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Minor changes to wording in the vignette. #17

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Minor changes to wording in the vignette. #17

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26 changes: 13 additions & 13 deletions vignettes/Intro_to_DataSpaceR.Rmd
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Expand Up @@ -16,26 +16,26 @@ This package provides a thin wrapper around [Rlabkey](https://cran.r-project.org

First, go to [DataSpace](https://dataspace.cavd.org) now and set yourself up with an account.

In order to connect to the CAVD DataSpace via `DataSpaceR`, you will need a `netrc` file in your home directory that will contain a `machine` name (hostname of DataSpace), and `login` and `password`. There are two ways to creat a netrc file.
In order to connect to the CAVD DataSpace via `DataSpaceR`, you will need a `netrc` file in your home directory that will contain a `machine` name (hostname of DataSpace), and `login` and `password`. There are two ways to create a `netrc` file.

### Creating a netrc file with `writeNetrc`

On your R console, create a netrc file using a function from `DataSpaceR`:
On your R console, create a `netrc` file using a function from `DataSpaceR`:

```{r, eval=FALSE}
DataSpaceR::writeNetrc("[email protected]", "yourSecretPassword")
```

This will create a netrc file in your home directory. Make sure you have a valid login and password.
This will create a `netrc` file in your home directory. Make sure you have a valid login and password.

### Manually creating a netrc file

***Alternatively***, you can manually create a netrc file in the computer running R.
***Alternatively***, you can manually create a netrc file.

* On Windows, this file sould be named `_netrc`
* On UNIX, it should be named `.netrc`
* On UNIX/Mac, it should be named `.netrc`
* The file should be located in the user's home directory, and the permissions on the file should be unreadable for everybody except the owner
* To determine home directory, run `Sys.getenv("HOME")` in R
* To determine your home directory, run `Sys.getenv("HOME")` in R

The following three lines must be included in the `.netrc` or `_netrc` file either separated by white space (spaces, tabs, or newlines) or commas. Multiple such blocks can exist in one file.

Expand All @@ -50,7 +50,7 @@ See [here](https://www.labkey.org/wiki/home/Documentation/page.view?name=netrc)

## Initiate a connection

We'll be looking at study `cvd256`. If you want to use a different study, change that string. The connections have state, so you can instantiate multiple connections to different studies simultaneously.
We'll be looking at study `cvd256`. If you want to use a different study, change that string. You can instantiate multiple connections to different studies simultaneously.

```{r connectDS}
library(DataSpaceR)
Expand All @@ -65,14 +65,14 @@ cvd256 <- con$getStudy("cvd256")
cvd256
```

`con$getStudy` creates a connection to the study `cvd256`. Printing the object shows where it's connected, to what study, and the available datasets.
`con$getStudy` creates a connection to the study `cvd256`. Printing the object shows where it's connected, to what study, and the available datasets.

```{r other-fields}
knitr::kable(cvd256$availableDatasets)
knitr::kable(cvd256$treatmentArm)
```

Available datasets and treatment arm information for the connection can be access by `availableDatasets` and `treatmentArm`.
Available datasets and treatment arm information for the connection can be accessed by `availableDatasets` and `treatmentArm`.


## Fetching datasets
Expand Down Expand Up @@ -107,22 +107,22 @@ See `?makeFilter` for more information on the syntax.

## Cross-study connection

To fetch data from multiple studies, simply create a connection at the project level.
To fetch data from multiple studies, create a connection at the project level.

```{r cross-connection}
cavd <- con$getStudy("")
```

This will instantiate a connection at the `CAVD` level. Most functions work cross study connections just like they do on single studies.

You can get a list of datasets available accross all studies.
You can get a list of datasets available across all studies.

```{r cross-connection-print}
cavd
knitr::kable(cavd$availableDatasets)
```

In cross-study connections, `getDataset` will combine the requested datasets. Note that in most cases the datasets will have too many subjects, making the filtering of the data a necessity. The `colFilter` argument can be used here, as described in the `getDataset` section.
In cross-study connections, `getDataset` will combine the requested datasets. Note that in most cases the datasets will have too many subjects for quick data transfer, making filtering of the data a necessity. The `colFilter` argument can be used here, as described in the `getDataset` section.

```{r cross-connection-dem}
conFilter <- makeFilter(c("species", "EQUAL", "Human"))
Expand All @@ -140,7 +140,7 @@ We can browse available saved groups via `availableGroups`.
knitr::kable(con$availableGroups)
```

To fetch data from a saved group, create a connection at the project level with a group ID. For example, we can connect to `mice` group which has group ID 208 by `getGroup`.
To fetch data from a saved group, create a connection at the project level with a group ID. For example, we can connect to `mice` group which has group ID 216 by `getGroup`.

```{r group-connection}
mice <- con$getGroup(216)
Expand Down