In lehmansociology/lehmansociology: Tools for Sociology Students Using R

library(ggplot2)
library(lehmansociology)
library(grid)
library(scales)
library(magrittr)
library("dplyr")
library(googlesheets)
library(broom)
library(xtable)
library(gridExtra)
# Set options for nicer looking documents
options(xtable.comment = FALSE)
knitr::opts_chunk$set(message=FALSE, warning=FALSE)

replaceCommas<-function(x){
  x<-as.numeric(gsub("\\,", "", x))
}

In this exercise we are going to explore how region of the country relates to the relationship of education and income.

First we create the data set.

# Set up some data
poverty13 <- select (poverty.counties, FIPStxt, Area_Name, PCTPOVALL_2013,
                     PCTPOV017_2013,
                     MEDHHINC_2013, Rural_urban_Continuum_Code_2013,
                     Urban_Influence_Code_2013)
poverty13$FIPS.Code <- as.integer(poverty13$FIPStxt)
poverty13$MEDHHINC_2013 <- replaceCommas(poverty13$MEDHHINC_2013)

education13 <- select(education.counties, Area.name, FIPS.Code,
                            Percent.of.adults.with.less.than.a.high.school.diploma..2009.2013,
                            Percent.of.adults.with.a.bachelor.s.degree.or.higher..2009.2013,
                            Percent.of.adults.with.less.than.a.high.school.diploma..2000,
                            Percent.of.adults.with.a.bachelor.s.degree.or.higher..2000
                          )

education_and_poverty_county <- merge(poverty13, education13, by='FIPS.Code')

persistent_child<-select(persistentpoverty.county, -c(Persistent.poverty, State, Area.name, Metro.status))
persistent_child$Persistent.child.poverty<-as.factor(persistent_child$Persistent.child.poverty)
levels(persistent_child$Persistent.child.poverty) <- c("No", "Yes")
persistent_all<-persistentpoverty
persistent_all$FIPS.Code <- as.integer(replaceCommas(persistent_all$`FIPS,Code`))
persistent_all <- select(persistent_all, -c(`FIPS,Code`, Area.name))

# Let's make this clean for merging with education_and_poverty
persistent <- merge(persistent_child, persistent_all, by = 'FIPS.Code')

education_and_poverty_county<-merge(persistent, education_and_poverty_county, by = 'FIPS.Code')

#type your code here
# First let's create the region data set
gs_region<-gs_url('https://docs.google.com/spreadsheets/d/1h_jY4A44WoSLkrqhwZZ9oJh51N2GybwVvGgEaY3n2gc/pubhtml')
region_data<-gs_read(gs_region)
# We need to change this column name because the map data uses the term region differently.
region_data$census_region <- region_data$region
region_data<-select(region_data, -c(region, FIPS.Code))

# Add the region variable to education_and_poverty by matching the FIPS code
education_and_poverty_county <- merge(education_and_poverty_county, region_data, 
                               by = 'State')

lehmansociology/lehmansociology documentation built on May 21, 2022, 9:06 p.m.