Table: Western Europe East Asia youth, students, teachers, and schools
Source:vignettes/ffv_tab_weea_pop_teachers_schools.Rmd
ffv_tab_weea_pop_teachers_schools.Rmd
library(tibble)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tidyr)
library(stringr)
library(readr)
library(kableExtra)
#>
#> Attaching package: 'kableExtra'
#> The following object is masked from 'package:dplyr':
#>
#> group_rows
library(PrjCompPPTS)
# If resave outputs to data, only do this during development
# Latex output file
verbose <- TRUE
bl_tex_save <- FALSE
spt_path_res <- file.path("..", "res-tab", fsep = .Platform$file.sep)
spn_tex_out <- file.path(spt_path_res, "tab_weea_lrce.tex", fsep = .Platform$file.sep)Implement PrjCompPPTS-11,summaries for Western Europe and East Asia stats.
Generate input file
Load prepped all location level files
Load file with interpolated data.
# Locaiton codes
ppts_code_wrk <- ppts_country_code
# We load in the global population data.
df_ppts <- ppts_easia_weuro_world_pchgRun PrjCompPPTS functions
Run functions to generate FLR, FPC, and FEL files. Run FLR.
# country list
ar_st_country_names <- c(
"CHN", "TWN", "JPN", "KOR",
"AUT", "DEU", "FRA", "NLD", "CHE"
)
df_ppts_sel <- df_ppts %>%
filter(location_code %in% ar_st_country_names)
# Generate PLR
df_flr_weea <- PrjCompPPTS::ff_ppts_lrce_flr(
df_ppts_sel,
st_year_bins_type = "1940t2020i01",
ar_it_years = c(
1960, 1970, 1980, 1990, 2000, 2010, 2020
),
verbose = TRUE
)
#> F-713479, S1
#> [1] 1960 1970 1980 1990 2000 2010 2020
#> F-713479, S2
#> dim of youth wide file: 52
#> dim of youth wide file: 7
#> F-713479, S2
#> [1] "var_rat_y2t"
#> [1] "var_lvl_youthpop"
#> [1] "var_lvl_teacher"
#> dim FLR: 52
#> dim FLR: 8
#> F-713479, S2
#> [1] "var_rat_s2t"
#> [1] "var_lvl_student"
#> [1] "var_lvl_teacher"
#> dim FLR: 52
#> dim FLR: 9
#> F-713479, S2
#> [1] "var_rat_y2s"
#> [1] "var_lvl_youthpop"
#> [1] "var_lvl_school"
#> dim FLR: 52
#> dim FLR: 10
#> F-713479, S2
#> [1] "var_rat_s2s"
#> [1] "var_lvl_student"
#> [1] "var_lvl_school"
#> dim FLR: 52
#> dim FLR: 11
#> F-713479, S3
#> [1] "location_code" "location_level" "year_bins" "var_lvl_school"
#> [5] "var_lvl_student" "var_lvl_teacher" "var_lvl_youthpop" "var_rat_y2t"
#> [9] "var_rat_s2t" "var_rat_y2s" "var_rat_s2s"
#> dim of youth wide ratio file: 52
#> dim of youth wide ratio file: 11Run FPC.
# Generate FPC
df_fpc_weea <- PrjCompPPTS::ff_ppts_lrce_fpc(
df_flr_weea,
ar_it_years_chg = c(1960, 1970, 1980, 1990, 2000, 2010, 2020),
ls_chg_years = list(
"chg_20v60" = c(1960, 2020),
"chg_20v70" = c(1970, 2020),
"chg_20v80" = c(1980, 2020),
"chg_20v90" = c(1990, 2020),
"chg_20v00" = c(2000, 2020),
"chg_20v10" = c(2010, 2020)
), verbose = TRUE
)
#> F-376864, S1
#> dim of youth wide ratio file: 395
#> dim of youth wide ratio file: 6
#> F-376864, S2
#> dim of youth wide ratio file: 395
#> dim of youth wide ratio file: 6
#> F-376864, S3
#> dim of youth wide ratio file: 144
#> dim of youth wide ratio file: 11
#> F-376864, SD3
#> [1] "chg_20v60"
#> [1] "year1960"
#> [1] "year2020"
#> dim FPC: 144
#> dim FPC: 12
#> F-376864, SD3
#> [1] "chg_20v70"
#> [1] "year1970"
#> [1] "year2020"
#> dim FPC: 144
#> dim FPC: 13
#> F-376864, SD3
#> [1] "chg_20v80"
#> [1] "year1980"
#> [1] "year2020"
#> dim FPC: 144
#> dim FPC: 14
#> F-376864, SD3
#> [1] "chg_20v90"
#> [1] "year1990"
#> [1] "year2020"
#> dim FPC: 144
#> dim FPC: 15
#> F-376864, SD3
#> [1] "chg_20v00"
#> [1] "year2000"
#> [1] "year2020"
#> dim FPC: 144
#> dim FPC: 16
#> F-376864, SD3
#> [1] "chg_20v10"
#> [1] "year2010"
#> [1] "year2020"
#> dim FPC: 144
#> dim FPC: 17
#> F-376864, S4
#> dim FPC: 144
#> dim FPC: 17
print(df_fpc_weea, n = 40)
#> # A tibble: 144 × 17
#> location_code location_level vartype variable year1960 year1970 year1980
#> <fct> <fct> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 AUT country lvl school 4393 3.97e3 3.45e3
#> 2 AUT country lvl student 516110 5.96e5 4.01e5
#> 3 AUT country lvl teacher 21499 2.48e4 2.75e4
#> 4 AUT country lvl youthpop 1566864 1.84e6 1.55e6
#> 5 AUT country rat y2t 72.9 7.40e1 5.63e1
#> 6 AUT country rat s2t 24.0 2.40e1 1.46e1
#> 7 AUT country rat y2s 357. 4.62e2 4.49e2
#> 8 AUT country rat s2s 117. 1.50e2 1.16e2
#> 9 CHE country lvl school NA NA NA
#> 10 CHE country lvl student 573052. 5.32e5 4.40e5
#> 11 CHE country lvl teacher 17449. 2.09e4 2.52e4
#> 12 CHE country lvl youthpop 1315049. 1.37e6 1.28e6
#> 13 CHE country rat y2t 75.4 6.53e1 5.07e1
#> 14 CHE country rat s2t 32.8 2.54e1 1.75e1
#> 15 CHE country rat y2s NA NA NA
#> 16 CHE country rat s2s NA NA NA
#> 17 CHN country lvl school 726484 9.61e5 9.17e5
#> 18 CHN country lvl student 93791000 1.05e8 1.46e8
#> 19 CHN country lvl teacher 2693000 3.61e6 5.50e6
#> 20 CHN country lvl youthpop 265641893 3.31e8 3.53e8
#> 21 CHN country rat y2t 98.6 9.15e1 6.41e1
#> 22 CHN country rat s2t 34.8 2.91e1 2.66e1
#> 23 CHN country rat y2s 366. 3.44e2 3.84e2
#> 24 CHN country rat s2s 129. 1.10e2 1.59e2
#> 25 DEU country lvl school NA NA NA
#> 26 DEU country lvl teacher NA NA NA
#> 27 DEU country lvl youthpop NA NA NA
#> 28 DEU country rat y2t NA NA NA
#> 29 DEU country rat y2s NA NA NA
#> 30 DEU country lvl student NA NA NA
#> 31 DEU country rat s2t NA NA NA
#> 32 DEU country rat s2s NA NA NA
#> 33 FRA country lvl school NA NA 6.07e4
#> 34 FRA country lvl student NA NA 6.13e6
#> 35 FRA country lvl teacher NA NA 2.92e5
#> 36 FRA country lvl youthpop NA NA 1.24e7
#> 37 FRA country rat y2t NA NA 4.24e1
#> 38 FRA country rat s2t NA NA 2.10e1
#> 39 FRA country rat y2s NA NA 2.04e2
#> 40 FRA country rat s2s NA NA 1.01e2
#> # ℹ 104 more rows
#> # ℹ 10 more variables: year1990 <dbl>, year2000 <dbl>, year2010 <dbl>,
#> # year2020 <dbl>, chg_20v60 <dbl>, chg_20v70 <dbl>, chg_20v80 <dbl>,
#> # chg_20v90 <dbl>, chg_20v00 <dbl>, chg_20v10 <dbl>Run FEL.
