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x <- c(1,3,2,5)
x[1] 1 3 2 5Show the Code
x = c(1,6,2)
x[1] 1 6 2Show the Code
y = c(1,4,3)Assignment 1 - February 10th, 2026
EPPS 6323 Knowledge Mining
R Programming Basic Commands
Lab 01
Create object using the assignment operator (<-, =)
Using function
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length(x) # What does length() do?[1] 3Show the Code
length(y)[1] 3Using +, -, *, /,^ operators
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x+y[1] 2 10 5Show the Code
ls() # List objects in the environment[1] "x" "y"Show the Code
rm(x,y) # Remove objects
ls()character(0)Show the Code
rm(list=ls()) # Danger! What does this do? Not recommended!Matrix operations
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?matrixstarting httpd help server ... doneShow the Code
x=matrix(data=c(1,2,3,4), nrow=2, ncol=2) # Create a 2x2 matrix object
x [,1] [,2]
[1,] 1 3
[2,] 2 4Show the Code
x=matrix(c(1,2,3,4),2,2)
matrix(c(1,2,3,4),2,2,byrow=T) # What about byrow=F? [,1] [,2]
[1,] 1 2
[2,] 3 4Show the Code
sqrt(x) # What does x look like? [,1] [,2]
[1,] 1.000000 1.732051
[2,] 1.414214 2.000000Show the Code
x [,1] [,2]
[1,] 1 3
[2,] 2 4Show the Code
x^2 [,1] [,2]
[1,] 1 9
[2,] 4 16Show the Code
x=rnorm(50) # Generate a vector of 50 numbers using the rnorm() function
y=x+rnorm(50,mean=50,sd=.1) # What does rnorm(50,mean=50,sd=.1) generate?
cor(x,y) # Correlation of x and y[1] 0.9966754Show the Code
set.seed(1303) # Set the seed for Random Number Generator (RNG) to generate values that are reproducible.
rnorm(50) [1] -1.1439763145 1.3421293656 2.1853904757 0.5363925179 0.0631929665
[6] 0.5022344825 -0.0004167247 0.5658198405 -0.5725226890 -1.1102250073
[11] -0.0486871234 -0.6956562176 0.8289174803 0.2066528551 -0.2356745091
[16] -0.5563104914 -0.3647543571 0.8623550343 -0.6307715354 0.3136021252
[21] -0.9314953177 0.8238676185 0.5233707021 0.7069214120 0.4202043256
[26] -0.2690521547 -1.5103172999 -0.6902124766 -0.1434719524 -1.0135274099
[31] 1.5732737361 0.0127465055 0.8726470499 0.4220661905 -0.0188157917
[36] 2.6157489689 -0.6931401748 -0.2663217810 -0.7206364412 1.3677342065
[41] 0.2640073322 0.6321868074 -1.3306509858 0.0268888182 1.0406363208
[46] 1.3120237985 -0.0300020767 -0.2500257125 0.0234144857 1.6598706557Show the Code
set.seed(3) # Try different seeds?
y=rnorm(100)Simple descriptive statistics (base)
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mean(y)[1] 0.01103557Show the Code
var(y)[1] 0.7328675Show the Code
sqrt(var(y))[1] 0.8560768Show the Code
sd(y)[1] 0.8560768Visualization using R Graphics (without packages)
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x=rnorm(100)
y=rnorm(100)
plot(x,y)
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plot(x,y, pch=20, col = "firebrick") # Scatterplot for two numeric variables by default
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plot(x,y, pch=20, col = "steelblue",xlab="this is the x-axis",ylab="this is the y-axis",main="Plot of X vs Y") # Add labels
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pdf("Figure01.pdf") # Save as pdf, add a path or it will be stored on the project directory
plot(x,y,pch=20, col="forestgreen") # Try different colors?
dev.off() # Close the file using the dev.off functionpng
2 Show the Code
x=seq(1,10) # Same as x=c(1:10)
x [1] 1 2 3 4 5 6 7 8 9 10Show the Code
x=1:10
x [1] 1 2 3 4 5 6 7 8 9 10Show the Code
x=seq(-pi,pi,length=50)
y=xLab 02
Indexing Data using []
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A=matrix(1:16,4,4)
A [,1] [,2] [,3] [,4]
[1,] 1 5 9 13
[2,] 2 6 10 14
[3,] 3 7 11 15
[4,] 4 8 12 16Show the Code
A[2,3][1] 10Show the Code
A[c(1,3),c(2,4)] [,1] [,2]
[1,] 5 13
[2,] 7 15Show the Code
A[1:3,2:4] [,1] [,2] [,3]
[1,] 5 9 13
[2,] 6 10 14
[3,] 7 11 15Show the Code
A[1:2,] [,1] [,2] [,3] [,4]
[1,] 1 5 9 13
[2,] 2 6 10 14Show the Code
A[,1:2] [,1] [,2]
[1,] 1 5
[2,] 2 6
[3,] 3 7
[4,] 4 8Show the Code
A[1,][1] 1 5 9 13Show the Code
A[-c(1,3),] # What does -c() do? [,1] [,2] [,3] [,4]
[1,] 2 6 10 14
[2,] 4 8 12 16Show the Code
A[-c(1,3),-c(1,3,4)][1] 6 8Show the Code
dim(A) # Dimensions[1] 4 4Loading Data from GitHub (remote)
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Auto=read.table("https://raw.githubusercontent.com/datageneration/knowledgemining/master/data/Auto.data")
Auto=read.table("https://raw.githubusercontent.com/datageneration/knowledgemining/master/data/Auto.data",header=T,na.strings="?")
Auto=read.csv("https://raw.githubusercontent.com/datageneration/knowledgemining/master/data/Auto.csv") # read csv file
# Which function reads data faster?
# Try using this simple method
# time1 = proc.time()
# Auto=read.csv("https://raw.githubusercontent.com/datageneration/knowledgemining/master/data/Auto.csv",header=T,na.strings="?")
# proc.time()-time1
# Check on data
dim(Auto)[1] 397 9Show the Code
Auto[1:4,] # select rows mpg cylinders displacement horsepower weight acceleration year origin
1 18 8 307 130 3504 12.0 70 1
2 15 8 350 165 3693 11.5 70 1
3 18 8 318 150 3436 11.0 70 1
4 16 8 304 150 3433 12.0 70 1
name
1 chevrolet chevelle malibu
2 buick skylark 320
3 plymouth satellite
4 amc rebel sstShow the Code
Auto=na.omit(Auto)
dim(Auto) # Notice the difference?[1] 397 9Show the Code
names(Auto)[1] "mpg" "cylinders" "displacement" "horsepower" "weight"
[6] "acceleration" "year" "origin" "name" Load data from ISLR website
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Auto=read.table("https://www.statlearning.com/s/Auto.data",header=T,na.strings="?")
