In-Class Exercise 1: Chapter 4 of Lattice
Load data file
## Rural Male Rural Female Urban Male Urban Female
## 50-54 11.7 8.7 15.4 8.4
## 55-59 18.1 11.7 24.3 13.6
## 60-64 26.9 20.3 37.0 19.3
## 65-69 41.0 30.9 54.6 35.1
## 70-74 66.0 54.3 71.1 50.0## [1] "matrix"Show the methods of dotplot in lattice package
## [1] dotplot.array* dotplot.default* dotplot.formula* dotplot.matrix*
## [5] dotplot.numeric* dotplot.table*
## see '?methods' for accessing help and source code
Plot the dotplot of death rates, show one column and four rows
dotplot(VADeaths, groups=FALSE,
layout=c(1, 4),
aspect=0.7,
origin=0,
type=c("p", "h"),
main="Death Rates in Virginia - 1940",
xlab="Rate (per 1000)")
Plot the scatter plot of death rates for the different age by people in one figure.
dotplot(VADeaths, type="o",
auto.key=list(lines=TRUE, space="right"),
main="Death Rates in Virginia - 1940",
xlab="Rate (per 1000)")
Plot the barplot of death rates for the different age by people
barchart(VADeaths, groups=FALSE,
layout=c(1, 4),
aspect=0.7,
reference=FALSE,
main="Death Rates in Virginia - 1940",
xlab="Rate (per 100)")
Load data file
data(postdoc, package="latticeExtra")
## Plot barplot of the proportion of the field by training and occupations
barchart(prop.table(postdoc, margin=1),
xlab="Proportion",
auto.key=list(adj=1))
Plot scatter plot of the proportion of the field by training and occupations
dotplot(prop.table(postdoc, margin=1),
groups=FALSE,
xlab="Proportion",
par.strip.text=list(abbreviate=TRUE, minlength=10))
## Plot scatter plot of the proportion of the field by training and occupations, Show in different panels.
dotplot(prop.table(postdoc, margin=1),
groups=FALSE,
index.cond=function(x, y) median(x),
xlab="Proportion",
layout=c(1, 5),
aspect=0.6,
scales=list(y=list(relation="free", rot=0)),
prepanel=function(x, y) {
list(ylim=levels(reorder(y, x)))
},
panel=function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
Load data file
data(Chem97, package="mlmRev")
## Create a table which include gcsescore, gender and Chem97
gcsescore.tab <- xtabs(~ gcsescore + gender, Chem97)
## Transform table to data frame
gcsescore.df <- as.data.frame(gcsescore.tab)
## Transform gcsescore to character and numeric
gcsescore.df$gcsescore <- as.numeric(as.character(gcsescore.df$gcsescore))Plot a histogram of GCSE scores by different gender
xyplot(Freq ~ gcsescore | gender,
data = gcsescore.df,
type="h",
layout=c(1, 2),
xlab="Average GCSE Score")
## Create a table which include score, gender and Chem97
score.tab <- xtabs(~score + gender, Chem97)
## Transform table to data frame
score.df <- as.data.frame(score.tab)
## Plot a histogram of scores by different gender
barchart(Freq ~ score | gender, score.df, origin=0)
In-Class Exercise 2: The Reading scores for the Student-Teacher ratio
Load data file
## distcod county district grspan enrltot teachers
## 1 75119 Alameda Sunol Glen Unified KK-08 195 10.90
## 2 61499 Butte Manzanita Elementary KK-08 240 11.15
## 3 61549 Butte Thermalito Union Elementary KK-08 1550 82.90
## 4 61457 Butte Golden Feather Union Elementary KK-08 243 14.00
## 5 61523 Butte Palermo Union Elementary KK-08 1335 71.50
## 6 62042 Fresno Burrel Union Elementary KK-08 137 6.40
## calwpct mealpct computer testscr compstu expnstu str avginc
## 1 0.5102 2.0408 67 690.80 0.3435898 6384.911 17.88991 22.690001
## 2 15.4167 47.9167 101 661.20 0.4208333 5099.381 21.52466 9.824000
## 3 55.0323 76.3226 169 643.60 0.1090323 5501.955 18.69723 8.978000
## 4 36.4754 77.0492 85 647.70 0.3497942 7101.831 17.35714 8.978000
## 5 33.1086 78.4270 171 640.85 0.1280899 5235.988 18.67133 9.080333
## 6 12.3188 86.9565 25 605.55 0.1824818 5580.147 21.40625 10.415000
## elpct readscr mathscr
## 1 0.000000 691.6 690.0
## 2 4.583333 660.5 661.9
## 3 30.000002 636.3 650.9
## 4 0.000000 651.9 643.5
## 5 13.857677 641.8 639.9
## 6 12.408759 605.7 605.4Create a new variable of student-teacher ratio
In fact, The raw data set have been existed a str variable which is same as new variable I was created.
