library(tidycensus)
library(tidyverse)

tableYear = 2020

#log on with API
census_api_key("7a853acf81fd5758228680556ac831138c40b83e")

#load variables
variables = load_variables(2020,"acs5/profile")
codes = variables$name
label0 = variables$label

#view variables
#varTable = table(variables$concept)
#write.table(varTable, file = "cat.txt", sep = ",", quote = FALSE, row.names = F)

#pull all tables with every variable
  
test = get_acs(geography = "county",
               state = "PR",
               year = tableYear,
               variables = codes)

#add label column
#create empty vector 
labelCol = c()

#amount of GEOIDS
for (x in 1:78){
  labelCol = c(labelCol, label0)
}

#combine test and cols
test["label"] = labelCol

#rearrange cols
test = test[c("NAME","label","variable","estimate","moe","GEOID")]

GEOIDS = table(test$GEOID)

#omit NA rows
noNA = na.omit(test)

DP02table = noNA %>% filter(startsWith(variable,"DP02"))

DP03table = noNA %>% filter(startsWith(variable,"DP03"))

DP04table = noNA %>% filter(startsWith(variable,"DP04"))

DP05table = noNA %>% filter(startsWith(variable,"DP05"))