# Setup Your API Connection in R

Sys.setenv(CENSUS_KEY= "insert key here")
readRenviron("~/.Renviron")
Sys.getenv("CENSUS_KEY")

# What Packages to Download

install.packages("dplyr")
install.packages("censusapi")
library(dplyr)
library(censusapi)

# How to View Available APIs

apis <- listCensusApis()
View(apis)

# How to View Metadata

census_metadata_groups <-
  listCensusMetadata(
    name="acs/acs5",
    vintage="2018",
    type="groups")

View(census_metadata_groups)

# How to View Geographies

census_metadata_geography <-
  listCensusMetadata(
    name="acs/acs5",
    vintage="2018",
    type="geographies",
    group="B15001")

View(census_metadata_geography)

# How to View Variables
census_metadata_variables <-
  listCensusMetadata(
    name="acs/acs5",
    vintage="2018",
    type="variables",
    group="B15001")

View(census_metadata_variables)

# How to Filter Out Annotations and Margin of Errors
census_metadata_variables <-
  census_metadata_variables %>%
  filter(
    grepl("EA",name)==FALSE,
    grepl("MA",name)==FALSE,
    grepl("M",name)==FALSE)

View(census_metadata_variables)

# How to Filter Down to 18 to 24 Year Olds
census_metadata_variables <-
  census_metadata_variables %>%
  filter(grepl("18 to 24",label,ignore.case=TRUE))

View(census_metadata_variables)

# How to Query a Census API
census <-
  getCensus(
    name="acs/acs5",
    vintage="2018",
    vars=c("NAME",
           "B15001_003E","B15001_004E","B15001_005E",
           "B15001_044E","B15001_045E","B15001_046E"),
    region="state:*")

View(census)

# How to Refrence the Variable List
variable_list <-
  c("B15001_003E","B15001_004E","B15001_005E",
    "B15001_044E","B15001_045E","B15001_046E")
census <-
  getCensus(
    name="acs/acs5",
    vintage="2018",
    vars=c("NAME",variable_list),
    region="state:*")

View(census)

# How to Query Only Kansas by County
variable_list <-
  c("B15001_003E","B15001_004E","B15001_005E",
    "B15001_044E","B15001_045E","B15001_046E")
census <-
  getCensus(
    name="acs/acs5",
    vintage="2018",
    vars=c("NAME",variable_list),
    region="county:*",
    regionin="state:20")

View(census)

# How to Mutate the Variables to Get Percent Without Diploma
census_clean <-
  census %>%
  transmute("county"=NAME,
            "total_male_18_to_24"=B15001_003E,
            "total_male_no_diploma_18_to_24"=
              (B15001_004E+B15001_005E),
            "percent_male_no_diploma"=
              (B15001_004E+B15001_005E)/B15001_003E,
            "total_female_18_to_24"=B15001_044E,
            "total_female_no_diploma_18_to_24"=
              (B15001_045E+B15001_046E),
            "percent_female_no_diploma"=
              (B15001_045E+B15001_046E)/B15001_044E)

View(census_clean)


# How to See Top Three for Male and Female
top_three_male <-
  census_clean %>%
  select(
    county,
    percent_male_no_diploma,
    total_male_18_to_24,
    total_male_no_diploma_18_to_24) %>%
  filter(percent_male_no_diploma!="Inf") %>%
  top_n(3,percent_male_no_diploma)

View(top_three_male)

top_three_female <-
  census_clean %>%
  select(
    county,
    percent_female_no_diploma,
    total_female_18_to_24,
    total_female_no_diploma_18_to_24) %>%
  filter(percent_female_no_diploma!="Inf") %>%
  top_n(3,percent_female_no_diploma)

View(top_three_female)