if (requireNamespace("DiagrammeR", quietly = TRUE)) {
show <- function(x, ...) DiagrammeR::grViz(gv(x, ...), width = "100%")
}else{
show <- function(x, ...) cat(d2(x), sep = "\n")
}While autodb takes a single data frame for input, its
data might not be “flat”. As an example, the data might be imported from
JSON, using the jsonlite package.
Such nested data frames can be a pain to work with in base R, because many functions aren’t designed to handle them. For example, consider the JSON data below.
[
{
"a": 1,
"b": [1],
"c": [1, 2],
"d": [
[1, 2],
[3, 4]
],
"e": [1, 2],
"f": [
[1, 2],
[3, 4]
],
"g": [1, 2],
"h": [[1], [1]],
"i": {
"i.1": 1,
"i.2": "a"
},
"k": [
[1, 4],
[2, 5],
[3, 6]
],
"l": [
[1, 4],
[2, 5],
[3, 6]
],
"m": [
{
"m.1": 1,
"m.2": 4
},
{
"m.1": 2,
"m.2": 5
},
{
"m.1": 3,
"m.2": 6
}
],
"n": [
{
"n.1": 1,
"n.2": 4
},
{
"n.1": 2,
"n.2": 5
},
{
"n.1": 3,
"n.2": 6
}
]
},
{
"a": 2,
"b": [2, 3],
"c": [3, 4],
"d": [
[5, 6],
[7, 8]
],
"e": [1, 2],
"f": [
[5, 6],
[7, 8]
],
"g": [2, 1],
"h": [[2], [2]],
"i": {
"i.1": 2,
"i.2": "b"
},
"k": [
[1, 4],
[2, 5],
[3, 6]
],
"l": [
[1, 4],
[2, 5],
[3, 6]
],
"m": [
{
"m.1": 1,
"m.2": 4
},
{
"m.1": 2,
"m.2": 5
},
{
"m.1": 3,
"m.2": 6
}
],
"n": [
{
"n.1": 1,
"n.2": 4
},
{
"n.1": 2,
"n.2": 5
},
{
"n.1": 3,
"n.2": 6
}
]
},
{
"a": 3,
"b": [4, 5, 6],
"c": [5, 6],
"d": [
[9, 10],
[11, 12]
],
"e": ["a", "b"],
"f": [
[9, 10],
[11, 12]
],
"g": [1, 2],
"h": [[1], [1]],
"i": {
"i.1": 3,
"i.2": "c"
},
"k": [
[1, 4],
[2, 5],
[3, 6]
],
"l": [
[1, 4],
[2, 5],
[3, 6]
],
"m": [
{
"m.1": 1,
"m.2": 4
},
{
"m.1": 2,
"m.2": 5
},
{
"m.1": 3,
"m.2": 6
}
],
"n": [
{
"n.1": 1,
"n.2": 4
},
{
"n.1": 2,
"n.2": 5
},
{
"n.1": 3,
"n.2": 6
}
]
},
{
"a": 4,
"b": [1],
"c": [3, 4],
"d": [
[9, 10],
[11, 12]
],
"e": [
["a"],
["b", "c"]
],
"f": [
[9, 10]
],
"g": [2, 1],
"h": [[3], [3]],
"i": {
"i.1": 4,
"i.2": "d"
},
"k": [
[1, 6],
[2, 7],
[3, 8],
[4, 9],
[5, 10]
],
"l": [
[7, 10],
[8, 11],
[9, 12]
],
"m": [
{
"m.1": 1,
"m.2": 6
},
{
"m.1": 2,
"m.2": 7
},
{
"m.1": 3,
"m.2": 8
},
{
"m.1": 4,
"m.2": 9
},
{
"m.1": 5,
"m.2": 10
}
],
"n": [
{
"n.1": 7,
"n.2": 10
},
{
"n.1": 8,
"n.2": 11
},
{
"n.1": 9,
"n.2": 12
}
]
},
{
"a": 5,
"b": [2, 3],
"c": [3, 4],
"d": [
[9, 10],
[11, 12]
],
"e": [
["a"],
["b", "c"]
],
"f": [
[9, 10]
],
"g": [1, 2],
"h": [[2], [2]],
"i": {
"i.1": 5,
"i.2": "e"
},
"k": [
[1, 6],
[2, 7],
[3, 8],
[4, 9],
[5, 10]
],
"l": [
[7, 10],
[8, 11],
[9, 12]
],
"m": [
{
"m.1": 1,
"m.2": 6
},
{
"m.1": 2,
"m.2": 7
},
{
"m.1": 3,
"m.2": 8
},
{
"m.1": 4,
"m.2": 9
},
{
"m.1": 5,
"m.2": 10
}
],
"n": [
{
"n.1": 7,
"n.2": 10
},
{
"n.1": 8,
"n.2": 11
},
{
"n.1": 9,
"n.2": 12
}
]
}
]R hides a lot of class details when printing; printing as a tibble hides less.