dim(Auto)[1] 397 9Additional Graphical and Numerical Summaries
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# plot(cylinders, mpg)
plot(Auto$cylinders, Auto$mpg)
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attach(Auto)
plot(cylinders, mpg)
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cylinders=as.factor(cylinders)
plot(cylinders, mpg)
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plot(cylinders, mpg, col="red")
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plot(cylinders, mpg, col="red", varwidth=T)
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plot(cylinders, mpg, col="red", varwidth=T,horizontal=T)
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plot(cylinders, mpg, col="red", varwidth=T, xlab="cylinders", ylab="MPG")
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hist(mpg)
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hist(mpg,col=2)
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hist(mpg,col=2,breaks=15)
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#pairs(Auto)
pairs(~ mpg + displacement + horsepower + weight + acceleration, Auto)
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plot(horsepower,mpg)
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# identify(horsepower,mpg,name) # Interactive: point and click the dot to identify cases
summary(Auto) mpg cylinders displacement horsepower weight
Min. : 9.00 Min. :3.000 Min. : 68.0 Min. : 46.0 Min. :1613
1st Qu.:17.50 1st Qu.:4.000 1st Qu.:104.0 1st Qu.: 75.0 1st Qu.:2223
Median :23.00 Median :4.000 Median :146.0 Median : 93.5 Median :2800
Mean :23.52 Mean :5.458 Mean :193.5 Mean :104.5 Mean :2970
3rd Qu.:29.00 3rd Qu.:8.000 3rd Qu.:262.0 3rd Qu.:126.0 3rd Qu.:3609
Max. :46.60 Max. :8.000 Max. :455.0 Max. :230.0 Max. :5140
NA's :5
acceleration year origin name
Min. : 8.00 Min. :70.00 Min. :1.000 Length:397
1st Qu.:13.80 1st Qu.:73.00 1st Qu.:1.000 Class :character
Median :15.50 Median :76.00 Median :1.000 Mode :character
Mean :15.56 Mean :75.99 Mean :1.574
3rd Qu.:17.10 3rd Qu.:79.00 3rd Qu.:2.000
Max. :24.80 Max. :82.00 Max. :3.000
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summary(mpg) Min. 1st Qu. Median Mean 3rd Qu. Max.
9.00 17.50 23.00 23.52 29.00 46.60 Linear Regression
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ptbu=c("MASS","ISLR")
lapply(ptbu, require, character.only = TRUE)Loading required package: MASSWarning: package 'MASS' was built under R version 4.5.2Loading required package: ISLRWarning: package 'ISLR' was built under R version 4.5.2
Attaching package: 'ISLR'The following object is masked _by_ '.GlobalEnv':
Auto[[1]]
[1] TRUE
[[2]]
[1] TRUEShow the Code
library(MASS)
library(ISLR)
# Simple Linear Regression
# fix(Boston)
names(Boston) [1] "crim" "zn" "indus" "chas" "nox" "rm" "age"
[8] "dis" "rad" "tax" "ptratio" "black" "lstat" "medv" Show the Code
# lm.fit=lm(medv~lstat)
attach(Boston)
lm.fit=lm(medv~lstat,data=Boston)
attach(Boston)The following objects are masked from Boston (pos = 3):
age, black, chas, crim, dis, indus, lstat, medv, nox, ptratio, rad,
rm, tax, znShow the Code
lm.fit=lm(medv~lstat)
lm.fit
Call:
lm(formula = medv ~ lstat)
Coefficients:
(Intercept) lstat
34.55 -0.95 Show the Code
summary(lm.fit)
Call:
lm(formula = medv ~ lstat)
Residuals:
Min 1Q Median 3Q Max
-15.168 -3.990 -1.318 2.034 24.500
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 34.55384 0.56263 61.41 <2e-16 ***
lstat -0.95005 0.03873 -24.53 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.216 on 504 degrees of freedom
Multiple R-squared: 0.5441, Adjusted R-squared: 0.5432
F-statistic: 601.6 on 1 and 504 DF, p-value: < 2.2e-16Show the Code
names(lm.fit) [1] "coefficients" "residuals" "effects" "rank"
[5] "fitted.values" "assign" "qr" "df.residual"
[9] "xlevels" "call" "terms" "model" Show the Code
coef(lm.fit)(Intercept) lstat
34.5538409 -0.9500494 Show the Code
confint(lm.fit) 2.5 % 97.5 %
(Intercept) 33.448457 35.6592247
lstat -1.026148 -0.8739505Show the Code
predict(lm.fit,data.frame(lstat=(c(5,10,15))), interval="confidence") fit lwr upr
1 29.80359 29.00741 30.59978
2 25.05335 24.47413 25.63256
3 20.30310 19.73159 20.87461Show the Code
predict(lm.fit,data.frame(lstat=(c(5,10,15))), interval="prediction") fit lwr upr
1 29.80359 17.565675 42.04151
2 25.05335 12.827626 37.27907
3 20.30310 8.077742 32.52846Show the Code
# What is the differnce between "conference" and "prediction" difference?
plot(lstat,medv)
abline(lm.fit)
abline(lm.fit,lwd=3)
abline(lm.fit,lwd=3,col="red")
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plot(lstat,medv,col="red")
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plot(lstat,medv,pch=16)
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plot(lstat,medv,pch="+")
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plot(1:20,1:20,pch=1:20)
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par(mfrow=c(2,2))
plot(lm.fit)
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plot(predict(lm.fit), residuals(lm.fit))
plot(predict(lm.fit), rstudent(lm.fit))
plot(hatvalues(lm.fit))
which.max(hatvalues(lm.fit))375
375 
Multiple Linear Regression
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lm.fit=lm(medv~lstat+age,data=Boston)
summary(lm.fit)
Call:
lm(formula = medv ~ lstat + age, data = Boston)
Residuals:
Min 1Q Median 3Q Max
-15.981 -3.978 -1.283 1.968 23.158
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.22276 0.73085 45.458 < 2e-16 ***
lstat -1.03207 0.04819 -21.416 < 2e-16 ***
age 0.03454 0.01223 2.826 0.00491 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.173 on 503 degrees of freedom
Multiple R-squared: 0.5513, Adjusted R-squared: 0.5495
F-statistic: 309 on 2 and 503 DF, p-value: < 2.2e-16Show the Code
lm.fit=lm(medv~.,data=Boston)
summary(lm.fit)
Call:
lm(formula = medv ~ ., data = Boston)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16Show the Code
library(car)Warning: package 'car' was built under R version 4.5.2Loading required package: carDataWarning: package 'carData' was built under R version 4.5.2Show the Code
vif(lm.fit) crim zn indus chas nox rm age dis
1.792192 2.298758 3.991596 1.073995 4.393720 1.933744 3.100826 3.955945
rad tax ptratio black lstat
7.484496 9.008554 1.799084 1.348521 2.941491 Show the Code
lm.fit1=lm(medv~.-age,data=Boston)
summary(lm.fit1)
Call:
