In the wake of the Great Recession of 2009, there has been a good deal of focus on employment statistics, one of the most important metrics policymakers use to gauge the overall strength of the economy. In the United States, the government measures unemployment using the Current Population Survey (CPS), which collects demographic and employment information from a wide range of Americans each month. In this exercise, we will employ the topics reviewed in the lectures as well as a few new techniques using the September 2013 version of this rich, nationally representative dataset (available online.
The observations in the dataset represent people surveyed in the September 2013 CPS who actually completed a survey. While the full dataset has 385 variables, in this exercise we will use a more compact version of the dataset, CPSData.csv, which has the following variables:
PeopleInHousehold: The number of people in the interviewee’s household.
Region: The census region where the interviewee lives.
State: The state where the interviewee lives.
MetroAreaCode: A code that identifies the metropolitan area in which the interviewee lives (missing if the interviewee does not live in a metropolitan area). The mapping from codes to names of metropolitan areas is provided in the file MetroAreaCodes.csv
Age: The age, in years, of the interviewee. 80 represents people aged 80-84, and 85 represents people aged 85 and higher.
**Married*: The marriage status of the interviewee.
Sex: The sex of the interviewee.
Education: The maximum level of education obtained by the interviewee.
**Race*: The race of the interviewee.
Hispanic: Whether the interviewee is of Hispanic ethnicity.
CountryOfBirthCode: A code identifying the country of birth of the interviewee. The mapping from codes to names of countries is provided in the file CountryCodes.csv
Citizenship: The United States citizenship status of the interviewee.
**EmploymentStatus*: The status of employment of the interviewee.
Industry: The industry of employment of the interviewee (only available if they are employed).
In this problem, we’ll take a look at how the stock dynamics of these companies have changed over time.
Load the dataset from CPSData.csv into a data frame called CPS, and view the dataset with the summary() and str() commands.
CPS = read.csv("CPSData.csv")
MetroAreaMap = read.csv("MetroAreaCodes.csv")
CountryMap = read.csv("CountryCodes.csv")str(CPS)
## 'data.frame': 131302 obs. of 14 variables:
## $ PeopleInHousehold : int 1 3 3 3 3 3 3 2 2 2 ...
## $ Region : Factor w/ 4 levels "Midwest","Northeast",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ State : Factor w/ 51 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ MetroAreaCode : int 26620 13820 13820 13820 26620 26620 26620 33660 33660 26620 ...
## $ Age : int 85 21 37 18 52 24 26 71 43 52 ...
## $ Married : Factor w/ 5 levels "Divorced","Married",..: 5 3 3 3 5 3 3 1 1 3 ...
## $ Sex : Factor w/ 2 levels "Female","Male": 1 2 1 2 1 2 2 1 2 2 ...
## $ Education : Factor w/ 8 levels "Associate degree",..: 1 4 4 6 1 2 4 4 4 2 ...
## $ Race : Factor w/ 6 levels "American Indian",..: 6 3 3 3 6 6 6 6 6 6 ...
## $ Hispanic : int 0 0 0 0 0 0 0 0 0 0 ...
## $ CountryOfBirthCode: int 57 57 57 57 57 57 57 57 57 57 ...
## $ Citizenship : Factor w/ 3 levels "Citizen, Native",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ EmploymentStatus : Factor w/ 5 levels "Disabled","Employed",..: 4 5 1 3 2 2 2 2 3 2 ...
## $ Industry : Factor w/ 14 levels "Agriculture, forestry, fishing, and hunting",..: NA 11 NA NA 11 4 14 4 NA 12 ...Explanation: From str(CPS), we can read that there are 131302 interviewees.
summary(CPS)
## PeopleInHousehold Region State MetroAreaCode Age Married Sex Education
## Min. : 1.000 Midwest :30684 California :11570 Min. :10420 Min. : 0.00 Divorced :11151 Female:67481 High school :30906
## 1st Qu.: 2.000 Northeast:25939 Texas : 7077 1st Qu.:21780 1st Qu.:19.00 Married :55509 Male :63821 Bachelor's degree :19443
## Median : 3.000 South :41502 New York : 5595 Median :34740 Median :39.00 Never Married:30772 Some college, no degree:18863
## Mean : 3.284 West :33177 Florida : 5149 Mean :35075 Mean :38.83 Separated : 2027 No high school diploma :16095
## 3rd Qu.: 4.000 Pennsylvania: 3930 3rd Qu.:41860 3rd Qu.:57.00 Widowed : 6505 Associate degree : 9913
## Max. :15.000 Illinois : 3912 Max. :79600 Max. :85.00 NA's :25338 (Other) :10744
## (Other) :94069 NA's :34238 NA's :25338
## Race Hispanic CountryOfBirthCode Citizenship EmploymentStatus
## American Indian : 1433 Min. :0.0000 Min. : 57.00 Citizen, Native :116639 Disabled : 5712
## Asian : 6520 1st Qu.:0.0000 1st Qu.: 57.00 Citizen, Naturalized: 7073 Employed :61733
## Black : 13913 Median :0.0000 Median : 57.00 Non-Citizen : 7590 Not in Labor Force:15246
## Multiracial : 2897 Mean :0.1393 Mean : 82.68 Retired :18619
## Pacific Islander: 618 3rd Qu.:0.0000 3rd Qu.: 57.00 Unemployed : 4203
## White :105921 Max. :1.0000 Max. :555.00 NA's :25789
##
## Industry
## Educational and health services :15017
## Trade : 8933
## Professional and business services: 7519
## Manufacturing : 6791
## Leisure and hospitality : 6364
## (Other) :21618
## NA's :65060
table(CPS$Industry)
##
## Agriculture, forestry, fishing, and hunting Armed forces Construction
## 1307 29 4387
## Educational and health services Financial Information
## 15017 4347 1328
## Leisure and hospitality Manufacturing Mining
## 6364 6791 550
## Other services Professional and business services Public administration
## 3224 7519 3186
## Trade Transportation and utilities
## 8933 3260Explanation: The output of summary(CPS) orders the levels of a factor variable like Industry from largest to smallest, so we can see that “Educational and health services” is the most common Industry. table(CPS$Industry) would have provided the breakdown across all industries.
sort(table(CPS$State))
##
## New Mexico Montana Mississippi Alabama West Virginia Arkansas Louisiana
## 1102 1214 1230 1376 1409 1421 1450
## Idaho Oklahoma Arizona Alaska Wyoming North Dakota South Carolina
## 1518 1523 1528 1590 1624 1645 1658
## Tennessee District of Columbia Kentucky Utah Nevada Vermont Kansas
## 1784 1791 1841 1842 1856 1890 1935
## Oregon Nebraska Massachusetts South Dakota Indiana Hawaii Missouri
## 1943 1949 1987 2000 2004 2099 2145
## Rhode Island Delaware Maine Washington Iowa New Jersey North Carolina
## 2209 2214 2263 2366 2528 2567 2619
## New Hampshire Wisconsin Georgia Connecticut Colorado Virginia Michigan
## 2662 2686 2807 2836 2925 2953 3063
## Minnesota Maryland Ohio Illinois Pennsylvania Florida New York
## 3139 3200 3678 3912 3930 5149 5595
## Texas California
## 7077 11570New Mexico.
sort(table(CPS$State))
##
## New Mexico Montana Mississippi Alabama West Virginia Arkansas Louisiana
## 1102 1214 1230 1376 1409 1421 1450
## Idaho Oklahoma Arizona Alaska Wyoming North Dakota South Carolina
## 1518 1523 1528 1590 1624 1645 1658
## Tennessee District of Columbia Kentucky Utah Nevada Vermont Kansas
## 1784 1791 1841 1842 1856 1890 1935
## Oregon Nebraska Massachusetts South Dakota Indiana Hawaii Missouri
## 1943 1949 1987 2000 2004 2099 2145
## Rhode Island Delaware Maine Washington Iowa New Jersey North Carolina
## 2209 2214 2263 2366 2528 2567 2619
## New Hampshire Wisconsin Georgia Connecticut Colorado Virginia Michigan
## 2662 2686 2807 2836 2925 2953 3063
## Minnesota Maryland Ohio Illinois Pennsylvania Florida New York
## 3139 3200 3678 3912 3930 5149 5595
## Texas California
## 7077 11570California.
table(CPS$Citizenship)
##
## Citizen, Native Citizen, Naturalized Non-Citizen
## 116639 7073 7590
m = table(CPS$Citizenship)
(m[1]+m[2])/(m[1]+m[2]+m[3])
## Citizen, Native
## 0.9421943Explanation: From table(CPS$Citizenship), we see that 123,712 of the 131,302 interviewees are citizens of the United States (either native or naturalized). This is a proportion of 123712/131302=0.942.
table(CPS$Race, CPS$Hispanic) >=250
##
## 0 1
## American Indian TRUE TRUE
## Asian TRUE FALSE
## Black TRUE TRUE
## Multiracial TRUE TRUE
## Pacific Islander TRUE FALSE
## White TRUE TRUEExplanation: The breakdown of race and Hispanic ethnicity can be obtained with table(CPS\(Race, CPS\)Hispanic).
summary(CPS)
## PeopleInHousehold Region State MetroAreaCode Age Married Sex Education
## Min. : 1.000 Midwest :30684 California :11570 Min. :10420 Min. : 0.00 Divorced :11151 Female:67481 High school :30906
## 1st Qu.: 2.000 Northeast:25939 Texas : 7077 1st Qu.:21780 1st Qu.:19.00 Married :55509 Male :63821 Bachelor's degree :19443
## Median : 3.000 South :41502 New York : 5595 Median :34740 Median :39.00 Never Married:30772 Some college, no degree:18863
## Mean : 3.284 West :33177 Florida : 5149 Mean :35075 Mean :38.83 Separated : 2027 No high school diploma :16095
## 3rd Qu.: 4.000 Pennsylvania: 3930 3rd Qu.:41860 3rd Qu.:57.00 Widowed : 6505 Associate degree : 9913
## Max. :15.000 Illinois : 3912 Max. :79600 Max. :85.00 NA's :25338 (Other) :10744
## (Other) :94069 NA's :34238 NA's :25338
## Race Hispanic CountryOfBirthCode Citizenship EmploymentStatus
## American Indian : 1433 Min. :0.0000 Min. : 57.00 Citizen, Native :116639 Disabled : 5712
## Asian : 6520 1st Qu.:0.0000 1st Qu.: 57.00 Citizen, Naturalized: 7073 Employed :61733
## Black : 13913 Median :0.0000 Median : 57.00 Non-Citizen : 7590 Not in Labor Force:15246
## Multiracial : 2897 Mean :0.1393 Mean : 82.68 Retired :18619
## Pacific Islander: 618 3rd Qu.:0.0000 3rd Qu.: 57.00 Unemployed : 4203
## White :105921 Max. :1.0000 Max. :555.00 NA's :25789
##
## Industry
## Educational and health services :15017
## Trade : 8933
## Professional and business services: 7519
## Manufacturing : 6791
## Leisure and hospitality : 6364
## (Other) :21618
## NA's :65060Explanation: This can be read from the output of summary(CPS).
table(CPS$Region, is.na(CPS$Married))
##
## FALSE TRUE
## Midwest 24609 6075
## Northeast 21432 4507
## South 33535 7967
## West 26388 6789
table(CPS$Sex, is.na(CPS$Married))
##
## FALSE TRUE
## Female 55264 12217
## Male 50700 13121
table(CPS$Age, is.na(CPS$Married))
##
## FALSE TRUE
## 0 0 1283
## 1 0 1559
## 2 0 1574
## 3 0 1693
## 4 0 1695
## 5 0 1795
## 6 0 1721
## 7 0 1681
## 8 0 1729
## 9 0 1748
## 10 0 1750
## 11 0 1721
## 12 0 1797
## 13 0 1802
## 14 0 1790
## 15 1795 0
## 16 1751 0
## 17 1764 0
## 18 1596 0
## 19 1517 0
## 20 1398 0
## 21 1525 0
## 22 1536 0
## 23 1638 0
## 24 1627 0
## 25 1604 0
## 26 1643 0
## 27 1657 0
## 28 1736 0
## 29 1645 0
## 30 1854 0
## 31 1762 0
## 32 1790 0
## 33 1804 0
## 34 1653 0
## 35 1716 0
## 36 1663 0
## 37 1531 0
## 38 1530 0
## 39 1542 0
## 40 1571 0
## 41 1673 0
## 42 1711 0
## 43 1819 0
## 44 1764 0
## 45 1749 0
## 46 1665 0
## 47 1647 0
## 48 1791 0
## 49 1989 0
## 50 1966 0
## 51 1931 0
## 52 1935 0
## 53 1994 0
## 54 1912 0
## 55 1895 0
## 56 1935 0
## 57 1827 0
## 58 1874 0
## 59 1758 0
## 60 1746 0
## 61 1735 0
## 62 1595 0
## 63 1596 0
## 64 1519 0
## 65 1569 0
## 66 1577 0
## 67 1227 0
## 68 1130 0
## 69 1062 0
## 70 1195 0
## 71 1031 0
## 72 941 0
## 73 896 0
## 74 842 0
## 75 763 0
## 76 729 0
## 77 698 0
## 78 659 0
## 79 661 0
## 80 2664 0
## 85 2446 0
table(CPS$Citizenship, is.na(CPS$Married))
##
## FALSE TRUE
## Citizen, Native 91956 24683
## Citizen, Naturalized 6910 163
## Non-Citizen 7098 492Explanation: For each possible value of Region, Sex, and Citizenship, there are both interviewees with missing and non-missing Married values. However, Married is missing for all interviewees Aged 0-14 and is present for all interviewees aged 15 and older. This is because the CPS does not ask about marriage status for interviewees 14 and younger.
