DataM L3 in-class exercise 1-5
Ching-Fang Wu
2021-10-20
Q1
The math attainment page has a dataset and a script of R code chunks. Generate a markdown file from the script to push the output in HTML for posting to course Moodle site.
input data
#first R session using math attainment data set
# read in a plain text file with variable names and assign a name to it
dta <- read.table("C:/Users/Ching-Fang Wu/Documents/dataM/math_attainment.txt", header = T)checking data
# structure of data
str(dta)## 'data.frame': 39 obs. of 3 variables:
## $ math2: int 28 56 51 13 39 41 30 13 17 32 ...
## $ math1: int 18 22 44 8 20 12 16 5 9 18 ...
## $ cc : num 328 406 387 167 328 ...math attainment的資料結構為data.frame，稱作資料框架。
#查看資料
View(dta) 用View()看資料框架，它是由直排的行(column)所組成，每一行代表一個變數。
# first 6 rows
head(dta)## math2 math1 cc
## 1 28 18 328.20
## 2 56 22 406.03
## 3 51 44 386.94
## 4 13 8 166.91
## 5 39 20 328.20
## 6 41 12 328.20descriptive statistics
# variable mean
colMeans(dta) #colMeans顧名思義是行平均## math2 math1 cc
## 28.76923 15.35897 188.83667#試試看rowMeans(列平均)
rowMeans(dta)## [1] 124.73333 161.34333 160.64667 62.63667 129.06667 127.06667 68.76000
## [8] 37.64667 41.40000 127.20667 126.73333 89.12000 79.45333 81.12000
## [15] 84.03667 56.08333 54.41667 90.51333 78.70333 24.24000 54.02667
## [22] 63.46333 37.75333 53.94000 85.14333 51.60667 64.75000 59.71667
## [29] 47.15667 57.60667 63.22333 79.47667 62.71667 74.38000 74.14333
## [36] 84.47667 81.89333 72.87000 55.27333# variable sd
#apply sd to dta
apply(dta, 2, sd) #2是column；1是row## math2 math1 cc
## 10.720029 7.744224 84.842513# correlation matrix 相關係數矩陣
cor(dta) ## math2 math1 cc
## math2 1.0000000 0.7443604 0.6570098
## math1 0.7443604 1.0000000 0.5956771
## cc 0.6570098 0.5956771 1.0000000plot data
# specify square plot region
par(pty="s")
#pty是指plot type繪圖類型或繪圖區域，"s"或"m"# scatter plot of math2 by math1
plot(math2 ~ math1, data=dta, xlim=c(0, 60), ylim=c(0, 60),xlab="Math score at Year 1", ylab="Math score at Year 2")
# add grid lines
grid() #增加輔助線
plot()是用來畫散布圖，表達方式是plot(Y~X)，或plot(x=X軸的值,y=Y軸的值)。其他設定：xlim=x軸範圍,ylim=y軸範圍,main=“圖片名稱”,xlab=“X軸名稱”,ylab=“Y軸名稱”
regression analysis
# regress math2 by math1
dta.lm <- lm(math2 ~ math1, data=dta)# show results
summary(dta.lm)##
## Call:
## lm(formula = math2 ~ math1, data = dta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.430 -5.521 -0.369 4.253 20.388
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.944 2.607 4.965 1.57e-05 ***
## math1 1.030 0.152 6.780 5.57e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.255 on 37 degrees of freedom
## Multiple R-squared: 0.5541, Adjusted R-squared: 0.542
## F-statistic: 45.97 on 1 and 37 DF, p-value: 5.571e-08截距項=12.94,斜率為1.03
# show anova table
anova(dta.lm)## Analysis of Variance Table
