSP2".

`distinct` is a function that removes duplicate data.

Next, let's extract the list of variables under **Secondary Energy|Electricity|**.

"|" has a special meaning in regular expressions, so to search for it we must use something like "\\\|". This is called escaping.

```{r}
sr15_sample_data %>% 
  filter(str_detect(variable, "^Secondary Energy\\|Electricity\\|")) %>% 
  select(variable) %>%
  distinct
```

## Visualization with interactive tool

Let's use the interactive interface of `mipplot` to filter and visualize the data.

### line plot

The first step is to perform a line plot, which can be done as follows:

```{r, eval=FALSE}
mipplot_interactive_line(sr15_sample_data)
```

You will then see a screen like the one below.

```{r, echo=FALSE, out.width="100%"}
knitr::include_graphics("screenshot_mipplot_interactive_line.png")
```

Select the various conditions and plot the graph.

The R code for reproducing the graph will be displayed on the screen.
You can copy and paste it to edit the plot as shown below.

```{r, fig.width=10, fig.height=7, out.width="100%"}
data_subset <- sr15_sample_data %>%
  filter( model %in% c("AIM/CGE 2.0", "GCAM 4.2", "IMAGE 3.0.1") ) %>%
  filter(2000 <= period) %>%
  filter(period <= 2100)

mipplot_line(
  data_subset,
  variable = c("Emissions|CO2"),
  scenario = c("SSP3-34", "SSP3-45", "SSP3-60", "SSP3-Baseline"),
  region = c("World"),
  legend = TRUE,
  language = 'en')[[1]]

```

### bar plot

Next, we will create a bar plot.
Since bar plot needs a rule table, we will use the rule `ar5_db_sample_rule_table` for AR5.

```{r, eval=FALSE}
mipplot_interactive_bar(sr15_sample_data, ar5_db_sample_rule_table)
```

```{r, echo=FALSE, out.width="100%"}
knitr::include_graphics("screenshot_mipplot_interactive_bar.png")
```

On the screen you will see the R code to reproduce the plot.
Copy and paste this to edit the plot as shown below.

```{r, fig.width=10, fig.height=7, out.width="100%"}
data_subset <- sr15_sample_data %>%
  filter(variable %in% c("Primary Energy", "Primary Energy|Coal", "Primary Energy|Gas", "Primary Energy|Oil", "Primary Energy|Nuclear", "Primary Energy|Hydro", "Primary Energy|Biomass", "Primary Energy|Solar", "Primary Energy|Wind", "Primary Energy|Geothermal", "Primary Energy|Ocean")) %>%
  filter(model %in% c("AIM/CGE 2.0", "GCAM 4.2", "IMAGE 3.0.1")) %>%
  filter(scenario %in% c("SSP3-34", "SSP3-45", "SSP3-60", "SSP3-Baseline"))

mipplot_bar(data_subset, ar5_db_sample_rule_table,
  region = c("World"),
  target_year = 2050,
  one_hundred_percent_stacked = FALSE,
  language = 'en')[[1]]

```


### area plot

Since area plot also requires a rule, we will use the rule `ar5_db_sample_rule_table` for AR5.

```{r, eval=FALSE}
mipplot_interactive_area(sr15_sample_data, ar5_db_sample_rule_table)
```

```{r, echo=FALSE, out.width="100%"}
knitr::include_graphics("screenshot_mipplot_interactive_area.png")
```

On the screen you will see the R code to reproduce the plot.
Copy and paste this to edit the plot as shown below.

```{r, fig.width=10, fig.height=7, out.width="100%"}
data_subset <- sr15_sample_data %>%
  filter(variable %in% c("Secondary Energy|Electricity", "Secondary Energy|Electricity|Coal", "Secondary Energy|Electricity|Gas", "Secondary Energy|Electricity|Oil", "Secondary Energy|Electricity|Nuclear", "Secondary Energy|Electricity|Hydro", "Secondary Energy|Electricity|Biomass", "Secondary Energy|Electricity|Solar", "Secondary Energy|Electricity|Wind", "Secondary Energy|Electricity|Geothermal", "Secondary Energy|Electricity|Ocean")) %>%
  filter(model %in% c("AIM/CGE 2.0", "GCAM 4.2", "IMAGE 3.0.1")) %>%
  filter(2000 <= period) %>%
  filter(period <= 2100)

mipplot_area(data_subset, ar5_db_sample_rule_table,
  region = c("World"),
  scenario = c("SSP3-Baseline"),
  one_hundred_percent_stacked = FALSE,
  language = 'en')[[1]]
```

## Output in another language

So far, we have plotted in English, but in addition to this, we can plot in Chinese (Simplified), Chinese (Traditional), Japanese, and Spanish.

All plots so far have specified `language = "en"` (English), so replace this with, for example, the following:

```{r, warning=FALSE, fig.width=10, fig.height=7, out.width="100%", eval=FALSE}
data_subset <- sr15_sample_data %>%
  filter(variable %in% c("Secondary Energy|Electricity", "Secondary Energy|Electricity|Coal", "Secondary Energy|Electricity|Gas", "Secondary Energy|Electricity|Oil", "Secondary Energy|Electricity|Nuclear", "Secondary Energy|Electricity|Hydro", "Secondary Energy|Electricity|Biomass", "Secondary Energy|Electricity|Solar", "Secondary Energy|Electricity|Wind", "Secondary Energy|Electricity|Geothermal", "Secondary Energy|Electricity|Ocean")) %>%
  filter(model %in% c("AIM/CGE 2.0", "GCAM 4.2", "IMAGE 3.0.1")) %>%
  filter(2000 <= period) %>%
  filter(period <= 2100)

mipplot_area(data_subset, ar5_db_sample_rule_table,
  region = c("World"),
  scenario = c("SSP3-Baseline"),
  one_hundred_percent_stacked = FALSE,
  language = 'jp')[[1]]  # <--------------- We changed here
```


The following table shows the languages and language codes that can be set.

| language | language code |
| ---- | ---- |
| English | en |
| Chinese (Simplified) | zh-cn |
| Chinese (Traditional) | zh-tw |
| Japanese | jp |
| Spanish | es |


## Output to PDF file

To output an image to a PDF file, do the following:

```{r, eval=FALSE, fig.width=10}
data_subset <- sr15_sample_data %>%
  filter(variable %in% c("Secondary Energy|Electricity", "Secondary Energy|Electricity|Coal", "Secondary Energy|Electricity|Gas", "Secondary Energy|Electricity|Oil", "Secondary Energy|Electricity|Nuclear", "Secondary Energy|Electricity|Hydro", "Secondary Energy|Electricity|Biomass", "Secondary Energy|Electricity|Solar", "Secondary Energy|Electricity|Wind", "Secondary Energy|Electricity|Geothermal", "Secondary Energy|Electricity|Ocean")) %>%
  filter(model %in% c("AIM/CGE 2.0", "GCAM 4.2", "IMAGE 3.0.1")) %>%
  filter(2000 <= period) %>%
  filter(period <= 2100)

# Save plot to `graph` variable
graph <- mipplot_area(data_subset, ar5_db_sample_rule_table,
  region = c("World"),
  scenario = c("SSP3-Baseline"),
  one_hundred_percent_stacked = FALSE,
  language = 'zh-cn')[[1]]

# Give `graph` to `mipplot_print_pdf`
# mipplot_print_pdf(graph)

```

This will bring up a dialog box to save the file.
You can save the file as a PDF file.