Tomonori Nagano 2015-12-31
- R scripts to analyze heritage language spakers in the U.S.
- Project: Source codes for “Demographics of Adult Heritage Language
Speakers in the United States: Differences by Region and Language and
Their Implications” (The Modern Language Journal, vol. 99, No. 4)
- Nagano, T. (2015). Demographics of Adult Heritage Language Speakers in the United States: Differences by Region and Language and their Implications. The Modern Language Journal, 99(4), 771-792.
- Script purpose: This R script will analyze the U.S. Census/ACE data and compute the number of adult heritage language speakers in the U.S. in different regions, at different time period (1980-2010), and by languages. See the following article for more information: Nagano, T. (2015). Demographics of Adult Heritage Language Speakers in the United States: Differences by Region and Language and their Implications. The Modern Language Journal, 99(4), 771-792.
- Note: Download the U.S. Census/ACE data from the IPUMS website
https://usa.ipums.org.
- For the published article, I used the following data:
- 1980: 5% state
- 1990: 5%
- 2000: 5%
- 2010: ACS
- The variables to extract in each dataset are: YEAR, DATANUM, SERIAL, HHWT, REGION, STATEFIP, COUNTY, METRO, METAREA (general), METAREAD (detailed), CITY, CITYPOP, CONSPUMA, CNTRY, GQ, NFAMS, MULTGEN (general), MULTGEND (detailed), PERNUM, PERWT, FAMSIZE, NCHILD, NCHLT5, FAMUNIT, ELDCH, YNGCH, NSIBS, MOMLOC, POPLOC, SUBFAM, RELATE (general), RELATED (detailed), SEX, AGE, MARST, HISPAN (general), HISPAND (detailed), BPL (general), BPLD (detailed), ANCESTR1 (general), ANCESTR1D (detailed), ANCESTR2 (general), ANCESTR2D (detailed), CITIZEN, YRIMMIG, YRSUSA1, YRSUSA2, LANGUAGE (general), LANGUAGED (detailed), SPEAKENG, RACESING (general), RACESINGD (detailed), EDUC (general), EDUCD (detailed), GRADEATT (general), GRADEATTD (detailed), OCC, IND, INCTOT, FTOTINC, POVERTY, OCCSCORE, and SEI
- Download the data as SPSS file (which can be read by R). The full data (about 2G-3G) will be extremely large and you won’t be able to process them unless you have a very powerful computer. Use sampled/partial data with the “Customize Sample Size” function on IPUMS if necessary.
- Screenshot of the IPUMS USA page (“Customize Sample Size”)
- For the published article, I used the following data:
- In the “data” folder, you can find sample data from the IPUMS USA page (0.3%-0.5% data). They are all in the SPSS data format (.sav). Unarchive them and change their filenames accordingly. Please note that they are all tiny sample data and may not show exactly the same results as those in the published article (they should be very close, though.)
- For a complete analysis, go to the IPUMS USA (https://usa.ipums.org/usa/) and download the whole data files from their website.
# clear the cache
rm(list = ls())
# set the current workd directly
setwd("~/Desktop")
# loading required packages (install with install.packages("xxxx") if the package is not installed)
library(ggplot2); library(gdata); library(xtable); library(gplots); library(foreign); library(ineq);- Load the data. Run the script several times by changing the “fileYear” variable.
# loading the data file (full data might take a lot of time to load)
# update fileYear to 1980, 1990, 2000, or 2010
fileYear="1980"
thisData <- drop.levels(as.data.frame(read.spss(paste("dataIPUMS2014/DATA_DOWNLOADED_FROM_IPUMS_SAMPLE",fileYear,".sav",sep=""))),reorder=FALSE)## Warning in read.spss(paste("dataIPUMS2014/DATA_DOWNLOADED_FROM_IPUMS_SAMPLE", :
## Duplicated levels in factor RELATED: Other non-relatives,
## Roomers/boarders/lodgers, Relative of employee, Military
## Warning in read.spss(paste("dataIPUMS2014/DATA_DOWNLOADED_FROM_IPUMS_SAMPLE", :
## Duplicated levels in factor BPLD: Br. Virgin Islands, ns
## Warning in read.spss(paste("dataIPUMS2014/DATA_DOWNLOADED_FROM_IPUMS_SAMPLE", :
## Undeclared level(s) 422, 441 added in variable: ANCESTR1
head(thisData)## YEAR SAMPLE SERIAL HHWT CLUSTER REGION STATEFIP COUNTYICP
## 1 1980 1980 5% 2 200 1.98e+12 Pacific Division Alaska 200
## 2 1980 1980 5% 2 200 1.98e+12 Pacific Division Alaska 200
## 3 1980 1980 5% 12 200 1.98e+12 Pacific Division Alaska 200
## 4 1980 1980 5% 32 200 1.98e+12 Pacific Division Alaska 200
## 5 1980 1980 5% 32 200 1.98e+12 Pacific Division Alaska 200
## 6 1980 1980 5% 42 200 1.98e+12 Pacific Division Alaska 200
## METRO
## 1 In metropolitan area: Central/principal city status indeterminable (mixed)
## 2 In metropolitan area: Central/principal city status indeterminable (mixed)
## 3 In metropolitan area: Central/principal city status indeterminable (mixed)
## 4 In metropolitan area: Central/principal city status indeterminable (mixed)
## 5 In metropolitan area: Central/principal city status indeterminable (mixed)
## 6 In metropolitan area: Central/principal city status indeterminable (mixed)
## METAREA METAREAD CITY CITYPOP STRATA CONSPUMA
## 1 Anchorage, AK Anchorage, AK Anchorage, AK 1744 24 540
## 2 Anchorage, AK Anchorage, AK Anchorage, AK 1744 24 540
## 3 Anchorage, AK Anchorage, AK Anchorage, AK 1744 27 540
## 4 Anchorage, AK Anchorage, AK Anchorage, AK 1744 25 540
## 5 Anchorage, AK Anchorage, AK Anchorage, AK 1744 25 540
## 6 Anchorage, AK Anchorage, AK Anchorage, AK 1744 7 540
## CNTRY GQ NFAMS MULTGEN
## 1 United States Households under 1970 definition 1 family or N/A 1 generation
## 2 United States Households under 1970 definition 1 family or N/A 1 generation
## 3 United States Households under 1970 definition 1 family or N/A 1 generation
## 4 United States Households under 1970 definition 1 family or N/A 1 generation
## 5 United States Households under 1970 definition 1 family or N/A 1 generation
## 6 United States Households under 1970 definition 1 family or N/A 1 generation
## MULTGEND PERNUM PERWT FAMUNIT
## 1 1 generation 1 200 1st family in household or group quarters
## 2 1 generation 2 200 1st family in household or group quarters
## 3 1 generation 1 200 1st