Using `egor` to analyse ego-centered network data

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")

First rows of alter data.
.ALTID	.EGOID	sex	age	age.years	country	income
1	1	m	46 - 55	48	USA	45625
2	1	m	0 - 17	5	Germany	52925
3	1	w	26 - 35	35	Australia	60225
4	1	w	0 - 17	3	Poland	25550
5	1	m	66 - 100	97	Australia	45260
6	1	w	26 - 35	29	Germany	8395

First rows of ego data.
.EGOID	sex	age	age.years	country	income
1	m	56 - 65	63	Australia	29930
2	m	26 - 35	33	Germany	17885
3	m	66 - 100	74	Germany	20805
4	w	18 - 25	21	Poland	29565
5	m	0 - 17	9	Germany	15330
6	m	0 - 17	6	Australia	23360

First rows of alter-alter tie data.
.EGOID	.SRCID	.TGTID	weight
20	1	2	0.6666667
25	6	10	0.6666667
9	6	8	0.6666667
31	2	10	0.6666667
24	1	12	0.3333333
11	9	11	0.3333333

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
#> # EGO data (active): 32 × 6
#>   .egoID sex   age      age.years country   income
#> *  <dbl> <chr> <fct>        <int> <chr>      <dbl>
#> 1      1 m     56 - 65         63 Australia  29930
#> 2      2 m     26 - 35         33 Germany    17885
#> 3      3 m     66 - 100        74 Germany    20805
#> 4      4 w     18 - 25         21 Poland     29565
#> 5      5 m     0 - 17           9 Germany    15330
#> # ℹ 27 more rows
#> # ALTER data: 384 × 7
#>   .altID .egoID sex   age     age.years country   income
#> *  <int>  <dbl> <chr> <fct>       <int> <chr>      <dbl>
#> 1      1      1 m     46 - 55        48 USA        45625
#> 2      2      1 m     0 - 17          5 Germany    52925
#> 3      3      1 w     26 - 35        35 Australia  60225
#> # ℹ 381 more rows
#> # AATIE data: 1,056 × 4
#>   .egoID .srcID .tgtID weight
#> *  <int>  <int>  <int>  <dbl>
#> 1     20      1      2  0.667
#> 2     25      6     10  0.667
#> 3      9      6      8  0.667
#> # ℹ 1,053 more rows

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, ]
#> # EGO data (active): 19 × 6
#>   .egoID sex   age      age.years country   income
#> *  <dbl> <chr> <fct>        <int> <chr>      <dbl>
#> 1      1 m     56 - 65         63 Australia  29930
#> 2      3 m     66 - 100        74 Germany    20805
#> 3      7 m     66 - 100        84 Australia  19345
#> 4      8 w     66 - 100       100 Poland     35040
#> 5      9 m     36 - 45         38 USA        64605
#> # ℹ 14 more rows
#> # ALTER data: 228 × 7
#>   .altID .egoID sex   age     age.years country   income
#> *  <int>  <dbl> <chr> <fct>       <int> <chr>      <dbl>
#> 1      1      1 m     46 - 55        48 USA        45625
#> 2      2      1 m     0 - 17          5 Germany    52925
#> 3      3      1 w     26 - 35        35 Australia  60225
#> # ℹ 225 more rows
#> # AATIE data: 641 × 4
#>   .egoID .srcID .tgtID weight
#> *  <int>  <int>  <int>  <dbl>
#> 1     25      6     10  0.667
#> 2      9      6      8  0.667
#> 3      7      3      6  0.667
#> # ℹ 638 more rows

Alter level:

subset(e1, e1$alter$sex == "w", unit = "alter")
#> # EGO data (active): 32 × 6
#>   .egoID sex   age      age.years country   income
#> *  <dbl> <chr> <fct>        <int> <chr>      <dbl>
#> 1      1 m     56 - 65         63 Australia  29930
#> 2      2 m     26 - 35         33 Germany    17885
#> 3      3 m     66 - 100        74 Germany    20805
#> 4      4 w     18 - 25         21 Poland     29565
#> 5      5 m     0 - 17           9 Germany    15330
#> # ℹ 27 more rows
#> # ALTER data: 204 × 7
#>   .altID .egoID sex   age     age.years country   income
#> *  <int>  <dbl> <chr> <fct>       <int> <chr>      <dbl>
#> 1      3      1 w     26 - 35        35 Australia  60225
#> 2      4      1 w     0 - 17          3 Poland     25550
#> 3      6      1 w     26 - 35        29 Germany     8395
#> # ℹ 201 more rows
#> # AATIE data: 300 × 4
#>   .egoID .srcID .tgtID weight
#> *  <int>  <int>  <int>  <dbl>
#> 1     25      6     10  0.667
#> 2      9      6      8  0.667
#> 3      7      3      6  0.667
#> # ℹ 297 more rows