# Generate FEL
df_fel_weea <- PrjCompPPTS::ff_ppts_lrce_fel(
df_fpc_weea,
verbose = TRUE
)
#> F-307302, S1
#> dim df_fpc_base: 36
#> dim df_fpc_base: 9
#> F-307302, S2
#> dim df_fel_long: 165
#> dim df_fel_long: 5
#> F-307302, S3
#> dim df_fel_wide: 43
#> dim df_fel_wide: 7
#> F-307302, S3
#> [1] "var_elas_y2t"
#> [1] "var_chg_youthpop"
#> [1] "var_chg_teacher"
#> dim FEL: 43
#> dim FEL: 8
#> F-307302, S3
#> [1] "var_elas_s2t"
#> [1] "var_chg_student"
#> [1] "var_chg_teacher"
#> dim FEL: 43
#> dim FEL: 9
#> F-307302, S3
#> [1] "var_elas_y2s"
#> [1] "var_chg_youthpop"
#> [1] "var_chg_school"
#> dim FEL: 43
#> dim FEL: 10
#> F-307302, S3
#> [1] "var_elas_s2s"
#> [1] "var_chg_student"
#> [1] "var_chg_school"
#> dim FEL: 43
#> dim FEL: 11
#> F-307302, S4
#> dim df_fel_elas: 43
#> dim df_fel_elas: 11
#> F-307302, S4
#> dim FEL: 36
#> dim FEL: 9
print(df_fel_weea, n = 40)
#> # A tibble: 36 × 9
#> location_code location_level variable elas_20v60 elas_20v70 elas_20v80
#> <fct> <fct> <chr> <dbl> <dbl> <dbl>
#> 1 AUT country y2t -0.245 -0.596 -0.480
#> 2 AUT country s2t -0.445 -0.829 -0.378
#> 3 AUT country y2s 0.573 1.24 1.35
#> 4 AUT country s2s 1.04 1.73 1.06
#> 5 CHE country y2t -0.00822 -0.0340 0.00839
#> 6 CHE country s2t -0.0349 -0.00148 0.174
#> 7 CHE country y2s NA NA NA
#> 8 CHE country s2s NA NA NA
#> 9 CHN country y2t -0.0427 -0.313 -1.71
#> 10 CHN country s2t 0.103 0.0240 -1.57
#> 11 CHN country y2s 0.0757 0.292 0.352
#> 12 CHN country s2s -0.183 -0.0224 0.322
#> 13 DEU country y2t NA NA NA
#> 14 DEU country y2s NA NA NA
#> 15 DEU country s2t NA NA NA
#> 16 DEU country s2s NA NA NA
#> 17 FRA country y2t NA NA -0.378
#> 18 FRA country s2t NA NA -1.13
#> 19 FRA country y2s NA NA 0.149
#> 20 FRA country s2s NA NA 0.443
#> 21 JPN country y2t -2.59 -2.50 4.45
#> 22 JPN country s2t -2.91 -2.27 4.82
#> 23 JPN country y2s 1.63 1.75 1.99
#> 24 JPN country s2s 1.83 1.58 2.15
#> 25 KOR country y2t NA -0.594 -0.842
#> 26 KOR country s2t NA -0.609 -0.888
#> 27 KOR country y2s NA -19.4 8.77
#> 28 KOR country s2s NA -19.9 9.26
#> 29 NLD country y2s NA NA NA
#> 30 NLD country s2s NA NA NA
#> 31 NLD country y2t NA NA NA
#> 32 NLD country s2t NA NA NA
#> 33 TWN country y2t NA NA -1.20
#> 34 TWN country s2t NA NA -1.18
#> 35 TWN country y2s NA NA -5.78
#> 36 TWN country s2s NA NA -5.68
#> # ℹ 3 more variables: elas_20v90 <dbl>, elas_20v00 <dbl>, elas_20v10 <dbl>Merge FPC and FEL, country and group
Now we combine the level and change information from FPC with the elasticity results from FEL.
We have the LRCE file, level, ratio, change, and elasticity.
df_lrce_weea <- df_fpc_weea %>% left_join(
df_fel_weea %>% mutate(vartype = "rat"),
by = (c(
"location_code" = "location_code",
"location_level" = "location_level",
"variable" = "variable",
"vartype" = "vartype"
))
)
print(glue::glue("dim df_lrce_weea: {dim(df_lrce_weea)}"))
#> dim df_lrce_weea: 144
#> dim df_lrce_weea: 23Finally, merge in country key info to FPC, country-level information.
df_lrce_weea <- df_lrce_weea %>%
left_join(
ppts_code_wrk %>%
select(
location_name, location_code,
location_region_group, location_region_group_code
),
by = c("location_code" = "location_code")
) %>%
# mutate(location_name_full = paste0(location_name, " (", location_code, ")")) %>%
mutate(location_name_full = paste0(location_name)) %>%
select(-location_name) %>%
select(location_name_full, location_region_group, everything()) %>%
arrange(location_region_group_code, location_name_full)Global Population Table
Prep Table inputs
First, select level and percentage changes year by year for four variables. Also get ratio and elasticities.