table(CPS$State, is.na(CPS$MetroAreaCode))
##
## FALSE TRUE
## Alabama 1020 356
## Alaska 0 1590
## Arizona 1327 201
## Arkansas 724 697
## California 11333 237
## Colorado 2545 380
## Connecticut 2593 243
## Delaware 1696 518
## District of Columbia 1791 0
## Florida 4947 202
## Georgia 2250 557
## Hawaii 1576 523
## Idaho 761 757
## Illinois 3473 439
## Indiana 1420 584
## Iowa 1297 1231
## Kansas 1234 701
## Kentucky 908 933
## Louisiana 1216 234
## Maine 909 1354
## Maryland 2978 222
## Massachusetts 1858 129
## Michigan 2517 546
## Minnesota 2150 989
## Mississippi 376 854
## Missouri 1440 705
## Montana 199 1015
## Nebraska 816 1133
## Nevada 1609 247
## New Hampshire 1148 1514
## New Jersey 2567 0
## New Mexico 832 270
## New York 5144 451
## North Carolina 1642 977
## North Dakota 432 1213
## Ohio 2754 924
## Oklahoma 1024 499
## Oregon 1519 424
## Pennsylvania 3245 685
## Rhode Island 2209 0
## South Carolina 1139 519
## South Dakota 595 1405
## Tennessee 1149 635
## Texas 6060 1017
## Utah 1455 387
## Vermont 657 1233
## Virginia 2367 586
## Washington 1937 429
## West Virginia 344 1065
## Wisconsin 1882 804
## Wyoming 0 1624table(CPS$State, is.na(CPS$MetroAreaCode))
##
## FALSE TRUE
## Alabama 1020 356
## Alaska 0 1590
## Arizona 1327 201
## Arkansas 724 697
## California 11333 237
## Colorado 2545 380
## Connecticut 2593 243
## Delaware 1696 518
## District of Columbia 1791 0
## Florida 4947 202
## Georgia 2250 557
## Hawaii 1576 523
## Idaho 761 757
## Illinois 3473 439
## Indiana 1420 584
## Iowa 1297 1231
## Kansas 1234 701
## Kentucky 908 933
## Louisiana 1216 234
## Maine 909 1354
## Maryland 2978 222
## Massachusetts 1858 129
## Michigan 2517 546
## Minnesota 2150 989
## Mississippi 376 854
## Missouri 1440 705
## Montana 199 1015
## Nebraska 816 1133
## Nevada 1609 247
## New Hampshire 1148 1514
## New Jersey 2567 0
## New Mexico 832 270
## New York 5144 451
## North Carolina 1642 977
## North Dakota 432 1213
## Ohio 2754 924
## Oklahoma 1024 499
## Oregon 1519 424
## Pennsylvania 3245 685
## Rhode Island 2209 0
## South Carolina 1139 519
## South Dakota 595 1405
## Tennessee 1149 635
## Texas 6060 1017
## Utah 1455 387
## Vermont 657 1233
## Virginia 2367 586
## Washington 1937 429
## West Virginia 344 1065
## Wisconsin 1882 804
## Wyoming 0 1624m = table(CPS$Region, is.na(CPS$MetroAreaCode))
prop.table(m,1)
##
## FALSE TRUE
## Midwest 0.6521314 0.3478686
## Northeast 0.7837619 0.2162381
## South 0.7621560 0.2378440
## West 0.7563372 0.2436628Explanation: We can then compute the proportion of interviewees in each region that live in a non-metropolitan area: 34.8% in the Midwest, 21.6% in the Northeast, 23.8% in the South, and 24.4% in the West.
sort(tapply(is.na(CPS$MetroAreaCode), CPS$State, mean))
## District of Columbia New Jersey Rhode Island California Florida Massachusetts Maryland
## 0.00000000 0.00000000 0.00000000 0.02048401 0.03923092 0.06492199 0.06937500
## New York Connecticut Illinois Colorado Arizona Nevada Texas
## 0.08060769 0.08568406 0.11221881 0.12991453 0.13154450 0.13308190 0.14370496
## Louisiana Pennsylvania Michigan Washington Georgia Virginia Utah
## 0.16137931 0.17430025 0.17825661 0.18131868 0.19843249 0.19844226 0.21009772
## Oregon Delaware New Mexico Hawaii Ohio Alabama Indiana
## 0.21821925 0.23396567 0.24500907 0.24916627 0.25122349 0.25872093 0.29141717
## Wisconsin South Carolina Minnesota Oklahoma Missouri Tennessee Kansas
## 0.29932986 0.31302774 0.31506849 0.32764281 0.32867133 0.35594170 0.36227390
## North Carolina Iowa Arkansas Idaho Kentucky New Hampshire Nebraska
## 0.37304315 0.48694620 0.49049965 0.49868248 0.50678979 0.56874530 0.58132376
## Maine Vermont Mississippi South Dakota North Dakota West Virginia Montana
## 0.59832081 0.65238095 0.69430894 0.70250000 0.73738602 0.75585522 0.83607908
## Alaska Wyoming
## 1.00000000 1.00000000Wisconsin.
sort(tapply(is.na(CPS$MetroAreaCode), CPS$State, mean))
## District of Columbia New Jersey Rhode Island California Florida Massachusetts Maryland
## 0.00000000 0.00000000 0.00000000 0.02048401 0.03923092 0.06492199 0.06937500
## New York Connecticut Illinois Colorado Arizona Nevada Texas
## 0.08060769 0.08568406 0.11221881 0.12991453 0.13154450 0.13308190 0.14370496
## Louisiana Pennsylvania Michigan Washington Georgia Virginia Utah
## 0.16137931 0.17430025 0.17825661 0.18131868 0.19843249 0.19844226 0.21009772
## Oregon Delaware New Mexico Hawaii Ohio Alabama Indiana
## 0.21821925 0.23396567 0.24500907 0.24916627 0.25122349 0.25872093 0.29141717
## Wisconsin South Carolina Minnesota Oklahoma Missouri Tennessee Kansas
## 0.29932986 0.31302774 0.31506849 0.32764281 0.32867133 0.35594170 0.36227390
## North Carolina Iowa Arkansas Idaho Kentucky New Hampshire Nebraska
## 0.37304315 0.48694620 0.49049965 0.49868248 0.50678979 0.56874530 0.58132376
## Maine Vermont Mississippi South Dakota North Dakota West Virginia Montana
## 0.59832081 0.65238095 0.69430894 0.70250000 0.73738602 0.75585522 0.83607908
## Alaska Wyoming
## 1.00000000 1.00000000Montana
Codes like MetroAreaCode and CountryOfBirthCode are a compact way to encode factor variables with text as their possible values, and they are therefore quite common in survey datasets. In fact, all but one of the variables in this dataset were actually stored by a numeric code in the original CPS datafile.
When analyzing a variable stored by a numeric code, we will often want to convert it into the values the codes represent. To do this, we will use a dictionary, which maps the the code to the actual value of the variable. We have provided dictionaries MetroAreaCodes.csv and CountryCodes.csv, which respectively map MetroAreaCode and CountryOfBirthCode into their true values. Read these two dictionaries into data frames MetroAreaMap and CountryMap.
str(MetroAreaMap)
## 'data.frame': 271 obs. of 2 variables:
## $ Code : int 460 3000 3160 3610 3720 6450 10420 10500 10580 10740 ...
## $ MetroArea: Factor w/ 271 levels "Akron, OH","Albany-Schenectady-Troy, NY",..: 12 92 97 117 122 195 1 3 2 4 ...str(CountryMap)
## 'data.frame': 149 obs. of 2 variables:
## $ Code : int 57 66 73 78 96 100 102 103 104 105 ...
## $ Country: Factor w/ 149 levels "Afghanistan",..: 139 57 105 135 97 3 11 18 24 37 ...To merge in the metropolitan areas, we want to connect the field MetroAreaCode from the CPS data frame with the field Code in MetroAreaMap. The following command merges the two data frames on these columns, overwriting the CPS data frame with the result:
CPS = merge(CPS, MetroAreaMap, by.x="MetroAreaCode", by.y="Code", all.x=TRUE)summary(CPS)
## MetroAreaCode PeopleInHousehold Region State Age Married Sex Education
## Min. :10420 Min. : 1.000 Midwest :30684 California :11570 Min. : 0.00 Divorced :11151 Female:67481 High school :30906
## 1st Qu.:21780 1st Qu.: 2.000 Northeast:25939 Texas : 7077 1st Qu.:19.00 Married :55509 Male :63821 Bachelor's degree :19443
## Median :34740 Median : 3.000 South :41502 New York : 5595 Median :39.00 Never Married:30772 Some college, no degree:18863
## Mean :35075 Mean : 3.284 West :33177 Florida : 5149 Mean :38.83 Separated : 2027 No high school diploma :16095
## 3rd Qu.:41860 3rd Qu.: 4.000 Pennsylvania: 3930 3rd Qu.:57.00 Widowed : 6505 Associate degree : 9913
## Max. :79600 Max. :15.000 Illinois : 3912 Max. :85.00 NA's :25338 (Other) :10744
## NA's :34238 (Other) :94069 NA's :25338
## Race Hispanic CountryOfBirthCode Citizenship EmploymentStatus
## American Indian : 1433 Min. :0.0000 Min. : 57.00 Citizen, Native :116639 Disabled : 5712
## Asian : 6520 1st Qu.:0.0000 1st Qu.: 57.00 Citizen, Naturalized: 7073 Employed :61733
## Black : 13913 Median :0.0000 Median : 57.00 Non-Citizen : 7590 Not in Labor Force:15246
## Multiracial : 2897 Mean :0.1393 Mean : 82.68 Retired :18619
## Pacific Islander: 618 3rd Qu.:0.0000 3rd Qu.: 57.00 Unemployed : 4203
## White :105921 Max. :1.0000 Max. :555.00 NA's :25789
##
## Industry MetroArea
## Educational and health services :15017 New York-Northern New Jersey-Long Island, NY-NJ-PA: 5409
## Trade : 8933 Washington-Arlington-Alexandria, DC-VA-MD-WV : 4177
## Professional and business services: 7519 Los Angeles-Long Beach-Santa Ana, CA : 4102
## Manufacturing : 6791 Philadelphia-Camden-Wilmington, PA-NJ-DE : 2855
## Leisure and hospitality : 6364 Chicago-Naperville-Joliet, IN-IN-WI : 2772
## (Other) :21618 (Other) :77749
## NA's :65060 NA's :34238MetroArea
summary(CPS)
## MetroAreaCode PeopleInHousehold Region State Age Married Sex Education
## Min. :10420 Min. : 1.000 Midwest :30684 California :11570 Min. : 0.00 Divorced :11151 Female:67481 High school :30906
## 1st Qu.:21780 1st Qu.: 2.000 Northeast:25939 Texas : 7077 1st Qu.:19.00 Married :55509 Male :63821 Bachelor's degree :19443
## Median :34740 Median : 3.000 South :41502 New York : 5595 Median :39.00 Never Married:30772 Some college, no degree:18863
## Mean :35075 Mean : 3.284 West :33177 Florida : 5149 Mean :38.83 Separated : 2027 No high school diploma :16095
## 3rd Qu.:41860 3rd Qu.: 4.000 Pennsylvania: 3930 3rd Qu.:57.00 Widowed : 6505 Associate degree : 9913
## Max. :79600 Max. :15.000 Illinois : 3912 Max. :85.00 NA's :25338 (Other) :10744
## NA's :34238 (Other) :94069 NA's :25338
## Race Hispanic CountryOfBirthCode Citizenship EmploymentStatus
## American Indian : 1433 Min. :0.0000 Min. : 57.00 Citizen, Native :116639 Disabled : 5712
## Asian : 6520 1st Qu.:0.0000 1st Qu.: 57.00 Citizen, Naturalized: 7073 Employed :61733
## Black : 13913 Median :0.0000 Median : 57.00 Non-Citizen : 7590 Not in Labor Force:15246
## Multiracial : 2897 Mean :0.1393 Mean : 82.68 Retired :18619
## Pacific Islander: 618 3rd Qu.:0.0000 3rd Qu.: 57.00 Unemployed : 4203
## White :105921 Max. :1.0000 Max. :555.00 NA's :25789
##
## Industry MetroArea
## Educational and health services :15017 New York-Northern New Jersey-Long Island, NY-NJ-PA: 5409
## Trade : 8933 Washington-Arlington-Alexandria, DC-VA-MD-WV : 4177
## Professional and business services: 7519 Los Angeles-Long Beach-Santa Ana, CA : 4102
## Manufacturing : 6791 Philadelphia-Camden-Wilmington, PA-NJ-DE : 