analysis.qmd

## Programmatic access

You can access the {{< meta redivis.dataset >}} data programmatically using the Redivis API for [R](https://apidocs.redivis.com/client-libraries/redivis-r) or [Python](https://apidocs.redivis.com/client-libraries/redivis-python).

::: {.panel-tabset}

## R

1. Install the redivis-r package:
```{.r}
devtools::install_github("redivis/redivis-r")
```

2. [Generate and set an API token](https://apidocs.redivis.com/client-libraries/redivis-r/getting-started#authentication).

3. Access the data:

```{.r}
library(redivis)

user <- redivis::user("{{< meta redivis.user >}}")
dataset <- user$dataset("{{< meta redivis.dataset >}}")
table <- dataset$table("{{< meta redivis.table >}}")

# Load table as tidyverse tibble
df <- table$to_tibble()
```

[View documentation](https://apidocs.redivis.com/client-libraries/redivis-r)

## Python

1. Install the redivis-python client library:
```{.python}
pip install --upgrade redivis
```

2. [Generate and set an API token](https://apidocs.redivis.com/client-libraries/redivis-python/getting-started#authentication).

3. Access the data:
```{.python}
import redivis

user = redivis.user("{{< meta redivis.user >}}")
dataset = user.dataset("{{< meta redivis.dataset >}}")
table = dataset.table("{{< meta redivis.table >}}")

# Load table as a dataframe
df = table.to_pandas_dataframe()
```

[View documentation](https://apidocs.redivis.com/client-libraries/redivis-python)

:::