//In the previous section you learned how to calculate total forest area lost in 
//the given region of interest using the reduceRegion method. Instead of 
//calculating the total loss, it would be helpful to compute the loss for 
//each year. The way to achieve this in Earth Engine is using a Grouped Reducer.

//To group output of reduceRegion(), you can specify a grouping band that 
//defines groups by integer pixel values. In the following example, we 
//slightly modify the previous code and add the lossYear band to the original
//image. Each pixel in the lossYear band contain values from 0 to 14 - indicating 
//the year in which the loss occurred. We also change the reducer to a grouped 
//reducer, specifying the band index of the grouping band (1) so the pixel 
//areas will be summed and grouped according to the value in the lossYear band. 

// Load country boundaries from LSIB.
var countries = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017');
// Get a feature collection with just the feature of the Republic of the Congo.
var congo = countries.filter(ee.Filter.eq('country_na', 'Germany'));

// Get the loss image.
// This dataset is updated yearly, so we get the latest version.
var gfc2017 = ee.Image("UMD/hansen/global_forest_change_2020_v1_8");
var lossImage = gfc2017.select(['loss']);
var lossAreaImage = lossImage.multiply(ee.Image.pixelArea());

var lossYear = gfc2017.select(['lossyear']);
var lossByYear = lossAreaImage.addBands(lossYear).reduceRegion({
  reducer: ee.Reducer.sum().group({
    groupField: 1
    }),
  geometry: congo,
  scale: 30,
  maxPixels: 1e10
});
print(lossByYear);

//Once you run the above code, you will see the yearly forest 
//loss area printed out in a nested list called groups. We can format 
//the output a little to make the result a dictionary, with year as the 
//key and loss area as the value. Notice that we are using the format() 
//method to convert the year values from 0-14 to 2000-2014.

var statsFormatted = ee.List(lossByYear.get('groups'))
  .map(function(el) {
    var d = ee.Dictionary(el);
    return [ee.Number(d.get('group')).format("20%02d"), d.get('sum')];
  });
var statsDictionary = ee.Dictionary(statsFormatted.flatten());
print(statsDictionary);

//Now that we have yearly loss numbers, we are ready to prepare a chart. 
//We will use the ui.Chart.array.values() method. This method takes an array 
//(or list) of input values and an array (or list) of labels for the X-axis. 
var chart = ui.Chart.array.values({
  array: statsDictionary.values(),
  axis: 0,
  xLabels: statsDictionary.keys()
}).setChartType('ColumnChart')
  .setOptions({
    title: 'Yearly Forest Loss in Germany',
    hAxis: {title: 'Year', format: '####'},
    vAxis: {title: 'Area (square meters)'},
    legend: { position: "none" },
    lineWidth: 1,
    pointSize: 3
  });
print(chart);

// This is how you can add a vector layer to the map view.
Map.addLayer(congo, {color: 'FF0000'}, 'Germany');