tests/ES/PopEsts.R
In jlivsey/Dvar: What the Package Does (One Line, Title Case)
# I mentioned that we could start by downloading or simply recording tables of partially cross-tabulated totals by county. You schema has 7 counties, so I suggest that we more or less haphazardly choose 4 counties each from 2 different states.
# The website is:
# https://www.census.gov/content/census/en/data/datasets/time-series/demo/popest/2010s-counties-detail.html
# First Try HH, Counties in Virginia pop by M/F, AGE <20, 20-64, 65+,
# Hisp and major CenRace
# Age-groups are 1-4, 5-13 and 14-18
# Details:
PopEstVA = read.csv("data/cc-est2019-alldata-51.csv")
dim(PopEstVA)
# [1] 30324 80
names(PopEstVA)
# [1] "SUMLEV" "STATE" "COUNTY" "STNAME" "CTYNAME" "YEAR"
# [7] "AGEGRP" "TOT_POP" "TOT_MALE" "TOT_FEMALE" "WA_MALE" "WA_FEMALE"
# [13] "BA_MALE" "BA_FEMALE" "IA_MALE" "IA_FEMALE" "AA_MALE" "AA_FEMALE"
# [19] "NA_MALE" "NA_FEMALE" "TOM_MALE" "TOM_FEMALE" "WAC_MALE" "WAC_FEMALE"
# [25] "BAC_MALE" "BAC_FEMALE" "IAC_MALE" "IAC_FEMALE" "AAC_MALE" "AAC_FEMALE"
# [31] "NAC_MALE" "NAC_FEMALE" "NH_MALE" "NH_FEMALE" "NHWA_MALE" "NHWA_FEMALE"
# [37] "NHBA_MALE" "NHBA_FEMALE" "NHIA_MALE" "NHIA_FEMALE" "NHAA_MALE" "NHAA_FEMALE"
# [43] "NHNA_MALE" "NHNA_FEMALE" "NHTOM_MALE" "NHTOM_FEMALE" "NHWAC_MALE" "NHWAC_FEMALE"
# [49] "NHBAC_MALE" "NHBAC_FEMALE" "NHIAC_MALE" "NHIAC_FEMALE" "NHAAC_MALE" "NHAAC_FEMALE"
# [55] "NHNAC_MALE" "NHNAC_FEMALE" "H_MALE" "H_FEMALE" "HWA_MALE" "HWA_FEMALE"
# [61] "HBA_MALE" "HBA_FEMALE" "HIA_MALE" "HIA_FEMALE" "HAA_MALE" "HAA_FEMALE"
# [67] "HNA_MALE" "HNA_FEMALE" "HTOM_MALE" "HTOM_FEMALE" "HWAC_MALE" "HWAC_FEMALE"
# [73] "HBAC_MALE" "HBAC_FEMALE" "HIAC_MALE" "HIAC_FEMALE" "HAAC_MALE" "HAAC_FEMALE"
# [79] "HNAC_MALE" "HNAC_FEMALE"
# Randomly select counties
county_list <- c(
"Culpeper County",
"Halifax County",
"Madison County",
"Northumberland County",
"Rockbridge County",
"Harrisonburg city",
"Hopewell city"
)
XtrCty = PopEstVA[PopEstVA$CTYNAME %in% county_list,-c(1,2,4)]
dim(XtrCty)
# [1] 1596 76
unique(XtrCty$CTYNAME)
# [1] Culpeper County Halifax County Madison County Northumberland County
# [5] Rockbridge County Harrisonburg city Hopewell city
## Want only YEAR=12 for the 2019 population estimates
## Selected 7 counties at random, above
XtrCty = XtrCty[XtrCty$YEAR==12,] ## now 133 x 77
CTYnames = as.character(XtrCty$CTYNAME[(1:7)*19])
CTYnames
# [1] "Culpeper County" "Halifax County" "Madison County" "Northumberland County"
# [5] "Rockbridge County" "Harrisonburg city" "Hopewell city"
### Now reduce variable set to the essential
VarSet = c("HWA_MALE", "HWA_FEMALE", "NHWA_MALE", "NHWA_FEMALE",
"HBA_MALE", "HBA_FEMALE", "NHBA_MALE", "NHBA_FEMALE",
"HAA_MALE", "HAA_FEMALE", "NHAA_MALE", "NHAA_FEMALE",