## distcod county district grspan enrltot teachers
## 1 75119 Alameda Sunol Glen Unified KK-08 195 10.90
## 2 61499 Butte Manzanita Elementary KK-08 240 11.15
## 3 61549 Butte Thermalito Union Elementary KK-08 1550 82.90
## 4 61457 Butte Golden Feather Union Elementary KK-08 243 14.00
## 5 61523 Butte Palermo Union Elementary KK-08 1335 71.50
## 6 62042 Fresno Burrel Union Elementary KK-08 137 6.40
## calwpct mealpct computer testscr compstu expnstu str avginc
## 1 0.5102 2.0408 67 690.80 0.3435898 6384.911 17.88991 22.690001
## 2 15.4167 47.9167 101 661.20 0.4208333 5099.381 21.52466 9.824000
## 3 55.0323 76.3226 169 643.60 0.1090323 5501.955 18.69723 8.978000
## 4 36.4754 77.0492 85 647.70 0.3497942 7101.831 17.35714 8.978000
## 5 33.1086 78.4270 171 640.85 0.1280899 5235.988 18.67133 9.080333
## 6 12.3188 86.9565 25 605.55 0.1824818 5580.147 21.40625 10.415000
## elpct readscr mathscr ST_R
## 1 0.000000 691.6 690.0 17.88991
## 2 4.583333 660.5 661.9 21.52466
## 3 30.000002 636.3 650.9 18.69723
## 4 0.000000 651.9 643.5 17.35714
## 5 13.857677 641.8 639.9 18.67133
## 6 12.408759 605.7 605.4 21.40625Divide the reading scores into three parts
Filter grspan == KK-08
## ─ Attaching packages ────────────────────────── tidyverse 1.3.0 ─## ✓ ggplot2 3.2.1 ✓ purrr 0.3.3
## ✓ tibble 2.1.3 ✓ dplyr 0.8.4
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.4.0## ─ Conflicts ─────────────────────────── tidyverse_conflicts() ─
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Plot the reading scores for the student-teacher ratio by different level of reading socres
xyplot(readscr~ST_R|RSCR, data=dta_n,
xlab="Student-Teacher ratio", ylab="Reading score", type=c("p", "g", "r"),
layout=c(3,1), pch=1)
In-Class Exercise 3:
Load data file
dta <- read.table("/Users/haolunfu/Documents/資料管理/week6/beautyCourseEval.txt", header = T)
head(dta)## eval beauty sex age minority tenure courseID
## 1 4.3 0.2015666 1 36 1 0 3
## 2 4.5 -0.8260813 0 59 0 1 0
## 3 3.7 -0.6603327 0 51 0 1 4
## 4 4.3 -0.7663125 1 40 0 1 2
## 5 4.4 1.4214450 1 31 0 0 0
## 6 4.2 0.5002196 0 62 0 1 0Show the details of the data
## 'data.frame': 463 obs. of 7 variables:
## $ eval : num 4.3 4.5 3.7 4.3 4.4 4.2 4 3.4 4.5 3.9 ...
## $ beauty : num 0.202 -0.826 -0.66 -0.766 1.421 ...
## $ sex : int 1 0 0 1 1 0 1 1 1 0 ...
## $ age : int 36 59 51 40 31 62 33 51 33 47 ...
## $ minority: int 1 0 0 0 0 0 0 0 0 0 ...
## $ tenure : int 0 1 1 1 0 1 0 1 0 0 ...
## $ courseID: int 3 0 4 2 0 0 4 0 0 4 ...Plot
xyplot(eval~beauty|as.factor(courseID), data=dta, type=c("p", "g", "r"),
layout=c(6,6),
xlab="Beauty judgment score", ylab="Average course evaluation score")
Try to list the beta (coefficients), but it can’t work on the plot function.
