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.
# Read in the datasets
CPS = read.csv("CPSData.csv")
MetroAreaMap = read.csv("MetroAreaCodes.csv")
CountryMap = read.csv("CountryCodes.csv")# Output a string
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 ...From str(CPS), we can read that there are 131302 interviewees.
# Output a summary
z = summary(CPS)
kable(z)| PeopleInHousehold | Region | State | MetroAreaCode | Age | Married | Sex | Education | Race | Hispanic | CountryOfBirthCode | Citizenship | EmploymentStatus | Industry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. : 1.000 | Midwest :30684 | California :11570 | Min. :10420 | Min. : 0.00 | Divorced :11151 | Female:67481 | High school :30906 | American Indian : 1433 | Min. :0.0000 | Min. : 57.00 | Citizen, Native :116639 | Disabled : 5712 | Educational and health services :15017 | |
| 1st Qu.: 2.000 | Northeast:25939 | Texas : 7077 | 1st Qu.:21780 | 1st Qu.:19.00 | Married :55509 | Male :63821 | Bachelor’s degree :19443 | Asian : 6520 | 1st Qu.:0.0000 | 1st Qu.: 57.00 | Citizen, Naturalized: 7073 | Employed :61733 | Trade : 8933 | |
| Median : 3.000 | South :41502 | New York : 5595 | Median :34740 | Median :39.00 | Never Married:30772 | NA | Some college, no degree:18863 | Black : 13913 | Median :0.0000 | Median : 57.00 | Non-Citizen : 7590 | Not in Labor Force:15246 | Professional and business services: 7519 | |
| Mean : 3.284 | West :33177 | Florida : 5149 | Mean :35075 | Mean :38.83 | Separated : 2027 | NA | No high school diploma :16095 | Multiracial : 2897 | Mean :0.1393 | Mean : 82.68 | NA | Retired :18619 | Manufacturing : 6791 | |
| 3rd Qu.: 4.000 | NA | Pennsylvania: 3930 | 3rd Qu.:41860 | 3rd Qu.:57.00 | Widowed : 6505 | NA | Associate degree : 9913 | Pacific Islander: 618 | 3rd Qu.:0.0000 | 3rd Qu.: 57.00 | NA | Unemployed : 4203 | Leisure and hospitality : 6364 | |
| Max. :15.000 | NA | Illinois : 3912 | Max. :79600 | Max. :85.00 | NA’s :25338 | NA | (Other) :10744 | White :105921 | Max. :1.0000 | Max. :555.00 | NA | NA’s :25789 | (Other) :21618 | |
| NA | NA | (Other) :94069 | NA’s :34238 | NA | NA | NA | NA’s :25338 | NA | NA | NA | NA | NA | NA’s :65060 |
# Tabulates the number of observations in the industry variable
z = table(CPS$Industry)
kable(z)| Var1 | Freq |
|---|---|
| Agriculture, forestry, fishing, and hunting | 1307 |
| Armed forces | 29 |
| Construction | 4387 |
| Educational and health services | 15017 |
| Financial | 4347 |
| Information | 1328 |
| Leisure and hospitality | 6364 |
| Manufacturing | 6791 |
| Mining | 550 |
| Other services | 3224 |
| Professional and business services | 7519 |
| Public administration | 3186 |
| Trade | 8933 |
| Transportation and utilities | 3260 |
The output of summary(CPS) orders the levels of a factor variable like Industry from largest to smallest, so we can see that “Educational and health services” is the most common Industry. table(CPS$Industry) would have provided the breakdown across all industries.
# Sorts the tabulation
z = sort(table(CPS$State))
kable(z)| Var1 | Freq |
|---|---|
| New Mexico | 1102 |
| Montana | 1214 |
| Mississippi | 1230 |
| Alabama | 1376 |
| West Virginia | 1409 |
| Arkansas | 1421 |
| Louisiana | 1450 |
| Idaho | 1518 |
| Oklahoma | 1523 |
| Arizona | 1528 |
| Alaska | 1590 |
| Wyoming | 1624 |
| North Dakota | 1645 |
| South Carolina | 1658 |
| Tennessee | 1784 |
| District of Columbia | 1791 |
| Kentucky | 1841 |
| Utah | 1842 |
| Nevada | 1856 |
| Vermont | 1890 |
| Kansas | 1935 |
| Oregon | 1943 |
| Nebraska | 1949 |
| Massachusetts | 1987 |
| South Dakota | 2000 |
| Indiana | 2004 |
| Hawaii | 2099 |
| Missouri | 2145 |
| Rhode Island | 2209 |
| Delaware | 2214 |
| Maine | 2263 |
| Washington | 2366 |
| Iowa | 2528 |
| New Jersey | 2567 |
| North Carolina | 2619 |
| New Hampshire | 2662 |
| Wisconsin | 2686 |
| Georgia | 2807 |
| Connecticut | 2836 |
| Colorado | 2925 |
| Virginia | 2953 |
| Michigan | 3063 |
| Minnesota | 3139 |
| Maryland | 3200 |
| Ohio | 3678 |
| Illinois | 3912 |
| Pennsylvania | 3930 |
| Florida | 5149 |
| New York | 5595 |
| Texas | 7077 |
| California | 11570 |
New Mexico.
# Sorts the tabulation
z = sort(table(CPS$State))
kable(z)| Var1 | Freq |
|---|---|
| New Mexico | 1102 |
| Montana | 1214 |
| Mississippi | 1230 |
| Alabama | 1376 |
| West Virginia | 1409 |
| Arkansas | 1421 |
| Louisiana | 1450 |
| Idaho | 1518 |
| Oklahoma | 1523 |
| Arizona | 1528 |
| Alaska | 1590 |
| Wyoming | 1624 |
| North Dakota | 1645 |
| South Carolina | 1658 |
| Tennessee | 1784 |
| District of Columbia | 1791 |
| Kentucky | 1841 |
| Utah | 1842 |
| Nevada | 1856 |
| Vermont | 1890 |
| Kansas | 1935 |
| Oregon | 1943 |
| Nebraska | 1949 |
| Massachusetts | 1987 |
| South Dakota | 2000 |
| Indiana | 2004 |
| Hawaii | 2099 |
| Missouri | 2145 |
| Rhode Island | 2209 |
| Delaware | 2214 |
| Maine | 2263 |
| Washington | 2366 |
| Iowa | 2528 |
| New Jersey | 2567 |
| North Carolina | 2619 |
| New Hampshire | 2662 |
| Wisconsin | 2686 |
| Georgia | 2807 |
| Connecticut | 2836 |
| Colorado | 2925 |
| Virginia | 2953 |
| Michigan | 3063 |
| Minnesota | 3139 |
| Maryland | 3200 |
| Ohio | 3678 |
| Illinois | 3912 |
| Pennsylvania | 3930 |
| Florida | 5149 |
| New York | 5595 |
| Texas | 7077 |
| California | 11570 |
California.
# Calculate the proportion
m = table(CPS$Citizenship)
kable(m)| Var1 | Freq |
|---|---|
| Citizen, Native | 116639 |
| Citizen, Naturalized | 7073 |
| Non-Citizen | 7590 |
(m[1]+m[2])/(m[1]+m[2]+m[3])
## Citizen, Native
## 0.9421943From table(CPS$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.
# Tabulates the races and hispanic variables
z = table(CPS$Race, CPS$Hispanic) >=250
kable(z)| 0 | 1 | |
|---|---|---|
| American Indian | TRUE | TRUE |
| Asian | TRUE | FALSE |
| Black | TRUE | TRUE |
| Multiracial | TRUE | TRUE |
| Pacific Islander | TRUE | FALSE |
| White | TRUE | TRUE |
The breakdown of race and Hispanic ethnicity can be obtained with table(CPS\(Race, CPS\)Hispanic).
# Outputs a summary
z = summary(CPS)
kable(z)| PeopleInHousehold | Region | State | MetroAreaCode | Age | Married | Sex | Education | Race | Hispanic | CountryOfBirthCode | Citizenship | EmploymentStatus | Industry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. : 1.000 | Midwest :30684 | California :11570 | Min. :10420 | Min. : 0.00 | Divorced :11151 | Female:67481 | High school :30906 | American Indian : 1433 | Min. :0.0000 | Min. : 57.00 | Citizen, Native :116639 | Disabled : 5712 | Educational and health services :15017 | |
| 1st Qu.: 2.000 | Northeast:25939 | Texas : 7077 | 1st Qu.:21780 | 1st Qu.:19.00 | Married :55509 | Male :63821 | Bachelor’s degree :19443 | Asian : 6520 | 1st Qu.:0.0000 | 1st Qu.: 57.00 | Citizen, Naturalized: 7073 | Employed :61733 | Trade : 8933 | |
| Median : 3.000 | South :41502 | New York : 5595 | Median :34740 | Median :39.00 | Never Married:30772 | NA | Some college, no degree:18863 | Black : 13913 | Median :0.0000 | Median : 57.00 | Non-Citizen : 7590 | Not in Labor Force:15246 | Professional and business services: 7519 | |
| Mean : 3.284 | West :33177 | Florida : 5149 | Mean :35075 | Mean :38.83 | Separated : 2027 | NA | No high school diploma :16095 | Multiracial : 2897 | Mean :0.1393 | Mean : 82.68 | NA | Retired :18619 | Manufacturing : 6791 | |
| 3rd Qu.: 4.000 | NA | Pennsylvania: 3930 | 3rd Qu.:41860 | 3rd Qu.:57.00 | Widowed : 6505 | NA | Associate degree : 9913 | Pacific Islander: 618 | 3rd Qu.:0.0000 | 3rd Qu.: 57.00 | NA | Unemployed : 4203 | Leisure and hospitality : 6364 | |
| Max. :15.000 | NA | Illinois : 3912 | Max. :79600 | Max. :85.00 | NA’s :25338 | NA | (Other) :10744 | White :105921 | Max. :1.0000 | Max. :555.00 | NA | NA’s :25789 | (Other) :21618 | |
| NA | NA | (Other) :94069 | NA’s :34238 | NA | NA | NA | NA’s :25338 | NA | NA | NA | NA | NA | NA’s :65060 | |
| This | can be read from the | output of summary | (CPS). |
# Tabulates various relationship amongst the variables
z = table(CPS$Region, is.na(CPS$Married))
kable(z)| FALSE | TRUE | |
|---|---|---|
| Midwest | 24609 | 6075 |
| Northeast | 21432 | 4507 |
| South | 33535 | 7967 |
| West | 26388 | 6789 |
z = table(CPS$Sex, is.na(CPS$Married))
kable(z)| FALSE | TRUE | |
|---|---|---|
| Female | 55264 | 12217 |
| Male | 50700 | 13121 |
z = table(CPS$Age, is.na(CPS$Married))
kable(z)| 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 |
z = table(CPS$Citizenship, is.na(CPS$Married))
kable(z)| FALSE | TRUE | |
|---|---|---|
| Citizen, Native | 91956 | 24683 |
| Citizen, Naturalized | 6910 | 163 |
| Non-Citizen | 7098 | 492 |
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.
# Tabulates the observations living in a non-MetroAreaCode
z = table(CPS$State, is.na(CPS$MetroAreaCode))
kable(z)| 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 | 1624 |
2 interviewees
# Tabulates the number of observations not living in a metro area
z = table(CPS$State, is.na(CPS$MetroAreaCode))
kable(z)| 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 | 1624 |
# Calculates the proprotion
m = table(CPS$Region, is.na(CPS$MetroAreaCode))
z = prop.table(m,1)
kable(m)| FALSE | TRUE | |
|---|---|---|
| Midwest | 20010 | 10674 |
| Northeast | 20330 | 5609 |
| South | 31631 | 9871 |
| West | 25093 | 8084 |
Explanation: 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.