lm(formula = medv ~ . - age, data = Boston)
Residuals:
Min 1Q Median 3Q Max
-15.6054 -2.7313 -0.5188 1.7601 26.2243
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 36.436927 5.080119 7.172 2.72e-12 ***
crim -0.108006 0.032832 -3.290 0.001075 **
zn 0.046334 0.013613 3.404 0.000719 ***
indus 0.020562 0.061433 0.335 0.737989
chas 2.689026 0.859598 3.128 0.001863 **
nox -17.713540 3.679308 -4.814 1.97e-06 ***
rm 3.814394 0.408480 9.338 < 2e-16 ***
dis -1.478612 0.190611 -7.757 5.03e-14 ***
rad 0.305786 0.066089 4.627 4.75e-06 ***
tax -0.012329 0.003755 -3.283 0.001099 **
ptratio -0.952211 0.130294 -7.308 1.10e-12 ***
black 0.009321 0.002678 3.481 0.000544 ***
lstat -0.523852 0.047625 -10.999 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.74 on 493 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7343
F-statistic: 117.3 on 12 and 493 DF, p-value: < 2.2e-16Show the Code
lm.fit1=update(lm.fit, ~.-age)Non-linear Transformations of the Predictors
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lm.fit2=lm(medv~lstat+I(lstat^2))
summary(lm.fit2)
Call:
lm(formula = medv ~ lstat + I(lstat^2))
Residuals:
Min 1Q Median 3Q Max
-15.2834 -3.8313 -0.5295 2.3095 25.4148
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 42.862007 0.872084 49.15 <2e-16 ***
lstat -2.332821 0.123803 -18.84 <2e-16 ***
I(lstat^2) 0.043547 0.003745 11.63 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.524 on 503 degrees of freedom
Multiple R-squared: 0.6407, Adjusted R-squared: 0.6393
F-statistic: 448.5 on 2 and 503 DF, p-value: < 2.2e-16Show the Code
lm.fit=lm(medv~lstat)
anova(lm.fit,lm.fit2)Analysis of Variance Table
Model 1: medv ~ lstat
Model 2: medv ~ lstat + I(lstat^2)
Res.Df RSS Df Sum of Sq F Pr(>F)
1 504 19472
2 503 15347 1 4125.1 135.2 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Show the Code
par(mfrow=c(2,2))
plot(lm.fit2)
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lm.fit5=lm(medv~poly(lstat,5))
summary(lm.fit5)
Call:
lm(formula = medv ~ poly(lstat, 5))
Residuals:
Min 1Q Median 3Q Max
-13.5433 -3.1039 -0.7052 2.0844 27.1153
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.5328 0.2318 97.197 < 2e-16 ***
poly(lstat, 5)1 -152.4595 5.2148 -29.236 < 2e-16 ***
poly(lstat, 5)2 64.2272 5.2148 12.316 < 2e-16 ***
poly(lstat, 5)3 -27.0511 5.2148 -5.187 3.10e-07 ***
poly(lstat, 5)4 25.4517 5.2148 4.881 1.42e-06 ***
poly(lstat, 5)5 -19.2524 5.2148 -3.692 0.000247 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.215 on 500 degrees of freedom
Multiple R-squared: 0.6817, Adjusted R-squared: 0.6785
F-statistic: 214.2 on 5 and 500 DF, p-value: < 2.2e-16Show the Code
summary(lm(medv~log(rm),data=Boston))
Call:
lm(formula = medv ~ log(rm), data = Boston)
Residuals:
Min 1Q Median 3Q Max
-19.487 -2.875 -0.104 2.837 39.816
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -76.488 5.028 -15.21 <2e-16 ***
log(rm) 54.055 2.739 19.73 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.915 on 504 degrees of freedom
Multiple R-squared: 0.4358, Adjusted R-squared: 0.4347
F-statistic: 389.3 on 1 and 504 DF, p-value: < 2.2e-16Qualitative Predictors
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# fix(Carseats)
names(Carseats) [1] "Sales" "CompPrice" "Income" "Advertising" "Population"
[6] "Price" "ShelveLoc" "Age" "Education" "Urban"
[11] "US" Show the Code
lm.fit=lm(Sales~.+Income:Advertising+Price:Age,data=Carseats)
summary(lm.fit)
Call:
lm(formula = Sales ~ . + Income:Advertising + Price:Age, data = Carseats)
Residuals:
Min 1Q Median 3Q Max
-2.9208 -0.7503 0.0177 0.6754 3.3413
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.5755654 1.0087470 6.519 2.22e-10 ***
CompPrice 0.0929371 0.0041183 22.567 < 2e-16 ***
Income 0.0108940 0.0026044 4.183 3.57e-05 ***
Advertising 0.0702462 0.0226091 3.107 0.002030 **
Population 0.0001592 0.0003679 0.433 0.665330
Price -0.1008064 0.0074399 -13.549 < 2e-16 ***
ShelveLocGood 4.8486762 0.1528378 31.724 < 2e-16 ***
ShelveLocMedium 1.9532620 0.1257682 15.531 < 2e-16 ***
Age -0.0579466 0.0159506 -3.633 0.000318 ***
Education -0.0208525 0.0196131 -1.063 0.288361
UrbanYes 0.1401597 0.1124019 1.247 0.213171
USYes -0.1575571 0.1489234 -1.058 0.290729
Income:Advertising 0.0007510 0.0002784 2.698 0.007290 **
Price:Age 0.0001068 0.0001333 0.801 0.423812
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.011 on 386 degrees of freedom
Multiple R-squared: 0.8761, Adjusted R-squared: 0.8719
F-statistic: 210 on 13 and 386 DF, p-value: < 2.2e-16Show the Code
attach(Carseats)
contrasts(ShelveLoc) Good Medium
Bad 0 0
Good 1 0
Medium 0 1Interaction Terms (including interaction and single effects)
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summary(lm(medv~lstat*age,data=Boston))
Call:
lm(formula = medv ~ lstat * age, data = Boston)
Residuals:
Min 1Q Median 3Q Max
-15.806 -4.045 -1.333 2.085 27.552
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 36.0885359 1.4698355 24.553 < 2e-16 ***
lstat -1.3921168 0.1674555 -8.313 8.78e-16 ***
age -0.0007209 0.0198792 -0.036 0.9711
lstat:age 0.0041560 0.0018518 2.244 0.0252 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.149 on 502 degrees of freedom
Multiple R-squared: 0.5557, Adjusted R-squared: 0.5531
F-statistic: 209.3 on 3 and 502 DF, p-value: < 2.2e-16Exploratory Analysis
Downloading Data-set
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library(haven)Warning: package 'haven' was built under R version 4.5.2Show the Code
TEDS_2016 <- haven::read_dta(
"https://github.com/datageneration/home/blob/master/DataProgramming/data/TEDS_2016.dta?raw=true"
)
names(TEDS_2016) [1] "District" "Sex" "Age" "Edu"
[5] "Arear" "Career" "Career8" "Ethnic"
[9] "Party" "PartyID" "Tondu" "Tondu3"
[13] "nI2" "votetsai" "green" "votetsai_nm"
[17] "votetsai_all" "Independence" "Unification" "sq"
[21] "Taiwanese" "edu" "female" "whitecollar"
[25] "lowincome" "income" "income_nm" "age"
[29] "KMT" "DPP" "npp" "noparty"
[33] "pfp" "South" "north" "Minnan_father"
[37] "Mainland_father" "Econ_worse" "Inequality" "inequality5"
[41] "econworse5" "Govt_for_public" "pubwelf5" "Govt_dont_care"
[45] "highincome" "votekmt" "votekmt_nm" "Blue"
[49] "Green" "No_Party" "voteblue" "voteblue_nm"
[53] "votedpp_1" "votekmt_1" Show the Code
str(TEDS_2016)tibble [1,690 × 54] (S3: tbl_df/tbl/data.frame)
$ District : dbl+lbl [1:1690] 201, 201, 201, 201, 201, 201, 201, 201, 201, 201, 201...