family in household or group quarters
## 4 1 generation 1 200 1st family in household or group quarters
## 5 1 generation 2 200 1st family in household or group quarters
## 6 1 generation 1 200 1st family in household or group quarters
## FAMSIZE SUBFAM MOMLOC POPLOC
## 1 2 family members present Group quarters or not in subfamily 0 0
## 2 2 family members present Group quarters or not in subfamily 0 0
## 3 1 family member present Group quarters or not in subfamily 0 0
## 4 2 family members present Group quarters or not in subfamily 0 0
## 5 2 family members present Group quarters or not in subfamily 0 0
## 6 2 family members present Group quarters or not in subfamily 0 0
## NCHILD NCHLT5 NSIBS ELDCH YNGCH
## 1 0 children present No children under age 5 0 siblings N/A N/A
## 2 0 children present No children under age 5 0 siblings N/A N/A
## 3 0 children present No children under age 5 0 siblings N/A N/A
## 4 0 children present No children under age 5 0 siblings N/A N/A
## 5 0 children present No children under age 5 0 siblings N/A N/A
## 6 0 children present No children under age 5 0 siblings N/A N/A
## RELATE RELATED SEX AGE MARST
## 1 Head/Householder Head/Householder Female 58 Married, spouse present
## 2 Spouse Spouse Male 59 Married, spouse present
## 3 Head/Householder Head/Householder Male 42 Never married/single
## 4 Head/Householder Head/Householder Male 24 Married, spouse present
## 5 Spouse Spouse Female 24 Married, spouse present
## 6 Head/Householder Head/Householder Male 37 Married, spouse present
## HISPAN HISPAND BPL BPLD
## 1 Not Hispanic Not Hispanic Illinois Illinois
## 2 Not Hispanic Not Hispanic Illinois Illinois
## 3 Not Hispanic Not Hispanic South Dakota South Dakota
## 4 Not Hispanic Not Hispanic Virginia Virginia
## 5 Not Hispanic Not Hispanic Maryland Maryland
## 6 Not Hispanic Not Hispanic West Virginia West Virginia
## ANCESTR1 ANCESTR1D ANCESTR2 ANCESTR2D CITIZEN
## 1 German German (1980) French French (1980) N/A
## 2 English English German German (1980) N/A
## 3 American American Not Reported Not Reported N/A
## 4 Scottish Scottish Not Reported Not Reported N/A
## 5 Irish, various subheads, Irish Not Reported Not Reported N/A
## 6 English English Not Reported Not Reported N/A
## YRIMMIG YRSUSA2 LANGUAGE LANGUAGED SPEAKENG
## 1 N/A N/A English English Yes, speaks only English
## 2 N/A N/A English English Yes, speaks only English
## 3 N/A N/A English English Yes, speaks only English
## 4 N/A N/A English English Yes, speaks only English
## 5 N/A N/A English English Yes, speaks only English
## 6 N/A N/A English English Yes, speaks only English
## EDUC EDUCD GRADEATT GRADEATTD
## 1 Grade 12 Grade 12 N/A N/A
## 2 1 year of college 1 year of college N/A N/A
## 3 5+ years of college 6 years of college (6+ in 1960-1970) N/A N/A
## 4 Grade 12 Grade 12 N/A N/A
## 5 Grade 12 Grade 12 N/A N/A
## 6 5+ years of college 6 years of college (6+ in 1960-1970) N/A N/A
## OCC IND INCTOT FTOTINC POVERTY OCCSCORE SEI RACESING RACESINGD
## 1 313 412 18310 21620 448 22 61 White White
## 2 508 421 3310 21620 448 33 48 White White
## 3 173 901 30005 30005 501 31 65 White White
## 4 0 0 3970 8975 186 11 18 White White
## 5 0 0 5005 8975 186 11 18 White White
## 6 0 0 20310 23815 494 11 18 White White
# Creating new variables
# Wyoming is 51 and American Samoa (52), Guam (53), Puerto Rico (54), and Virgin Islands (55)
USStates <- c("Alabama","Alaska","Arizona","Arkansas","California","Colorado","Connecticut","Delaware","District of Columbia","Florida","Georgia","Hawaii","Idaho","Illinois","Indiana","Iowa","Kansas","Kentucky","Louisiana","Maine","Maryland","Massachusetts","Michigan","Minnesota","Mississippi","Missouri","Montana","Nebraska","Nevada","New Hampshire","New Jersey","New Mexico","New York","North Carolina","North Dakota","Ohio","Oklahoma","Oregon","Pennsylvania","Rhode Island","South Carolina","South Dakota","Tennessee","Texas","Utah","Vermont","Virginia","Washington","West Virginia","Wisconsin","Wyoming","American Samoa","Guam","Puerto Rico")
thisData$USBorn <- thisData$BPL %in% USStates
thisData$AGE_ORIG <- thisData$AGE
thisData$AGE <- as.numeric(levels(thisData$AGE))[thisData$AGE]## Warning: NAs introduced by coercion
thisData[is.na(thisData$AGE),"AGE"] <- 0 # "less than 1 year old"
thisData$ADULT <- thisData$AGE > 18
thisData$YRIMMIG_ORIG <- thisData$YRIMMIG
if (fileYear == "1980"){
levels(thisData$YRIMMIG) <- c(NA,"1949","1959","1964","1969","1974","1980")
} else if (fileYear == "1990"){
levels(thisData$YRIMMIG) <- c(NA,"1949","1959","1964","1969","1974","1979","1981","1984","1986","1990")
} else if (fileYear == "2000"){
levels(thisData$YRIMMIG) <- c("N/A","1910","1914","1919","1920","1921","1922","1923","1924","1925","1926","1927","1928","1929","1930","1935","1936","1937","1938","1939","1940","1941","1942","1943","1944","1945","1946","1947","1948","1949","1950","1951","1952","1953","1954","1955","1956","1957","1958","1959","1960","1961","1962","1963","1964","1965","1966","1967","1968","1969","1970","1971","1972","1973","1974","1975","1976","1977","1978","1979","1980","1981","1982","1983","1984","1985","1986","1987","1988","1989","1990","1991","1992","1993","1994","1995","1996","1997","1998","1999","2000")
} else if (fileYear == "2010"){
levels(thisData$YRIMMIG) <- c(NA,"1919","1920","1921","1922","1923","1924","1925","1926","1927","1928","1929","1930","1932","1934","1935","1936","1937","1938","1939","1940","1941","1942","1943","1944","1945","1946","1947","1948","1949","1950","1951","1952","1953","1954","1955","1956","1957","1958","1959","1960","1961","1962","1963","1964","1965","1966","1967","1968","1969","1970","1971","1972","1973","1974","1975","1976","1977","1978","1979","1980","1981","1982","1983","1984","1985","1986","1987","1988","1989","1990","1991","1992","1993","1994","1995","1996","1997","1998","1999","2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010")
}- See the article for more discussion on how to define heritage speakers using the Census/ACS data.