# Sorts the tabulation
z = sort(tapply(is.na(CPS$MetroAreaCode), CPS$State, mean))
kable(z)| x | |
|---|---|
| District of Columbia | 0.00000000 |
| New Jersey | 0.00000000 |
| Rhode Island | 0.00000000 |
| California | 0.02048401 |
| Florida | 0.03923092 |
| Massachusetts | 0.06492199 |
| Maryland | 0.06937500 |
| New York | 0.08060769 |
| Connecticut | 0.08568406 |
| Illinois | 0.11221881 |
| Colorado | 0.12991453 |
| Arizona | 0.13154450 |
| Nevada | 0.13308190 |
| Texas | 0.14370496 |
| Louisiana | 0.16137931 |
| Pennsylvania | 0.17430025 |
| Michigan | 0.17825661 |
| Washington | 0.18131868 |
| Georgia | 0.19843249 |
| Virginia | 0.19844226 |
| Utah | 0.21009772 |
| Oregon | 0.21821925 |
| Delaware | 0.23396567 |
| New Mexico | 0.24500907 |
| Hawaii | 0.24916627 |
| Ohio | 0.25122349 |
| Alabama | 0.25872093 |
| Indiana | 0.29141717 |
| Wisconsin | 0.29932986 |
| South Carolina | 0.31302774 |
| Minnesota | 0.31506849 |
| Oklahoma | 0.32764281 |
| Missouri | 0.32867133 |
| Tennessee | 0.35594170 |
| Kansas | 0.36227390 |
| North Carolina | 0.37304315 |
| Iowa | 0.48694620 |
| Arkansas | 0.49049965 |
| Idaho | 0.49868248 |
| Kentucky | 0.50678979 |
| New Hampshire | 0.56874530 |
| Nebraska | 0.58132376 |
| Maine | 0.59832081 |
| Vermont | 0.65238095 |
| Mississippi | 0.69430894 |
| South Dakota | 0.70250000 |
| North Dakota | 0.73738602 |
| West Virginia | 0.75585522 |
| Montana | 0.83607908 |
| Alaska | 1.00000000 |
| Wyoming | 1.00000000 |
Wisconsin.
# Sorts the tabulation
z = sort(tapply(is.na(CPS$MetroAreaCode), CPS$State, mean))
kable(z)| x | |
|---|---|
| District of Columbia | 0.00000000 |
| New Jersey | 0.00000000 |
| Rhode Island | 0.00000000 |
| California | 0.02048401 |
| Florida | 0.03923092 |
| Massachusetts | 0.06492199 |
| Maryland | 0.06937500 |
| New York | 0.08060769 |
| Connecticut | 0.08568406 |
| Illinois | 0.11221881 |
| Colorado | 0.12991453 |
| Arizona | 0.13154450 |
| Nevada | 0.13308190 |
| Texas | 0.14370496 |
| Louisiana | 0.16137931 |
| Pennsylvania | 0.17430025 |
| Michigan | 0.17825661 |
| Washington | 0.18131868 |
| Georgia | 0.19843249 |
| Virginia | 0.19844226 |
| Utah | 0.21009772 |
| Oregon | 0.21821925 |
| Delaware | 0.23396567 |
| New Mexico | 0.24500907 |
| Hawaii | 0.24916627 |
| Ohio | 0.25122349 |
| Alabama | 0.25872093 |
| Indiana | 0.29141717 |
| Wisconsin | 0.29932986 |
| South Carolina | 0.31302774 |
| Minnesota | 0.31506849 |
| Oklahoma | 0.32764281 |
| Missouri | 0.32867133 |
| Tennessee | 0.35594170 |
| Kansas | 0.36227390 |
| North Carolina | 0.37304315 |
| Iowa | 0.48694620 |
| Arkansas | 0.49049965 |
| Idaho | 0.49868248 |
| Kentucky | 0.50678979 |
| New Hampshire | 0.56874530 |
| Nebraska | 0.58132376 |
| Maine | 0.59832081 |
| Vermont | 0.65238095 |
| Mississippi | 0.69430894 |
| South Dakota | 0.70250000 |
| North Dakota | 0.73738602 |
| West Virginia | 0.75585522 |
| Montana | 0.83607908 |
| Alaska | 1.00000000 |
| Wyoming | 1.00000000 |
Montana
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.
# Outputs the string
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 ...271 observations.
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 ...149 observations.
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:
# Merge data
CPS = merge(CPS, MetroAreaMap, by.x="MetroAreaCode", by.y="Code", all.x=TRUE)# Output a summary
z = summary(CPS)
kable(z)| MetroAreaCode | PeopleInHousehold | Region | State | Age | Married | Sex | Education | Race | Hispanic | CountryOfBirthCode | Citizenship | EmploymentStatus | Industry | MetroArea | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. :10420 | Min. : 1.000 | Midwest :30684 | California :11570 | Min. : 0.00 | Divorced :11151 | Female:67481 | High school :30906 | American Indian : 1433 | Min. :0.0000 | Min. : 57.00 | Citizen, Native :116639 | Disabled : 5712 | Educational and health services :15017 | New York-Northern New Jersey-Long Island, NY-NJ-PA: 5409 | |
| 1st Qu.:21780 | 1st Qu.: 2.000 | Northeast:25939 | Texas : 7077 | 1st Qu.:19.00 | Married :55509 | Male :63821 | Bachelor’s degree :19443 | Asian : 6520 | 1st Qu.:0.0000 | 1st Qu.: 57.00 | Citizen, Naturalized: 7073 | Employed :61733 | Trade : 8933 | Washington-Arlington-Alexandria, DC-VA-MD-WV : 4177 | |
| Median :34740 | Median : 3.000 | South :41502 | New York : 5595 | Median :39.00 | Never Married:30772 | NA | Some college, no degree:18863 | Black : 13913 | Median :0.0000 | Median : 57.00 | Non-Citizen : 7590 | Not in Labor Force:15246 | Professional and business services: 7519 | Los Angeles-Long Beach-Santa Ana, CA : 4102 | |
| Mean :35075 | Mean : 3.284 | West :33177 | Florida : 5149 | Mean :38.83 | Separated : 2027 | NA | No high school diploma :16095 | Multiracial : 2897 | Mean :0.1393 | Mean : 82.68 | NA | Retired :18619 | Manufacturing : 6791 | Philadelphia-Camden-Wilmington, PA-NJ-DE : 2855 | |
| 3rd Qu.:41860 | 3rd Qu.: 4.000 | NA | Pennsylvania: 3930 | 3rd Qu.:57.00 | Widowed : 6505 | NA | Associate degree : 9913 | Pacific Islander: 618 | 3rd Qu.:0.0000 | 3rd Qu.: 57.00 | NA | Unemployed : 4203 | Leisure and hospitality : 6364 | Chicago-Naperville-Joliet, IN-IN-WI : 2772 | |
| Max. :79600 | Max. :15.000 | NA | Illinois : 3912 | Max. :85.00 | NA’s :25338 | NA | (Other) :10744 | White :105921 | Max. :1.0000 | Max. :555.00 | NA | NA’s :25789 | (Other) :21618 | (Other) :77749 | |
| NA’s :34238 | NA | NA | (Other) :94069 | NA | NA | NA | NA’s :25338 | NA | NA | NA | NA | NA | NA’s :65060 | NA’s :34238 |
MetroArea
# Output a summary
z = summary(CPS)
kable(z)| MetroAreaCode | PeopleInHousehold | Region | State | Age | Married | Sex | Education | Race | Hispanic | CountryOfBirthCode | Citizenship | EmploymentStatus | Industry | MetroArea | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. :10420 | Min. : 1.000 | Midwest :30684 | California :11570 | Min. : 0.00 | Divorced :11151 | Female:67481 | High school :30906 | American Indian : 1433 | Min. :0.0000 | Min. : 57.00 | Citizen, Native :116639 | Disabled : 5712 | Educational and health services :15017 | New York-Northern New Jersey-Long Island, NY-NJ-PA: 5409 | |
| 1st Qu.:21780 | 1st Qu.: 2.000 | Northeast:25939 | Texas : 7077 | 1st Qu.:19.00 | Married :55509 | Male :63821 | Bachelor’s degree :19443 | Asian : 6520 | 1st Qu.:0.0000 | 1st Qu.: 57.00 | Citizen, Naturalized: 7073 | Employed :61733 | Trade : 8933 | Washington-Arlington-Alexandria, DC-VA-MD-WV : 4177 | |
| Median :34740 | Median : 3.000 | South :41502 | New York : 5595 | Median :39.00 | Never Married:30772 | NA | Some college, no degree:18863 | Black : 13913 | Median :0.0000 | Median : 57.00 | Non-Citizen : 7590 | Not in Labor Force:15246 | Professional and business services: 7519 | Los Angeles-Long Beach-Santa Ana, CA : 4102 | |
| Mean :35075 | Mean : 3.284 | West :33177 | Florida : 5149 | Mean :38.83 | Separated : 2027 | NA | No high school diploma :16095 | Multiracial : 2897 | Mean :0.1393 | Mean : 82.68 | NA | Retired :18619 | Manufacturing : 6791 | Philadelphia-Camden-Wilmington, PA-NJ-DE : 2855 | |
| 3rd Qu.:41860 | 3rd Qu.: 4.000 | NA | Pennsylvania: 3930 | 3rd Qu.:57.00 | Widowed : 6505 | NA | Associate degree : 9913 | Pacific Islander: 618 | 3rd Qu.:0.0000 | 3rd Qu.: 57.00 | NA | Unemployed : 4203 | Leisure and hospitality : 6364 | Chicago-Naperville-Joliet, IN-IN-WI : 2772 | |
| Max. :79600 | Max. :15.000 | NA | Illinois : 3912 | Max. :85.00 | NA’s :25338 | NA | (Other) :10744 | White :105921 | Max. :1.0000 | Max. :555.00 | NA | NA’s :25789 | (Other) :21618 | (Other) :77749 | |
| NA’s :34238 | NA | NA | (Other) :94069 | NA | NA | NA | NA’s :25338 | NA | NA | NA | NA | NA | NA’s :65060 | NA’s :34238 |
34238 interviewees.
