# A complete database research workflow (Matlab & RStudio)

Nuvolos supports a query-analyze-insert cycle to help researchers query, analyse and store data safely in a simple, integrated workflow. The workflow is demonstrated in two languages, but can be extended to any languages supported by Nuvolos.

## Matlab workflow

A standard skeleton scientific workflow in Matlab can be broken down into three main steps:

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

### Querying relevant data

For the mock example, we are going to work with the Fama-French factor set that is available for our Demo user, we will be focusing on the [North America](https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_5developed.html) 5-factor table. The `NORTH_AMERICA_5_FACTORS` table has been [distributed to the instance we are working in](/features/database-integration.md#distribute-data-you-need).

After opening the Matlab application, the following bit of code will return the entire database table as a Matlab table-type object. The query we are executing is a merge of a monthly stock series table (for Apple monthly stock prices) and the Fama-French factor table as follows.

The SQL query:

```
SELECT NAF.*, SM.MPRC, 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
```

The code that executes the query, the above string is saved in `query_string`.

```
con = get_connection();
dataset_factor = select(con, query_string);
```

### The simple analysis

The previous step resulted in `dataset_factor` containing a Matlab `Table` object that holds the data. The `fitlm` method fits a linear regression on the table with an R-style formula. We then write back the fitted values to the Table as a column.

```
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;
```

### Storing results in the database

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

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

## RStudio workflow

A standard skeleton scientific workflow in 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

### Querying relevant data

For the mock example, we are going to work with the Fama-French factor set that is available for our Demo user, we will be focusing on the [North America](https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_5developed.html) 5-factor table. The `NORTH_AMERICA_5_FACTORS` [table has been distributed to the instance we are working in](/features/database-integration.md#distribute-data-you-need).

After opening the RStudio application, the following bit of code will return the entire database table as an R data frame object. The query we are executing is a merge of a monthly stock series table (for Apple monthly stock prices) and the Fama-French factor table as follows.

The SQL query:

```
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
```

The code that executes the query, the above string is saved in `query_string`.

```
conn <- nuvolos::get_connection()
dataset_factor <- dbGetQuery(conn, query_string)
```

### The simple analysis

The previous step resulted in `dataset_factor` containing an R `data.frame` object that holds the data. The `lm` method fits a linear regression on the data frame. We put the fitted values to the data frame.

```
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
```

### Storing results in the database

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

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


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