Using egor to analyse ego-centered network data

Till Krenz

2021-01-14

The egor Package

egor provides

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 w 66 - 100 75 Australia 42340
2 1 m 18 - 25 17 Germany 730
3 1 w 66 - 100 91 Australia 23360
4 1 m 0 - 17 10 USA 27010
5 1 m 66 - 100 67 Poland 33215
6 1 m 0 - 17 10 USA 31755
First rows of ego data.
.EGOID sex age age.years country income
1 w 36 - 45 45 USA 36135
2 m 36 - 45 37 Germany 35040
3 m 26 - 35 28 Australia 63875
4 w 0 - 17 2 USA 31755
5 m 56 - 65 56 Germany 14600
6 m 26 - 35 31 Poland 21900
First rows of alter-alter tie data.
.EGOID .SRCID .TGTID weight
32 13 18 0.3333333
18 11 22 0.6666667
28 5 19 0.6666667
19 5 6 1.0000000
32 2 19 0.6666667
22 3 15 0.6666667

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 x 6
#>   .egoID sex   age     age.years country   income
#>   <chr>  <fct> <fct>       <int> <fct>      <dbl>
#> 1 1      w     36 - 45        45 USA        36135
#> 2 2      m     36 - 45        37 Germany    35040
#> 3 3      m     26 - 35        28 Australia  63875
#> 4 4      w     0 - 17          2 USA        31755
#> 5 5      m     56 - 65        56 Germany    14600
#> # ALTER data: 478 x 7
#>   .altID .egoID sex   age      age.years country   income
#>   <chr>  <chr>  <fct> <fct>        <int> <fct>      <dbl>
#> 1 1      1      w     66 - 100        75 Australia  42340
#> 2 2      1      m     18 - 25         17 Germany      730
#> 3 3      1      w     66 - 100        91 Australia  23360
#> # AATIE data: 1,858 x 4
#>   .egoID .srcID .tgtID weight
#>   <chr>  <chr>  <chr>   <dbl>
#> 1 32     13     18      0.333
#> 2 18     11     22      0.667
#> 3 28     5      19      0.667

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

Import

There are currently three importing functions that read the data exported from data collection tools from the harddrive 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): 23 x 6
#>   .egoID sex   age      age.years country income
#>   <chr>  <fct> <fct>        <int> <fct>    <dbl>
#> 1 1      w     36 - 45         45 USA      36135
#> 2 2      m     36 - 45         37 Germany  35040
#> 3 5      m     56 - 65         56 Germany  14600
#> 4 7      w     36 - 45         43 Poland   15695
#> 5 9      w     66 - 100        73 Poland   31390
#> # ALTER data: 334 x 7
#>   .altID .egoID sex   age      age.years country   income
#>   <chr>  <chr>  <fct> <fct>        <int> <fct>      <dbl>
#> 1 1      1      w     66 - 100        75 Australia  42340
#> 2 2      1      m     18 - 25         17 Germany      730
#> 3 3      1      w     66 - 100        91 Australia  23360
#> # AATIE data: 1,296 x 4
#>   .egoID .srcID .tgtID weight
#>   <chr>  <chr>  <chr>   <dbl>
#> 1 18     11     22      0.667
#> 2 28     5      19      0.667
#> 3 22     3      15      0.667

Alter level:

subset(e1, e1$alter$sex == "w", unit = "alter")
#> # EGO data (active): 32 x 6
#>   .egoID sex   age     age.years country   income
#>   <chr>  <fct> <fct>       <int> <fct>      <dbl>
#> 1 1      w     36 - 45        45 USA        36135
#> 2 2      m     36 - 45        37 Germany    35040
#> 3 3      m     26 - 35        28 Australia  63875
#> 4 4      w     0 - 17          2 USA        31755
#> 5 5      m     56 - 65        56 Germany    14600
#> # ALTER data: 181 x 7
#>   .altID .egoID sex   age      age.years country   income
#>   <chr>  <chr>  <fct> <fct>        <int> <fct>      <dbl>
#> 1 1      1      w     66 - 100        75 Australia  42340
#> 2 3      1      w     66 - 100        91 Australia  23360
#> 3 1      2      w     66 - 100        75 Australia  42340
#> # AATIE data: 281 x 4
#>   .egoID .srcID .tgtID weight
#>   <chr>  <chr>  <chr>   <dbl>
#> 1 18     11     22      0.667
#> 2 11     10     12      0.333
#> 3 24     10     11      0.333

