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h-full"},U.createElement(En,{input:a,onChange:o})),U.createElement("div",{className:"divider my-4 -mx-2"}),U.createElement("div",{className:"flex-grow h-full"},U.createElement(Tn,{input:a,codeStr:n,key:n,outputType:u,onChangeOutputType:c})))))))}}}]); \ No newline at end of file diff --git a/docs/api/groupby/index.html b/docs/api/groupby/index.html index 5efdc4c..30fa371 100644 --- a/docs/api/groupby/index.html +++ b/docs/api/groupby/index.html @@ -8,10 +8,10 @@ groupBy API | tidy.js - + - + @@ -43,10 +43,10 @@
tidy(
data,
groupBy(['str', 'ing'], [
summarize({ total: sum('value') })
], groupBy.levels({
levels: ['entries-object', 'object'],
single: true
}))
)
// output:
[
{ // <-- this level is an "entries object"
"key": "a",
"values": { // <-- this level is an "object"
"x": {"str": "a", "ing": "x", "total": 1},
"y": {"str": "a", "ing": "y", "total": 9},
"z": {"str": "a", "ing": "z", "total": 11}
}
},
{
"key": "b",
"values": {
"x": {"str": "b", "ing": "x", "total": 300},
"y": {"str": "b", "ing": "y", "total": 300},
"z": {"str": "b", "ing": "z", "total": 400}
}
}
]
tidy(
data,
groupBy(['str', 'ing'], [
summarize({ total: sum('value') })
], groupBy.levels({
levels: ['object', 'entries-object'], // swapped order
single: true
}))
)
// output:
{ // <-- this level is an "object"
"a": [ // <-- this level is "entries object"s
{"key": "x", "values": {"str": "a", "ing": "x", "total": 1}},
{"key": "y", "values": {"str": "a", "ing": "y", "total": 9}},
{"key": "z", "values": {"str": "a", "ing": "z", "total": 11}}
],
"b": [
{"key": "x", "values": {"str": "b", "ing": "x", "total": 300}},
{"key": "y", "values": {"str": "b", "ing": "y", "total": 300}},
{"key": "z", "values": {"str": "b", "ing": "z", "total": 400}}
]
}

Custom Levels Export#

For a more advanced export, a custom levels export can be specified by providing a LevelSpec for the value of levels:

// LevelSpec:
{
createEmptySubgroup: () => any;
addSubgroup: (
parentGrouped: any,
newSubgroup: any,
key: any,
level: number
) => void;
addLeaf: (
parentGrouped: any,
key: any,
values: any[],
level: number
) => void;
}

Probably best to just look at the source code of the existing groupBy methods to get an idea of how to use this.

Edit this page
Last updated on by Peter Beshai
- + - + diff --git a/docs/api/item/index.html b/docs/api/item/index.html index 57e68bc..3d52359 100644 --- a/docs/api/item/index.html +++ b/docs/api/item/index.html @@ -8,10 +8,10 @@ Item Function API | tidy.js - + - + @@ -23,10 +23,10 @@

Item Function API

Mapping functions that map from a single item (or row) to a value. Typically used with mutate.

rate#

Returns a function that computes a rate (numerator / denominator), setting the value to 0 if denominator = 0 and numerator = 0. If denominator or numerator are nully, the result is undefined.

Parameters#

numerator#

| string /* key of object */
| (item: object, index: number, array: object[]) => number

Either a key of the object or an accessor function that returns a number given the object. This will be used as the numerator when computing the rate.

denominator#

| string /* key of object */
| (item: object, index: number, array: object[]) => number

Either a key of the object or an accessor function that returns a number given the object. This will be used as the denominator when computing the rate.

options?#

{
predicate?: (item: object, index: number, array: object[]) => boolean
allowDivideByZero?: boolean
}
  • allowDivideByZero = false: If true, evaluates division when denominator is 0 (typically resulting in Infinity), otherwise rates with 0 denominators are undefined.
  • predicate: Optional parameter that when provided will return undefined as the value of the rate when the predicate function returns false. This is typically used for only computing rates when there is sufficient sample size (i.e., checking if the denominator is large enough). For example:
rate('numerator', 'denominator', { predicate: d => d.denominator > 1000 })

