# Managing Data

There’s not much point creating interactive web pages if they don’t have something to interact with. To provide that, we need something to store data and something to serve it. We could build one program to do both, but experience teaches that it’s better to create one for each so that they are easier to understand, test, and maintain. After tossing a coin, we decide to start with the data store; s:server will look at how to build a server.

## Data Formats

The most widely used text format for tabular data is undoubtedly comma-separated values or CSV. Each row of the table is a line in the file; the values within each row—i.e., the columns—are separated by commas. Numbers appear as themselves; strings may or may not be wrapped in quotation marks, unless they contain commas themselves, in which case they definitely are:

"maroon",128,0,0
"olive",128,128,0
"aqua",0,255,255
"fuchsia",255,0,255


The first line of a CSV file is often a header row that defines the names of the columns. For example, the small table shown above would better be represented as:

"name","red","green","blue"
"maroon",128,0,0
"olive",128,128,0
"aqua",0,255,255
"fuchsia",255,0,255


Tragically, CSV doesn’t require the first row to be a header, and CSV files usually don’t specify units or data types. We can guess that the values in the table above are integers, but it’s all too common to have a CSV file whose columns are labelled “height” and “weight” without any indication of whether the heights are in feet or meters or the weights in pounds or kilograms.

CSV is good for tabular data, but a lot of data doesn’t neatly fit into rows and columns. A format for hierarchical data that is popular with many programmers is JSON, which stands for JavaScript Object Notation. It supports a subset of the syntax for values, arrays, and objects in JavaScript, so that (for example) we can store configuration values for a program like this:

{
"name" : "DataExplorer",
"version" : "1.2.1",
"preferences" : {
"colorscheme" : "dark",
"autofill" : true
},
"last_opened" : [
"raw/biotic.dat",
"raw/genomic.dat",
"cooked/inferred.dat"
]
}


JSON can be used for tabular data as well. The whole table is an array, and each record is an object with name-value pairs:

[
{"name": "maroon", "red": 128, "green": 0, "blue": 0},
{"name": "olive", "red": 128, "green": 128, "blue": 0},
{"name": "aqua", "red": 0, "green": 255, "blue": 255},
{"name": "fuchsia", "red": 255, "green": 0, "blue": 255}
]


Repeating field names like this is wasteful compared to listing them once at the top of a table, but it does mean that the fields within rows can be accessed directly using expressions like colors[1].red.

## Slicing Data

The data we will use as an example is available in a variety of formats from https://figshare.com/articles/Portal_Project_Teaching_Database/1314459. We will focus on surveys.csv, which has over 35,500 records. That’s a lot to look at, so we will create a 10-record slice for testing.

Although it would be easy to take the first ten, or the last, there’s a good chance that neither would be representative of the data as a whole. Instead, we will write a little script that selects N records at random. Since it doesn’t need to be efficient, we will do something simple:

const fs = require('fs')

const [inputFile, numLines, outputFile] = process.argv.splice(2)
.split('\n')
const sample = lines.slice(1)
.map(line => [Math.random(), line])
.sort((left, right) => { return left[0] - right[0] })
.slice(0, parseInt(numLines))
.map(pair => pair[1])
fs.writeFileSync(outputFile, header + '\n' + sample.join('\n'))


We run this on the command line:

$node select-random.js ../../data/surveys.csv 10 slice.csv  and get this: record_id,month,day,year,plot_id,species_id,sex,hindfoot_length,weight 18501,3,14,1991,13,OT,M,21,28 2283,1,15,1980,11,OL,M,21,23 19941,5,2,1992,1,PP,M,22,13 27413,12,29,1997,5,,,, 16002,5,9,1989,19,SC,,, 28813,11,21,1998,12,DO,M,35,56 9338,7,4,1984,11,DO,F,35,57 28336,8,22,1998,7,PB,M,26,23 25323,3,16,1997,9,DM,F,33,26 6785,10,23,1982,5,DM,F,37,45  Running it again will probably generate a different data slice, since we’re not specifying a random number generation seed. We are bad people, and will fix this in the exercises. ## Data Manager Rather arbitrarily, we decide that our data manager will be able to answer two questions: 1. How many records do we have and what range of years do they cover? This is the kind of opening question that many client programs will ask. 2. What are the minimum, average, and maximum values for weight and hindfoot length by year for a given range of years? This would be very specific to a particular kind of client program; a good service would either provide many such specialized queries or provide a way to apply common aggregation functions to particular columns. We will use PapaParse to parse our CSV, so our first step is to install it: $ npm install papaparse


After loading the library and reading our test data file a couple of times, we break down and read the documentation, then come up with this as the first version of our data manager:

const fs = require('fs')
const papa = require('papaparse')

class DataManager {

constructor (filename) {
const options = {header: true, dynamicTyping: true}
this.data = papa.parse(raw, options).data
}
}

module.exports = DataManager


What our hubris made us miss in our first couple of attempts was that the options object controls how the parser behaves. Here, we tell it to interpret the first row as a header (which sets column names) and to convert things that look like numbers to numbers (the dynamicTyping option). The output of papa.parse looks like this:

{ data:
[ { record_id: 18501,
month: 3,
day: 14,
year: 1991,
plot_id: 13,
species_id: 'OT',
sex: 'M',
hindfoot_length: 21,
weight: 28 },

...eight more records...

