---
title: "Topic-Speaker Bipartite Network for useR! 2025 Conference"
subtitle: "Connections between speakers and topics"
description: "Exploring the connections between speakers and topics at the upcoming useR! 2025 conference using network analysis. This visualization uses a bipartite network approach to reveal how speakers cluster around five key topics in the R community: Visualization, Machine Learning, Data Wrangling, Package Development, and Reproducibility."
author: "Steven Ponce"
date: "2025-04-29"
categories: ["TidyTuesday", "2025"]
tags: [
"network-analysis", "bipartite-network", "useR2025", "conference-data", "ggplot2", "igraph", "r-community", "data-science", "network-visualization", "speaker-topics"
]
image: "thumbnails/tt_2025_17.png"
format:
html:
toc: true
toc-depth: 5
code-link: true
code-fold: true
code-tools: true
code-summary: "Show code"
editor_options:
chunk_output_type: inline
execute:
freeze: true
cache: true
error: false
message: false
warning: false
eval: true
# filters:
# - social-share
# share:
# permalink: "https://stevenponce.netlify.app/data_visualizations/TidyTuesday/2025/tt_2025_17.html"
# description: "#TidyTuesday week 17: Visualizing speaker-topic connections for the useR! 2025 conference through a bipartite network graph that reveals community clustering and cross-disciplinary interests."
#
# twitter: true
# linkedin: true
# email: true
# facebook: false
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# stumble: false
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# mastodon: true
# bsky: true
---
{#fig-1}
### <mark> **Steps to Create this Graphic** </mark>
#### 1. Load Packages & Setup
```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
tidyverse, # Easily Install and Load the 'Tidyverse'
ggtext, # Improved Text Rendering Support for 'ggplot2'
showtext, # Using Fonts More Easily in R Graphs
janitor, # Simple Tools for Examining and Cleaning Dirty Data
skimr, # Compact and Flexible Summaries of Data
scales, # Scale Functions for Visualization
glue, # Interpreted String Literals
here, # A Simpler Way to Find Your Files
igraph, # Network Analysis and Visualization
ggrepel, # Automatically Position Non-Overlapping Text Labels with 'ggplot2'
camcorder # Record Your Plot History
)
})
### |- figure size ----
gg_record(
dir = here::here("temp_plots"),
device = "png",
width = 8,
height = 8,
units = "in",
dpi = 320
)
# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```
#### 2. Read in the Data
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
tt <- tidytuesdayR::tt_load(2025, week = 17)
user2025_raw <- tt$user2025 |> clean_names()
tidytuesdayR::readme(tt)
rm(tt)
```
#### 3. Examine the Data
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false
glimpse(user2025_raw)
skim(user2025_raw)
```
#### 4. Tidy Data
```{r}
#| label: tidy
#| warning: false
### |- tidy data ----
user2025 <- user2025_raw |>
mutate(
keywords_list = str_split(keywords, ", "),
co_authors = replace_na(co_authors, "")
)
# Define Topic Mapping
topic_definitions <- tribble(
~pattern, ~topic,
"visualization|ggplot2|shiny|dashboard|interactive", "Visualization",
"machine learning|deep learning|statistics|modeling|neural", "Machine Learning",
"data wrangling|tidyverse|data.table|cleaning|manipulation", "Data Wrangling",
"reproducibility|rmarkdown|workflow|version control|git", "Reproducibility",
"package|cran|bioconductor|github", "Package Development"
)
# Expand to keyword-topic map
keyword_topics <- map_dfr(seq_len(nrow(topic_definitions)), function(i) {
pattern <- topic_definitions$pattern[i]
topic <- topic_definitions$topic[i]
matched <- unique(unlist(user2025$keywords_list))[str_detect(unique(unlist(user2025$keywords_list)), regex(pattern, ignore_case = TRUE))]
tibble(keyword = matched, topic = topic)
}) |> distinct()
# Create Edges ----
speaker_topic_edges <- user2025 |>
unnest(keywords_list) |>
inner_join(keyword_topics, by = c("keywords_list" = "keyword")) |>
select(speakers, topic) |>
distinct()
# Build Graph ----
g <- graph_from_data_frame(speaker_topic_edges |> rename(from = speakers, to = topic), directed = FALSE)
# Set node attributes
V(g)$type <- ifelse(V(g)$name %in% speaker_topic_edges$speakers, "speaker", "topic")
V(g)$label <- ifelse(V(g)$type == "speaker", str_trunc(str_extract(V(g)$name, "^[^(]+"), 25), V(g)$name)
deg <- degree(g)
V(g)$size <- ifelse(V(g)$type == "speaker", rescale(deg, to = c(3.5, 6)), 10)