2855
## Leisure and hospitality : 6364 Chicago-Naperville-Joliet, IN-IN-WI : 2772
## (Other) :21618 (Other) :77749
## NA's :65060 NA's :34238sort(table(CPS$MetroArea))
##
## Appleton-Oshkosh-Neenah, WI Grand Rapids-Muskegon-Holland, MI Greenville-Spartanburg-Anderson, SC
## 0 0 0
## Hinesville-Fort Stewart, GA Jamestown, NY Kalamazoo-Battle Creek, MI
## 0 0 0
## Portsmouth-Rochester, NH-ME Bowling Green, KY Ocean City, NJ
## 0 29 30
## Springfield, OH Bloomington-Normal IL Valdosta, GA
## 34 40 42
## Warner Robins, GA Tallahassee, FL Columbia, MO
## 42 43 47
## Punta Gorda, FL Midland, TX Niles-Benton Harbor, MI
## 48 51 51
## Johnson City, TN Santa Fe, NM Prescott, AZ
## 52 52 54
## Vineland-Millville-Bridgeton, NJ Hickory-Morgantown-Lenoir, NC Madera, CA
## 54 57 57
## Columbus, GA-AL Joplin, MO Panama City-Lynn Haven, FL
## 59 59 59
## Chico, CA Anniston-Oxford, AL Napa, CA
## 60 61 61
## Anderson, IN Florence, AL Jacksonville, NC
## 62 63 63
## Johnstown, PA Lubbock, TX Monroe, MI
## 63 63 63
## Anderson, SC Farmington, NM Athens-Clark County, GA
## 64 64 65
## Gulfport-Biloxi, MS Longview, TX Macon, GA
## 65 65 65
## Leominster-Fitchburg-Gardner, MA Roanoke, VA Santa-Cruz-Watsonville, CA
## 66 66 66
## Kingsport-Bristol, TN-VA Albany, GA Bellingham, WA
## 67 68 70
## Gainesville, FL Jackson, MI Binghamton, NY
## 70 70 73
## Lynchburg, VA Saginaw-Saginaw Township North, MI Salisbury, MD
## 73 74 74
## Barnstable Town, MA Ocala, FL Springfield, IL
## 75 76 76
## Fayetteville, NC Michigan City-La Porte, IN San Luis Obispo-Paso Robles, CA
## 77 77 77
## Holland-Grand Haven, MI Tuscaloosa, AL Brownsville-Harlingen, TX
## 78 78 79
## Vero Beach, FL Waco, TX Fort Walton Beach-Crestview-Destin, FL
## 79 79 80
## Utica-Rome, NY Decatur, IL Lake Charles, LA
## 80 81 81
## South Bend-Mishawaka, IN-MI Altoona, PA Huntington-Ashland, WV-KY-OH
## 81 82 82
## Medford, OR Naples-Marco Island, FL St. Cloud, MN
## 82 82 82
## Ann Arbor, MI Oshkosh-Neenah, WI Hagerstown-Martinsburg, MD-WV
## 85 85 86
## Bremerton-Silverdale, WA Erie, PA Kankakee-Bradley, IL
## 87 87 87
## Kingston, NY Amarillo, TX Laredo, TX
## 87 88 89
## Harrisonburg, VA Muskegon-Norton Shores, MI Trenton-Ewing, NJ
## 90 90 91
## Decatur, Al Wausau, WI Lawton, OK
## 96 96 97
## Lawrence, KS El Centro, CA Evansville, IN-KY
## 98 99 99
## Janesville, WI Olympia, WA Spartanburg, SC
## 99 99 99
## Killeen-Temple-Fort Hood, TX Flint, MI Myrtle Beach-Conway-North Myrtle Beach, SC
## 101 102 102
## Montgomery, AL Bloomington, IN Salinas, CA
## 103 104 104
## Fort Smith, AR-OK Merced, CA Las Cruses, NM
## 105 106 107
## Pensacola-Ferry Pass-Brent, FL Port St. Lucie-Fort Pierce, FL Eau Claire, WI
## 107 109 110
## Mobile, AL Atlantic City, NJ Danbury, CT
## 110 111 112
## Peoria, IL Yakima, WA La Crosse, WI
## 112 112 114
## Rockford, IL Asheville, NC Victoria, TX
## 114 116 116
## Coeur d'Alene, ID Huntsville, AL York-Hanover, PA
## 117 117 117
## Canton-Massillon, OH Lansing-East Lansing, MI Racine, WI
## 118 119 119
## Visalia-Porterville, CA Champaign-Urbana, IL Beaumont-Port Author, TX
## 121 122 123
## Appleton,WI Duluth, MN-WI Kalamazoo-Portage, MI
## 125 126 127
## Winston-Salem, NC Santa Rosa-Petaluma, CA Pueblo, CO
## 127 129 130
## Iowa City, IA Corpus Christi, TX Santa Barbara-Santa Maria-Goleta, CA
## 131 132 132
## Vallejo-Fairfield, CA Fort Wayne, IN Green Bay, WI
## 133 136 136
## Bend, OR Deltona-Daytona Beach-Ormond Beach, FL Reading, PA
## 140 140 142
## Worcester, MA-CT Cape Coral-Fort Myers, FL Shreveport-Bossier City, LA
## 144 146 146
## Lakeland-Winter Haven, FL Youngstown-Warren-Boardman, OH Springfield, MA-CT
## 149 153 155
## Lancaster, PA Spokane, WA Waterloo-Cedar Falls, IA
## 156 156 156
## Waterbury, CT Modesto, CA Augusta-Richmond County, GA-SC
## 157 158 161
## Springfield, MO Greeley, CO Chattanooga, TN-GA
## 161 162 167
## Knoxville, TN Palm Bay-Melbourne-Titusville, FL Salem, OR
## 168 168 170
## Boulder, CO Harrisburg-Carlisle, PA Scranton-Wilkes Barre, PA
## 171 174 176
## Monroe, LA Lafayette, LA Topeka, KS
## 179 181 182
## Greenville, SC Durham, NC Sarasota-Bradenton-Venice, FL
## 185 189 192
## Stockton, CA McAllen-Edinburg-Pharr, TX Cedar Rapids, IA
## 193 195 196
## Eugene-Springfield, OR Lexington-Fayette, KY Billings, MT
## 196 198 199
## Poughkeepsie-Newburgh-Middletown, NY Savannah, GA Norwich-New London, CT-RI
## 201 202 203
## Fort Collins-Loveland, CO Bangor, ME Fayetteville-Springdale-Rogers, AR-MO
## 206 208 215
## Jackson, MS Syracuse, NY Akron, OH
## 222 223 231
## Charleston-North Charleston, SC Toledo, OH Davenport-Moline-Rock Island, IA-IL
## 232 235 240
## El Paso, TX Bakersfield, CA Greensboro-High Point, NC
## 244 245 251
## Baton Rouge, LA Charleston, WV Rochester-Dover, NH-ME
## 262 262 262
## Oxnard-Thousand Oaks-Ventura, CA Albany-Schenectady-Troy, NY Dayton, OH
## 267 268 268
## Madison, WI Columbia, SC Tucson, AZ
## 284 291 302
## Fresno, CA Grand Rapids-Wyoming, MI Rochester, NY
## 303 304 307
## Provo-Orem, UT Reno-Sparks, NV Tulsa, OK
## 309 310 323
## Allentown-Bethlehem-Easton, PA-NJ Raleigh-Cary, NC Buffalo-Niagara Falls, NY
## 334 336 344
## Memphis, TN-MS-AR New Orleans-Metairie-Kenner, LA Colorado Springs, CO
## 348 367 372
## Birmingham-Hoover, AL Jacksonville, FL Little Rock-North Little Rock, AR
## 392 393 404
## Ogden-Clearfield, UT Wichita, KS Fargo, ND-MN
## 423 427 432
## Dover, DE Richmond, VA Des Moines, IA
## 456 490 501
## Nashville-Davidson-Murfreesboro, TN New Haven, CT Austin-Round Rock, TX
## 505 506 516
## Charlotte-Gastonia-Concord, NC-SC Louisville, KY-IN Columbus, OH
## 517 519 551
## Indianapolis, IN Sioux Falls, SD Virginia Beach-Norfolk-Newport News, VA-NC
## 570 595 597
## Oklahoma City, OK San Antonio, TX Albuquerque, NM
## 604 607 609
## Orlando, FL Boise City-Nampa, ID Burlington-South Burlington, VT
## 610 644 657
## Sacramento-Arden-Arcade-Roseville, CA San Jose-Sunnyvale-Santa Clara, CA Cleveland-Elyria-Mentor, OH
## 667 670 681
## Portland-South Portland, ME Milwaukee-Waukesha-West Allis, WI Cincinnati-Middletown, OH-KY-IN
## 701 714 719
## Salt Lake City, UT Bridgeport-Stamford-Norwalk, CT Pittsburgh, PA
## 723 730 732
## Tampa-St. Petersburg-Clearwater, FL Hartford-West Hartford-East Hartford, CT San Diego-Carlsbad-San Marcos, CA
## 842 885 907
## St. Louis, MO-IL Omaha-Council Bluffs, NE-IA Kansas City, MO-KS
## 956 957 962
## Phoenix-Mesa-Scottsdale, AZ Portland-Vancouver-Beaverton, OR-WA Seattle-Tacoma-Bellevue, WA
## 971 1089 1255
## Riverside-San Bernardino, CA Las Vegas-Paradise, NV Detroit-Warren-Livonia, MI
## 1290 1299 1354
## San Francisco-Oakland-Fremont, CA Baltimore-Towson, MD Denver-Aurora, CO
## 1386 1483 1504
## Atlanta-Sandy Springs-Marietta, GA Miami-Fort Lauderdale-Miami Beach, FL Honolulu, HI
## 1552 1554 1576
## Houston-Baytown-Sugar Land, TX Dallas-Fort Worth-Arlington, TX Minneapolis-St Paul-Bloomington, MN-WI
## 1649 1863 1942
## Boston-Cambridge-Quincy, MA-NH Providence-Fall River-Warwick, MA-RI Chicago-Naperville-Joliet, IN-IN-WI
## 2229 2284 2772
## Philadelphia-Camden-Wilmington, PA-NJ-DE Los Angeles-Long Beach-Santa Ana, CA Washington-Arlington-Alexandria, DC-VA-MD-WV
## 2855 4102 4177
## New York-Northern New Jersey-Long Island, NY-NJ-PA
## 5409Explanation: From table(CPS$MetroArea), we can read that Boston-Cambridge-Quincy, MA-NH has the largest number of interviewees of these options, with 2229.
sort(tapply(CPS$Hispanic, CPS$MetroArea, mean))
## Anderson, SC Ann Arbor, MI Barnstable Town, MA
## 0.000000000 0.000000000 0.000000000
## Bloomington-Normal IL Bloomington, IN Bowling Green, KY
## 0.000000000 0.000000000 0.000000000
## Decatur, IL Eau Claire, WI Florence, AL
## 0.000000000 0.000000000 0.000000000
## Hagerstown-Martinsburg, MD-WV Harrisonburg, VA Huntington-Ashland, WV-KY-OH
## 0.000000000 0.000000000 0.000000000
## Huntsville, AL Jackson, MI Johnstown, PA
## 0.000000000 0.000000000 0.000000000
## Macon, GA Mobile, AL Salisbury, MD
## 0.000000000 0.000000000 0.000000000
## Savannah, GA Warner Robins, GA Dayton, OH
## 0.000000000 0.000000000 0.003731343
## Monroe, LA Knoxville, TN Charleston, WV
## 0.005586592 0.005952381 0.007633588
## Appleton,WI Jackson, MS Burlington-South Burlington, VT
## 0.008000000 0.009009009 0.009132420
## Montgomery, AL Wausau, WI Portland-South Portland, ME
## 0.009708738 0.010416667 0.011412268
## Oshkosh-Neenah, WI Altoona, PA St. Cloud, MN
## 0.011764706 0.012195122 0.012195122
## Holland-Grand Haven, MI Akron, OH Springfield, IL
## 0.012820513 0.012987013 0.013157895
## Bellingham, WA Bangor, ME Kingsport-Bristol, TN-VA
## 0.014285714 0.014423077 0.014925373
## Cedar Rapids, IA Gulfport-Biloxi, MS Duluth, MN-WI
## 0.015306122 0.015384615 0.015873016
## Pittsburgh, PA Joplin, MO Charleston-North Charleston, SC
## 0.016393443 0.016949153 0.017241379
## Buffalo-Niagara Falls, NY La Crosse, WI Niles-Benton Harbor, MI
## 0.017441860 0.017543860 0.019607843
## Evansville, IN-KY Spartanburg, SC Bend, OR
## 0.020202020 0.020202020 0.021428571
## Muskegon-Norton Shores, MI Erie, PA Harrisburg-Carlisle, PA
## 0.022222222 0.022988506 0.022988506
## Madison, WI Lake Charles, LA Fargo, ND-MN
## 0.024647887 0.024691358 0.025462963
## Coeur d'Alene, ID Spokane, WA Saginaw-Saginaw Township North, MI
## 0.025641026 0.025641026 0.027027027
## Lynchburg, VA Pensacola-Ferry Pass-Brent, FL Memphis, TN-MS-AR
## 0.027397260 0.028037383 0.028735632
## Springfield, OH Billings, MT Janesville, WI
## 0.029411765 0.030150754 0.030303030
## Roanoke, VA St. Louis, MO-IL Iowa City, IA
## 0.030303030 0.030334728 0.030534351
## Rochester-Dover, NH-ME Kalamazoo-Portage, MI Youngstown-Warren-Boardman, OH
## 0.030534351 0.031496063 0.032679739
## Champaign-Urbana, IL Toledo, OH Fort Wayne, IN
## 0.032786885 0.034042553 0.036764706
## Little Rock-North Little Rock, AR Detroit-Warren-Livonia, MI Greenville, SC
## 0.037128713 0.037666174 0.037837838
## Baton Rouge, LA Johnson City, TN Louisville, KY-IN
## 0.038167939 0.038461538 0.038535645
## Michigan City-La Porte, IN Flint, MI Cincinnati-Middletown, OH-KY-IN
## 