> For the complete documentation index, see [llms.txt](https://docs.nuvolos.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.nuvolos.com/how-to-guides/workflows-for-researchers/a-complete-database-research-workflow-matlab-and-rstudio.md).

# Database research workflow - Matlab & RStudio

<mark style="color:$primary;">**Outcome**</mark>\
You query data from the Scientific Data Warehouse, analyse it in MATLAB or RStudio, and write results back as a new table.

<mark style="color:$primary;">**Before you start**</mark>

* Tables are activated in your space. See [Reference › Data storage](/reference/data-storage.md) if not.
* You have at least one MATLAB or RStudio application configured.
* You have identified the source tables for your analysis.

Nuvolos supports a *query–analyse–insert* cycle. The example below uses the Fama-French 5-factor table (`NORTH_AMERICA_5_FACTORS`) joined with a monthly stock returns table - the same pattern extends to any tables you have access to.

A standard skeleton scientific workflow in MATLAB/RStudio can be broken down into three main steps:

1. Query research-relevant data
2. Analyse, transform or otherwise manipulate data
3. Store results

You can create more complex workflows, but they will usually be built from three-step modules like the one above.

## MATLAB workflow

{% stepper %}
{% step %}

### Querying relevant data

After opening the MATLAB application, define your SQL query as a string and execute it:

{% code overflow="wrap" %}

```sql
query_string = "SELECT NAF.*, SM.MPRC, SM.MRET*100 AS SM_MRET_100, SM.MTCAP " + ...
               "FROM NORTH_AMERICA_5_FACTORS NAF " + ...
               "INNER JOIN TIME_SERIES_MONTHLY SM " + ...
               "ON SM.MCALDT = NAF.DATE " + ...
               "WHERE KYPERMNO = 14593";
 
con = get_connection();
dataset_factor = select(con, query_string);
```

{% endcode %}
{% endstep %}

{% step %}

### The simple analysis

Use MATLAB's fitlm to fit a linear regression with an R-style formula:

{% code overflow="wrap" %}

```sql
dataset_factor.EXCESS_RETURN = dataset_factor.SM_MRET_100 - dataset_factor.RF;
 
mod = fitlm(dataset_factor, ...
  'EXCESS_RETURN ~ (-1) + MKT_RF + SMB + HML + RMW + CMA');
 
dataset_factor.FIT_FACTOR_5 = mod.Fitted;
```

{% endcode %}
{% endstep %}

{% step %}

### Write results back

Use the sqlwrite command to push the results back to the warehouse as a new table:

{% code overflow="wrap" %}

```sql
sqlwrite(con, 'APPLE_5FACTOR_FIT', dataset_factor);
```

{% endcode %}
{% endstep %}
{% endstepper %}

## RStudio workflow

{% stepper %}
{% step %}

### Querying relevant data

After opening the RStudio application, define your SQL query as a string and execute it:

{% code overflow="wrap" %}

```sql
query_string <- "
SELECT NAF.*, SM.MPRC, SM.MRET*100 AS SM_MRET_100, SM.MTCAP
FROM NORTH_AMERICA_5_FACTORS NAF
INNER JOIN TIME_SERIES_MONTHLY SM
  ON SM.MCALDT = NAF.DATE
WHERE KYPERMNO = 14593
"
 
conn <- nuvolos::get_connection()
dataset_factor <- dbGetQuery(conn, query_string)
```

{% endcode %}
{% endstep %}

{% step %}

### The simple analysis

Use RStudio's fitlm to fit a linear regression with an R-style formula:

{% code overflow="wrap" %}

```sql
dataset_factor$EXCESS_RETURN <- dataset_factor$SM_MRET_100 - dataset_factor$RF
 
mod <- lm(EXCESS_RETURN ~ (-1) + MKT_RF + SMB + HML + RMW + CMA,
          dataset_factor)
 
dataset_factor$FIT_FACTOR_5 <- mod$fitted.values
```

{% endcode %}
{% endstep %}

{% step %}

### Write results back

As a final step, we write back the results using the data upload command for R:

{% code overflow="wrap" %}

```sql
DBI::dbWriteTable(conn, name = "APPLE_5FACTOR_FIT",
                  value = dataset_factor, batch_rows = 10000)
```

{% endcode %}
{% endstep %}
{% endstepper %}

For the full data warehouse reference - Python and Stata connectors, off-Nuvolos access, ODBC drivers, big-data exports - see [Reference › Data storage](/reference/data-storage.md).