Retrieving Data
The demographics category of cancerprof contains 11 unique functions to pull data from the demographics page of State Cancer Profile.
These functions are: demo_crowding(),
demo_education(), demo_food(),
demo_income(), demo_insurance(),
demo_mobility(), demo_non_english_language(),
demo_population(), demo_poverty(),
demo_svi(), demo_workforce()
Each of these functions require various parameters that must be specified to pull data. Please refer to function documentation for more details.
Demo Crowding
Demo crowding Always requires 4 arguments:
area, areatype, crowding, and
race
crowding <- demo_crowding(
area = "WA",
areatype = "county",
crowding = "household with >1 person per room",
race = "All Races (includes Hispanic)"
)
head(crowding, n = 3)
#> County FIPS Percent Households Rank
#> 1 Columbia County 53013 1.4 25 2111 of 3143
#> 2 Jefferson County 53031 1.4 211 2095 of 3143
#> 3 Whitman County 53075 1.4 246 2090 of 3143Demo Education
Demo education has 5 arguments: area,
areatype, education, sex,
race.
Depending on the education argument, the required arguments will change
# at least high school - requires arguments: area, areatype, education, sex
education1 <- demo_education(
area = "wa",
areatype = "county",
education = "at least high school",
sex = "males"
)
head(education1, n = 3)
#> County FIPS Percent Households Rank
#> 1 Whitman County 53075 95.5 11789 3037 of 3143
#> 2 Kitsap County 53035 95.1 91509 3012 of 3143
#> 3 Island County 53029 95.0 29073 2995 of 3143
# at least bachelors degree - requires arguments:
# area, areatype, education, sex, race
education2 <- demo_education(
area = "usa",
areatype = "state",
education = "at least bachelors degree",
sex = "both sexes",
race = "all races (includes hispanic)"
)
head(education2, n = 3)
#> State FIPS Percent Households Rank
#> 1 West Virginia 54000 21.8 278281 52 of 52
#> 2 Mississippi 28000 23.2 458928 51 of 52
#> 3 Arkansas 05000 24.3 491269 50 of 52
# less than 9th grade - requires arguments: area, areatype, education
education3 <- demo_education(
area = "pr",
areatype = "hsa",
education = "less than 9th grade"
)
head(education3, n = 3)
#> Health_Service_Area HSA_Code Percent Households Rank
#> 1 Puerto Rico 0995 14.1 337405 935 of 950Demo Food
Demo food has 4 arguments: area, areatype,
food, race.
# limited access to healthy food - requires arguments: area, areatype, food
food1 <- demo_food(
area = "usa",
areatype = "state",
food = "limited access to healthy food"
)
head(food1, n = 3)
#> State FIPS Percent People
#> 1 New Mexico 35000 13 268515
#> 2 Louisiana 22000 11 483383
#> 3 Mississippi 28000 11 337505
# food insecurity - requires arguments: area, areatype, food, race
food2 <- demo_food(
area = "pr",
areatype = "county",
food = "food insecurity",
race = "all races (includes hispanic)"
)
head(food2, n = 3)
#> County FIPS Percent
#> 1 Puerto Rico 72001 NADemo Income
Demo income Always requires 4 arguments:
area, areatype, income,
race.
# limited access to healthy food - requires arguments: area, areatype, food
income1 <- demo_income(
area = "wa",
areatype = "county",
income = "median household income",
race = "all races (includes hispanic)"
)
head(income1, n = 3)
#> County FIPS Dollars Rank
#> 1 Whitman County 53075 43613 2700 of 3142
#> 2 Ferry County 53019 45907 2529 of 3142
#> 3 Garfield County 53023 50625 2168 of 3142
# food insecurity - requires arguments: area, areatype, food, race
income2 <- demo_income(
area = "usa",
areatype = "state",
income = "median family income",
race = "all races (includes hispanic)"
)
head(income2, n = 3)
#> State FIPS Dollars Rank
#> 1 Puerto Rico 72001 26745 52 of 52
#> 2 Mississippi 28000 62802 51 of 52
#> 3 Arkansas 05000 65673 50 of 52Demo Insurance
Demo insurance has 6 arguments: area, areatype, insurance, sex, age, race.
