[R] Update function examples to show how to open CSV dataset with partitioning and a schema

I feel like this might be a duplicate of a previous ticket, but can't find it.

```r

library(dplyr)
library(arrow)

# all good!
tf <- tempfile()
dir.create(tf)
write_dataset(mtcars, tf, format = "csv")
open_dataset(tf, format = "csv") %>% collect()
#> # A tibble: 32 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # … with 22 more rows

# all good
tf <- tempfile()
dir.create(tf)
write_dataset(group_by(mtcars, cyl), tf, format = "csv")
open_dataset(tf, format = "csv") %>% collect()
#> # A tibble: 32 × 11
#> mpg disp hp drat wt qsec vs am gear carb cyl
#>
#> 1 22.8 108 93 3.85 2.32 18.6 1 1 4 1 4
#> 2 24.4 147. 62 3.69 3.19 20 1 0 4 2 4
#> 3 22.8 141. 95 3.92 3.15 22.9 1 0 4 2 4
#> 4 32.4 78.7 66 4.08 2.2 19.5 1 1 4 1 4
#> 5 30.4 75.7 52 4.93 1.62 18.5 1 1 4 2 4
#> 6 33.9 71.1 65 4.22 1.84 19.9 1 1 4 1 4
#> 7 21.5 120. 97 3.7 2.46 20.0 1 0 3 1 4
#> 8 27.3 79 66 4.08 1.94 18.9 1 1 4 1 4
#> 9 26 120. 91 4.43 2.14 16.7 0 1 5 2 4
#> 10 30.4 95.1 113 3.77 1.51 16.9 1 1 5 2 4
#> # … with 22 more rows
list.files(tf)
#> [1] "cyl=4" "cyl=6" "cyl=8"

# hive-style=FALSE leads to no `cyl` column, which, sure, makes sense
tf <- tempfile()
dir.create(tf)
write_dataset(group_by(mtcars, cyl), tf, format = "csv", hive_style = FALSE)
open_dataset(tf, format = "csv") %>% collect()
#> # A tibble: 32 × 10
#> mpg disp hp drat wt qsec vs am gear carb
#>
#> 1 22.8 108 93 3.85 2.32 18.6 1 1 4 1
#> 2 24.4 147. 62 3.69 3.19 20 1 0 4 2
#> 3 22.8 141. 95 3.92 3.15 22.9 1 0 4 2
#> 4 32.4 78.7 66 4.08 2.2 19.5 1 1 4 1
#> 5 30.4 75.7 52 4.93 1.62 18.5 1 1 4 2
#> 6 33.9 71.1 65 4.22 1.84 19.9 1 1 4 1
#> 7 21.5 120. 97 3.7 2.46 20.0 1 0 3 1
#> 8 27.3 79 66 4.08 1.94 18.9 1 1 4 1
#> 9 26 120. 91 4.43 2.14 16.7 0 1 5 2
#> 10 30.4 95.1 113 3.77 1.51 16.9 1 1 5 2
#> # … with 22 more rows
list.files(tf)
#> [1] "4" "6" "8"

# *but* if we try to add it in via a schema, it doesn't work

desired_schema <- schema(mpg = float64(), disp = float64(), hp = int64(), drat = float64(),
wt = float64(), qsec = float64(), vs = int64(), am = int64(),
gear = int64(), carb = int64(), cyl = int64())

tf <- tempfile()
dir.create(tf)
write_dataset(group_by(mtcars, cyl), tf, format = "csv", hive_style = FALSE)
open_dataset(tf, format = "csv", schema = desired_schema) %>% collect()
#> Error in `dplyr::collect()`:
#> ! Invalid: Could not open CSV input source '/tmp/RtmpnInOwc/file13f0d38c5b994/4/part-0.csv': Invalid: CSV parse error: Row #1: Expected 11 columns, got 10: "mpg","disp","hp","drat","wt","qsec","vs","am","gear","carb"
#> /home/nic2/arrow/cpp/src/arrow/csv/parser.cc:477 (ParseLine(values_writer, parsed_writer, data, data_end, is_final, &line_end, bulk_filter))
#> /home/nic2/arrow/cpp/src/arrow/csv/parser.cc:566 ParseChunk( &values_writer, &parsed_writer, data, data_end, is_final, rows_in_chunk, &data, &finished_parsing, bulk_filter)
#> /home/nic2/arrow/cpp/src/arrow/csv/reader.cc:426 parser->ParseFinal(views, &parsed_size)
#> /home/nic2/arrow/cpp/src/arrow/compute/exec/exec_plan.cc:573 iterator_.Next()
#> /home/nic2/arrow/cpp/src/arrow/record_batch.cc:337 ReadNext(&batch)
#> /home/nic2/arrow/cpp/src/arrow/record_batch.cc:351 ToRecordBatches()
list.files(tf)
#> [1] "4" "6" "8"

```

If I include a schema which does not include the partitioning column, it works, but I lost the partitioning column

```r

# this works minus the col
desired_schema <- schema(mpg = float64(), disp = float64(), hp = int64(), drat = float64(),
wt = float64(), qsec = float64(), vs = int64(), am = int64(),
gear = int64(), carb = int64())

tf <- tempfile()
dir.create(tf)
write_dataset(group_by(mtcars, cyl), tf, format = "csv", hive_style = FALSE)
open_dataset(tf, format = "csv", schema = desired_schema, skip = 1) %>% collect()
```

**Reporter**: [Nicola Crane](https://issues.apache.org/jira/browse/ARROW-17700) / @thisisnic

_{**Note**: *This issue was originally created as [ARROW-17700](https://issues.apache.org/jira/browse/ARROW-17700). Please see the [migration documentation](https://github.com/apache/arrow/issues/14542) for further details.*}

The entry points are write_dataset() and open_dataset() in the R examples; start by running the CSV reproducer with hive_style = FALSE and an explicit schema. Review the relevant R function examples and update them to demonstrate partitioning with a schema while retaining the partitioning column; done when the examples run and show the intended collected result.

Written by the indexing model from the issue text.