Skip to content

Commit

Permalink
update the readme
Browse files Browse the repository at this point in the history
  • Loading branch information
@vikram-rawat
vikram-rawat committed Mar 20, 2024
1 parent 1218d1f commit 97dc81d
Show file tree
Hide file tree
Showing 2 changed files with 160 additions and 66 deletions.
107 changes: 75 additions & 32 deletions README.Rmd
View file
Original file line number Diff line number Diff line change
Expand Up @@ -54,18 +54,22 @@ Always start with creating a config file for keeping all your basic environment

Then you can read it with config package. make sure you don't add a drv name in the YML file and also avoid using the password directly into Yml file. Use environment variable instead. read.sql have 1 function to create either a connection or a pool directly from this list.

dw <- config::get("datawarehouse")
```{r eval=FALSE}
conn <- read.sql::rs_create_conn(
driver = RPostgres::Postgres(),
param_list = dw
)
dw <- config::get("datawarehouse")
pool <- read.sql::rs_create_conn(
driver = RPostgres::Postgres(),
param_list = dw,
pool = TRUE
)
conn <- read.sql::rs_create_conn(
driver = RPostgres::Postgres(),
param_list = dw
)
pool <- read.sql::rs_create_conn(
driver = RPostgres::Postgres(),
param_list = dw,
pool = TRUE
)
```

These functions come in handy when you want to re-establish a connection. So I preferred to use them in the file.

Expand Down Expand Up @@ -115,39 +119,78 @@ where

what if you want to make this query reuse able and use multiple parameters. You could use SQL interpolations like this.

select
*
from
iris
where
`Sepal.Length` > ?minsepal
and
`Petal.Length` < ?minpetal
```{sql eval=FALSE}
select
*
from
iris
where
`Sepal.Length` > ?minsepal
and
`Petal.Length` < ?minpetal
```

and then you could use it in the function as this.

query <- read.sql::rs_interpolate(
sql_query = sql_query_object, # object created from rs_read_query function
sql_conn = conn,
query_params = list(
minsepal = 5,
minpetal = 5
)
)
```{r eval=FALSE}
sql_query_object <- read.sql::rs_interpolate(
sql_query = sql_query_object, # object created from rs_read_query function
sql_conn = conn,
query_params = list(
minsepal = 5,
minpetal = 5
)
)
```


query_params is an optional argument. You would only need it when you want to interpolate a value in the SQL file you need.

### update meta data in SQL query

sometimes you will feel like you want to dynamically update the table name or column name in a query. This functionality is not available in `DBI::sqlInterpolate` function. Hence this has to be dealt with seperately. So there is an optional parameter called `meta_query_params` in the function `rs_interpolate` which will simply replace anything inside the `{}` curly brackets by matching it with named list. liket this

```{sql eval=FALSE}
select
*
from
{main_table}
where
{column1} >= ?mincol1
and
{column2} >= ?mincol2
```

to run a query like this we need to pass 2 values like this.

```{r eval=FALSE}
query_obj <- read.sql::rs_interpolate(
sql_query = sql_query_object, # object created from rs_read_query function
sql_conn = conn,
meta_query_params = list(
main_table = "iris",
column1 = "`Sepal.Length`",
column2 = "`Petal.Length`"
),
query_params = list(
mincol1 = 5,
mincol2 = 5
)
)
```



### execute_sql_file

most common function you will use most of the time is this. This will execute the query that is read from the file or modified by function rs_interpolate.

query_obj |>
read.sql::rs_execute(
sql_conn = conn,
query_params = list(
minsepal = 5,
minpetal = 5
)
sql_conn = conn
)

The only thing different about it is that it has a method argument where if you need results from the DB you should use `get` all lower case. If you want to execute a delete, or update statement you should use `post` all lower case.
Expand Down
119 changes: 85 additions & 34 deletions README.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -53,25 +53,29 @@ name in the YML file and also avoid using the password directly into Yml
file. Use environment variable instead. read.sql have 1 function to
create either a connection or a pool directly from this list.