# level
df_lvl_weea <- df_lrce_weea %>%
filter(vartype == "lvl") %>%
select(location_name_full, variable, contains("year"))
# change
df_chg_weea <- df_lrce_weea %>%
filter(vartype == "lvl") %>%
select(location_name_full, variable, contains("chg"))
# ratio
df_rat_weea <- df_lrce_weea %>%
filter(vartype == "rat") %>%
select(location_name_full, variable, contains("year"))
# elasticity
df_elas_weea <- df_lrce_weea %>%
filter(vartype == "rat") %>%
select(location_name_full, variable, contains("elas"))Second, reshape time variables from each table above from wide to long.
# level file
df_lvl_weea_long <- df_lvl_weea %>%
pivot_longer(
cols = starts_with("year"),
names_to = c("yearset"),
names_pattern = paste0("year(.*)"),
values_to = "stats"
) %>%
mutate(type = "1lvl")
# change file
df_chg_weea_long <- df_chg_weea %>%
pivot_longer(
cols = starts_with("chg"),
names_to = c("yearset"),
names_pattern = paste0("chg_(.*)"),
values_to = "stats"
) %>%
mutate(type = "2chg")
# ratio file
df_rat_weea_long <- df_rat_weea %>%
pivot_longer(
cols = starts_with("year"),
names_to = c("yearset"),
names_pattern = paste0("year(.*)"),
values_to = "stats"
) %>%
mutate(type = "3rat")
# elasticity file
df_elas_weea_long <- df_elas_weea %>%
pivot_longer(
cols = starts_with("elas"),
names_to = c("yearset"),
names_pattern = paste0("elas_(.*)"),
values_to = "stats"
) %>%
mutate(type = "4elas")Third, combine long files, and convert countries to columns, reshape to wide.
# combine all long files
df_weea_long_jnt <- bind_rows(
df_lvl_weea_long,
df_chg_weea_long,
df_rat_weea_long,
df_elas_weea_long
)
# country name modification
df_weea_long_jnt <- df_weea_long_jnt %>%
mutate(
location_name_full =
ifelse(location_name_full == "Korea, Rep.", "Korea", location_name_full)
)
# reshape long to wide
df_weea_wide <- df_weea_long_jnt %>%
pivot_wider(
id_cols = c("type", "variable", "yearset"),
names_from = location_name_full,
names_prefix = "j_",
values_from = stats
)Fourth, organize columns, and generate four subsets.
# Select variables
tab3_weea <- df_weea_wide %>%
select(
type, variable, yearset,
j_China, j_Japan, j_Korea, j_Taiwan,
j_Austria, j_Germany, j_France, j_Netherlands, j_Switzerland
)
# Four variables
tab3_weea_lvl <- tab3_weea %>% filter(type == "1lvl")
tab3_weea_chg <- tab3_weea %>% filter(type == "2chg")
tab3_weea_rat <- tab3_weea %>% filter(type == "3rat")
tab3_weea_elas <- tab3_weea %>% filter(type == "4elas")Fifth, format each type of variables separately.
# Format variables, level in millions
tab3_weea_lvl_school <- tab3_weea_lvl %>%
filter(variable == "school") %>%
mutate_at(
vars(contains("j_")),
list(~ paste0(
format(round(. / 1000, 1),
nsmall = 0, big.mark = ","
)
))
)
tab3_weea_lvl_teacher <- tab3_weea_lvl %>%
filter(variable == "teacher") %>%
mutate_at(
vars(contains("j_")),
list(~ paste0(
format(round(. / 1000, 1),
nsmall = 0, big.mark = ","
)
))
)
tab3_weea_lvl_youth <- tab3_weea_lvl %>%
filter(variable == "youthpop") %>%
mutate_at(
vars(contains("j_")),
list(~ paste0(
format(round(. / 1000000, 1),
nsmall = 0, big.mark = ","
)
))
)
tab3_weea_lvl_student <- tab3_weea_lvl %>%
filter(variable == "student") %>%
mutate_at(
vars(contains("j_")),
list(~ paste0(
format(round(. / 1000000, 1),
nsmall = 0, big.mark = ","
)
))
)
# Change in percent
tab3_weea_chg <- tab3_weea_chg %>%
mutate_at(
vars(contains("j_")),
list(~ paste0(
format(round(., 2) * 100,
nsmall = 0,
big.mark = ","
),
"%"
))
)
# Change in ratio
tab3_weea_rat <- tab3_weea_rat %>%
mutate_at(
vars(contains("j_")),
list(~ paste0(
format(round(., 0), nsmall = 0, big.mark = ",")
))
)
# Change in ratio
tab3_weea_elas <- tab3_weea_elas %>%
mutate_at(
vars(contains("j_")),
list(~ paste0(
format(round(., 2), nsmall = 0, big.mark = ",")
))
)Sixth, combine all four stats again, now that formatting is done.