# Sort the tabulation
z = sort(table(CPS$MetroArea))
kable(z)| Var1 | Freq |
|---|---|
| Appleton-Oshkosh-Neenah, WI | 0 |
| Grand Rapids-Muskegon-Holland, MI | 0 |
| Greenville-Spartanburg-Anderson, SC | 0 |
| Hinesville-Fort Stewart, GA | 0 |
| Jamestown, NY | 0 |
| Kalamazoo-Battle Creek, MI | 0 |
| Portsmouth-Rochester, NH-ME | 0 |
| Bowling Green, KY | 29 |
| Ocean City, NJ | 30 |
| Springfield, OH | 34 |
| Bloomington-Normal IL | 40 |
| Valdosta, GA | 42 |
| Warner Robins, GA | 42 |
| Tallahassee, FL | 43 |
| Columbia, MO | 47 |
| Punta Gorda, FL | 48 |
| Midland, TX | 51 |
| Niles-Benton Harbor, MI | 51 |
| Johnson City, TN | 52 |
| Santa Fe, NM | 52 |
| Prescott, AZ | 54 |
| Vineland-Millville-Bridgeton, NJ | 54 |
| Hickory-Morgantown-Lenoir, NC | 57 |
| Madera, CA | 57 |
| Columbus, GA-AL | 59 |
| Joplin, MO | 59 |
| Panama City-Lynn Haven, FL | 59 |
| Chico, CA | 60 |
| Anniston-Oxford, AL | 61 |
| Napa, CA | 61 |
| Anderson, IN | 62 |
| Florence, AL | 63 |
| Jacksonville, NC | 63 |
| Johnstown, PA | 63 |
| Lubbock, TX | 63 |
| Monroe, MI | 63 |
| Anderson, SC | 64 |
| Farmington, NM | 64 |
| Athens-Clark County, GA | 65 |
| Gulfport-Biloxi, MS | 65 |
| Longview, TX | 65 |
| Macon, GA | 65 |
| Leominster-Fitchburg-Gardner, MA | 66 |
| Roanoke, VA | 66 |
| Santa-Cruz-Watsonville, CA | 66 |
| Kingsport-Bristol, TN-VA | 67 |
| Albany, GA | 68 |
| Bellingham, WA | 70 |
| Gainesville, FL | 70 |
| Jackson, MI | 70 |
| Binghamton, NY | 73 |
| Lynchburg, VA | 73 |
| Saginaw-Saginaw Township North, MI | 74 |
| Salisbury, MD | 74 |
| Barnstable Town, MA | 75 |
| Ocala, FL | 76 |
| Springfield, IL | 76 |
| Fayetteville, NC | 77 |
| Michigan City-La Porte, IN | 77 |
| San Luis Obispo-Paso Robles, CA | 77 |
| Holland-Grand Haven, MI | 78 |
| Tuscaloosa, AL | 78 |
| Brownsville-Harlingen, TX | 79 |
| Vero Beach, FL | 79 |
| Waco, TX | 79 |
| Fort Walton Beach-Crestview-Destin, FL | 80 |
| Utica-Rome, NY | 80 |
| Decatur, IL | 81 |
| Lake Charles, LA | 81 |
| South Bend-Mishawaka, IN-MI | 81 |
| Altoona, PA | 82 |
| Huntington-Ashland, WV-KY-OH | 82 |
| Medford, OR | 82 |
| Naples-Marco Island, FL | 82 |
| St. Cloud, MN | 82 |
| Ann Arbor, MI | 85 |
| Oshkosh-Neenah, WI | 85 |
| Hagerstown-Martinsburg, MD-WV | 86 |
| Bremerton-Silverdale, WA | 87 |
| Erie, PA | 87 |
| Kankakee-Bradley, IL | 87 |
| Kingston, NY | 87 |
| Amarillo, TX | 88 |
| Laredo, TX | 89 |
| Harrisonburg, VA | 90 |
| Muskegon-Norton Shores, MI | 90 |
| Trenton-Ewing, NJ | 91 |
| Decatur, Al | 96 |
| Wausau, WI | 96 |
| Lawton, OK | 97 |
| Lawrence, KS | 98 |
| El Centro, CA | 99 |
| Evansville, IN-KY | 99 |
| Janesville, WI | 99 |
| Olympia, WA | 99 |
| Spartanburg, SC | 99 |
| Killeen-Temple-Fort Hood, TX | 101 |
| Flint, MI | 102 |
| Myrtle Beach-Conway-North Myrtle Beach, SC | 102 |
| Montgomery, AL | 103 |
| Bloomington, IN | 104 |
| Salinas, CA | 104 |
| Fort Smith, AR-OK | 105 |
| Merced, CA | 106 |
| Las Cruses, NM | 107 |
| Pensacola-Ferry Pass-Brent, FL | 107 |
| Port St. Lucie-Fort Pierce, FL | 109 |
| Eau Claire, WI | 110 |
| Mobile, AL | 110 |
| Atlantic City, NJ | 111 |
| Danbury, CT | 112 |
| Peoria, IL | 112 |
| Yakima, WA | 112 |
| La Crosse, WI | 114 |
| Rockford, IL | 114 |
| Asheville, NC | 116 |
| Victoria, TX | 116 |
| Coeur d’Alene, ID | 117 |
| Huntsville, AL | 117 |
| York-Hanover, PA | 117 |
| Canton-Massillon, OH | 118 |
| Lansing-East Lansing, MI | 119 |
| Racine, WI | 119 |
| Visalia-Porterville, CA | 121 |
| Champaign-Urbana, IL | 122 |
| Beaumont-Port Author, TX | 123 |
| Appleton,WI | 125 |
| Duluth, MN-WI | 126 |
| Kalamazoo-Portage, MI | 127 |
| Winston-Salem, NC | 127 |
| Santa Rosa-Petaluma, CA | 129 |
| Pueblo, CO | 130 |
| Iowa City, IA | 131 |
| Corpus Christi, TX | 132 |
| Santa Barbara-Santa Maria-Goleta, CA | 132 |
| Vallejo-Fairfield, CA | 133 |
| Fort Wayne, IN | 136 |
| Green Bay, WI | 136 |
| Bend, OR | 140 |
| Deltona-Daytona Beach-Ormond Beach, FL | 140 |
| Reading, PA | 142 |
| Worcester, MA-CT | 144 |
| Cape Coral-Fort Myers, FL | 146 |
| Shreveport-Bossier City, LA | 146 |
| Lakeland-Winter Haven, FL | 149 |
| Youngstown-Warren-Boardman, OH | 153 |
| Springfield, MA-CT | 155 |
| Lancaster, PA | 156 |
| Spokane, WA | 156 |
| Waterloo-Cedar Falls, IA | 156 |
| Waterbury, CT | 157 |
| Modesto, CA | 158 |
| Augusta-Richmond County, GA-SC | 161 |
| Springfield, MO | 161 |
| Greeley, CO | 162 |
| Chattanooga, TN-GA | 167 |
| Knoxville, TN | 168 |
| Palm Bay-Melbourne-Titusville, FL | 168 |
| Salem, OR | 170 |
| Boulder, CO | 171 |
| Harrisburg-Carlisle, PA | 174 |
| Scranton-Wilkes Barre, PA | 176 |
| Monroe, LA | 179 |
| Lafayette, LA | 181 |
| Topeka, KS | 182 |
| Greenville, SC | 185 |
| Durham, NC | 189 |
| Sarasota-Bradenton-Venice, FL | 192 |
| Stockton, CA | 193 |
| McAllen-Edinburg-Pharr, TX | 195 |
| Cedar Rapids, IA | 196 |
| Eugene-Springfield, OR | 196 |
| Lexington-Fayette, KY | 198 |
| Billings, MT | 199 |
| Poughkeepsie-Newburgh-Middletown, NY | 201 |
| Savannah, GA | 202 |
| Norwich-New London, CT-RI | 203 |
| Fort Collins-Loveland, CO | 206 |
| Bangor, ME | 208 |
| Fayetteville-Springdale-Rogers, AR-MO | 215 |
| Jackson, MS | 222 |
| Syracuse, NY | 223 |
| Akron, OH | 231 |
| Charleston-North Charleston, SC | 232 |
| Toledo, OH | 235 |
| Davenport-Moline-Rock Island, IA-IL | 240 |
| El Paso, TX | 244 |
| Bakersfield, CA | 245 |
| Greensboro-High Point, NC | 251 |
| Baton Rouge, LA | 262 |
| Charleston, WV | 262 |
| Rochester-Dover, NH-ME | 262 |
| Oxnard-Thousand Oaks-Ventura, CA | 267 |
| Albany-Schenectady-Troy, NY | 268 |
| Dayton, OH | 268 |
| Madison, WI | 284 |
| Columbia, SC | 291 |
| Tucson, AZ | 302 |
| Fresno, CA | 303 |
| Grand Rapids-Wyoming, MI | 304 |
| Rochester, NY | 307 |
| Provo-Orem, UT | 309 |
| Reno-Sparks, NV | 310 |
| Tulsa, OK | 323 |
| Allentown-Bethlehem-Easton, PA-NJ | 334 |
| Raleigh-Cary, NC | 336 |
| Buffalo-Niagara Falls, NY | 344 |
| Memphis, TN-MS-AR | 348 |
| New Orleans-Metairie-Kenner, LA | 367 |
| Colorado Springs, CO | 372 |
| Birmingham-Hoover, AL | 392 |
| Jacksonville, FL | 393 |
| Little Rock-North Little Rock, AR | 404 |
| Ogden-Clearfield, UT | 423 |
| Wichita, KS | 427 |
| Fargo, ND-MN | 432 |
| Dover, DE | 456 |
| Richmond, VA | 490 |
| Des Moines, IA | 501 |
| Nashville-Davidson-Murfreesboro, TN | 505 |
| New Haven, CT | 506 |
| Austin-Round Rock, TX | 516 |
| Charlotte-Gastonia-Concord, NC-SC | 517 |
| Louisville, KY-IN | 519 |
| Columbus, OH | 551 |
| Indianapolis, IN | 570 |
| Sioux Falls, SD | 595 |
| Virginia Beach-Norfolk-Newport News, VA-NC | 597 |
| Oklahoma City, OK | 604 |
| San Antonio, TX | 607 |
| Albuquerque, NM | 609 |
| Orlando, FL | 610 |
| Boise City-Nampa, ID | 644 |
| Burlington-South Burlington, VT | 657 |
| Sacramento-Arden-Arcade-Roseville, CA | 667 |
| San Jose-Sunnyvale-Santa Clara, CA | 670 |
| Cleveland-Elyria-Mentor, OH | 681 |
| Portland-South Portland, ME | 701 |
| Milwaukee-Waukesha-West Allis, WI | 714 |
| Cincinnati-Middletown, OH-KY-IN | 719 |
| Salt Lake City, UT | 723 |
| Bridgeport-Stamford-Norwalk, CT | 730 |
| Pittsburgh, PA | 732 |
| Tampa-St. Petersburg-Clearwater, FL | 842 |
| Hartford-West Hartford-East Hartford, CT | 885 |
| San Diego-Carlsbad-San Marcos, CA | 907 |
| St. Louis, MO-IL | 956 |
| Omaha-Council Bluffs, NE-IA | 957 |
| Kansas City, MO-KS | 962 |
| Phoenix-Mesa-Scottsdale, AZ | 971 |
| Portland-Vancouver-Beaverton, OR-WA | 1089 |
| Seattle-Tacoma-Bellevue, WA | 1255 |
| Riverside-San Bernardino, CA | 1290 |
| Las Vegas-Paradise, NV | 1299 |
| Detroit-Warren-Livonia, MI | 1354 |
| San Francisco-Oakland-Fremont, CA | 1386 |
| Baltimore-Towson, MD | 1483 |
| Denver-Aurora, CO | 1504 |
| Atlanta-Sandy Springs-Marietta, GA | 1552 |
| Miami-Fort Lauderdale-Miami Beach, FL | 1554 |
| Honolulu, HI | 1576 |
| Houston-Baytown-Sugar Land, TX | 1649 |
| Dallas-Fort Worth-Arlington, TX | 1863 |
| Minneapolis-St Paul-Bloomington, MN-WI | 1942 |
| Boston-Cambridge-Quincy, MA-NH | 2229 |
| Providence-Fall River-Warwick, MA-RI | 2284 |
| Chicago-Naperville-Joliet, IN-IN-WI | 2772 |
| Philadelphia-Camden-Wilmington, PA-NJ-DE | 2855 |
| Los Angeles-Long Beach-Santa Ana, CA | 4102 |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 4177 |
| New York-Northern New Jersey-Long Island, NY-NJ-PA | 5409 |
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 the tabulation
z = sort(tapply(CPS$Hispanic, CPS$MetroArea, mean))96.6% of the interviewees from Laredo, TX, are of Hispanic ethnicity, the highest proportion among metropolitan areas in the United States.