..@ label : chr "District"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:73] 201 401 501 502 701 702 703 704 801 802 ...
.. ..- attr(*, "names")= chr [1:73] "Yi Lan County Single District" "Hsinchu County Single District" "Miaoli County 1st District" "Miaoli County 2nd District" ...
$ Sex : dbl+lbl [1:1690] 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1,...
..@ label : chr "Sex"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:2] 1 2
.. ..- attr(*, "names")= chr [1:2] "Male" "Female"
$ Age : dbl+lbl [1:1690] 4, 2, 5, 4, 5, 5, 5, 4, 5, 4, 5, 1, 5, 3, 4, 5, 4, 5,...
..@ label : chr "Age"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:5] 1 2 3 4 5
.. ..- attr(*, "names")= chr [1:5] "20-29" "30-39" "40-49" "50-59" ...
$ Edu : dbl+lbl [1:1690] 4, 5, 5, 2, 1, 2, 1, 5, 1, 1, 1, 2, 1, 5, 5, 1, 3, 4,...
..@ label : chr "Education"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:6] 1 2 3 4 5 9
.. ..- attr(*, "names")= chr [1:6] "Below elementary school" "Junior high school" "Senior high school" "College" ...
$ Arear : dbl+lbl [1:1690] 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
..@ label : chr "Area"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:6] 1 2 3 4 5 6
.. ..- attr(*, "names")= chr [1:6] "Taipei, New Taipei, Keelung and Yi Lan" "Taoyuan, Hsinchu and Miaoli" "Taichung, Changhua and Nantou" "Yunlin, Chiayi and Tainan" ...
$ Career : dbl+lbl [1:1690] 1, 2, 1, 4, 3, 2, 4, 1, 4, 3, 3, 5, 5, 4, 1, 5, 2, 2,...
..@ label : chr "Occupations5"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:5] 1 2 3 4 5
.. ..- attr(*, "names")= chr [1:5] "Hight-class WHITE COLLAR" "Low-class WHITE COLLAR" "FARMER" "WORKER" ...
$ Career8 : dbl+lbl [1:1690] 1, 3, 1, 4, 5, 7, 4, 2, 4, 5, 5, 7, 7, 7, 2, 7, 3, 1,...
..@ label : chr "Occupation8"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:8] 1 2 3 4 5 6 7 8
.. ..- attr(*, "names")= chr [1:8] "Civil servants" "Managers and Professionals (priv.)" "CLERKS (priv.)" "Labor (priv.)" ...
$ Ethnic : dbl+lbl [1:1690] 1, 2, 2, 1, 9, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 9, 2, 2,...
..@ label : chr "Ethnic"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:4] 1 2 3 9
.. ..- attr(*, "names")= chr [1:4] "Taiwanese" "Both" "Chinese" "Noresponse"
$ Party : dbl+lbl [1:1690] 25, 25, 3, 25, 25, 6, 25, 24, 25, 25, 6, 5, 25, ...
..@ label : chr "Party Preference"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:26] 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr [1:26] "Strongly support KMT" "Somewhat support KMT" "Lean to KMT" "Somewhat lean to KMT" ...
$ PartyID : dbl+lbl [1:1690] 9, 9, 1, 9, 9, 2, 9, 6, 9, 9, 2, 2, 9, 1, 1, 9, 9, 9,...
..@ label : chr "Party Identification"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:7] 1 2 3 4 5 6 9
.. ..- attr(*, "names")= chr [1:7] "KMT" "DPP" "NP" "PFP" ...
$ Tondu : dbl+lbl [1:1690] 3, 5, 3, 5, 9, 4, 9, 6, 9, 9, 5, 5, 9, 5, 4, 9, 9, 4,...
..@ label : chr "Position on unification and independence"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:7] 1 2 3 4 5 6 9
.. ..- attr(*, "names")= chr [1:7] "Immediate unification" "Maintain the status quo,move toward unification" "Maintain the status quo, decide either unification or independence" "Maintain the status quo forever" ...
$ Tondu3 : dbl+lbl [1:1690] 2, 3, 2, 3, 9, 2, 9, 3, 9, 9, 3, 3, 9, 3, 2, 9, 9, 2,...
..@ label : chr "3 categories of TONDU"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:4] 1 2 3 9
.. ..- attr(*, "names")= chr [1:4] "Unification" "Maintain the status quo" "Independence" "Nonresponse"
$ nI2 : dbl+lbl [1:1690] 3, 98, 98, 3, 98, 98, 98, 3, 98, 1, 2, 98, 98, ...
..@ label : chr "Who is the current the premier of our country?"
..@ format.stata: chr "%10.0g"
..@ labels : Named num [1:5] 1 2 3 95 98
.. ..- attr(*, "names")= chr [1:5] "Correct" "Incorrect" "I know but can't remember the name" "Refuse to answer" ...
$ votetsai : num [1:1690] NA 1 0 NA NA 1 1 1 1 NA ...
..- attr(*, "format.stata")= chr "%9.0g"
$ green : num [1:1690] 0 0 0 0 0 1 0 1 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ votetsai_nm : num [1:1690] NA 1 0 NA NA 1 1 1 1 NA ...
..- attr(*, "format.stata")= chr "%9.0g"
$ votetsai_all : num [1:1690] 0 1 0 0 0 1 1 1 1 NA ...
..- attr(*, "format.stata")= chr "%9.0g"
$ Independence : num [1:1690] 0 1 0 1 0 0 0 1 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ Unification : num [1:1690] 0 0 0 0 0 0 0 0 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ sq : num [1:1690] 1 0 1 0 0 1 0 0 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ Taiwanese : num [1:1690] 1 0 0 1 0 1 0 1 1 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ edu : num [1:1690] 4 5 5 2 1 2 1 5 1 1 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ female : num [1:1690] 1 1 0 0 1 1 0 1 1 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ whitecollar : num [1:1690] 1 1 1 0 0 1 0 1 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ lowincome : num [1:1690] 4 4 5 4 3 5 2 5 5 5 ...
..- attr(*, "label")= chr "How serious do you think low income of salaryman?"
..- attr(*, "format.stata")= chr "%9.0g"
$ income : num [1:1690] 8 7 8 5 5.5 9 1 10 2 5.5 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ income_nm : num [1:1690] 8 7 8 5 NA 9 1 10 2 NA ...
..- attr(*, "format.stata")= chr "%9.0g"
$ age : num [1:1690] 59 39 63 55 76 64 75 54 64 59 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ KMT : num [1:1690] 0 0 1 0 0 0 0 0 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ DPP : num [1:1690] 0 0 0 0 0 1 0 0 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ npp : num [1:1690] 0 0 0 0 0 0 0 1 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ noparty : num [1:1690] 1 1 0 1 1 0 1 0 1 1 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ pfp : num [1:1690] 0 0 0 0 0 0 0 0 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ South : num [1:1690] 0 0 0 0 0 0 0 0 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ north : num [1:1690] 1 1 1 1 1 1 1 1 1 1 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ Minnan_father : num [1:1690] 1 1 1 1 1 1 1 1 1 1 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ Mainland_father: num [1:1690] 0 0 0 0 0 0 0 0 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ Econ_worse : num [1:1690] 0 0 1 1 0 1 1 1 1 1 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ Inequality : num [1:1690] 1 1 1 1 0 1 0 1 1 1 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ inequality5 : num [1:1690] 4 5 5 5 3 5 3 5 5 5 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ econworse5 : num [1:1690] 3 3 4 5 3 4 4 5 5 5 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ Govt_for_public: num [1:1690] 1 1 1 0 0 0 0 0 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ pubwelf5 : num [1:1690] 5 5 4 1 3 2 2 1 3 2 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ Govt_dont_care : num [1:1690] 0 0 1 1 0 1 1 1 0 1 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ highincome : num [1:1690] 1 1 1 1 NA 1 0 1 0 NA ...