V(g)$color <- ifelse(V(g)$type == "speaker", "#F4978E", "#99C1DE")
V(g)$shape <- 21
# Compute Layout ----
set.seed(42)
layout_coords <- layout_with_fr(g) + matrix(rnorm(length(V(g)) * 2, sd = 0.15), ncol = 2)
# Build Plot Data ----
nodes_df <- data.frame(
name = V(g)$name,
type = V(g)$type,
color = V(g)$color,
shape = V(g)$shape,
size = V(g)$size,
label = V(g)$label,
x = layout_coords[, 1],
y = layout_coords[, 2]
)
edges_df <- as_data_frame(g) |>
left_join(nodes_df |> select(name, x_from = x, y_from = y), by = c("from" = "name")) |>
left_join(nodes_df |> select(name, x_to = x, y_to = y), by = c("to" = "name"))
# Label high-degree speakers only
high_deg_names <- names(deg[deg >= 2 & V(g)$type == "speaker"])
speaker_labels_df <- nodes_df |> filter(name %in% high_deg_names)
```
#### 5. Visualization Parameters
```{r}
#| label: params
#| include: true
#| warning: false
### |- plot aesthetics ----
colors <- get_theme_colors(
palette = c(
"speaker" = "#F4978E", "topic" = "#99C1DE"
)
)
### |- titles and caption ----
title_text <- str_glue("Topic-Speaker Bipartite Network for useR! 2025 Conference")
subtitle_text <- str_glue("Connections between speakers and topics")
# Create caption
caption_text <- create_social_caption(
tt_year = 2025,
tt_week = 17,
source_text = "Program for the useR! 2025 conference"
)
### |- fonts ----
setup_fonts()
fonts <- get_font_families()
### |- plot theme ----
# Start with base theme
base_theme <- create_base_theme(colors)
# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
base_theme,
theme(
# Text styling
plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
# Axis elements
axis.title = element_blank(),
axis.text = element_blank(),
# Grid elements
panel.grid.minor = element_blank(),
panel.grid.major = element_line(),
# Legend elements
legend.position = "plot",
legend.direction = "horizontal",
legend.title = element_text(family = fonts$text, size = rel(0.8), face = "bold"),
legend.text = element_text(family = fonts$text, size = rel(0.7)),
# Plot margins
plot.margin = margin(t = 20, r = 20, b = 20, l = 20),
)
)
# Set theme
theme_set(weekly_theme)
```
#### 6. Plot
```{r}
#| label: plot
#| warning: false
### |- Plot ----
p <- ggplot() +
# Geoms
geom_segment(
data = edges_df, aes(x = x_from, y = y_from, xend = x_to, yend = y_to),
color = "#CCCCCC", alpha = 0.5, size = 0.3
) +
geom_point(
data = nodes_df,
aes(x = x, y = y, fill = type, size = size),
shape = 21, stroke = 0.4, color = "#333333", show.legend = FALSE
) +
geom_text(
data = nodes_df |> filter(type == "topic"),
aes(x = x, y = y, label = label),
color = "#1A1A1A", size = 5, fontface = "bold"
) +
geom_text_repel(
data = speaker_labels_df,
aes(x = x, y = y, label = label),
color = "#333333", size = 3.2,
max.overlaps = 20, point.padding = 0.3, box.padding = 0.4,
segment.color = "#AAAAAA", segment.alpha = 0.6,
force = 0.5, seed = 42
) +
# Scales
scale_fill_manual(values = colors$palette) +
scale_size_identity() +
coord_equal(clip = "off") +
# Labs
labs(
title = title_text,
subtitle = subtitle_text,
caption = caption_text,
x = NULL,
y = NULL,
) +
# Theme
theme(
plot.title = element_text(
size = rel(1.4),
family = fonts$title,
face = "bold",
color = colors$title,
lineheight = 1.1,
margin = margin(t = 5, b = 5)
),
plot.subtitle = element_text(
size = rel(0.85),
family = fonts$subtitle,
color = alpha(colors$subtitle, 0.9),
lineheight = 1.2,
margin = margin(t = 5, b = 10)
),
plot.caption = element_markdown(
size = rel(0.65),
family = fonts$caption,
color = colors$caption,
hjust = 0.5,
margin = margin(t = 10)
)
)
```
#### 7. Save
```{r}
#| label: save
#| warning: false
### |- plot image ----
save_plot(
plot = p,
type = "tidytuesday",
year = 2025,
week = 17,
width = 8,
height = 8
)
```
#### 8. Session Info
::: {.callout-tip collapse="true"}
##### Expand for Session Info
```{r, echo = FALSE}
#| eval: true
#| warning: false
sessionInfo()
```
:::
#### 9. GitHub Repository
::: {.callout-tip collapse="true"}
##### Expand for GitHub Repo
The complete code for this analysis is available in [`tt_2025_17.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/tt_2025_17.qmd).
For the full repository, [click here](https://github.com/poncest/personal-website/).
:::
#### 10. References
::: {.callout-tip collapse="true"}
##### Expand for References
1. Data Sources:
- TidyTuesday 2025 Week 17: [useR! 2025 program](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-04-29)
:::