0.038961039 0.039215686 0.040333797
## Lexington-Fayette, KY Lawrence, KS Albany-Schenectady-Troy, NY
## 0.040404040 0.040816327 0.041044776
## Binghamton, NY Punta Gorda, FL Sioux Falls, SD
## 0.041095890 0.041666667 0.042016807
## Columbia, MO York-Hanover, PA Gainesville, FL
## 0.042553191 0.042735043 0.042857143
## Richmond, VA Springfield, MO Columbus, OH
## 0.042857143 0.043478261 0.043557169
## Rockford, IL Albany, GA Sarasota-Bradenton-Venice, FL
## 0.043859649 0.044117647 0.046875000
## Valdosta, GA Anniston-Oxford, AL South Bend-Mishawaka, IN-MI
## 0.047619048 0.049180328 0.049382716
## Virginia Beach-Norfolk-Newport News, VA-NC Minneapolis-St Paul-Bloomington, MN-WI Decatur, Al
## 0.050251256 0.052008239 0.052083333
## Birmingham-Hoover, AL Palm Bay-Melbourne-Titusville, FL Winston-Salem, NC
## 0.053571429 0.053571429 0.055118110
## Dover, DE Bremerton-Silverdale, WA Rochester, NY
## 0.057017544 0.057471264 0.058631922
## Myrtle Beach-Conway-North Myrtle Beach, SC Racine, WI Honolulu, HI
## 0.058823529 0.058823529 0.059644670
## Cleveland-Elyria-Mentor, OH Asheville, NC Lafayette, LA
## 0.060205580 0.060344828 0.060773481
## Peoria, IL Monroe, MI Anderson, IN
## 0.062500000 0.063492063 0.064516129
## Provo-Orem, UT Ocean City, NJ Panama City-Lynn Haven, FL
## 0.064724919 0.066666667 0.067796610
## Kingston, NY Nashville-Davidson-Murfreesboro, TN Boston-Cambridge-Quincy, MA-NH
## 0.068965517 0.069306931 0.069537909
## Tallahassee, FL Omaha-Council Bluffs, NE-IA Indianapolis, IN
## 0.069767442 0.070010449 0.071929825
## New Haven, CT Des Moines, IA Utica-Rome, NY
## 0.073122530 0.073852295 0.075000000
## Greensboro-High Point, NC Vero Beach, FL Canton-Massillon, OH
## 0.075697211 0.075949367 0.076271186
## Eugene-Springfield, OR Chattanooga, TN-GA Philadelphia-Camden-Wilmington, PA-NJ-DE
## 0.076530612 0.077844311 0.078458844
## Columbia, SC Syracuse, NY Shreveport-Bossier City, LA
## 0.079037801 0.080717489 0.082191781
## Baltimore-Towson, MD Worcester, MA-CT Lansing-East Lansing, MI
## 0.082265678 0.083333333 0.084033613
## Medford, OR Milwaukee-Waukesha-West Allis, WI Atlanta-Sandy Springs-Marietta, GA
## 0.085365854 0.085434174 0.085695876
## Fort Smith, AR-OK Allentown-Bethlehem-Easton, PA-NJ Hickory-Morgantown-Lenoir, NC
## 0.085714286 0.086826347 0.087719298
## Seattle-Tacoma-Bellevue, WA Atlantic City, NJ Leominster-Fitchburg-Gardner, MA
## 0.088446215 0.090090090 0.090909091
## Jacksonville, FL Davenport-Moline-Rock Island, IA-IL Augusta-Richmond County, GA-SC
## 0.091603053 0.091666667 0.093167702
## Boise City-Nampa, ID Topeka, KS Portland-Vancouver-Beaverton, OR-WA
## 0.093167702 0.093406593 0.094582185
## Deltona-Daytona Beach-Ormond Beach, FL Port St. Lucie-Fort Pierce, FL Lancaster, PA
## 0.100000000 0.100917431 0.102564103
## Tuscaloosa, AL Norwich-New London, CT-RI Hartford-West Hartford-East Hartford, CT
## 0.102564103 0.103448276 0.105084746
## Oklahoma City, OK Waterloo-Cedar Falls, IA Durham, NC
## 0.107615894 0.108974359 0.111111111
## New Orleans-Metairie-Kenner, LA Bridgeport-Stamford-Norwalk, CT Fort Walton Beach-Crestview-Destin, FL
## 0.111716621 0.112328767 0.112500000
## Providence-Fall River-Warwick, MA-RI Tulsa, OK Kankakee-Bradley, IL
## 0.114273205 0.114551084 0.114942529
## Chico, CA Charlotte-Gastonia-Concord, NC-SC Raleigh-Cary, NC
## 0.116666667 0.117988395 0.119047619
## Colorado Springs, CO Olympia, WA Fort Collins-Loveland, CO
## 0.120967742 0.121212121 0.121359223
## Washington-Arlington-Alexandria, DC-VA-MD-WV Kansas City, MO-KS Athens-Clark County, GA
## 0.121378980 0.121621622 0.123076923
## Lawton, OK Green Bay, WI Jacksonville, NC
## 0.123711340 0.125000000 0.126984127
## Prescott, AZ Trenton-Ewing, NJ Wichita, KS
## 0.129629630 0.131868132 0.133489461
## Lakeland-Winter Haven, FL Scranton-Wilkes Barre, PA Grand Rapids-Wyoming, MI
## 0.134228188 0.136363636 0.138157895
## Ogden-Clearfield, UT Boulder, CO Fayetteville-Springdale-Rogers, AR-MO
## 0.144208038 0.146198830 0.148837209
## Santa-Cruz-Watsonville, CA Salt Lake City, UT Fayetteville, NC
## 0.151515152 0.154910097 0.155844156
## Ocala, FL Tampa-St. Petersburg-Clearwater, FL Greeley, CO
## 0.157894737 0.159144893 0.160493827
## Chicago-Naperville-Joliet, IN-IN-WI Naples-Marco Island, FL Reno-Sparks, NV
## 0.167388167 0.182926829 0.196774194
## San Francisco-Oakland-Fremont, CA Columbus, GA-AL Vallejo-Fairfield, CA
## 0.199855700 0.203389831 0.210526316
## Reading, PA Salem, OR Orlando, FL
## 0.211267606 0.211764706 0.213114754
## Springfield, MA-CT Beaumont-Port Author, TX New York-Northern New Jersey-Long Island, NY-NJ-PA
## 0.219354839 0.227642276 0.228508042
## Napa, CA Denver-Aurora, CO Santa Rosa-Petaluma, CA
## 0.229508197 0.232047872 0.232558140
## Farmington, NM San Luis Obispo-Paso Robles, CA Waterbury, CT
## 0.234375000 0.246753247 0.248407643
## Las Vegas-Paradise, NV Phoenix-Mesa-Scottsdale, AZ Amarillo, TX
## 0.251732102 0.254376931 0.261363636
## Sacramento-Arden-Arcade-Roseville, CA San Diego-Carlsbad-San Marcos, CA Poughkeepsie-Newburgh-Middletown, NY
## 0.263868066 0.269018743 0.273631841
## Dallas-Fort Worth-Arlington, TX Lubbock, TX Longview, TX
## 0.283950617 0.285714286 0.292307692
## Pueblo, CO Austin-Round Rock, TX San Jose-Sunnyvale-Santa Clara, CA
## 0.307692308 0.310077519 0.316417910
## Stockton, CA Waco, TX Danbury, CT
## 0.321243523 0.329113924 0.339285714
## Modesto, CA Midland, TX Yakima, WA
## 0.341772152 0.352941176 0.357142857
## Houston-Baytown-Sugar Land, TX Oxnard-Thousand Oaks-Ventura, CA Killeen-Temple-Fort Hood, TX
## 0.359005458 0.359550562 0.386138614
## Santa Barbara-Santa Maria-Goleta, CA Vineland-Millville-Bridgeton, NJ Fresno, CA
## 0.401515152 0.407407407 0.409240924
## Visalia-Porterville, CA Cape Coral-Fort Myers, FL Albuquerque, NM
## 0.438016529 0.438356164 0.441707718
## Los Angeles-Long Beach-Santa Ana, CA Santa Fe, NM Victoria, TX
## 0.460263286 0.461538462 0.465517241
## Miami-Fort Lauderdale-Miami Beach, FL Bakersfield, CA Riverside-San Bernardino, CA
## 0.467824968 0.489795918 0.502325581
## Tucson, AZ Las Cruses, NM Salinas, CA
## 0.506622517 0.542056075 0.557692308
## Merced, CA Corpus Christi, TX Madera, CA
## 0.566037736 0.606060606 0.614035088
## San Antonio, TX El Centro, CA El Paso, TX
## 0.644151565 0.686868687 0.790983607
## Brownsville-Harlingen, TX McAllen-Edinburg-Pharr, TX Laredo, TX
## 0.797468354 0.948717949 0.966292135Explanation: 96.6% of the interviewees from Laredo, TX, are of Hispanic ethnicity, the highest proportion among metropolitan areas in the United States.
sort(tapply(CPS$Race == "Asian", CPS$MetroArea, mean))
## Albany, GA Altoona, PA Amarillo, TX
## 0.000000000 0.000000000 0.000000000
## Anderson, IN Appleton,WI Asheville, NC
## 0.000000000 0.000000000 0.000000000
## Barnstable Town, MA Beaumont-Port Author, TX Billings, MT
## 0.000000000 0.000000000 0.000000000
## Binghamton, NY Bloomington, IN Bowling Green, KY
## 0.000000000 0.000000000 0.000000000
## Canton-Massillon, OH Charleston, WV Chico, CA
## 0.000000000 0.000000000 0.000000000
## Columbus, GA-AL Decatur, IL Durham, NC
## 0.000000000 0.000000000 0.000000000
## Eau Claire, WI El Paso, TX Erie, PA
## 0.000000000 0.000000000 0.000000000
## Farmington, NM Florence, AL Hagerstown-Martinsburg, MD-WV
## 0.000000000 0.000000000 0.000000000
## Huntsville, AL Jackson, MI Jackson, MS
## 0.000000000 0.000000000 0.000000000
## Janesville, WI Johnson City, TN Joplin, MO
## 0.000000000 0.000000000 0.000000000
## Kankakee-Bradley, IL Killeen-Temple-Fort Hood, TX Kingsport-Bristol, TN-VA
## 0.000000000 0.000000000 0.000000000
## Knoxville, TN Lafayette, LA Lansing-East Lansing, MI
## 0.000000000 0.000000000 0.000000000
## Laredo, TX Leominster-Fitchburg-Gardner, MA Longview, TX
## 0.000000000 0.000000000 0.000000000
## Lubbock, TX Lynchburg, VA Macon, GA
## 0.000000000 0.000000000 0.000000000
## Madera, CA McAllen-Edinburg-Pharr, TX Michigan City-La Porte, IN
## 0.000000000 0.000000000 0.000000000
## Midland, TX Monroe, MI Muskegon-Norton Shores, MI
## 0.000000000 0.000000000 0.000000000
## Myrtle Beach-Conway-North Myrtle Beach, SC Niles-Benton Harbor, MI Ocean City, NJ
## 0.000000000 0.000000000 0.000000000
## Oshkosh-Neenah, WI Port St. Lucie-Fort Pierce, FL Poughkeepsie-Newburgh-Middletown, NY
## 0.000000000 0.000000000 0.000000000
## Pueblo, CO Punta Gorda, FL Racine, WI
## 0.000000000 0.000000000 0.000000000
## Reading, PA Roanoke, VA Rockford, IL
## 0.000000000 0.000000000 0.000000000
## Saginaw-Saginaw Township North, MI Salem, OR Salisbury, MD
## 0.000000000 0.000000000 0.000000000
## Santa-Cruz-Watsonville, CA Santa Fe, NM Scranton-Wilkes Barre, PA
## 0.000000000 0.000000000 0.000000000
## Shreveport-Bossier City, LA South Bend-Mishawaka, IN-MI Spartanburg, SC
## 0.000000000 0.000000000 0.000000000
## Springfield, MA-CT Springfield, OH St. Cloud, MN
## 0.000000000 0.000000000 0.000000000
## Tallahassee, FL Tuscaloosa, AL Utica-Rome, NY
## 0.000000000 0.000000000 0.000000000
## Valdosta, GA Vero Beach, FL Victoria, TX
## 0.000000000 0.000000000 0.000000000
## Vineland-Millville-Bridgeton, NJ Waco, TX Waterbury, CT
## 0.000000000 0.000000000 0.000000000
## Wausau, WI St. Louis, MO-IL New Orleans-Metairie-Kenner, LA
## 0.000000000 0.002092050 0.002724796
## San Antonio, TX Charleston-North Charleston, SC Monroe, LA
## 0.003294893 0.004310345 0.005586592
## Chattanooga, TN-GA Modesto, CA Bend, OR
## 0.005988024 0.006329114 0.007142857
## Dayton, OH Santa Barbara-Santa Maria-Goleta, CA Santa Rosa-Petaluma, CA
## 0.007462687 0.007575758 0.007751938
## Toledo, OH Coeur d'Alene, ID York-Hanover, PA
## 0.008510638 0.008547009 0.008547009
## Yakima, WA Grand Rapids-Wyoming, MI Sioux Falls, SD
## 0.008928571 0.009868421 0.010084034
## Evansville, IN-KY Lawrence, KS Cleveland-Elyria-Mentor, OH
## 0.010101010 0.010204082 0.010279001
## Lawton, OK Boise City-Nampa, ID Harrisburg-Carlisle, PA
## 0.010309278 0.010869565 0.011494253
## Kingston, NY Louisville, KY-IN Medford, OR
## 0.011494253 0.011560694 0.012195122
## Greeley, CO Springfield, MO Birmingham-Hoover, AL