..- attr(*, "format.stata")= chr "%9.0g"
$ votekmt : num [1:1690] 0 0 1 0 0 0 0 0 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ votekmt_nm : num [1:1690] NA 0 1 NA NA 0 0 0 0 NA ...
..- attr(*, "format.stata")= chr "%9.0g"
$ Blue : num [1:1690] 0 0 0 0 0 0 0 0 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ Green : num [1:1690] 0 0 0 0 0 0 0 0 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ No_Party : num [1:1690] 0 0 0 0 0 0 0 0 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ voteblue : num [1:1690] 0 0 1 0 0 0 0 0 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ voteblue_nm : num [1:1690] NA 0 1 NA NA 0 0 0 0 NA ...
..- attr(*, "format.stata")= chr "%9.0g"
$ votedpp_1 : num [1:1690] NA 1 0 NA NA 1 1 1 1 0 ...
..- attr(*, "format.stata")= chr "%9.0g"
$ votekmt_1 : num [1:1690] NA 0 1 NA NA 0 0 0 0 0 ...
..- attr(*, "format.stata")= chr "%9.0g"Show the Code
summary(TEDS_2016) District Sex Age Edu Arear
Min. : 201 Min. :1.000 Min. :1.0 Min. :1.000 Min. :1.000
1st Qu.:1401 1st Qu.:1.000 1st Qu.:2.0 1st Qu.:2.000 1st Qu.:1.000
Median :6406 Median :1.000 Median :3.0 Median :3.000 Median :3.000
Mean :4661 Mean :1.486 Mean :3.3 Mean :3.334 Mean :2.744
3rd Qu.:6604 3rd Qu.:2.000 3rd Qu.:5.0 3rd Qu.:5.000 3rd Qu.:4.000
Max. :6806 Max. :2.000 Max. :5.0 Max. :9.000 Max. :6.000
Career Career8 Ethnic Party
Min. :1.000 Min. :1.000 Min. :1.000 Min. : 1.00
1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.: 5.00
Median :2.000 Median :4.000 Median :1.000 Median : 7.00
Mean :2.683 Mean :3.811 Mean :1.658 Mean :13.02
3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:2.000 3rd Qu.:25.00
Max. :5.000 Max. :8.000 Max. :9.000 Max. :26.00
PartyID Tondu Tondu3 nI2
Min. :1.000 Min. :1.000 Min. :1.000 Min. : 1.00
1st Qu.:2.000 1st Qu.:3.000 1st Qu.:2.000 1st Qu.: 1.00
Median :2.000 Median :4.000 Median :2.000 Median : 3.00
Mean :4.522 Mean :4.127 Mean :2.667 Mean :35.13
3rd Qu.:9.000 3rd Qu.:5.000 3rd Qu.:3.000 3rd Qu.:98.00
Max. :9.000 Max. :9.000 Max. :9.000 Max. :98.00
votetsai green votetsai_nm votetsai_all
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
Median :1.0000 Median :0.0000 Median :1.0000 Median :1.0000
Mean :0.6265 Mean :0.3781 Mean :0.6265 Mean :0.5478
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
NA's :429 NA's :429 NA's :248
Independence Unification sq Taiwanese
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
Median :0.0000 Median :0.0000 Median :1.0000 Median :1.0000
Mean :0.2888 Mean :0.1225 Mean :0.5172 Mean :0.6272
3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
edu female whitecollar lowincome
Min. :1.000 Min. :0.0000 Min. :0.0000 Min. :1.000
1st Qu.:2.000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:4.000
Median :3.000 Median :0.0000 Median :1.0000 Median :5.000
Mean :3.301 Mean :0.4864 Mean :0.5373 Mean :4.343
3rd Qu.:5.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:5.000
Max. :5.000 Max. :1.0000 Max. :1.0000 Max. :5.000
NA's :10
income income_nm age KMT
Min. : 1.000 Min. : 1.000 Min. : 20.00 Min. :0.0000
1st Qu.: 3.000 1st Qu.: 2.000 1st Qu.: 35.00 1st Qu.:0.0000
Median : 5.500 Median : 5.000 Median : 49.00 Median :0.0000
Mean : 5.324 Mean : 5.281 Mean : 49.11 Mean :0.2296
3rd Qu.: 7.000 3rd Qu.: 8.000 3rd Qu.: 61.00 3rd Qu.:0.0000
Max. :10.000 Max. :10.000 Max. :100.00 Max. :1.0000
NA's :330
DPP npp noparty pfp
Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. :0.00000
1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000
Median :0.0000 Median :0.00000 Median :0.0000 Median :0.00000
Mean :0.3497 Mean :0.02544 Mean :0.3716 Mean :0.01893
3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:0.00000
Max. :1.0000 Max. :1.00000 Max. :1.0000 Max. :1.00000
South north Minnan_father Mainland_father
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
Median :0.0000 Median :0.0000 Median :1.0000 Median :0.0000
Mean :0.4947 Mean :0.4799 Mean :0.7225 Mean :0.1024
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
Econ_worse Inequality inequality5 econworse5
Min. :0.0000 Min. :0.0000 Min. :1.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:4.000 1st Qu.:3.000
Median :1.0000 Median :1.0000 Median :5.000 Median :4.000
Mean :0.5544 Mean :0.9355 Mean :4.495 Mean :3.644
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:5.000 3rd Qu.:4.000
Max. :1.0000 Max. :1.0000 Max. :5.000 Max. :5.000
Govt_for_public pubwelf5 Govt_dont_care highincome
Min. :0.0000 Min. :1.000 Min. :0.0000 Min. :0.0000
1st Qu.:0.0000 1st Qu.:2.000 1st Qu.:0.0000 1st Qu.:0.0000
Median :0.0000 Median :3.000 Median :0.0000 Median :1.0000
Mean :0.4249 Mean :2.877 Mean :0.4988 Mean :0.5765
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :1.0000 Max. :5.000 Max. :1.0000 Max. :1.0000
NA's :330
votekmt votekmt_nm Blue Green No_Party
Min. :0.0000 Min. :0.0000 Min. :0 Min. :0 Min. :0
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0 1st Qu.:0 1st Qu.:0
Median :0.0000 Median :0.0000 Median :0 Median :0 Median :0
Mean :0.2053 Mean :0.2752 Mean :0 Mean :0 Mean :0
3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
Max. :1.0000 Max. :1.0000 Max. :0 Max. :0 Max. :0
NA's :429
voteblue voteblue_nm votedpp_1 votekmt_1
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
Median :0.0000 Median :0.0000 Median :1.0000 Median :0.0000
Mean :0.2787 Mean :0.3735 Mean :0.5256 Mean :0.2309
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
NA's :429 NA's :187 NA's :187 Graphical and Numeral Summaries
Show the Code
# 1) Scatter-style plot (only meaningful if x is numeric-ish)
plot(TEDS_2016$Age, TEDS_2016$income)
Show the Code