"HIA_MALE", "HIA_FEMALE", "NHIA_MALE", "NHIA_FEMALE",
"HNA_MALE", "HNA_FEMALE", "NHNA_MALE", "NHNA_FEMALE",
"H_MALE" , "H_FEMALE", "NH_MALE", "NH_FEMALE",
"HTOM_MALE","HTOM_FEMALE","NHTOM_MALE","NHTOM_FEMALE")
XtrCty = XtrCty[,-(2:3)] ### 133 x 75
XtrCty = XtrCty[,VarSet]
dim(XtrCty)
# [1] 133 28
# Copied from ES .rlog file
CTYnum <- c(660, 47, 83, 670, 113, 133, 163) ### but counties are in alphabetical
### order in original file with
### all counties before all cities
CTYnum = sort(CTYnum)
### Now create an array
XtrArr = array(data.matrix(XtrCty),
c(19,7,2,2,7),
dimnames=list(paste0("Agegp",0:18),
county_list,
c("M","F"),
c("Hsp","NH"),
c("WA","BA","AA","IA","NPIA","Tot","2+"))
)
XtrCty[0:18,1:4] ## data for 1st county
# HWA_MALE HWA_FEMALE NHWA_MALE NHWA_FEMALE
# 5454 2643 2445 17975 18726
# 5455 295 245 1005 993
# 5456 265 308 1117 1098
# 5457 279 274 1115 1216
# 5458 230 251 1092 1032
# 5459 168 149 884 879
# 5460 129 129 1059 1056
# 5461 160 146 1058 1130
# 5462 236 194 1200 1163
# 5463 244 205 1040 1114
# 5464 203 158 1191 1189
# 5465 142 101 1284 1269
# 5466 128 104 1447 1481
# 5467 62 73 1211 1366
# 5468 69 48 1071 1140
# 5469 11 28 912 1061
# 5470 11 18 653 690
# 5471 7 10 406 443
XtrArr[,1,,,1]
XtrCty[0:18,1:4]
# HWA_MALE HWA_FEMALE NHWA_MALE NHWA_FEMALE
# 5454 2643 2445 17975 18726
# 5455 295 245 1005 993
# 5456 265 308 1117 1098
# 5457 279 274 1115 1216
# 5458 230 251 1092 1032
# 5459 168 149 884 879
# 5460 129 129 1059 1056
# 5461 160 146 1058 1130
# 5462 236 194 1200 1163
# 5463 244 205 1040 1114
# 5464 203 158 1191 1189
# 5465 142 101 1284 1269
# 5466 128 104 1447 1481
# 5467 62 73 1211 1366
# 5468 69 48 1071 1140
# 5469 11 28 912 1061
# 5470 11 18 653 690
# 5471 7 10 406 443
XtrArr[,1,,,1]
# , , Hsp
#
# M F
# Agegp0 2643 2445
# Agegp1 295 245
# Agegp2 265 308
# Agegp3 279 274
# Agegp4 230 251
# Agegp5 168 149
# Agegp6 129 129
# Agegp7 160 146
# Agegp8 236 194
# Agegp9 244 205
# Agegp10 203 158
# Agegp11 142 101
# Agegp12 128 104
# Agegp13 62 73
# Agegp14 69 48
# Agegp15 11 28
# Agegp16 11 18
# Agegp17 7 10
# Agegp18 4 4
#
# , , NH
#
# M F
# Agegp0 17975 18726
# Agegp1 1005 993
# Agegp2 1117 1098
# Agegp3 1115 1216
# Agegp4 1092 1032
# Agegp5 884 879
# Agegp6 1059 1056
# Agegp7 1058 1130
# Agegp8 1200 1163
# Agegp9 1040 1114
# Agegp10 1191 1189
# Agegp11 1284 1269
# Agegp12 1447 1481
# Agegp13 1211 1366
# Agegp14 1071 1140
# Agegp15 912 1061
# Agegp16 653 690
# Agegp17 406 443
# Agegp18 230 406
### This worked properly
## Now collapse Age-groups: Age-groups are 1-4, 5-13 and 14-18
## 0 was total and is omitted
XtrArr2 = array(0, c(3,7,2,2,7), dimnames=c(list(
c("0-19","20-64","65+")),dimnames(XtrArr)[-1]))
dimnames(XtrArr2)