##
## Response: math2
## Df Sum Sq Mean Sq F value Pr(>F)
## math1 1 2419.6 2419.59 45.973 5.571e-08 ***
## Residuals 37 1947.3 52.63
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# 畫迴歸的趨勢線
plot(math2 ~ math1, data=dta, xlim=c(0, 60), ylim=c(0, 60),xlab="Math score at Year 1", ylab="Math score at Year 2")+abline(dta.lm,lty=2)+title("Mathematics Attainment")
## integer(0)#lty是線的類型(0=blank, 1=solid (default), 2=dashed, 3=dotted, 4=dotdash, 5=longdash, 6=twodash)
#lwd是線的粗細
#title("Mathematics Attainment")是add plot title整理：plot()畫散布圖後，可以在畫布上用其他函式增添其他訊息，如points()=畫上點；legend()=作上標記；abline()=畫上線
diagnostics
# specify maximum plot region
par(pty="m")#
plot(scale(resid(dta.lm)) ~ fitted(dta.lm),
ylim=c(-3.5, 3.5), type="n",
xlab="Fitted values", ylab="Standardized residuals")
#Add Text to a Plot
text(fitted(dta.lm), scale(resid(dta.lm)), labels=rownames(dta), cex=0.5)
#加上輔助線
grid()
# add a horizontal red dash line
abline(h=0, lty=2, col="red")
normality check
#
qqnorm(scale(resid(dta.lm)))
qqline(scale(resid(dta.lm)))
grid()
Q2
The notation, women{datasets}, indicates that a data object by the name women is in the datasets package. This package is preloaded when R is invoked. Explain the difference between c(women) and c(as.matrix(women)) using the women{datasets}.
women## height weight
## 1 58 115
## 2 59 117
## 3 60 120
## 4 61 123
## 5 62 126
## 6 63 129
## 7 64 132
## 8 65 135
## 9 66 139
## 10 67 142
## 11 68 146
## 12 69 150
## 13 70 154
## 14 71 159
## 15 72 164women資料集裡面有15位女性的身高、體重的數據
str(women)## 'data.frame': 15 obs. of 2 variables:
## $ height: num 58 59 60 61 62 63 64 65 66 67 ...
## $ weight: num 115 117 120 123 126 129 132 135 139 142 ...women的資料結構(Structure)是data.frame
x<-c(women)
str(x)## List of 2
## $ height: num [1:15] 58 59 60 61 62 63 64 65 66 67 ...
## $ weight: num [1:15] 115 117 120 123 126 129 132 135 139 142 ...c(women)把data frame變成list
y<-as.matrix(women)women原為data.frame，透過as.matrix()可轉換為matrix資料結構
str(y)## num [1:15, 1:2] 58 59 60 61 62 63 64 65 66 67 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:2] "height" "weight"is.matrix(y)## [1] TRUEz<-c(as.matrix(women)) is.vector(z)## [1] TRUE結論： women的資料結構為data.frame，c(women)的資料結構為list。 as.matrix(women)的資料結構為matrix，c(as.matrix(women))的資料結構為vector。
疑問1：所以c()會讓資料結構”升級”? 疑問2：Matrices和Data frames都有row、column，但兩者究竟有何不同?
Q3
Use help to examine the coding scheme for the mother’s race variable in the birthwt{MASS} dataset.
The MASS comes with the base R installation but is not automatically loaded when R is invoked.
How many black mothers are there in this data frame?
What does the following R command do? > c(“White”, “Black”, “Other”)[birthwt$race]
#用data這個指令，從套件MASS中載入birthwt這個資料集
data(birthwt,package = "MASS")head(birthwt)## low age lwt race smoke ptl ht ui ftv bwt