# Adult heritage language speakers based on the definition by Nagano (2015)
thisData$YRIMMIG <- as.numeric(levels(thisData$YRIMMIG))[thisData$YRIMMIG]
thisData$YRIMMIG <- as.numeric(fileYear)-as.numeric(thisData$YRIMMIG)
thisData$IMMIGADULT <- thisData$YRIMMIG > 18
thisData[is.na(thisData$IMMIGADULT),"IMMIGADULT"] <- FALSE
thisData$SPEAKENGWell <- thisData$SPEAKENG == "Yes, speaks very well" | thisData$SPEAKENG == "Yes, speaks well"
thisData$SPEAKNoENG <- thisData$SPEAKENG == "Does not speak English"
thisData$SPEAKHL <- !(thisData$LANGUAGE == "N/A or blank" | thisData$LANGUAGE == "English")
# checking states, counties, and CONSPUMA
length(levels(thisData$STATEFIP))## [1] 51
mean(xtabs(PERWT ~ STATEFIP, data=thisData))## [1] 4452216
sqrt(var(xtabs(PERWT ~ STATEFIP, data=thisData)))## [1] 4714314
length(levels(as.factor(thisData$COUNTY)))## [1] 107
mean(xtabs(PERWT ~ COUNTYICP, data=thisData))## [1] 2122084
sqrt(var(xtabs(PERWT ~ COUNTYICP, data=thisData)))## [1] 9054487
# HL conditions: age of 18 or above, speak a HL at home, not "Does not speak English", and not immigrated after 18
HLConditions <- thisData$ADULT==TRUE & thisData$SPEAKHL==TRUE & !thisData$SPEAKNoENG & !thisData$IMMIGADULT
# adult population
adultPopulation <- xtabs(PERWT ~ STATEFIP, data=thisData)
head(adultPopulation)## STATEFIP
## Alabama Alaska Arizona Arkansas California Colorado
## 3945600 412200 2726000 2275000 23750400 2883600
write.csv(adultPopulation,file=paste("Output00_TableAdultStatesPopulation",fileYear,".csv",sep=""))
# all people
sum(thisData[,"PERWT"])## [1] 227063000
# all people who speak HL at home
sum(thisData[thisData$SPEAKHL==TRUE,"PERWT"])## [1] 23813400
# all people who are HL according to my criteira
sum(thisData[HLConditions,"PERWT"])## [1] 14071800
# U.S. population by race
tableRace <- xtabs(PERWT ~ RACESING, data=thisData)
head(tableRace)## RACESING
## White Black
## 194825200 26703800
## American Indian/Alaska Native Asian and/or Pacific Islander
## 1553000 3759600
## Other race, non-Hispanic
## 221400
write.csv(tableRace,file=paste("Output01_TableRace",fileYear,".csv",sep=""))
# U.S. Hispanic population
tableHipanics <- xtabs(PERWT ~ HISPAN, data=thisData)
head(tableHipanics)## HISPAN
## Not Hispanic Mexican Puerto Rican Cuban Other
## 212335200 8757600 2036600 807800 3125800
write.csv(tableHipanics,file=paste("Output02_TableRaceHispanic",fileYear,".csv",sep=""))
# Heritage language speakers by state
HLTableOrder = rev(order(colSums(xtabs(PERWT ~ STATEFIP + LANGUAGE, data=thisData[HLConditions,]))))
tableAllHLSpeakersState <- xtabs(PERWT ~ STATEFIP + LANGUAGE, data=thisData[thisData$SPEAKHL==TRUE,])
head(tableAllHLSpeakersState)## LANGUAGE
## STATEFIP N/A or blank English German Yiddish, Jewish Dutch Swedish
## Alabama 0 0 11800 400 1600 200
## Alaska 0 0 2400 0 400 200
## Arizona 0 0 14000 1400 1600 1000
## Arkansas 0 0 6200 0 800 200
## California 0 0 175800 27800 39000 11400
## Colorado 0 0 28800 800 1000 600
## LANGUAGE
## STATEFIP Danish Norwegian Icelandic Italian French Spanish Portuguese
## Alabama 0 0 0 800 8800 18400 0
## Alaska 0 1200 0 200 800 3600 200
## Arizona 200 1200 0 6400 12600 344800 1600
## Arkansas 0 0 0 1000 5200 12000 0
## California 11400 9800 1000 137800 104200 3248800 77800
## Colorado 200 1200 0 5600 11600 181600 800
## LANGUAGE
## STATEFIP Rumanian Celtic Greek Albanian Russian
## Alabama 0 0 800 0 0
## Alaska 0 200 0 0 1600
## Arizona 0 0 2000 0 400
## Arkansas 0 200 600 0 600
## California 3000 1600 40800 200 30400
## Colorado 0 0 2800 0 1600
## LANGUAGE
## STATEFIP Ukrainian, Ruthenian, Little Russian Czech Polish Slovak
## Alabama 0 0 0 200
## Alaska 0 0 0 0
## Arizona 600 400 3000 0
## Arkansas 0 400 600 400
## California 7000 9600 22400 1200
## Colorado 800 1800 3000 400
## LANGUAGE
## STATEFIP Serbo-Croatian, Yugoslavian, Slavonian Slovene Lithuanian
## Alabama 400 0 200
## Alaska 0 0 0
## Arizona 2800 0 1000
## Arkansas 200 0 200
## California 16200 1200 7200
## Colorado 3000 200 600
## LANGUAGE
## STATEFIP Other Balto-Slavic Armenian Persian, Iranian, Farsi
## Alabama 0 0 1000
## Alaska 0 0 0
## Arizona 0 200 400
## Arkansas 0 0 800
## California 1000 57800 32200
## Colorado 0 200 1400
## LANGUAGE
## STATEFIP Other Persian dialects Hindi and related Romany, Gypsy Finnish
## Alabama 0 2800 0 0
## Alaska 0 400 0 0
## Arizona 0 2000 0 800
## Arkansas 0 0 0 0
## California 200 46600 400 5800
## Colorado 0 400 0 400
## LANGUAGE
## STATEFIP Magyar, Hungarian Uralic Turkish Other Altaic
## Alabama 0 0 200 0
## Alaska 600 0 0 0
## Arizona 1400 200 600 0
## Arkansas 400 0 400 0
## California 21400 2400 3800 400
## Colorado 2000 400 0 0
## LANGUAGE
## STATEFIP Caucasian, Georgian, Avar Basque Dravidian Kurukh Burushaski
## Alabama 0 0 400 0 0
## Alaska 0 0 0 0 0
## Arizona 0 0 0 0 0
## Arkansas 0 0 0 0 0
## California 0 1600 3000 1400 0
## Colorado 0 0 800 0 0
## LANGUAGE
## STATEFIP Chinese Tibetan Burmese, Lisu, Lolo Thai, Siamese, Lao Japanese
## Alabama 1200 0 0 400 1600
## Alaska 0 0 0 600 1800
## Arizona 4000 0 0 400 2200
## Arkansas 400 0 0 0 600
## California 272400 1600 2600 23800 122600
## Colorado 3400 0 0 1000 4400
## LANGUAGE
## STATEFIP Korean Vietnamese Other East/Southeast Asian Indonesian
## Alabama 1000 1600 0 0
## Alaska 1400 0 0 0
## Arizona 3200 200 0 200
## Arkansas 800 800 0 0
## California 87800 67000 3200 2400
## Colorado 2600 1600 0 0
## LANGUAGE
## STATEFIP Other Malayan Filipino, Tagalog Micronesian, Polynesian Hawaiian
## Alabama 0 0 0 200
## Alaska 0 800 0 0
## Arizona 0 1400 200 0
## Arkansas 0 600 0 0
## California 6600 214400 24200 1400
## Colorado 0 1800 400 200
## LANGUAGE
## STATEFIP Arabic Near East Arabic dialect Hebrew, Israeli
## Alabama 1000 0 0
## Alaska 200 0 0
## Arizona 1600 0 0
## Arkansas 1600 0 0
## California 42800 3400 16800
## Colorado 3600 0 1200
## LANGUAGE
## STATEFIP Amharic, Ethiopian, etc Hamitic Sub-Saharan Africa African, n.s
## Alabama 0 0 400 0
## Alaska 0 0 200 0
## Arizona 200 0 0 200
## Arkansas 0 0 400 400
## California 1200 1200 5600 200
## Colorado 200 200 1400 0
## LANGUAGE
## STATEFIP American Indian (all) Other or not reported
## Alabama 600 4600
## Alaska 29200 200
## Arizona 108200 2000
## Arkansas 800 4000
## California 10600 34000
## Colorado 1800 2800
write.csv(tableAllHLSpeakersState[,HLTableOrder],file=paste("Output03_TableAllHLSpeakersByState",fileYear,".csv",sep=""))
# Heritage language speakers by region
tableHLSpeakersRegion <- xtabs(PERWT ~ REGION + LANGUAGE, data=thisData[HLConditions,])
head(tableHLSpeakersRegion)## LANGUAGE