# 2) Treat Age as categorical (like cylinders)
TEDS_2016$Age_f <- as.factor(TEDS_2016$Age)
plot(TEDS_2016$Age_f, TEDS_2016$income)
Show the Code
plot(TEDS_2016$Age_f, TEDS_2016$income, col="red")
Show the Code
plot(TEDS_2016$Age_f, TEDS_2016$income, col="red", varwidth=TRUE)
Show the Code
plot(TEDS_2016$Age_f, TEDS_2016$income, col="red", varwidth=TRUE, horizontal=TRUE)
Show the Code
plot(TEDS_2016$Age_f, TEDS_2016$income, col="red", varwidth=TRUE,
xlab="Age group (1–5)", ylab="Income (1–10)")
Show the Code
# 3) Histogram (like hist(mpg))
hist(TEDS_2016$income)
Show the Code
hist(TEDS_2016$income, col=2)
Show the Code
hist(TEDS_2016$income, col=2, breaks=15)
Show the Code
# 4) Pairs plot: pick numeric variables that exist in TEDS
pairs(~ income + age + inequality5 + econworse5 + pubwelf5, data=TEDS_2016)
Show the Code
# 5) Another scatterplot (like plot(horsepower, mpg))
plot(TEDS_2016$age, TEDS_2016$income)
Show the Code
# 6) Summaries
summary(TEDS_2016) District Sex Age Edu Arear
Min. : 201 Min. :1.000 Min. :1.0 Min. :1.000 Min. :1.000
1st Qu.:1401 1st Qu.:1.000 1st Qu.:2.0 1st Qu.:2.000 1st Qu.:1.000
Median :6406 Median :1.000 Median :3.0 Median :3.000 Median :3.000
Mean :4661 Mean :1.486 Mean :3.3 Mean :3.334 Mean :2.744
3rd Qu.:6604 3rd Qu.:2.000 3rd Qu.:5.0 3rd Qu.:5.000 3rd Qu.:4.000
Max. :6806 Max. :2.000 Max. :5.0 Max. :9.000 Max. :6.000
Career Career8 Ethnic Party
Min. :1.000 Min. :1.000 Min. :1.000 Min. : 1.00
1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.: 5.00
Median :2.000 Median :4.000 Median :1.000 Median : 7.00
Mean :2.683 Mean :3.811 Mean :1.658 Mean :13.02
3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:2.000 3rd Qu.:25.00
Max. :5.000 Max. :8.000 Max. :9.000 Max. :26.00
PartyID Tondu Tondu3 nI2
Min. :1.000 Min. :1.000 Min. :1.000 Min. : 1.00
1st Qu.:2.000 1st Qu.:3.000 1st Qu.:2.000 1st Qu.: 1.00
Median :2.000 Median :4.000 Median :2.000 Median : 3.00
Mean :4.522 Mean :4.127 Mean :2.667 Mean :35.13
3rd Qu.:9.000 3rd Qu.:5.000 3rd Qu.:3.000 3rd Qu.:98.00
Max. :9.000 Max. :9.000 Max. :9.000 Max. :98.00
votetsai green votetsai_nm votetsai_all
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
Median :1.0000 Median :0.0000 Median :1.0000 Median :1.0000
Mean :0.6265 Mean :0.3781 Mean :0.6265 Mean :0.5478
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
NA's :429 NA's :429 NA's :248
Independence Unification sq Taiwanese
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
Median :0.0000 Median :0.0000 Median :1.0000 Median :1.0000
Mean :0.2888 Mean :0.1225 Mean :0.5172 Mean :0.6272
3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
edu female whitecollar lowincome
Min. :1.000 Min. :0.0000 Min. :0.0000 Min. :1.000
1st Qu.:2.000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:4.000
Median :3.000 Median :0.0000 Median :1.0000 Median :5.000
Mean :3.301 Mean :0.4864 Mean :0.5373 Mean :4.343
3rd Qu.:5.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:5.000
Max. :5.000 Max. :1.0000 Max. :1.0000 Max. :5.000
NA's :10
income income_nm age KMT
Min. : 1.000 Min. : 1.000 Min. : 20.00 Min. :0.0000
1st Qu.: 3.000 1st Qu.: 2.000 1st Qu.: 35.00 1st Qu.:0.0000
Median : 5.500 Median : 5.000 Median : 49.00 Median :0.0000
Mean : 5.324 Mean : 5.281 Mean : 49.11 Mean :0.2296
3rd Qu.: 7.000 3rd Qu.: 8.000 3rd Qu.: 61.00 3rd Qu.:0.0000
Max. :10.000 Max. :10.000 Max. :100.00 Max. :1.0000
NA's :330
DPP npp noparty pfp
Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. :0.00000
1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000
Median :0.0000 Median :0.00000 Median :0.0000 Median :0.00000
Mean :0.3497 Mean :0.02544 Mean :0.3716 Mean :0.01893
3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:0.00000
Max. :1.0000 Max. :1.00000 Max. :1.0000 Max. :1.00000
South north Minnan_father Mainland_father
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
Median :0.0000 Median :0.0000 Median :1.0000 Median :0.0000
Mean :0.4947 Mean :0.4799 Mean :0.7225 Mean :0.1024
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
Econ_worse Inequality inequality5 econworse5
Min. :0.0000 Min. :0.0000 Min. :1.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:4.000 1st Qu.:3.000
Median :1.0000 Median :1.0000 Median :5.000 Median :4.000
Mean :0.5544 Mean :0.9355 Mean :4.495 Mean :3.644
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:5.000 3rd Qu.:4.000
Max. :1.0000 Max. :1.0000 Max. :5.000 Max. :5.000
Govt_for_public pubwelf5 Govt_dont_care highincome
Min. :0.0000 Min. :1.000 Min. :0.0000 Min. :0.0000
1st Qu.:0.0000 1st Qu.:2.000 1st Qu.:0.0000 1st Qu.:0.0000
Median :0.0000 Median :3.000 Median :0.0000 Median :1.0000
Mean :0.4249 Mean :2.877 Mean :0.4988 Mean :0.5765
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :1.0000 Max. :5.000 Max. :1.0000 Max. :1.0000
NA's :330
votekmt votekmt_nm Blue Green No_Party
Min. :0.0000 Min. :0.0000 Min. :0 Min. :0 Min. :0
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0 1st Qu.:0 1st Qu.:0
Median :0.0000 Median :0.0000 Median :0 Median :0 Median :0
Mean :0.2053 Mean :0.2752 Mean :0 Mean :0 Mean :0
3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
Max. :1.0000 Max. :1.0000 Max. :0 Max. :0 Max. :0
NA's :429
voteblue voteblue_nm votedpp_1 votekmt_1 Age_f
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 1:264
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 2:282
Median :0.0000 Median :0.0000 Median :1.0000 Median :0.0000 3:317
Mean :0.2787 Mean :0.3735 Mean :0.5256 Mean :0.2309 4:337
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000 5:490
Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
NA's :429 NA's :187 NA's :187 Show the Code
