182_calculate_rain.js

// dataand data1 are two strings with rainfall records of a few cities for months from January to December. The records of towns are separated by \n. The name of each town is followed by :.

// data and towns can be seen in "Your Test Cases:".
// Task:

//     function: mean(town, strng) should return the average of rainfall for the city town and the strng data or data1 (In R and Julia this function is called avg).
//     function: variance(town, strng) should return the variance of rainfall for the city town and the strng data or data1.

// Examples:

// mean("London", data), 51.19(9999999999996) 
// variance("London", data), 57.42(833333333374)

// Notes:

//     if functions mean or variance have as parameter town a city which has no records return -1 or -1.0 (depending on the language)

//     Don't truncate or round: the tests will pass if abs(your_result - test_result) <= 1e-2 or abs((your_result - test_result) / test_result) <= 1e-6 depending on the language.

//     Shell
//         Shell tests only variance.
//         In "function "variance" $1 is "data", $2 is "town".

//     A ref: http://www.mathsisfun.com/data/standard-deviation.html

//     data and data1 (can be named d0 and d1 depending on the language; see "Sample Tests:") are adapted from: http://www.worldclimate.com

//P we are given the name of a town, and two datasets

//R: We are returning either the mean of the total data from the datasets, or the variance of the datasets

//E: we are given Rome and data in the function mean => find how many times rome occurs then divide the total value by that number

const data = 
     "Rome:Jan 81.2,Feb 63.2,Mar 70.3,Apr 55.7,May 53.0,Jun 36.4,Jul 17.5,Aug 27.5,Sep 60.9,Oct 117.7,Nov 111.0,Dec 97.9" + "\n" +
     "London:Jan 48.0,Feb 38.9,Mar 39.9,Apr 42.2,May 47.3,Jun 52.1,Jul 59.5,Aug 57.2,Sep 55.4,Oct 62.0,Nov 59.0,Dec 52.9" + "\n" +
     "Paris:Jan 182.3,Feb 120.6,Mar 158.1,Apr 204.9,May 323.1,Jun 300.5,Jul 236.8,Aug 192.9,Sep 66.3,Oct 63.3,Nov 83.2,Dec 154.7" + "\n" +
     "NY:Jan 108.7,Feb 101.8,Mar 131.9,Apr 93.5,May 98.8,Jun 93.6,Jul 102.2,Aug 131.8,Sep 92.0,Oct 82.3,Nov 107.8,Dec 94.2" + "\n" +
     "Vancouver:Jan 145.7,Feb 121.4,Mar 102.3,Apr 69.2,May 55.8,Jun 47.1,Jul 31.3,Aug 37.0,Sep 59.6,Oct 116.3,Nov 154.6,Dec 171.5" + "\n" +
     "Sydney:Jan 103.4,Feb 111.0,Mar 131.3,Apr 129.7,May 123.0,Jun 129.2,Jul 102.8,Aug 80.3,Sep 69.3,Oct 82.6,Nov 81.4,Dec 78.2" + "\n" +
     "Bangkok:Jan 10.6,Feb 28.2,Mar 30.7,Apr 71.8,May 189.4,Jun 151.7,Jul 158.2,Aug 187.0,Sep 319.9,Oct 230.8,Nov 57.3,Dec 9.4" + "\n" +
     "Tokyo:Jan 49.9,Feb 71.5,Mar 106.4,Apr 129.2,May 144.0,Jun 176.0,Jul 135.6,Aug 148.5,Sep 216.4,Oct 194.1,Nov 95.6,Dec 54.4" + "\n" +
     "Beijing:Jan 3.9,Feb 4.7,Mar 8.2,Apr 18.4,May 33.0,Jun 78.1,Jul 224.3,Aug 170.0,Sep 58.4,Oct 18.0,Nov 9.3,Dec 2.7" + "\n" +
     "Lima:Jan 1.2,Feb 0.9,Mar 0.7,Apr 0.4,May 0.6,Jun 1.8,Jul 4.4,Aug 3.1,Sep 3.3,Oct 1.7,Nov 0.5,Dec 0.7";
const data1 =
     "Rome:Jan 90.2,Feb 73.2,Mar 80.3,Apr 55.7,May 53.0,Jun 36.4,Jul 17.5,Aug 27.5,Sep 60.9,Oct 147.7,Nov 121.0,Dec 97.9" + "\n" +
     "London:Jan 58.0,Feb 38.9,Mar 49.9,Apr 42.2,May 67.3,Jun 52.1,Jul 59.5,Aug 77.2,Sep 55.4,Oct 62.0,Nov 69.0,Dec 52.9" + "\n" +
     "Paris:Jan 182.3,Feb 120.6,Mar 188.1,Apr 204.9,May 323.1,Jun 350.5,Jul 336.8,Aug 192.9,Sep 66.3,Oct 63.3,Nov 83.2,Dec 154.7" + "\n" +
     "NY:Jan 128.7,Feb 121.8,Mar 151.9,Apr 93.5,May 98.8,Jun 93.6,Jul 142.2,Aug 131.8,Sep 92.0,Oct 82.3,Nov 107.8,Dec 94.2" + "\n" +
     "Vancouver:Jan 155.7,Feb 121.4,Mar 132.3,Apr 69.2,May 85.8,Jun 47.1,Jul 31.3,Aug 37.0,Sep 69.6,Oct 116.3,Nov 154.6,Dec 171.5" + "\n" +
     "Sydney:Jan 123.4,Feb 111.0,Mar 151.3,Apr 129.7,May 123.0,Jun 159.2,Jul 102.8,Aug 90.3,Sep 69.3,Oct 82.6,Nov 81.4,Dec 78.2" + "\n" +
     "Bangkok:Jan 20.6,Feb 28.2,Mar 40.7,Apr 81.8,May 189.4,Jun 151.7,Jul 198.2,Aug 197.0,Sep 319.9,Oct 230.8,Nov 57.3,Dec 9.4" + "\n" +
     "Tokyo:Jan 59.9,Feb 81.5,Mar 106.4,Apr 139.2,May 144.0,Jun 186.0,Jul 155.6,Aug 148.5,Sep 216.4,Oct 194.1,Nov 95.6,Dec 54.4" + "\n" +
     "Beijing:Jan 13.9,Feb 14.7,Mar 18.2,Apr 18.4,May 43.0,Jun 88.1,Jul 224.3,Aug 170.0,Sep 58.4,Oct 38.0,Nov 19.3,Dec 2.7" + "\n" +
     "Lima:Jan 11.2,Feb 10.9,Mar 10.7,Apr 10.4,May 10.6,Jun 11.8,Jul 14.4,Aug 13.1,Sep 23.3,Oct 1.7,Nov 0.5,Dec 10.7";