Usage#

const data = [
{ str: 'foo', value: 3, value2: 0 },
{ str: 'foo', value: 1, value2: 1 },
{ str: 'bar', value: 3, value2: null },
{ str: 'bar', value: null, value2: 4 },
{ str: 'bar', value: 0, value2: 0 },
{ str: 'bar', value: 7, value2: 35 },
];
tidy(
data,
mutate({
rate1: rate(
(d) => d.value * 100,
(d) => d.value2
),
rate2: rate('value', 'value2'),
})
)
// output:
[
{ str: 'foo', value: 3, value2: 0, rate1: Infinity, rate2: Infinity },
{ str: 'foo', value: 1, value2: 1, rate1: 100, rate2: 1 },
{ str: 'bar', value: 3, value2: null, rate1: undefined, rate2: undefined, },
{ str: 'bar', value: null, value2: 4, rate1: 0, rate2: undefined },
{ str: 'bar', value: 0, value2: 0, rate1: 0, rate2: 0 },
{ str: 'bar', value: 7, value2: 35, rate1: 20, rate2: 0.2 },
]
Edit this page
Last updated on by Peter Beshai
- + - + diff --git a/docs/api/math/index.html b/docs/api/math/index.html index 4794524..3906a82 100644 --- a/docs/api/math/index.html +++ b/docs/api/math/index.html @@ -8,10 +8,10 @@ Tidy Math API | tidy.js - + - + @@ -22,10 +22,10 @@

Tidy Math API

add#

Adds two numbers together, returning undefined if either are nully. Optionally replaces nully values with 0.

Parameters#

a#

number | null | undefined

The first operand

b#

number | null | undefined

The second operand

nullyZero? = false#

boolean

If true, nully (null and undefined) values are replaced with 0 before performing the addition.

Usage#

TMath.add(1, 2) // 3
TMath.add(null, 1) // undefined
TMath.add(1, null) // undefined
TMath.add(null, null) // undefined
TMath.add(null, 1, true) // 1
TMath.add(1, null, true) // 1
TMath.add(null, null, true) // 0

rate#

Computes a rate (numerator / denominator), setting the value to 0 if denominator = 0 and numerator = 0. If denominator or numerator are nully, the result is undefined.

See also item::rate

Parameters#

numerator#

number | null | undefined

The value to use as numerator.

denominator#

number | null | undefined

The value to use as denominator.

allowDivideByZero?#

boolean | undefined

If true, evaluates division when denominator is 0 (typically resulting in Infinity), otherwise rates with 0 denominators are undefined.

Usage#

TMath.rate(5, 10); // 0.5
TMath.rate(5, null); // undefined
TMath.rate(undefined, 15); // undefined
TMath.rate(0, 0); // undefined
TMath.rate(5, 0); // undefined
TMath.rate(5, 0, true); // Infinity

subtract#

Subtracts two numbers, returning undefined if either are nully. Optionally replaces nully values with 0.

Parameters#

a#

number | null | undefined

The first operand

b#

number | null | undefined

The second operand

nullyZero? = false#

boolean

If true, nully (null and undefined) values are replaced with 0 before performing the subtraction.

