kjhnet

Some network data, for teaching purposes.

• Iliad data are from Gabriel Rossman (2017) “Glory and Gore”, Contexts 16(3):42–47. Original data files available at https://osf.io/jasf4/.

• Sociology Top 25 Jobs data are from John Robert Warren, (2019) “How Much Do You Have to Publish to Get a Job in a Top Sociology Department? Or to Get Tenure? Trends Over a Generation”, Sociological Science February 27, 2019 doi://10.15195/v6.a7. Original data files available at https://www.rob-warren.com/pub_trends.html.

Installation

kjhnet is a data package.

Install direct from GitHub

You can install the beta version of kjhnet from GitHub with:

remotes::install_github("kjhealy/kjhnet")

Loading the data

The package works best with the ggraph, tidygraph, and broader tidyverse libraries.

library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr     1.1.4     ✔ readr     2.1.5
#> ✔ forcats   1.0.0     ✔ stringr   1.5.1
#> ✔ ggplot2   3.5.0     ✔ tibble    3.2.1
#> ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
#> ✔ purrr     1.0.2     
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggraph)
library(tidygraph)
#> 
#> Attaching package: 'tidygraph'
#> 
#> The following object is masked from 'package:stats':
#> 
#>     filter

Load the data:

library(kjhnet)

Example

Some off-label use of Rob Warren’s Top 25 Sociology Jobs data.

library(ggraph)
library(tidygraph)

set_graph_style(family = "Myriad Pro SemiCondensed")

clean_dept_names <- function(x){
    x <- stringr::str_replace(x, "California-", "")
    x <- stringr::str_replace(x, "SUNY-", "")
    x <- stringr::str_replace(x, "SUNY-", "")
    x <- stringr::str_replace(x, "Illinois-Chicago", "UIC")
    x <- stringr::str_replace(x, "U of Illinois", "UIUC")
    x <- stringr::str_replace(x, "U of Pennsylvania", "U Penn")
    x <- stringr::str_replace(x, "San Francisco", "UCSF")
    x <- stringr::str_replace(x, "North Carolina", "UNC")
    x <- stringr::str_replace(x, "N.C. State", "NC State")
    x
}

(p1 <- socjobs %>% 
  filter(top25phd == "Yes", phd_dept != "Texas", job_dept != "Texas") %>%
  select(phd_dept, job_dept) %>%
  mutate(phd_dept = clean_dept_names(phd_dept),
         job_dept = clean_dept_names(job_dept)) %>%
  group_by(phd_dept, job_dept) %>%
  tally() %>% 
  select(from = phd_dept, to = job_dept, weight = n) %>%
  mutate(scale_weight = scale(weight, center = FALSE)) %>% 
  filter(weight > 0) %>% 
  as_tbl_graph() %>%
  mutate(centrality = centrality_eigen(weights = weight)) %>%
  ggraph(layout = "graphopt") + 
  geom_edge_fan(aes(alpha = weight),
                arrow = arrow(length = unit(3, 'mm'), type = "closed"), 
                start_cap = circle(2, 'mm'),
                end_cap = circle(8, 'mm')) + 
  geom_node_label(aes(label = name)) + 
  scale_edge_alpha_continuous(name = "N Hires") + 
  labs(title = "New Assistant Professor Exchanges within the Top 25 Sociology Departments", 
       subtitle = "New Ph.D. hires, 1990-2017. Data show absolute number of within-network hires only.\nHires to and from outside the Top 25 are not shown. No adjustments are made for departmental or cohort sizes.", 
       caption = "Data: Warren (2019). Data exclude UT Austin.") + 
  theme_graph(base_family = "Myriad Pro SemiCondensed") + 
    theme(legend.position = "top") 
)
  
ggsave(here("man/figures/README-example-1.png"), p1, height = 10, width = 15, dpi = 300)