function mean(town, strng) {
     //P Verify that the town passed into the funtion exists in the data set
     //split the data set so that you can navigate to the city provided in the town variable
     //take that string and replace the : with a , so we can further split that string into another array
     //now that we have the city's yearly rain in an array, we want to break each entry into its own array
     //each month we split into its own array by splitting on the whitespace
     //each time we iterate through a month, we add it to the total month rain count
     //for each month, we are splitting it into an array with two elements - the second element will be the total rain count for that month
     //for each month, we target month[1] and add it to the total rain fall
     //after we have total rain fall and total months that had rain, we can return total rain / total months to find the mean
     let cities = strng.split('\n')
     let listOfCities = []
     cities.forEach(element=>{
          listOfCities.push(element.split(':')[0])
     })
     if (!listOfCities.includes(town)){
          return -1
     }
     let monthCount = 0
     let totalRain = 0
     cities.forEach(element=>{
          if (element.includes(town)){
               element = element.replace(':', ',')
               monthCount = element
               element = element.split(',')
               monthCount = element.length - 1
               
               element.forEach((month,index)=>{
                    
                    if (index != 0){
                         month = month.split(' ')
                         totalRain += Number(month[1])
                    }
               })
               
          }
     })
     console.log(listOfCities)
     return totalRain / monthCount
}
function variance(town, strng) {
    //P
    //To find the variance, first we need the mean
    //after calling mean function we need to create a deviations array
     //after deviations array, we have to iterate through the data set again like we did before
     //to find the variance, we must find the squared deviation from the mean for each month
     //split each month on the empty space so that month[1] will be the total rain fall
     //for each month, we subtract the total rainfall for that month from the mean, then square it, then push it onto the deviations array
     //after all total rain fall is squared and added into deviations, we use the reduce function to get the total deviation, then we divide it by the length of the deviations array to find the variance
     //return variance
     cities = strng.split('\n')
     let listOfCities = []
     cities.forEach(element=>{
          listOfCities.push(element.split(':')[0])
     })
     if (!listOfCities.includes(town)){
          return -1
     }
     let meanRain = mean(town,strng)
     let deviations = []

     
     cities.forEach(city =>{
          if (city.includes(town)){
               city = city.replace(':',',')
               let cityRainYear = city.split(',')
               cityRainYear.forEach((month,index)=>{
                    if (index != 0){
                         month = month.split(' ')
                         deviations.push((meanRain - Number(month[1])) ** 2)
                         
                    }
               })
          }
     })
     let totalDeviation = deviations.reduce((acc,c)=>acc += c,0)
     return totalDeviation / deviations.length
}

console.log(variance('London', data))