## 85 0 19 182 2 0 0 0 1 0 2523
## 86 0 33 155 3 0 0 0 0 3 2551
## 87 0 20 105 1 1 0 0 0 1 2557
## 88 0 21 108 1 1 0 0 1 2 2594
## 89 0 18 107 1 1 0 0 1 0 2600
## 91 0 21 124 3 0 0 0 0 0 2622str(birthwt)## 'data.frame': 189 obs. of 10 variables:
## $ low : int 0 0 0 0 0 0 0 0 0 0 ...
## $ age : int 19 33 20 21 18 21 22 17 29 26 ...
## $ lwt : int 182 155 105 108 107 124 118 103 123 113 ...
## $ race : int 2 3 1 1 1 3 1 3 1 1 ...
## $ smoke: int 0 0 1 1 1 0 0 0 1 1 ...
## $ ptl : int 0 0 0 0 0 0 0 0 0 0 ...
## $ ht : int 0 0 0 0 0 0 0 0 0 0 ...
## $ ui : int 1 0 0 1 1 0 0 0 0 0 ...
## $ ftv : int 0 3 1 2 0 0 1 1 1 0 ...
## $ bwt : int 2523 2551 2557 2594 2600 2622 2637 2637 2663 2665 ...# 從dataframe中取出race這個vector
birthwt$race## [1] 2 3 1 1 1 3 1 3 1 1 3 3 3 3 1 1 2 1 3 1 3 1 1 3 3 1 1 1 2 2 2 1 2 1 2 1 1
## [38] 1 1 1 2 1 2 1 1 1 1 3 1 3 1 3 1 1 3 3 3 3 3 3 3 3 3 1 3 3 3 3 1 2 1 3 3 2
## [75] 1 2 1 1 2 1 1 1 3 3 3 3 3 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 3 1 3 2 1 1 1 2 1
## [112] 3 1 1 1 3 1 3 1 3 1 3 1 1 1 1 1 1 1 1 3 1 2 3 3 3 3 2 3 1 1 1 3 3 1 1 2 1
## [149] 3 3 3 1 1 1 1 3 2 1 2 3 1 3 3 3 2 1 3 3 1 1 2 2 2 3 3 1 1 1 1 2 3 3 1 3 1
## [186] 3 3 2 1race是數值(num)，不知道各自代表甚麼人種。
#
c("White", "Black", "Other")[birthwt$race]## [1] "Black" "Other" "White" "White" "White" "Other" "White" "Other" "White"
## [10] "White" "Other" "Other" "Other" "Other" "White" "White" "Black" "White"
## [19] "Other" "White" "Other" "White" "White" "Other" "Other" "White" "White"
## [28] "White" "Black" "Black" "Black" "White" "Black" "White" "Black" "White"
## [37] "White" "White" "White" "White" "Black" "White" "Black" "White" "White"
## [46] "White" "White" "Other" "White" "Other" "White" "Other" "White" "White"
## [55] "Other" "Other" "Other" "Other" "Other" "Other" "Other" "Other" "Other"
## [64] "White" "Other" "Other" "Other" "Other" "White" "Black" "White" "Other"
## [73] "Other" "Black" "White" "Black" "White" "White" "Black" "White" "White"
## [82] "White" "Other" "Other" "Other" "Other" "Other" "White" "White" "White"
## [91] "White" "Other" "White" "White" "White" "White" "White" "White" "White"
## [100] "White" "White" "White" "Other" "White" "Other" "Black" "White" "White"
## [109] "White" "Black" "White" "Other" "White" "White" "White" "Other" "White"
## [118] "Other" "White" "Other" "White" "Other" "White" "White" "White" "White"
## [127] "White" "White" "White" "White" "Other" "White" "Black" "Other" "Other"
## [136] "Other" "Other" "Black" "Other" "White" "White" "White" "Other" "Other"
## [145] "White" "White" "Black" "White" "Other" "Other" "Other" "White" "White"
## [154] "White" "White" "Other" "Black" "White" "Black" "Other" "White" "Other"
## [163] "Other" "Other" "Black" "White" "Other" "Other" "White" "White" "Black"
## [172] "Black" "Black" "Other" "Other" "White" "White" "White" "White" "Black"
## [181] "Other" "Other" "White" "Other" "White" "Other" "Other" "Black" "White"c(“White”, “Black”,“Other”)是將”White”編碼為1,“Black”=2,“Other”=3。
#table()函數可知道向量中每個值出現幾次
table(birthwt$race)##
## 1 2 3
## 96 26 672=Black，有26個。
Q4
Regarding UCBAdmissions{datasets} data object, what does the output > UCBAdmissions[,1,] > UCBAdmissions[,1,1] > UCBAdmissions[1,1,]
of each of the above R statements mean, respectively?