## REGION N/A or blank English German Yiddish, Jewish Dutch
## New England Division 0 0 27800 6600 5200
## Middle Atlantic Division 0 0 175200 90400 9600
## East North Central Div 0 0 216000 13800 18000
## West North Central Div 0 0 169800 1800 8400
## South Atlantic Division 0 0 93600 25400 7600
## East South Central Div 0 0 25600 400 1400
## LANGUAGE
## REGION Swedish Danish Norwegian Icelandic Italian French
## New England Division 5400 1600 1200 200 137400 317800
## Middle Atlantic Division 6400 3200 5400 600 502400 146200
## East North Central Div 11200 3200 11600 600 121400 81800
## West North Central Div 17200 4200 41400 600 17400 29600
## South Atlantic Division 4200 2400 2200 400 71600 140400
## East South Central Div 200 0 0 0 5800 31000
## LANGUAGE
## REGION Spanish Portuguese Rumanian Celtic Greek Albanian
## New England Division 134400 106800 1200 1800 30000 2400
## Middle Atlantic Division 1154200 32800 3400 4800 71000 3600
## East North Central Div 468800 2600 5600 2200 52000 1800
## West North Central Div 97400 1200 0 1200 3200 0
## South Atlantic Division 622800 9400 800 2800 29200 200
## East South Central Div 51200 400 0 400 3000 0
## LANGUAGE
## REGION Russian Ukrainian, Ruthenian, Little Russian Czech
## New England Division 2400 2600 1400
## Middle Atlantic Division 39400 26800 12400
## East North Central Div 14000 9800 19600
## West North Central Div 2200 400 26000
## South Atlantic Division 6000 2800 3600
## East South Central Div 400 0 0
## LANGUAGE
## REGION Polish Slovak
## New England Division 71200 2200
## Middle Atlantic Division 227200 34600
## East North Central Div 221200 22000
## West North Central Div 13400 800
## South Atlantic Division 29800 3400
## East South Central Div 1400 600
## LANGUAGE
## REGION Serbo-Croatian, Yugoslavian, Slavonian Slovene
## New England Division 1600 400
## Middle Atlantic Division 30600 2400
## East North Central Div 39400 6000
## West North Central Div 4200 200
## South Atlantic Division 4200 800
## East South Central Div 400 200
## LANGUAGE
## REGION Lithuanian Other Balto-Slavic Armenian
## New England Division 13800 0 8800
## Middle Atlantic Division 13000 2000 10000
## East North Central Div 12200 4000 3800
## West North Central Div 1000 0 200
## South Atlantic Division 3200 0 3800
## East South Central Div 400 400 400
## LANGUAGE
## REGION Persian, Iranian, Farsi Other Persian dialects
## New England Division 4200 0
## Middle Atlantic Division 5800 200
## East North Central Div 10200 0
## West North Central Div 5400 200
## South Atlantic Division 10800 1000
## East South Central Div 2000 200
## LANGUAGE
## REGION Hindi and related Romany, Gypsy Finnish
## New England Division 4000 0 4200
## Middle Atlantic Division 44200 600 2400
## East North Central Div 34600 800 17000
## West North Central Div 7800 0 13200
## South Atlantic Division 20200 0 3200
## East South Central Div 3400 0 0
## LANGUAGE
## REGION Magyar, Hungarian Uralic Turkish Other Altaic
## New England Division 7200 0 1200 400
## Middle Atlantic Division 31600 0 8600 0
## East North Central Div 24800 200 2400 400
## West North Central Div 1600 0 200 400
## South Atlantic Division 9400 0 2200 0
## East South Central Div 600 0 600 200
## LANGUAGE
## REGION Caucasian, Georgian, Avar Basque Dravidian Kurukh
## New England Division 0 0 1800 200
## Middle Atlantic Division 200 0 9400 0
## East North Central Div 200 0 5200 0
## West North Central Div 0 0 2400 0
## South Atlantic Division 0 0 2400 400
## East South Central Div 0 0 200 0
## LANGUAGE
## REGION Burushaski Chinese Tibetan Burmese, Lisu, Lolo
## New England Division 0 18600 200 0
## Middle Atlantic Division 0 89800 1000 0
## East North Central Div 0 30000 2000 0
## West North Central Div 0 10800 400 200
## South Atlantic Division 0 26000 600 800
## East South Central Div 0 3000 200 0
## LANGUAGE
## REGION Thai, Siamese, Lao Japanese Korean Vietnamese
## New England Division 1200 3200 4000 2200
## Middle Atlantic Division 7800 20800 32800 8600
## East North Central Div 9000 14400 23800 5400
## West North Central Div 3200 6000 6200 9400
## South Atlantic Division 6600 9800 28000 12600
## East South Central Div 1600 2800 5200 4400
## LANGUAGE
## REGION Other East/Southeast Asian Indonesian Other Malayan
## New England Division 0 400 800
## Middle Atlantic Division 200 1600 800
## East North Central Div 800 1800 1600
## West North Central Div 0 800 400
## South Atlantic Division 600 400 0
## East South Central Div 0 200 400
## LANGUAGE
## REGION Filipino, Tagalog Micronesian, Polynesian Hawaiian
## New England Division 3200 600 200
## Middle Atlantic Division 34000 1200 600
## East North Central Div 32000 1200 400
## West North Central Div 5000 600 0
## South Atlantic Division 27600 2200 0
## East South Central Div 2400 400 200
## LANGUAGE
## REGION Arabic Near East Arabic dialect Hebrew, Israeli
## New England Division 9000 200 6200
## Middle Atlantic Division 30600 600 28000
## East North Central Div 33800 2200 4000
## West North Central Div 5400 0 1000
## South Atlantic Division 15400 200 5600
## East South Central Div 3400 0 800
## LANGUAGE
## REGION Amharic, Ethiopian, etc Hamitic Sub-Saharan Africa
## New England Division 200 0 1600
## Middle Atlantic Division 400 800 9000
## East North Central Div 4000 400 5600
## West North Central Div 400 0 3000
## South Atlantic Division 1200 800 6200
## East South Central Div 0 0 800
## LANGUAGE
## REGION African, n.s American Indian (all)
## New England Division 200 2800
## Middle Atlantic Division 1000 6000
## East North Central Div 600 8000
## West North Central Div 800 18800
## South Atlantic Division 200 6200
## East South Central Div 0 4000
## LANGUAGE
## REGION Other or not reported
## New England Division 9800
## Middle Atlantic Division 27200
## East North Central Div 33400
## West North Central Div 11400
## South Atlantic Division 32600
## East South Central Div 10000
write.csv(tableHLSpeakersRegion[,HLTableOrder],file=paste("Output04_TableHLSpeakersByRegion",fileYear,".csv",sep=""))
# Heritage language speakers by language and by state
tableHLSpeakersState <- xtabs(PERWT ~ STATEFIP + LANGUAGE, data=thisData[HLConditions,])
head(tableHLSpeakersState)## LANGUAGE
