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Posted to commits@sedona.apache.org by ji...@apache.org on 2023/04/01 07:30:08 UTC
[sedona] branch master updated: [DOCS] Fix spelling (#813)
This is an automated email from the ASF dual-hosted git repository.
jiayu pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/sedona.git
The following commit(s) were added to refs/heads/master by this push:
new 86b90fc4 [DOCS] Fix spelling (#813)
86b90fc4 is described below
commit 86b90fc41a342088d20429ebcd61a95b2f757903
Author: John Bampton <jb...@users.noreply.github.com>
AuthorDate: Sat Apr 1 17:30:02 2023 +1000
[DOCS] Fix spelling (#813)
---
R/R/data_interface.R | 4 ++--
R/README.md | 2 +-
R/vignettes/articles/apache-sedona.Rmd | 2 +-
docs/api/sql/Raster-loader.md | 2 +-
docs/tutorial/sql.md | 6 +++---
5 files changed, 8 insertions(+), 8 deletions(-)
diff --git a/R/R/data_interface.R b/R/R/data_interface.R
index ecf35ce3..4d68a514 100644
--- a/R/R/data_interface.R
+++ b/R/R/data_interface.R
@@ -423,7 +423,7 @@ spark_read_shapefile <- function(sc,
lapply(names(options), function(name) {
if (!name %in% c("")) {
- warning(paste0("Ignoring unkown option '", name,"'"))
+ warning(paste0("Ignoring unknown option '", name,"'"))
}
})
@@ -452,7 +452,7 @@ spark_read_geojson <- function(sc,
if ("skip_syntactically_invalid_geometries" %in% names(options)) final_skip <- options[["skip_syntactically_invalid_geometries"]] else final_skip <- TRUE
lapply(names(options), function(name) {
if (!name %in% c("allow_invalid_geometries", "skip_syntactically_invalid_geometries")) {
- warning(paste0("Ignoring unkown option '", name,"'"))
+ warning(paste0("Ignoring unknown option '", name,"'"))
}
})
diff --git a/R/README.md b/R/README.md
index e50f644b..1deccb89 100644
--- a/R/README.md
+++ b/R/README.md
@@ -57,4 +57,4 @@ mean_area_sdf <- polygon_sdf %>%
print(mean_area_sdf)
```
-Notice that all of the above can open up many interesting possiblities. For example, one can extract ML features from geospatial data in Spark dataframes, build a ML pipeline using `ml_*` family of functions in `{sparklyr}` to work with such features, and if the output of a ML model happens to be a geospatial object as well, one can even apply visualization routines in `{apache.sedona}` to visualize the difference between any predicted geometry and the corresponding ground truth.
\ No newline at end of file
+Notice that all of the above can open up many interesting possibilities. For example, one can extract ML features from geospatial data in Spark dataframes, build a ML pipeline using `ml_*` family of functions in `{sparklyr}` to work with such features, and if the output of a ML model happens to be a geospatial object as well, one can even apply visualization routines in `{apache.sedona}` to visualize the difference between any predicted geometry and the corresponding ground truth.
diff --git a/R/vignettes/articles/apache-sedona.Rmd b/R/vignettes/articles/apache-sedona.Rmd
index ba10a25e..db03b7c0 100644
--- a/R/vignettes/articles/apache-sedona.Rmd
+++ b/R/vignettes/articles/apache-sedona.Rmd
@@ -180,7 +180,7 @@ data_tbl %>%
## Manipulating
-The dbplyr interface transparently translates dbplyr worklfows into SQL, and gives access to all Apache Sedona SQL functions:
+The dbplyr interface transparently translates dbplyr workflows into SQL, and gives access to all Apache Sedona SQL functions:
* [Vector functions](../../../api/sql/Function/)
* [Vector predicates](../../../api/sql/Predicate/)
diff --git a/docs/api/sql/Raster-loader.md b/docs/api/sql/Raster-loader.md
index 2da7cd48..cdb0f24e 100644
--- a/docs/api/sql/Raster-loader.md
+++ b/docs/api/sql/Raster-loader.md
@@ -5,7 +5,7 @@ Sedona provides two types of raster DataFrame loaders. They both use Sedona buil
## Load any raster to RasterUDT format
-The raster loader of Sedona leverages Spark built-in binary data source and works with several RS RasterUDT constrcutors to produce RasterUDT type. Each raster is a row in the resulting DataFrame and stored in a `RasterUDT` format.
+The raster loader of Sedona leverages Spark built-in binary data source and works with several RS RasterUDT constructors to produce RasterUDT type. Each raster is a row in the resulting DataFrame and stored in a `RasterUDT` format.
### Load raster to a binary DataFrame
diff --git a/docs/tutorial/sql.md b/docs/tutorial/sql.md
index 3aa197e0..4a0f60f5 100644
--- a/docs/tutorial/sql.md
+++ b/docs/tutorial/sql.md
@@ -305,7 +305,7 @@ For Postgis there is no need to add a query to convert geometry types since it's
=== "Scala"
```scala
- // For any JDBC data source, inluding Postgis.
+ // For any JDBC data source, including Postgis.
val df = sparkSession.read.format("jdbc")
// Other options.
.option("query", "SELECT id, ST_AsBinary(geom) as geom FROM my_table")
@@ -323,7 +323,7 @@ For Postgis there is no need to add a query to convert geometry types since it's
=== "Java"
```java
- // For any JDBC data source, inluding Postgis.
+ // For any JDBC data source, including Postgis.
Dataset<Row> df = sparkSession.read().format("jdbc")
// Other options.
.option("query", "SELECT id, ST_AsBinary(geom) as geom FROM my_table")
@@ -341,7 +341,7 @@ For Postgis there is no need to add a query to convert geometry types since it's
=== "Python"
```python
- # For any JDBC data source, inluding Postgis.
+ # For any JDBC data source, including Postgis.
df = (sparkSession.read.format("jdbc")
# Other options.
.option("query", "SELECT id, ST_AsBinary(geom) as geom FROM my_table")