2020-04-13-In-Class-2-Trellis

讀取資料

library(Ecdat)

## Loading required package: Ecfun

## 
## Attaching package: 'Ecfun'

## The following object is masked from 'package:base':
## 
##     sign

## 
## Attaching package: 'Ecdat'

## The following object is masked from 'package:datasets':
## 
##     Orange

dat <- Ecdat::Caschool

另創造一個ratio變項(師生比)

dta <- subset(dat, dat$grspan=="KK-08")[,c(5,6,16)]
dta$ratio <- round(print(dta$enrltot/dta$teachers),2)

##   [1] 17.88991 21.52466 18.69723 17.35714 18.67133 21.40625 19.50000 20.89412
##   [9] 19.94737 21.23810 21.00000 20.60000 20.00822 18.02778 20.25196 16.97787
##  [17] 16.50980 22.70402 19.91111 18.33333 22.61905 19.44828 25.05263 20.67544
##  [25] 18.68235 19.26667 19.25000 20.54545 20.60697 21.07268 21.53581 19.90400
##  [33] 21.19407 21.86535 18.32965 16.22857 19.17857 20.27737 22.98614 20.44444
##  [41] 19.82085 23.20522 23.30189 21.18829 20.87179 19.01749 21.91938 20.10124
##  [49] 21.47651 20.37509 22.44648 22.89524 20.49797 22.25658 21.56436 19.47737
##  [57] 21.78339 19.14000 18.11050 20.68243 22.62361 21.54545 21.15289 16.63333
##  [65] 21.14438 19.78182 18.98373 17.66767 17.75499 15.27273 14.00000 20.59613
##  [73] 16.31169 21.12796 17.48801 17.88679 19.30676 21.28684 20.19560 24.95000
##  [81] 18.13043 20.00000 18.72951 18.25000 18.99257 19.88764 19.37895 20.46259
##  [89] 22.29157 20.70474 19.06005 20.23247 19.69012 20.36254 19.75422 19.37977
##  [97] 22.92351 19.37340 19.15516 21.30000 18.30357 21.07926 18.79121 19.59016
## [105] 20.87187 21.11500 19.91049 17.81285 18.13333 19.22221 18.66071 19.60000
## [113] 19.28384 22.81818 21.37363 20.02041 21.49861 15.42857 22.40000 20.12709
## [121] 19.03797 17.34216 17.01863 20.80000 21.15385 18.45833 19.40766 19.56896
## [129] 21.50120 17.52941 16.43017 19.79654 17.18613 17.61589 20.12537 22.16667
## [137] 19.96154 19.03945 15.22436 21.14475 19.64390 21.04869 20.17544 21.39130
## [145] 20.00832 17.66667 18.22055 20.27100 20.19895 21.38424 20.97368 20.00000
## [153] 17.15328 18.95714 19.74533 16.42623 16.62540 20.07416 17.99544 19.39130
## [161] 16.42857 24.41345 18.26415 18.95504 21.03896 20.74074 18.10000 19.84615
## [169] 21.60000 22.44242 23.01438 18.28664 19.26544 22.66667 19.29412 17.36364
## [177] 19.82143 20.43378 21.03721 19.92462 19.00986 23.82222 19.36908 19.82857
## [185] 15.25886 17.16129 21.81333 19.07471 25.78512 18.21261 18.16606 16.97297
## [193] 21.50087 20.60000 16.99029 20.77955 19.88506 21.39882 20.49751 19.36376
## [201] 17.65958 21.01796 19.05565 22.53846 21.10787 20.05135 14.20176 18.47687
## [209] 18.63542 20.94595 21.08548 18.69288 20.86808 19.82558 19.75000 19.50000
## [217] 18.39081 18.78676 19.77018 19.33333 23.08492 21.06299 18.68687 20.77024
## [225] 20.13280 20.66963 22.28155 20.60027 20.82734 19.22492 17.65477 16.49773
## [233] 19.78261 22.30216 17.73077 20.44836 20.37169 20.16479 21.61538 20.56143
## [241] 19.95551 21.18387 18.81042 20.57838 18.32461 18.82063 20.81633 20.00000
## [249] 19.68182 19.39018 19.94437 20.79109 19.20354 19.02439 17.62058 19.29374
## [257] 20.33949 17.89130 19.93548 20.14179 23.55637 21.46479 19.19101 20.13080
## [265] 25.80000 18.77774 19.70109 20.99721 20.00000 20.98325 21.64262 20.02967
## [273] 19.81140 18.00000 19.35811 20.17912 21.11986 23.38974 22.18182 19.94283
## [281] 17.78826 20.89195 19.83851 20.68622 18.18182 18.89224 24.88889 18.58065
## [289] 18.04000 21.45455 19.92342 22.54608 18.19743 20.10768 19.15984 19.54545
## [297] 18.39150 19.17990 19.39771 21.67827 19.28889 20.34927 20.96416 19.46039
## [305] 19.28572 20.90021 20.59574 19.95122 18.84973 18.11787 19.18341 22.00000
## [313] 21.58416 18.27778 19.37472 18.90909 16.40693 15.59140 18.70694 18.32985
## [321] 17.90235 18.91156 20.32497 20.02457 24.00000 17.60784 19.34853 19.67847
## [329] 18.72861 15.88235 17.98825 19.23937 19.59906 18.58848 17.65537 17.57976
## [337] 18.75000 18.10241 18.80207 18.77231 20.70707 17.69978 21.48329 19.57567
## [345] 17.34931 16.26228 17.70045 20.12882 14.54214 19.15261 17.36574 15.13899
## [353] 15.40704 18.86534 16.47413 17.86263 21.88586 20.20000 19.03640

將資料分為“L”, “M”,“H”

quantile(dta$readscr, probs=seq(from=0, to=1, by=.1))

##     0%    10%    20%    30%    40%    50%    60%    70%    80%    90%   100% 
## 604.50 628.48 636.66 642.64 650.16 654.90 659.78 663.96 669.56 679.64 704.00

levels <- with(dta, cut(readscr, ordered=T, breaks=c(0, 645, 660, 710), labels=c("L", "M","H")))
dta.1 <- cbind(dta,levels)

繪圖

library(lattice)
xyplot(readscr ~ ratio | levels, data=dta.1, layout=c(3, 1), xlab="Student-Teacher ratio", ylab="Reading score", type=c('p', 'g', 'r'))