Trellis in_exercise2 0413

in_class exercise 2

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':
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##     Orange

library(tibble)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(lattice)
data(Caschool,package="Ecdat")
knitr::kable(head(Caschool))

distcod	county	district	grspan	enrltot	teachers	calwpct	mealpct	computer	testscr	compstu	expnstu	str	avginc	elpct	readscr	mathscr
75119	Alameda	Sunol Glen Unified	KK-08	195	10.90	0.5102	2.0408	67	690.80	0.3435898	6384.911	17.88991	22.690001	0.000000	691.6	690.0
61499	Butte	Manzanita Elementary	KK-08	240	11.15	15.4167	47.9167	101	661.20	0.4208333	5099.381	21.52466	9.824000	4.583334	660.5	661.9
61549	Butte	Thermalito Union Elementary	KK-08	1550	82.90	55.0323	76.3226	169	643.60	0.1090323	5501.955	18.69723	8.978000	30.000002	636.3	650.9
61457	Butte	Golden Feather Union Elementary	KK-08	243	14.00	36.4754	77.0492	85	647.70	0.3497942	7101.831	17.35714	8.978000	0.000000	651.9	643.5
61523	Butte	Palermo Union Elementary	KK-08	1335	71.50	33.1086	78.4270	171	640.85	0.1280899	5235.988	18.67133	9.080333	13.857677	641.8	639.9
62042	Fresno	Burrel Union Elementary	KK-08	137	6.40	12.3188	86.9565	25	605.55	0.1824818	5580.147	21.40625	10.415000	12.408759	605.7	605.4

dta <- as_tibble(Ecdat::Caschool)
dta1 <-dta %>% mutate(STratio=enrltot/teachers,readscr_f = cut(readscr, breaks=quantile(readscr, probs=c(0, .33, .67, 1)), 
                   label=c("Low", "Middle", "High"), ordered=T)) 
lattice::xyplot(readscr ~ STratio | readscr_f, data=dta1, xlab="Student-Teacher ratio", ylab="Reading score", type=c("p", "g", "r"),auto.key=list(columns=2))