# Sort the tabulation
z = sort(tapply(CPS$Race == "Asian", CPS$MetroArea, mean))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 the tabulation
z = sort(tapply(CPS$Education == "No high school diploma", CPS$MetroArea, mean))
kable(z)| x |
|---|
# Sort the tabulation
z = sort(tapply(CPS$Education == "No high school diploma", CPS$MetroArea, mean, na.rm=TRUE))
kable(z)| x | |
|---|---|
| Iowa City, IA | 0.02912621 |
| Bowling Green, KY | 0.03703704 |
| Kalamazoo-Portage, MI | 0.05050505 |
| Champaign-Urbana, IL | 0.05154639 |
| Bremerton-Silverdale, WA | 0.05405405 |
| Lawrence, KS | 0.05952381 |
| Bloomington-Normal IL | 0.06060606 |
| Jacksonville, NC | 0.06122449 |
| Eau Claire, WI | 0.06250000 |
| Palm Bay-Melbourne-Titusville, FL | 0.06666667 |
| Salisbury, MD | 0.06779661 |
| Gainesville, FL | 0.06896552 |
| Fort Collins-Loveland, CO | 0.06936416 |
| Altoona, PA | 0.07142857 |
| Madison, WI | 0.07423581 |
| Tallahassee, FL | 0.07500000 |
| Fargo, ND-MN | 0.07902736 |
| Albany-Schenectady-Troy, NY | 0.07929515 |
| Ocean City, NJ | 0.08000000 |
| Lakeland-Winter Haven, FL | 0.08130081 |
| Billings, MT | 0.08280255 |
| Coeur d’Alene, ID | 0.08333333 |
| Burlington-South Burlington, VT | 0.08394161 |
| Akron, OH | 0.08421053 |
| Ann Arbor, MI | 0.08695652 |
| Asheville, NC | 0.08695652 |
| Pensacola-Ferry Pass-Brent, FL | 0.08695652 |
| Oshkosh-Neenah, WI | 0.08823529 |
| Rochester-Dover, NH-ME | 0.08928571 |
| Knoxville, TN | 0.08965517 |
| Pittsburgh, PA | 0.09060403 |
| Barnstable Town, MA | 0.09090909 |
| Bridgeport-Stamford-Norwalk, CT | 0.09563758 |
| Johnstown, PA | 0.09615385 |
| Austin-Round Rock, TX | 0.09629630 |
| La Crosse, WI | 0.09677419 |
| Boulder, CO | 0.09701493 |
| Charleston-North Charleston, SC | 0.09890110 |
| Fort Wayne, IN | 0.09900990 |
| Roanoke, VA | 0.10169492 |
| Prescott, AZ | 0.10204082 |
| Santa Rosa-Petaluma, CA | 0.10280374 |
| Evansville, IN-KY | 0.10389610 |
| Spokane, WA | 0.10434783 |
| Poughkeepsie-Newburgh-Middletown, NY | 0.10559006 |
| Tampa-St. Petersburg-Clearwater, FL | 0.10579710 |
| Grand Rapids-Wyoming, MI | 0.10612245 |
| Portland-South Portland, ME | 0.10638298 |
| Honolulu, HI | 0.10739300 |
| Michigan City-La Porte, IN | 0.10769231 |
| Eugene-Springfield, OR | 0.11038961 |
| Boston-Cambridge-Quincy, MA-NH | 0.11080485 |
| Bend, OR | 0.11111111 |
| Vero Beach, FL | 0.11428571 |
| Sarasota-Bradenton-Venice, FL | 0.11464968 |
| Fort Walton Beach-Crestview-Destin, FL | 0.11475410 |
| Flint, MI | 0.11538462 |
| Cedar Rapids, IA | 0.11564626 |
| Minneapolis-St Paul-Bloomington, MN-WI | 0.11638204 |
| Portland-Vancouver-Beaverton, OR-WA | 0.11657143 |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 0.11683748 |
| Mobile, AL | 0.11702128 |
| Scranton-Wilkes Barre, PA | 0.11724138 |
| Topeka, KS | 0.11724138 |
| Colorado Springs, CO | 0.11764706 |
| Olympia, WA | 0.11764706 |
| Reno-Sparks, NV | 0.11764706 |
| Appleton,WI | 0.11827957 |
| Santa Fe, NM | 0.11904762 |
| Virginia Beach-Norfolk-Newport News, VA-NC | 0.11909651 |
| Allentown-Bethlehem-Easton, PA-NJ | 0.11929825 |
| Rochester, NY | 0.12132353 |
| Seattle-Tacoma-Bellevue, WA | 0.12168793 |
| Kansas City, MO-KS | 0.12172775 |
| Napa, CA | 0.12244898 |
| Duluth, MN-WI | 0.12264151 |
| New Haven, CT | 0.12354312 |
| Canton-Massillon, OH | 0.12371134 |
| Fayetteville, NC | 0.12500000 |
| San Luis Obispo-Paso Robles, CA | 0.12500000 |
| Worcester, MA-CT | 0.12605042 |
| Philadelphia-Camden-Wilmington, PA-NJ-DE | 0.12717253 |
| Davenport-Moline-Rock Island, IA-IL | 0.12727273 |
| Waterloo-Cedar Falls, IA | 0.12800000 |
| Pueblo, CO | 0.12844037 |
| Baton Rouge, LA | 0.12871287 |
| Racine, WI | 0.12903226 |
| Des Moines, IA | 0.12944162 |
| Detroit-Warren-Livonia, MI | 0.12964642 |
| Omaha-Council Bluffs, NE-IA | 0.12972973 |
| Richmond, VA | 0.12990196 |
| Savannah, GA | 0.13013699 |
| Danbury, CT | 0.13043478 |
| Bloomington, IN | 0.13095238 |
| Valdosta, GA | 0.13157895 |
| Wausau, WI | 0.13157895 |
| Deltona-Daytona Beach-Ormond Beach, FL | 0.13178295 |
| Tulsa, OK | 0.13178295 |
| Harrisburg-Carlisle, PA | 0.13286713 |
| Las Vegas-Paradise, NV | 0.13307985 |
| Myrtle Beach-Conway-North Myrtle Beach, SC | 0.13333333 |
| Provo-Orem, UT | 0.13366337 |
| Anderson, IN | 0.13461538 |
| Chico, CA | 0.13461538 |
| St. Louis, MO-IL | 0.13461538 |
| Niles-Benton Harbor, MI | 0.13513514 |
| Ogden-Clearfield, UT | 0.13571429 |
| Baltimore-Towson, MD | 0.13583333 |
| Buffalo-Niagara Falls, NY | 0.13684211 |
| Milwaukee-Waukesha-West Allis, WI | 0.13693694 |
| Chicago-Naperville-Joliet, IN-IN-WI | 0.13737734 |
| Louisville, KY-IN | 0.13785047 |
| Lynchburg, VA | 0.13793103 |
| Peoria, IL | 0.13829787 |
| Sioux Falls, SD | 0.13832200 |
| Ocala, FL | 0.13888889 |
| Leominster-Fitchburg-Gardner, MA | 0.14035088 |
| Oklahoma City, OK | 0.14137214 |
| San Diego-Carlsbad-San Marcos, CA | 0.14188267 |
| Jacksonville, FL | 0.14244186 |
| Atlantic City, NJ | 0.14285714 |
| Holland-Grand Haven, MI | 0.14285714 |
| Medford, OR | 0.14285714 |
| Naples-Marco Island, FL | 0.14285714 |
| Punta Gorda, FL | 0.14285714 |
| Victoria, TX | 0.14285714 |
| Winston-Salem, NC | 0.14285714 |
| Salt Lake City, UT | 0.14338235 |
| Atlanta-Sandy Springs-Marietta, GA | 0.14421553 |
| Decatur, IL | 0.14516129 |
| Springfield, IL | 0.14516129 |
| Monroe, MI | 0.14545455 |
| Denver-Aurora, CO | 0.14574558 |
| Hartford-West Hartford-East Hartford, CT | 0.14574899 |
| Greeley, CO | 0.14615385 |
| San Francisco-Oakland-Fremont, CA | 0.14651368 |
| Boise City-Nampa, ID | 0.14653465 |
| Greenville, SC | 0.14666667 |
| Birmingham-Hoover, AL | 0.14678899 |
| Saginaw-Saginaw Township North, MI | 0.14754098 |
| Santa-Cruz-Watsonville, CA | 0.14814815 |
| Trenton-Ewing, NJ | 0.14814815 |
| Lexington-Fayette, KY | 0.14838710 |
| San Jose-Sunnyvale-Santa Clara, CA | 0.14922481 |
| Bellingham, WA | 0.15000000 |
| Norwich-New London, CT-RI | 0.15060241 |
| Lubbock, TX | 0.15094340 |
| Huntington-Ashland, WV-KY-OH | 0.15151515 |
| St. Cloud, MN | 0.15151515 |
| Jackson, MS | 0.15168539 |
| Dayton, OH | 0.15207373 |
| Chattanooga, TN-GA | 0.15217391 |
| Syracuse, NY | 0.15428571 |
| New York-Northern New Jersey-Long Island, NY-NJ-PA | 0.15573586 |
| Columbia, SC | 0.15600000 |
| Columbus, OH | 0.15617716 |
| Memphis, TN-MS-AR | 0.15714286 |
| Orlando, FL | 0.16108787 |
| Warner Robins, GA | 0.16216216 |
| Cleveland-Elyria-Mentor, OH | 0.16250000 |
| Columbia, MO | 0.16279070 |
| Durham, NC | 0.16326531 |
| Miami-Fort Lauderdale-Miami Beach, FL | 0.16356589 |
| Indianapolis, IN | 0.16371681 |
| Albuquerque, NM | 0.16424116 |
| Cape Coral-Fort Myers, FL | 0.16528926 |
| Amarillo, TX | 0.16666667 |
| Anniston-Oxford, AL | 0.16666667 |
| Athens-Clark County, GA | 0.16666667 |
| Binghamton, NY | 0.16666667 |
| Phoenix-Mesa-Scottsdale, AZ | 0.16687737 |
| Green Bay, WI | 0.16831683 |
| Bangor, ME | 0.16860465 |
| Providence-Fall River-Warwick, MA-RI | 0.16915688 |
| Muskegon-Norton Shores, MI | 0.16923077 |
| Tuscaloosa, AL | 0.16949153 |
| Rockford, IL | 0.17021277 |
| Las Cruses, NM | 0.17283951 |
| Gulfport-Biloxi, MS | 0.17307692 |
| Huntsville, AL | 0.17391304 |
| Utica-Rome, NY | 0.17391304 |
| Fort Smith, AR-OK | 0.17441860 |
| Charlotte-Gastonia-Concord, NC-SC | 0.17444717 |
| El Centro, CA | 0.17567568 |
| Erie, PA | 0.17567568 |
| Jackson, MI | 0.17741935 |
| Cincinnati-Middletown, OH-KY-IN | 0.17773788 |
| Springfield, MA-CT | 0.17829457 |
| Reading, PA | 0.17857143 |
| Vallejo-Fairfield, CA | 0.17924528 |
| Salem, OR | 0.17985612 |
| Nashville-Davidson-Murfreesboro, TN | 0.18112245 |
| Johnson City, TN | 0.18181818 |
| Wichita, KS | 0.18181818 |
| York-Hanover, PA | 0.18181818 |
| Janesville, WI | 0.18292683 |
| Lansing-East Lansing, MI | 0.18348624 |
| Greensboro-High Point, NC | 0.18357488 |
| Decatur, Al | 0.18421053 |
| Albany, GA | 0.18604651 |
| Augusta-Richmond County, GA-SC | 0.18796992 |
| Charleston, WV | 0.18834081 |
| Shreveport-Bossier City, LA | 0.18918919 |
| Raleigh-Cary, NC | 0.18959108 |
| Toledo, OH | 0.18965517 |
| Spartanburg, SC | 0.18987342 |
| Dallas-Fort Worth-Arlington, TX | 0.19077135 |
| Sacramento-Arden-Arcade-Roseville, CA | 0.19136961 |
| Santa Barbara-Santa Maria-Goleta, CA | 0.19191919 |
| Monroe, LA | 0.19205298 |
| Dover, DE | 0.19220056 |
| South Bend-Mishawaka, IN-MI | 0.19354839 |
| Fayetteville-Springdale-Rogers, AR-MO | 0.19393939 |
| Columbus, GA-AL | 0.19607843 |
| Kingston, NY | 0.19696970 |
| Port St. Lucie-Fort Pierce, FL | 0.19767442 |
| Waterbury, CT | 0.19852941 |
| Little Rock-North Little Rock, AR | 0.19939577 |
| Springfield, MO | 0.20000000 |
| Modesto, CA | 0.20325203 |
| Houston-Baytown-Sugar Land, TX | 0.20439739 |
| Oxnard-Thousand Oaks-Ventura, CA | 0.20657277 |
| Anderson, SC | 0.20689655 |
| Midland, TX | 0.21052632 |
| New Orleans-Metairie-Kenner, LA | 0.21088435 |
| Fresno, CA | 0.21120690 |
| Lake Charles, LA | 0.21739130 |
| Visalia-Porterville, CA | 0.21782178 |
| San Antonio, TX | 0.22004357 |
| Hagerstown-Martinsburg, MD-WV | 0.22222222 |
| Yakima, WA | 0.22222222 |
| Hickory-Morgantown-Lenoir, NC | 0.22448980 |
| Los Angeles-Long Beach-Santa Ana, CA | 0.22882883 |
| Panama City-Lynn Haven, FL | 0.22916667 |
| Harrisonburg, VA | 0.23287671 |
| Kankakee-Bradley, IL | 0.23437500 |
| Beaumont-Port Author, TX | 0.23469388 |
| Youngstown-Warren-Boardman, OH | 0.23622047 |
| Riverside-San Bernardino, CA | 0.23780488 |
| Farmington, NM | 0.23913043 |
| Killeen-Temple-Fort Hood, TX | 0.24050633 |
| Waco, TX | 0.24074074 |
| Montgomery, AL | 0.24137931 |
| Tucson, AZ | 0.24603175 |
| Lafayette, LA | 0.24822695 |
| Joplin, MO | 0.25000000 |
| Stockton, CA | 0.25333333 |
| Brownsville-Harlingen, TX | 0.25396825 |
| Lancaster, PA | 0.26771654 |
| Bakersfield, CA | 0.27218935 |
| Vineland-Millville-Bridgeton, NJ | 0.27500000 |
| Lawton, OK | 0.28000000 |
| Merced, CA | 0.28358209 |
| Corpus Christi, TX | 0.29702970 |
| El Paso, TX | 0.30219780 |
| Springfield, OH | 0.31034483 |
| Florence, AL | 0.32075472 |
| Madera, CA | 0.33333333 |
| Salinas, CA | 0.34090909 |
| Laredo, TX | 0.34426230 |
| Kingsport-Bristol, TN-VA | 0.36363636 |
| Longview, TX | 0.38297872 |
| McAllen-Edinburg-Pharr, TX | 0.38297872 |
| Macon, GA | 0.40816327 |
We can see that Iowa City, IA had 2.9% of interviewees not finish high school, the smallest value of any metropolitan area.
