#Medium reduce

We have data which represents a movement of currency pair EUR/USD in the last year 2016. Our data includes three parameters for each day and looks like this { forex: 'EUR/USD', date: '2016-01-01', value: 1.021304401 }. From that data we would like an answer to a question, was average of pair USD/EUR higher in the first half of 2016 or in second?

To find out we use two different solutions.

We implemented four functions.

• customMap in map.ts
• customReduce in reduce.ts
• customFilter in filter.ts
• asynchronousAverage in asynchronous.ts

To find out the average of pair USD/EUR in the first half of 2016 we use first three functions (filter, map and reduce). First, we filter the whole data, using only pairs from the first half of 2016 (date < '2016-07-01'). Then we map our data because we have values for pair EUR/USD, but we were interested in pair USD/EUR, we have to inverse our values (value = 1 / value). Now when we have an array with values we are looking, we calculate a sum of these elements with reduce function and then divide this value by a number of elements in a current array. The result is an average value of pair USD/EUR in the first half of 2016.

To find out the average of pair USD/EUR in the second half of 2016 we use the fourth function (asynchronousAverage). If we look what we've done in the first step we can quickly see, that we've called method array.forEach() for three times (for each function). In case we already know, that we'll need all three functions (map, reduce and filter) we can optimize execution and done everything in one loop. In function asynchronousAverage, we use only one loop and check for each element if it belongs in the second half of 2016 (date > '2016-06-30'), reverse its value (value = 1 / value) and add to our average.

#Usage

$ git clone -b izak https://github.com/irmana/catenate-starter-tasks-Izak88.git
$ cd .\catenate-starter-tasks-Izak88\medium-reduce\
$ npm install
$ npm start

Now open your browser at http://localhost:3000

#Time spent 3h

#Licence MIT