data(UCBAdmissions,package = "datasets")UCBAdmissions## , , Dept = A
##
## Gender
## Admit Male Female
## Admitted 512 89
## Rejected 313 19
##
## , , Dept = B
##
## Gender
## Admit Male Female
## Admitted 353 17
## Rejected 207 8
##
## , , Dept = C
##
## Gender
## Admit Male Female
## Admitted 120 202
## Rejected 205 391
##
## , , Dept = D
##
## Gender
## Admit Male Female
## Admitted 138 131
## Rejected 279 244
##
## , , Dept = E
##
## Gender
## Admit Male Female
## Admitted 53 94
## Rejected 138 299
##
## , , Dept = F
##
## Gender
## Admit Male Female
## Admitted 22 24
## Rejected 351 317看起來很像多重列聯表，呈現在不同科系A~F，“錄取、不錄取”vs”男性、女性”之間的人數。
str(UCBAdmissions)## 'table' num [1:2, 1:2, 1:6] 512 313 89 19 353 207 17 8 120 205 ...
## - attr(*, "dimnames")=List of 3
## ..$ Admit : chr [1:2] "Admitted" "Rejected"
## ..$ Gender: chr [1:2] "Male" "Female"
## ..$ Dept : chr [1:6] "A" "B" "C" "D" ...UCBAdmissions的資料結構是table，有3個list，依序是Admit、Gender、Dept。
UCBAdmissions[Admit,Gender,Dept]
#各系所錄取之男女生人數
UCBAdmissions[1,,] #1是Admitted、2是Rejected## Dept
## Gender A B C D E F
## Male 512 353 120 138 53 22
## Female 89 17 202 131 94 24#各系所男生錄取與不錄取人數
UCBAdmissions[,1,] #1是男性、2是女性## Dept
## Admit A B C D E F
## Admitted 512 353 120 138 53 22
## Rejected 313 207 205 279 138 351#Dept = A，男性，錄取與不錄取的人數
UCBAdmissions[,1,1] #[, 1=男性, 1=Dept=A]## Admitted Rejected
## 512 313#各系所男性錄取人數
UCBAdmissions[1,1,]#[1=錄取,1=男性,]## A B C D E F
## 512 353 120 138 53 22Q5
What happens when the following command is entered? > help(ls(“package:MASS”)[92])
Use the observation to find out how many items there are in the package MASS.
#help(ls("package:MASS")[92])
#length(ls('package:MASS'))
data(package = "MASS") #會跳出一個視窗列出MASS中所有的datasets，但是不會下載到電腦中出現錯誤訊息 Error in help(ls(“package:MASS”)[92]) : ‘topic’ should be a name, length-one character vector or reserved word。
ls(data(package = "MASS"))## [1] "footer" "header" "results" "title"data(package = "MASS")$result #發現這樣可以列出package中所有的datasets的Package LibPath、Item、Title## Package LibPath Item
## [1,] "MASS" "C:/Program Files/R/R-4.1.1/library" "Aids2"
## [2,] "MASS" "C:/Program Files/R/R-4.1.1/library" "Animals"
## [3,] "MASS" "C:/Program Files/R/R-4.1.1/library" "Boston"
## [4,] "MASS" "C:/Program Files/R/R-4.1.1/library" "Cars93"
## [5,] "MASS" "C:/Program Files/R/R-4.1.1/library" "Cushings"
## [6,] "MASS" "C:/Program Files/R/R-4.1.1/library" "DDT"
## [7,] "MASS" "C:/Program Files/R/R-4.1.1/library" "GAGurine"
## [8,] "MASS" "C:/Program Files/R/R-4.1.1/library" "Insurance"
## [9,] "MASS" "C:/Program Files/R/R-4.1.1/library" "Melanoma"
## [10,] "MASS" "C:/Program Files/R/R-4.1.1/library" "OME"
## [11,] "MASS" "C:/Program Files/R/R-4.1.1/library" "Pima.te"
## [12,] "MASS" "C:/Program Files/R/R-4.1.1/library" "Pima.tr"
## [13,] "MASS" "C:/Program Files/R/R-4.1.1/library" "Pima.tr2"
## [14,] "MASS" "C:/Program Files/R/R-4.1.1/library" "Rabbit"
## [15,] "MASS" "C:/Program