Note you need to set up an API key for tidycensus. Please check the package notes first.
library(tidyverse)## -- Attaching packages ------------------------------------------------------------------------------------------------------------ tidyverse 1.2.1 --## v ggplot2 3.2.0 v purrr 0.3.2
## v tibble 2.1.3 v dplyr 0.8.3
## v tidyr 1.0.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0## -- Conflicts --------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(tidycensus)This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
yolo_age_hisp <-
get_estimates(geography = "county",
product = "characteristics",
breakdown = c("SEX", "AGEGROUP", "HISP"),
breakdown_labels = TRUE,
state = "CA",
county = "Yolo")
yolo_age_hisp## # A tibble: 210 x 6
## GEOID NAME value SEX AGEGROUP HISP
## <chr> <chr> <dbl> <chr> <fct> <chr>
## 1 06113 Yolo County, Cali~ 215959 Both sex~ All ages Both Hispanic O~
## 2 06113 Yolo County, Cali~ 105170 Male All ages Both Hispanic O~
## 3 06113 Yolo County, Cali~ 147821 Both sex~ All ages Non-Hispanic
## 4 06113 Yolo County, Cali~ 12437 Both sex~ Age 0 to 4 y~ Both Hispanic O~
## 5 06113 Yolo County, Cali~ 6357 Male Age 0 to 4 y~ Both Hispanic O~
## 6 06113 Yolo County, Cali~ 6892 Both sex~ Age 0 to 4 y~ Non-Hispanic
## 7 06113 Yolo County, Cali~ 2719 Male Age 0 to 4 y~ Hispanic
## 8 06113 Yolo County, Cali~ 110789 Female All ages Both Hispanic O~
## 9 06113 Yolo County, Cali~ 6080 Female Age 0 to 4 y~ Both Hispanic O~
## 10 06113 Yolo County, Cali~ 71277 Male All ages Non-Hispanic
## # ... with 200 more rowsyolo_compare <- yolo_age_hisp %>%
filter(str_detect(AGEGROUP, "^Age"),
HISP != "Both Hispanic Origins",
SEX != "Both sexes") %>%
mutate(value = if_else(SEX == "Male", -value, value))
library(ggplot2)
gg_yolo_compare <- ggplot(yolo_compare, aes(x = AGEGROUP, y = value, fill = SEX)) +
geom_bar(stat = "identity", width = 1) +
theme_minimal(
# base_family = "Roboto"
) +
scale_y_continuous(labels = function(y) paste0(abs(y / 1000), "k")) +
scale_x_discrete(labels = function(x) gsub("Age | years", "", x)) +
scale_fill_manual(values = c("darkred", "navy")) +
coord_flip() +
facet_wrap(~ HISP) +
labs(x = "",
y = "2017 Census Bureau population estimate",
title = "Yolo County, California",
fill = "",
caption = "Data source: US Census Bureau population estimates & tidycensus R package")
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutplotly::ggplotly(gg_yolo_compare)sessionInfo()## R version 3.6.1 (2019-07-05)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18362)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] plotly_4.9.0 tidycensus_0.9.2 forcats_0.4.0 stringr_1.4.0
## [5] dplyr_0.8.3 purrr_0.3.2 readr_1.3.1 tidyr_1.0.0
## [9] tibble_2.1.3 ggplot2_3.2.0 tidyverse_1.2.1
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## loaded via a namespace (and not attached):
## [1] httr_1.4.1 jsonlite_1.6 viridisLite_0.3.0
## [4] modelr_0.1.4 shiny_1.3.2 assertthat_0.2.1
## [7] sp_1.3-1 cellranger_1.1.0 yaml_2.2.0
## [10] pillar_1.4.2 backports_1.1.4 lattice_0.20-38
## [13] glue_1.3.1 uuid_0.1-2 digest_0.6.21
## [16] promises_1.0.1 rvest_0.3.4 colorspace_1.4-1
## [19] htmltools_0.3.6 httpuv_1.5.1 pkgconfig_2.0.2
## [22] broom_0.5.2 haven_2.1.1 xtable_1.8-4
## [25] scales_1.0.0 later_0.8.0 tigris_0.8.2
## [28] generics_0.0.2 withr_2.1.2 lazyeval_0.2.2
## [31] cli_1.1.0 magrittr_1.5 crayon_1.3.4
## [34] readxl_1.3.1 mime_0.7 maptools_0.9-5
## [37] evaluate_0.14 fansi_0.4.0 nlme_3.1-140
## [40] xml2_1.2.2 foreign_0.8-71 class_7.3-15
## [43] tools_3.6.1 data.table_1.12.2 hms_0.5.1
## [46] lifecycle_0.1.0 munsell_0.5.0 compiler_3.6.1
## [49] e1071_1.7-2 rlang_0.4.0 classInt_0.3-3
## [52] units_0.6-3 grid_3.6.1 rstudioapi_0.10
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## [70] stringi_1.4.3 Rcpp_1.0.2 vctrs_0.2.0
## [73] sf_0.7-7 tidyselect_0.2.5 xfun_0.9