## STATEFIP N/A or blank English German Yiddish, Jewish Dutch Swedish
## Alabama 0 0 7400 200 800 200
## Alaska 0 0 1800 0 400 0
## Arizona 0 0 9800 1000 800 600
## Arkansas 0 0 4000 0 400 200
## California 0 0 96200 13600 19400 6400
## Colorado 0 0 15800 200 1000 400
## LANGUAGE
## STATEFIP Danish Norwegian Icelandic Italian French Spanish Portuguese
## Alabama 0 0 0 400 5800 13200 0
## Alaska 0 0 0 200 600 3200 200
## Arizona 0 600 0 3600 8200 198400 1000
## Arkansas 0 0 0 1000 3600 7600 0
## California 5200 5000 1000 81000 70400 1701400 44800
## Colorado 200 800 0 4400 7800 134800 600
## LANGUAGE
## STATEFIP Rumanian Celtic Greek Albanian Russian
## Alabama 0 0 600 0 0
## Alaska 0 200 0 0 600
## Arizona 0 0 1400 0 200
## Arkansas 0 200 400 0 0
## California 2200 400 24000 200 12600
## Colorado 0 0 1800 0 600
## LANGUAGE
## STATEFIP Ukrainian, Ruthenian, Little Russian Czech Polish Slovak
## Alabama 0 0 0 200
## Alaska 0 0 0 0
## Arizona 200 0 2400 0
## Arkansas 0 200 200 200
## California 2400 5200 13400 800
## Colorado 400 1600 1400 400
## LANGUAGE
## STATEFIP Serbo-Croatian, Yugoslavian, Slavonian Slovene Lithuanian
## Alabama 200 0 200
## Alaska 0 0 0
## Arizona 800 0 1000
## Arkansas 0 0 200
## California 9200 200 1800
## Colorado 1600 200 200
## LANGUAGE
## STATEFIP Other Balto-Slavic Armenian Persian, Iranian, Farsi
## Alabama 0 0 600
## Alaska 0 0 0
## Arizona 0 200 200
## Arkansas 0 0 800
## California 400 35800 23000
## Colorado 0 200 1200
## LANGUAGE
## STATEFIP Other Persian dialects Hindi and related Romany, Gypsy Finnish
## Alabama 0 1800 0 0
## Alaska 0 400 0 0
## Arizona 0 1400 0 600
## Arkansas 0 0 0 0
## California 0 30000 400 4200
## Colorado 0 400 0 200
## LANGUAGE
## STATEFIP Magyar, Hungarian Uralic Turkish Other Altaic
## Alabama 0 0 200 0
## Alaska 400 0 0 0
## Arizona 1200 200 400 0
## Arkansas 200 0 200 0
## California 9200 400 2600 400
## Colorado 1000 0 0 0
## LANGUAGE
## STATEFIP Caucasian, Georgian, Avar Basque Dravidian Kurukh Burushaski
## Alabama 0 0 200 0 0
## Alaska 0 0 0 0 0
## Arizona 0 0 0 0 0
## Arkansas 0 0 0 0 0
## California 0 1600 3000 800 0
## Colorado 0 0 400 0 0
## LANGUAGE
## STATEFIP Chinese Tibetan Burmese, Lisu, Lolo Thai, Siamese, Lao Japanese
## Alabama 400 0 0 400 1000
## Alaska 0 0 0 600 1600
## Arizona 2000 0 0 200 2000
## Arkansas 200 0 0 0 600
## California 156000 800 2000 14400 84000
## Colorado 2000 0 0 200 2600
## LANGUAGE
## STATEFIP Korean Vietnamese Other East/Southeast Asian Indonesian
## Alabama 800 800 0 0
## Alaska 1200 0 0 0
## Arizona 1800 0 0 200
## Arkansas 600 800 0 0
## California 57000 37200 2000 1400
## Colorado 2400 1400 0 0
## LANGUAGE
## STATEFIP Other Malayan Filipino, Tagalog Micronesian, Polynesian Hawaiian
## Alabama 0 0 0 200
## Alaska 0 600 0 0
## Arizona 0 400 200 0
## Arkansas 0 400 0 0
## California 3800 149600 16000 1400
## Colorado 0 800 400 200
## LANGUAGE
## STATEFIP Arabic Near East Arabic dialect Hebrew, Israeli
## Alabama 1000 0 0
## Alaska 200 0 0
## Arizona 1200 0 0
## Arkansas 1000 0 0
## California 28400 2200 10400
## Colorado 3000 0 1000
## LANGUAGE
## STATEFIP Amharic, Ethiopian, etc Hamitic Sub-Saharan Africa African, n.s
## Alabama 0 0 400 0
## Alaska 0 0 200 0
## Arizona 200 0 0 200
## Arkansas 0 0 400 200
## California 1200 1000 3800 200
## Colorado 200 0 800 0
## LANGUAGE
## STATEFIP American Indian (all) Other or not reported
## Alabama 600 1000
## Alaska 20200 0
## Arizona 58200 1400
## Arkansas 400 2600
## California 8000 19200
## Colorado 1200 2000
write.csv(tableHLSpeakersState[,HLTableOrder],file=paste("Output05_TableHLSpeakersByState",fileYear,".csv",sep=""))
# Heritage language speakers by language and by PUMA
tableHLSpeakersCONSPUMA <- xtabs(PERWT ~ CONSPUMA + LANGUAGE, data=thisData[HLConditions,])
head(tableHLSpeakersCONSPUMA)## LANGUAGE
## CONSPUMA N/A or blank English German Yiddish, Jewish Dutch Swedish Danish
## 1 0 0 1600 0 200 0 0
## 2 0 0 800 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 600 0 0 200 0
## 5 0 0 4400 200 600 0 0
## 6 0 0 400 0 0 0 0
## LANGUAGE
## CONSPUMA Norwegian Icelandic Italian French Spanish Portuguese Rumanian Celtic
## 1 0 0 0 400 1600 0 0 0
## 2 0 0 0 600 600 0 0 0
## 3 0 0 0 0 400 0 0 0
## 4 0 0 0 800 2800 0 0 0
## 5 0 0 400 4000 7800 0 0 0
## 6 0 0 0 200 2400 0 0 0
## LANGUAGE
## CONSPUMA Greek Albanian Russian Ukrainian, Ruthenian, Little Russian Czech
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 200 0 0 0 0
## 5 400 0 0 0 0
## 6 0 0 0 0 0
## LANGUAGE
## CONSPUMA Polish Slovak Serbo-Croatian, Yugoslavian, Slavonian Slovene
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 200 0 0
## 5 0 0 200 0
## 6 0 0 0 0
## LANGUAGE
## CONSPUMA Lithuanian Other Balto-Slavic Armenian Persian, Iranian, Farsi
## 1 0 0 0 400
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 200 0 0 200
## 6 0 0 0 0
## LANGUAGE
## CONSPUMA Other Persian dialects Hindi and related Romany, Gypsy Finnish
## 1 0 0 0 0
## 2 0 200 0 0
## 3 0 0 0 0
## 4 0 400 0 0
## 5 0 1200 0 0
## 6 0 0 0 0
## LANGUAGE
## CONSPUMA Magyar, Hungarian Uralic Turkish Other Altaic
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 200 0
## 6 0 0 0 0
## LANGUAGE
## CONSPUMA Caucasian, Georgian, Avar Basque Dravidian Kurukh Burushaski Chinese
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 200
## 4 0 0 0 0 0 0
## 5 0 0 200 0 0 200
## 6 0 0 0 0 0 0
## LANGUAGE
## CONSPUMA Tibetan Burmese, Lisu, Lolo Thai, Siamese, Lao Japanese Korean
## 1 0 0 200 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 200
## 4 0 0 0 0 400
## 5 0 0 200 1000 200
## 6 0 0 0 0 0
## LANGUAGE
## CONSPUMA Vietnamese Other East/Southeast Asian Indonesian Other Malayan
## 1 600 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 200 0 0 0
## 6 0 0 0 0
## LANGUAGE
## CONSPUMA Filipino, Tagalog Micronesian, Polynesian Hawaiian Arabic
## 1 0 0 200 400
## 2 0 0 0 400
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 200
## 6 0 0 0 0
## LANGUAGE
## CONSPUMA Near East Arabic dialect Hebrew, Israeli Amharic, Ethiopian, etc
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## LANGUAGE
## CONSPUMA Hamitic Sub-Saharan Africa African, n.s American Indian (all)
## 1 0 0 0 200
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 200 0 0
## 5 0 200 0 400
## 6 0 0 0 28600
## LANGUAGE
## CONSPUMA Other or not reported
## 1 0
## 2 0
## 3 0
## 4 600
## 5 400
## 6 0
write.csv(tableHLSpeakersCONSPUMA[,HLTableOrder],file=paste("Output06_TableHLSpeakersByCONSPUMA",fileYear,".csv",sep=""))
tableHLSpeakersSpeakMETRO <- xtabs(PERWT ~ METRO + LANGUAGE, data=thisData[HLConditions,])
head(tableHLSpeakersSpeakMETRO)## LANGUAGE