Please note that the age arguments for
"both sexes" is different than “Males and”Females”
Check function documentations for more details
Only Areatype "state" can select Race, otherwise race
should always be "all races (includes hispanic)"
insurance1 <- demo_insurance(
area = "usa",
areatype = "state",
insurance = "% Insured in demographic group, all income levels",
sex = "both sexes",
age = "18 to 64 years",
race = "white (non-hispanic)"
)
head(insurance1, n = 3)
#> State FIPS Percent People Rank
#> 1 Oklahoma 40000 84.6 1256749 51 of 51
#> 2 Mississippi 28000 85.2 809125 50 of 51
#> 3 Wyoming 56000 85.4 238655 49 of 51
insurance2 <- demo_insurance(
area = "wa",
areatype = "county",
insurance = "% Insured in demographic group, all income levels",
sex = "males",
age = "18 to 64 years"
)
head(insurance2, n = 3)
#> County FIPS Percent People Rank
#> 1 Adams County 53001 73.8 4073 2890 of 3141
#> 2 Yakima County 53077 77.2 54771 2679 of 3141
#> 3 Grant County 53025 79.4 23081 2464 of 3141Demo Mobility
Demo mobility Always requires 3 arguments:
area, areatype, mobility. The
function defaults to "all races",
"both sexes", "ages 1+"
mobility1 <- demo_mobility(
area = "usa",
areatype = "state",
mobility = "moved, same county (in past year)"
)
head(mobility1, n = 3)
#> State FIPS Percent People Rank
#> 1 Nevada 32000 10.6 321900 51 of 51
#> 2 Arizona 04000 10.2 716304 50 of 51
#> 3 District of Columbia 11001 10.2 68557 49 of 51
mobility2 <- demo_mobility(
area = "WA",
areatype = "county",
mobility = "moved, different county, same state (in past year)"
)
head(mobility2, n = 3)
#> County FIPS Percent People Rank
#> 1 Kittitas County 53037 12.7 5563 3114 of 3142
#> 2 Whitman County 53075 10.9 5224 3093 of 3142
#> 3 Grays Harbor County 53027 5.8 4314 2619 of 3142Demo Language
Demo Language Always requires 3 arguments:
area, areatype, language. The
function defaults to "all races",
"both sexes", "ages 14+"
non_english1 <- demo_language(
area = "wa",
areatype = "county",
language = "language isolation"
)
head(non_english1, n = 3)
#> County FIPS Percent Households Rank
#> 1 Adams County 53001 18.9 1165 3127 of 3142
#> 2 Franklin County 53021 11.0 3044 3087 of 3142
#> 3 Grant County 53025 8.6 2810 3044 of 3142
non_english2 <- demo_language(
area = "usa",
areatype = "state",
language = "language isolation"
)
head(non_english2, n = 3)
#> State FIPS Percent Households Rank
#> 1 California 06000 8.5 1119486 51 of 51
#> 2 New York 36000 7.6 571749 50 of 51
#> 3 Texas 48000 7.1 731111 49 of 51Demo Population
Demo Population has 5 arguments: area,
areatype, population, race,
sex. The population argument is used to input a population
variable such as age, race, or sex. Please note that this different from
the race and sex arguments and different population variables will
default race, sex, and age.