## 0.012345679 0.012422360 0.012755102
## Waterloo-Cedar Falls, IA Provo-Orem, UT Youngstown-Warren-Boardman, OH
## 0.012820513 0.012944984 0.013071895
## Ocala, FL Allentown-Bethlehem-Easton, PA-NJ Corpus Christi, TX
## 0.013157895 0.014970060 0.015151515
## Dover, DE Charlotte-Gastonia-Concord, NC-SC Sarasota-Bradenton-Venice, FL
## 0.015350877 0.015473888 0.015625000
## Kalamazoo-Portage, MI Winston-Salem, NC Johnstown, PA
## 0.015748031 0.015748031 0.015873016
## Colorado Springs, CO Champaign-Urbana, IL Napa, CA
## 0.016129032 0.016393443 0.016393443
## Panama City-Lynn Haven, FL Memphis, TN-MS-AR Columbus, OH
## 0.016949153 0.017241379 0.018148820
## Prescott, AZ Las Cruses, NM Pensacola-Ferry Pass-Brent, FL
## 0.018518519 0.018691589 0.018691589
## Spokane, WA Fort Collins-Loveland, CO Flint, MI
## 0.019230769 0.019417476 0.019607843
## Savannah, GA Tucson, AZ El Centro, CA
## 0.019801980 0.019867550 0.020202020
## Eugene-Springfield, OR Davenport-Moline-Rock Island, IA-IL Deltona-Daytona Beach-Ormond Beach, FL
## 0.020408163 0.020833333 0.021428571
## Topeka, KS Cincinnati-Middletown, OH-KY-IN Little Rock-North Little Rock, AR
## 0.021978022 0.022253129 0.022277228
## Albany-Schenectady-Troy, NY Baton Rouge, LA Bremerton-Silverdale, WA
## 0.022388060 0.022900763 0.022988506
## Bangor, ME Naples-Marco Island, FL Indianapolis, IN
## 0.024038462 0.024390244 0.024561404
## Augusta-Richmond County, GA-SC Holland-Grand Haven, MI Fayetteville, NC
## 0.024844720 0.025641026 0.025974026
## Ogden-Clearfield, UT Rochester-Dover, NH-ME Virginia Beach-Norfolk-Newport News, VA-NC
## 0.026004728 0.026717557 0.026800670
## Lakeland-Winter Haven, FL Columbia, SC Fargo, ND-MN
## 0.026845638 0.027491409 0.027777778
## Bellingham, WA Montgomery, AL Omaha-Council Bluffs, NE-IA
## 0.028571429 0.029126214 0.029258098
## Akron, OH Wichita, KS Athens-Clark County, GA
## 0.030303030 0.030444965 0.030769231
## Gulfport-Biloxi, MS Anderson, SC Denver-Aurora, CO
## 0.030769231 0.031250000 0.031914894
## Greenville, SC Philadelphia-Camden-Wilmington, PA-NJ-DE Harrisonburg, VA
## 0.032432432 0.032924694 0.033333333
## Cape Coral-Fort Myers, FL Kansas City, MO-KS Worcester, MA-CT
## 0.034246575 0.034303534 0.034722222
## Oklahoma City, OK Hickory-Morgantown-Lenoir, NC Lexington-Fayette, KY
## 0.034768212 0.035087719 0.035353535
## Miami-Fort Lauderdale-Miami Beach, FL Palm Bay-Melbourne-Titusville, FL Salt Lake City, UT
## 0.035392535 0.035714286 0.035961272
## Mobile, AL Huntington-Ashland, WV-KY-OH Richmond, VA
## 0.036363636 0.036585366 0.036734694
## Fort Wayne, IN Fort Walton Beach-Crestview-Destin, FL Des Moines, IA
## 0.036764706 0.037500000 0.037924152
## Phoenix-Mesa-Scottsdale, AZ Pittsburgh, PA Bridgeport-Stamford-Norwalk, CT
## 0.038105046 0.038251366 0.038356164
## Providence-Fall River-Warwick, MA-RI Tampa-St. Petersburg-Clearwater, FL Duluth, MN-WI
## 0.038966725 0.039192399 0.039682540
## Syracuse, NY Albuquerque, NM Decatur, Al
## 0.040358744 0.041050903 0.041666667
## Portland-South Portland, ME Gainesville, FL Detroit-Warren-Livonia, MI
## 0.042796006 0.042857143 0.043574594
## Trenton-Ewing, NJ New Haven, CT Jacksonville, NC
## 0.043956044 0.047430830 0.047619048
## Milwaukee-Waukesha-West Allis, WI Jacksonville, FL Burlington-South Burlington, VT
## 0.047619048 0.048346056 0.048706240
## Anniston-Oxford, AL Tulsa, OK Raleigh-Cary, NC
## 0.049180328 0.049535604 0.050595238
## Orlando, FL Fayetteville-Springdale-Rogers, AR-MO San Luis Obispo-Paso Robles, CA
## 0.050819672 0.051162791 0.051948052
## Boston-Cambridge-Quincy, MA-NH Austin-Round Rock, TX Buffalo-Niagara Falls, NY
## 0.052041274 0.052325581 0.052325581
## Springfield, IL Iowa City, IA Peoria, IL
## 0.052631579 0.053435115 0.053571429
## Madison, WI Merced, CA Fort Smith, AR-OK
## 0.056338028 0.056603774 0.057142857
## Nashville-Davidson-Murfreesboro, TN Lancaster, PA Baltimore-Towson, MD
## 0.057425743 0.057692308 0.057990560
## Reno-Sparks, NV Chicago-Naperville-Joliet, IN-IN-WI Boulder, CO
## 0.058064516 0.058441558 0.058479532
## Houston-Baytown-Sugar Land, TX Riverside-San Bernardino, CA Danbury, CT
## 0.061249242 0.062015504 0.062500000
## Dallas-Fort Worth-Arlington, TX Columbia, MO Rochester, NY
## 0.062801932 0.063829787 0.065146580
## Cedar Rapids, IA Hartford-West Hartford-East Hartford, CT Portland-Vancouver-Beaverton, OR-WA
## 0.066326531 0.066666667 0.069788797
## Washington-Arlington-Alexandria, DC-VA-MD-WV Atlanta-Sandy Springs-Marietta, GA Norwich-New London, CT-RI
## 0.070624850 0.072809278 0.073891626
## Lake Charles, LA Oxnard-Thousand Oaks-Ventura, CA Bloomington-Normal IL
## 0.074074074 0.074906367 0.075000000
## Brownsville-Harlingen, TX Minneapolis-St Paul-Bloomington, MN-WI Las Vegas-Paradise, NV
## 0.075949367 0.076725026 0.078521940
## Greensboro-High Point, NC Bakersfield, CA Ann Arbor, MI
## 0.079681275 0.081632653 0.082352941
## La Crosse, WI Green Bay, WI Visalia-Porterville, CA
## 0.087719298 0.088235294 0.090909091
## Seattle-Tacoma-Bellevue, WA New York-Northern New Jersey-Long Island, NY-NJ-PA Salinas, CA
## 0.099601594 0.104270660 0.125000000
## Olympia, WA Los Angeles-Long Beach-Santa Ana, CA San Diego-Carlsbad-San Marcos, CA
## 0.131313131 0.135056070 0.142227122
## Sacramento-Arden-Arcade-Roseville, CA Atlantic City, NJ Stockton, CA
## 0.142428786 0.144144144 0.155440415
## Warner Robins, GA Fresno, CA Vallejo-Fairfield, CA
## 0.166666667 0.184818482 0.203007519
## San Jose-Sunnyvale-Santa Clara, CA San Francisco-Oakland-Fremont, CA Honolulu, HI
## 0.241791045 0.246753247 0.501903553Explanation: We can read from the sorted output that Honolulu, HI; San Francisco-Oakland-Fremont, CA; San Jose-Sunnyvale-Santa Clara, CA; and Vallejo-Fairfield, CA had at least 20% of their interviewees of the Asian race.
Normally, we would look at the sorted proportion of interviewees from each metropolitan area who have not received a high school diploma with the command:
sort(tapply(CPS$Education == "No high school diploma", CPS$MetroArea, mean))
## named numeric(0)sort(tapply(CPS$Education == "No high school diploma", CPS$MetroArea, mean, na.rm=TRUE))
## Iowa City, IA Bowling Green, KY Kalamazoo-Portage, MI
## 0.02912621 0.03703704 0.05050505
## Champaign-Urbana, IL Bremerton-Silverdale, WA Lawrence, KS
## 0.05154639 0.05405405 0.05952381
## Bloomington-Normal IL Jacksonville, NC Eau Claire, WI
## 0.06060606 0.06122449 0.06250000
## Palm Bay-Melbourne-Titusville, FL Salisbury, MD Gainesville, FL
## 0.06666667 0.06779661 0.06896552
## Fort Collins-Loveland, CO Altoona, PA Madison, WI
## 0.06936416 0.07142857 0.07423581
## Tallahassee, FL Fargo, ND-MN Albany-Schenectady-Troy, NY
## 0.07500000 0.07902736 0.07929515
## Ocean City, NJ Lakeland-Winter Haven, FL Billings, MT
## 0.08000000 0.08130081 0.08280255
## Coeur d'Alene, ID Burlington-South Burlington, VT Akron, OH
## 0.08333333 0.08394161 0.08421053
## Ann Arbor, MI Asheville, NC Pensacola-Ferry Pass-Brent, FL
## 0.08695652 0.08695652 0.08695652
## Oshkosh-Neenah, WI Rochester-Dover, NH-ME Knoxville, TN
## 0.08823529 0.08928571 0.08965517
## Pittsburgh, PA Barnstable Town, MA Bridgeport-Stamford-Norwalk, CT
## 0.09060403 0.09090909 0.09563758
## Johnstown, PA Austin-Round Rock, TX La Crosse, WI
## 0.09615385 0.09629630 0.09677419
## Boulder, CO Charleston-North Charleston, SC Fort Wayne, IN
## 0.09701493 0.09890110 0.09900990
## Roanoke, VA Prescott, AZ Santa Rosa-Petaluma, CA
## 0.10169492 0.10204082 0.10280374
## Evansville, IN-KY Spokane, WA Poughkeepsie-Newburgh-Middletown, NY
## 0.10389610 0.10434783 0.10559006
## Tampa-St. Petersburg-Clearwater, FL Grand Rapids-Wyoming, MI Portland-South Portland, ME
## 0.10579710 0.10612245 0.10638298
## Honolulu, HI Michigan City-La Porte, IN Eugene-Springfield, OR
## 0.10739300 0.10769231 0.11038961
## Boston-Cambridge-Quincy, MA-NH Bend, OR Vero Beach, FL
## 0.11080485 0.11111111 0.11428571
## Sarasota-Bradenton-Venice, FL Fort Walton Beach-Crestview-Destin, FL Flint, MI
## 0.11464968 0.11475410 0.11538462
## Cedar Rapids, IA Minneapolis-St Paul-Bloomington, MN-WI Portland-Vancouver-Beaverton, OR-WA
## 0.11564626 0.11638204 0.11657143
## Washington-Arlington-Alexandria, DC-VA-MD-WV Mobile, AL Scranton-Wilkes Barre, PA
## 0.11683748 0.11702128 0.11724138
## Topeka, KS Colorado Springs, CO Olympia, WA
## 0.11724138 0.11764706 0.11764706
## Reno-Sparks, NV Appleton,WI Santa Fe, NM
## 0.11764706 0.11827957 0.11904762
## Virginia Beach-Norfolk-Newport News, VA-NC Allentown-Bethlehem-Easton, PA-NJ Rochester, NY
## 0.11909651 0.11929825 0.12132353
## Seattle-Tacoma-Bellevue, WA Kansas City, MO-KS Napa, CA
## 0.12168793 0.12172775 0.12244898
## Duluth, MN-WI New Haven, CT Canton-Massillon, OH
## 0.12264151 0.12354312 0.12371134
## Fayetteville, NC San Luis Obispo-Paso Robles, CA Worcester, MA-CT
## 0.12500000 0.12500000 0.12605042
## Philadelphia-Camden-Wilmington, PA-NJ-DE Davenport-Moline-Rock Island, IA-IL Waterloo-Cedar Falls, IA
## 0.12717253 0.12727273 0.12800000
## Pueblo, CO Baton Rouge, LA Racine, WI
## 0.12844037 0.12871287 0.12903226
## Des Moines, IA Detroit-Warren-Livonia, MI Omaha-Council Bluffs, NE-IA
## 0.12944162 0.12964642 0.12972973
## Richmond, VA Savannah, GA Danbury, CT
## 0.12990196 0.13013699 0.13043478
## Bloomington, IN Valdosta, GA Wausau, WI
## 0.13095238 0.13157895 0.13157895
## Deltona-Daytona Beach-Ormond Beach, FL Tulsa, OK Harrisburg-Carlisle, PA
## 0.13178295 0.13178295 0.13286713
## Las Vegas-Paradise, NV Myrtle Beach-Conway-North Myrtle Beach, SC Provo-Orem, UT
## 0.13307985 0.13333333 0.13366337
## Anderson, IN Chico, CA St. Louis, MO-IL
## 0.13461538 0.13461538 0.13461538
## Niles-Benton Harbor, MI Ogden-Clearfield, UT Baltimore-Towson, MD
## 0.13513514 0.13571429 0.13583333
## Buffalo-Niagara Falls, NY Milwaukee-Waukesha-West Allis, WI Chicago-Naperville-Joliet, IN-IN-WI
## 0.13684211 0.13693694 0.13737734
## Louisville, KY-IN Lynchburg, VA Peoria, IL
## 0.13785047 0.13793103 0.13829787
## Sioux Falls, SD Ocala, FL Leominster-Fitchburg-Gardner, MA
## 0.13832200 0.13888889 0.14035088
## Oklahoma