Assignment4.R
aleigey
Thu Nov 19 20:58:00 2015
#Title: Assignment 4 for WFED540
#Author: Andrew Leigey
#Date: 11/19/15
#Output: html_document
# ************************************
# ASSIGNMENT 4, WFED 540 *
# ************************************
# ************************************
# READING Mroz Labor Supply Dataset *
# from Ecdat Package *
# *
# The Mroz dataset contains 753 *
# observations of individuals in the *
# United States. The code below *
# installs the Ecdat package *
# containing the Mroz dataset, loads *
# Ecdat, accesses the Mroz dataset, *
# lists the names of variables in *
# Mroz dataset, and summarizes each *
# variable in the Mroz dataset. Mroz *
# variables do not include missing *
# data. Your entry of the command, *
# "?Mroz", displays documentation *
# for the Mroz dataset. *
# ************************************
require(Ecdat)## Loading required package: Ecdat
## Loading required package: Ecfun
##
## Attaching package: 'Ecdat'
##
## The following object is masked from 'package:datasets':
##
## Orangedata(Mroz)
names(Mroz)## [1] "work" "hoursw" "child6" "child618" "agew"
## [6] "educw" "hearnw" "wagew" "hoursh" "ageh"
## [11] "educh" "wageh" "income" "educwm" "educwf"
## [16] "unemprate" "city" "experience"summary(Mroz)## work hoursw child6 child618
## yes:325 Min. : 0.0 Min. :0.0000 Min. :0.000
## no :428 1st Qu.: 0.0 1st Qu.:0.0000 1st Qu.:0.000
## Median : 288.0 Median :0.0000 Median :1.000
## Mean : 740.6 Mean :0.2377 Mean :1.353
## 3rd Qu.:1516.0 3rd Qu.:0.0000 3rd Qu.:2.000
## Max. :4950.0 Max. :3.0000 Max. :8.000
## agew educw hearnw wagew
## Min. :30.00 Min. : 5.00 Min. : 0.000 Min. :0.00
## 1st Qu.:36.00 1st Qu.:12.00 1st Qu.: 0.000 1st Qu.:0.00
## Median :43.00 Median :12.00 Median : 1.625 Median :0.00
## Mean :42.54 Mean :12.29 Mean : 2.375 Mean :1.85
## 3rd Qu.:49.00 3rd Qu.:13.00 3rd Qu.: 3.788 3rd Qu.:3.58
## Max. :60.00 Max. :17.00 Max. :25.000 Max. :9.98
## hoursh ageh educh wageh
## Min. : 175 Min. :30.00 Min. : 3.00 Min. : 0.4121
## 1st Qu.:1928 1st Qu.:38.00 1st Qu.:11.00 1st Qu.: 4.7883
## Median :2164 Median :46.00 Median :12.00 Median : 6.9758
## Mean :2267 Mean :45.12 Mean :12.49 Mean : 7.4822
## 3rd Qu.:2553 3rd Qu.:52.00 3rd Qu.:15.00 3rd Qu.: 9.1667
## Max. :5010 Max. :60.00 Max. :17.00 Max. :40.5090
## income educwm educwf unemprate
## Min. : 1500 Min. : 0.000 Min. : 0.000 Min. : 3.000
## 1st Qu.:15428 1st Qu.: 7.000 1st Qu.: 7.000 1st Qu.: 7.500
## Median :20880 Median :10.000 Median : 7.000 Median : 7.500
## Mean :23081 Mean : 9.251 Mean : 8.809 Mean : 8.624
## 3rd Qu.:28200 3rd Qu.:12.000 3rd Qu.:12.000 3rd Qu.:11.000
## Max. :96000 Max. :17.000 Max. :17.000 Max. :14.000
## city experience
## no :269 Min. : 0.00
## yes:484 1st Qu.: 4.00
## Median : 9.00
## Mean :10.63
## 3rd Qu.:15.00
## Max. :45.00# ************************************
# Assignment 4 *
# ************************************
require(ggvis)## Loading required package: ggvisrequire(corrplot)## Loading required package: corrplotrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(knitr)## Loading required package: knitrrequire(rmarkdown)## Loading required package: rmarkdown# 1. Select four continuous variables from Mroz.