Usage#

TMath.subtract(1, 2) // -1
TMath.subtract(null, 1) // undefined
TMath.subtract(1, null) // undefined
TMath.subtract(null, null) // undefined
TMath.subtract(null, 1, true) // -1
TMath.subtract(1, null, true) // 1
TMath.subtract(null, null, true) // 0
Edit this page
Last updated on by Peter Beshai
- + - + diff --git a/docs/api/pivot/index.html b/docs/api/pivot/index.html index 86231a4..21f6579 100644 --- a/docs/api/pivot/index.html +++ b/docs/api/pivot/index.html @@ -8,10 +8,10 @@ Pivot API | tidy.js - + - + @@ -27,10 +27,10 @@
tidy(
data,
pivotWider({
namesFrom: 'place',
valuesFrom: 'val',
})
);
// output:
[
{ type: 'one', canada: 1, usa: 10, campbell: 4, brampton: 8 },
{ type: 'two', brampton: 7, boston: 3, usa: 11 },
{ type: 'three', canada: 20 },
]
const data = [
{ type: 'one', other: 5, place: 'canada', val: 1, secval: 91 },
{ type: 'one', other: 5, place: 'usa', val: 10, secval: 910 },
{ type: 'one', other: 6, place: 'campbell', val: 4, secval: 94 },
{ type: 'one', other: 6, place: 'brampton', val: 8, secval: 98 },
{ type: 'two', other: 5, place: 'brampton', val: 7, secval: 97 },
{ type: 'two', other: 5, place: 'boston', val: 3, secval: 93 },
{ type: 'two', other: 6, place: 'usa', val: 11, secval: 911 },
{ type: 'three', other: 5, place: 'canada', val: 20, secval: 920 },
];
const results = tidy(
data,
pivotWider({
namesFrom: ['place', 'other'],
valuesFrom: ['val', 'secval'],
valuesFillMap: { val: 0, secval: -1 },
})
);
// output:
[{
secval_boston_5: -1,
secval_boston_6: -1,
secval_brampton_5: -1,
secval_brampton_6: 98,
secval_campbell_5: -1,
secval_campbell_6: 94,
secval_canada_5: 91,
secval_canada_6: -1,
secval_usa_5: 910,
secval_usa_6: -1,
type: 'one',
val_boston_5: 0,
val_boston_6: 0,
val_brampton_5: 0,
val_brampton_6: 8,
val_campbell_5: 0,
val_campbell_6: 4,
val_canada_5: 1,
val_canada_6: 0,
val_usa_5: 10,
val_usa_6: 0,
},
{
secval_boston_5: 93,
secval_boston_6: -1,
secval_brampton_5: 97,
secval_brampton_6: -1,
secval_campbell_5: -1,
secval_campbell_6: -1,
secval_canada_5: -1,
secval_canada_6: -1,
secval_usa_5: -1,
secval_usa_6: 911,
type: 'two',
val_boston_5: 3,
val_boston_6: 0,
val_brampton_5: 7,
val_brampton_6: 0,
val_campbell_5: 0,
val_campbell_6: 0,
val_canada_5: 0,
val_canada_6: 0,
val_usa_5: 0,
val_usa_6: 11,
},
{
secval_boston_5: -1,
secval_boston_6: -1,
secval_brampton_5: -1,
secval_brampton_6: -1,
secval_campbell_5: -1,
secval_campbell_6: -1,
secval_canada_5: 920,
secval_canada_6: -1,
secval_usa_5: -1,
secval_usa_6: -1,
type: 'three',
val_boston_5: 0,
val_boston_6: 0,
val_brampton_5: 0,
val_brampton_6: 0,
val_campbell_5: 0,
val_campbell_6: 0,
val_canada_5: 20,
val_canada_6: 0,
val_usa_5: 0,
val_usa_6: 0,
}]
Edit this page
Last updated on by ellemenno
- + - + diff --git a/docs/api/selectors/index.html b/docs/api/selectors/index.html index ceaf6fb..208e39e 100644 --- a/docs/api/selectors/index.html +++ b/docs/api/selectors/index.html @@ -8,10 +8,10 @@ Selectors API | tidy.js - + - + @@ -34,10 +34,10 @@
tidy(data, select([startsWith('bAR')]))
// output:
[{ foobar: 300, FoObAR: 90 }]
tidy(data, select([startsWith('bAR', false)]))
// output:
[{ FoObAR: 90 }]
Edit this page
Last updated on by Peter Beshai
- + - + diff --git a/docs/api/sequences/index.html b/docs/api/sequences/index.html index 1edd261..49ffffb 100644 --- a/docs/api/sequences/index.html +++ b/docs/api/sequences/index.html @@ -8,10 +8,10 @@ Sequence API | tidy.js - + - + @@ -25,10 +25,10 @@
fullSeqDate(data, 'date', 'day', 1)
// output:
[
new Date('2020-04-01'),
new Date('2020-04-02'),
new Date('2020-04-03'),
new Date('2020-04-04'),
new Date('2020-04-05'),
new Date('2020-04-06'),
new Date('2020-04-07'),
new Date('2020-04-08'),
]

fullSeqDateISOString#

Creates a full sequence of date ISO string values for a key given a set of data. The bounds of the sequence are determined from the existing values within the data. Typically used in combination with expand or complete.

Similar to fullSeq.

Parameters#

key#

| string /* key of item */
| (item: object) => string /* date ISO string */

The key within the data to expand the sequence across.

granularity#

'second' | 'minute' | 'day' | 'week' | 'month' | 'year' = 'day'

The granularity to increment the data by.

period#

number = 1

The gap between each value in the data (how much the sequence increments by).