City, OK San Diego-Carlsbad-San Marcos, CA Jacksonville, FL
## 0.14137214 0.14188267 0.14244186
## Atlantic City, NJ Holland-Grand Haven, MI Medford, OR
## 0.14285714 0.14285714 0.14285714
## Naples-Marco Island, FL Punta Gorda, FL Victoria, TX
## 0.14285714 0.14285714 0.14285714
## Winston-Salem, NC Salt Lake City, UT Atlanta-Sandy Springs-Marietta, GA
## 0.14285714 0.14338235 0.14421553
## Decatur, IL Springfield, IL Monroe, MI
## 0.14516129 0.14516129 0.14545455
## Denver-Aurora, CO Hartford-West Hartford-East Hartford, CT Greeley, CO
## 0.14574558 0.14574899 0.14615385
## San Francisco-Oakland-Fremont, CA Boise City-Nampa, ID Greenville, SC
## 0.14651368 0.14653465 0.14666667
## Birmingham-Hoover, AL Saginaw-Saginaw Township North, MI Santa-Cruz-Watsonville, CA
## 0.14678899 0.14754098 0.14814815
## Trenton-Ewing, NJ Lexington-Fayette, KY San Jose-Sunnyvale-Santa Clara, CA
## 0.14814815 0.14838710 0.14922481
## Bellingham, WA Norwich-New London, CT-RI Lubbock, TX
## 0.15000000 0.15060241 0.15094340
## Huntington-Ashland, WV-KY-OH St. Cloud, MN Jackson, MS
## 0.15151515 0.15151515 0.15168539
## Dayton, OH Chattanooga, TN-GA Syracuse, NY
## 0.15207373 0.15217391 0.15428571
## New York-Northern New Jersey-Long Island, NY-NJ-PA Columbia, SC Columbus, OH
## 0.15573586 0.15600000 0.15617716
## Memphis, TN-MS-AR Orlando, FL Warner Robins, GA
## 0.15714286 0.16108787 0.16216216
## Cleveland-Elyria-Mentor, OH Columbia, MO Durham, NC
## 0.16250000 0.16279070 0.16326531
## Miami-Fort Lauderdale-Miami Beach, FL Indianapolis, IN Albuquerque, NM
## 0.16356589 0.16371681 0.16424116
## Cape Coral-Fort Myers, FL Amarillo, TX Anniston-Oxford, AL
## 0.16528926 0.16666667 0.16666667
## Athens-Clark County, GA Binghamton, NY Phoenix-Mesa-Scottsdale, AZ
## 0.16666667 0.16666667 0.16687737
## Green Bay, WI Bangor, ME Providence-Fall River-Warwick, MA-RI
## 0.16831683 0.16860465 0.16915688
## Muskegon-Norton Shores, MI Tuscaloosa, AL Rockford, IL
## 0.16923077 0.16949153 0.17021277
## Las Cruses, NM Gulfport-Biloxi, MS Huntsville, AL
## 0.17283951 0.17307692 0.17391304
## Utica-Rome, NY Fort Smith, AR-OK Charlotte-Gastonia-Concord, NC-SC
## 0.17391304 0.17441860 0.17444717
## El Centro, CA Erie, PA Jackson, MI
## 0.17567568 0.17567568 0.17741935
## Cincinnati-Middletown, OH-KY-IN Springfield, MA-CT Reading, PA
## 0.17773788 0.17829457 0.17857143
## Vallejo-Fairfield, CA Salem, OR Nashville-Davidson-Murfreesboro, TN
## 0.17924528 0.17985612 0.18112245
## Johnson City, TN Wichita, KS York-Hanover, PA
## 0.18181818 0.18181818 0.18181818
## Janesville, WI Lansing-East Lansing, MI Greensboro-High Point, NC
## 0.18292683 0.18348624 0.18357488
## Decatur, Al Albany, GA Augusta-Richmond County, GA-SC
## 0.18421053 0.18604651 0.18796992
## Charleston, WV Shreveport-Bossier City, LA Raleigh-Cary, NC
## 0.18834081 0.18918919 0.18959108
## Toledo, OH Spartanburg, SC Dallas-Fort Worth-Arlington, TX
## 0.18965517 0.18987342 0.19077135
## Sacramento-Arden-Arcade-Roseville, CA Santa Barbara-Santa Maria-Goleta, CA Monroe, LA
## 0.19136961 0.19191919 0.19205298
## Dover, DE South Bend-Mishawaka, IN-MI Fayetteville-Springdale-Rogers, AR-MO
## 0.19220056 0.19354839 0.19393939
## Columbus, GA-AL Kingston, NY Port St. Lucie-Fort Pierce, FL
## 0.19607843 0.19696970 0.19767442
## Waterbury, CT Little Rock-North Little Rock, AR Springfield, MO
## 0.19852941 0.19939577 0.20000000
## Modesto, CA Houston-Baytown-Sugar Land, TX Oxnard-Thousand Oaks-Ventura, CA
## 0.20325203 0.20439739 0.20657277
## Anderson, SC Midland, TX New Orleans-Metairie-Kenner, LA
## 0.20689655 0.21052632 0.21088435
## Fresno, CA Lake Charles, LA Visalia-Porterville, CA
## 0.21120690 0.21739130 0.21782178
## San Antonio, TX Hagerstown-Martinsburg, MD-WV Yakima, WA
## 0.22004357 0.22222222 0.22222222
## Hickory-Morgantown-Lenoir, NC Los Angeles-Long Beach-Santa Ana, CA Panama City-Lynn Haven, FL
## 0.22448980 0.22882883 0.22916667
## Harrisonburg, VA Kankakee-Bradley, IL Beaumont-Port Author, TX
## 0.23287671 0.23437500 0.23469388
## Youngstown-Warren-Boardman, OH Riverside-San Bernardino, CA Farmington, NM
## 0.23622047 0.23780488 0.23913043
## Killeen-Temple-Fort Hood, TX Waco, TX Montgomery, AL
## 0.24050633 0.24074074 0.24137931
## Tucson, AZ Lafayette, LA Joplin, MO
## 0.24603175 0.24822695 0.25000000
## Stockton, CA Brownsville-Harlingen, TX Lancaster, PA
## 0.25333333 0.25396825 0.26771654
## Bakersfield, CA Vineland-Millville-Bridgeton, NJ Lawton, OK
## 0.27218935 0.27500000 0.28000000
## Merced, CA Corpus Christi, TX El Paso, TX
## 0.28358209 0.29702970 0.30219780
## Springfield, OH Florence, AL Madera, CA
## 0.31034483 0.32075472 0.33333333
## Salinas, CA Laredo, TX Kingsport-Bristol, TN-VA
## 0.34090909 0.34426230 0.36363636
## Longview, TX McAllen-Edinburg-Pharr, TX Macon, GA
## 0.38297872 0.38297872 0.40816327Explanation: We can see that Iowa City, IA had 2.9% of interviewees not finish high school, the smallest value of any metropolitan area.
CPS = merge(CPS, CountryMap, by.x="CountryOfBirthCode", by.y="Code", all.x=TRUE)summary(CPS)
## CountryOfBirthCode MetroAreaCode PeopleInHousehold Region State Age Married Sex
## Min. : 57.00 Min. :10420 Min. : 1.000 Midwest :30684 California :11570 Min. : 0.00 Divorced :11151 Female:67481
## 1st Qu.: 57.00 1st Qu.:21780 1st Qu.: 2.000 Northeast:25939 Texas : 7077 1st Qu.:19.00 Married :55509 Male :63821
## Median : 57.00 Median :34740 Median : 3.000 South :41502 New York : 5595 Median :39.00 Never Married:30772
## Mean : 82.68 Mean :35075 Mean : 3.284 West :33177 Florida : 5149 Mean :38.83 Separated : 2027
## 3rd Qu.: 57.00 3rd Qu.:41860 3rd Qu.: 4.000 Pennsylvania: 3930 3rd Qu.:57.00 Widowed : 6505
## Max. :555.00 Max. :79600 Max. :15.000 Illinois : 3912 Max. :85.00 NA's :25338
## NA's :34238 (Other) :94069
## Education Race Hispanic Citizenship EmploymentStatus
## High school :30906 American Indian : 1433 Min. :0.0000 Citizen, Native :116639 Disabled : 5712
## Bachelor's degree :19443 Asian : 6520 1st Qu.:0.0000 Citizen, Naturalized: 7073 Employed :61733
## Some college, no degree:18863 Black : 13913 Median :0.0000 Non-Citizen : 7590 Not in Labor Force:15246
## No high school diploma :16095 Multiracial : 2897 Mean :0.1393 Retired :18619
## Associate degree : 9913 Pacific Islander: 618 3rd Qu.:0.0000 Unemployed : 4203
## (Other) :10744 White :105921 Max. :1.0000 NA's :25789
## NA's :25338
## Industry MetroArea Country
## Educational and health services :15017 New York-Northern New Jersey-Long Island, NY-NJ-PA: 5409 United States:115063
## Trade : 8933 Washington-Arlington-Alexandria, DC-VA-MD-WV : 4177 Mexico : 3921
## Professional and business services: 7519 Los Angeles-Long Beach-Santa Ana, CA : 4102 Philippines : 839
## Manufacturing : 6791 Philadelphia-Camden-Wilmington, PA-NJ-DE : 2855 India : 770
## Leisure and hospitality : 6364 Chicago-Naperville-Joliet, IN-IN-WI : 2772 China : 581
## (Other) :21618 (Other) :77749 (Other) : 9952
## NA's :65060 NA's :34238 NA's : 176Explanation: From summary(CPS), we can read that Country is the name of the added variable, and that it has 176 missing values.
sort(table(CPS$Country))
##
## Cyprus Kosovo Oceania, not specified Other U. S. Island Areas Wales
## 0 0 0 0 0
## Northern Ireland Tanzania Azerbaijan Czechoslovakia St. Kitts--Nevis
## 2 2 3 3 3
## Georgia Barbados Denmark Latvia Samoa
## 5 6 6 6 6
## Senegal Singapore Slovakia Tonga Zimbabwe
## 6 6 6 6 6
## South America, not specified St. Lucia Algeria Americas, not specified Belize
## 7 7 9 9 9
## Fiji St. Vincent and the Grenadines Bahamas Finland Kuwait
## 9 9 10 10 10
## Lithuania Czech Republic Dominica Paraguay Croatia
## 10 11 11 11 12
## Macedonia Moldova Antigua and Barbuda Belgium Bermuda
## 12 12 13 13 13
## Bolivia Grenada Sudan Cape Verde Eritrea
## 13 13 13 15 15
## Sierra Leone Uganda Austria Morocco Sri Lanka
## 15 15 17 17 17
## U. S. Virgin Islands Uruguay Albania Norway Europe, not specified
## 17 17 18 18 19
## Uzbekistan West Indies, not specified Malaysia Serbia Azores
## 19 19 20 20 22
## USSR New Zealand Switzerland Yemen Belarus
## 22 23 23 23 24
## Scotland Yugoslavia Hungary Afghanistan Indonesia
## 24 24 25 26 26
## Netherlands Sweden Bulgaria Costa Rica Saudi Arabia
## 28 28 29 29 29
## Guam Cameroon Syria Armenia Jordan
## 31 32 32 35 36
## Chile Asia, not specified Ireland Spain Bangladesh
## 37 39 39 41 42
## Australia Nepal Panama Lebanon Myanmar (Burma)
## 43 44 44 45 45
## South Africa Turkey Cambodia Liberia Kenya
## 48 48 49 52 55
## Romania Greece Israel Trinidad and Tobago Bosnia & Herzegovina
## 55 56 57 60 61
## Venezuela Argentina Hong Kong Portugal Egypt
## 61 64 64 64 65
## Somalia France South Korea Ghana Nicaragua
## 72 73 73 76 76
## Ethiopia Elsewhere Nigeria Iraq Laos
## 80 81 85 97 98
## Taiwan Ukraine Guyana Pakistan United Kingdom
## 102 104 109 109 111
## Thailand Africa, not specified Ecuador Peru Iran
## 128 129 136 136 144
## Italy Brazil Poland Haiti Russia
## 149 159 162 167 173
## England Japan Honduras Columbia Jamaica
## 179 187 189 206 217
## Guatemala Dominican Republic Korea Canada Cuba
## 309 330 334 410 426
## Germany Vietnam El Salvador Puerto Rico China
## 438 458 477 518 581
## India Philippines Mexico United States
## 770 839 3921 115063Explanation: From the summary(CPS) output, or alternately sort(table(CPS$Country)), we see that the top two countries of birth were United States and Mexico, both of which are in North America. The third highest value, 839, was for the Philippines.
m = table(CPS$MetroArea == "New York-Northern New Jersey-Long Island, NY-NJ-PA", CPS$Country != "United States")
prop.table(m,1)
##
## FALSE TRUE
## FALSE 0.8607228 0.1392772
## TRUE 0.6913397 0.3086603Explanation: From table(CPS$MetroArea == “New York-Northern New Jersey-Long Island, NY-NJ-PA”, CPS$Country != “United States”), we can see that 1668 of interviewees from this metropolitan area were born outside the United States and 3736 were born in the United States (it turns out an additional 5 have a missing country of origin). Therefore, the proportion is 1668/(1668+3736)=0.309.
