LISS alter relations

Goal

Create alter relationship variable based on LISS alter ids.

Set

Packages and data import

#package
library(tidyverse)

#disable the scientific notation in R (else all the id's will be in scientific notation)
options(scipen = 999)

load("data/data-processed/liss_merged/liss_core_merged_V3_240624.Rdata")

Data prep

#extract the alter id variable from the lisscdn long file. 
liss_alters <- liss_long %>% 
  select(nomem_encr,
         survey_wave,
         starts_with("alter_id")) %>% 
  rename(alter_id.1 = alter_id_1, #replace alter sep _ with ., so we can use it in pivot
         alter_id.2 = alter_id_2,
         alter_id.3 = alter_id_3,
         alter_id.4 = alter_id_4,
         alter_id.5 = alter_id_5)

#reshape a long file with observations of alter in survey and egonet. 
alters_long <- liss_alters %>%
  pivot_longer(cols = starts_with("alter_id"),
               names_to = c("names", "alter"),
               values_to = "alter_id",
               names_pattern = "(.+)\\.(.+)")

Variable creation

#group by nomem_encr and alter id and count how many times an alter is member of network
#also calculate how many waves a member is part of the network.
alters_long <- alters_long %>% 
  group_by(nomem_encr, alter_id) %>% 
  mutate(rl_alter = ifelse(is.na(alter_id), NA, 1:n())) %>%
  ungroup()

Cleaning of data

#identify duplicate alters in a survey/ego combination. I will remove them from the analysis
duplicates <- alters_long %>% 
  group_by(nomem_encr, survey_wave) %>% 
  mutate(n_alter_wave = sum(!is.na(alter_id)),
         n_unique_alter = sum(!is.na(unique(na.omit(alter_id))))) %>% 
  ungroup() %>% 
  mutate(selection = as.numeric(n_unique_alter == n_alter_wave)) %>% 
  filter(selection == 0) %>% 
  select(nomem_encr) %>% 
  distinct() %>% 
  pull(nomem_encr)

#we remove 1597 ego's from the data
alters_long <- alters_long %>% 
  filter(!nomem_encr %in% duplicates)

Data export

#reshape into long file so we can can merge with LISS wide
alters_wide <- alters_long %>% 
  select(-names, -alter_id) %>% 
  pivot_wider(id_cols = c("nomem_encr", "survey_wave"), #first step of pivot
              names_from = "alter",
              names_prefix = "rl_alter",
              values_from = c("rl_alter")) %>% 
  pivot_wider(id_cols = "nomem_encr", #second step of pivot
              names_from = "survey_wave",
              values_from = 3:7,
              names_sep = ".")

#merge with liss wide (inner_join)
liss_wide <- liss_wide %>% 
  inner_join(alters_wide, 
            by = "nomem_encr")

#reshape data into liss_long.
liss_long <- liss_wide %>%
  pivot_longer(cols = 2:ncol(liss_wide), 
               names_to = c("measure", "survey_wave"),
               names_pattern = "(.+)\\.(.+)",
               values_to = "value") %>%
  pivot_wider(names_from = measure,
              values_from = value) %>% 
  mutate(survey_wave = as.numeric(survey_wave))

#clean global environment.
#only keep the wide and long files. 
rm(list=ls()[! ls() %in% c("liss_wide","liss_long")])

#save.image
save.image(file = "data/data-processed/liss_merged/lisscdn_afterrel_V4_240624.Rdata")

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Copyright © 2024 Jeroense Thijmen