# Combine dataset
CPS = merge(CPS, CountryMap, by.x="CountryOfBirthCode", by.y="Code", all.x=TRUE)# Ouput summary
z = summary(CPS)
kable(z)| CountryOfBirthCode | MetroAreaCode | PeopleInHousehold | Region | State | Age | Married | Sex | Education | Race | Hispanic | Citizenship | EmploymentStatus | Industry | MetroArea | Country | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. : 57.00 | Min. :10420 | Min. : 1.000 | Midwest :30684 | California :11570 | Min. : 0.00 | Divorced :11151 | Female:67481 | High school :30906 | American Indian : 1433 | Min. :0.0000 | Citizen, Native :116639 | Disabled : 5712 | Educational and health services :15017 | New York-Northern New Jersey-Long Island, NY-NJ-PA: 5409 | United States:115063 | |
| 1st Qu.: 57.00 | 1st Qu.:21780 | 1st Qu.: 2.000 | Northeast:25939 | Texas : 7077 | 1st Qu.:19.00 | Married :55509 | Male :63821 | Bachelor’s degree :19443 | Asian : 6520 | 1st Qu.:0.0000 | Citizen, Naturalized: 7073 | Employed :61733 | Trade : 8933 | Washington-Arlington-Alexandria, DC-VA-MD-WV : 4177 | Mexico : 3921 | |
| Median : 57.00 | Median :34740 | Median : 3.000 | South :41502 | New York : 5595 | Median :39.00 | Never Married:30772 | NA | Some college, no degree:18863 | Black : 13913 | Median :0.0000 | Non-Citizen : 7590 | Not in Labor Force:15246 | Professional and business services: 7519 | Los Angeles-Long Beach-Santa Ana, CA : 4102 | Philippines : 839 | |
| Mean : 82.68 | Mean :35075 | Mean : 3.284 | West :33177 | Florida : 5149 | Mean :38.83 | Separated : 2027 | NA | No high school diploma :16095 | Multiracial : 2897 | Mean :0.1393 | NA | Retired :18619 | Manufacturing : 6791 | Philadelphia-Camden-Wilmington, PA-NJ-DE : 2855 | India : 770 | |
| 3rd Qu.: 57.00 | 3rd Qu.:41860 | 3rd Qu.: 4.000 | NA | Pennsylvania: 3930 | 3rd Qu.:57.00 | Widowed : 6505 | NA | Associate degree : 9913 | Pacific Islander: 618 | 3rd Qu.:0.0000 | NA | Unemployed : 4203 | Leisure and hospitality : 6364 | Chicago-Naperville-Joliet, IN-IN-WI : 2772 | China : 581 | |
| Max. :555.00 | Max. :79600 | Max. :15.000 | NA | Illinois : 3912 | Max. :85.00 | NA’s :25338 | NA | (Other) :10744 | White :105921 | Max. :1.0000 | NA | NA’s :25789 | (Other) :21618 | (Other) :77749 | (Other) : 9952 | |
| NA | NA’s :34238 | NA | NA | (Other) :94069 | NA | NA | NA | NA’s :25338 | NA | NA | NA | NA | NA’s :65060 | NA’s :34238 | NA’s : 176 |
From summary(CPS), we can read that Country is the name of the added variable, and that it has 176 missing values.
# Sorts the tabulation
z = sort(table(CPS$Country))
kable(z)| Var1 | Freq |
|---|---|
| Cyprus | 0 |
| Kosovo | 0 |
| Oceania, not specified | 0 |
| Other U. S. Island Areas | 0 |
| Wales | 0 |
| Northern Ireland | 2 |
| Tanzania | 2 |
| Azerbaijan | 3 |
| Czechoslovakia | 3 |
| St. Kitts–Nevis | 3 |
| Georgia | 5 |
| Barbados | 6 |
| Denmark | 6 |
| Latvia | 6 |
| Samoa | 6 |
| Senegal | 6 |
| Singapore | 6 |
| Slovakia | 6 |
| Tonga | 6 |
| Zimbabwe | 6 |
| South America, not specified | 7 |
| St. Lucia | 7 |
| Algeria | 9 |
| Americas, not specified | 9 |
| Belize | 9 |
| Fiji | 9 |
| St. Vincent and the Grenadines | 9 |
| Bahamas | 10 |
| Finland | 10 |
| Kuwait | 10 |
| Lithuania | 10 |
| Czech Republic | 11 |
| Dominica | 11 |
| Paraguay | 11 |
| Croatia | 12 |
| Macedonia | 12 |
| Moldova | 12 |
| Antigua and Barbuda | 13 |
| Belgium | 13 |
| Bermuda | 13 |
| Bolivia | 13 |
| Grenada | 13 |
| Sudan | 13 |
| Cape Verde | 15 |
| Eritrea | 15 |
| Sierra Leone | 15 |
| Uganda | 15 |
| Austria | 17 |
| Morocco | 17 |
| Sri Lanka | 17 |
| U. S. Virgin Islands | 17 |
| Uruguay | 17 |
| Albania | 18 |
| Norway | 18 |
| Europe, not specified | 19 |
| Uzbekistan | 19 |
| West Indies, not specified | 19 |
| Malaysia | 20 |
| Serbia | 20 |
| Azores | 22 |
| USSR | 22 |
| New Zealand | 23 |
| Switzerland | 23 |
| Yemen | 23 |
| Belarus | 24 |
| Scotland | 24 |
| Yugoslavia | 24 |
| Hungary | 25 |
| Afghanistan | 26 |
| Indonesia | 26 |
| Netherlands | 28 |
| Sweden | 28 |
| Bulgaria | 29 |
| Costa Rica | 29 |
| Saudi Arabia | 29 |
| Guam | 31 |
| Cameroon | 32 |
| Syria | 32 |
| Armenia | 35 |
| Jordan | 36 |
| Chile | 37 |
| Asia, not specified | 39 |
| Ireland | 39 |
| Spain | 41 |
| Bangladesh | 42 |
| Australia | 43 |
| Nepal | 44 |
| Panama | 44 |
| Lebanon | 45 |
| Myanmar (Burma) | 45 |
| South Africa | 48 |
| Turkey | 48 |
| Cambodia | 49 |
| Liberia | 52 |
| Kenya | 55 |
| Romania | 55 |
| Greece | 56 |
| Israel | 57 |
| Trinidad and Tobago | 60 |
| Bosnia & Herzegovina | 61 |
| Venezuela | 61 |
| Argentina | 64 |
| Hong Kong | 64 |
| Portugal | 64 |
| Egypt | 65 |
| Somalia | 72 |
| France | 73 |
| South Korea | 73 |
| Ghana | 76 |
| Nicaragua | 76 |
| Ethiopia | 80 |
| Elsewhere | 81 |
| Nigeria | 85 |
| Iraq | 97 |
| Laos | 98 |
| Taiwan | 102 |
| Ukraine | 104 |
| Guyana | 109 |
| Pakistan | 109 |
| United Kingdom | 111 |
| Thailand | 128 |
| Africa, not specified | 129 |
| Ecuador | 136 |
| Peru | 136 |
| Iran | 144 |
| Italy | 149 |
| Brazil | 159 |
| Poland | 162 |
| Haiti | 167 |
| Russia | 173 |
| England | 179 |
| Japan | 187 |
| Honduras | 189 |
| Columbia | 206 |
| Jamaica | 217 |
| Guatemala | 309 |
| Dominican Republic | 330 |
| Korea | 334 |
| Canada | 410 |
| Cuba | 426 |
| Germany | 438 |
| Vietnam | 458 |
| El Salvador | 477 |
| Puerto Rico | 518 |
| China | 581 |
| India | 770 |
| Philippines | 839 |
| Mexico | 3921 |
| United States | 115063 |
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.
# Calculates the proportion
m = table(CPS$MetroArea == "New York-Northern New Jersey-Long Island, NY-NJ-PA", CPS$Country != "United States")
z = prop.table(m,1)
kable(z)| FALSE | TRUE | |
|---|---|---|
| FALSE | 0.8607228 | 0.1392772 |
| TRUE | 0.6913397 | 0.3086603 |
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.
