library(tidycensus)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.0 v dplyr 1.0.5
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()census_api_key('021009b495f1a330accaee1f188f81c820c8a338', install = TRUE, overwrite = TRUE)## Your original .Renviron will be backed up and stored in your R HOME directory if needed.## Your API key has been stored in your .Renviron and can be accessed by Sys.getenv("CENSUS_API_KEY").
## To use now, restart R or run `readRenviron("~/.Renviron")`## [1] "021009b495f1a330accaee1f188f81c820c8a338"readRenviron("~/.Renviron")## Loading Variables
v_1 = load_variables(year = 2019, "acs5", cache = TRUE)
head(v_1)## # A tibble: 6 x 3
## name label concept
## <chr> <chr> <chr>
## 1 B01001_001 Estimate!!Total: SEX BY AGE
## 2 B01001_002 Estimate!!Total:!!Male: SEX BY AGE
## 3 B01001_003 Estimate!!Total:!!Male:!!Under 5 years SEX BY AGE
## 4 B01001_004 Estimate!!Total:!!Male:!!5 to 9 years SEX BY AGE
## 5 B01001_005 Estimate!!Total:!!Male:!!10 to 14 years SEX BY AGE
## 6 B01001_006 Estimate!!Total:!!Male:!!15 to 17 years SEX BY AGEv_1 %>% filter(str_detect(str_to_lower(label), "wireless"))## # A tibble: 6 x 3
## name label concept
## <chr> <chr> <chr>
## 1 B28001_~ Estimate!!Total:!!Has one or more types of c~ TYPES OF COMPUTERS IN ~
## 2 B28001_~ Estimate!!Total:!!Has one or more types of c~ TYPES OF COMPUTERS IN ~
## 3 B28010_~ Estimate!!Total:!!Has one or more types of c~ COMPUTERS IN HOUSEHOLD
## 4 B28010_~ Estimate!!Total:!!Has one or more types of c~ COMPUTERS IN HOUSEHOLD
## 5 B99282_~ Estimate!!Total:!!Tablet or other portable w~ ALLOCATION OF HOUSEHOL~
## 6 B99282_~ Estimate!!Total:!!Tablet or other portable w~ ALLOCATION OF HOUSEHOL~## Graph based on chosen variable
v_2 <- get_acs(geography = "county", state = "WA", variables = "B28001_008")## Getting data from the 2015-2019 5-year ACShead(v_2)## # A tibble: 6 x 5
## GEOID NAME variable estimate moe
## <chr> <chr> <chr> <dbl> <dbl>
## 1 53001 Adams County, Washington B28001_008 50 32
## 2 53003 Asotin County, Washington B28001_008 84 47
## 3 53005 Benton County, Washington B28001_008 472 132
## 4 53007 Chelan County, Washington B28001_008 350 159
## 5 53009 Clallam County, Washington B28001_008 456 118
## 6 53011 Clark County, Washington B28001_008 876 154v_2 %>% ggplot(aes(x = estimate, y = reorder(NAME, estimate))) + geom_point()
## Graph of Counties that CPs in the Household
v_3 <- get_acs(geography = "county", state = "WA", variables = "B28001_008", geometry = TRUE)## Getting data from the 2015-2019 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.##
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|======================================================================| 100%v_3 = v_3 %>% mutate(NAME = str_replace(NAME, "County,Washington", ""))
head(v_3)## Simple feature collection with 6 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -124.3549 ymin: 45.54354 xmax: -118.1983 ymax: 47.94077
## Geodetic CRS: NAD83
## GEOID NAME variable estimate moe
## 1 53035 Kitsap County, Washington B28001_008 655 147
## 2 53021 Franklin County, Washington B28001_008 245 155
## 3 53011 Clark County, Washington B28001_008 876 154
## 4 53027 Grays Harbor County, Washington B28001_008 455 133
## 5 53005 Benton County, Washington B28001_008 472 132
## 6 53015 Cowlitz County, Washington B28001_008 501 140
## geometry
## 1 MULTIPOLYGON (((-122.5049 4...
## 2 MULTIPOLYGON (((-119.457 46...
## 3 MULTIPOLYGON (((-122.796 45...
## 4 MULTIPOLYGON (((-123.8845 4...
## 5 MULTIPOLYGON (((-119.8751 4...
## 6 MULTIPOLYGON (((-123.2183 4...ggplot(v_3) + geom_sf(aes(fill = estimate)) + geom_sf_label(aes(label = NAME))## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Bar Graph
v_4 <- get_acs(geography = 'county', state = 'WA', variables = 'B28001_008', geometry = TRUE)## Getting data from the 2015-2019 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`. v_4 %>% ggplot(aes(x = NAME, y = estimate)) + geom_col() + coord_flip()
##Pie Chart
v_5 <- get_acs(geography = 'county', state = 'WA', variables = 'B28001_008', geometry = TRUE)## Getting data from the 2015-2019 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.ggplot(v_5, aes(x = 100, y = NAME, fill = estimate)) + geom_col() + coord_polar(theta = 'y')