sort(tapply(CPS$Country == "India", CPS$MetroArea, sum, na.rm=TRUE))
## Akron, OH Albany-Schenectady-Troy, NY Albany, GA
## 0 0 0
## Allentown-Bethlehem-Easton, PA-NJ Altoona, PA Amarillo, TX
## 0 0 0
## Anderson, IN Ann Arbor, MI Anniston-Oxford, AL
## 0 0 0
## Appleton,WI Asheville, NC Athens-Clark County, GA
## 0 0 0
## Augusta-Richmond County, GA-SC Bangor, ME Barnstable Town, MA
## 0 0 0
## Baton Rouge, LA Beaumont-Port Author, TX Bellingham, WA
## 0 0 0
## Bend, OR Billings, MT Binghamton, NY
## 0 0 0
## Bloomington, IN Boulder, CO Bowling Green, KY
## 0 0 0
## Bremerton-Silverdale, WA Buffalo-Niagara Falls, NY Canton-Massillon, OH
## 0 0 0
## Cape Coral-Fort Myers, FL Cedar Rapids, IA Champaign-Urbana, IL
## 0 0 0
## Charleston, WV Chattanooga, TN-GA Chico, CA
## 0 0 0
## Coeur d'Alene, ID Colorado Springs, CO Columbia, MO
## 0 0 0
## Columbus, GA-AL Columbus, OH Corpus Christi, TX
## 0 0 0
## Danbury, CT Davenport-Moline-Rock Island, IA-IL Dayton, OH
## 0 0 0
## Decatur, Al Decatur, IL Denver-Aurora, CO
## 0 0 0
## Dover, DE Duluth, MN-WI Durham, NC
## 0 0 0
## Eau Claire, WI El Centro, CA El Paso, TX
## 0 0 0
## Erie, PA Eugene-Springfield, OR Evansville, IN-KY
## 0 0 0
## Fargo, ND-MN Farmington, NM Fayetteville, NC
## 0 0 0
## Flint, MI Florence, AL Fort Collins-Loveland, CO
## 0 0 0
## Fort Smith, AR-OK Fort Walton Beach-Crestview-Destin, FL Gainesville, FL
## 0 0 0
## Grand Rapids-Wyoming, MI Greeley, CO Green Bay, WI
## 0 0 0
## Greensboro-High Point, NC Gulfport-Biloxi, MS Hagerstown-Martinsburg, MD-WV
## 0 0 0
## Harrisonburg, VA Hickory-Morgantown-Lenoir, NC Holland-Grand Haven, MI
## 0 0 0
## Huntington-Ashland, WV-KY-OH Huntsville, AL Jackson, MI
## 0 0 0
## Jackson, MS Jacksonville, NC Janesville, WI
## 0 0 0
## Johnson City, TN Johnstown, PA Joplin, MO
## 0 0 0
## Kalamazoo-Portage, MI Kankakee-Bradley, IL Killeen-Temple-Fort Hood, TX
## 0 0 0
## Kingsport-Bristol, TN-VA Kingston, NY Knoxville, TN
## 0 0 0
## La Crosse, WI Lafayette, LA Lake Charles, LA
## 0 0 0
## Lakeland-Winter Haven, FL Lancaster, PA Lansing-East Lansing, MI
## 0 0 0
## Laredo, TX Las Cruses, NM Lawton, OK
## 0 0 0
## Leominster-Fitchburg-Gardner, MA Lexington-Fayette, KY Longview, TX
## 0 0 0
## Louisville, KY-IN Lubbock, TX Lynchburg, VA
## 0 0 0
## Macon, GA Madera, CA McAllen-Edinburg-Pharr, TX
## 0 0 0
## Medford, OR Merced, CA Michigan City-La Porte, IN
## 0 0 0
## Midland, TX Mobile, AL Modesto, CA
## 0 0 0
## Monroe, LA Monroe, MI Montgomery, AL
## 0 0 0
## Muskegon-Norton Shores, MI Myrtle Beach-Conway-North Myrtle Beach, SC Napa, CA
## 0 0 0
## Niles-Benton Harbor, MI Ocala, FL Ocean City, NJ
## 0 0 0
## Oshkosh-Neenah, WI Palm Bay-Melbourne-Titusville, FL Panama City-Lynn Haven, FL
## 0 0 0
## Pensacola-Ferry Pass-Brent, FL Port St. Lucie-Fort Pierce, FL Portland-South Portland, ME
## 0 0 0
## Poughkeepsie-Newburgh-Middletown, NY Prescott, AZ Pueblo, CO
## 0 0 0
## Punta Gorda, FL Racine, WI Raleigh-Cary, NC
## 0 0 0
## Reading, PA Richmond, VA Riverside-San Bernardino, CA
## 0 0 0
## Roanoke, VA Rockford, IL Saginaw-Saginaw Township North, MI
## 0 0 0
## Salem, OR Salinas, CA Salisbury, MD
## 0 0 0
## San Antonio, TX San Luis Obispo-Paso Robles, CA Santa-Cruz-Watsonville, CA
## 0 0 0
## Santa Barbara-Santa Maria-Goleta, CA Santa Fe, NM Santa Rosa-Petaluma, CA
## 0 0 0
## Sarasota-Bradenton-Venice, FL Savannah, GA Scranton-Wilkes Barre, PA
## 0 0 0
## Shreveport-Bossier City, LA Sioux Falls, SD South Bend-Mishawaka, IN-MI
## 0 0 0
## Spartanburg, SC Spokane, WA Springfield, MA-CT
## 0 0 0
## Springfield, MO Springfield, OH St. Cloud, MN
## 0 0 0
## St. Louis, MO-IL Stockton, CA Tallahassee, FL
## 0 0 0
## Toledo, OH Topeka, KS Tuscaloosa, AL
## 0 0 0
## Utica-Rome, NY Valdosta, GA Vallejo-Fairfield, CA
## 0 0 0
## Vero Beach, FL Victoria, TX Vineland-Millville-Bridgeton, NJ
## 0 0 0
## Virginia Beach-Norfolk-Newport News, VA-NC Waco, TX Waterbury, CT
## 0 0 0
## Waterloo-Cedar Falls, IA Wausau, WI Wichita, KS
## 0 0 0
## Worcester, MA-CT Yakima, WA York-Hanover, PA
## 0 0 0
## Youngstown-Warren-Boardman, OH Anderson, SC Bloomington-Normal IL
## 0 1 1
## Boise City-Nampa, ID Cincinnati-Middletown, OH-KY-IN Columbia, SC
## 1 1 1
## Greenville, SC Harrisburg-Carlisle, PA Jacksonville, FL
## 1 1 1
## Lawrence, KS Naples-Marco Island, FL New Orleans-Metairie-Kenner, LA
## 1 1 1
## Olympia, WA Provo-Orem, UT Syracuse, NY
## 1 1 1
## Tucson, AZ Atlantic City, NJ Bakersfield, CA
## 1 2 2
## Birmingham-Hoover, AL Burlington-South Burlington, VT Charleston-North Charleston, SC
## 2 2 2
## Cleveland-Elyria-Mentor, OH Deltona-Daytona Beach-Ormond Beach, FL Fort Wayne, IN
## 2 2 2
## Las Vegas-Paradise, NV Memphis, TN-MS-AR Miami-Fort Lauderdale-Miami Beach, FL
## 2 2 2
## Nashville-Davidson-Murfreesboro, TN Ogden-Clearfield, UT Oklahoma City, OK
## 2 2 2
## Oxnard-Thousand Oaks-Ventura, CA Phoenix-Mesa-Scottsdale, AZ Rochester, NY
## 2 2 2
## Salt Lake City, UT Springfield, IL Winston-Salem, NC
## 2 2 2
## Albuquerque, NM Iowa City, IA Madison, WI
## 3 3 3
## Norwich-New London, CT-RI Reno-Sparks, NV Visalia-Porterville, CA
## 3 3 3
## Charlotte-Gastonia-Concord, NC-SC Indianapolis, IN Omaha-Council Bluffs, NE-IA
## 4 4 4
## Peoria, IL Rochester-Dover, NH-ME San Diego-Carlsbad-San Marcos, CA
## 4 4 4
## Trenton-Ewing, NJ Tulsa, OK Orlando, FL
## 4 4 5
## Seattle-Tacoma-Bellevue, WA Austin-Round Rock, TX Brownsville-Harlingen, TX
## 5 6 6
## Des Moines, IA Little Rock-North Little Rock, AR New Haven, CT
## 6 6 6
## Portland-Vancouver-Beaverton, OR-WA Warner Robins, GA Tampa-St. Petersburg-Clearwater, FL
## 6 6 7
## Fayetteville-Springdale-Rogers, AR-MO Sacramento-Arden-Arcade-Roseville, CA Honolulu, HI
## 8 8 9
## Boston-Cambridge-Quincy, MA-NH Kansas City, MO-KS Bridgeport-Stamford-Norwalk, CT
## 11 11 12
## Milwaukee-Waukesha-West Allis, WI Providence-Fall River-Warwick, MA-RI Houston-Baytown-Sugar Land, TX
## 12 14 15
## Baltimore-Towson, MD Fresno, CA Pittsburgh, PA
## 16 16 16
## Dallas-Fort Worth-Arlington, TX Los Angeles-Long Beach-Santa Ana, CA San Jose-Sunnyvale-Santa Clara, CA
## 18 19 19
## Minneapolis-St Paul-Bloomington, MN-WI Hartford-West Hartford-East Hartford, CT Atlanta-Sandy Springs-Marietta, GA
## 23 26 27
## San Francisco-Oakland-Fremont, CA Detroit-Warren-Livonia, MI Chicago-Naperville-Joliet, IN-IN-WI
## 27 30 31
## Philadelphia-Camden-Wilmington, PA-NJ-DE Washington-Arlington-Alexandria, DC-VA-MD-WV New York-Northern New Jersey-Long Island, NY-NJ-PA
## 32 50 96
sort(tapply(CPS$Country == "Brazil", CPS$MetroArea, sum, na.rm=TRUE))
## Albany-Schenectady-Troy, NY Albany, GA Allentown-Bethlehem-Easton, PA-NJ
## 0 0 0
## Altoona, PA Amarillo, TX Anderson, IN
## 0 0 0
## Anderson, SC Ann Arbor, MI Anniston-Oxford, AL
## 0 0 0
## Appleton,WI Asheville, NC Athens-Clark County, GA
## 0 0 0
## Atlantic City, NJ Augusta-Richmond County, GA-SC Austin-Round Rock, TX
## 0 0 0
## Bakersfield, CA Baltimore-Towson, MD Bangor, ME
## 0 0 0
## Baton Rouge, LA Beaumont-Port Author, TX Bellingham, WA
## 0 0 0
## Bend, OR Billings, MT Binghamton, NY
## 0 0 0
## Birmingham-Hoover, AL Bloomington-Normal IL Bloomington, IN
## 0 0 0
## Boise City-Nampa, ID Boulder, CO Bowling Green, KY
## 0 0 0
## Brownsville-Harlingen, TX Buffalo-Niagara Falls, NY Burlington-South Burlington, VT
## 0 0 0
## Cedar Rapids, IA Champaign-Urbana, IL Charleston-North Charleston, SC
## 0 0 0
## Charleston, WV Chattanooga, TN-GA Cleveland-Elyria-Mentor, OH
## 0 0 0
## Coeur d'Alene, ID Colorado Springs, CO Columbia, MO
## 0 0 0
## Columbus, GA-AL Columbus, OH Corpus Christi, TX
## 0 0 0
## Dayton, OH Decatur, Al Decatur, IL
## 0 0 0
## Deltona-Daytona Beach-Ormond Beach, FL Des Moines, IA Detroit-Warren-Livonia, MI
## 0 0 0
## Dover, DE Duluth, MN-WI Durham, NC
## 0 0 0
## Eau Claire, WI El Centro, CA El Paso, TX
## 0 0 0
## Erie, PA Eugene-Springfield, OR Evansville, IN-KY
## 0 0 0
## Fargo, ND-MN Farmington, NM Fayetteville-Springdale-Rogers, AR-MO
## 0 0 0
## Fayetteville, NC Flint, MI Florence, AL
## 0 0 0
## Fort Collins-Loveland, CO Fort Smith, AR-OK Fort Walton Beach-Crestview-Destin, FL
## 0 0 0
## Fort Wayne, IN Fresno, CA Gainesville, FL
## 0 0 0
## Grand Rapids-Wyoming, MI Greeley, CO Green Bay, WI
## 0 0 0
## Greensboro-High Point, NC Greenville, SC Gulfport-Biloxi, MS
## 0 0 0
## Hagerstown-Martinsburg, MD-WV Harrisburg-Carlisle, PA Harrisonburg, VA
## 0 0 0
## Hickory-Morgantown-Lenoir, NC Holland-Grand Haven, MI Honolulu, HI
## 0 0 0
## Houston-Baytown-Sugar Land, TX Huntington-Ashland, WV-KY-OH Huntsville, AL
## 0 0 0
## Indianapolis, IN Iowa City, IA Jackson, MI
## 0 0 0
## Jackson, MS Jacksonville, NC Janesville, WI
## 0 0 0
## Johnson City, TN Johnstown, PA Joplin, MO
## 0 0 0
## Kalamazoo-Portage, MI Kankakee-Bradley, IL Killeen-Temple-Fort Hood, TX
## 0 0 0
## Kingsport-Bristol, TN-VA Kingston, NY Knoxville, TN
## 0 0 0
## La Crosse, WI Lafayette, LA Lake Charles, LA
## 0 0 0
## Lakeland-Winter Haven, FL Lancaster, PA Lansing-East Lansing, MI
## 0 0 0
## Laredo, TX Las Cruses, NM Las Vegas-Paradise, NV
## 0 0 0
## Lawrence, KS Lawton, OK Lexington-Fayette, KY
## 0 0 0
## Little Rock-North Little Rock, AR Longview, TX Lubbock, TX
## 0 0 0
## Lynchburg, VA Macon, GA Madera, CA
## 0 0 0
## Madison, WI McAllen-Edinburg-Pharr, TX Medford, OR
## 0 0 0
## Memphis, TN-MS-AR Merced, CA Michigan City-La Porte, IN
## 0 0 0
## Midland, TX Milwaukee-Waukesha-West Allis, WI Mobile, AL
## 0 0 0
## Modesto, CA Monroe, MI Muskegon-Norton Shores, MI
## 0 0 0
## Myrtle Beach-Conway-North Myrtle Beach, SC Napa, CA Naples-Marco Island, FL
## 0 0 0
## Nashville-Davidson-Murfreesboro, TN New Haven, CT New Orleans-Metairie-Kenner, LA
## 0 0 0
## Niles-Benton Harbor, MI Norwich-New London, CT-RI Ocala, FL
## 0 0 0
## Ocean City, NJ Ogden-Clearfield, UT Oklahoma