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Filter extension

Source: R/ext_filter.R
ext_filter.Rd

[Experimental] ext_filter() implements Common Query Language (CQL2) filter extension on rstac. This extension expands the filter capabilities providing a query language to construct more complex expressions. CQL2 is an OGC standard and defines how filters can be constructed. It supports predicates for standard data types like strings, numbers, and boolean as well as for spatial geometries (point, lines, polygons) and temporal data (instants and intervals).

[Experimental] cql2_json() and cql2_text() are helper functions that can be used to show how expressions are converted into CQL2 standard, either JSON or TEXT formats.

rstac translates R expressions to CQL2, allowing users to express their filter criteria using R language. For more details on how to create CQL2 expressions in rstac. See the details section.

Usage

ext_filter(q, expr, lang = NULL, crs = NULL)

cql2_json(expr)

cql2_text(expr)

Arguments

q

a RSTACQuery object expressing a STAC query criteria.

expr

a valid R expression to be translated to CQL2 (see details).

lang

a character value indicating which CQL2 representation to be used. It can be either "cql2-text" (for plain text) or "cql2-json" (for JSON format). If NULL (default), "cql2-text" is used for HTTP GET requests and "cql2-json" for POST requests.

crs

an optional character value informing the coordinate reference system used by geometry objects. If NULL (default), STAC services assume "WGS 84".

Value

A RSTACQuery object with the subclass ext_filter containing all request parameters to be passed to get_request() or post_request() function.

Details

To allow users to express filter criteria in R language, rstac takes advantage of the abstract syntax tree (AST) to translate R expressions to CQL2 expressions. The following topics describe the correspondences between rstac expressions and CQL2 operators.

Standard comparison operators

  • ==, >=, <=, >, <, and != operators correspond to =, >=, <=, >, <, and <> in CQL2, respectively.

  • function is_null(a) and !is_null(a) corresponds to a IS NULL and a IS NOT NULL CQL2 operators, respectively.

Advanced comparison operators

  • a %like% b corresponds to CQL2 a LIKE b, a and b strings values.

  • between(a, b, c) corresponds to CQL2 a BETWEEN b AND c, where b and c integer values.

  • a %in% b corresponds to CQL2 a IN (b), where b should be a list of values of the same type as a.

Spatial operators

  • functions s_intersects(a, b), s_touches(a, b), s_within(a, b), s_overlaps(a, b), s_crosses(a, b), and s_contains(a, b) corresponds to CQL2 S_INTERSECTS(a, b), S_TOUCHES(a, b), S_WITHIN(a, b), S_OVERLAPS(a, b), S_CROSSES(a, b), and S_CONTAINS(a, b) operators, respectively. Here, a and b should be geometry objects. rstac accepts sf, sfc, sfg, list (representing GeoJSON objects), or character (representing either GeoJSON or WKT).

  • NOTE: All of the above spatial object types, except for the character, representing a WKT, may lose precision due to numeric truncation when R converts numbers to JSON text. WKT strings are sent "as is" to the service. Therefore, the only way for users to retain precision on spatial objects is to represent them as a WKT string. However, user can control numeric precision using the options(stac_digits = ...). The default value is 15 digits.

Temporal operators

  • functions date(a), timestamp(a), and interval(a, b) corresponds to CQL2 DATE(a), TIMESTAMP(a), and INTERVAL(a, b) operators, respectively. These functions create literal temporal values. The first two define an instant type, and the third an interval type.

  • functions t_after(a, b), t_before(a, b), t_contains(a, b), t_disjoint(a, b), t_during(a, b), t_equals(a, b), t_finishedby(a, b), t_finishes(a, b), t_intersects(a, b), t_meets(a, b), t_meet(a, b), t_metby(a, b), t_overlappedby(a, b), t_overlaps(a, b), t_startedby(a, b), and t_starts(a, b) corresponds to CQL2 T_AFTER(a, b), T_BEFORE(a, b), T_CONTAINS(a, b), T_DISJOINT(a, b), T_DURING(a, b), T_EQUALS(a, b), T_FINISHEDBY(a, b), T_FINISHES(a, b), T_INTERSECTS(a, b), T_MEETS(a, b), T_MEET(a, b), T_METBY(a, b), T_OVERLAPPEDBY(a, b), T_OVERLAPS(a, b), T_STARTEDBY(a, b), and T_STARTS(a, b) operators, respectively. Here, a and b are temporal values (instant or interval, depending on function).

Array Operators

  • R unnamed lists (or vectors of size > 1) are translated to arrays by rstac. list() and c() functions always create array values in CQL2 context, no matter the number of its arguments.