Files/R/R-4.1.1/library" "Rubber"
## [16,] "MASS" "C:/Program Files/R/R-4.1.1/library" "SP500"
## [17,] "MASS" "C:/Program Files/R/R-4.1.1/library" "Sitka"
## [18,] "MASS" "C:/Program Files/R/R-4.1.1/library" "Sitka89"
## [19,] "MASS" "C:/Program Files/R/R-4.1.1/library" "Skye"
## [20,] "MASS" "C:/Program Files/R/R-4.1.1/library" "Traffic"
## [21,] "MASS" "C:/Program Files/R/R-4.1.1/library" "UScereal"
## [22,] "MASS" "C:/Program Files/R/R-4.1.1/library" "UScrime"
## [23,] "MASS" "C:/Program Files/R/R-4.1.1/library" "VA"
## [24,] "MASS" "C:/Program Files/R/R-4.1.1/library" "abbey"
## [25,] "MASS" "C:/Program Files/R/R-4.1.1/library" "accdeaths"
## [26,] "MASS" "C:/Program Files/R/R-4.1.1/library" "anorexia"
## [27,] "MASS" "C:/Program Files/R/R-4.1.1/library" "bacteria"
## [28,] "MASS" "C:/Program Files/R/R-4.1.1/library" "beav1"
## [29,] "MASS" "C:/Program Files/R/R-4.1.1/library" "beav2"
## [30,] "MASS" "C:/Program Files/R/R-4.1.1/library" "biopsy"
## [31,] "MASS" "C:/Program Files/R/R-4.1.1/library" "birthwt"
## [32,] "MASS" "C:/Program Files/R/R-4.1.1/library" "cabbages"
## [33,] "MASS" "C:/Program Files/R/R-4.1.1/library" "caith"
## [34,] "MASS" "C:/Program Files/R/R-4.1.1/library" "cats"
## [35,] "MASS" "C:/Program Files/R/R-4.1.1/library" "cement"
## [36,] "MASS" "C:/Program Files/R/R-4.1.1/library" "chem"
## [37,] "MASS" "C:/Program Files/R/R-4.1.1/library" "coop"
## [38,] "MASS" "C:/Program Files/R/R-4.1.1/library" "cpus"
## [39,] "MASS" "C:/Program Files/R/R-4.1.1/library" "crabs"
## [40,] "MASS" "C:/Program Files/R/R-4.1.1/library" "deaths"
## [41,] "MASS" "C:/Program Files/R/R-4.1.1/library" "drivers"
## [42,] "MASS" "C:/Program Files/R/R-4.1.1/library" "eagles"
## [43,] "MASS" "C:/Program Files/R/R-4.1.1/library" "epil"
## [44,] "MASS" "C:/Program Files/R/R-4.1.1/library" "farms"
## [45,] "MASS" "C:/Program Files/R/R-4.1.1/library" "fgl"
## [46,] "MASS" "C:/Program Files/R/R-4.1.1/library" "forbes"
## [47,] "MASS" "C:/Program Files/R/R-4.1.1/library" "galaxies"
## [48,] "MASS" "C:/Program Files/R/R-4.1.1/library" "gehan"
## [49,] "MASS" "C:/Program Files/R/R-4.1.1/library" "genotype"
## [50,] "MASS" "C:/Program Files/R/R-4.1.1/library" "geyser"
## [51,] "MASS" "C:/Program Files/R/R-4.1.1/library" "gilgais"
## [52,] "MASS" "C:/Program Files/R/R-4.1.1/library" "hills"
## [53,] "MASS" "C:/Program Files/R/R-4.1.1/library" "housing"
## [54,] "MASS" "C:/Program Files/R/R-4.1.1/library" "immer"
## [55,] "MASS" "C:/Program Files/R/R-4.1.1/library" "leuk"
## [56,] "MASS" "C:/Program Files/R/R-4.1.1/library" "mammals"
## [57,] "MASS" "C:/Program Files/R/R-4.1.1/library" "mcycle"
## [58,] "MASS" "C:/Program Files/R/R-4.1.1/library" "menarche"
## [59,] "MASS" "C:/Program Files/R/R-4.1.1/library" "michelson"
## [60,] "MASS" "C:/Program Files/R/R-4.1.1/library" "minn38"
## [61,] "MASS" "C:/Program Files/R/R-4.1.1/library" "motors"
## [62,] "MASS" "C:/Program