# combine
tab3_weea_jnt <- bind_rows(
tab3_weea_lvl_school,
tab3_weea_lvl_teacher,
tab3_weea_lvl_youth,
tab3_weea_lvl_student,
tab3_weea_chg,
tab3_weea_rat,
tab3_weea_elas
)
# replace variable names for sorting
tab3_weea_jnt <- tab3_weea_jnt %>%
mutate(variable_sort = case_when(
variable == "school" ~ "1school",
variable == "teacher" ~ "2teacher",
variable == "student" ~ "3student",
variable == "youthpop" ~ "4youthpop",
variable == "s2s" ~ "1s2s",
variable == "y2s" ~ "2y2s",
variable == "s2t" ~ "3s2t",
variable == "y2t" ~ "4y2t"
))
# Year sorting
tab3_weea_jnt <- tab3_weea_jnt %>%
mutate(yearset = case_when(
yearset == "20v00" ~ "2020 vs 2000",
yearset == "20v10" ~ "2020 vs 2010",
yearset == "20v60" ~ "2020 vs 1960",
yearset == "20v70" ~ "2020 vs 1970",
yearset == "20v80" ~ "2020 vs 1980",
yearset == "20v90" ~ "2020 vs 1990",
TRUE ~ yearset
))
# replace NAs
tab3_weea_jnt <- tab3_weea_jnt %>%
mutate_at(vars(starts_with("j_")), ~ str_replace(., "NA%", "")) %>%
mutate_at(vars(starts_with("j_")), ~ str_replace(., "NA", ""))
print(tab3_weea_jnt, n = 200)
#> # A tibble: 104 × 13
#> type variable yearset j_China j_Japan j_Korea j_Taiwan j_Austria j_Germany
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1lvl school 1960 "726.5" "26.9" " " " " "4.4" " "
#> 2 1lvl school 1970 "961.1" "24.8" "6.0" " " "4.0" " "
#> 3 1lvl school 1980 "917.3" "24.9" "6.5" "2.4" "3.5" " "
#> 4 1lvl school 1990 "766.1" "24.8" "6.3" "2.5" "3.4" "18.0"
#> 5 1lvl school 2000 "553.6" "24.1" "5.3" "2.6" "3.4" "17.3"
#> 6 1lvl school 2010 "257.4" "22.0" "5.9" "2.7" "3.2" "16.3"
#> 7 1lvl school 2020 "158.0" "19.5" "6.1" "2.6" "3.0" "15.4"
#> 8 1lvl teacher 1960 "2,693… "360.7" " " " " "21.5" " "
#> 9 1lvl teacher 1970 "3,612… "367.9" "101.1" " " "24.8" " "
#> 10 1lvl teacher 1980 "5,499… "468.0" "119.1" " 69.1" "27.5" " "
#> 11 1lvl teacher 1990 "5,582… "444.2" "136.8" " 82.6" "29.4" "181.0"
#> 12 1lvl teacher 2000 "5,860… "407.6" "140.0" "101.6" "33.9" "215.4"
#> 13 1lvl teacher 2010 "5,617… "419.8" "176.8" " 99.6" "32.6" "228.4"
#> 14 1lvl teacher 2020 "6,434… "422.6" "189.3" " 97.0" "37.3" "237.8"
#> 15 1lvl youthpop 1960 "265.6" "28.2" " " " " "1.6" " "
#> 16 1lvl youthpop 1970 "330.7" "24.9" "13.5" " " "1.8" " "
#> 17 1lvl youthpop 1980 "352.6" "27.5" "12.9" "5.7" "1.5" " "
#> 18 1lvl youthpop 1990 "324.5" "22.8" "10.9" "5.5" "1.3" "12.8"
#> 19 1lvl youthpop 2000 "313.0" "18.8" " 9.7" "4.7" "1.3" "12.9"
#> 20 1lvl youthpop 2010 "249.6" "17.1" " 8.0" "3.6" "1.2" "11.1"
#> 21 1lvl youthpop 2020 "249.9" "15.7" " 6.5" "3.0" "1.3" "11.6"
#> 22 1lvl student 1960 " 93.8" "12.6" " " " " "0.5" " "
#> 23 1lvl student 1970 "105.3" " 9.5" "5.7" " " "0.6" " "
#> 24 1lvl student 1980 "146.3" "11.8" "5.7" "2.2" "0.4" " "
#> 25 1lvl student 1990 "122.4" " 9.4" "4.9" "2.4" "0.4" " "
#> 26 1lvl student 2000 "130.1" " 7.4" "4.0" "1.9" "0.4" "3.4"
#> 27 1lvl student 2010 " 99.4" " 7.0" "3.3" "1.5" "0.3" "2.8"
#> 28 1lvl student 2020 "107.3" " 6.3" "2.7" "1.2" "0.3" "2.8"
#> 29 2chg school 2020 vs… "-78%" "-27%" " " " " "-31%" " "
#> 30 2chg school 2020 vs… "-84%" "-21%" " 3%" " " "-24%" " "
#> 31 2chg school 2020 vs… "-83%" "-22%" " -6%" " 8%" "-13%" " "
#> 32 2chg school 2020 vs… "-79%" "-21%" " -3%" " 6%" "-11%" "-14%"
#> 33 2chg school 2020 vs… "-71%" "-19%" " 16%" " 1%" "-10%" "-11%"
#> 34 2chg school 2020 vs… "-39%" "-11%" " 5%" " -1%" " -5%" " -5%"
#> 35 2chg student 2020 vs… " 14%" "-50%" " " " " "-33%" " "
#> 36 2chg student 2020 vs… " 2%" "-34%" "-53%" " " "-42%" " "
#> 37 2chg student 2020 vs… "-27%" "-47%" "-52%" "-47%" "-13%" " "
#> 38 2chg student 2020 vs… "-12%" "-33%" "-45%" "-50%" " -7%" " "
#> 39 2chg student 2020 vs… "-18%" "-14%" "-33%" "-39%" "-12%" "-15%"
#> 40 2chg student 2020 vs… " 8%" "-10%" "-18%" "-23%" " 6%" " 0%"
#> 41 2chg teacher 2020 vs… "139%" " 17%" " " " " " 73%" " "
#> 42 2chg teacher 2020 vs… " 78%" " 15%" " 87%" " " " 50%" " "
#> 43 2chg teacher 2020 vs… " 17%" "-10%" " 59%" " 40%" " 35%" " "
#> 44 2chg teacher 2020 vs… " 15%" " -5%" " 38%" " 17%" " 27%" " 31%"
#> 45 2chg teacher 2020 vs… " 10%" " 4%" " 35%" " -5%" " 10%" " 10%"
#> 46 2chg teacher 2020 vs… " 15%" " 1%" " 7%" " -3%" " 14%" " 4%"
#> 47 2chg youthpop 2020 vs… " -6%" "-44%" " " " " "-18%" " "