  • functions a_equals(a, b), a_contains(a, b), a_containedby(a, b), and a_overlaps(a, b) corresponds to CQL2 A_EQUALS(a, b), A_CONTAINS(a, b), A_CONTAINEDBY(a, b), and A_OVERLAPS(a, b) operators, respectively. Here, a and b should be arrays.

Note

The specification states that double-quoted identifiers should be interpreted as properties. However, the R language does not distinguish double quote from single quote strings. The right way to represent double quoted properties in R is to use the escape character (), for example "date"`.

See also

ext_query(), stac_search(), post_request(), endpoint(), before_request(), after_response(), content_response()

Examples

if (FALSE) {
# Standard comparison operators in rstac:
# Creating a stac search query
req <- rstac::stac("https://planetarycomputer.microsoft.com/api/stac/v1") %>%
  rstac::stac_search(limit = 5)

# Equal operator '=' with collection property
req %>% ext_filter(collection == "sentinel-2-l2a") %>% post_request()

# Not equal operator '!=' with collection property
req %>% ext_filter(collection != "sentinel-2-l2a") %>% post_request()

# Less than operator '<' with cloud_cover property
req %>% ext_filter(`eo:cloud_cover` < 10) %>% post_request()

# Greater than operator '>' with vegetation_percentage property
req %>% ext_filter(`s2:vegetation_percentage` > 50) %>% post_request()

# Less than or equal operator '<=' with datetime property
req %>% ext_filter(datetime <= "1986-01-01") %>% post_request()

# Greater than or equal '>=' with AND operator
req %>% ext_filter(collection == "sentinel-2-l2a"   &&
                   `s2:vegetation_percentage` >= 50 &&
                   `eo:cloud_cover` <= 10) %>% post_request()
# Advanced comparison operators
# 'LIKE' operator
req %>% ext_filter(collection %like% "modis%") %>% post_request()

# 'IN' operator
req %>% ext_filter(
  collection %in% c("landsat-c2-l2", "sentinel-2-l2a") &&
    datetime > "2019-01-01" &&
    datetime < "2019-06-01") %>%
  post_request()

# Spatial operator
# Lets create a polygon with list
polygon <- list(
  type = "Polygon",
  coordinates = list(
    matrix(c(-62.34499836, -8.57414572,
             -62.18858174, -8.57414572,
             -62.18858174, -8.15351185,
             -62.34499836, -8.15351185,
             -62.34499836, -8.57414572),
           ncol = 2, byrow = TRUE)
  )
)
# 'S_INTERSECTS' spatial operator with polygon and geometry property
req %>% ext_filter(collection == "sentinel-2-l2a" &&
                   s_intersects(geometry, {{polygon}})) %>% post_request()

# 'S_CONTAINS' spatial operator with point and geometry property
point <- list(type = "Point", coordinates = c(-62.45792211, -8.61158488))
req %>% ext_filter(collection == "landsat-c2-l2" &&
                   s_contains(geometry, {{point}})) %>% post_request()

# 'S_CROSSES' spatial operator with linestring and geometry property
linestring <- list(
  type = "LineString",
  coordinates = matrix(
         c(-62.55735320, -8.43329465, -62.21791603, -8.36815014),
         ncol = 2, byrow = TRUE
  )
)
req %>% ext_filter(collection == "landsat-c2-l2" &&
                   s_crosses(geometry, {{linestring}})) %>% post_request()

# Temporal operator
# 'T_INTERSECTS' temporal operator with datetime property
req %>% ext_filter(
  collection == "landsat-c2-l2" &&
    t_intersects(datetime, interval("1985-07-16T05:32:00Z",
                                    "1985-07-24T16:50:35Z"))) %>%
 post_request()

# 'T_DURING' temporal operator with datetime property
req %>%
 ext_filter(collection == "landsat-c2-l2" &&
            t_during(datetime,
            interval("2022-07-16T05:32:00Z", ".."))) %>%
 post_request()

# 'T_BEFORE' temporal operator with datetime property
req %>%
 ext_filter(collection == "landsat-c2-l2" &&
            t_before(datetime, timestamp("2022-07-16T05:32:00Z"))) %>%
 post_request()

# 'T_AFTER' temporal operator with datetime property
req %>%
 ext_filter(collection == "landsat-c2-l2" &&
            t_after(datetime, timestamp("2022-07-16T05:32:00Z"))) %>%
  post_request()

# Shows how CQL2 expression (TEXT format)
cql2_text(collection == "landsat-c2-l2" &&
  s_crosses(geometry, {{linestring}}))

# Shows how CQL2 expression (JSON format)
cql2_json(collection == "landsat-c2-l2" &&
            t_after(datetime, timestamp("2022-07-16T05:32:00Z")))
}