Usage#

const data = [
{ str: 'foo', timestamp: '2020-04-01T00:00:00.000Z' },
{ str: 'foo', timestamp: '2020-04-02T00:00:00.000Z' },
{ str: 'foo', timestamp: '2020-04-03T00:00:00.000Z' },
{ str: 'foo', timestamp: '2020-04-06T00:00:00.000Z' },
{ str: 'foo', timestamp: '2020-04-08T00:00:00.000Z' },
];
fullSeqDateISOString(data, 'timestamp', 'day', 1)
// output:
[
'2020-04-01T00:00:00.000Z',
'2020-04-02T00:00:00.000Z',
'2020-04-03T00:00:00.000Z',
'2020-04-04T00:00:00.000Z',
'2020-04-05T00:00:00.000Z',
'2020-04-06T00:00:00.000Z',
'2020-04-07T00:00:00.000Z',
'2020-04-08T00:00:00.000Z',
]
Edit this page
Last updated on by konsalex
- + - + diff --git a/docs/api/summary/index.html b/docs/api/summary/index.html index 0dfa0a9..47b81c0 100644 --- a/docs/api/summary/index.html +++ b/docs/api/summary/index.html @@ -8,10 +8,10 @@ Summarizer API | tidy.js - + - + @@ -36,10 +36,10 @@
tidy(data, summarize({
value: sum('value'),
valueFoo: sum('value', { predicate: d => d.str === 'foo' })
})
// output:
[{ value: 15, valueFoo: 4 }]

variance#

Computes the variance as per d3-array::variance.

Parameters#

key#

| string /* key of object */
| (item: object, index: number, array: object[]) => number

Either the key to compute the value over or an accessor function that maps a given item to the value to compute over.

Usage#

const data = [
{ str: 'foo', value: 3 },
{ str: 'foo', value: 1 },
{ str: 'bar', value: 3 },
{ str: 'bar', value: 1 },
{ str: 'bar', value: 7 },
];
tidy(data, summarize({
value: variance('value'),
})
// output:
[{ value: 6 }]
Edit this page
Last updated on by Peter Beshai
- + - + diff --git a/docs/api/tidy/index.html b/docs/api/tidy/index.html index 2dbac78..e6bd32d 100644 --- a/docs/api/tidy/index.html +++ b/docs/api/tidy/index.html @@ -8,10 +8,10 @@ Tidy Function API | tidy.js - + - + @@ -82,10 +82,10 @@
tidy(data, transmute({
value_x2: (d) => d.value * 2,
value_x4: (d) => d.value_x2 * 2,
}));
// output:
[
{ value_x2: 6, value_x4: 12 },
{ value_x2: 2, value_x4: 4 },
{ value_x2: 14, value_x4: 28 },
]

when#

Conditionally runs a tidy subflow based on the result of a boolean or predicate function.

Parameters#

predicate#

| boolean
| (items: object[]) => boolean

When true, or the function results in true, the subflow is run, otherwise the input items are passed through unmodified.

fns#

Array<(items: object[]) => object[]>

Array of tidy functions to run on the input data when the predicate is true.

Usage#

const data = [{ x: 1 }, { x: 2 }, { x: 3 }];
tidy(data,
when(true, [
mutate({ y: 52 })
])
);
// output:
[{ x: 1, y: 52 }, { x: 2, y: 52 }, { x: 3, y: 52 }]
tidy(data,
when((items) => items.length === 2, [
mutate({ y: 52 })
])
);
// output:
[{ x: 1 }, { x: 2 }, { x: 3 }]
Edit this page
Last updated on by Peter Beshai
- + - + diff --git a/docs/api/vector/index.html b/docs/api/vector/index.html index 5ab8087..a11a0a0 100644 --- a/docs/api/vector/index.html +++ b/docs/api/vector/index.html @@ -8,10 +8,10 @@ Vector Function API | tidy.js - + - + @@ -29,10 +29,10 @@
tidy(data, mutateWithSummary({
movingAvg: roll(3, mean('value'), { partial: true }),
}))
// output:
[
{ str: 'foo', value: 3, movingAvg: 3 / 1 }, // partial
{ str: 'foo', value: 1, movingAvg: 4 / 2 }, // partial
{ str: 'bar', value: 3, movingAvg: 7 / 3 },
{ str: 'bar', value: 1, movingAvg: 5 / 3 },
{ str: 'bar', value: 7, movingAvg: 11 / 3 },
]

rowNumber#

Computes the row number for each item in the array. Note this can also be accomplished with a simple mutate, if you prefer:

mutate({ row: (_, i) => i })

Parameters#

options#

{
startAt?: number
}
  • startAt = 0 what to start counting at, default is 0 – so the first row is row 0, then row 1 for the second row.