## METRO N/A or blank
## Metropolitan status indeterminable (mixed) 0
## Not in metropolitan area 0
## In metropolitan area: In central/principal city 0
## In metropolitan area: Not in central/principal city 0
## In metropolitan area: Central/principal city status indeterminable (mixed) 0
## LANGUAGE
## METRO English
## Metropolitan status indeterminable (mixed) 0
## Not in metropolitan area 0
## In metropolitan area: In central/principal city 0
## In metropolitan area: Not in central/principal city 0
## In metropolitan area: Central/principal city status indeterminable (mixed) 0
## LANGUAGE
## METRO German
## Metropolitan status indeterminable (mixed) 124400
## Not in metropolitan area 241000
## In metropolitan area: In central/principal city 159000
## In metropolitan area: Not in central/principal city 298600
## In metropolitan area: Central/principal city status indeterminable (mixed) 162000
## LANGUAGE
## METRO Yiddish, Jewish
## Metropolitan status indeterminable (mixed) 1600
## Not in metropolitan area 2600
## In metropolitan area: In central/principal city 85200
## In metropolitan area: Not in central/principal city 57200
## In metropolitan area: Central/principal city status indeterminable (mixed) 9000
## LANGUAGE
## METRO Dutch
## Metropolitan status indeterminable (mixed) 7000
## Not in metropolitan area 17200
## In metropolitan area: In central/principal city 10800
## In metropolitan area: Not in central/principal city 31200
## In metropolitan area: Central/principal city status indeterminable (mixed) 17200
## LANGUAGE
## METRO Swedish
## Metropolitan status indeterminable (mixed) 10000
## Not in metropolitan area 13000
## In metropolitan area: In central/principal city 13600
## In metropolitan area: Not in central/principal city 17200
## In metropolitan area: Central/principal city status indeterminable (mixed) 7000
## LANGUAGE
## METRO Danish
## Metropolitan status indeterminable (mixed) 1200
## Not in metropolitan area 5800
## In metropolitan area: In central/principal city 3800
## In metropolitan area: Not in central/principal city 7800
## In metropolitan area: Central/principal city status indeterminable (mixed) 5400
## LANGUAGE
## METRO Norwegian
## Metropolitan status indeterminable (mixed) 26200
## Not in metropolitan area 20800
## In metropolitan area: In central/principal city 9400
## In metropolitan area: Not in central/principal city 15200
## In metropolitan area: Central/principal city status indeterminable (mixed) 7600
## LANGUAGE
## METRO Icelandic
## Metropolitan status indeterminable (mixed) 600
## Not in metropolitan area 600
## In metropolitan area: In central/principal city 1400
## In metropolitan area: Not in central/principal city 1200
## In metropolitan area: Central/principal city status indeterminable (mixed) 800
## LANGUAGE
## METRO Italian
## Metropolitan status indeterminable (mixed) 53800
## Not in metropolitan area 51400
## In metropolitan area: In central/principal city 354000
## In metropolitan area: Not in central/principal city 409800
## In metropolitan area: Central/principal city status indeterminable (mixed) 112400
## LANGUAGE
## METRO French
## Metropolitan status indeterminable (mixed) 193600
## Not in metropolitan area 261200
## In metropolitan area: In central/principal city 236000
## In metropolitan area: Not in central/principal city 248200
## In metropolitan area: Central/principal city status indeterminable (mixed) 222600
## LANGUAGE
## METRO Spanish
## Metropolitan status indeterminable (mixed) 438400
## Not in metropolitan area 621200
## In metropolitan area: In central/principal city 2581400
## In metropolitan area: Not in central/principal city 1753000
## In metropolitan area: Central/principal city status indeterminable (mixed) 1043400
## LANGUAGE
## METRO Portuguese
## Metropolitan status indeterminable (mixed) 25000
## Not in metropolitan area 16600
## In metropolitan area: In central/principal city 42400
## In metropolitan area: Not in central/principal city 57200
## In metropolitan area: Central/principal city status indeterminable (mixed) 65200
## LANGUAGE
## METRO Rumanian
## Metropolitan status indeterminable (mixed) 200
## Not in metropolitan area 0
## In metropolitan area: In central/principal city 8200
## In metropolitan area: Not in central/principal city 4200
## In metropolitan area: Central/principal city status indeterminable (mixed) 1200
## LANGUAGE
## METRO Celtic
## Metropolitan status indeterminable (mixed) 2600
## Not in metropolitan area 2800
## In metropolitan area: In central/principal city 3800
## In metropolitan area: Not in central/principal city 5000
## In metropolitan area: Central/principal city status indeterminable (mixed) 1800
## LANGUAGE
## METRO Greek
## Metropolitan status indeterminable (mixed) 11400
## Not in metropolitan area 7400
## In metropolitan area: In central/principal city 98000
## In metropolitan area: Not in central/principal city 84800
## In metropolitan area: Central/principal city status indeterminable (mixed) 26400
## LANGUAGE
## METRO Albanian
## Metropolitan status indeterminable (mixed) 1000
## Not in metropolitan area 0
## In metropolitan area: In central/principal city 4200
## In metropolitan area: Not in central/principal city 2800
## In metropolitan area: Central/principal city status indeterminable (mixed) 200
## LANGUAGE
## METRO Russian
## Metropolitan status indeterminable (mixed) 2400
## Not in metropolitan area 3800
## In metropolitan area: In central/principal city 43200
## In metropolitan area: Not in central/principal city 27000
## In metropolitan area: Central/principal city status indeterminable (mixed) 9800
## LANGUAGE
## METRO Ukrainian, Ruthenian, Little Russian
## Metropolitan status indeterminable (mixed) 3200
## Not in metropolitan area 2600
## In metropolitan area: In central/principal city 12000
## In metropolitan area: Not in central/principal city 22400
## In metropolitan area: Central/principal city status indeterminable (mixed) 7400
## LANGUAGE
## METRO Czech
## Metropolitan status indeterminable (mixed) 11200
## Not in metropolitan area 29400
## In metropolitan area: In central/principal city 14400
## In metropolitan area: Not in central/principal city 24400
## In metropolitan area: Central/principal city status indeterminable (mixed) 19200
## LANGUAGE
## METRO Polish
## Metropolitan status indeterminable (mixed) 41800
## Not in metropolitan area 48800
## In metropolitan area: In central/principal city 199800
## In metropolitan area: Not in central/principal city 230600
## In metropolitan area: Central/principal city status indeterminable (mixed) 78600
## LANGUAGE
## METRO Slovak
## Metropolitan status indeterminable (mixed) 5600
## Not in metropolitan area 7200
## In metropolitan area: In central/principal city 10800
## In metropolitan area: Not in central/principal city 29200
## In metropolitan area: Central/principal city status indeterminable (mixed) 13000
## LANGUAGE
## METRO Serbo-Croatian, Yugoslavian, Slavonian