{ record_id: 6785,
month: 10,
day: 23,
year: 1982,
plot_id: 5,
species_id: 'DM',
sex: 'F',
hindfoot_length: 37,
weight: 45 } ],
errors: [],
meta:
{ delimiter: ',',
linebreak: '\n',
aborted: false,
truncated: false,
cursor: 350,
fields:
[ 'record_id',
'month',
'day',
'year',
'plot_id',
'species_id',
'sex',
'hindfoot_length',
'weight' ] } }


so using papa.parse(raw, options).data gets the data we want as JSON. Let’s write a method to get some overall statistics:

  getSurveyStats () {
return {
year_low : this._get(this.data, 'year', Math.min),
year_high : this._get(this.data, 'year', Math.max),
record_count : this.data.length
}
}

// ...other methods...

_get(values, field, func) {
return func(...values.map(rec => rec[field]).filter(val => !isNaN(val)))
}


Functions like Math.min and Math.max take any number of scalar values as arguments, but do not directly process arrays. However, the notation func(...array) means “pass all the values in the array as separate arguments”, which saves us from writing our own minimum and maximum functions. Thus, func(...this.data.map(rec => rec[field])) means “select the specified field from each record in this.data to create an array of fields, then pass all of those values as arguments to func. We include an underscore to the start of the name of _get to indicate that we intend it to be used only inside DataManager and not to be called elsewhere.

Adding the method to get weight and hindfoot length for a range of years is comparatively straightforward. First, we write a function to calculate the average of one or more arguments:

const _average = (...values) => {
let sum = 0
for (let v of values) {
sum += v
}
return sum / values.length
}


It would be more natural for _average to take an array rather than a variable number of arguments, but we want to be able to use it in the same way that we use Math.min and Math.max, so we have to conform to their signature.

After some thought we realize that it’s possible for subset to be empty - that is, it’s possible that there are years that have no data in our data set. We should filter these out, to prevent unnecessary effort being made to render summary statistics with NaN values. Remembering that empty arrays are not falsy in JavaScript (s:basics), we decide to test that the subset returned by filtering for each year contains at least one entry.

The last thing that we need to ensure is that each data object has a unique key, which will make it much easier for React to efficiently update the display of the data when we are ready to render it.

The method to get the values for a range of years is now:

  getSurveyRange (minYear, maxYear) {
return Array(1 + maxYear - minYear)
.fill(0)
.map((v, i) => minYear + i)
.map(year => {
const subset = this.data.filter(r => r.year === year)
if (subset.length) {
return {
key  : toString(year),
year : year,
min_hindfoot_length :this._get(subset,
'hindfoot_length', Math.min),
ave_hindfoot_length : this._get(subset,
'hindfoot_length', _average),
max_hindfoot_length : this._get(subset,
'hindfoot_length', Math.max),
min_weight : this._get(subset, 'weight', Math.min),
ave_weight : this._get(subset, 'weight', _average),
max_weight : this._get(subset, 'weight', Math.max)
}
}
})
}


## Exercises

### Tracing Data

Trace the execution of the utility program that creates a small sample of the original data, explaining what is passed into each of the chained methods calls.

### Unrandom

Programs that rely on random numbers are impossible to test because there’s (deliberately) no way to predict their output. Luckily, computer programs don’t actually use random numbers: they use pseudo-random numbers that are generated in a repeatable but unpredictable way. Given the same initial seed, a pseudo-random number generator will always produce the same sequence of values.

There is no way to set a seed for Math.random out of the box, but the seedrandom package provides an add-on function for this purpose. Install the package and modify the slice selection utility so that it takes a word or phrase as a command-line argument and uses it to seed the random number generator.

### One Record Per Year

Another way to slice the data for testing purposes is to select one record from each year. Write a small command-line JavaScript program that:

1. Reads all the data from the CSV.
2. Keeps the first record it finds for each year.
3. Prints these records formatted as SQL insert statements.

### Error Handling

Modify DataManager’s constructor so that it checks for errors.

### Generalization

Modify getSurveyRange so that it can be called like this:

getSurveyRange(minYear, maxYear, 'hindfoot_length', 'weight')


i.e., so that the names of the fields whose minimum, average, and maximum values are wanted can be passed as strings, and the method will automatically create the right names and values in its result.

## Key Points

• Small tabular datasets are commonly stored as Comma-Separated Values (CSV).
• CSV can only represent regular data, and CSV files usually don’t include units.
• Nested data is commonly stored using JavaScript Object Notation (JSON).
• JSON representations of tabular data often include redundant (and therefore possibly inconsistent) specifications of column names.
• PapaParse is a robust CSV parsing library that produces JSON output.