Usage#

const data = [
{ str: 'foo', value: 3 },
{ str: 'foo', value: 1 },
{ str: 'bar', value: 3 },
{ str: 'bar', value: 1 },
{ str: 'bar', value: 7 },
];
tidy(data, mutateWithSummary({
row: rowNumber(),
rowFrom1: rowNumber({ startAt: 1})
}))
// output:
[
{ str: 'foo', value: 3, row: 0, rowFrom1: 1 },
{ str: 'foo', value: 1, row: 1, rowFrom1: 2 },
{ str: 'bar', value: 3, row: 2, rowFrom1: 3 },
{ str: 'bar', value: 1, row: 3, rowFrom1: 4 },
{ str: 'bar', value: 7, row: 4, rowFrom1: 5 },
]
Edit this page
Last updated on by Peter Beshai
- + - + diff --git a/docs/examples/moving_average_example/index.html b/docs/examples/moving_average_example/index.html index 6db1849..7c802b5 100644 --- a/docs/examples/moving_average_example/index.html +++ b/docs/examples/moving_average_example/index.html @@ -8,10 +8,10 @@ Moving Average Examples | tidy.js - + - + @@ -23,10 +23,10 @@

Moving Average Examples

When you want to compute a moving average on your data, the main tools at your disposal will be mutate and roll. Let's go over a few examples of how to use them together.

Moving Average#

Given input data:

Input Data

[{"value": 1}, {"value": 2}, {"value": 3}, {"value": 7}, {"value": 5}]

If we want to compute a 3-item moving average of the value key, we can do so as follows:

tidy(
data,
mutateWithSummary({
movingAvg: roll(3, mean('value')),
})
)

Here we're providing a value of 3 for the window width argument of roll and specifying the aggregation to run as the summarizer function mean. This produces the following output:

Output Data

[
{"value": 1},
{"value": 2},
{"value": 3, "movingAvg": 2},
{"value": 7, "movingAvg": 4},
{"value": 5, "movingAvg": 5}
]

Note that by default roll will only compute the aggregate value if the full window width of data is available. Since the window sizes for the first and second elements are 1 and 2, which are less than our width argument of 3, their movingAvg value is undefined.

To compute the aggregate value even when the window width is less than our desired width, we can pass in an options argument with partial set to true:

tidy(
data,
mutateWithSummary({
movingAvg: roll(3, mean('value'), { partial: true }),
})
)

With this change, the output becomes:

Output Data with Partial

[
{"value": 1, "movingAvg": 1},
{"value": 2, "movingAvg": 1.5},
{"value": 3, "movingAvg": 2},
{"value": 7, "movingAvg": 4},
{"value": 5, "movingAvg": 5}
]

And that's all there is to doing simple moving averages with tidy.

Moving Average of Rates#

Working with rates is generally a bit more challenging than plain ol' numbers since we want to sum the numerator and denominator separately in each window as opposed to averaging the sum of rates from each item. Here's where the meanRate summarizer comes in handy.

Input Data

[
{"numer": 1, "denom": 2},
{"numer": 2, "denom": 6},
{"numer": 3, "denom": 7},
{"numer": 7, "denom": 12},
{"numer": 5, "denom": 6}
]

If we want to compute a 3-item moving average of the rate defined by numer / denom, we can do so as follows:

tidy(
data,
mutateWithSummary({
movingAvg: roll(3, meanRate('numer', 'denom')),
})
)

Output Data

[
{"numer": 1, "denom": 2},
{"numer": 2, "denom": 6},
{"numer": 3, "denom": 7, "movingAvg": 0.4},
{"numer": 7, "denom": 12, "movingAvg": 0.48},
{"numer": 5, "denom": 6, "movingAvg": 0.6}
]

Note that the incorrect way of doing it would involve averaging the rates:

tidy(
data,
mutate({
rate: rate('numer', 'denom'),
}),
mutateWithSummary({
movingAvgWrong: roll(3, mean('rate')),
movingAvgRight: roll(3, meanRate('numer', 'denom')),
})
)

Output Data with Wrong

numerdenomratemovingAvgWrongmovingAvgRight
1.002.000.500
2.006.000.333
3.007.000.4290.4210.400
7.0012.00.5830.4480.480
5.006.000.8330.6150.600

Moving Average by Date#

When working with time series data, we often want be able to re-aggregate the data to a variety of different granularities: e.g., a 7-day moving average (7DMA), 45DMA, 5-week moving average (5WMA), daily, weekly, and so on. For this purpose, we can use the summarizeMomentGranularity tidy function. Note this requires our dates to be stored as moment objects and that moment is installed as a dependency in our project.

