[DOCS] Standardize Markdown code blocks: word case and whitespace (#1060

)

R/README.md

@@ -11,7 +11,7 @@ enabling higher-level access through a `{dplyr}` backend and familiar R function
 ## Installation
 To use Apache Sedona from R, you just need to install the apache.sedona package; Spark dependencies are managed directly by the package.
 
-``` r
+```r
 # Install released version from CRAN
 install.packages("apache.sedona")
 ```
@@ -21,7 +21,7 @@ To use the development version, you will need both the latest version of the pac
 
 To get the latest R package from GtiHub:
 
-``` r
+```r
 # Install development version from GitHub
 devtools::install_github("apache/sedona/R")
 ```
@@ -40,7 +40,7 @@ The path to the sedona-spark-shaded jars needs to be put in the `SEDONA_JAR_FILE
 
 The first time you load Sedona, Spark will download all the dependent jars, which can take a few minutes and cause the connection to timeout. You can either retry (some jars will already be downloaded and cached) or increase the `"sparklyr.connect.timeout"` parameter in the sparklyr config.
 
-``` r
+```r
 library(sparklyr)
 library(apache.sedona)
 
@@ -51,7 +51,7 @@ sc <- spark_connect(master = "local")
 polygon_sdf <- spark_read_geojson(sc, location = "/tmp/polygon.json")
 ```
 
-``` r
+```r
 mean_area_sdf <- polygon_sdf %>%
   dplyr::summarize(mean_area = mean(ST_Area(geometry)))
 print(mean_area_sdf)

R/vignettes/articles/apache-sedona.Rmd

@@ -362,7 +362,7 @@ to Sedona visualization routines. For example, the following is
 essentially the R equivalent of [this example in
 Scala](https://github.com/apache/sedona/blob/f6b1c5e24bdb67d2c8d701a9b2af1fb5658fdc4d/viz/src/main/scala/org/apache/sedona/viz/showcase/ScalaExample.scala#L142-L160).
 
-``` {r}
+```{r}
 resolution_x <- 1000
 resolution_y <- 600
 boundary <- c(-126.790180, -64.630926, 24.863836, 50.000)

README.md

@@ -62,11 +62,11 @@ Apache Sedona is a widely used framework for working with spatial data, and it h
 This example loads NYC taxi trip records and taxi zone information stored as .CSV files on AWS S3 into Sedona spatial dataframes. It then performs spatial SQL query on the taxi trip datasets to filter out all records except those within the Manhattan area of New York. The example also shows a spatial join operation that matches taxi trip records to zones based on whether the taxi trip lies within the geographical extents of the zone. Finally, the last code snippet integrates the output of Sedona with GeoPandas and plots the spatial distribution of both datasets.
 
 #### Load NYC taxi trips and taxi zones data from CSV Files Stored on AWS S3
-``` python
+```python
 taxidf = sedona.read.format('csv').option("header","true").option("delimiter", ",").load("s3a://your-directory/data/nyc-taxi-data.csv")
 taxidf = taxidf.selectExpr('ST_Point(CAST(Start_Lon AS Decimal(24,20)), CAST(Start_Lat AS Decimal(24,20))) AS pickup', 'Trip_Pickup_DateTime', 'Payment_Type', 'Fare_Amt')
 ```
-``` python
+```python
 zoneDf = sedona.read.format('csv').option("delimiter", ",").load("s3a://your-directory/data/TIGER2018_ZCTA5.csv")
 zoneDf = zoneDf.selectExpr('ST_GeomFromWKT(_c0) as zone', '_c1 as zipcode')
 ```
@@ -105,7 +105,7 @@ We provide a Docker image for Apache Sedona with Python JupyterLab and a single-
 
 * To install the Python package: 
 
-  ``` 
+  ```
   pip install apache-sedona
   ```
 * To Compile the source code, please refer to [Sedona website](https://sedona.apache.org/latest-snapshot/setup/compile/)

docs/api/flink/Function.md

@@ -1246,7 +1246,7 @@ Format: `ST_H3CellDistance(cell1: Long, cell2: Long)`
 Since: `v1.5.0`
 
