The goal of befproj is to make forecasts of a
population. The method used is a common projection method called cohort
component method. In this example data we will make a projection for
Umea municipality .
You can install the released version of befproj from CRAN with:
install.packages("befproj")This is a simple example how to use the function
bef_components and extract results for the population
components. The first argument is the start population. The second
argument is the assumptions about fertility and death rates etc. The
third argument is the year with our start population. The first few rows
and columns are shown below:
library(befproj)
bef.1 <- bef_components(startpop_data, assump_data, 2019)
bef.1[2:6,2:5]
#> netmigration.men netmigration.tot birts.boys births.girls
#> 2020 541.0532 1115.083 784.4031 741.6729
#> 2021 480.7252 1086.154 798.4692 754.9728
#> 2022 515.1171 1089.646 810.5600 766.4050
#> 2023 461.5626 1061.646 819.9212 775.2562
#> 2024 486.9417 1067.007 826.8813 781.8372This is a simple example how to use the function bef_raw
and extract results for the age specific numbers. The first six lines
are shown:
bef.2 <- bef_raw(startpop_data, assump_data, 2019)
head(bef.2)
#> year agegroup2 agegroup total_2 men_2 women_2 age.n
#> 1 2019 [0,1) [0,1) 1487 784 703 0
#> 2 2019 [1,2) [1,6) 1515 799 716 1
#> 3 2019 [2,3) [1,6) 1546 802 744 2
#> 4 2019 [3,4) [1,6) 1488 775 713 3
#> 5 2019 [4,5) [1,6) 1484 767 717 4
#> 6 2019 [5,6) [1,6) 1501 772 729 5This is a simple example how to use the function
bef_proj and extract results for yearly growth:
bef.3 <- bef_proj(startpop_data, assump_data, 2019)
tail(bef.3)
#> growth
#> 2025 1671.514
#> 2026 1647.338
#> 2027 1630.896
#> 2028 1603.411
#> 2029 1562.460
#> 2030 1490.897