If projects are not using moment but would like this functionality, please get in touch and we can figure out a reusable solution.

Let's look at an example where we compute a 2-week moving average of value.

Input Data

datevalue
"2020-01-01T00:00:00.000Z"3.00
"2020-01-02T00:00:00.000Z"1.00
"2020-01-03T00:00:00.000Z"4.00
"2020-01-04T00:00:00.000Z"5.00
"2020-01-05T00:00:00.000Z"2.00
"2020-01-06T00:00:00.000Z"4.00
"2020-01-07T00:00:00.000Z"3.00
"2020-01-08T00:00:00.000Z"1.00
1 of 4

The first step is to rollup our data from being at a daily granularity to say a weekly granularity before we compute our moving average.

tidy(
data,
summarizeMomentGranularity('w', { value: sum('value') })
);

At this point, our data has been summed up per week.

Output Weekly Data

valuedatetimestamp
15.02019-12-30T00:00:00.000Z2019-12-30T00:00:00.000Z
21.02020-01-06T00:00:00.000Z2020-01-06T00:00:00.000Z
20.02020-01-13T00:00:00.000Z2020-01-13T00:00:00.000Z
19.02020-01-20T00:00:00.000Z2020-01-20T00:00:00.000Z

We are now ready to compute our moving average:

tidy(
data,
summarizeMomentGranularity('w', { value: sum('value') }),
mutateWithSummary({
movingAvg: roll(2, mean('value'))
})
);

Output Data

valuedatetimestampmovingAvg
15.02019-12-23T00:00:00.000Z2019-12-23T00:00:00.000Z
21.02019-12-30T00:00:00.000Z2019-12-30T00:00:00.000Z18.0
20.02020-01-06T00:00:00.000Z2020-01-06T00:00:00.000Z20.5
19.02020-01-13T00:00:00.000Z2020-01-13T00:00:00.000Z19.5

And we're done, in two steps we've moved from daily values in our input data to weekly values which include a 2-week moving average.

Moving Average by Date with Zerofill#

We often end up with data that is missing values for some dates, which throws off the windows used to compute moving averages. To get around this, we can use complete to expand our data to include zeroes (or other values) where data is missing in a sequence.

Consider the following data:

Input Data

datevalue
"2020-01-06T00:00:00.000Z"4.00
"2020-01-07T00:00:00.000Z"3.00
"2020-01-08T00:00:00.000Z"1.00
"2020-01-11T00:00:00.000Z"2.00
"2020-01-12T00:00:00.000Z"5.00
"2020-01-13T00:00:00.000Z"6.00
"2020-01-14T00:00:00.000Z"1.00
"2020-01-17T00:00:00.000Z"3.00
1 of 2

It's missing values for January 9, 10, 15, 16, and 18. If we run our tidy flow on it to compute a 7-day moving average without accounting for these missing values we get

tidy(
data,
mutateWithSummary({
movingAvg: roll(7, mean('value'), { partial: true })
})
);

Output Data

datevaluemovingAvg
2020-01-06T00:00:00.000Z4.004.00
2020-01-07T00:00:00.000Z3.003.50
2020-01-08T00:00:00.000Z1.002.67
2020-01-11T00:00:00.000Z2.002.50
2020-01-12T00:00:00.000Z5.003.00
2020-01-13T00:00:00.000Z6.003.50
2020-01-14T00:00:00.000Z1.003.14
2020-01-17T00:00:00.000Z3.003.00
1 of 2

As you can see, the moving average doesn't account for the missing days.

