This function imports ego-centered network data, stored in a single file, providing ego, alter and edge data. This data format is used by the Allbus 2010 (GESIS) and similar social surveys.
onefile_to_egor(
egos,
netsize = NULL,
ID.vars = list(ego = "egoID"),
attr.start.col,
attr.end.col,
max.alters,
aa.first.var,
aa.regex = NULL,
var.wise = FALSE,
...
)Data frame containing ego data (egos as cases)
Numeric, network size values are used to filter out empty alter entries. If the alter data is not structured in a way, where valid alters are stored before the invalid alters, pass NULL here and filter out invalid alters afterwards.
Character. For onefile_to_egor only the name of the ego ID needs to be provided.
Index or name of the first column containing alter attributes.
Index or name of the last column containing alter attributes.
Maximum number of alters.
First column containing alter-alter relations/ edges.
A Perl regular expression with name capture,
intended to be run on column names and capturing via named
capture the following regex groups: "attr", "src", and
"tgt", representing the edge attribute being captured, the
source (or the first alter identified), and the target (or the
second alter identified) of the edge, respectively. See regex
for more information.
Logical value indicating if the alter attributes are sorted variable wise (defaults to FALSE).
additional arguments to egor().
An egor object is returned. It is a list of three data frames:
(1) ego: dataframe of all
egos and their attributes;
(2) alter: dataframe of all alters;
(3) aatie: dataframe of alter alter ties/ edges
Muller, C., Wellman, B., & Marin, A. (1999). How to Use SPSS to Study Ego-Centered Networks. Bulletin de Methodologie Sociologique, 64(1), 83-100.
path_to_one_file_8 <- system.file("extdata", "one_file_8.csv", package = "egor")
egos_8 <- read.csv2(path_to_one_file_8)
onefile_to_egor(
egos = egos_8, netsize = egos_8$netsize,
attr.start.col = "alter.sex.1",
attr.end.col = "alter.age.8",
aa.first.var = "X1.to.2",
max.alters = 8)
#> Sorting data by egoID:
#> Done.
#> Transforming alters data to long format:
#> Done.
#> Transforming wide dyad data to edgelist:
#> Done.
#> Note: Make sure to filter out alter-alter ties with invalid weight values.
#> Filtering out empty alter entries using provided network size values:
#> Done.
#> # EGO data (active): 16 × 5
#> .egoID sex age netsize egoID.1
#> * <chr> <chr> <chr> <int> <int>
#> 1 1 w 66 - 100 8 1
#> 2 2 m 18 - 25 8 2
#> 3 3 w 26 - 35 8 3
#> 4 4 w 0 - 17 8 4
#> 5 5 m 36 - 45 8 5
#> # … with 11 more rows
#> # ALTER data: 128 × 4
#> .altID .egoID alter.sex. alter.age.
#> * <chr> <chr> <chr> <chr>
#> 1 1 1 w 66 - 100
#> 2 2 1 w 0 - 17
#> 3 3 1 m 56 - 65
#> # … with 125 more rows
#> # AATIE data: 448 × 4
#> .egoID .srcID .tgtID weight
#> * <chr> <chr> <chr> <int>
#> 1 1 1 2 2
#> 2 1 1 3 3
#> 3 1 1 4 1
#> # … with 445 more rows