datapage/analysis.qmd at main · datapages/datapage · GitHub

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## Programmatic access

```{r}
library(glue)

quarto <- yaml::read_yaml("_quarto.yml")
ds_opts <- c("dataset", "workflow")
ds <- ds_opts |> purrr::keep(\(d) !is.null(quarto$redivis[[d]]))
```

You can access data from `{{< meta redivis.`r ds` >}}` 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 >}}")
`r ds` <- user$`r ds`("{{< meta redivis.`r ds` >}}")
table <- dataset$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 >}}")
`r ds` = user.`r ds`("{{< meta redivis.`r ds` >}}")
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)

:::