# Calculates the proportion
z = sort(tapply(CPS$Country == "India", CPS$MetroArea, sum, na.rm=TRUE))
kable(z)| x | |
|---|---|
| Akron, OH | 0 |
| Albany-Schenectady-Troy, NY | 0 |
| Albany, GA | 0 |
| Allentown-Bethlehem-Easton, PA-NJ | 0 |
| Altoona, PA | 0 |
| Amarillo, TX | 0 |
| Anderson, IN | 0 |
| Ann Arbor, MI | 0 |
| Anniston-Oxford, AL | 0 |
| Appleton,WI | 0 |
| Asheville, NC | 0 |
| Athens-Clark County, GA | 0 |
| Augusta-Richmond County, GA-SC | 0 |
| Bangor, ME | 0 |
| Barnstable Town, MA | 0 |
| Baton Rouge, LA | 0 |
| Beaumont-Port Author, TX | 0 |
| Bellingham, WA | 0 |
| Bend, OR | 0 |
| Billings, MT | 0 |
| Binghamton, NY | 0 |
| Bloomington, IN | 0 |
| Boulder, CO | 0 |
| Bowling Green, KY | 0 |
| Bremerton-Silverdale, WA | 0 |
| Buffalo-Niagara Falls, NY | 0 |
| Canton-Massillon, OH | 0 |
| Cape Coral-Fort Myers, FL | 0 |
| Cedar Rapids, IA | 0 |
| Champaign-Urbana, IL | 0 |
| Charleston, WV | 0 |
| Chattanooga, TN-GA | 0 |
| Chico, CA | 0 |
| Coeur d’Alene, ID | 0 |
| Colorado Springs, CO | 0 |
| Columbia, MO | 0 |
| Columbus, GA-AL | 0 |
| Columbus, OH | 0 |
| Corpus Christi, TX | 0 |
| Danbury, CT | 0 |
| Davenport-Moline-Rock Island, IA-IL | 0 |
| Dayton, OH | 0 |
| Decatur, Al | 0 |
| Decatur, IL | 0 |
| Denver-Aurora, CO | 0 |
| Dover, DE | 0 |
| Duluth, MN-WI | 0 |
| Durham, NC | 0 |
| Eau Claire, WI | 0 |
| El Centro, CA | 0 |
| El Paso, TX | 0 |
| Erie, PA | 0 |
| Eugene-Springfield, OR | 0 |
| Evansville, IN-KY | 0 |
| Fargo, ND-MN | 0 |
| Farmington, NM | 0 |
| Fayetteville, NC | 0 |
| Flint, MI | 0 |
| Florence, AL | 0 |
| Fort Collins-Loveland, CO | 0 |
| Fort Smith, AR-OK | 0 |
| Fort Walton Beach-Crestview-Destin, FL | 0 |
| Gainesville, FL | 0 |
| Grand Rapids-Wyoming, MI | 0 |
| Greeley, CO | 0 |
| Green Bay, WI | 0 |
| Greensboro-High Point, NC | 0 |
| Gulfport-Biloxi, MS | 0 |
| Hagerstown-Martinsburg, MD-WV | 0 |
| Harrisonburg, VA | 0 |
| Hickory-Morgantown-Lenoir, NC | 0 |
| Holland-Grand Haven, MI | 0 |
| Huntington-Ashland, WV-KY-OH | 0 |
| Huntsville, AL | 0 |
| Jackson, MI | 0 |
| Jackson, MS | 0 |
| Jacksonville, NC | 0 |
| Janesville, WI | 0 |
| Johnson City, TN | 0 |
| Johnstown, PA | 0 |
| Joplin, MO | 0 |
| Kalamazoo-Portage, MI | 0 |
| Kankakee-Bradley, IL | 0 |
| Killeen-Temple-Fort Hood, TX | 0 |
| Kingsport-Bristol, TN-VA | 0 |
| Kingston, NY | 0 |
| Knoxville, TN | 0 |
| La Crosse, WI | 0 |
| Lafayette, LA | 0 |
| Lake Charles, LA | 0 |
| Lakeland-Winter Haven, FL | 0 |
| Lancaster, PA | 0 |
| Lansing-East Lansing, MI | 0 |
| Laredo, TX | 0 |
| Las Cruses, NM | 0 |
| Lawton, OK | 0 |
| Leominster-Fitchburg-Gardner, MA | 0 |
| Lexington-Fayette, KY | 0 |
| Longview, TX | 0 |
| Louisville, KY-IN | 0 |
| Lubbock, TX | 0 |
| Lynchburg, VA | 0 |
| Macon, GA | 0 |
| Madera, CA | 0 |
| McAllen-Edinburg-Pharr, TX | 0 |
| Medford, OR | 0 |
| Merced, CA | 0 |
| Michigan City-La Porte, IN | 0 |
| Midland, TX | 0 |
| Mobile, AL | 0 |
| Modesto, CA | 0 |
| Monroe, LA | 0 |
| Monroe, MI | 0 |
| Montgomery, AL | 0 |
| Muskegon-Norton Shores, MI | 0 |
| Myrtle Beach-Conway-North Myrtle Beach, SC | 0 |
| Napa, CA | 0 |
| Niles-Benton Harbor, MI | 0 |
| Ocala, FL | 0 |
| Ocean City, NJ | 0 |
| Oshkosh-Neenah, WI | 0 |
| Palm Bay-Melbourne-Titusville, FL | 0 |
| Panama City-Lynn Haven, FL | 0 |
| Pensacola-Ferry Pass-Brent, FL | 0 |
| Port St. Lucie-Fort Pierce, FL | 0 |
| Portland-South Portland, ME | 0 |
| Poughkeepsie-Newburgh-Middletown, NY | 0 |
| Prescott, AZ | 0 |
| Pueblo, CO | 0 |
| Punta Gorda, FL | 0 |
| Racine, WI | 0 |
| Raleigh-Cary, NC | 0 |
| Reading, PA | 0 |
| Richmond, VA | 0 |
| Riverside-San Bernardino, CA | 0 |
| Roanoke, VA | 0 |
| Rockford, IL | 0 |
| Saginaw-Saginaw Township North, MI | 0 |
| Salem, OR | 0 |
| Salinas, CA | 0 |
| Salisbury, MD | 0 |
| San Antonio, TX | 0 |
| San Luis Obispo-Paso Robles, CA | 0 |
| Santa-Cruz-Watsonville, CA | 0 |
| Santa Barbara-Santa Maria-Goleta, CA | 0 |
| Santa Fe, NM | 0 |
| Santa Rosa-Petaluma, CA | 0 |
| Sarasota-Bradenton-Venice, FL | 0 |
| Savannah, GA | 0 |
| Scranton-Wilkes Barre, PA | 0 |
| Shreveport-Bossier City, LA | 0 |
| Sioux Falls, SD | 0 |
| South Bend-Mishawaka, IN-MI | 0 |
| Spartanburg, SC | 0 |
| Spokane, WA | 0 |
| Springfield, MA-CT | 0 |
| Springfield, MO | 0 |
| Springfield, OH | 0 |
| St. Cloud, MN | 0 |
| St. Louis, MO-IL | 0 |
| Stockton, CA | 0 |
| Tallahassee, FL | 0 |
| Toledo, OH | 0 |
| Topeka, KS | 0 |
| Tuscaloosa, AL | 0 |
| Utica-Rome, NY | 0 |
| Valdosta, GA | 0 |
| Vallejo-Fairfield, CA | 0 |
| Vero Beach, FL | 0 |
| Victoria, TX | 0 |
| Vineland-Millville-Bridgeton, NJ | 0 |
| Virginia Beach-Norfolk-Newport News, VA-NC | 0 |
| Waco, TX | 0 |
| Waterbury, CT | 0 |
| Waterloo-Cedar Falls, IA | 0 |
| Wausau, WI | 0 |
| Wichita, KS | 0 |
| Worcester, MA-CT | 0 |
| Yakima, WA | 0 |
| York-Hanover, PA | 0 |
| Youngstown-Warren-Boardman, OH | 0 |
| Anderson, SC | 1 |
| Bloomington-Normal IL | 1 |
| Boise City-Nampa, ID | 1 |
| Cincinnati-Middletown, OH-KY-IN | 1 |
| Columbia, SC | 1 |
| Greenville, SC | 1 |
| Harrisburg-Carlisle, PA | 1 |
| Jacksonville, FL | 1 |
| Lawrence, KS | 1 |
| Naples-Marco Island, FL | 1 |
| New Orleans-Metairie-Kenner, LA | 1 |
| Olympia, WA | 1 |
| Provo-Orem, UT | 1 |
| Syracuse, NY | 1 |
| Tucson, AZ | 1 |
| Atlantic City, NJ | 2 |
| Bakersfield, CA | 2 |
| Birmingham-Hoover, AL | 2 |
| Burlington-South Burlington, VT | 2 |
| Charleston-North Charleston, SC | 2 |
| Cleveland-Elyria-Mentor, OH | 2 |
| Deltona-Daytona Beach-Ormond Beach, FL | 2 |
| Fort Wayne, IN | 2 |
| Las Vegas-Paradise, NV | 2 |
| Memphis, TN-MS-AR | 2 |
| Miami-Fort Lauderdale-Miami Beach, FL | 2 |
| Nashville-Davidson-Murfreesboro, TN | 2 |
| Ogden-Clearfield, UT | 2 |
| Oklahoma City, OK | 2 |
| Oxnard-Thousand Oaks-Ventura, CA | 2 |
| Phoenix-Mesa-Scottsdale, AZ | 2 |
| Rochester, NY | 2 |
| Salt Lake City, UT | 2 |
| Springfield, IL | 2 |
| Winston-Salem, NC | 2 |
| Albuquerque, NM | 3 |
| Iowa City, IA | 3 |
| Madison, WI | 3 |
| Norwich-New London, CT-RI | 3 |
| Reno-Sparks, NV | 3 |
| Visalia-Porterville, CA | 3 |
| Charlotte-Gastonia-Concord, NC-SC | 4 |
| Indianapolis, IN | 4 |
| Omaha-Council Bluffs, NE-IA | 4 |
| Peoria, IL | 4 |
| Rochester-Dover, NH-ME | 4 |
| San Diego-Carlsbad-San Marcos, CA | 4 |
| Trenton-Ewing, NJ | 4 |
| Tulsa, OK | 4 |
| Orlando, FL | 5 |
| Seattle-Tacoma-Bellevue, WA | 5 |
| Austin-Round Rock, TX | 6 |
| Brownsville-Harlingen, TX | 6 |
| Des Moines, IA | 6 |
| Little Rock-North Little Rock, AR | 6 |
| New Haven, CT | 6 |
| Portland-Vancouver-Beaverton, OR-WA | 6 |
| Warner Robins, GA | 6 |
| Tampa-St. Petersburg-Clearwater, FL | 7 |
| Fayetteville-Springdale-Rogers, AR-MO | 8 |
| Sacramento-Arden-Arcade-Roseville, CA | 8 |
| Honolulu, HI | 9 |
| Boston-Cambridge-Quincy, MA-NH | 11 |
| Kansas City, MO-KS | 11 |
| Bridgeport-Stamford-Norwalk, CT | 12 |
| Milwaukee-Waukesha-West Allis, WI | 12 |
| Providence-Fall River-Warwick, MA-RI | 14 |
| Houston-Baytown-Sugar Land, TX | 15 |
| Baltimore-Towson, MD | 16 |
| Fresno, CA | 16 |
| Pittsburgh, PA | 16 |
| Dallas-Fort Worth-Arlington, TX | 18 |
| Los Angeles-Long Beach-Santa Ana, CA | 19 |
| San Jose-Sunnyvale-Santa Clara, CA | 19 |
| Minneapolis-St Paul-Bloomington, MN-WI | 23 |
| Hartford-West Hartford-East Hartford, CT | 26 |
| Atlanta-Sandy Springs-Marietta, GA | 27 |
| San Francisco-Oakland-Fremont, CA | 27 |
| Detroit-Warren-Livonia, MI | 30 |
| Chicago-Naperville-Joliet, IN-IN-WI | 31 |
| Philadelphia-Camden-Wilmington, PA-NJ-DE | 32 |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 50 |
| New York-Northern New Jersey-Long Island, NY-NJ-PA | 96 |