Refer my classmate procedure (Thanks Jay Liao), I use function(x, y) coefficients(lm(y ~ x))[2] to reorder.
xyplot(eval~beauty|as.factor(courseID), data=dta, type=c("p", "g", "r"),
layout=c(6,6), index.cond = function(x, y) coefficients(lm(y ~ x))[2],
xlab="Beauty judgment score", ylab="Average course evaluation score")
In-Class Exercise 4: Brain Size and IQ
Load data file
## Sbj Gender FSIQ VIQ PIQ Weight Height MRICount
## 1 1 Female 133 132 124 118 64.5 816932
## 2 2 Male 140 150 124 NA 72.5 1001121
## 3 3 Male 139 123 150 143 73.3 1038437
## 4 4 Male 133 129 128 172 68.8 965353
## 5 5 Female 137 132 134 147 65.0 951545
## 6 6 Female 99 90 110 146 69.0 928799Transform the different type of IQ from wide to long format
##
## Attaching package: 'reshape'## The following object is masked from 'package:dplyr':
##
## rename## The following objects are masked from 'package:tidyr':
##
## expand, smithsQ: Are there gender differences in the three IQ scores?
## A: Yes, in our visual inspection, but specificially in FSIQ (small effect), VIQ (large effect), not in PIQ (no effect).
Q: Is the relationship between height and weight gender dependent?
xyplot(Weight ~ Height, groups=Gender,
data=dta, xlab="Height", ylab="Weight",
type=c('p', 'g', 'r'))
## A: Yes, in our visual inspection, gender modulate the weight and height.
Q: Is the relationship between IQ and brainsize (as measured by MRIcount) gender dependent?
xyplot(IQ ~ MRICount|Type, groups=Gender,
data=dta_n, xlab="Brain Size", ylab="IQ",
type=c('p', 'g', 'r'))
## A: Because I couldn’t observe the significant pattern in the figure, I conducted linear regression model to examine the gender effect.
##
## Call:
## lm(formula = FSIQ ~ MRICount + Gender, data = dta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.410 -20.011 0.924 21.978 36.094
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.371e+01 5.644e+01 -0.774 0.44358
## MRICount 1.804e-04 6.516e-05 2.768 0.00876 **
## GenderMale -1.353e+01 9.302e+00 -1.455 0.15418
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.46 on 37 degrees of freedom
## Multiple R-squared: 0.1751, Adjusted R-squared: 0.1305
## F-statistic: 3.927 on 2 and 37 DF, p-value: 0.02842The results revealed that no gender effect on Brain size to FSIQ
##
## Call:
## lm(formula = VIQ ~ MRICount + Gender, data = dta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.840 -18.429 -1.268 17.487 35.537
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.491e+01 5.687e+01 -0.262 0.7946
## MRICount 1.442e-04 6.567e-05 2.195 0.0345 *
## GenderMale -7.492e+00 9.374e+00 -0.799 0.4293
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.63 on 37 degrees of freedom
## Multiple R-squared: 0.1289, Adjusted R-squared: 0.08184
## F-statistic: 2.738 on 2 and 37 DF, p-value: 0.0778The results revealed that no gender effect on Brain size to VIQ
##
## Call:
## lm(formula = PIQ ~ MRICount + Gender, data = dta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.696 -15.155 -6.259 16.898 37.771
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.985e+01 5.069e+01 -1.181 0.24530
## MRICount 1.974e-04 5.853e-05 3.373 0.00176 **
## GenderMale -1.705e+01 8.355e+00 -2.041 0.04844 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.17 on 37 degrees of freedom
## Multiple R-squared: 0.2357, Adjusted R-squared: 0.1944
## F-statistic: 5.704 on 2 and 37 DF, p-value: 0.006929