City, OK
## 0 0 0
## Olympia, WA Omaha-Council Bluffs, NE-IA Oshkosh-Neenah, WI
## 0 0 0
## Palm Bay-Melbourne-Titusville, FL Panama City-Lynn Haven, FL Peoria, IL
## 0 0 0
## Pittsburgh, PA Port St. Lucie-Fort Pierce, FL Portland-South Portland, ME
## 0 0 0
## Portland-Vancouver-Beaverton, OR-WA Poughkeepsie-Newburgh-Middletown, NY Prescott, AZ
## 0 0 0
## Provo-Orem, UT Pueblo, CO Punta Gorda, FL
## 0 0 0
## Raleigh-Cary, NC Reading, PA Reno-Sparks, NV
## 0 0 0
## Richmond, VA Riverside-San Bernardino, CA Roanoke, VA
## 0 0 0
## Rochester-Dover, NH-ME Rockford, IL Saginaw-Saginaw Township North, MI
## 0 0 0
## Salinas, CA Salisbury, MD San Antonio, TX
## 0 0 0
## San Diego-Carlsbad-San Marcos, CA San Luis Obispo-Paso Robles, CA Santa-Cruz-Watsonville, CA
## 0 0 0
## Santa Barbara-Santa Maria-Goleta, CA Santa Fe, NM Santa Rosa-Petaluma, CA
## 0 0 0
## Sarasota-Bradenton-Venice, FL Savannah, GA Scranton-Wilkes Barre, PA
## 0 0 0
## Shreveport-Bossier City, LA Sioux Falls, SD South Bend-Mishawaka, IN-MI
## 0 0 0
## Spartanburg, SC Spokane, WA Springfield, IL
## 0 0 0
## Springfield, MA-CT Springfield, MO Springfield, OH
## 0 0 0
## St. Cloud, MN St. Louis, MO-IL Stockton, CA
## 0 0 0
## Syracuse, NY Tallahassee, FL Toledo, OH
## 0 0 0
## Topeka, KS Tucson, AZ Tulsa, OK
## 0 0 0
## Tuscaloosa, AL Utica-Rome, NY Valdosta, GA
## 0 0 0
## Vallejo-Fairfield, CA Vero Beach, FL Victoria, TX
## 0 0 0
## Vineland-Millville-Bridgeton, NJ Visalia-Porterville, CA Waco, TX
## 0 0 0
## Warner Robins, GA Waterloo-Cedar Falls, IA Wausau, WI
## 0 0 0
## Winston-Salem, NC Worcester, MA-CT Yakima, WA
## 0 0 0
## York-Hanover, PA Youngstown-Warren-Boardman, OH Akron, OH
## 0 0 1
## Albuquerque, NM Atlanta-Sandy Springs-Marietta, GA Bremerton-Silverdale, WA
## 1 1 1
## Cape Coral-Fort Myers, FL Chico, CA Cincinnati-Middletown, OH-KY-IN
## 1 1 1
## Denver-Aurora, CO Hartford-West Hartford-East Hartford, CT Kansas City, MO-KS
## 1 1 1
## Leominster-Fitchburg-Gardner, MA Louisville, KY-IN Minneapolis-St Paul-Bloomington, MN-WI
## 1 1 1
## Monroe, LA Montgomery, AL Oxnard-Thousand Oaks-Ventura, CA
## 1 1 1
## Pensacola-Ferry Pass-Brent, FL Racine, WI Rochester, NY
## 1 1 1
## Salem, OR San Jose-Sunnyvale-Santa Clara, CA Seattle-Tacoma-Bellevue, WA
## 1 1 1
## Tampa-St. Petersburg-Clearwater, FL Trenton-Ewing, NJ Virginia Beach-Norfolk-Newport News, VA-NC
## 1 1 1
## Waterbury, CT Wichita, KS Barnstable Town, MA
## 1 1 2
## Charlotte-Gastonia-Concord, NC-SC Chicago-Naperville-Joliet, IN-IN-WI Columbia, SC
## 2 2 2
## Dallas-Fort Worth-Arlington, TX Jacksonville, FL Orlando, FL
## 2 2 2
## Sacramento-Arden-Arcade-Roseville, CA Canton-Massillon, OH Phoenix-Mesa-Scottsdale, AZ
## 2 3 3
## Providence-Fall River-Warwick, MA-RI Salt Lake City, UT Davenport-Moline-Rock Island, IA-IL
## 3 3 4
## Philadelphia-Camden-Wilmington, PA-NJ-DE Danbury, CT San Francisco-Oakland-Fremont, CA
## 4 5 6
## Bridgeport-Stamford-Norwalk, CT New York-Northern New Jersey-Long Island, NY-NJ-PA Washington-Arlington-Alexandria, DC-VA-MD-WV
## 7 7 8
## Los Angeles-Long Beach-Santa Ana, CA Miami-Fort Lauderdale-Miami Beach, FL Boston-Cambridge-Quincy, MA-NH
## 9 16 18
sort(tapply(CPS$Country == "Somalia", CPS$MetroArea, sum, na.rm=TRUE))
## Akron, OH Albany-Schenectady-Troy, NY Albany, GA
## 0 0 0
## Albuquerque, NM Allentown-Bethlehem-Easton, PA-NJ Altoona, PA
## 0 0 0
## Amarillo, TX Anderson, IN Anderson, SC
## 0 0 0
## Ann Arbor, MI Anniston-Oxford, AL Appleton,WI
## 0 0 0
## Asheville, NC Athens-Clark County, GA Atlanta-Sandy Springs-Marietta, GA
## 0 0 0
## Atlantic City, NJ Augusta-Richmond County, GA-SC Austin-Round Rock, TX
## 0 0 0
## Bakersfield, CA Baltimore-Towson, MD Bangor, ME
## 0 0 0
## Barnstable Town, MA Baton Rouge, LA Beaumont-Port Author, TX
## 0 0 0
## Bellingham, WA Bend, OR Billings, MT
## 0 0 0
## Binghamton, NY Birmingham-Hoover, AL Bloomington-Normal IL
## 0 0 0
## Bloomington, IN Boise City-Nampa, ID Boston-Cambridge-Quincy, MA-NH
## 0 0 0
## Boulder, CO Bowling Green, KY Bremerton-Silverdale, WA
## 0 0 0
## Bridgeport-Stamford-Norwalk, CT Brownsville-Harlingen, TX Buffalo-Niagara Falls, NY
## 0 0 0
## Canton-Massillon, OH Cape Coral-Fort Myers, FL Cedar Rapids, IA
## 0 0 0
## Champaign-Urbana, IL Charleston-North Charleston, SC Charleston, WV
## 0 0 0
## Charlotte-Gastonia-Concord, NC-SC Chattanooga, TN-GA Chicago-Naperville-Joliet, IN-IN-WI
## 0 0 0
## Chico, CA Cincinnati-Middletown, OH-KY-IN Cleveland-Elyria-Mentor, OH
## 0 0 0
## Coeur d'Alene, ID Colorado Springs, CO Columbia, MO
## 0 0 0
## Columbia, SC Columbus, GA-AL Corpus Christi, TX
## 0 0 0
## Dallas-Fort Worth-Arlington, TX Danbury, CT Davenport-Moline-Rock Island, IA-IL
## 0 0 0
## Decatur, Al Decatur, IL Deltona-Daytona Beach-Ormond Beach, FL
## 0 0 0
## Denver-Aurora, CO Des Moines, IA Detroit-Warren-Livonia, MI
## 0 0 0
## Dover, DE Duluth, MN-WI Durham, NC
## 0 0 0
## Eau Claire, WI El Centro, CA El Paso, TX
## 0 0 0
## Erie, PA Eugene-Springfield, OR Evansville, IN-KY
## 0 0 0
## Farmington, NM Fayetteville-Springdale-Rogers, AR-MO Fayetteville, NC
## 0 0 0
## Flint, MI Florence, AL Fort Collins-Loveland, CO
## 0 0 0
## Fort Smith, AR-OK Fort Walton Beach-Crestview-Destin, FL Fort Wayne, IN
## 0 0 0
## Fresno, CA Gainesville, FL Grand Rapids-Wyoming, MI
## 0 0 0
## Greeley, CO Green Bay, WI Greensboro-High Point, NC
## 0 0 0
## Greenville, SC Gulfport-Biloxi, MS Hagerstown-Martinsburg, MD-WV
## 0 0 0
## Harrisburg-Carlisle, PA Harrisonburg, VA Hartford-West Hartford-East Hartford, CT
## 0 0 0
## Hickory-Morgantown-Lenoir, NC Holland-Grand Haven, MI Honolulu, HI
## 0 0 0
## Huntington-Ashland, WV-KY-OH Huntsville, AL Indianapolis, IN
## 0 0 0
## Iowa City, IA Jackson, MI Jackson, MS
## 0 0 0
## Jacksonville, FL Jacksonville, NC Janesville, WI
## 0 0 0
## Johnson City, TN Johnstown, PA Joplin, MO
## 0 0 0
## Kalamazoo-Portage, MI Kankakee-Bradley, IL Kansas City, MO-KS
## 0 0 0
## Killeen-Temple-Fort Hood, TX Kingsport-Bristol, TN-VA Kingston, NY
## 0 0 0
## Knoxville, TN La Crosse, WI Lafayette, LA
## 0 0 0
## Lake Charles, LA Lakeland-Winter Haven, FL Lancaster, PA
## 0 0 0
## Lansing-East Lansing, MI Laredo, TX Las Cruses, NM
## 0 0 0
## Las Vegas-Paradise, NV Lawrence, KS Lawton, OK
## 0 0 0
## Leominster-Fitchburg-Gardner, MA Lexington-Fayette, KY Little Rock-North Little Rock, AR
## 0 0 0
## Longview, TX Los Angeles-Long Beach-Santa Ana, CA Louisville, KY-IN
## 0 0 0
## Lubbock, TX Lynchburg, VA Macon, GA
## 0 0 0
## Madera, CA Madison, WI McAllen-Edinburg-Pharr, TX
## 0 0 0
## Medford, OR Memphis, TN-MS-AR Merced, CA
## 0 0 0
## Miami-Fort Lauderdale-Miami Beach, FL Michigan City-La Porte, IN Midland, TX
## 0 0 0
## Milwaukee-Waukesha-West Allis, WI Mobile, AL Modesto, CA
## 0 0 0
## Monroe, LA Monroe, MI Montgomery, AL
## 0 0 0
## Muskegon-Norton Shores, MI Myrtle Beach-Conway-North Myrtle Beach, SC Napa, CA
## 0 0 0
## Naples-Marco Island, FL Nashville-Davidson-Murfreesboro, TN New Haven, CT
## 0 0 0
## New Orleans-Metairie-Kenner, LA New York-Northern New Jersey-Long Island, NY-NJ-PA Niles-Benton Harbor, MI
## 0 0 0
## Norwich-New London, CT-RI Ocala, FL Ocean City, NJ
## 0 0 0
## Ogden-Clearfield, UT Oklahoma City, OK Olympia, WA
## 0 0 0
## Omaha-Council Bluffs, NE-IA Orlando, FL Oshkosh-Neenah, WI
## 0 0 0
## Oxnard-Thousand Oaks-Ventura, CA Palm Bay-Melbourne-Titusville, FL Panama City-Lynn Haven, FL
## 0 0 0
## Pensacola-Ferry Pass-Brent, FL Peoria, IL Philadelphia-Camden-Wilmington, PA-NJ-DE
## 0 0 0
## Pittsburgh, PA Port St. Lucie-Fort Pierce, FL Poughkeepsie-Newburgh-Middletown, NY
## 0 0 0
## Prescott, AZ Providence-Fall River-Warwick, MA-RI Provo-Orem, UT
## 0 0 0
## Pueblo, CO Punta Gorda, FL Racine, WI
## 0 0 0
## Raleigh-Cary, NC Reading, PA Reno-Sparks, NV
## 0 0 0
## Riverside-San Bernardino, CA Roanoke, VA Rochester-Dover, NH-ME
## 0 0 0
## Rochester, NY Rockford, IL Sacramento-Arden-Arcade-Roseville, CA
## 0 0 0
## Saginaw-Saginaw Township North, MI Salem, OR Salinas, CA
## 0 0 0
## Salisbury, MD Salt Lake City, UT San Antonio, TX
## 0 0 0
## San Diego-Carlsbad-San Marcos, CA San Francisco-Oakland-Fremont, CA San Jose-Sunnyvale-Santa Clara, CA
## 0 0 0
## San Luis Obispo-Paso Robles, CA Santa-Cruz-Watsonville, CA Santa Barbara-Santa Maria-Goleta, CA
## 0 0 0
## Santa Fe, NM Santa Rosa-Petaluma, CA Sarasota-Bradenton-Venice, FL
## 0 0 0
## Savannah, GA Scranton-Wilkes Barre, PA Shreveport-Bossier City, LA
## 0 0 0
## South Bend-Mishawaka, IN-MI Spartanburg, SC Spokane, WA
## 0 0 0
## Springfield, IL Springfield, MA-CT Springfield, MO
## 0 0 0
## Springfield, OH St. Louis, MO-IL Stockton, CA
## 0 0 0
## Syracuse, NY Tallahassee, FL Tampa-St. Petersburg-Clearwater, FL
## 0 0 0
## Toledo, OH Topeka, KS Trenton-Ewing, NJ
## 0 0 0
## Tucson, AZ Tulsa, OK Tuscaloosa, AL
## 0 0 0
## Utica-Rome, NY Valdosta, GA Vallejo-Fairfield, CA
## 0 0 0
## Vero Beach, FL Victoria, TX Vineland-Millville-Bridgeton, NJ
## 0 0 0
## Virginia Beach-Norfolk-Newport News, VA-NC Visalia-Porterville, CA Waco, TX
## 0 0 0
## Warner Robins, GA Washington-Arlington-Alexandria, DC-VA-MD-WV Waterbury, CT
## 0 0 0
## Waterloo-Cedar Falls, IA Wausau, WI Wichita, KS
## 0 0 0
## Winston-Salem, NC Worcester, MA-CT Yakima, WA
## 0 0 0
## York-Hanover, PA Youngstown-Warren-Boardman, OH Dayton, OH
## 0 0 1
## Richmond, VA Houston-Baytown-Sugar Land, TX Sioux Falls, SD
## 1 2 2
## Burlington-South Burlington, VT Portland-South Portland, ME Portland-Vancouver-Beaverton, OR-WA
## 3 3 3
## Columbus, OH Fargo, ND-MN Phoenix-Mesa-Scottsdale, AZ
## 5 5 7
## Seattle-Tacoma-Bellevue, WA St. Cloud, MN Minneapolis-St Paul-Bloomington, MN-WI
## 7 7 17Explanation: We see that New York has the most interviewees born in India (96), Boston has the most born in Brazil (18), and Minneapolis has the most born in Somalia (17).