test

R.R

@@ -7,6 +7,7 @@ tableYear = 2020
 census_api_key("7a853acf81fd5758228680556ac831138c40b83e")
 
 #load variables
+pueblos = 78
 variables = load_variables(2020,"acs5/profile")
 codes = variables$name
 label0 = variables$label
@@ -27,7 +28,7 @@ test = get_acs(geography = "county",
 labelCol = c()
 
 #amount of GEOIDS
-for (x in 1:78){
+for (x in 1:pueblos){
   labelCol = c(labelCol, label0)
 }
 

fetchTables.py

@@ -0,0 +1,66 @@
+import pandas as pd
+import rpy2.robjects as robjects
+
+#hellooo
+
+robjects.r('''
+        library(tidycensus)
+library(tidyverse)
+
+tableYear = 2020
+
+#log on with API
+census_api_key("7a853acf81fd5758228680556ac831138c40b83e")
+
+#load variables
+pueblos = 78
+variables = load_variables(2020,"acs5/profile")
+codes = variables$name
+label0 = variables$label
+
+#view variables
+#varTable = table(variables$concept)
+#write.table(varTable, file = "cat.txt", sep = ",", quote = FALSE, row.names = F)
+
+#pull all tables with every variable
+  
+test = get_acs(geography = "county",
+               state = "PR",
+               year = tableYear,
+               variables = codes)
+
+#add label column
+#create empty vector 
+labelCol = c()
+
+#amount of GEOIDS
+for (x in 1:pueblos){
+  labelCol = c(labelCol, label0)
+}
+
+#combine test and cols
+test["label"] = labelCol
+
+#rearrange cols
+test = test[c("NAME","label","variable","estimate","moe","GEOID")]
+
+GEOIDS = table(test$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"))
+        ''')
+        
+table02 = robjects.r('''DP02table''')
+table03 = robjects.r('''DP03table''')
+table04 = robjects.r('''DP04table''')
+table05 = robjects.r('''DP05table''')
+
+print(table02)