## a b c d e f g h i.i.1 i.i.2
## 1 1 1 1, 2 1, 3, 2, 4 1, 2 1, 3, 2, 4 1, 2 1, 1 1 a
## 2 2 2, 3 3, 4 5, 7, 6, 8 1, 2 5, 7, 6, 8 2, 1 2, 2 2 b
## 3 3 4, 5, 6 5, 6 9, 11, 10, 12 a, b 9, 11, 10, 12 1, 2 1, 1 3 c
## 4 4 1 3, 4 9, 11, 10, 12 a, b, c 9, 10 2, 1 3, 3 4 d
## 5 5 2, 3 3, 4 9, 11, 10, 12 a, b, c 9, 10 1, 2 2, 2 5 e
## k l
## 1 1, 2, 3, 4, 5, 6 1, 2, 3, 4, 5, 6
## 2 1, 2, 3, 4, 5, 6 1, 2, 3, 4, 5, 6
## 3 1, 2, 3, 4, 5, 6 1, 2, 3, 4, 5, 6
## 4 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 7, 8, 9, 10, 11, 12
## 5 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 7, 8, 9, 10, 11, 12
## m n
## 1 1, 2, 3, 4, 5, 6 1, 2, 3, 4, 5, 6
## 2 1, 2, 3, 4, 5, 6 1, 2, 3, 4, 5, 6
## 3 1, 2, 3, 4, 5, 6 1, 2, 3, 4, 5, 6
## 4 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 7, 8, 9, 10, 11, 12
## 5 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 7, 8, 9, 10, 11, 12## # A tibble: 5 × 13
## a b c d e f g
## <int> <list> <list> <list> <list> <list> <list>
## 1 1 <int [1]> <int [2]> <int [2 × 2]> <int [2]> <int [2 × 2]> <int [2]>
## 2 2 <int [2]> <int [2]> <int [2 × 2]> <int [2]> <int [2 × 2]> <int [2]>
## 3 3 <int [3]> <int [2]> <int [2 × 2]> <chr [2]> <int [2 × 2]> <int [2]>
## 4 4 <int [1]> <int [2]> <int [2 × 2]> <list [2]> <int [1 × 2]> <int [2]>
## 5 5 <int [2]> <int [2]> <int [2 × 2]> <list [2]> <int [1 × 2]> <int [2]>
## h i$i.1 $i.2 k l m
## <list> <int> <chr> <list> <list> <list>
## 1 <int [2 × 1]> 1 a <int [3 × 2]> <int [3 × 2]> <df [3 × 2]>
## 2 <int [2 × 1]> 2 b <int [3 × 2]> <int [3 × 2]> <df [3 × 2]>
## 3 <int [2 × 1]> 3 c <int [3 × 2]> <int [3 × 2]> <df [3 × 2]>
## 4 <int [2 × 1]> 4 d <int [5 × 2]> <int [3 × 2]> <df [5 × 2]>
## 5 <int [2 × 1]> 5 e <int [5 × 2]> <int [3 × 2]> <df [5 × 2]>
## n
## <list>
## 1 <df [3 × 2]>
## 2 <df [3 × 2]>
## 3 <df [3 × 2]>
## 4 <df [3 × 2]>
## 5 <df [3 × 2]>autodb has some ability to deal with nested data:
specifically, where columns contain lists, data frames, or matrices.
When plotting, column classes are described with any common nested class
information within angle brackets (<>), and common
dimensions are given within square brackets ([]):
The first dimension isn’t at the top level, since the number of rows
is already given in the table’s header. For example, column
i is a data frame, so has the same number of rows as the
main data frame: the size information only gives its column count.
nest_level can be set to limit how deeply nested the
given class information can be:
Dependency discovery and decomposition work as expected:
Also as expected, rejoining the resulting database into a single data frame gives back the original:
## [1] TRUE## a b c d e f g h i.i.1 i.i.2
## 1 1 1 1, 2 1, 3, 2, 4 1, 2 1, 3, 2, 4 1, 2 1, 1 1 a
## 2 2 2, 3 3, 4 5, 7, 6, 8 1, 2 5, 7, 6, 8 2, 1 2, 2 2 b
## 3 3 4, 5, 6 5, 6 9, 11, 10, 12 a, b 9, 11, 10, 12 1, 2 1, 1 3 c
## 4 4 1 3, 4 9, 11, 10, 12 a, b, c 9, 10 2, 1 3, 3 4 d
## 5 5 2, 3 3, 4 9, 11, 10, 12 a, b, c 9, 10 1, 2 2, 2 5 e
## k l
## 1 1, 2, 3, 4, 5, 6 1, 2, 3, 4, 5, 6
## 2 1, 2, 3, 4, 5, 6 1, 2, 3, 4, 5, 6
## 3 1, 2, 3, 4, 5, 6 1, 2, 3, 4, 5, 6
## 4 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 7, 8, 9, 10, 11, 12
## 5 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 7, 8, 9, 10, 11, 12
## m n
## 1 1, 2, 3, 4, 5, 6 1, 2, 3, 4, 5, 6
## 2 1, 2, 3, 4, 5, 6 1, 2, 3, 4, 5, 6
## 3 1, 2, 3, 4, 5, 6 1, 2, 3, 4, 5, 6
## 4 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 7, 8, 9, 10, 11, 12
## 5 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 7, 8, 9, 10, 11, 12