# [[1]]
# [1] "0-19" "20-64" "65+"
#
# [[2]]
# [1] "Cty47" "Cty83" "Cty113" "Cty133" "Cty163" "Cty660" "Cty670"
#
# [[3]]
# [1] "M" "F"
#
# [[4]]
# [1] "Hsp" "NH"
#
# [[5]]
# [1] "WA" "BA" "AA" "IA" "NPIA" "Tot" "2+"
XtrArr2[1,,,,] = apply(XtrArr[1:4,,,,],2:5,sum)
XtrArr2[2,,,,] = apply(XtrArr[5:13,,,,],2:5,sum)
XtrArr2[3,,,,] = apply(XtrArr[14:18,,,,],2:5,sum)
## Now finish off by redefining Race categories with
# "Tot" changed to "Other" after subtracting from Tot
# the sum of all other categories
XtrArr2[,,,,"Tot"] = XtrArr2[,,,,"Tot"] -
apply(XtrArr2[,,,,c(1:5,7)],1:4,sum)
summary(c(XtrArr2))
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.0 1.0 18.0 706.4 161.5 22033.0
dimnames(XtrArr2)[[5]][6] = "Oth"
### FINALLY RENAME this table "CrossArr"
CrossArr = XtrArr2
dimnames(CrossArr)
# [[1]]
# [1] "0-19" "20-64" "65+"
#
# [[2]]
# [1] "Cty47" "Cty83" "Cty113" "Cty133" "Cty163" "Cty660" "Cty670"
#
# [[3]]
# [1] "M" "F"
#
# [[4]]
# [1] "Hsp" "NH"
#
# [[5]]
# [1] "WA" "BA" "AA" "IA" "NPIA" "Oth" "2+"
## and removed XtrArr2
# save.image("C:\\EricStf\\CensusProj\\DiffPrivacy\\PopEsts-VA\\PopEst.RData")
## AN EXAMPLE OF A Little sub-table follows:
CrossArr[,1,1,2,]
# WA BA AA IA NPIA Oth 2+
# 0-19 21212 4526 511 92 10 0 1115
# 20-64 10255 2515 235 54 7 0 366
# 65+ 4253 661 43 14 1 0 44
## NB: the race-category "Other" is empty
apply(CrossArr[,,,,"Oth"],2,sum)
# Cty47 Cty83 Cty113 Cty133 Cty163 Cty660 Cty670
# 0 0 0 0 0 0 0
jlivsey/Dvar documentation built on July 13, 2024, 6:10 a.m.
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# I mentioned that we could start by downloading or simply recording tables of partially cross-tabulated totals by county. You schema has 7 counties, so I suggest that we more or less haphazardly choose 4 counties each from 2 different states.
# The website is:
# https://www.census.gov/content/census/en/data/datasets/time-series/demo/popest/2010s-counties-detail.html
# First Try HH, Counties in Virginia pop by M/F, AGE <20, 20-64, 65+,
# Hisp and major CenRace
# Age-groups are 1-4, 5-13 and 14-18
# Details:
PopEstVA = read.csv("data/cc-est2019-alldata-51.csv")
dim(PopEstVA)
# [1] 30324 80
names(PopEstVA)
# [1] "SUMLEV" "STATE" "COUNTY" "STNAME" "CTYNAME" "YEAR"
# [7] "AGEGRP" "TOT_POP" "TOT_MALE" "TOT_FEMALE" "WA_MALE" "WA_FEMALE"
# [13] "BA_MALE" "BA_FEMALE" "IA_MALE" "IA_FEMALE" "AA_MALE" "AA_FEMALE"
# [19] "NA_MALE" "NA_FEMALE" "TOM_MALE" "TOM_FEMALE" "WAC_MALE" "WAC_FEMALE"
# [25] "BAC_MALE" "BAC_FEMALE" "IAC_MALE" "IAC_FEMALE" "AAC_MALE" "AAC_FEMALE"
# [31] "NAC_MALE" "NAC_FEMALE" "NH_MALE" "NH_FEMALE" "NHWA_MALE" "NHWA_FEMALE"
# [37] "NHBA_MALE" "NHBA_FEMALE" "NHIA_MALE" "NHIA_FEMALE" "NHAA_MALE" "NHAA_FEMALE"
# [43] "NHNA_MALE" "NHNA_FEMALE" "NHTOM_MALE" "NHTOM_FEMALE" "NHWAC_MALE" "NHWAC_FEMALE"
# [49] "NHBAC_MALE" "NHBAC_FEMALE" "NHIAC_MALE" "NHIAC_FEMALE" "NHAAC_MALE" "NHAAC_FEMALE"
# [55] "NHNAC_MALE" "NHNAC_FEMALE" "H_MALE" "H_FEMALE" "HWA_MALE" "HWA_FEMALE"
# [61] "HBA_MALE" "HBA_FEMALE" "HIA_MALE" "HIA_FEMALE" "HAA_MALE" "HAA_FEMALE"
# [67] "HNA_MALE" "HNA_FEMALE" "HTOM_MALE" "HTOM_FEMALE" "HWAC_MALE" "HWAC_FEMALE"
# [73] "HBAC_MALE" "HBAC_FEMALE" "HIAC_MALE" "HIAC_FEMALE" "HAAC_MALE" "HAAC_FEMALE"
# [79] "HNAC_MALE" "HNAC_FEMALE"
# Randomly select counties
county_list <- c(
