---
title: "Getting Started with scimapR"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Getting Started with scimapR}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
resource_files:
- figures/logo.svg
- figures/logo-dark.svg
---
[](https://github.com/CTTIR/scimapR/actions/workflows/R-CMD-check.yaml)
[](https://cttir.github.io/scimapR/)
[](https://CRAN.R-project.org/package=scimapR)
[](https://app.codecov.io/gh/CTTIR/scimapR?branch=main)
[](https://cran.r-project.org/package=scimapR)
[](https://cran.r-project.org/package=scimapR)
[](https://opensource.org/licenses/MIT)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 7,
fig.height = 5
)
```
## What is scimapR?
scimapR is a comprehensive toolkit for bibliometric and scientometric
analysis in R. It is designed as a complement to the foundational
[bibliometrix](https://www.bibliometrix.org/) package, adding:
- **Live corpus refresh** with staleness tracking and locking
- **Research questions as first-class objects** with LLM-grounded screening
- **Reproducible-by-construction corpus certificates** for exact re-derivation
- **Author trajectory analysis** with topic pivots and collaborator turnover
- **Equity and representation auditing** with built-in epistemic humility
- **LLM-grounded corpus chat** with retrieval-constrained citations
## Quick start
```{r setup}
library(scimapR)
# Generate a synthetic example corpus
corpus <- sm_example_corpus(n_works = 100, seed = 42)
print(corpus)
```
## Exploring the corpus
```{r works}
# View works
head(corpus$works[, c("work_id", "title", "year", "cited_by_count")])
```
```{r authors}
# View authors
head(corpus$authors[, c("author_id", "display_name")])
```
## Visualisation
All plots use viridis colour palettes by default.
```{r production, fig.cap = "Annual production"}
sm_plot_production(corpus)
```
```{r top-authors, fig.cap = "Top authors"}
sm_plot_top(corpus, level = "authors", n = 10)
```
## Networks and large graphs
Network plots such as `sm_plot_citation_network()` and `sm_plot_collab()`
return `ggraph` objects whose layout is computed lazily at print time. On large
graphs that lazy layout (and the embedded graph) can crash a knitr/`callr`/
workflowr render subprocess. For documents, pass `precompute = TRUE`: the layout
is computed eagerly and a self-contained plain `ggplot` is returned, which you
can `saveRDS()` and print later in the document without recomputing anything ---
even for a graph with a couple of thousand nodes.
The `max_nodes` cap is **opt-in** (`NULL` by default), so existing renders are
never silently downsampled; set it only when you deliberately want to bound a
very large graph (a `cli` message reports when it engages).
```{r network, eval = requireNamespace("ggraph", quietly = TRUE)}
p <- sm_plot_citation_network(corpus, precompute = TRUE)
# saveRDS(p, "fig_network.rds") # print later in the document, cheaply
p
```
## Filtering
```{r filter}
recent <- sm_filter_works(corpus, year_range = c(2020, 2024))
nrow(recent$works)
```
## Next steps
- See `vignette("ingestion")` for building corpora from real data
- See `vignette("relationship-to-bibliometrix")` for interop with bibliometrix
- See `vignette("embeddings-and-clusters")` for semantic analysis
- Run `sm_run_app()` for the interactive Shiny explorer