#> 48 2chg youthpop 2020 vs… "-24%" "-37%" "-52%" " " "-30%" " "
#> 49 2chg youthpop 2020 vs… "-29%" "-43%" "-50%" "-48%" "-17%" " "
#> 50 2chg youthpop 2020 vs… "-23%" "-31%" "-40%" "-46%" " -1%" " -9%"
#> 51 2chg youthpop 2020 vs… "-20%" "-16%" "-33%" "-37%" " -5%" "-10%"
#> 52 2chg youthpop 2020 vs… " 0%" " -8%" "-19%" "-18%" " 4%" " 5%"
#> 53 3rat y2t 1960 " 99" " 78" " " " " " 73" " "
#> 54 3rat y2t 1970 " 92" " 68" " 134" " " " 74" " "
#> 55 3rat y2t 1980 " 64" " 59" " 108" " 83" " 56" " "
#> 56 3rat y2t 1990 " 58" " 51" " 80" " 67" " 44" " 71"
#> 57 3rat y2t 2000 " 53" " 46" " 69" " 46" " 40" " 60"
#> 58 3rat y2t 2010 " 44" " 41" " 45" " 36" " 38" " 49"
#> 59 3rat y2t 2020 " 39" " 37" " 34" " 31" " 34" " 49"
#> 60 3rat s2t 1960 " 35" " 35" " " " " " 24" " "
#> 61 3rat s2t 1970 " 29" " 26" " 57" " " " 24" " "
#> 62 3rat s2t 1980 " 27" " 25" " 48" " 32" " 15" " "
#> 63 3rat s2t 1990 " 22" " 21" " 36" " 29" " 13" " "
#> 64 3rat s2t 2000 " 22" " 18" " 29" " 19" " 12" " 16"
#> 65 3rat s2t 2010 " 18" " 17" " 19" " 15" " 10" " 12"
#> 66 3rat s2t 2020 " 17" " 15" " 14" " 12" " 9" " 12"
#> 67 3rat y2s 1960 " 366" "1,050" " " " " "357" " "
#> 68 3rat y2s 1970 " 344" "1,006" "2,265" " " "462" " "
#> 69 3rat y2s 1980 " 384" "1,104" "1,990" "2,364" "449" " "
#> 70 3rat y2s 1990 " 424" " 919" "1,721" "2,222" "384" "709"
#> 71 3rat y2s 2000 " 565" " 778" "1,840" "1,809" "401" "746"
#> 72 3rat y2s 2010 " 970" " 777" "1,363" "1,362" "388" "681"
#> 73 3rat y2s 2020 "1,582" " 802" "1,062" "1,126" "426" "751"
#> 74 3rat s2s 1960 " 129" " 469" " " " " "117" " "
#> 75 3rat s2s 1970 " 110" " 383" " 964" " " "150" " "
#> 76 3rat s2s 1980 " 159" " 474" " 872" " 920" "116" " "
#> 77 3rat s2s 1990 " 160" " 378" " 769" " 947" "110" " "
#> 78 3rat s2s 2000 " 235" " 306" " 763" " 741" "117" "194"
#> 79 3rat s2s 2010 " 386" " 318" " 564" " 571" "103" "174"
#> 80 3rat s2s 2020 " 679" " 323" " 440" " 446" "115" "184"
#> 81 4elas y2t 2020 vs… "-0.04" " -2.5… " " " " "-0.24" " "
#> 82 4elas y2t 2020 vs… "-0.31" " -2.5… " -0.5… " " "-0.60" " "
#> 83 4elas y2t 2020 vs… "-1.71" " 4.4… " -0.8… " -1.20" "-0.48" " "
#> 84 4elas y2t 2020 vs… "-1.51" " 6.4… " -1.0… " -2.66" "-0.04" "-0.29"
#> 85 4elas y2t 2020 vs… "-2.06" " -4.4… " -0.9… " 8.14" "-0.46" "-0.96"
#> 86 4elas y2t 2020 vs… " 0.01" "-12.7… " -2.6… " 6.99" " 0.31" " 1.12"
#> 87 4elas s2t 2020 vs… " 0.10" " -2.9… " " " " "-0.44" " "
#> 88 4elas s2t 2020 vs… " 0.02" " -2.2… " -0.6… " " "-0.83" " "
#> 89 4elas s2t 2020 vs… "-1.57" " 4.8… " -0.8… " -1.18" "-0.38" " "
#> 90 4elas s2t 2020 vs… "-0.81" " 6.7… " -1.1… " -2.88" "-0.24" " "
#> 91 4elas s2t 2020 vs… "-1.79" " -3.9… " -0.9… " 8.60" "-1.15" "-1.47"
#> 92 4elas s2t 2020 vs… " 0.54" "-14.9… " -2.5… " 8.73" " 0.42" " 0.04"
#> 93 4elas y2s 2020 vs… " 0.08" " 1.6… " " " " " 0.57" " "
#> 94 4elas y2s 2020 vs… " 0.29" " 1.7… "-19.4… " " " 1.24" " "
#> 95 4elas y2s 2020 vs… " 0.35" " 1.9… " 8.7… " -5.78" " 1.35" " "
#> 96 4elas y2s 2020 vs… " 0.29" " 1.4… " 11.9… " -8.01" " 0.10" " 0.64"
#> 97 4elas y2s 2020 vs… " 0.28" " 0.8… " -2.0… "-31.02" " 0.45" " 0.94"
#> 98 4elas y2s 2020 vs… " 0.00" " 0.7… " -4.0… " 16.17" "-0.89" "-0.89"
#> 99 4elas s2s 2020 vs… "-0.18" " 1.8… " " " " " 1.04" " "
#> 100 4elas s2s 2020 vs… "-0.02" " 1.5… "-19.9… " " " 1.73" " "
#> 101 4elas s2s 2020 vs… " 0.32" " 2.1… " 9.2… " -5.68" " 1.06" " "
#> 102 4elas s2s 2020 vs… " 0.16" " 1.5… " 13.1… " -8.66" " 0.60" " "
#> 103 4elas s2s 2020 vs… " 0.25" " 0.7… " -2.0… "-32.75" " 1.14" " 1.44"
#> 104 4elas s2s 2020 vs… "-0.20" " 0.8… " -4.0… " 20.19" "-1.22" "-0.03"
#> # ℹ 4 more variables: j_France <chr>, j_Netherlands <chr>, j_Switzerland <chr>,
#> # variable_sort <chr>Finally, count by group, generate labeling string
# Group and arrange
tab3_weea_jnt <- tab3_weea_jnt %>%
arrange(
type, variable_sort, yearset,
)
# get counts
df_typevar_counts <- tab3_weea_jnt %>%
group_by(
type, variable_sort, variable,
) %>%
summarize(group_count = n()) %>%
ungroup() %>%
mutate(group_count_start = cumsum(group_count) - group_count + 1) %>%
mutate(group_count_end = cumsum(group_count)) %>%
select(group_count_start, group_count_end, everything())
#> `summarise()` has grouped output by 'type', 'variable_sort'. You can override
#> using the `.groups` argument.