"Culpeper County",
"Halifax County",
"Madison County",
"Northumberland County",
"Rockbridge County",
"Harrisonburg city",
"Hopewell city"
)
XtrCty = PopEstVA[PopEstVA$CTYNAME %in% county_list,-c(1,2,4)]
dim(XtrCty)
# [1] 1596 76
unique(XtrCty$CTYNAME)
# [1] Culpeper County Halifax County Madison County Northumberland County
# [5] Rockbridge County Harrisonburg city Hopewell city
## Want only YEAR=12 for the 2019 population estimates
## Selected 7 counties at random, above
XtrCty = XtrCty[XtrCty$YEAR==12,] ## now 133 x 77
CTYnames = as.character(XtrCty$CTYNAME[(1:7)*19])
CTYnames
# [1] "Culpeper County" "Halifax County" "Madison County" "Northumberland County"
# [5] "Rockbridge County" "Harrisonburg city" "Hopewell city"
### Now reduce variable set to the essential
VarSet = c("HWA_MALE", "HWA_FEMALE", "NHWA_MALE", "NHWA_FEMALE",
"HBA_MALE", "HBA_FEMALE", "NHBA_MALE", "NHBA_FEMALE",
"HAA_MALE", "HAA_FEMALE", "NHAA_MALE", "NHAA_FEMALE",
"HIA_MALE", "HIA_FEMALE", "NHIA_MALE", "NHIA_FEMALE",
"HNA_MALE", "HNA_FEMALE", "NHNA_MALE", "NHNA_FEMALE",
"H_MALE" , "H_FEMALE", "NH_MALE", "NH_FEMALE",
"HTOM_MALE","HTOM_FEMALE","NHTOM_MALE","NHTOM_FEMALE")
XtrCty = XtrCty[,-(2:3)] ### 133 x 75
XtrCty = XtrCty[,VarSet]
dim(XtrCty)
# [1] 133 28
# Copied from ES .rlog file
CTYnum <- c(660, 47, 83, 670, 113, 133, 163) ### but counties are in alphabetical
### order in original file with
### all counties before all cities
CTYnum = sort(CTYnum)
### Now create an array
XtrArr = array(data.matrix(XtrCty),
c(19,7,2,2,7),
dimnames=list(paste0("Agegp",0:18),
county_list,
c("M","F"),
c("Hsp","NH"),
c("WA","BA","AA","IA","NPIA","Tot","2+"))
)
XtrCty[0:18,1:4] ## data for 1st county
# HWA_MALE HWA_FEMALE NHWA_MALE NHWA_FEMALE
# 5454 2643 2445 17975 18726
# 5455 295 245 1005 993
# 5456 265 308 1117 1098
# 5457 279 274 1115 1216
# 5458 230 251 1092 1032
# 5459 168 149 884 879
# 5460 129 129 1059 1056
# 5461 160 146 1058 1130
# 5462 236 194 1200 1163
# 5463 244 205 1040 1114
# 5464 203 158 1191 1189
# 5465 142 101 1284 1269
# 5466 128 104 1447 1481
# 5467 62 73 1211 1366
# 5468 69 48 1071 1140
# 5469 11 28 912 1061
# 5470 11 18 653 690
# 5471 7 10 406 443
XtrArr[,1,,,1]
XtrCty[0:18,1:4]