z = sort(tapply(CPS$Country == "Brazil", CPS$MetroArea, sum, na.rm=TRUE))
kable(z)| x | |
|---|---|
| Albany-Schenectady-Troy, NY | 0 |
| Albany, GA | 0 |
| Allentown-Bethlehem-Easton, PA-NJ | 0 |
| Altoona, PA | 0 |
| Amarillo, TX | 0 |
| Anderson, IN | 0 |
| Anderson, SC | 0 |
| Ann Arbor, MI | 0 |
| Anniston-Oxford, AL | 0 |
| Appleton,WI | 0 |
| Asheville, NC | 0 |
| Athens-Clark County, GA | 0 |
| Atlantic City, NJ | 0 |
| Augusta-Richmond County, GA-SC | 0 |
| Austin-Round Rock, TX | 0 |
| Bakersfield, CA | 0 |
| Baltimore-Towson, MD | 0 |
| Bangor, ME | 0 |
| Baton Rouge, LA | 0 |
| Beaumont-Port Author, TX | 0 |
| Bellingham, WA | 0 |
| Bend, OR | 0 |
| Billings, MT | 0 |
| Binghamton, NY | 0 |
| Birmingham-Hoover, AL | 0 |
| Bloomington-Normal IL | 0 |
| Bloomington, IN | 0 |
| Boise City-Nampa, ID | 0 |
| Boulder, CO | 0 |
| Bowling Green, KY | 0 |
| Brownsville-Harlingen, TX | 0 |
| Buffalo-Niagara Falls, NY | 0 |
| Burlington-South Burlington, VT | 0 |
| Cedar Rapids, IA | 0 |
| Champaign-Urbana, IL | 0 |
| Charleston-North Charleston, SC | 0 |
| Charleston, WV | 0 |
| Chattanooga, TN-GA | 0 |
| Cleveland-Elyria-Mentor, OH | 0 |
| Coeur d’Alene, ID | 0 |
| Colorado Springs, CO | 0 |
| Columbia, MO | 0 |
| Columbus, GA-AL | 0 |
| Columbus, OH | 0 |
| Corpus Christi, TX | 0 |
| Dayton, OH | 0 |
| Decatur, Al | 0 |
| Decatur, IL | 0 |
| Deltona-Daytona Beach-Ormond Beach, FL | 0 |
| Des Moines, IA | 0 |
| Detroit-Warren-Livonia, MI | 0 |
| Dover, DE | 0 |
| Duluth, MN-WI | 0 |
| Durham, NC | 0 |
| Eau Claire, WI | 0 |
| El Centro, CA | 0 |
| El Paso, TX | 0 |
| Erie, PA | 0 |
| Eugene-Springfield, OR | 0 |
| Evansville, IN-KY | 0 |
| Fargo, ND-MN | 0 |
| Farmington, NM | 0 |
| Fayetteville-Springdale-Rogers, AR-MO | 0 |
| Fayetteville, NC | 0 |
| Flint, MI | 0 |
| Florence, AL | 0 |
| Fort Collins-Loveland, CO | 0 |
| Fort Smith, AR-OK | 0 |
| Fort Walton Beach-Crestview-Destin, FL | 0 |
| Fort Wayne, IN | 0 |
| Fresno, CA | 0 |
| Gainesville, FL | 0 |
| Grand Rapids-Wyoming, MI | 0 |
| Greeley, CO | 0 |
| Green Bay, WI | 0 |
| Greensboro-High Point, NC | 0 |
| Greenville, SC | 0 |
| Gulfport-Biloxi, MS | 0 |
| Hagerstown-Martinsburg, MD-WV | 0 |
| Harrisburg-Carlisle, PA | 0 |
| Harrisonburg, VA | 0 |
| Hickory-Morgantown-Lenoir, NC | 0 |
| Holland-Grand Haven, MI | 0 |
| Honolulu, HI | 0 |
| Houston-Baytown-Sugar Land, TX | 0 |
| Huntington-Ashland, WV-KY-OH | 0 |
| Huntsville, AL | 0 |
| Indianapolis, IN | 0 |
| Iowa City, IA | 0 |
| Jackson, MI | 0 |
| Jackson, MS | 0 |
| Jacksonville, NC | 0 |
| Janesville, WI | 0 |
| Johnson City, TN | 0 |
| Johnstown, PA | 0 |
| Joplin, MO | 0 |
| Kalamazoo-Portage, MI | 0 |
| Kankakee-Bradley, IL | 0 |
| Killeen-Temple-Fort Hood, TX | 0 |
| Kingsport-Bristol, TN-VA | 0 |
| Kingston, NY | 0 |
| Knoxville, TN | 0 |
| La Crosse, WI | 0 |
| Lafayette, LA | 0 |
| Lake Charles, LA | 0 |
| Lakeland-Winter Haven, FL | 0 |
| Lancaster, PA | 0 |
| Lansing-East Lansing, MI | 0 |
| Laredo, TX | 0 |
| Las Cruses, NM | 0 |
| Las Vegas-Paradise, NV | 0 |
| Lawrence, KS | 0 |
| Lawton, OK | 0 |
| Lexington-Fayette, KY | 0 |
| Little Rock-North Little Rock, AR | 0 |
| Longview, TX | 0 |
| Lubbock, TX | 0 |
| Lynchburg, VA | 0 |
| Macon, GA | 0 |
| Madera, CA | 0 |
| Madison, WI | 0 |
| McAllen-Edinburg-Pharr, TX | 0 |
| Medford, OR | 0 |
| Memphis, TN-MS-AR | 0 |
| Merced, CA | 0 |
| Michigan City-La Porte, IN | 0 |
| Midland, TX | 0 |
| Milwaukee-Waukesha-West Allis, WI | 0 |
| Mobile, AL | 0 |
| Modesto, CA | 0 |
| Monroe, MI | 0 |
| Muskegon-Norton Shores, MI | 0 |
| Myrtle Beach-Conway-North Myrtle Beach, SC | 0 |
| Napa, CA | 0 |
| Naples-Marco Island, FL | 0 |
| Nashville-Davidson-Murfreesboro, TN | 0 |
| New Haven, CT | 0 |
| New Orleans-Metairie-Kenner, LA | 0 |
| Niles-Benton Harbor, MI | 0 |
| Norwich-New London, CT-RI | 0 |
| Ocala, FL | 0 |
| Ocean City, NJ | 0 |
| Ogden-Clearfield, UT | 0 |
| Oklahoma City, OK | 0 |
| Olympia, WA | 0 |
| Omaha-Council Bluffs, NE-IA | 0 |
| Oshkosh-Neenah, WI | 0 |
| Palm Bay-Melbourne-Titusville, FL | 0 |
| Panama City-Lynn Haven, FL | 0 |
| Peoria, IL | 0 |
| Pittsburgh, PA | 0 |
| Port St. Lucie-Fort Pierce, FL | 0 |
| Portland-South Portland, ME | 0 |
| Portland-Vancouver-Beaverton, OR-WA | 0 |
| Poughkeepsie-Newburgh-Middletown, NY | 0 |
| Prescott, AZ | 0 |
| Provo-Orem, UT | 0 |
| Pueblo, CO | 0 |
| Punta Gorda, FL | 0 |
| Raleigh-Cary, NC | 0 |
| Reading, PA | 0 |
| Reno-Sparks, NV | 0 |
| Richmond, VA | 0 |
| Riverside-San Bernardino, CA | 0 |
| Roanoke, VA | 0 |
| Rochester-Dover, NH-ME | 0 |
| Rockford, IL | 0 |
| Saginaw-Saginaw Township North, MI | 0 |
| Salinas, CA | 0 |
| Salisbury, MD | 0 |
| San Antonio, TX | 0 |
| San Diego-Carlsbad-San Marcos, CA | 0 |
| San Luis Obispo-Paso Robles, CA | 0 |
| Santa-Cruz-Watsonville, CA | 0 |
| Santa Barbara-Santa Maria-Goleta, CA | 0 |
| Santa Fe, NM | 0 |
| Santa Rosa-Petaluma, CA | 0 |
| Sarasota-Bradenton-Venice, FL | 0 |
| Savannah, GA | 0 |
| Scranton-Wilkes Barre, PA | 0 |
| Shreveport-Bossier City, LA | 0 |
| Sioux Falls, SD | 0 |
| South Bend-Mishawaka, IN-MI | 0 |
| Spartanburg, SC | 0 |
| Spokane, WA | 0 |
| Springfield, IL | 0 |
| Springfield, MA-CT | 0 |
| Springfield, MO | 0 |
| Springfield, OH | 0 |
| St. Cloud, MN | 0 |
| St. Louis, MO-IL | 0 |
| Stockton, CA | 0 |
| Syracuse, NY | 0 |
| Tallahassee, FL | 0 |
| Toledo, OH | 0 |
| Topeka, KS | 0 |
| Tucson, AZ | 0 |
| Tulsa, OK | 0 |
| Tuscaloosa, AL | 0 |
| Utica-Rome, NY | 0 |
| Valdosta, GA | 0 |
| Vallejo-Fairfield, CA | 0 |
| Vero Beach, FL | 0 |
| Victoria, TX | 0 |
| Vineland-Millville-Bridgeton, NJ | 0 |
| Visalia-Porterville, CA | 0 |
| Waco, TX | 0 |
| Warner Robins, GA | 0 |
| Waterloo-Cedar Falls, IA | 0 |
| Wausau, WI | 0 |
| Winston-Salem, NC | 0 |
| Worcester, MA-CT | 0 |
| Yakima, WA | 0 |
| York-Hanover, PA | 0 |
| Youngstown-Warren-Boardman, OH | 0 |
| Akron, OH | 1 |
| Albuquerque, NM | 1 |
| Atlanta-Sandy Springs-Marietta, GA | 1 |
| Bremerton-Silverdale, WA | 1 |
| Cape Coral-Fort Myers, FL | 1 |
| Chico, CA | 1 |
| Cincinnati-Middletown, OH-KY-IN | 1 |
| Denver-Aurora, CO | 1 |
| Hartford-West Hartford-East Hartford, CT | 1 |
| Kansas City, MO-KS | 1 |
| Leominster-Fitchburg-Gardner, MA | 1 |
| Louisville, KY-IN | 1 |
| Minneapolis-St Paul-Bloomington, MN-WI | 1 |
| Monroe, LA | 1 |
| Montgomery, AL | 1 |
| Oxnard-Thousand Oaks-Ventura, CA | 1 |
| Pensacola-Ferry Pass-Brent, FL | 1 |
| Racine, WI | 1 |
| Rochester, NY | 1 |
| Salem, OR | 1 |
| San Jose-Sunnyvale-Santa Clara, CA | 1 |
| Seattle-Tacoma-Bellevue, WA | 1 |
| Tampa-St. Petersburg-Clearwater, FL | 1 |
| Trenton-Ewing, NJ | 1 |
| Virginia Beach-Norfolk-Newport News, VA-NC | 1 |
| Waterbury, CT | 1 |
| Wichita, KS | 1 |
| Barnstable Town, MA | 2 |
| Charlotte-Gastonia-Concord, NC-SC | 2 |
| Chicago-Naperville-Joliet, IN-IN-WI | 2 |
| Columbia, SC | 2 |
| Dallas-Fort Worth-Arlington, TX | 2 |
| Jacksonville, FL | 2 |
| Orlando, FL | 2 |
| Sacramento-Arden-Arcade-Roseville, CA | 2 |
| Canton-Massillon, OH | 3 |
| Phoenix-Mesa-Scottsdale, AZ | 3 |
| Providence-Fall River-Warwick, MA-RI | 3 |
| Salt Lake City, UT | 3 |
| Davenport-Moline-Rock Island, IA-IL | 4 |
| Philadelphia-Camden-Wilmington, PA-NJ-DE | 4 |
| Danbury, CT | 5 |
| San Francisco-Oakland-Fremont, CA | 6 |
| Bridgeport-Stamford-Norwalk, CT | 7 |
| New York-Northern New Jersey-Long Island, NY-NJ-PA | 7 |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 8 |
| Los Angeles-Long Beach-Santa Ana, CA | 9 |
| Miami-Fort Lauderdale-Miami Beach, FL | 16 |
| Boston-Cambridge-Quincy, MA-NH | 18 |