 Example:
-```SQL
+```sql
 select ST_H3CellDistance(ST_H3CellIDs(ST_GeomFromWKT('POINT(1 2)'), 8, true)[1], ST_H3CellIDs(ST_GeomFromWKT('POINT(1.23 1.59)'), 8, true)[1])
 ```
 
@@ -1291,7 +1291,7 @@ Format: `ST_H3CellIDs(geom: geometry, level: Int, fullCover: true)`
 Since: `v1.5.0`
 
 Example:
-```SQL
+```sql
 SELECT ST_H3CellIDs(ST_GeomFromText('LINESTRING(1 3 4, 5 6 7)'), 6, true)
 ```
 
@@ -1318,7 +1318,7 @@ Format: `ST_H3KRing(cell: Long, k: Int, exactRing: Boolean)`
 Since: `v1.5.0`
 
 Example:
-```SQL
+```sql
 select ST_H3KRing(ST_H3CellIDs(ST_GeomFromWKT('POINT(1 2)'), 8, true)[1], 1, false), ST_H3KRing(ST_H3CellIDs(ST_GeomFromWKT('POINT(1 2)'), 8, true)[1], 1, true)
 ```
 
@@ -1342,7 +1342,7 @@ Format: `ST_H3ToGeom(cells: Array[Long])`
 Since: `v1.5.0`
 
 Example:
-```SQL
+```sql
 SELECT ST_H3ToGeom(ST_H3CellIDs(ST_GeomFromWKT('POINT(1 2)'), 8, true)[0], 1, true))
 ```
 
@@ -2189,7 +2189,7 @@ Since: `v1.4.0`
 
 Example:
 
-```SQL
+```sql
 SELECT ST_S2CellIDs(ST_GeomFromText('LINESTRING(1 3 4, 5 6 7)'), 6)
 ```
 

docs/api/sql/Function.md

@@ -1257,7 +1257,7 @@ Format: `ST_H3CellDistance(cell1: Long, cell2: Long)`
 Since: `v1.5.0`
 
 Spark SQL example:
-```SQL
+```sql
 select ST_H3CellDistance(ST_H3CellIDs(ST_GeomFromWKT('POINT(1 2)'), 8, true)[0], ST_H3CellIDs(ST_GeomFromWKT('POINT(1.23 1.59)'), 8, true)[0])
 ```
 
@@ -1302,7 +1302,7 @@ Format: `ST_H3CellIDs(geom: geometry, level: Int, fullCover: Boolean)`
 Since: `v1.5.0`
 
 Spark SQL example:
-```SQL
+```sql
 SELECT ST_H3CellIDs(ST_GeomFromText('LINESTRING(1 3 4, 5 6 7)'), 6, true)
 ```
 
@@ -1329,7 +1329,7 @@ Format: `ST_H3KRing(cell: Long, k: Int, exactRing: Boolean)`
 Since: `v1.5.0`
 
 Spark SQL example:
-```SQL
+```sql
 SELECT ST_H3KRing(ST_H3CellIDs(ST_GeomFromWKT('POINT(1 2)'), 8, true)[0], 1, true) cells union select ST_H3KRing(ST_H3CellIDs(ST_GeomFromWKT('POINT(1 2)'), 8, true)[0], 1, false) cells
 ```
 
@@ -1354,7 +1354,7 @@ Format: `ST_H3ToGeom(cells: Array[Long])`
 Since: `v1.5.0`
 
 Spark SQL example:
-```SQL
+```sql
 SELECT ST_H3ToGeom(ST_H3CellIDs(ST_GeomFromWKT('POINT(1 2)'), 8, true)[0], 1, true))
 ```
 
@@ -2199,7 +2199,7 @@ Since: `v1.4.0`
 
 Spark SQL Example:
 
-```SQL
+```sql
 SELECT ST_S2CellIDs(ST_GeomFromText('LINESTRING(1 3 4, 5 6 7)'), 6)
 ```