## Metropolitan status indeterminable (mixed) 3400
## Not in metropolitan area 5000
## In metropolitan area: In central/principal city 38000
## In metropolitan area: Not in central/principal city 42000
## In metropolitan area: Central/principal city status indeterminable (mixed) 8600
## LANGUAGE
## METRO Slovene
## Metropolitan status indeterminable (mixed) 1000
## Not in metropolitan area 800
## In metropolitan area: In central/principal city 2600
## In metropolitan area: Not in central/principal city 5600
## In metropolitan area: Central/principal city status indeterminable (mixed) 1000
## LANGUAGE
## METRO Lithuanian
## Metropolitan status indeterminable (mixed) 5200
## Not in metropolitan area 2800
## In metropolitan area: In central/principal city 18800
## In metropolitan area: Not in central/principal city 15000
## In metropolitan area: Central/principal city status indeterminable (mixed) 6200
## LANGUAGE
## METRO Other Balto-Slavic
## Metropolitan status indeterminable (mixed) 600
## Not in metropolitan area 400
## In metropolitan area: In central/principal city 2400
## In metropolitan area: Not in central/principal city 2200
## In metropolitan area: Central/principal city status indeterminable (mixed) 1200
## LANGUAGE
## METRO Armenian
## Metropolitan status indeterminable (mixed) 2400
## Not in metropolitan area 1200
## In metropolitan area: In central/principal city 33600
## In metropolitan area: Not in central/principal city 23200
## In metropolitan area: Central/principal city status indeterminable (mixed) 4200
## LANGUAGE
## METRO Persian, Iranian, Farsi
## Metropolitan status indeterminable (mixed) 4400
## Not in metropolitan area 5000
## In metropolitan area: In central/principal city 30800
## In metropolitan area: Not in central/principal city 27600
## In metropolitan area: Central/principal city status indeterminable (mixed) 9800
## LANGUAGE
## METRO Other Persian dialects
## Metropolitan status indeterminable (mixed) 0
## Not in metropolitan area 400
## In metropolitan area: In central/principal city 400
## In metropolitan area: Not in central/principal city 1000
## In metropolitan area: Central/principal city status indeterminable (mixed) 0
## LANGUAGE
## METRO Hindi and related
## Metropolitan status indeterminable (mixed) 5000
## Not in metropolitan area 6600
## In metropolitan area: In central/principal city 56200
## In metropolitan area: Not in central/principal city 77600
## In metropolitan area: Central/principal city status indeterminable (mixed) 20000
## LANGUAGE
## METRO Romany, Gypsy
## Metropolitan status indeterminable (mixed) 0
## Not in metropolitan area 0
## In metropolitan area: In central/principal city 800
## In metropolitan area: Not in central/principal city 1400
## In metropolitan area: Central/principal city status indeterminable (mixed) 0
## LANGUAGE
## METRO Finnish
## Metropolitan status indeterminable (mixed) 7600
## Not in metropolitan area 19800
## In metropolitan area: In central/principal city 8000
## In metropolitan area: Not in central/principal city 13200
## In metropolitan area: Central/principal city status indeterminable (mixed) 3800
## LANGUAGE
## METRO Magyar, Hungarian
## Metropolitan status indeterminable (mixed) 4800
## Not in metropolitan area 7000
## In metropolitan area: In central/principal city 27200
## In metropolitan area: Not in central/principal city 38400
## In metropolitan area: Central/principal city status indeterminable (mixed) 13400
## LANGUAGE
## METRO Uralic
## Metropolitan status indeterminable (mixed) 0
## Not in metropolitan area 200
## In metropolitan area: In central/principal city 400
## In metropolitan area: Not in central/principal city 200
## In metropolitan area: Central/principal city status indeterminable (mixed) 0
## LANGUAGE
## METRO Turkish
## Metropolitan status indeterminable (mixed) 200
## Not in metropolitan area 400
## In metropolitan area: In central/principal city 8000
## In metropolitan area: Not in central/principal city 8000
## In metropolitan area: Central/principal city status indeterminable (mixed) 3000
## LANGUAGE
## METRO Other Altaic
## Metropolitan status indeterminable (mixed) 600
## Not in metropolitan area 200
## In metropolitan area: In central/principal city 800
## In metropolitan area: Not in central/principal city 0
## In metropolitan area: Central/principal city status indeterminable (mixed) 600
## LANGUAGE
## METRO Caucasian, Georgian, Avar
## Metropolitan status indeterminable (mixed) 0
## Not in metropolitan area 0
## In metropolitan area: In central/principal city 200
## In metropolitan area: Not in central/principal city 200
## In metropolitan area: Central/principal city status indeterminable (mixed) 200
## LANGUAGE
## METRO Basque
## Metropolitan status indeterminable (mixed) 0
## Not in metropolitan area 800
## In metropolitan area: In central/principal city 200
## In metropolitan area: Not in central/principal city 800
## In metropolitan area: Central/principal city status indeterminable (mixed) 1000
## LANGUAGE
## METRO Dravidian
## Metropolitan status indeterminable (mixed) 2000
## Not in metropolitan area 1000
## In metropolitan area: In central/principal city 8800
## In metropolitan area: Not in central/principal city 12400
## In metropolitan area: Central/principal city status indeterminable (mixed) 3000
## LANGUAGE
## METRO Kurukh
## Metropolitan status indeterminable (mixed) 0
## Not in metropolitan area 0
## In metropolitan area: In central/principal city 1000
## In metropolitan area: Not in central/principal city 0
## In metropolitan area: Central/principal city status indeterminable (mixed) 400
## LANGUAGE
## METRO Burushaski
## Metropolitan status indeterminable (mixed) 0
## Not in metropolitan area 0
## In metropolitan area: In central/principal city 0
## In metropolitan area: Not in central/principal city 0
## In metropolitan area: Central/principal city status indeterminable (mixed) 0
## LANGUAGE
## METRO Chinese
## Metropolitan status indeterminable (mixed) 7400
## Not in metropolitan area 13000
## In metropolitan area: In central/principal city 200000
## In metropolitan area: Not in central/principal city 133200
## In metropolitan area: Central/principal city status indeterminable (mixed) 31400
## LANGUAGE
## METRO Tibetan
## Metropolitan status indeterminable (mixed) 1600
## Not in metropolitan area 0
## In metropolitan area: In central/principal city 3200
## In metropolitan area: Not in central/principal city 400
## In metropolitan area: Central/principal city status indeterminable (mixed) 2000
## LANGUAGE
