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?
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 317UCBAdmissions[,1,]
#> Dept
#> Admit A B C D E F
#> Admitted 512 353 120 138 53 22
#> Rejected 313 207 205 279 138 351此為各系男生之情形。
此為Dept A 的男性狀態。
此為各系admitted的男性人數。
Concerning the chickwts{datasets}, explain the difference between
chickwts[,2]
chickwts[“feed”]
head(chickwts)
#> weight feed
#> 1 179 horsebean
#> 2 160 horsebean
#> 3 136 horsebean
#> 4 227 horsebean
#> 5 217 horsebean
#> 6 168 horsebean
str(chickwts)
#> 'data.frame': 71 obs. of 2 variables:
#> $ weight: num 179 160 136 227 217 168 108 124 143 140 ...
#> $ feed : Factor w/ 6 levels "casein","horsebean",..: 2 2 2 2 2 2 2 2 2 2 ...chickwts[,2]
#> [1] horsebean horsebean horsebean horsebean horsebean horsebean horsebean
#> [8] horsebean horsebean horsebean linseed linseed linseed linseed
#> [15] linseed linseed linseed linseed linseed linseed linseed
#> [22] linseed soybean soybean soybean soybean soybean soybean
#> [29] soybean soybean soybean soybean soybean soybean soybean
#> [36] soybean sunflower sunflower sunflower sunflower sunflower sunflower
#> [43] sunflower sunflower sunflower sunflower sunflower sunflower meatmeal
#> [50] meatmeal meatmeal meatmeal meatmeal meatmeal meatmeal meatmeal
#> [57] meatmeal meatmeal meatmeal casein casein casein casein
#> [64] casein casein casein casein casein casein casein
#> [71] casein
#> Levels: casein horsebean linseed meatmeal soybean sunflowerchickwts["feed"]
#> feed
#> 1 horsebean
#> 2 horsebean
#> 3 horsebean
#> 4 horsebean
#> 5 horsebean
#> 6 horsebean
#> 7 horsebean
#> 8 horsebean
#> 9 horsebean
#> 10 horsebean
#> 11 linseed
#> 12 linseed
#> 13 linseed
#> 14 linseed
#> 15 linseed
#> 16 linseed
#> 17 linseed
#> 18 linseed
#> 19 linseed
#> 20 linseed
#> 21 linseed
#> 22 linseed
#> 23 soybean
#> 24 soybean
#> 25 soybean
#> 26 soybean
#> 27 soybean
#> 28 soybean
#> 29 soybean
#> 30 soybean
#> 31 soybean
#> 32 soybean
#> 33 soybean
#> 34 soybean
#> 35 soybean
#> 36 soybean
#> 37 sunflower
#> 38 sunflower
#> 39 sunflower
#> 40 sunflower
#> 41 sunflower
#> 42 sunflower
#> 43 sunflower
#> 44 sunflower
#> 45 sunflower
#> 46 sunflower
#> 47 sunflower
#> 48 sunflower
#> 49 meatmeal
#> 50 meatmeal
#> 51 meatmeal
#> 52 meatmeal
#> 53 meatmeal
#> 54 meatmeal
#> 55 meatmeal
#> 56 meatmeal
#> 57 meatmeal
#> 58 meatmeal
#> 59 meatmeal
#> 60 casein
#> 61 casein
#> 62 casein
#> 63 casein
#> 64 casein
#> 65 casein
#> 66 casein
#> 67 casein
#> 68 casein
#> 69 casein
#> 70 casein
#> 71 caseinchickwts[,2]
此為 chickwts 第二行(column)的值。
chickwts[“feed”]
此為 chickwts feed 這一行的資料。
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])
It shows:
menarche {MASS}
This data frame contains the following columns:
Age Average age of the group. (The groups are reasonably age homogeneous.)
Total Total number of children in the group.
Menarche Number who have reached menarche.
ls(“package:MASS”)
The result in R was
[1] “abbey” “accdeaths” “addterm”
[4] “Aids2” “Animals” “anorexia”
[7] “area” “as.fractions” “bacteria”
[10] “bandwidth.nrd” “bcv” “beav1”
[13] “beav2” “biopsy” “birthwt”
[16] “Boston” “boxcox” “cabbages”
[19] “caith” “Cars93” “cats”
[22] “cement” “chem” “con2tr”
[25] “contr.sdif” “coop” “corresp”
[28] “cov.mcd” “cov.mve” “cov.rob”
[31] “cov.trob” “cpus” “crabs”
[34] “Cushings” “DDT” “deaths”
[37] “denumerate” “dose.p” “drivers”
[40] “dropterm” “eagles” “enlist”
[43] “epil” “eqscplot” “farms”
[46] “fbeta” “fgl” “fitdistr”
[49] “forbes” “fractions” “frequency.polygon” [52] “GAGurine” “galaxies” “gamma.dispersion” [55] “gamma.shape” “gehan” “genotype”
[58] “geyser” “gilgais” “ginv”
[61] “glm.convert” “glm.nb” “glmmPQL”
[64] “hills” “hist.FD” “hist.scott”
[67] “housing” “huber” “hubers”
[70] “immer” “Insurance” “is.fractions”
[73] “isoMDS” “kde2d” “lda”
[76] “ldahist” “leuk” “lm.gls”
[79] “lm.ridge” “lmsreg” “lmwork”
[82] “loglm” “loglm1” “logtrans”
[85] “lqs” “lqs.formula” “ltsreg”
[88] “mammals” “mca” “mcycle”
[91] “Melanoma” “menarche” “michelson”
[94] “minn38” “motors” “muscle”
[97] “mvrnorm” “nclass.freq” “neg.bin”
[100] “negative.binomial” “negexp.SSival” “newcomb”
[103] “nlschools” “npk” “npr1”
[106] “Null” “oats” “OME”
[109] “painters” “parcoord” “petrol”
[112] “phones” “Pima.te” “Pima.tr”
[115] “Pima.tr2” “polr” “psi.bisquare”
[118] “psi.hampel” “psi.huber” “qda”
[121] “quine” “Rabbit” “rational”
[124] “renumerate” “rlm” “rms.curv”
[127] “rnegbin” “road” “rotifer”
[130] “Rubber” “sammon” “select”
[133] “Shepard” “ships” “shoes”
[136] “shrimp” “shuttle” “Sitka”
[139] “Sitka89” “Skye” “snails”
[142] “SP500” “stdres” “steam”
[145] “stepAIC” “stormer” “studres”
[148] “survey” “synth.te” “synth.tr”
[151] “theta.md” “theta.ml” “theta.mm”
[154] “topo” “Traffic” “truehist”
[157] “ucv” “UScereal” “UScrime”
[160] “VA” “waders” “whiteside”
[163] “width.SJ” “write.matrix” “wtloss”
There are 165 items in the package MASS.