This homework is designed to give you practice with calculating error bars (confidence intervals) with ddply and using ggplot2 graphics to produce insightful plots of the results.

library(plyr) library(dplyr) library(ggplot2) 

You will continue using theadultdata set that you first encountered on Homework 3. This data set is loaded below.

adult.data <- read.csv("http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data", header=FALSE, fill=FALSE, strip.white=T, col.names=c("age", "type_employer", "fnlwgt", "education", "education_num","marital", "occupation", "relationship", "race","sex", "capital_gain", "capital_loss", "hr_per_week","country", "income")) adult.data <- mutate(adult.data, high.income = as.numeric(income == ">50K")) 

Problem 1: Calculating and plotting error bars for a 1-sample t-test

(a) Usingddplyand 1-sample t-testing, construct a table that shows the averagecapital_gainacrosseducation, along with the lower and upper endpoints of a 95% confidence interval. Your table should look something like:

 education mean lower upper 1 10th 404.5745 91.893307 717.2557 2 11th 215.0979 144.306937 285.8888 3 12th 284.0878 126.824531 441.3510 ... # Edit me 

(b) Reorder the levels of the factor in your summary table to correspond to ascending order of education. E.g., Preschool is the lowest, 1st-4th the next lowest, etc. You may find thefactor(..., levels = ...)command helpful here. For the post-high school grades, you can use the ordering: Assoc-voc, Assoc-acdm, Some-college, Bachelors, Masters, Prof-school, Doctorate.

# Edit me 

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