Alter-alter tie level:

subset(e1, e1$aatie$weight > 0.5, unit = "aatie")
#> # EGO data (active): 32 x 6
#>   .egoID sex   age     age.years country   income
#>   <chr>  <fct> <fct>       <int> <fct>      <dbl>
#> 1 1      w     36 - 45        45 USA        36135
#> 2 2      m     36 - 45        37 Germany    35040
#> 3 3      m     26 - 35        28 Australia  63875
#> 4 4      w     0 - 17          2 USA        31755
#> 5 5      m     56 - 65        56 Germany    14600
#> # ALTER data: 478 x 7
#>   .altID .egoID sex   age      age.years country   income
#>   <chr>  <chr>  <fct> <fct>        <int> <fct>      <dbl>
#> 1 1      1      w     66 - 100        75 Australia  42340
#> 2 2      1      m     18 - 25         17 Germany      730
#> 3 3      1      w     66 - 100        91 Australia  23360
#> # AATIE data: 1,241 x 4
#>   .egoID .srcID .tgtID weight
#>   <chr>  <chr>  <chr>   <dbl>
#> 1 18     11     22      0.667
#> 2 28     5      19      0.667
#> 3 19     5      6       1

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 borrwed 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): 13 x 6
#>   .egoID sex   age      age.years country   income
#>   <chr>  <fct> <fct>        <int> <fct>      <dbl>
#> 1 1      w     36 - 45         45 USA        36135
#> 2 3      m     26 - 35         28 Australia  63875
#> 3 8      m     0 - 17           1 Australia  37960
#> 4 10     w     66 - 100        74 Germany    43435
#> 5 11     m     66 - 100        81 Germany    71905
#> # ALTER data: 192 x 7
#>   .altID .egoID sex   age      age.years country   income
#>   <chr>  <chr>  <fct> <fct>        <int> <fct>      <dbl>
#> 1 1      1      w     66 - 100        75 Australia  42340
#> 2 2      1      m     18 - 25         17 Germany      730
#> 3 3      1      w     66 - 100        91 Australia  23360
#> # AATIE data: 703 x 4
#>   .egoID .srcID .tgtID weight
#>   <chr>  <chr>  <chr>   <dbl>
#> 1 22     3      15      0.667
#> 2 14     3      7       0.333
#> 3 15     5      8       0.333

e1 %>% 
  activate(alter) %>% 
  filter(country %in% c("USA", "Poland"))
#> # EGO data: 32 x 6
#>   .egoID sex   age     age.years country   income
#>   <chr>  <fct> <fct>       <int> <fct>      <dbl>
#> 1 1      w     36 - 45        45 USA        36135
#> 2 2      m     36 - 45        37 Germany    35040
#> 3 3      m     26 - 35        28 Australia  63875
#> # ALTER data (active): 239 x 7
#>   .altID .egoID sex   age      age.years country income
#>   <chr>  <chr>  <fct> <fct>        <int> <fct>    <dbl>
#> 1 4      1      m     0 - 17          10 USA      27010
#> 2 5      1      m     66 - 100        67 Poland   33215
#> 3 6      1      m     0 - 17          10 USA      31755
#> 4 8      1      m     66 - 100        97 USA      67890
#> 5 4      2      m     0 - 17          10 USA      27010
#> # AATIE data: 454 x 4
#>   .egoID .srcID .tgtID weight
#>   <chr>  <chr>  <chr>   <dbl>
#> 1 32     13     18      0.333
#> 2 18     11     22      0.667
#> 3 19     5      6       1

e1 %>% 
  activate(aatie) %>% 
  filter(weight > 0.7)
#> # EGO data: 32 x 6
#>   .egoID sex   age     age.years country   income
#>   <chr>  <fct> <fct>       <int> <fct>      <dbl>
#> 1 1      w     36 - 45        45 USA        36135
#> 2 2      m     36 - 45        37 Germany    35040
#> 3 3      m     26 - 35        28 Australia  63875
#> # ALTER data: 478 x 7
#>   .altID .egoID sex   age      age.years country   income
#>   <chr>  <chr>  <fct> <fct>        <int> <fct>      <dbl>
#> 1 1      1      w     66 - 100        75 Australia  42340
#> 2 2      1      m     18 - 25         17 Germany      730
#> 3 3      1      w     66 - 100        91 Australia  23360
#> # AATIE data (active): 598 x 4
#>   .egoID .srcID .tgtID weight
#>   <chr>  <chr>  <chr>   <dbl>
#> 1 19     5      6           1
#> 2 13     12     17          1
#> 3 8      3      13          1
#> 4 32     8      14          1
#> 5 21     3      13          1

Analyse

Try these function to analyse you egor object.

Summary

summary(e1)
#> 32 Egos/ Ego Networks 
#> 478 Alters 
#> Min. Netsize 6 
#> Average Netsize 14.9375 
#> Max. Netsize 24 
#> Average Density 0.503451632597652 
#> Alter survey design:
#>   Maximum nominations: Inf

