The Allbus 2010: ego-centered network data
The Allbus 2010 splits the respondents into two groups. Both groups
were presented different name generators.
- Allbus name generator - the generated alters are called “Freunde”
(friends in German) in the data (max. 3 persons, “spent time with in
private, not living in same household”)
- GSS name generator - these alters are called “Kontakte” (contacts in
German) in the dataset (max. 5 persons, “discussed important
matters”)
For more information please consult the Allbus documentation. ## Load
packages and data
library(egor)
library(purrr)
library(haven)
In addition to egor, this vignette uses the haven
package, to import the SPSS file of the Allbus 2010 and the purrr package, that
provides enhanced functional programming functions. The purrr
functions used in this vignette are map*() functions, which are
similar in their functionality to base R’s lapply().
When using haven to import the original Allbus data, that
would look like this.
raw_data <- read_sav("ZA4610_A10.SAV")
For the purpose of the vignette we are loading a simulated data
instead.
data("allbus_2010_simulated")
raw_data <- allbus_2010_simulated
The Allbus variable names are quite technical ranging from V1 to
V981. Fortunately the haven data import preserves the SPSS
variable labels, that describe each variable in more detail. We are
going to convert these labels into a format, that allows us to use them
as variable names.
The code below extracts all variable labels and eliminates all
non-characters from the labels and substitutes spaces with
underscores.
var_labels <- map_chr(raw_data, ~attr(., "label"))
var_labels <- gsub("[,\\.:;><?+()-]", " ", var_labels)
var_labels <- gsub("\\s+", "_", trimws(var_labels))
The variable labels for the ego-centered network module need some
special treatment, so that egor can give useful names to the alter
variables. We are deleting the first part from those labels, that is
shared by all variables in each split.
var_labels <- gsub("FREUND_IN_._", "", var_labels)
var_labels <- gsub("^KONTAKT_._", "", var_labels)
Now we can use the cleaned up variable labels as names for
our data.
names(raw_data) <- make.unique(var_labels, sep = "")
Convert the data to egor objects
We are going to create two separate ego objects for each split.
Starting with the “Freunde” split.
First we filter out only those respondents from split 1.
split_freunde <-
raw_data %>%
filter(FRAGEBOGENSPLIT_F020 == 1)
Now we use the onefile_to_egor() function to convert the
data to an egor object. This function needs a few arguments in order for
it to be able to locate the alter data and alter-alter tie data in the
dataset.
e_freunde <- onefile_to_egor(
egos = split_freunde,
ID.vars = list(ego = "IDENTIFIKATIONSNUMMER_DES_BEFRAGTEN"),
netsize = split_freunde$ANZ_GENANNTER_NETZWERKPERS_SPLIT_1,
attr.start.col = "GESCHLECHT",
attr.end.col = "SPANNUNGEN_KONFLIKTE2",
aa.first.var = "KENNEN_SICH_A_B",
max.alters = 3)
#> Sorting data by egoID:
#> Done.
#> Transforming alters data to long format:
#> Done.
#> Transforming wide dyad data to edgelist:
#> Done.
#> Note: Make sure to filter out alter-alter ties with invalid weight values.
#> Filtering out empty alter entries using provided network size values:
#> Done.
The onefile_to_egor() function prints some messages during
the conversion, that are supposed to help us to identify problems in
case something something goes wrong.
We also see a NOTE, that tells us that we need to filter out invalid
alter-alter ties. In this case those are ties with a weight of 2, since
Allbus codes not existing ties with 2 here.
attr(raw_data$KENNEN_SICH_A_B, "labels")
#> TRIFFT NICHT ZU KENNEN SICH GUT KENNEN SICH NICHT KEINE ANGABE
#> 0 1 2 9
“KENNEN SICH NICHT” means “don’t know each other” in german.
We can filter the alter-alter ties using the activate() and
filter() functions.
e_freunde <-
e_freunde %>%
activate(aatie) %>%
filter(weight != 2) %>%
activate(ego)
Next we repeat the same steps for split 2. Here we need to filter out
the weight value 3 from the alter-alter ties and of adjust some
arguments according to the position of the data in the dataset and the
maximum amount of alters that the respondents were allowed to
nominate.
split_kontakte <-
raw_data %>%
filter(FRAGEBOGENSPLIT_F020 == 2)
e_kontakte <- onefile_to_egor(
egos = split_kontakte,
ID.vars = list(ego = "IDENTIFIKATIONSNUMMER_DES_BEFRAGTEN"),
netsize = split_kontakte$ANZ_GENANNTER_NETZWERKPERS_SPLIT_2,
attr.start.col = "GESCHLECHT3",
attr.end.col = "SPANNUNGEN_KONFLIKTE7",
aa.first.var = "KENNEN_SICH_KONTAKT_A_B",
max.alters = 5)
#> 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.
e_kontakte <-
e_kontakte %>%
activate(aatie) %>%
filter(weight != 3) %>%
activate(ego)
Visualize and analyze
Now we can visualize and analyze the Allbus data. A few
demonstrations follow. For an overview of available options, please see
the main vignette of egor “Using egor to analyse
ego-centered network data”.
plot(e_freunde, ego_no = 4, x_dim = 2, y_dim = 1)
plot(e_kontakte, ego_no = 4, x_dim = 2, y_dim = 1)
e_freunde <-
e_freunde%>%
activate(alter) %>%
mutate(WO_GEBOREN = droplevels(as_factor(WO_GEBOREN)),
KONTAKTE = droplevels(as_factor(KONTAKTE)))
plot_egograms(e_freunde,
ego_no = 4,
x_dim = 1,
y_dim = 1, venn_var = "KONTAKTE",
pie_var = "WO_GEBOREN")
e_kontakte <-
e_kontakte %>%
activate(alter) %>%
mutate(WO_GEBOREN = droplevels(as_factor(WO_GEBOREN)),
KONTAKTE = droplevels(as_factor(KONTAKTE)))
plot_egograms(e_kontakte,
ego_no = 4,
x_dim = 1,
y_dim = 1,
venn_var = "KONTAKTE" ,
pie_var = "WO_GEBOREN")