# Generate group names
df_typevar_counts <- df_typevar_counts %>%
mutate(type_lab = case_when(
type == "1lvl" ~ "Number of",
type == "2chg" ~ "Percentage change in",
type == "3rat" ~ "Ratio",
type == "4elas" ~ "Elasticity"
)) %>%
mutate(variable_lab = case_when(
(type == "1lvl" & variable == "school") ~ "schools (1000s)",
(type == "1lvl" & variable == "teacher") ~ "teachers (1000s)",
(type == "1lvl" & variable == "student") ~ "students (mil.)",
(type == "1lvl" & variable == "youthpop") ~ "youth 0-14 (mil.)",
(type == "2chg" & variable == "school") ~ "schools",
(type == "2chg" & variable == "teacher") ~ "teachers",
(type == "2chg" & variable == "student") ~ "students",
(type == "2chg" & variable == "youthpop") ~ "youth 0-14",
(type == "3rat" & variable == "s2s") ~ "Student/School",
(type == "3rat" & variable == "y2s") ~ "Youth/School",
(type == "3rat" & variable == "s2t") ~ "Student/Teacher",
(type == "3rat" & variable == "y2t") ~ "Youth/Teacher",
(type == "4elas" & variable == "s2s") ~ "$\\Delta$\\%Student/$\\Delta$\\%School",
(type == "4elas" & variable == "y2s") ~ "$\\Delta$\\%Youth/$\\Delta$\\%School",
(type == "4elas" & variable == "s2t") ~ "$\\Delta$\\%Student/$\\Delta$\\%Teacher",
(type == "4elas" & variable == "y2t") ~ "$\\Delta$\\%Youth/$\\Delta$\\%Teacher"
)) %>%
unite(row_name, type_lab:variable_lab, sep = " ")
# print
print(df_typevar_counts, n = 100)
#> # A tibble: 16 × 7
#> group_count_start group_count_end type variable_sort variable group_count
#> <dbl> <int> <chr> <chr> <chr> <int>
#> 1 1 7 1lvl 1school school 7
#> 2 8 14 1lvl 2teacher teacher 7
#> 3 15 21 1lvl 3student student 7
#> 4 22 28 1lvl 4youthpop youthpop 7
#> 5 29 34 2chg 1school school 6
#> 6 35 40 2chg 2teacher teacher 6
#> 7 41 46 2chg 3student student 6
#> 8 47 52 2chg 4youthpop youthpop 6
#> 9 53 59 3rat 1s2s s2s 7
#> 10 60 66 3rat 2y2s y2s 7
#> 11 67 73 3rat 3s2t s2t 7
#> 12 74 80 3rat 4y2t y2t 7
#> 13 81 86 4elas 1s2s s2s 6
#> 14 87 92 4elas 2y2s y2s 6
#> 15 93 98 4elas 3s2t s2t 6
#> 16 99 104 4elas 4y2t y2t 6
#> # ℹ 1 more variable: row_name <chr>Generate table
Now we generate the table.
ar_st_kableformat <- c("latex", "html")
for (st_kableformat in ar_st_kableformat) {
# j_China, j_Japan, j_Korea, j_Taiwan,
# j_Austria, j_Germany, j_France, j_Netherlands, j_Switzerland
# Column names
ar_st_col_names <- c(
"Years",
"China",
"Japan",
"Korea",
"Taiwan",
"Austria",
"Germany",
"France",
"Netherlands",
"Switzerland"
)
# Define column groups, grouping the names above
# =1/3/2 are number of columns group title covers
ar_st_col_groups <- c(
" " = 1,
"East Asia" = 4,
"Western Europe" = 5
)
# Second, we construct main table, and add styling.
bk_weea <- kbl(
tab3_weea_jnt %>%
select(-type, -variable, -variable_sort),
format = st_kableformat,
label = "main:tab:weaa:pop:teachers:schools",
# escape = F,
linesep = "",
booktabs = T,
longtable = T,
align = "c",
caption = "East Asia and Western Europe: Schools, Teachers, Students, and Youth",
col.names = ar_st_col_names
) %>%
# see https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html#Bootstrap_table_classes
kable_styling(
bootstrap_options = c("striped", "hover", "condensed", "responsive"),
full_width = F, position = "left"
)
# Third, we add in column groups.
bk_weea <- bk_weea %>% add_header_above(ar_st_col_groups)
# Fourth, we add in row groups.
for (it_group in seq(1, dim(df_typevar_counts)[1])) {
# Reion full name info
st_loc <- as.character(df_typevar_counts[[it_group, "row_name"]])
# groups start and end
it_group_count_start <- df_typevar_counts[[it_group, "group_count_start"]]
it_group_count_end <- df_typevar_counts[[it_group, "group_count_end"]]
# Add to table
bk_weea <- bk_weea %>%
pack_rows(
st_loc, it_group_count_start, it_group_count_end,
latex_gap_space = "1em",
latex_align = "c",
hline_after = TRUE
)
}
# Fifth, column formatting.
fl_width_country <- 1.5
st_width_country <- paste0(fl_width_country, "cm")
bk_weea <- bk_weea %>%
column_spec(1, width = st_width_country) %>%
column_spec(2:10, width = "1.0cm")
# Final adjustments
# Headings on all pages, note use `sub` to replace first midrule
st_headend <- paste0(
"\\midrule\\endhead\n",
"\\addlinespace[0.2em]\\hline\\addlinespace[0.2em]\n",
"\\multicolumn{10}{r}{\\emph{Continued on next page}}\\\\\n",
"\\endfoot\\endlastfoot"
)
bk_weea <- sub(bk_weea,
pattern = "\\midrule", replacement = st_headend,
fixed = TRUE
)
# country-names left-align
bk_weea <- gsub(bk_weea,
pattern = paste0("\\centering\\arraybackslash}p{", st_width_country, "}"),
replacement = paste0("\\raggedright\\arraybackslash}p{", st_width_country, "}"),
fixed = TRUE
)
# midrule replacing hline
bk_weea <- gsub(bk_weea,
pattern = "hline",
replacement = "midrule", fixed = TRUE
)
# Sixth, display.
# pl_bk_asset_count <- bk_asset_count %>% as_image()
if (st_kableformat == "html") {
print(dim(bk_weea))
} else {
if (bl_tex_save) {
fileConn <- file(spn_tex_out)
writeLines(bk_weea, fileConn)
close(fileConn)
if (verbose) {
print(glue::glue("F-945386, S1"))
print(glue::glue("Latex saved: {spn_tex_out}"))
}
}
}
}
#> Warning in pack_rows(., st_loc, it_group_count_start, it_group_count_end, : latex_align parameter is not used in HTML Mode,
#> use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_group_count_start, it_group_count_end, : latex_align parameter is not used in HTML Mode,
#> use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_group_count_start, it_group_count_end, : latex_align parameter is not used in HTML Mode,
#> use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_group_count_start, it_group_count_end, : latex_align parameter is not used in HTML Mode,
#> use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_group_count_start, it_group_count_end, : latex_align parameter is not used in HTML Mode,
#> use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_group_count_start, it_group_count_end, : latex_align parameter is not used in HTML Mode,
#> use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_group_count_start, it_group_count_end, : latex_align parameter is not used in HTML Mode,
#> use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_group_count_start, it_group_count_end, : latex_align parameter is not used in HTML Mode,
#> use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_group_count_start, it_group_count_end, : latex_align parameter is not used in HTML Mode,
#> use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_group_count_start, it_group_count_end, : latex_align parameter is not used in HTML Mode,
#> use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_group_count_start, it_group_count_end, : latex_align parameter is not used in HTML Mode,
#> use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_group_count_start, it_group_count_end, : latex_align parameter is not used in HTML Mode,
#> use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_group_count_start, it_group_count_end, : latex_align parameter is not used in HTML Mode,
#> use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_group_count_start, it_group_count_end, : latex_align parameter is not used in HTML Mode,
#> use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_group_count_start, it_group_count_end, : latex_align parameter is not used in HTML Mode,
#> use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_group_count_start, it_group_count_end, : latex_align parameter is not used in HTML Mode,
#> use label_row_css instead.