Files/R/R-4.1.1/library" "muscle"
## [63,] "MASS" "C:/Program Files/R/R-4.1.1/library" "newcomb"
## [64,] "MASS" "C:/Program Files/R/R-4.1.1/library" "nlschools"
## [65,] "MASS" "C:/Program Files/R/R-4.1.1/library" "npk"
## [66,] "MASS" "C:/Program Files/R/R-4.1.1/library" "npr1"
## [67,] "MASS" "C:/Program Files/R/R-4.1.1/library" "oats"
## [68,] "MASS" "C:/Program Files/R/R-4.1.1/library" "painters"
## [69,] "MASS" "C:/Program Files/R/R-4.1.1/library" "petrol"
## [70,] "MASS" "C:/Program Files/R/R-4.1.1/library" "phones"
## [71,] "MASS" "C:/Program Files/R/R-4.1.1/library" "quine"
## [72,] "MASS" "C:/Program Files/R/R-4.1.1/library" "road"
## [73,] "MASS" "C:/Program Files/R/R-4.1.1/library" "rotifer"
## [74,] "MASS" "C:/Program Files/R/R-4.1.1/library" "ships"
## [75,] "MASS" "C:/Program Files/R/R-4.1.1/library" "shoes"
## [76,] "MASS" "C:/Program Files/R/R-4.1.1/library" "shrimp"
## [77,] "MASS" "C:/Program Files/R/R-4.1.1/library" "shuttle"
## [78,] "MASS" "C:/Program Files/R/R-4.1.1/library" "snails"
## [79,] "MASS" "C:/Program Files/R/R-4.1.1/library" "steam"
## [80,] "MASS" "C:/Program Files/R/R-4.1.1/library" "stormer"
## [81,] "MASS" "C:/Program Files/R/R-4.1.1/library" "survey"
## [82,] "MASS" "C:/Program Files/R/R-4.1.1/library" "synth.te"
## [83,] "MASS" "C:/Program Files/R/R-4.1.1/library" "synth.tr"
## [84,] "MASS" "C:/Program Files/R/R-4.1.1/library" "topo"
## [85,] "MASS" "C:/Program Files/R/R-4.1.1/library" "waders"
## [86,] "MASS" "C:/Program Files/R/R-4.1.1/library" "whiteside"
## [87,] "MASS" "C:/Program Files/R/R-4.1.1/library" "wtloss"
## Title
## [1,] "Australian AIDS Survival Data"
## [2,] "Brain and Body Weights for 28 Species"
## [3,] "Housing Values in Suburbs of Boston"
## [4,] "Data from 93 Cars on Sale in the USA in 1993"
## [5,] "Diagnostic Tests on Patients with Cushing's Syndrome"
## [6,] "DDT in Kale"
## [7,] "Level of GAG in Urine of Children"
## [8,] "Numbers of Car Insurance claims"
## [9,] "Survival from Malignant Melanoma"
## [10,] "Tests of Auditory Perception in Children with OME"
## [11,] "Diabetes in Pima Indian Women"
## [12,] "Diabetes in Pima Indian Women"
## [13,] "Diabetes in Pima Indian Women"
## [14,] "Blood Pressure in Rabbits"
## [15,] "Accelerated Testing of Tyre Rubber"
## [16,] "Returns of the Standard and Poors 500"
## [17,] "Growth Curves for Sitka Spruce Trees in 1988"
## [18,] "Growth Curves for Sitka Spruce Trees in 1989"
## [19,] "AFM Compositions of Aphyric Skye Lavas"
## [20,] "Effect of Swedish Speed Limits on Accidents"
## [21,] "Nutritional and Marketing Information on US Cereals"
## [22,] "The Effect of Punishment Regimes on Crime Rates"
## [23,] "Veteran's Administration Lung Cancer Trial"
## [24,] "Determinations of Nickel Content"
## [25,] "Accidental Deaths in the US 1973-1978"
## [26,] "Anorexia Data on Weight Change"
## [27,] "Presence of Bacteria after Drug Treatments"