Alter-alter tie level:

subset(e1, e1$aatie$weight > 0.5, unit = "aatie")
#> # EGO data (active): 32 × 6
#>   .egoID sex   age      age.years country   income
#> *  <dbl> <chr> <fct>        <int> <chr>      <dbl>
#> 1      1 m     56 - 65         63 Australia  29930
#> 2      2 m     26 - 35         33 Germany    17885
#> 3      3 m     66 - 100        74 Germany    20805
#> 4      4 w     18 - 25         21 Poland     29565
#> 5      5 m     0 - 17           9 Germany    15330
#> # ℹ 27 more rows
#> # ALTER data: 384 × 7
#>   .altID .egoID sex   age     age.years country   income
#> *  <int>  <dbl> <chr> <fct>       <int> <chr>      <dbl>
#> 1      1      1 m     46 - 55        48 USA        45625
#> 2      2      1 m     0 - 17          5 Germany    52925
#> 3      3      1 w     26 - 35        35 Australia  60225
#> # ℹ 381 more rows
#> # AATIE data: 721 × 4
#>   .egoID .srcID .tgtID weight
#> *  <int>  <int>  <int>  <dbl>
#> 1     20      1      2  0.667
#> 2     25      6     10  0.667
#> 3      9      6      8  0.667
#> # ℹ 718 more rows

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)
#> # EGO data (active): 10 × 6
#>   .egoID sex   age     age.years country   income
#> *  <dbl> <chr> <fct>       <int> <chr>      <dbl>
#> 1      9 m     36 - 45        38 USA        64605
#> 2     10 m     0 - 17         14 Australia  49275
#> 3     11 w     26 - 35        27 Germany    37960
#> 4     12 m     56 - 65        57 Germany    54750
#> 5     15 w     26 - 35        28 Germany    46720
#> # ℹ 5 more rows
#> # ALTER data: 120 × 7
#>   .altID .egoID sex   age     age.years country   income
#> *  <int>  <dbl> <chr> <fct>       <int> <chr>      <dbl>
#> 1      1      9 m     46 - 55        48 USA        45625
#> 2      2      9 m     0 - 17          5 Germany    52925
#> 3      3      9 w     26 - 35        35 Australia  60225
#> # ℹ 117 more rows
#> # AATIE data: 333 × 4
#>   .egoID .srcID .tgtID weight
#> *  <int>  <int>  <int>  <dbl>
#> 1     20      1      2  0.667
#> 2      9      6      8  0.667
#> 3     11      9     11  0.333
#> # ℹ 330 more rows

e1 %>% 
  activate(alter) %>% 
  filter(country %in% c("USA", "Poland"))
#> # EGO data: 32 × 6
#>   .egoID sex   age      age.years country   income
#> *  <dbl> <chr> <fct>        <int> <chr>      <dbl>
#> 1      1 m     56 - 65         63 Australia  29930
#> 2      2 m     26 - 35         33 Germany    17885
#> 3      3 m     66 - 100        74 Germany    20805
#> # ℹ 29 more rows
#> # ALTER data (active): 180 × 7
#>   .altID .egoID sex   age     age.years country income
#> *  <int>  <dbl> <chr> <fct>       <int> <chr>    <dbl>
#> 1      1      1 m     46 - 55        48 USA      45625
#> 2      4      1 w     0 - 17          3 Poland   25550
#> 3      7      1 m     26 - 35        32 USA      54020
#> 4      8      1 w     46 - 55        49 USA      60955
#> 5     11      1 w     46 - 55        54 Poland    9490
#> # ℹ 175 more rows
#> # AATIE data: 218 × 4
#>   .egoID .srcID .tgtID weight
#> *  <int>  <int>  <int>  <dbl>
#> 1     31      2     10  0.667
#> 2     24      1     12  0.333
#> 3      7      3      6  0.667
#> # ℹ 215 more rows