#> NULL
bk_weea| Years | China | Japan | Korea | Taiwan | Austria | Germany | France | Netherlands | Switzerland |
|---|---|---|---|---|---|---|---|---|---|
| Number of schools (1000s) | |||||||||
| 1960 | 726.5 | 26.9 | 4.4 | ||||||
| 1970 | 961.1 | 24.8 | 6.0 | 4.0 | |||||
| 1980 | 917.3 | 24.9 | 6.5 | 2.4 | 3.5 | 60.7 | |||
| 1990 | 766.1 | 24.8 | 6.3 | 2.5 | 3.4 | 18.0 | 56.7 | 9.3 | |
| 2000 | 553.6 | 24.1 | 5.3 | 2.6 | 3.4 | 17.3 | 53.0 | 7.8 | |
| 2010 | 257.4 | 22.0 | 5.9 | 2.7 | 3.2 | 16.3 | 49.0 | 7.5 | 4.5 |
| 2020 | 158.0 | 19.5 | 6.1 | 2.6 | 3.0 | 15.4 | 45.1 | 6.7 | 4.6 |
| Number of teachers (1000s) | |||||||||
| 1960 | 2,693.0 | 360.7 | 21.5 | 17.4 | |||||
| 1970 | 3,612.0 | 367.9 | 101.1 | 24.8 | 20.9 | ||||
| 1980 | 5,499.0 | 468.0 | 119.1 | 69.1 | 27.5 | 291.5 | 25.2 | ||
| 1990 | 5,582.0 | 444.2 | 136.8 | 82.6 | 29.4 | 181.0 | 309.9 | 30.4 | |
| 2000 | 5,860.0 | 407.6 | 140.0 | 101.6 | 33.9 | 215.4 | 317.7 | 131.7 | 36.6 |
| 2010 | 5,617.0 | 419.8 | 176.8 | 99.6 | 32.6 | 228.4 | 316.1 | 182.0 | 44.1 |
| 2020 | 6,434.2 | 422.6 | 189.3 | 97.0 | 37.3 | 237.8 | 321.0 | 137.3 | 54.8 |
| Number of students (mil.) | |||||||||
| 1960 | 93.8 | 12.6 | 0.5 | 0.6 | |||||
| 1970 | 105.3 | 9.5 | 5.7 | 0.6 | 0.5 | ||||
| 1980 | 146.3 | 11.8 | 5.7 | 2.2 | 0.4 | 6.1 | 0.4 | ||
| 1990 | 122.4 | 9.4 | 4.9 | 2.4 | 0.4 | 5.8 | 1.5 | 0.4 | |
| 2000 | 130.1 | 7.4 | 4.0 | 1.9 | 0.4 | 3.4 | 5.4 | 1.6 | 0.5 |
| 2010 | 99.4 | 7.0 | 3.3 | 1.5 | 0.3 | 2.8 | 5.6 | 1.6 | 0.4 |
| 2020 | 107.3 | 6.3 | 2.7 | 1.2 | 0.3 | 2.8 | 5.4 | 1.5 | 0.5 |
| Number of youth 0-14 (mil.) | |||||||||
| 1960 | 265.6 | 28.2 | 1.6 | 1.3 | |||||
| 1970 | 330.7 | 24.9 | 13.5 | 1.8 | 1.4 | ||||
| 1980 | 352.6 | 27.5 | 12.9 | 5.7 | 1.5 | 12.4 | 1.3 | ||
| 1990 | 324.5 | 22.8 | 10.9 | 5.5 | 1.3 | 12.8 | 11.7 | 2.7 | 1.2 |
| 2000 | 313.0 | 18.8 | 9.7 | 4.7 | 1.3 | 12.9 | 11.5 | 2.9 | 1.3 |
| 2010 | 249.6 | 17.1 | 8.0 | 3.6 | 1.2 | 11.1 | 12.0 | 2.9 | 1.2 |
| 2020 | 249.9 | 15.7 | 6.5 | 3.0 | 1.3 | 11.6 | 11.9 | 2.7 | 1.3 |
| Percentage change in schools | |||||||||
| 2020 vs 1960 | -78% | -27% | -31% | ||||||
| 2020 vs 1970 | -84% | -21% | 3% | -24% | |||||
| 2020 vs 1980 | -83% | -22% | -6% | 8% | -13% | -26% | |||
| 2020 vs 1990 | -79% | -21% | -3% | 6% | -11% | -14% | -20% | -29% | |
| 2020 vs 2000 | -71% | -19% | 16% | 1% | -10% | -11% | -15% | -14% | |
| 2020 vs 2010 | -39% | -11% | 5% | -1% | -5% | -5% | -8% | -11% | 2% |
| Percentage change in teachers | |||||||||
| 2020 vs 1960 | 139% | 17% | 73% | 214% | |||||
| 2020 vs 1970 | 78% | 15% | 87% | 50% | 162% | ||||
| 2020 vs 1980 | 17% | -10% | 59% | 40% | 35% | 10% | 117% | ||
| 2020 vs 1990 | 15% | -5% | 38% | 17% | 27% | 31% | 4% | 80% | |
| 2020 vs 2000 | 10% | 4% | 35% | -5% | 10% | 10% | 1% | 4% | 50% |
| 2020 vs 2010 | 15% | 1% | 7% | -3% | 14% | 4% | 2% | -25% | 24% |
| Percentage change in students | |||||||||
| 2020 vs 1960 | 14% | -50% | -33% | -7% | |||||
| 2020 vs 1970 | 2% | -34% | -53% | -42% | 0% | ||||
| 2020 vs 1980 | -27% | -47% | -52% | -47% | -13% | -11% | 20% | ||
| 2020 vs 1990 | -12% | -33% | -45% | -50% | -7% | -6% | -2% | 29% | |
| 2020 vs 2000 | -18% | -14% | -33% | -39% | -12% | -15% | 0% | -9% | 12% |
| 2020 vs 2010 | 8% | -10% | -18% | -23% | 6% | 0% | -2% | -9% | 22% |
| Percentage change in youth 0-14 | |||||||||
| 2020 vs 1960 | -6% | -44% | -18% | -2% | |||||
| 2020 vs 1970 | -24% | -37% | -52% | -30% | -5% | ||||
| 2020 vs 1980 | -29% | -43% | -50% | -48% | -17% | -4% | 1% | ||
| 2020 vs 1990 | -23% | -31% | -40% | -46% | -1% | -9% | 2% | 1% | 10% |
