o v e r v i e w U S C e n s u s d a ta : a nU S C e n s u s d a ta : a n o v e r v i e w ANALYZING...

DataCamp Analyzing US Census Data in R

US Census data: anoverview

ANALYZING US CENSUS DATA IN R

Kyle WalkerInstructor


Course overview

What you'll learn:

How to acquire US Census data with the tidycensus R package

How to wrangle US Census data with tidyverse tools

How to use the R tigris package to acquire US Census Bureau boundary data

How to visualize and map US Census Bureau data in R with ggplot2


About your instructor

Fields: spatial demography & spatial data science

R developer: tidycensus, tigris, & idbr packages


US Census Bureau Data


The US Census Bureau API

To get started using US Census data in R,

Example key: "rw6pozt48ur2ugc8kg69x5phdrtnuhb2cb1subd6"

sign up for a Census API keylibrary(tidycensus)

census_api_key("YOUR KEY GOES HERE", install = TRUE)

https://api.census.gov/data/key_signup.html


Using decennial Census data with tidycensusstate_pop <- get_decennial(geography = "state",

variables = "P001001")

head(state_pop)

# A tibble: 6 x 4

GEOID NAME variable value

<chr> <chr> <chr> <dbl>

1 01 Alabama P001001 4779736

2 02 Alaska P001001 710231

3 04 Arizona P001001 6392017

4 05 Arkansas P001001 2915918

5 06 California P001001 37253956

6 08 Colorado P001001 5029196


Using ACS data with tidycensusstate_income <- get_acs(geography = "state",

variables = "B19013_001")

head(state_income)

# A tibble: 6 x 5

GEOID NAME variable estimate moe

<chr> <chr> <chr> <dbl> <dbl>

1 01 Alabama B19013_001 44758 314

2 02 Alaska B19013_001 74444 809

3 04 Arizona B19013_001 51340 231

4 05 Arkansas B19013_001 42336 234

5 06 California B19013_001 63783 188

6 08 Colorado B19013_001 62520 287


Let's get started!



Basic tidycensusfunctionality




Geography in tidycensus

Legal entities: geography = "county"

Statistical entities: geography = "tract"

Available geographies

https://walkerke.github.io/tidycensus/articles/basic-usage.html#geography-in-tidycensus


Geography and variables in tidycensuscounty_income <- get_acs(geography = "county",

variables = "B19013_001")

county_income

# A tibble: 3,220 x 5



1 01001 Autauga County, Alabama B19013_001 53099 2631

2 01003 Baldwin County, Alabama B19013_001 51365 991

3 01005 Barbour County, Alabama B19013_001 33956 2655

4 01007 Bibb County, Alabama B19013_001 39776 3306

5 01009 Blount County, Alabama B19013_001 46212 2443

6 01011 Bullock County, Alabama B19013_001 29335 5435

7 01013 Butler County, Alabama B19013_001 34315 2904

8 01015 Calhoun County, Alabama B19013_001 41954 1381

9 01017 Chambers County, Alabama B19013_001 36027 1870

10 01019 Cherokee County, Alabama B19013_001 38925 2598

# ... with 3,210 more rows


Geographic subsets in tidycensustexas_income <- get_acs(geography = "county",

variables = c(hhincome = "B19013_001"),

state = "TX")

texas_income

# A tibble: 254 x 5



1 48001 Anderson County, Texas hhincome 42146 2539

2 48003 Andrews County, Texas hhincome 70121 7053

3 48005 Angelina County, Texas hhincome 44185 2107

4 48007 Aransas County, Texas hhincome 44851 4261

5 48009 Archer County, Texas hhincome 62407 5368

6 48011 Armstrong County, Texas hhincome 65000 9415

7 48013 Atascosa County, Texas hhincome 53181 4114

8 48015 Austin County, Texas hhincome 56681 4903

9 48017 Bailey County, Texas hhincome 40589 8438

10 48019 Bandera County, Texas hhincome 55434 4503

# ... with 244 more rows


Wide data with tidycensusget_acs(geography = "county",

variables = c(hhincome = "B19013_001",

medage = "B01002_001"),

state = "TX",

output = "wide")

