Using egor to analyse ego-centered network data
Author: Till Krenz
Date: 2025-10-30

# Using `egor` to analyse ego-centered network data

#### Till Krenz

#### 2025-10-30

## 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: ¹​dy_cou_Australia_Australia, ²​dy_cou_Australia_Germany,
    #> #   ³​dy_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   25.2
    #>  2      2   23.2
    #>  3      3   22.4
    #>  4      4   23.8
    #>  5      5   22.0
    #>  6      6   25.4
    #>  7      7   25.7
    #>  8      8   24.2
    #>  9      9   25.2
    #> 10     10   24.0
    #> 11     11   22.1
    #> 12     12   24.1
    #> 13     13   24.7
    #> 14     14   22.7
    #> 15     15   24.0

## 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.