---
title: "GDAL SQL"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{GDAL SQL}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(lazysf)
```


GDAL is a complex tool and there are many intricacies to using SQL with it. We recommend using [gdalraster](https://CRAN.R-project.org/package=gdalraster) instead, it has control over
SQL dialect and sophisticated support for working with GDAL ready data. 

We haven't explored this comprehensively so [feedback is very welcome](https://github.com/hypertidy/lazysf/issues).


## OGRSQL 

https://gdal.org/en/stable/user/ogr_sql_dialect.html


## Real databases and file databases


PG, GPKG etc

https://gdal.org/en/stable/user/sql_sqlite_dialect.html

## Virtual SQL

shapefile, OpenFileGDB etc. 

ESRI "Geodatabases", known as 'OpenFileGDB' to GDAL *are not real databases*. Amazing!  Sadly this means we cannot perform subquerys, which occurs with some contructs. 

For example, this is fine - filter where on variables and then count the number of rows. There is only one 'SELECT ...' statement. 

```R
## here f is a file path to the directory containing list_2d_building_polys_hobart from TheLIST opendata
lazysf(f) %>% filter(SHAPE_Area < 15) %>% tally() %>% show_query()
<SQL>
SELECT COUNT(*) AS "n"
FROM "list_2d_building_polys_hobart"
WHERE ("SHAPE_Area" < 15.0)

## we predicted this woud work, even on our not-a-database geodatabase
lazysf(f) %>% filter(SHAPE_Area < 15) %>% tally() %>% collect()
# A tibble: 1 x 1
      n
  <int>
1    56
```

However, if we do something more complex like a GROUP BY and then `slice_min()` we cannot run the query. 

```R
lazysf(f) %>% group_by(BUILD_TY) %>% slice_min(SHAPE_Area) %>% show_query()
<SQL>
SELECT "BUILD_ID", "BUILD_TY", "BUILD_NAME", "BLD_PUR", "MEAN_HGT", "UFI", "CREATED_ON", "LIST_GUID", "NOM_REG_NO", "SHAPE_Length", "SHAPE_Area", "SHAPE"
FROM (SELECT "BUILD_ID", "BUILD_TY", "BUILD_NAME", "BLD_PUR", "MEAN_HGT", "UFI", "CREATED_ON", "LIST_GUID", "NOM_REG_NO", "SHAPE_Length", "SHAPE_Area", "SHAPE", RANK() OVER (PARTITION BY "BUILD_TY" ORDER BY "SHAPE_Area") AS "q01"
FROM "list_2d_building_polys_hobart") "q01"
WHERE ("q01" <= 1)


lazysf(f) %>% group_by(BUILD_TY) %>% slice_min(SHAPE_Area)
Error in CPL_read_ogr(dsn, layer, query, as.character(options), quiet,  : 
  Query execution failed, cannot open layer.
In addition: Warning message:
In CPL_read_ogr(dsn, layer, query, as.character(options), quiet,  :

```

Even a simpler version `slice_min()` without grouping fails because it would be generating a subquery to do the work (this might change, but for now consider if you can re-imagine your query as something that will not require nested statements, or write it yourself.)

```R
lazysf(f) %>% slice_min(SHAPE_Area) %>% show_query()
<SQL>
SELECT "BUILD_ID", "BUILD_TY", "BUILD_NAME", "BLD_PUR", "MEAN_HGT", "UFI", "CREATED_ON", "LIST_GUID", "NOM_REG_NO", "SHAPE_Length", "SHAPE_Area", "SHAPE"
FROM (SELECT "BUILD_ID", "BUILD_TY", "BUILD_NAME", "BLD_PUR", "MEAN_HGT", "UFI", "CREATED_ON", "LIST_GUID", "NOM_REG_NO", "SHAPE_Length", "SHAPE_Area", "SHAPE", RANK() OVER (ORDER BY "SHAPE_Area") AS "q01"
FROM "list_2d_building_polys_hobart") "q01"
WHERE ("q01" <= 1)
```

At worst, we can GROUP_BY and filter in the database and then sub select. 

Note that this kind of query works on a "real database", so consider relating your experiences to your friendly local data provider. 



```{r real-gpkg}
library(lazysf)
library(dplyr)
f <- system.file("gpkg/nc.gpkg", package = "sf", mustWork = TRUE)
#lazysf(f) %>% group_by(BIR74) %>% slice_min(PERIMETER) %>% show_query()


#lazysf(f) %>% group_by(BIR74) %>% slice_min(PERIMETER) %>% select(NAME, PERIMETER, geom) %>% st_as_sf()
```

Other things are less obvious, why doesn't this work? The tally fails but filter/collect is fine

```R
lazysf("C:/temp/temp.xlsx") %>% group_by(a) %>% tally() %>% show_query()
<SQL>
SELECT "a", COUNT(*) AS "n"
FROM "Encoding Time"
GROUP BY "a"


lazysf("C:/temp/temp.xlsx") %>% filter(d < 675868) %>% group_by(a) %>% collect()
# A tibble: 31 x 6
# Groups:   a [1]
   a               
....
```