## [28,] "Body Temperature Series of Beaver 1"
## [29,] "Body Temperature Series of Beaver 2"
## [30,] "Biopsy Data on Breast Cancer Patients"
## [31,] "Risk Factors Associated with Low Infant Birth Weight"
## [32,] "Data from a cabbage field trial"
## [33,] "Colours of Eyes and Hair of People in Caithness"
## [34,] "Anatomical Data from Domestic Cats"
## [35,] "Heat Evolved by Setting Cements"
## [36,] "Copper in Wholemeal Flour"
## [37,] "Co-operative Trial in Analytical Chemistry"
## [38,] "Performance of Computer CPUs"
## [39,] "Morphological Measurements on Leptograpsus Crabs"
## [40,] "Monthly Deaths from Lung Diseases in the UK"
## [41,] "Deaths of Car Drivers in Great Britain 1969-84"
## [42,] "Foraging Ecology of Bald Eagles"
## [43,] "Seizure Counts for Epileptics"
## [44,] "Ecological Factors in Farm Management"
## [45,] "Measurements of Forensic Glass Fragments"
## [46,] "Forbes' Data on Boiling Points in the Alps"
## [47,] "Velocities for 82 Galaxies"
## [48,] "Remission Times of Leukaemia Patients"
## [49,] "Rat Genotype Data"
## [50,] "Old Faithful Geyser Data"
## [51,] "Line Transect of Soil in Gilgai Territory"
## [52,] "Record Times in Scottish Hill Races"
## [53,] "Frequency Table from a Copenhagen Housing Conditions Survey"
## [54,] "Yields from a Barley Field Trial"
## [55,] "Survival Times and White Blood Counts for Leukaemia Patients"
## [56,] "Brain and Body Weights for 62 Species of Land Mammals"
## [57,] "Data from a Simulated Motorcycle Accident"
## [58,] "Age of Menarche in Warsaw"
## [59,] "Michelson's Speed of Light Data"
## [60,] "Minnesota High School Graduates of 1938"
## [61,] "Accelerated Life Testing of Motorettes"
## [62,] "Effect of Calcium Chloride on Muscle Contraction in Rat Hearts"
## [63,] "Newcomb's Measurements of the Passage Time of Light"
## [64,] "Eighth-Grade Pupils in the Netherlands"
## [65,] "Classical N, P, K Factorial Experiment"
## [66,] "US Naval Petroleum Reserve No. 1 data"
## [67,] "Data from an Oats Field Trial"
## [68,] "The Painter's Data of de Piles"
## [69,] "N. L. Prater's Petrol Refinery Data"
## [70,] "Belgium Phone Calls 1950-1973"
## [71,] "Absenteeism from School in Rural New South Wales"
## [72,] "Road Accident Deaths in US States"
## [73,] "Numbers of Rotifers by Fluid Density"
## [74,] "Ships Damage Data"
## [75,] "Shoe wear data of Box, Hunter and Hunter"
## [76,] "Percentage of Shrimp in Shrimp Cocktail"
## [77,] "Space Shuttle Autolander Problem"
## [78,] "Snail Mortality Data"
## [79,] "The Saturated Steam Pressure Data"
## [80,] "The Stormer Viscometer Data"
## [81,] "Student Survey Data"
## [82,] "Synthetic Classification Problem"
## [83,] "Synthetic Classification Problem"
## [84,] "Spatial Topographic Data"
## [85,] "Counts of Waders at 15 Sites in South Africa"
## [86,] "House Insulation: Whiteside's Data"
## [87,] "Weight Loss Data from an Obese Patient"所以package”MASS”中總計有87個items。