e1 %>% 
  activate(aatie) %>% 
  filter(weight > 0.7)
#> # EGO data: 32 × 6
#>   .egoID sex   age      age.years country   income
#> *  <dbl> <chr> <fct>        <int> <chr>      <dbl>
#> 1      1 m     56 - 65         63 Australia  29930
#> 2      2 m     26 - 35         33 Germany    17885
#> 3      3 m     66 - 100        74 Germany    20805
#> # ℹ 29 more rows
#> # ALTER data: 384 × 7
#>   .altID .egoID sex   age     age.years country   income
#> *  <int>  <dbl> <chr> <fct>       <int> <chr>      <dbl>
#> 1      1      1 m     46 - 55        48 USA        45625
#> 2      2      1 m     0 - 17          5 Germany    52925
#> 3      3      1 w     26 - 35        35 Australia  60225
#> # ℹ 381 more rows
#> # AATIE data (active): 374 × 4
#>   .egoID .srcID .tgtID weight
#> *  <int>  <int>  <int>  <dbl>
#> 1     26      2     10      1
#> 2     15      6     10      1
#> 3     16      3      6      1
#> 4     24      2      8      1
#> 5     26      6     11      1
#> # ℹ 369 more rows

Analyse

Try these function to analyse you egor object.

Summary

summary(e1)
#> 32 Egos/ Ego Networks 
#> 384 Alters 
#> Min. Netsize 12 
#> Average Netsize 12 
#> Max. Netsize 12 
#> Average Density 0.5 
#> Alter survey design:
#>   Maximum nominations: Inf

Density

ego_density(e1)
#> # A tibble: 32 × 2
#>    .egoID density
#>     <dbl>   <dbl>
#>  1      1   0.485
#>  2      2   0.5  
#>  3      3   0.5  
#>  4      4   0.409
#>  5      5   0.561
#>  6      6   0.455
#>  7      7   0.652
#>  8      8   0.485
#>  9      9   0.515
#> 10     10   0.515
#> # ℹ 22 more rows

Composition

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

.egoID	0 - 17	18 - 25	26 - 35	36 - 45	46 - 55	56 - 65	66 - 100
1	0.1666667	NA	0.2500000	NA	0.3333333	0.0833333	0.1666667
2	0.3333333	0.1666667	NA	0.0833333	0.1666667	NA	0.2500000
3	0.1666667	0.1666667	0.0833333	NA	0.1666667	0.0833333	0.3333333
4	0.0833333	0.0833333	0.1666667	NA	0.2500000	0.0833333	0.3333333
5	0.2500000	0.1666667	NA	0.0833333	0.1666667	NA	0.3333333
6	0.1666667	0.0833333	0.2500000	NA	0.2500000	0.0833333	0.1666667

Diversity

alts_diversity_count(e1, "age")
#> # A tibble: 32 × 2
#>    .egoID diversity
#>     <dbl>     <dbl>
#>  1      1         5
#>  2      2         5
#>  3      3         6
#>  4      4         6
#>  5      5         5
#>  6      6         6
#>  7      7         5
#>  8      8         5
#>  9      9         5
#> 10     10         5
#> # ℹ 22 more rows
alts_diversity_entropy(e1, "age")
#> # A tibble: 32 × 2
#>    .egoID entropy
#>     <dbl>   <dbl>
#>  1      1    2.19
#>  2      2    2.19
#>  3      3    2.42
#>  4      4    2.36
#>  5      5    2.19
#>  6      6    2.46
#>  7      7    2.08
#>  8      8    2.13
#>  9      9    2.19
#> 10     10    2.19
#> # ℹ 22 more rows

Ego-Alter Homophily (EI-Index)

comp_ei(e1, "age", "age")
#> # A tibble: 32 × 2
#>    .egoID    ei
#>     <dbl> <dbl>
#>  1      1 0.833
#>  2      2 1    
#>  3      3 0.333
#>  4      4 0.833
#>  5      5 0.5  
#>  6      6 0.667
#>  7      7 0.333
#>  8      8 0.333
#>  9      9 1    
#> 10     10 0.333
#> # ℹ 22 more rows