summary(TEDS_2016$income) Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 3.000 5.500 5.324 7.000 10.000 Linear Regression
Show the Code
names(TEDS_2016) [1] "District" "Sex" "Age" "Edu"
[5] "Arear" "Career" "Career8" "Ethnic"
[9] "Party" "PartyID" "Tondu" "Tondu3"
[13] "nI2" "votetsai" "green" "votetsai_nm"
[17] "votetsai_all" "Independence" "Unification" "sq"
[21] "Taiwanese" "edu" "female" "whitecollar"
[25] "lowincome" "income" "income_nm" "age"
[29] "KMT" "DPP" "npp" "noparty"
[33] "pfp" "South" "north" "Minnan_father"
[37] "Mainland_father" "Econ_worse" "Inequality" "inequality5"
[41] "econworse5" "Govt_for_public" "pubwelf5" "Govt_dont_care"
[45] "highincome" "votekmt" "votekmt_nm" "Blue"
[49] "Green" "No_Party" "voteblue" "voteblue_nm"
[53] "votedpp_1" "votekmt_1" "Age_f" Show the Code
# Fit the model (no attach needed)
lm.fit <- lm(income ~ age, data = TEDS_2016)
lm.fit
Call:
lm(formula = income ~ age, data = TEDS_2016)
Coefficients:
(Intercept) age
6.97331 -0.03359 Show the Code
summary(lm.fit)
Call:
lm(formula = income ~ age, data = TEDS_2016)
Residuals:
Min 1Q Median 3Q Max
-5.2680 -2.1596 0.1427 1.8068 5.7137
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.973309 0.201284 34.644 <2e-16 ***
age -0.033587 0.003877 -8.662 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.679 on 1688 degrees of freedom
Multiple R-squared: 0.04256, Adjusted R-squared: 0.04199
F-statistic: 75.03 on 1 and 1688 DF, p-value: < 2.2e-16Show the Code
names(lm.fit) [1] "coefficients" "residuals" "effects" "rank"
[5] "fitted.values" "assign" "qr" "df.residual"
[9] "xlevels" "call" "terms" "model" Show the Code
coef(lm.fit)(Intercept) age
6.97330884 -0.03358744 Show the Code
confint(lm.fit) 2.5 % 97.5 %
(Intercept) 6.57851695 7.36810073
age -0.04119265 -0.02598223Show the Code
predict(lm.fit, data.frame(age = c(25, 40, 60)), interval = "confidence") fit lwr upr
1 6.133623 5.910078 6.357167
2 5.629811 5.484406 5.775216
3 4.958062 4.805778 5.110347Show the Code
predict(lm.fit, data.frame(age = c(25, 40, 60)), interval = "prediction") fit lwr upr
1 6.133623 0.8743390 11.39291
2 5.629811 0.3732689 10.88635
3 4.958062 -0.2986747 10.21480Show the Code
plot(TEDS_2016$age, TEDS_2016$income)
abline(lm.fit)
abline(lm.fit, lwd=3)
abline(lm.fit, lwd=3, col="red")
Show the Code
plot(TEDS_2016$age, TEDS_2016$income, col="red")
Show the Code
plot(TEDS_2016$age, TEDS_2016$income, pch=16)
Show the Code
plot(TEDS_2016$age, TEDS_2016$income, pch="+")
Show the Code
par(mfrow=c(2,2))
plot(lm.fit)
Show the Code
plot(predict(lm.fit), residuals(lm.fit))
plot(predict(lm.fit), rstudent(lm.fit))
plot(hatvalues(lm.fit))
which.max(hatvalues(lm.fit))216
216 Show the Code
lm.edu <- lm(income ~ as.factor(Edu), data = TEDS_2016)
summary(lm.edu)
Call:
lm(formula = income ~ as.factor(Edu), data = TEDS_2016)
Residuals:
Min 1Q Median 3Q Max
-5.4511 -2.0435 0.1325 1.5489 5.9565
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.0435 0.1430 28.280 < 2e-16 ***
as.factor(Edu)2 0.3789 0.2477 1.530 0.126
as.factor(Edu)3 0.8240 0.1874 4.398 1.16e-05 ***
as.factor(Edu)4 2.0049 0.2363 8.485 < 2e-16 ***
as.factor(Edu)5 2.4076 0.1793 13.426 < 2e-16 ***
as.factor(Edu)9 0.6565 0.8239 0.797 0.426
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.566 on 1684 degrees of freedom
Multiple R-squared: 0.1239, Adjusted R-squared: 0.1213
F-statistic: 47.63 on 5 and 1684 DF, p-value: < 2.2e-16Show the Code
plot(as.factor(TEDS_2016$Edu), TEDS_2016$income, col="red", varwidth=TRUE)
Multiple Linear Regression
Show the Code
install.packages("car")Warning: package 'car' is in use and will not be installedShow the Code
library(car)
# (Assumes TEDS_2016 is already loaded)
# Optional: remove rows with missing values for variables used
TEDS_lm <- subset(TEDS_2016,
!is.na(income) & !is.na(age) & !is.na(edu) &
!is.na(female) & !is.na(whitecollar))
# 1) Multiple regression like: medv ~ lstat + age
lm.fit <- lm(income ~ age + edu, data = TEDS_lm)
summary(lm.fit)
Call:
lm(formula = income ~ age + edu, data = TEDS_lm)
Residuals:
Min 1Q Median 3Q Max
-5.4722 -2.1345 0.1528 1.6347 6.1448
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.077706 0.376811 8.168 6.11e-16 ***
age 0.002125 0.004769 0.445 0.656
edu 0.649999 0.053811 12.079 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.575 on 1677 degrees of freedom
Multiple R-squared: 0.1193, Adjusted R-squared: 0.1183
F-statistic: 113.6 on 2 and 1677 DF, p-value: < 2.2e-16Show the Code
# 2) "All predictors" version (safe approach: choose a set of numeric predictors)
lm.fit_all <- lm(income ~ age + edu + female + whitecollar + inequality5 + econworse5 + pubwelf5,
data = TEDS_lm)
summary(lm.fit_all)
Call:
lm(formula = income ~ age + edu + female + whitecollar + inequality5 +
econworse5 + pubwelf5, data = TEDS_lm)
Residuals:
Min 1Q Median 3Q Max
-5.7429 -2.0731 0.0982 1.7548 6.6150
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.3530388 0.6345931 5.284 1.43e-07 ***
age -0.0004934 0.0047755 -0.103 0.91772
edu 0.4773002 0.0618942 7.712 2.12e-14 ***
female -0.1360900 0.1265180 -1.076 0.28224
whitecollar 0.7143387 0.1494450 4.780 1.91e-06 ***
inequality5 -0.0175512 0.0883940 -0.199 0.84263
econworse5 -0.0657466 0.0852948 -0.771 0.44093
pubwelf5 0.1467219 0.0566765 2.589 0.00972 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.552 on 1672 degrees of freedom
Multiple R-squared: 0.1375, Adjusted R-squared: 0.1339
F-statistic: 38.08 on 7 and 1672 DF, p-value: < 2.2e-16Show the Code
# VIF for multicollinearity (works on lm objects)
vif(lm.fit_all) age edu female whitecollar inequality5 econworse5