# A tibble: 254 x 6

GEOID NAME hhincomeE hhincomeM medageE medageM

<chr> <chr> <dbl> <dbl> <dbl> <dbl>

1 48001 Anderson County, Texas 42146 2539 38.9 0.5

2 48003 Andrews County, Texas 70121 7053 31.2 0.8

3 48005 Angelina County, Texas 44185 2107 36.7 0.3

4 48007 Aransas County, Texas 44851 4261 50.7 1.1

5 48009 Archer County, Texas 62407 5368 44.1 0.7

6 48011 Armstrong County, Texas 65000 9415 45.9 2.8

7 48013 Atascosa County, Texas 53181 4114 35.4 0.2

8 48015 Austin County, Texas 56681 4903 40.8 0.4

9 48017 Bailey County, Texas 40589 8438 34.4 1.1

10 48019 Bandera County, Texas 55434 4503 51.3 0.9



Let's practice!



Searching for data withtidycensus




Searching for Census variables

To find Census variable IDs, use:

Online resources like

Built-in variable searching in tidycensus

Census Reporter

https://censusreporter.org/


Choosing a dataset to searchv16 <- load_variables(year = 2016,

dataset = "acs5",

cache = TRUE)

v16

# A tibble: 22,815 x 3

name label concept

<chr> <chr> <chr>

1 B00001_001 Estimate!!Total UNWEIGHTED...

2 B00002_001 Estimate!!Total UNWEIGHTED...

3 B01001_001 Estimate!!Total SEX BY AGE

4 B01001_002 Estimate!!Total!!Male SEX BY AGE

5 B01001_003 Estimate!!Total!!Male!!Under 5 years SEX BY AGE

6 B01001_004 Estimate!!Total!!Male!!5 to 9 years SEX BY AGE



9 B01001_007 Estimate!!Total!!Male!!18 and 19 years SEX BY AGE

10 B01001_008 Estimate!!Total!!Male!!20 years SEX BY AGE

# ... with 22,805 more rows


Filtering a variables datasetlibrary(tidyverse)

B19001 <- filter(v16, str_detect(name, "B19001"))

B19001

# A tibble: 170 x 3

name label concept

<chr> <chr> <chr>

1 B19001_001E Estimate!!Total HOUSEHOLD INCOME…

2 B19001_002E ...Less than $10,000 HOUSEHOLD INCOME…

3 B19001_003E ...$10,000 to $14,999 HOUSEHOLD INCOME…










ACS variable structure

Anatomy of an ACS variable B19001_002E:

B: refers to base table. Other prefixes: C, DP, S.

19001: the table ID

002: the variable code within the table

E: refers to estimate.

optional in tidycensus functions, which return both E and M for each variable.


Let's practice!



Visualizing Census datawith ggplot2




ggplot2: a layered grammar of graphics in R


Example: plotting income by statelibrary(tidycensus)

library(tidyverse)

ne_income <- get_acs(geography = "state",

variables = "B19013_001",

survey = "acs1",

state = c("ME", "NH", "VT", "MA",

"RI", "CT", "NY"))

ggplot(ne_income, aes(x = estimate, y = NAME)) +

geom_point()



Customizing ggplot2 graphics of ACS dataggplot(ne_income,

aes(x = estimate,

y = reorder(NAME, estimate))) +

geom_point(color = "navy", size = 4) +

scale_x_continuous(labels = scales::dollar) +

theme_minimal(base_size = 14) +

labs(x = "2016 ACS estimate",

y = "",

title = "Median household income by state")



Let's practice!


Date post:	22-Mar-2020
Category:	Documents
Upload:	others
View:	4 times
Download:	0 times

o v e r v i e w U S C e n s u s d a ta : a nU S C e n s u s d a ta : a n o v e r v i e w ANALYZING...

Documents