| 2020 vs 2000 | -20% | -16% | -33% | -37% | -5% | -10% | 3% | -7% | 3% |
| 2020 vs 2010 | 0% | -8% | -19% | -18% | 4% | 5% | -1% | -6% | 10% |
| Ratio Student/School | |||||||||
| 1960 | 129 | 469 | 117 | ||||||
| 1970 | 110 | 383 | 964 | 150 | |||||
| 1980 | 159 | 474 | 872 | 920 | 116 | 101 | |||
| 1990 | 160 | 378 | 769 | 947 | 110 | 102 | 164 | ||
| 2000 | 235 | 306 | 763 | 741 | 117 | 194 | 103 | 212 | |
| 2010 | 386 | 318 | 564 | 571 | 103 | 174 | 113 | 220 | 96 |
| 2020 | 679 | 323 | 440 | 446 | 115 | 184 | 120 | 224 | 115 |
| Ratio Youth/School | |||||||||
| 1960 | 366 | 1,050 | 357 | ||||||
| 1970 | 344 | 1,006 | 2,265 | 462 | |||||
| 1980 | 384 | 1,104 | 1,990 | 2,364 | 449 | 204 | |||
| 1990 | 424 | 919 | 1,721 | 2,222 | 384 | 709 | 206 | 292 | |
| 2000 | 565 | 778 | 1,840 | 1,809 | 401 | 746 | 217 | 379 | |
| 2010 | 970 | 777 | 1,363 | 1,362 | 388 | 681 | 245 | 389 | 260 |
| 2020 | 1,582 | 802 | 1,062 | 1,126 | 426 | 751 | 264 | 411 | 280 |
| Ratio Student/Teacher | |||||||||
| 1960 | 35 | 35 | 24 | 33 | |||||
| 1970 | 29 | 26 | 57 | 24 | 25 | ||||
| 1980 | 27 | 25 | 48 | 32 | 15 | 21 | 17 | ||
| 1990 | 22 | 21 | 36 | 29 | 13 | 19 | 14 | ||
| 2000 | 22 | 18 | 29 | 19 | 12 | 16 | 17 | 12 | 13 |
| 2010 | 18 | 17 | 19 | 15 | 10 | 12 | 18 | 9 | 10 |
| 2020 | 17 | 15 | 14 | 12 | 9 | 12 | 17 | 11 | 10 |
| Ratio Youth/Teacher | |||||||||
| 1960 | 99 | 78 | 73 | 75 | |||||
| 1970 | 92 | 68 | 134 | 74 | 65 | ||||
| 1980 | 64 | 59 | 108 | 83 | 56 | 42 | 51 | ||
| 1990 | 58 | 51 | 80 | 67 | 44 | 71 | 38 | 39 | |
| 2000 | 53 | 46 | 69 | 46 | 40 | 60 | 36 | 22 | 34 |
| 2010 | 44 | 41 | 45 | 36 | 38 | 49 | 38 | 16 | 27 |
| 2020 | 39 | 37 | 34 | 31 | 34 | 49 | 37 | 20 | 24 |
| Elasticity %Student/%School | |||||||||
| 2020 vs 1960 | -0.18 | 1.83 | 1.04 | ||||||
| 2020 vs 1970 | -0.02 | 1.58 | -19.93 | 1.73 | |||||
| 2020 vs 1980 | 0.32 | 2.15 | 9.26 | -5.68 | 1.06 | 0.44 | |||
| 2020 vs 1990 | 0.16 | 1.53 | 13.16 | -8.66 | 0.60 | 0.29 | 0.09 | ||
| 2020 vs 2000 | 0.25 | 0.76 | -2.04 | -32.75 | 1.14 | 1.44 | 0.01 | 0.65 | |
| 2020 vs 2010 | -0.20 | 0.88 | -4.04 | 20.19 | -1.22 | -0.03 | 0.27 | 0.85 | 11.78 |
| Elasticity %Youth/%School | |||||||||
| 2020 vs 1960 | 0.08 | 1.63 | 0.57 | ||||||
| 2020 vs 1970 | 0.29 | 1.75 | -19.44 | 1.24 | |||||
| 2020 vs 1980 | 0.35 | 1.99 | 8.77 | -5.78 | 1.35 | 0.15 | |||
| 2020 vs 1990 | 0.29 | 1.47 | 11.90 | -8.01 | 0.10 | 0.64 | -0.08 | -0.02 | |
| 2020 vs 2000 | 0.28 | 0.87 | -2.03 | -31.02 | 0.45 | 0.94 | -0.22 | 0.49 | |
| 2020 vs 2010 | 0.00 | 0.75 | -4.07 | 16.17 | -0.89 | -0.89 | 0.13 | 0.54 | 5.26 |
| Elasticity %Student/%Teacher | |||||||||
| 2020 vs 1960 | 0.10 | -2.91 | -0.44 | -0.03 | |||||
| 2020 vs 1970 | 0.02 | -2.27 | -0.61 | -0.83 | 0.00 | ||||
| 2020 vs 1980 | -1.57 | 4.82 | -0.89 | -1.18 | -0.38 | -1.13 | 0.17 | ||
| 2020 vs 1990 | -0.81 | 6.72 | -1.16 | -2.88 | -0.24 | -1.67 | 0.36 | ||
| 2020 vs 2000 | -1.79 | -3.94 | -0.94 | 8.60 | -1.15 | -1.47 | -0.22 | -2.17 | 0.24 |
| 2020 vs 2010 | 0.54 | -14.97 | -2.59 | 8.73 | 0.42 | 0.04 | -1.40 | 0.38 | 0.89 |
| Elasticity %Youth/%Teacher | |||||||||
| 2020 vs 1960 | -0.04 | -2.59 | -0.24 | -0.01 | |||||
| 2020 vs 1970 | -0.31 | -2.50 | -0.59 | -0.60 | -0.03 | ||||
| 2020 vs 1980 | -1.71 | 4.45 | -0.84 | -1.20 | -0.48 | -0.38 | 0.01 | ||
| 2020 vs 1990 | -1.51 | 6.42 | -1.05 | -2.66 | -0.04 | -0.29 | 0.48 | 0.12 | |
| 2020 vs 2000 | -2.06 | -4.49 | -0.93 | 8.14 | -0.46 | -0.96 | 3.21 | -1.62 | 0.06 |
| 2020 vs 2010 | 0.01 | -12.71 | -2.61 | 6.99 | 0.31 | 1.12 | -0.66 | 0.24 | 0.40 |