dw <- config::get("datawarehouse")
``` r

dw <- config::get("datawarehouse")

conn <- read.sql::rs_create_conn(
driver = RPostgres::Postgres(),
param_list = dw
)
conn <- read.sql::rs_create_conn(
driver = RPostgres::Postgres(),
param_list = dw
)

pool <- read.sql::rs_create_conn(
driver = RPostgres::Postgres(),
param_list = dw,
pool = TRUE
)
pool <- read.sql::rs_create_conn(
driver = RPostgres::Postgres(),
param_list = dw,
pool = TRUE
)
```

These functions come in handy when you want to re-establish a
connection. So I preferred to use them in the file.

Then there are only 3 functions that remain

``` r

conn <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")

DBI::dbWriteTable(conn,"iris", iris)
Expand All @@ -85,6 +89,7 @@ imagine you have a code like this.
### get_sql_query

``` sql

select
*
from
Expand All @@ -93,6 +98,8 @@ limit
5
```

<div class="knitsql-table">

| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species |
|-------------:|------------:|-------------:|------------:|:--------|
| 5.1 | 3.5 | 1.4 | 0.2 | setosa |
Expand All @@ -103,13 +110,16 @@ limit

5 records

</div>

In this case you can use the function like this

query <- read.sql::rs_read_query(filepath = "path/to/sql/file")

Now suppose you have a query like this.

``` sql

select
*
from
Expand All @@ -120,6 +130,8 @@ where
`Petal.Length` < 1.7
```

<div class="knitsql-table">

| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species |
|-------------:|------------:|-------------:|------------:|:--------|
| 5.1 | 3.5 | 1.4 | 0.2 | setosa |
Expand All @@ -135,46 +147,85 @@ where

Displaying records 1 - 10

</div>

what if you want to make this query reuse able and use multiple
parameters. You could use SQL interpolations like this.

select
*
from
iris
where
`Sepal.Length` > ?minsepal
and
`Petal.Length` < ?minpetal
``` sql

select
*
from
iris
where
`Sepal.Length` > ?minsepal
and
`Petal.Length` < ?minpetal
```

and then you could use it in the function as this.

query <- read.sql::rs_interpolate(
sql_query = sql_query_object, # object created from rs_read_query function
sql_conn = conn,
query_params = list(
minsepal = 5,
minpetal = 5
)
)
``` r
sql_query_object <- read.sql::rs_interpolate(
sql_query = sql_query_object, # object created from rs_read_query function
sql_conn = conn,
query_params = list(
minsepal = 5,
minpetal = 5
)
)
```

query_params is an optional argument. You would only need it when you
want to interpolate a value in the SQL file you need.

### update meta data in SQL query

sometimes you will feel like you want to dynamically update the table
name or column name in a query. This functionality is not available in
`DBI::sqlInterpolate` function. Hence this has to be dealt with
seperately. So there is an optional parameter called `meta_query_params`
in the function `rs_interpolate` which will simply replace anything
inside the `{}` curly brackets by matching it with named list. liket
this

``` sql
select
*
from
{main_table}
where
{column1} >= ?mincol1
and
{column2} >= ?mincol2
```

to run a query like this we need to pass 2 values like this.

``` r
query_obj <- read.sql::rs_interpolate(
sql_query = sql_query_object, # object created from rs_read_query function
sql_conn = conn,
meta_query_params = list(
main_table = "iris",
column1 = "`Sepal.Length`",
column2 = "`Petal.Length`"
),
query_params = list(
mincol1 = 5,
mincol2 = 5
)
)
```

### execute_sql_file

most common function you will use most of the time is this. This will
execute the query that is read from the file or modified by function
rs_interpolate.

read.sql::rs_execute(
sql_conn = conn,
query_params = list(
minsepal = 5,
minpetal = 5
)
)
query_obj \|\> read.sql::rs_execute( sql_conn = conn )

The only thing different about it is that it has a method argument where
if you need results from the DB you should use `get` all lower case. If
Expand Down

0 comments on commit 97dc81d

Please sign in to comment.