To get around this, we can use complete on the data to expand it to include rows for when there is data missing from the expected day-by-day sequence. Since our date column uses moments, we will have to convert it to an ISO timestamp string first so we can use fullSeqDateISOString on it. This can be done via summarizeMomentGranularity as shown above or with a simple mutate since we're going to work with daily data.

tidy(
data,
mutate({ timestamp: d => d.date.toISOString() }),
complete(
{ timestamp: fullSeqDateISOString('timestamp', 'day') },
{ value: 0 }
),
mutateWithSummary({
movingAvg: roll(7, mean('value'), { partial: true })
})
);

Alternative example with summarizeMomentGranularity – granularity should match in fullSeqDateISOString and summarizeMomentGranularity

tidy(
data,
summarizeMomentGranularity('day', { value: sum('value') }),
complete(
{ timestamp: fullSeqDateISOString('timestamp', 'day') },
{ value: 0 }
),
mutateWithSummary({
movingAvg: roll(7, mean('value'), { partial: true })
})
);

Output Data

timestampdatevaluemovingAvg
2020-01-06T00:00:00.000Z2020-01-06T00:00:00.000Z4.004.00
2020-01-07T00:00:00.000Z2020-01-07T00:00:00.000Z3.003.50
2020-01-08T00:00:00.000Z2020-01-08T00:00:00.000Z1.002.67
2020-01-09T00:00:00.000Z0.002.00
2020-01-10T00:00:00.000Z0.001.60
2020-01-11T00:00:00.000Z2020-01-11T00:00:00.000Z2.001.67
2020-01-12T00:00:00.000Z2020-01-12T00:00:00.000Z5.002.14
2020-01-13T00:00:00.000Z2020-01-13T00:00:00.000Z6.002.43
1 of 2

Now we can see that rows have been added with value: 0 for the missing dates in the sequence, which enables our moving average to be computed correctly.

Edit this page
Last updated on by Peter Beshai
- + - + diff --git a/docs/getting_started/index.html b/docs/getting_started/index.html index 6d29496..3e12a6d 100644 --- a/docs/getting_started/index.html +++ b/docs/getting_started/index.html @@ -8,10 +8,10 @@ Getting Started | tidy.js - + - + @@ -25,10 +25,10 @@
const data = [
{ key: 'group1', value: 10 },
{ key: 'group2', value: 9 },
{ key: 'group1', value: 7 }
]
const results = tidy(
data,
groupBy('key', [
summarize({ total: sum('value') })
])
)

This produces the following results:

[
{ "key": "group1", "total": 17 },
{ "key": "group2", "total": 9 },
]
Edit this page
Last updated on by Peter Beshai
- + - + diff --git a/docs/tidy-moment/tidy_moment/index.html b/docs/tidy-moment/tidy_moment/index.html index 147c827..48164de 100644 --- a/docs/tidy-moment/tidy_moment/index.html +++ b/docs/tidy-moment/tidy_moment/index.html @@ -8,10 +8,10 @@ tidy-moment API | tidy.js - + - + @@ -23,10 +23,10 @@

tidy-moment API

Dependency: moment >= 2.0.0

Note these functions are not included in the core tidy.js package and must be installed separately.

summarizeMomentGranularity#

Summarizes input data into bins set by a granularity (day, week, month, year) of UTC moment dates. Assumes input data is by day. By default uses date and timestamp as the keys to track the date, but can be configured via options.

Typically used as a precursor to a moving average computed by a subsequence mutate({ value: roll(...) }) command.

Parameters#

granularity#

| 'd'
| 'days'
| 'w'
| 'weeks'
| 'm'
| 'months'
| 'q'
| 'quarters'
| 'y'
| 'years'

summarizeSpec#

{
[string /* key in output */]: (items: object[]) => any
}

Mapping from key to summary functions, the same as used in summarize.

options?#

{
dateKey?: string = 'date',
timestampKey?: string = 'timestamp',
rest?: (key: string) => (items: object[]) => any
}
  • dateKey = 'date': The key in the input objects where the UTC moment dates are stored. Will be modified to be at the specified granularity.
  • timestampKey = 'timestamp': An ISO string of the rolled up date will be saved on this key.
  • rest = first: The same as used in summarize, defaults to first value encountered.

