library(mortAAR) library(magrittr)
Data from four neolithic gallery graves in central Germany [@czarnetzki_menschlichen_1966].
td <- gallery_graves
Inspect the data. Show the first ten rows of the data set:
td %>% head(., n = 10) %>% knitr::kable()
Replace: "?" with NA
values.
td %>% replace(td == "?", NA) -> td
td %>% head(., n = 10) %>% knitr::kable()
Translate "inf_I", "inf_I" and "juv" into numeric age ranges [@martin_lehrbuch_1928, pp. 580].
td <- td %>% replace(td == "inf_I", "0-6") %>% replace(td == "inf_II", "7-13") %>% replace(td == "juv", "14-19")
td %>% head(., n = 10) %>% knitr::kable()
Remove rows that do not have age information.
td <- td %>% dplyr::filter(!is.na(age))
td %>% head(., n = 10) %>% knitr::kable()
Make a decision on individual 139 from Niedertiefenbach with age less or equal 60.
td[td$indnr == "139" & td$site == "Niedertiefenbach", ]$age <- "50-60"
td %>% head(n = 10) %>% knitr::kable()
Separate the age range column.
td <- td %>% tidyr::separate(age, c("from", "to"))
td %>% head(., n = 10) %>% knitr::kable()
Adjust variable types.
td <- td %>% transform( from = as.numeric(from), to = as.numeric(to) )
Control the flow of the analysis by exemplifying what the different variables of the input data stand for.
# tdlist <- td %>% # plyr::dlply("site", identity) td_prepared <- prep.life.table( td, dec = NA, agebeg = "from", ageend = "to", group = "site", method = "Standard", agerange = "included" )
td_result <- td_prepared %>% life.table()
td_result %>% plot(display = c("qx", "dx", "lx"))
td_result %>% plot(display = c("ex", "rel_popx"))
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