r-conway

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

#read in data
a<- read_csv('./data/usa_00005.csv')
#remove Hawaii and Alaska and pick out those of working age
b<- a %>% filter(AGE>=15 & AGE<=65 & !(STATEFIP %in% c(2,15)))

r-conway

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

#read in data
a<- read_csv('./data/usa_00005.csv')
#remove Hawaii and Alaska and pick out those of working age
b<- a %>% filter(AGE>=15 & AGE<=65 & !(STATEFIP %in% c(2,15)))

r-conway

library(dplyr)
library(readr)
library(tidyr)
#enter data
a <- read_csv('./data/usa_00001.csv')

#we should remove all data from alask and hawaii
aa <- a %>% filter(!(YEAR < 1960 & STATEFIP %in% c(2,15)))

#code number for race into words using factor

r-conway

#Enter variables
titles<-c("The Waste Land", "Lord of the Flies", "Grapes of Wrath", "Pride and Prejudice", "Frankenstein", "Little Women", "Jane Eyre", "The Things they Carried", "Huckleberry Finn", "The Old Man and the Sea")
author<-c("Thomas Eliot", "William Golding", "John Steinbeck", "Jane Austen", "Mary Shelley", "Louisa May Alcott", "Charlotte Bronte", "Tim O'Brien", "Mark Twain", "Ernest Hemmingway")
pyear<-c(1922, 1954, 1939, 1813, 1818, 1868, 1847, 1990, 1884, 1952)
stock<-c(12, 4, 7, 1, 8, 9, 3, 2, 17, 5)
price<-c(5.50, 10, 12, 20, 18.50, 22, 16, 25, 8, 15)

#create dataframe
books<-data.frame(titles, author, pyear, stock, price)

r-conway

program