Usage#

const data = [
{ str: 'foo', date: moment.utc('2020-01-01'), value: 3 },
{ str: 'foo', date: moment.utc('2020-01-03'), value: 1 },
{ str: 'bar', date: moment.utc('2020-01-10'), value: 3 },
{ str: 'bar', date: moment.utc('2020-01-21'), value: 1 },
{ str: 'bar', date: moment.utc('2020-01-29'), value: 2 },
{ str: 'bar', date: moment.utc('2020-02-01'), value: 5 },
];
tidy(
data,
summarizeMomentGranularity('weeks', { value: sum('value') })
);
// output:
[
{
value: 4,
str: 'foo',
date: moment.utc('2019-12-30'),
timestamp: '2019-12-30T00:00:00.000Z',
},
{
value: 3,
str: 'bar',
date: moment.utc('2020-01-06'),
timestamp: '2020-01-06T00:00:00.000Z',
},
{
value: 1,
str: 'bar',
date: moment.utc('2020-01-20'),
timestamp: '2020-01-20T00:00:00.000Z',
},
{
value: 7,
str: 'bar',
date: moment.utc('2020-01-27'),
timestamp: '2020-01-27T00:00:00.000Z',
},
]
Edit this page
Last updated on by Andy Reagan
- + - + diff --git a/index.html b/index.html index 54a27d8..2010969 100644 --- a/index.html +++ b/index.html @@ -8,20 +8,20 @@ tidy.js - + - +

Your Docusaurus site did not load properly.

A very common reason is a wrong site baseUrl configuration.

Current configured baseUrl = /tidy/

We suggest trying baseUrl =

Tidy.js Logo

Tidy up your data with JavaScript

Get Started

Readable code

tidy.js prioritizes making your data transformations readable, so future you and your teammates can get up and running quickly.

Standard transformation verbs

Inspired by dplyr and the tidyverse in R, tidy.js is built using battle-tested verbs that can handle any data wrangling need.

Work with plain JS objects

No wrapper classes needed — all tidy.js needs is an array of plain old-fashioned JS objects to get started.

Function List

Here's a quick jumping off point to see the API for all the functions provided by tidy.js.

Tidy Functions

addItems / addRowsarrange / sortcompletecountdebugdistinctexpandfillfilterfullJoingroupByinnerJoinleftJoinmapmutatemutateWithSummaryrenamereplaceNullyselect / pickslicesliceHeadsliceTailsliceMinsliceMaxsliceSamplesummarizesummarizeAllsummarizeAtsummarizeIftallytotaltotalAlltotalAttotalIftransmutewhen

Grouping with groupBy

groupBygroupBy.groupedgroupBy.entriesgroupBy.entriesObjectgroupBy.objectgroupBy.mapgroupBy.keysgroupBy.valuesgroupBy.levels

Summarizers

deviationfirstlastmaxmeanmeanRatemedianminnnDistinctsumvariance

Item Functions

rate

Vector Functions

cumsumlagleadrollrowNumber

Pivot

pivotLongerpivotWider

Sequences

fullSeqfullSeqDatefullSeqDateISOString

Selectors

containsendsWitheverythingmatchesnegatenumRangestartsWith

TMath

addratesubtract
- + - + \ No newline at end of file diff --git a/playground/index.html b/playground/index.html index eab8351..af6d718 100644 --- a/playground/index.html +++ b/playground/index.html @@ -8,22 +8,22 @@ Playground | tidy.js - + - + - +
Loading...

Input

namempgcyldisphpdratwtqsecvsamgearcarb
Mazda RX421.06.001601103.902.6216.50.001.004.004.00
Mazda RX4 Wag21.06.001601103.902.8817.00.001.004.004.00
Datsun 71022.84.0010893.03.852.3218.61.001.004.001.00
Hornet 4 Drive21.46.002581103.083.2119.41.000.003.001.00
Hornet Sportabout18.78.003601753.153.4417.00.000.003.002.00
Valiant18.16.002251052.763.4620.21.000.003.001.00
Duster 36014.38.003602453.213.5715.80.000.003.004.00
Merc 240D24.44.0014762.03.693.1920.01.000.004.002.00
1 of 4

Output

cylgearnmpghpwt
8.003.0012.015.01944.10
4.004.008.0026.976.02.38
6.004.004.0019.81173.09
4.005.002.0028.21021.83
6.003.002.0019.81083.34
8.005.002.0015.43003.37
4.003.001.0021.597.02.46
6.005.001.0019.71752.77
- + - + - + \ No newline at end of file diff --git a/runtime~main.b5ba7626.js b/runtime~main.fdc3a0bc.js similarity index 93% rename from runtime~main.b5ba7626.js rename to runtime~main.fdc3a0bc.js index 8a4abb6..042eb81 100644 --- a/runtime~main.b5ba7626.js +++ b/runtime~main.fdc3a0bc.js @@ -1 +1 @@ -!function(e){function r(r){for(var n,c,f=r[0],u=r[1],i=r[2],d=0,b=[];d Search the documentation | tidy.js - + - - + +

Search the documentation

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