1.663786 2.195045 1.031693 1.431585 1.067853 1.125201
pubwelf5
1.134655 Show the Code
# 3) Remove one variable (like ~ . - age)
lm.fit1 <- update(lm.fit_all, ~ . - age)
summary(lm.fit1)
Call:
lm(formula = income ~ edu + female + whitecollar + inequality5 +
econworse5 + pubwelf5, data = TEDS_lm)
Residuals:
Min 1Q Median 3Q Max
-5.7373 -2.0670 0.0868 1.7487 6.6077
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.32021 0.54915 6.046 1.83e-09 ***
edu 0.48105 0.05016 9.591 < 2e-16 ***
female -0.13567 0.12641 -1.073 0.28334
whitecollar 0.71297 0.14881 4.791 1.81e-06 ***
inequality5 -0.01735 0.08835 -0.196 0.84431
econworse5 -0.06658 0.08489 -0.784 0.43298
pubwelf5 0.14635 0.05654 2.588 0.00973 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.551 on 1673 degrees of freedom
Multiple R-squared: 0.1375, Adjusted R-squared: 0.1344
F-statistic: 44.45 on 6 and 1673 DF, p-value: < 2.2e-16Show the Code
# same thing, just showing the update call directly (like your last line)
lm.fit1b <- update(lm.fit_all, ~ . - age)Non-linear Transformations of the Predictors
Show the Code
# Assumes TEDS_2016 is already loaded
# Keep only complete cases for variables we use
TEDS_poly <- subset(TEDS_2016, !is.na(income) & !is.na(age) & age > 0)
# Quadratic model: income ~ age + age^2
lm.fit2 <- lm(income ~ age + I(age^2), data = TEDS_poly)
summary(lm.fit2)
Call:
lm(formula = income ~ age + I(age^2), data = TEDS_poly)
Residuals:
Min 1Q Median 3Q Max
-5.0805 -2.1022 0.0833 1.9390 5.8965
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.2530458 0.5137085 12.172 <2e-16 ***
age -0.0015759 0.0213619 -0.074 0.941
I(age^2) -0.0003162 0.0002075 -1.524 0.128
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.678 on 1687 degrees of freedom
Multiple R-squared: 0.04387, Adjusted R-squared: 0.04274
F-statistic: 38.71 on 2 and 1687 DF, p-value: < 2.2e-16Show the Code
# Linear model: income ~ age
lm.fit <- lm(income ~ age, data = TEDS_poly)
# Compare linear vs quadratic (nested models)
anova(lm.fit, lm.fit2)Analysis of Variance Table
Model 1: income ~ age
Model 2: income ~ age + I(age^2)
Res.Df RSS Df Sum of Sq F Pr(>F)
1 1688 12115
2 1687 12098 1 16.653 2.3221 0.1277Show the Code
# Diagnostic plots for quadratic model
par(mfrow = c(2,2))
plot(lm.fit2)
Show the Code
# 5th-degree polynomial model
lm.fit5 <- lm(income ~ poly(age, 5), data = TEDS_poly)
summary(lm.fit5)
Call:
lm(formula = income ~ poly(age, 5), data = TEDS_poly)
Residuals:
Min 1Q Median 3Q Max
-5.0082 -2.1101 0.0696 1.9206 6.0061
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.32367 0.06516 81.698 <2e-16 ***
poly(age, 5)1 -23.20597 2.67880 -8.663 <2e-16 ***
poly(age, 5)2 -4.08076 2.67880 -1.523 0.128
poly(age, 5)3 2.67482 2.67880 0.999 0.318
poly(age, 5)4 1.65380 2.67880 0.617 0.537
poly(age, 5)5 2.01326 2.67880 0.752 0.452
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.679 on 1684 degrees of freedom
Multiple R-squared: 0.04498, Adjusted R-squared: 0.04214
F-statistic: 15.86 on 5 and 1684 DF, p-value: 2.709e-15Show the Code
# Log-transform example (like log(rm) in Boston)
# Here: income ~ log(age)
summary(lm(income ~ log(age), data = TEDS_poly))
Call:
lm(formula = income ~ log(age), data = TEDS_poly)
Residuals:
Min 1Q Median 3Q Max
-5.4546 -2.2035 0.2057 1.7965 5.4716
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.8391 0.6791 15.96 < 2e-16 ***
log(age) -1.4401 0.1765 -8.16 6.49e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.685 on 1688 degrees of freedom
Multiple R-squared: 0.03795, Adjusted R-squared: 0.03738
F-statistic: 66.58 on 1 and 1688 DF, p-value: 6.486e-16Qualitative Predictors
Show the Code
# Make sure dataset is loaded
# library(haven)
# TEDS_2016 <- read_dta("...")
# Remove missing values for variables used
TEDS_int <- subset(TEDS_2016,
!is.na(income) & !is.na(age) &
!is.na(edu) & !is.na(inequality5) &
!is.na(econworse5))
# Convert Edu to factor (like ShelveLoc)
TEDS_int$Edu_f <- as.factor(TEDS_int$Edu)
# Full model with interactions
lm.fit <- lm(income ~ age + edu + inequality5 + econworse5 +
age:inequality5 + edu:econworse5,
data = TEDS_int)
summary(lm.fit)
Call:
lm(formula = income ~ age + edu + inequality5 + econworse5 +
age:inequality5 + edu:econworse5, data = TEDS_int)
Residuals:
Min 1Q Median 3Q Max
-5.6087 -2.1391 0.0931 1.7489 6.3191
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.976064 1.538607 0.634 0.525919
age 0.047037 0.022437 2.096 0.036200 *
edu 0.768491 0.213360 3.602 0.000325 ***
inequality5 0.474062 0.268390 1.766 0.077525 .
econworse5 -0.006731 0.201756 -0.033 0.973388
age:inequality5 -0.009996 0.004949 -2.020 0.043580 *
edu:econworse5 -0.033004 0.055953 -0.590 0.555373
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.573 on 1673 degrees of freedom
Multiple R-squared: 0.1229, Adjusted R-squared: 0.1198
F-statistic: 39.07 on 6 and 1673 DF, p-value: < 2.2e-16Interactive Terms
Show the Code
# Remove missing values for variables used
TEDS_int <- subset(TEDS_2016,
!is.na(income) &
!is.na(age) &
!is.na(inequality5))
summary(lm(income ~ age * inequality5, data = TEDS_int))
Call:
lm(formula = income ~ age * inequality5, data = TEDS_int)
Residuals:
Min 1Q Median 3Q Max
-5.4089 -2.1819 0.1467 1.7899 5.9328
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.930127 1.236825 3.986 7e-05 ***
age 0.013582 0.022670 0.599 0.5492
inequality5 0.462457 0.275073 1.681 0.0929 .
age:inequality5 -0.010654 0.005046 -2.112 0.0349 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.676 on 1686 degrees of freedom
Multiple R-squared: 0.04562, Adjusted R-squared: 0.04392
F-statistic: 26.86 on 3 and 1686 DF, p-value: < 2.2e-16