Density

ego_density(e1)
#> # A tibble: 32 x 2
#>    .egoID density
#>    <chr>    <dbl>
#>  1 1        0.5  
#>  2 2        0.485
#>  3 3        0.55 
#>  4 4        0.449
#>  5 5        0.467
#>  6 6        0.375
#>  7 7        0.491
#>  8 8        0.463
#>  9 9        0.417
#> 10 10       0.455
#> # … with 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 0.3750000 0.1250000 NA NA NA NA 0.5000000
10 0.3636364 0.0909091 NA NA NA NA 0.5454545
11 0.2777778 0.0555556 0.0555556 0.0555556 0.1111111 0.0555556 0.3888889
12 0.3076923 0.0769231 0.0769231 NA NA NA 0.5384615
13 0.2727273 0.0454545 0.1363636 0.0909091 0.0909091 0.0454545 0.3181818
14 0.2857143 0.0476190 0.1428571 0.0476190 0.0952381 0.0476190 0.3333333

Diversity

alts_diversity_count(e1, "age")
#> # A tibble: 32 x 2
#>    .egoID diversity
#>    <chr>      <dbl>
#>  1 1              3
#>  2 2              7
#>  3 3              6
#>  4 4              7
#>  5 5              3
#>  6 6              6
#>  7 7              3
#>  8 8              7
#>  9 9              3
#> 10 10             3
#> # … with 22 more rows
alts_diversity_entropy(e1, "age")
#> # A tibble: 32 x 2
#>    .egoID entropy
#>    <chr>    <dbl>
#>  1 1         1.41
#>  2 2         2.29
#>  3 3         2.05
#>  4 4         2.25
#>  5 5         1.36
#>  6 6         2.05
#>  7 7         1.32
#>  8 8         2.25
#>  9 9         1.39
#> 10 10        1.32
#> # … with 22 more rows

Ego-Alter Homophily (EI-Index)

comp_ei(e1, "age", "age")
#> # A tibble: 32 x 2
#>    .egoID      ei
#>    <chr>    <dbl>
#>  1 1       1     
#>  2 2       0.895 
#>  3 3       0.875 
#>  4 4       0.412 
#>  5 5       1     
#>  6 6       0.875 
#>  7 7       1     
#>  8 8       0.412 
#>  9 9       0.111 
#> 10 10     -0.0909
#> # … with 22 more rows

EI-Index for Alter-Alter Ties

EI(e1, "age") %>%
  head() %>%
  kable()
.egoID ei 0 - 17 18 - 25 66 - 100 26 - 35 36 - 45 46 - 55 56 - 65
1 0.0843373 -0.1764706 NaN 0.2558140 NA NA NA NA
2 -0.1018011 -0.1005291 NaN -0.1428571 NaN NaN -0.3333333 NaN
3 -0.0368932 0.0909091 NaN -0.0985915 NaN NA NaN NaN
4 0.1460259 0.1818182 NaN 0.1208791 NaN NaN NaN NaN
5 -0.1523179 -0.1851852 NaN -0.1627907 NA NA NA NA
6 0.0987342 0.2903226 NaN 0.0400000 NaN NA NaN NaN

Count attribute combinations in alter-alter ties/ dyads

# return results as "wide" tibble
  count_dyads(
    object = e1,
    alter_var_name = "country"
  )
#> # A tibble: 32 x 11
#>    .egoID dy_cou_Australi… dy_cou_Australi… dy_cou_Australi… dy_cou_Australi…
#>    <chr>             <int>            <int>            <int>            <int>
#>  1 1                     1                2                2                5
#>  2 10                    1                4                4                3
#>  3 11                    0                8                7               10
#>  4 12                    2                2                4                7
#>  5 13                    3               16               16               11
#>  6 14                    5               15               10                7
#>  7 15                    1                9                9               11
#>  8 16                    2                5                5                6
#>  9 17                    3               16                9               13
#> 10 18                    2               16               12               13
#> # … with 22 more rows, and 6 more variables: dy_cou_Germany_USA <int>,
#> #   dy_cou_USA_USA <int>, dy_cou_Germany_Germany <int>,
#> #   dy_cou_Germany_Poland <int>, dy_cou_Poland_Poland <int>,
#> #   dy_cou_Poland_USA <int>

# return results as "long" tibble
  count_dyads(
    object = e1,
    alter_var_name = "country",
    return_as = "long"
  )
#> # A tibble: 292 x 3
#>    .egoID dyads                   n
#>    <chr>  <chr>               <int>
#>  1 1      Australia_Australia     1
#>  2 1      Australia_Germany       2
#>  3 1      Australia_Poland        2
#>  4 1      Australia_USA           5
#>  5 1      Germany_USA             2
#>  6 1      USA_USA                 2
#>  7 10     Australia_Australia     1
#>  8 10     Australia_Germany       4
#>  9 10     Australia_Poland        4
#> 10 10     Australia_USA           3
#> # … with 282 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 x 2
#>    .egoID result
#>     <dbl>  <dbl>
#>  1      1   27.0
#>  2      2   25.2
#>  3      3   25.3
#>  4      4   28.9
#>  5      5   26.8
#>  6      6   28.9
#>  7      7   27.3
#>  8      8   28.5
#>  9      9   25.2
#> 10     10   25.1
#> 11     11   26.8
#> 12     12   27.3
#> 13     13   27.2
#> 14     14   28.3
#> 15     15   39.0

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

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.