EI-Index for Alter-Alter Ties

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

.egoID	ei	0 - 17	26 - 35	46 - 55	56 - 65	66 - 100	18 - 25	36 - 45
1	0.5000000	-0.1764706	0.25	1.0000000	NaN	1.0000000	NA	NA
2	-0.0526316	0.1688312	NA	-0.2500000	NA	-0.3500000	1.0000000	NaN
3	-0.1692308	1.0000000	NaN	1.0000000	NaN	-0.2213740	-0.2903226	NA
4	0.0132159	NaN	1.00	-0.2413793	NaN	0.2000000	NaN	NA
5	0.0163934	1.0000000	NA	-0.3333333	NA	-0.0322581	-0.3793103	NaN
6	0.1076923	-0.3333333	1.00	0.1818182	NaN	-0.3793103	NaN	NA

Count attribute combinations in alter-alter ties/ dyads

# return results as "wide" tibble
  count_dyads(
    object = e1,
    alter_var_name = "country"
  )
#> # A tibble: 32 × 11
#>    .egoID dy_cou_Australia_Austr…¹ dy_cou_Australia_Ger…² dy_cou_Australia_Pol…³
#>     <dbl>                    <int>                  <int>                  <int>
#>  1      1                        2                      6                      3
#>  2      2                        0                      2                      0
#>  3      3                        4                      6                      4
#>  4      4                        1                      1                      1
#>  5      5                        2                     11                      4
#>  6      6                        2                      1                      1
#>  7      7                        0                      5                      7
#>  8      8                        1                      7                      1
#>  9      9                        1                      6                      4
#> 10     10                        0                      3                      1
#> # ℹ 22 more rows
#> # ℹ abbreviated names: ¹dy_cou_Australia_Australia, ²dy_cou_Australia_Germany,
#> #   ³dy_cou_Australia_Poland
#> # ℹ 7 more variables: dy_cou_Australia_USA <int>, dy_cou_Germany_Germany <int>,
#> #   dy_cou_Germany_Poland <int>, dy_cou_Germany_USA <int>,
#> #   dy_cou_Poland_USA <int>, dy_cou_USA_USA <int>, dy_cou_Poland_Poland <int>

# return results as "long" tibble
  count_dyads(
    object = e1,
    alter_var_name = "country",
    return_as = "long"
  )
#> # A tibble: 278 × 3
#>    .egoID dyads                   n
#>     <dbl> <chr>               <int>
#>  1      1 Australia_Australia     2
#>  2      1 Australia_Germany       6
#>  3      1 Australia_Poland        3
#>  4      1 Australia_USA           3
#>  5      1 Germany_Germany         3
#>  6      1 Germany_Poland          4
#>  7      1 Germany_USA             6
#>  8      1 Poland_USA              2
#>  9      1 USA_USA                 3
#> 10      2 Australia_Germany       2
#> # ℹ 268 more rows

`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)
#> # A tibble: 15 × 2
#>    .egoID result
#>     <dbl>  <dbl>
#>  1      1   26.6
#>  2      2   28.7
#>  3      3   29.9
#>  4      4   28.4
#>  5      5   25.6
#>  6      6   27.8
#>  7      7   28.5
#>  8      8   28.6
#>  9      9   28.6
#> 10     10   29.7
#> 11     11   29.7
#> 12     12   29.7
#> 13     13   26.2
#> 14     14   28.6
#> 15     15   28.2

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)

egor Vis App

Conversions

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

Till Krenz

2024-02-01

The `egor` Package

Import

Manipulate

Base R

activate() and dplyr verbs

Analyse

Summary

Density

Composition

Diversity

Ego-Alter Homophily (EI-Index)

EI-Index for Alter-Alter Ties

Count attribute combinations in alter-alter ties/ dyads

`comp_ply()`

Visualize

Clustered Graphs

`igraph` & `network` plotting

Shiny App for Visualization

Conversions

Using `egor` to analyse ego-centered network data

Till Krenz

2024-02-01

The egor Package

Import

Manipulate

Base R

activate() and dplyr verbs

Analyse

Summary

Density

Composition

Diversity

Ego-Alter Homophily (EI-Index)

EI-Index for Alter-Alter Ties

Count attribute combinations in alter-alter ties/ dyads

comp_ply()

Visualize

Clustered Graphs

igraph & network plotting

Shiny App for Visualization

Conversions

The `egor` Package

`comp_ply()`

`igraph` & `network` plotting