z = sort(tapply(CPS$Country == "Somalia", CPS$MetroArea, sum, na.rm=TRUE))
kable(z)| x | |
|---|---|
| Akron, OH | 0 |
| Albany-Schenectady-Troy, NY | 0 |
| Albany, GA | 0 |
| Albuquerque, NM | 0 |
| Allentown-Bethlehem-Easton, PA-NJ | 0 |
| Altoona, PA | 0 |
| Amarillo, TX | 0 |
| Anderson, IN | 0 |
| Anderson, SC | 0 |
| Ann Arbor, MI | 0 |
| Anniston-Oxford, AL | 0 |
| Appleton,WI | 0 |
| Asheville, NC | 0 |
| Athens-Clark County, GA | 0 |
| Atlanta-Sandy Springs-Marietta, GA | 0 |
| Atlantic City, NJ | 0 |
| Augusta-Richmond County, GA-SC | 0 |
| Austin-Round Rock, TX | 0 |
| Bakersfield, CA | 0 |
| Baltimore-Towson, MD | 0 |
| Bangor, ME | 0 |
| Barnstable Town, MA | 0 |
| Baton Rouge, LA | 0 |
| Beaumont-Port Author, TX | 0 |
| Bellingham, WA | 0 |
| Bend, OR | 0 |
| Billings, MT | 0 |
| Binghamton, NY | 0 |
| Birmingham-Hoover, AL | 0 |
| Bloomington-Normal IL | 0 |
| Bloomington, IN | 0 |
| Boise City-Nampa, ID | 0 |
| Boston-Cambridge-Quincy, MA-NH | 0 |
| Boulder, CO | 0 |
| Bowling Green, KY | 0 |
| Bremerton-Silverdale, WA | 0 |
| Bridgeport-Stamford-Norwalk, CT | 0 |
| Brownsville-Harlingen, TX | 0 |
| Buffalo-Niagara Falls, NY | 0 |
| Canton-Massillon, OH | 0 |
| Cape Coral-Fort Myers, FL | 0 |
| Cedar Rapids, IA | 0 |
| Champaign-Urbana, IL | 0 |
| Charleston-North Charleston, SC | 0 |
| Charleston, WV | 0 |
| Charlotte-Gastonia-Concord, NC-SC | 0 |
| Chattanooga, TN-GA | 0 |
| Chicago-Naperville-Joliet, IN-IN-WI | 0 |
| Chico, CA | 0 |
| Cincinnati-Middletown, OH-KY-IN | 0 |
| Cleveland-Elyria-Mentor, OH | 0 |
| Coeur d’Alene, ID | 0 |
| Colorado Springs, CO | 0 |
| Columbia, MO | 0 |
| Columbia, SC | 0 |
| Columbus, GA-AL | 0 |
| Corpus Christi, TX | 0 |
| Dallas-Fort Worth-Arlington, TX | 0 |
| Danbury, CT | 0 |
| Davenport-Moline-Rock Island, IA-IL | 0 |
| Decatur, Al | 0 |
| Decatur, IL | 0 |
| Deltona-Daytona Beach-Ormond Beach, FL | 0 |
| Denver-Aurora, CO | 0 |
| Des Moines, IA | 0 |
| Detroit-Warren-Livonia, MI | 0 |
| Dover, DE | 0 |
| Duluth, MN-WI | 0 |
| Durham, NC | 0 |
| Eau Claire, WI | 0 |
| El Centro, CA | 0 |
| El Paso, TX | 0 |
| Erie, PA | 0 |
| Eugene-Springfield, OR | 0 |
| Evansville, IN-KY | 0 |
| Farmington, NM | 0 |
| Fayetteville-Springdale-Rogers, AR-MO | 0 |
| Fayetteville, NC | 0 |
| Flint, MI | 0 |
| Florence, AL | 0 |
| Fort Collins-Loveland, CO | 0 |
| Fort Smith, AR-OK | 0 |
| Fort Walton Beach-Crestview-Destin, FL | 0 |
| Fort Wayne, IN | 0 |
| Fresno, CA | 0 |
| Gainesville, FL | 0 |
| Grand Rapids-Wyoming, MI | 0 |
| Greeley, CO | 0 |
| Green Bay, WI | 0 |
| Greensboro-High Point, NC | 0 |
| Greenville, SC | 0 |
| Gulfport-Biloxi, MS | 0 |
| Hagerstown-Martinsburg, MD-WV | 0 |
| Harrisburg-Carlisle, PA | 0 |
| Harrisonburg, VA | 0 |
| Hartford-West Hartford-East Hartford, CT | 0 |
| Hickory-Morgantown-Lenoir, NC | 0 |
| Holland-Grand Haven, MI | 0 |
| Honolulu, HI | 0 |
| Huntington-Ashland, WV-KY-OH | 0 |
| Huntsville, AL | 0 |
| Indianapolis, IN | 0 |
| Iowa City, IA | 0 |
| Jackson, MI | 0 |
| Jackson, MS | 0 |
| Jacksonville, FL | 0 |
| Jacksonville, NC | 0 |
| Janesville, WI | 0 |
| Johnson City, TN | 0 |
| Johnstown, PA | 0 |
| Joplin, MO | 0 |
| Kalamazoo-Portage, MI | 0 |
| Kankakee-Bradley, IL | 0 |
| Kansas City, MO-KS | 0 |
| Killeen-Temple-Fort Hood, TX | 0 |
| Kingsport-Bristol, TN-VA | 0 |
| Kingston, NY | 0 |
| Knoxville, TN | 0 |
| La Crosse, WI | 0 |
| Lafayette, LA | 0 |
| Lake Charles, LA | 0 |
| Lakeland-Winter Haven, FL | 0 |
| Lancaster, PA | 0 |
| Lansing-East Lansing, MI | 0 |
| Laredo, TX | 0 |
| Las Cruses, NM | 0 |
| Las Vegas-Paradise, NV | 0 |
| Lawrence, KS | 0 |
| Lawton, OK | 0 |
| Leominster-Fitchburg-Gardner, MA | 0 |
| Lexington-Fayette, KY | 0 |
| Little Rock-North Little Rock, AR | 0 |
| Longview, TX | 0 |
| Los Angeles-Long Beach-Santa Ana, CA | 0 |
| Louisville, KY-IN | 0 |
| Lubbock, TX | 0 |
| Lynchburg, VA | 0 |
| Macon, GA | 0 |
| Madera, CA | 0 |
| Madison, WI | 0 |
| McAllen-Edinburg-Pharr, TX | 0 |
| Medford, OR | 0 |
| Memphis, TN-MS-AR | 0 |
| Merced, CA | 0 |
| Miami-Fort Lauderdale-Miami Beach, FL | 0 |
| Michigan City-La Porte, IN | 0 |
| Midland, TX | 0 |
| Milwaukee-Waukesha-West Allis, WI | 0 |
| Mobile, AL | 0 |
| Modesto, CA | 0 |
| Monroe, LA | 0 |
| Monroe, MI | 0 |
| Montgomery, AL | 0 |
| Muskegon-Norton Shores, MI | 0 |
| Myrtle Beach-Conway-North Myrtle Beach, SC | 0 |
| Napa, CA | 0 |
| Naples-Marco Island, FL | 0 |
| Nashville-Davidson-Murfreesboro, TN | 0 |
| New Haven, CT | 0 |
| New Orleans-Metairie-Kenner, LA | 0 |
| New York-Northern New Jersey-Long Island, NY-NJ-PA | 0 |
| Niles-Benton Harbor, MI | 0 |
| Norwich-New London, CT-RI | 0 |
| Ocala, FL | 0 |
| Ocean City, NJ | 0 |
| Ogden-Clearfield, UT | 0 |
| Oklahoma City, OK | 0 |
| Olympia, WA | 0 |
| Omaha-Council Bluffs, NE-IA | 0 |
| Orlando, FL | 0 |
| Oshkosh-Neenah, WI | 0 |
| Oxnard-Thousand Oaks-Ventura, CA | 0 |
| Palm Bay-Melbourne-Titusville, FL | 0 |
| Panama City-Lynn Haven, FL | 0 |
| Pensacola-Ferry Pass-Brent, FL | 0 |
| Peoria, IL | 0 |
| Philadelphia-Camden-Wilmington, PA-NJ-DE | 0 |
| Pittsburgh, PA | 0 |
| Port St. Lucie-Fort Pierce, FL | 0 |
| Poughkeepsie-Newburgh-Middletown, NY | 0 |
| Prescott, AZ | 0 |
| Providence-Fall River-Warwick, MA-RI | 0 |
| Provo-Orem, UT | 0 |
| Pueblo, CO | 0 |
| Punta Gorda, FL | 0 |
| Racine, WI | 0 |
| Raleigh-Cary, NC | 0 |
| Reading, PA | 0 |
| Reno-Sparks, NV | 0 |
| Riverside-San Bernardino, CA | 0 |
| Roanoke, VA | 0 |
| Rochester-Dover, NH-ME | 0 |
| Rochester, NY | 0 |
| Rockford, IL | 0 |
| Sacramento-Arden-Arcade-Roseville, CA | 0 |
| Saginaw-Saginaw Township North, MI | 0 |
| Salem, OR | 0 |
| Salinas, CA | 0 |
| Salisbury, MD | 0 |
| Salt Lake City, UT | 0 |
| San Antonio, TX | 0 |
| San Diego-Carlsbad-San Marcos, CA | 0 |
| San Francisco-Oakland-Fremont, CA | 0 |
| San Jose-Sunnyvale-Santa Clara, CA | 0 |
| San Luis Obispo-Paso Robles, CA | 0 |
| Santa-Cruz-Watsonville, CA | 0 |
| Santa Barbara-Santa Maria-Goleta, CA | 0 |
| Santa Fe, NM | 0 |
| Santa Rosa-Petaluma, CA | 0 |
| Sarasota-Bradenton-Venice, FL | 0 |
| Savannah, GA | 0 |
| Scranton-Wilkes Barre, PA | 0 |
| Shreveport-Bossier City, LA | 0 |
| South Bend-Mishawaka, IN-MI | 0 |
| Spartanburg, SC | 0 |
| Spokane, WA | 0 |
| Springfield, IL | 0 |
| Springfield, MA-CT | 0 |
| Springfield, MO | 0 |
| Springfield, OH | 0 |
| St. Louis, MO-IL | 0 |
| Stockton, CA | 0 |
| Syracuse, NY | 0 |
| Tallahassee, FL | 0 |
| Tampa-St. Petersburg-Clearwater, FL | 0 |
| Toledo, OH | 0 |
| Topeka, KS | 0 |
| Trenton-Ewing, NJ | 0 |
| Tucson, AZ | 0 |
| Tulsa, OK | 0 |
| Tuscaloosa, AL | 0 |
| Utica-Rome, NY | 0 |
| Valdosta, GA | 0 |
| Vallejo-Fairfield, CA | 0 |
| Vero Beach, FL | 0 |
| Victoria, TX | 0 |
| Vineland-Millville-Bridgeton, NJ | 0 |
| Virginia Beach-Norfolk-Newport News, VA-NC | 0 |
| Visalia-Porterville, CA | 0 |
| Waco, TX | 0 |
| Warner Robins, GA | 0 |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 0 |
| Waterbury, CT | 0 |
| Waterloo-Cedar Falls, IA | 0 |
| Wausau, WI | 0 |
| Wichita, KS | 0 |
| Winston-Salem, NC | 0 |
| Worcester, MA-CT | 0 |
| Yakima, WA | 0 |
| York-Hanover, PA | 0 |
| Youngstown-Warren-Boardman, OH | 0 |
| Dayton, OH | 1 |
| Richmond, VA | 1 |
| Houston-Baytown-Sugar Land, TX | 2 |
| Sioux Falls, SD | 2 |
| Burlington-South Burlington, VT | 3 |
| Portland-South Portland, ME | 3 |
| Portland-Vancouver-Beaverton, OR-WA | 3 |
| Columbus, OH | 5 |
| Fargo, ND-MN | 5 |
| Phoenix-Mesa-Scottsdale, AZ | 7 |
| Seattle-Tacoma-Bellevue, WA | 7 |
| St. Cloud, MN | 7 |
| Minneapolis-St Paul-Bloomington, MN-WI | 17 |
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).