If you select "foreign born" for population, you
must provide another race for the race argument
#
population1 <- demo_population(
area = "wa",
areatype = "county",
population = "foreign born",
race = "black",
sex = "females"
)
head(population1, n = 3)
#> County FIPS Percent People Rank
#> 1 Columbia County 53013 0 0 1666 of 2885
#> 2 Grays Harbor County 53027 0 0 1666 of 2885
#> 3 Jefferson County 53031 0 0 1666 of 2885
population2 <- demo_population(
area = "ca",
areatype = "county",
population = "males",
race = "all races (includes hispanic)"
)
head(population2, n = 3)
#> County FIPS Percent People Rank
#> 1 Lassen County 06035 64.8 21361 3134 of 3143
#> 2 Kings County 06031 55.2 83872 3015 of 3143
#> 3 Mono County 06051 54.8 7284 2987 of 3143
population3 <- demo_population(
area = "usa",
areatype = "state",
population = "age under 18",
race = "all races (includes hispanic)",
sex = "both sexes"
)
head(population3, n = 3)
#> State FIPS Percent People Rank
#> 1 Puerto Rico 72001 18.0 597277 52 of 52
#> 2 District of Columbia 11001 18.3 125022 51 of 52
#> 3 Vermont 50000 18.5 118889 50 of 52Demo Poverty
Demo poverty has 5 arguments: area,
areatype, poverty, race,
sex. The function defaults to "all ages"
The "persistent poverty" and
"persons <150% of poverty" poverty argument will default
to "all races", "both sexes",
"all ages".
The "families below poverty" poverty argument will
require a race argument and default to "both sexes" and
"all ages".
The "persons below poverty" poverty argument will
require a race argument and a sex argument, and default to
"all ages".
# Persistent poverty
poverty1 <- demo_poverty(
area = "WA",
areatype = "county",
poverty = "persistent poverty"
)
head(poverty1, n = 3)
#> County FIPS Persistent Poverty
#> 1 Whitman County 53075 yes
#> 2 Adams County 53001 no
#> 3 Asotin County 53003 no
# Families below poverty
poverty2 <- demo_poverty(
area = "usa",
areatype = "state",
poverty = "families below poverty",
race = "black"
)
head(poverty2, n = 3)
#> State FIPS Percent People Rank
#> 1 Puerto Rico 72001 40.9 33658 52 of 52
#> 2 Wyoming 56000 33.1 349 51 of 52
#> 3 Iowa 19000 26.6 6200 50 of 52
# Persons below poverty
poverty3 <- demo_poverty(
area = "usa",
areatype = "state",
poverty = "persons below poverty",
race = "black",
sex = "males"
)
head(poverty3, n = 3)
#> State FIPS Percent People Rank
#> 1 Puerto Rico 72001 42.2 67037 52 of 52
#> 2 Louisiana 22000 28.3 188456 51 of 52
#> 3 Mississippi 28000 28.2 139358 50 of 52Demo Social Vulnerability Index (SVI)
Demo svi Always requires 2 arguments:
area, svi. The function defaults to
"all races", "both sexes",
"all ages".
Please note that the areatype argument is not available for this
function because areatype is limited to "county"
svi1 <- demo_svi(
area = "WA",
svi = "overall"
)
head(svi1, n = 3)
#> County FIPS Score
#> 1 Adams County 53001 0.9656
#> 2 Yakima County 53077 0.9570
#> 3 Okanogan County 53047 0.9532
svi2 <- demo_svi(
area = "usa",
svi = "socioeconomic status"
)
head(svi2, n = 3)
#> County FIPS Score
#> 1 Oglala Lakota/Shannon County, South Dakota 46102 1.0000
#> 2 Macon County, Georgia 13193 0.9997
#> 3 Humphreys County, Mississippi 28053 0.9994Workforce
Demo svi Always requires 5 arguments:
area, areatype, workforce,
race, sex. The function defaults to “ages
16+”
workforce1 <- demo_workforce(
area = "WA",
areatype = "county",
workforce = "unemployed",
race = "all races (includes hispanic)",
sex = "both sexes"
)
head(workforce1, n = 3)
#> County FIPS Percent People_Unemployed Rank
#> 1 Garfield County 53023 11.0 110 3045 of 3143
#> 2 Jefferson County 53031 7.6 953 2735 of 3143
#> 3 Whitman County 53075 7.5 1849 2700 of 3143
workforce2 <- demo_workforce(
area = "usa",
areatype = "state",
workforce = "unemployed",
race = "all races (includes hispanic)",
sex = "females"
)
head(workforce2, n = 3)
#> State FIPS Percent People_Unemployed Rank
#> 1 Puerto Rico 72001 14.5 85552 52 of 52
#> 2 Nevada 32000 7.2 50777 51 of 52
#> 3 District of Columbia 11001 7.1 14769 50 of 52