Using `egor` to analyse ego-centered network data

In egor: Import and Analyse Ego-Centered Network Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>")

library(knitr)
library(egor)

The egor Package

egor provides

• import functions
• egor object organizes ego-centered network, allowing for a smooth workflow
• dplyr methods: enable tidy data analysis strategies
• descriptive analysis (network composition, density, homophily, diversity)
• visualization (clustered graphs, egographs, egogram)
• interactive visualization app

An egor object contains all data levels associated with ego-centered network analysis, those levels are: ego, alter, alter-alter ties. By providing the egor()-function with data.frames containing data corresponding to these data levels, we construct an egor object. Here is an example of what the data.frames could look like. Pay attention to the ID variables connecting the levels with each other.

library(egor)

data("alters32")
data("egos32")
data("aaties32") 

alters32 %>%
  head() %>%
  kable(caption = "First rows of alter data.")

egos32 %>%
  head() %>%
  kable(caption = "First rows of ego data.")

aaties32 %>%
  head() %>%
  kable(caption = "First rows of alter-alter tie data.")

All three data.frames contain an egoID identifying a unique ego and connecting their personal data to the alter and alter-alter tie data. The alterID is in the alter data is reused in the alter-alter tie data in the Source and Target columns.

Let's create an egor object from the data we just loaded.

e1 <- egor(alters = alters32,
           egos = egos32,
           aaties = aaties32,
           ID.vars = list(
             ego = ".EGOID",
             alter = ".ALTID",
             source = ".SRCID",
             target = ".TGTID"))
e1

An [egor] object is a [list] of three [tibbles], named "ego", "alter" and "aatie", containing ego, alter and alter-alter tie data.

Import

There are currently three importing functions that read the data from disk and load them as an egor object.

read_openeddi()
read_egoweb()
read_egonet()

In addition there are three functions that help with the transformation of common data formats of ego-centered network data into egor objects:

onefile_to_egor()
twofiles_to_egor()
threefiles_to_egor()

Manipulate

Manipulating an egor object can be done with base R functions or with dplyr verbs.

Base R

The different data levels of an egor object can be manipulated using square bracket subsetting or the subset() function.

Ego level:

e1[e1$ego$age.years > 35, ]

Alter level:

subset(e1, e1$alter$sex == "w", unit = "alter")

Alter-alter tie level:

subset(e1, e1$aatie$weight > 0.5, unit = "aatie")

activate() and dplyr verbs

An egor object can be manipulated with dplyr verbs. Using the activate() command, the data level to execute manipulations on, can be changed. This concept is borrowed from the tidygraph package.

If the manipulation leads to the deletion of egos, the respective alters and alter-alter ties are deleted as well. Similarly deletions of alters lead to the exclusion of the alter-alter ties of the deleted alters.

e1 %>% 
  filter(income > 36000)

e1 %>% 
  activate(alter) %>% 
  filter(country %in% c("USA", "Poland"))

e1 %>% 
  activate(aatie) %>% 
  filter(weight > 0.7)

Analyse

Try these function to analyse you egor object.

Summary

summary(e1)

Density

ego_density(e1)

Composition

composition(e1, "age") %>%
  head() %>%
  kable()

Diversity

alts_diversity_count(e1, "age")
alts_diversity_entropy(e1, "age")

Ego-Alter Homophily (EI-Index)

comp_ei(e1, "age", "age")

EI-Index for Alter-Alter Ties

EI(e1, "age") %>%
  head() %>%
  kable()

Count attribute combinations in alter-alter ties/ dyads

# return results as "wide" tibble
  count_dyads(
    object = e1,
    alter_var_name = "country"
  )

# return results as "long" tibble
  count_dyads(
    object = e1,
    alter_var_name = "country",
    return_as = "long"
  )

comp_ply()

comp_ply() applies a user-defined function on an alter attribute and returns a numeric vector with the results. It can be used to apply base R functions like sd(), mean() or functions from other packages.

e2 <- make_egor(15, 32)
comp_ply(e2, "age.years", sd, na.rm = TRUE)

Visualize

Clustered Graphs

data("egor32")

# Simplify networks to clustered graphs, stored as igraph objects
graphs <- clustered_graphs(egor32, "age") 

# Visualize
par(mfrow = c(2,2), mar = c(0,0,0,0))
vis_clustered_graphs(graphs[1:3], 
                     node.size.multiplier = 1, 
                     edge.width.multiplier = 1,
                     label.size = 0.6)

graphs2 <- clustered_graphs(make_egor(50, 50)[1:4], "country") 

vis_clustered_graphs(graphs2[1:3], 
                     node.size.multiplier = 1, 
                     edge.width.multiplier = 3,
                     label.size = 0.6,
                     labels = FALSE)

igraph & network plotting

• as_igraph() converts an egor object to a list of igraph objects.
• as_network() converts an egor object to a list of network objects.

par(mar = c(0, 0, 0, 0), mfrow = c(2, 2))
purrr::walk(as_igraph(egor32)[1:4], plot)
purrr::walk(as_network(egor32)[1:4], plot)

plot(egor32)

plot(make_egor(32,16), venn_var = "sex", pie_var = "country", type = "egogram")

Shiny App for Visualization

egor_vis_app() starts a Shiny app which offers a graphical interface for adjusting the visualization parameters of the networks stored in an egor object.

egor_vis_app(egor32)

Conversions

With as_igraph() and as_network() all ego networks are transformed into a list of igraph/network objects.

egor documentation built on March 31, 2023, 11:33 p.m.