Mroz4var<-Mroz%>% select(hoursw, agew, income, experience)
Mroz4var## hoursw agew income experience
## 1 1610 32 16310 14
## 2 1656 30 21800 5
## 3 1980 35 21040 15
## 4 456 34 7300 6
## 5 1568 31 27300 7
## 6 2032 54 19495 33
## 7 1440 37 21152 11
## 8 1020 54 18900 35
## 9 1458 48 20405 24
## 10 1600 39 20425 21
## 11 1969 33 32300 15
## 12 1960 42 28700 14
## 13 240 30 15500 0
## 14 997 43 16860 14
## 15 1848 43 31431 6
## 16 1224 35 19180 9
## 17 1400 43 18600 20
## 18 640 39 19151 6
## 19 2000 45 18100 23
## 20 1324 35 20300 9
## 21 2215 42 30419 5
## 22 1680 30 14090 11
## 23 1600 48 22679 18
## 24 800 45 12160 15
## 25 1955 31 12487 4
## 26 660 43 29850 21
## 27 525 59 18100 31
## 28 1904 32 26000 9
## 29 1516 31 26100 7
## 30 346 42 17730 7
## 31 1040 50 6719 32
## 32 732 59 18550 11
## 33 1880 36 24600 16
## 34 1680 51 23100 14
## 35 2081 45 24656 27
## 36 690 42 15897 0
## 37 4210 46 20320 17
## 38 2205 46 21384 28
## 39 1952 51 25561 24
## 40 1302 30 36550 11
## 41 112 30 15810 1
## 42 893 57 25500 14
## 43 583 31 24000 6
## 44 480 48 22172 10
## 45 1900 30 17930 6
## 46 576 34 7000 4
## 47 2056 48 25300 10
## 48 1984 45 16212 22
## 49 2640 51 22650 16
## 50 240 30 6985 6
## 51 1173 46 30000 12
## 52 3640 58 18500 32
## 53 340 37 16658 15
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## 722 0 32 47500 4
## 723 0 48 28900 8
## 724 0 41 12400 18
## 725 0 49 6531 7
## 726 0 59 22422 15
## 727 0 58 22200 7
## 728 0 41 77000 8
## 729 0 45 88000 8
## 730 0 30 26040 3
## 731 0 41 63500 10
## 732 0 30 12100 9
## 733 0 53 17505 24
## 734 0 31 18000 12
## 735 0 43 28069 2
## 736 0 31 14000 6
## 737 0 51 8117 18
## 738 0 43 11895 17
## 739 0 31 45250 7
## 740 0 48 31106 6
## 741 0 31 4000 10
## 742 0 44 40500 5
## 743 0 48 21620 7
## 744 0 53 23426 11
## 745 0 42 26000 14
## 746 0 39 7840 5
## 747 0 32 6800 2
## 748 0 36 5330 4
## 749 0 40 28200 5
## 750 0 31 10000 14
## 751 0 43 9952 4
## 752 0 60 24984 15
## 753 0 39 28363 12# 2. Estimate Pearson Product-Moment Correlations
# for four pairs of variables.
# Load the cormat functions
# Loaded this way because the cormat
# package is not available (for R version 3.2.2).
source("http://www.sthda.com/upload/rquery_cormat.r")
rquery.cormat(Mroz4var)## $r
## hoursw experience agew income
## hoursw 1
## experience 0.4 1
## agew -0.033 0.33 1
## income 0.15 -0.028 0.052 1
##
## $p
## hoursw experience agew income
## hoursw 0
## experience 0 0
## agew 0.36 0 0
## income 5.6e-05 0.45 0.15 0
##
## $sym
## hoursw experience agew income
## hoursw 1
## experience . 1
## agew . 1
## income 1
## attr(,"legend")
## [1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 1# 3. Test null hypotheses that the population
# correlations = 0 for the four pairs of
# variables you selected.
#The null hypotheses is that the four pairs of variables selected will have
#correlations of zero for all the correlation coefficients tested. Therefore,
# since none of the correlation coefficients equal zero then we must reject the null
#hypotheses that there is no correlation between the four pairs of variables we
#selected. Instead we must accept the alternative hypotheses that there is a
#correlation between all the four pairs of variables selected, even though some of
#the relations are small in size.
# 4. Using ggvis, plot scatterplots containing
# points and a smooth line for the four pairs of
# variable you selected.
require(ggvis)
#experience and hoursw scatterplots and smooth line
Mroz4var%>%ggvis(~experience, ~hoursw)%>%layer_points()%>%layer_smooths()#experience and agew scatterplots and smooth line
Mroz4var%>%ggvis(~experience, ~agew)%>%layer_points()%>%layer_smooths()#income and hoursw scatterplots and smooth line
Mroz4var%>%ggvis(~income, ~hoursw)%>%layer_points()%>%layer_smooths()#income and agew scatterplots and smooth line
Mroz4var%>%ggvis(~income, ~agew)%>%layer_points()%>%layer_smooths()# 5. Produce correlograms and heat maps for the
# four pairs of variables you selected.
#correlogram
rquery.cormat(Mroz4var)
## $r
## hoursw experience agew income
## hoursw 1
## experience 0.4 1
## agew -0.033 0.33 1
## income 0.15 -0.028 0.052 1
##
## $p
## hoursw experience agew income
## hoursw 0
## experience 0 0
## agew 0.36 0 0
## income 5.6e-05 0.45 0.15 0
##
## $sym
## hoursw experience agew income
## hoursw 1
## experience . 1
## agew . 1
## income 1
## attr(,"legend")
## [1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 1#heatmap
cormat<-rquery.cormat(Mroz4var, graphType="heatmap")
# 6. Create an RMarkdown .Rmd file that documents the
# processes and outcomes for steps (1) through (5).
# 7. Create an RPubs html site from your .Rmd file.
# 8. In a private Piazza message to your instructors,
# post the URL to your RPubs file and upload your
# .Rmd file.```