## METRO Burmese, Lisu, Lolo
## Metropolitan status indeterminable (mixed) 0
## Not in metropolitan area 0
## In metropolitan area: In central/principal city 600
## In metropolitan area: Not in central/principal city 2400
## In metropolitan area: Central/principal city status indeterminable (mixed) 0
## LANGUAGE
## METRO Thai, Siamese, Lao
## Metropolitan status indeterminable (mixed) 3400
## Not in metropolitan area 4000
## In metropolitan area: In central/principal city 22400
## In metropolitan area: Not in central/principal city 17600
## In metropolitan area: Central/principal city status indeterminable (mixed) 6200
## LANGUAGE
## METRO Japanese
## Metropolitan status indeterminable (mixed) 5600
## Not in metropolitan area 24800
## In metropolitan area: In central/principal city 102600
## In metropolitan area: Not in central/principal city 84200
## In metropolitan area: Central/principal city status indeterminable (mixed) 20000
## LANGUAGE
## METRO Korean
## Metropolitan status indeterminable (mixed) 7600
## Not in metropolitan area 9000
## In metropolitan area: In central/principal city 73200
## In metropolitan area: Not in central/principal city 82800
## In metropolitan area: Central/principal city status indeterminable (mixed) 25400
## LANGUAGE
## METRO Vietnamese
## Metropolitan status indeterminable (mixed) 6000
## Not in metropolitan area 6800
## In metropolitan area: In central/principal city 45000
## In metropolitan area: Not in central/principal city 35400
## In metropolitan area: Central/principal city status indeterminable (mixed) 17200
## LANGUAGE
## METRO Other East/Southeast Asian
## Metropolitan status indeterminable (mixed) 600
## Not in metropolitan area 1000
## In metropolitan area: In central/principal city 1000
## In metropolitan area: Not in central/principal city 1800
## In metropolitan area: Central/principal city status indeterminable (mixed) 1200
## LANGUAGE
## METRO Indonesian
## Metropolitan status indeterminable (mixed) 200
## Not in metropolitan area 1800
## In metropolitan area: In central/principal city 3200
## In metropolitan area: Not in central/principal city 800
## In metropolitan area: Central/principal city status indeterminable (mixed) 2200
## LANGUAGE
## METRO Other Malayan
## Metropolitan status indeterminable (mixed) 800
## Not in metropolitan area 600
## In metropolitan area: In central/principal city 1400
## In metropolitan area: Not in central/principal city 4800
## In metropolitan area: Central/principal city status indeterminable (mixed) 1200
## LANGUAGE
## METRO Filipino, Tagalog
## Metropolitan status indeterminable (mixed) 7200
## Not in metropolitan area 20400
## In metropolitan area: In central/principal city 146000
## In metropolitan area: Not in central/principal city 130200
## In metropolitan area: Central/principal city status indeterminable (mixed) 28000
## LANGUAGE
## METRO Micronesian, Polynesian
## Metropolitan status indeterminable (mixed) 800
## Not in metropolitan area 2000
## In metropolitan area: In central/principal city 14200
## In metropolitan area: Not in central/principal city 11600
## In metropolitan area: Central/principal city status indeterminable (mixed) 6400
## LANGUAGE
## METRO Hawaiian
## Metropolitan status indeterminable (mixed) 400
## Not in metropolitan area 1200
## In metropolitan area: In central/principal city 2000
## In metropolitan area: Not in central/principal city 4000
## In metropolitan area: Central/principal city status indeterminable (mixed) 200
## LANGUAGE
## METRO Arabic
## Metropolitan status indeterminable (mixed) 5000
## Not in metropolitan area 8400
## In metropolitan area: In central/principal city 55800
## In metropolitan area: Not in central/principal city 56800
## In metropolitan area: Central/principal city status indeterminable (mixed) 19200
## LANGUAGE
## METRO Near East Arabic dialect
## Metropolitan status indeterminable (mixed) 0
## Not in metropolitan area 200
## In metropolitan area: In central/principal city 2800
## In metropolitan area: Not in central/principal city 2800
## In metropolitan area: Central/principal city status indeterminable (mixed) 600
## LANGUAGE
## METRO Hebrew, Israeli
## Metropolitan status indeterminable (mixed) 1200
## Not in metropolitan area 2800
## In metropolitan area: In central/principal city 32200
## In metropolitan area: Not in central/principal city 19600
## In metropolitan area: Central/principal city status indeterminable (mixed) 4000
## LANGUAGE
## METRO Amharic, Ethiopian, etc
## Metropolitan status indeterminable (mixed) 0
## Not in metropolitan area 200
## In metropolitan area: In central/principal city 4600
## In metropolitan area: Not in central/principal city 3000
## In metropolitan area: Central/principal city status indeterminable (mixed) 200
## LANGUAGE
## METRO Hamitic
## Metropolitan status indeterminable (mixed) 0
## Not in metropolitan area 0
## In metropolitan area: In central/principal city 1800
## In metropolitan area: Not in central/principal city 800
## In metropolitan area: Central/principal city status indeterminable (mixed) 600
## LANGUAGE
## METRO Sub-Saharan Africa
## Metropolitan status indeterminable (mixed) 1800
## Not in metropolitan area 2400
## In metropolitan area: In central/principal city 21600
## In metropolitan area: Not in central/principal city 8200
## In metropolitan area: Central/principal city status indeterminable (mixed) 4200
## LANGUAGE
## METRO African, n.s
## Metropolitan status indeterminable (mixed) 0
## Not in metropolitan area 200
## In metropolitan area: In central/principal city 2800
## In metropolitan area: Not in central/principal city 600
## In metropolitan area: Central/principal city status indeterminable (mixed) 400
## LANGUAGE
## METRO American Indian (all)
## Metropolitan status indeterminable (mixed) 17000
## Not in metropolitan area 144800
## In metropolitan area: In central/principal city 22200
## In metropolitan area: Not in central/principal city 26400
## In metropolitan area: Central/principal city status indeterminable (mixed) 11200
## LANGUAGE
## METRO Other or not reported
## Metropolitan status indeterminable (mixed) 16200
## Not in metropolitan area 32800
## In metropolitan area: In central/principal city 41000
## In metropolitan area: Not in central/principal city 60800
## In metropolitan area: Central/principal city status indeterminable (mixed) 27600
write.csv(tableHLSpeakersSpeakMETRO[,HLTableOrder],file=paste("Output07_TableHLSpeakersByMetro",fileYear,".csv",sep=""))
sum(thisData[thisData$USBorn==FALSE,"PERWT"])/sum(thisData[,"PERWT"])## [1] 0.06691623