---

Names

---

Names

Tabel 1: Surnames by surname, rank and time

NAXT1_raw <- 
  statgl_url("NAXT1", lang = language) %>% 
  statgl_fetch(
    surname   = px_all(),
    rank      = 0,
    time      = px_top(),
    .col_code = TRUE
  ) %>% 
  as_tibble()
  
NAXT1 <- 
  NAXT1_raw %>% 
  filter(value >= 1000) %>% 
  arrange(desc(value)) %>% 
  mutate(surname = surname %>% fct_reorder(value, .fun = sum, .desc = TRUE)) %>% 
  spread(rank, value)

col <- paste(colnames(NAXT1)[3], "> 1000")
  
NAXT1 %>% 
  select(-time) %>% 
  statgl_table(col.names = c(" ", col)) %>% 
  pack_rows(index = NAXT1[["time"]] %>% table())

surname	Count
2021
Petersen	2.087
Olsen	1.704
Jensen	1.494
Nielsen	1.346
Hansen	1.033

See the table in our Statbank: NRX01

Tabel 2: First names by first name, unit, sex and time

helper <- 
  statgl_url("NAXT5", lang = language) %>% 
  statgl_fetch(
    unit         = 0,
    sex          = px_all(),
    time         = px_top(1),
    "first name" = px_all(),
    .col_code    = FALSE
  ) %>% 
  as_tibble()

NAXT5_raw <- 
  statgl_url("NAXT5", lang = language) %>% 
  statgl_fetch(
    unit         = 0,
    sex          = px_all(),
    time         = px_top(1),
    "first name" = px_all(),
    .col_code    = TRUE
  ) %>% 
  as_tibble()

NAXT5 <- 
  NAXT5_raw %>% 
  drop_na() %>% 
  arrange(sex, desc(value)) %>% 
  filter(value >= 350) %>% 
  arrange(sex) %>% 
  mutate(`first name` = `first name` %>% fct_inorder()) %>% 
  spread(unit, value)

col <- paste(NAXT5_raw[["unit"]][1], "> 350")

NAXT5 %>% 
  select(-sex, -time) %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col)) %>% 
  pack_rows(index = NAXT5[["time"]] %>% table()) %>% 
  pack_rows(index = NAXT5[["sex"]] %>% table())

	Count
2021
Men
Hans	825
Jens	669
Karl	625
Peter	574
Lars	492
Niels	459
Kristian	407
Ole	394
Women
Ane	569
Karen	389
Marie	380

See the table in our Statbank: NAXT5

---

Last updated: 17. april 2024