SirSamAlot280

#Load packages
library(dplyr)
library(readr)
library(ggplot2)
library(RColorBrewer)
library(scales)

#Read in selected IPUMs data
ipums <- read_csv('./data/10_27.csv')

SirSamAlot280

#Load packages
library(readr)
library(dplyr)
library(ggplot2)
library(RColorBrewer)

#Read in appropriate IPUMs data; Change HHWT & PERWT from decimals to intergers.
ipums <- read_csv('./data/10_20.csv',col_types=cols(HHWT=col_double(),PERWT=col_double()))

#Filter out data for Alaska and Hawaii before 1960.

SirSamAlot280

#Load packages
library(readr)
library(dplyr)
library(ggplot2)
library(RColorBrewer)
library(ggmap)
library(maptools)
library(gtools)
library('gganimate')

SirSamAlot280

library(readr)
library(dplyr)
library(ggplot2)
library(RColorBrewer)

#Read in selected IPUMS data; Filter out data for Hawaii and Alaska 
#except for 1960 and onwards
a <- read_csv('./data/10_6.csv') %>% filter(YEAR>=1960 | !STATEFIP %in% c(2,15))

#Create a varaible named Generation to denote which individuals are 

SirSamAlot280

library(dplyr)
library(readr)
library(ggplot2)
library(RColorBrewer)

#Read in selected IPUMs data; filter out data pertaining to Alaska and Hawaii, while
#also limiting the data to only individuals from the age 15-65
a <- read_csv('./data/9_29.csv') %>% 
  filter(AGE>=15 & AGE<=65 & !(STATEFIP %in% c(2,15)))