# HWA_MALE HWA_FEMALE NHWA_MALE NHWA_FEMALE
# 5454 2643 2445 17975 18726
# 5455 295 245 1005 993
# 5456 265 308 1117 1098
# 5457 279 274 1115 1216
# 5458 230 251 1092 1032
# 5459 168 149 884 879
# 5460 129 129 1059 1056
# 5461 160 146 1058 1130
# 5462 236 194 1200 1163
# 5463 244 205 1040 1114
# 5464 203 158 1191 1189
# 5465 142 101 1284 1269
# 5466 128 104 1447 1481
# 5467 62 73 1211 1366
# 5468 69 48 1071 1140
# 5469 11 28 912 1061
# 5470 11 18 653 690
# 5471 7 10 406 443
XtrArr[,1,,,1]
# , , Hsp
#
# M F
# Agegp0 2643 2445
# Agegp1 295 245
# Agegp2 265 308
# Agegp3 279 274
# Agegp4 230 251
# Agegp5 168 149
# Agegp6 129 129
# Agegp7 160 146
# Agegp8 236 194
# Agegp9 244 205
# Agegp10 203 158
# Agegp11 142 101
# Agegp12 128 104
# Agegp13 62 73
# Agegp14 69 48
# Agegp15 11 28
# Agegp16 11 18
# Agegp17 7 10
# Agegp18 4 4
#
# , , NH
#
# M F
# Agegp0 17975 18726
# Agegp1 1005 993
# Agegp2 1117 1098
# Agegp3 1115 1216
# Agegp4 1092 1032
# Agegp5 884 879
# Agegp6 1059 1056
# Agegp7 1058 1130
# Agegp8 1200 1163
# Agegp9 1040 1114
# Agegp10 1191 1189
# Agegp11 1284 1269
# Agegp12 1447 1481
# Agegp13 1211 1366
# Agegp14 1071 1140
# Agegp15 912 1061
# Agegp16 653 690
# Agegp17 406 443
# Agegp18 230 406
### This worked properly
## Now collapse Age-groups: Age-groups are 1-4, 5-13 and 14-18
## 0 was total and is omitted
XtrArr2 = array(0, c(3,7,2,2,7), dimnames=c(list(
c("0-19","20-64","65+")),dimnames(XtrArr)[-1]))
dimnames(XtrArr2)
# [[1]]
# [1] "0-19" "20-64" "65+"
#
# [[2]]
# [1] "Cty47" "Cty83" "Cty113" "Cty133" "Cty163" "Cty660" "Cty670"
#
# [[3]]
# [1] "M" "F"
#
# [[4]]
# [1] "Hsp" "NH"
#
# [[5]]
# [1] "WA" "BA" "AA" "IA" "NPIA" "Tot" "2+"
XtrArr2[1,,,,] = apply(XtrArr[1:4,,,,],2:5,sum)
XtrArr2[2,,,,] = apply(XtrArr[5:13,,,,],2:5,sum)
XtrArr2[3,,,,] = apply(XtrArr[14:18,,,,],2:5,sum)
## Now finish off by redefining Race categories with
# "Tot" changed to "Other" after subtracting from Tot
# the sum of all other categories
XtrArr2[,,,,"Tot"] = XtrArr2[,,,,"Tot"] -
apply(XtrArr2[,,,,c(1:5,7)],1:4,sum)
summary(c(XtrArr2))
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.0 1.0 18.0 706.4 161.5 22033.0
dimnames(XtrArr2)[[5]][6] = "Oth"
### FINALLY RENAME this table "CrossArr"
CrossArr = XtrArr2
dimnames(CrossArr)
# [[1]]
# [1] "0-19" "20-64" "65+"
#
# [[2]]
# [1] "Cty47" "Cty83" "Cty113" "Cty133" "Cty163" "Cty660" "Cty670"
#
# [[3]]
# [1] "M" "F"
#
# [[4]]
# [1] "Hsp" "NH"
#
# [[5]]
# [1] "WA" "BA" "AA" "IA" "NPIA" "Oth" "2+"
## and removed XtrArr2
# save.image("C:\\EricStf\\CensusProj\\DiffPrivacy\\PopEsts-VA\\PopEst.RData")
## AN EXAMPLE OF A Little sub-table follows:
CrossArr[,1,1,2,]
# WA BA AA IA NPIA Oth 2+
# 0-19 21212 4526 511 92 10 0 1115
# 20-64 10255 2515 235 54 7 0 366
# 65+ 4253 661 43 14 1 0 44
## NB: the race-category "Other" is empty
apply(CrossArr[,,,,"Oth"],2,sum)
# Cty47 Cty83 Cty113 Cty133 Cty163 Cty660 Cty670
# 0 0 0 0 0 0 0
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