0330 HW 1-3
HW1
Select at random one school per county in the data set Caschool{Ecdat} and draw a scatter diagram of average math score mathscr against average reading score readscr for the sampled data set. Make sure your results are reproducible (e.g., the same random sample will be drawn each time).
load the data and check data structure, display first 6 rows of Vocab
## Warning: package 'Ecdat' was built under R version 3.6.3## Warning: package 'Ecfun' was built under R version 3.6.3## distcod county district grspan enrltot teachers
## 1 75119 Alameda Sunol Glen Unified KK-08 195 10.90
## 2 61499 Butte Manzanita Elementary KK-08 240 11.15
## 3 61549 Butte Thermalito Union Elementary KK-08 1550 82.90
## 4 61457 Butte Golden Feather Union Elementary KK-08 243 14.00
## 5 61523 Butte Palermo Union Elementary KK-08 1335 71.50
## 6 62042 Fresno Burrel Union Elementary KK-08 137 6.40
## calwpct mealpct computer testscr compstu expnstu str avginc
## 1 0.5102 2.0408 67 690.80 0.3435898 6384.911 17.88991 22.690001
## 2 15.4167 47.9167 101 661.20 0.4208333 5099.381 21.52466 9.824000
## 3 55.0323 76.3226 169 643.60 0.1090323 5501.955 18.69723 8.978000
## 4 36.4754 77.0492 85 647.70 0.3497942 7101.831 17.35714 8.978000
## 5 33.1086 78.4270 171 640.85 0.1280899 5235.988 18.67133 9.080333
## 6 12.3188 86.9565 25 605.55 0.1824818 5580.147 21.40625 10.415000
## elpct readscr mathscr
## 1 0.000000 691.6 690.0
## 2 4.583333 660.5 661.9
## 3 30.000002 636.3 650.9
## 4 0.000000 651.9 643.5
## 5 13.857677 641.8 639.9
## 6 12.408759 605.7 605.4## 'data.frame': 420 obs. of 17 variables:
## $ distcod : int 75119 61499 61549 61457 61523 62042 68536 63834 62331 67306 ...
## $ county : Factor w/ 45 levels "Alameda","Butte",..: 1 2 2 2 2 6 29 11 6 25 ...
## $ district: Factor w/ 409 levels "Ackerman Elementary",..: 362 214 367 132 270 53 152 383 263 94 ...
## $ grspan : Factor w/ 2 levels "KK-06","KK-08": 2 2 2 2 2 2 2 2 2 1 ...
## $ enrltot : int 195 240 1550 243 1335 137 195 888 379 2247 ...
## $ teachers: num 10.9 11.1 82.9 14 71.5 ...
## $ calwpct : num 0.51 15.42 55.03 36.48 33.11 ...
## $ mealpct : num 2.04 47.92 76.32 77.05 78.43 ...
## $ computer: int 67 101 169 85 171 25 28 66 35 0 ...
## $ testscr : num 691 661 644 648 641 ...
## $ compstu : num 0.344 0.421 0.109 0.35 0.128 ...
## $ expnstu : num 6385 5099 5502 7102 5236 ...
## $ str : num 17.9 21.5 18.7 17.4 18.7 ...
## $ avginc : num 22.69 9.82 8.98 8.98 9.08 ...
## $ elpct : num 0 4.58 30 0 13.86 ...
## $ readscr : num 692 660 636 652 642 ...
## $ mathscr : num 690 662 651 644 640 ...sample by strata
remain those rows are concistent with smaple_dta
## Warning: package 'dplyr' was built under R version 3.6.3remain those rows are concistent with smaple_dta
## distcod county district grspan enrltot teachers
## 1 75119 Alameda Sunol Glen Unified KK-08 195 10.90
## 2 61457 Butte Golden Feather Union Elementary KK-08 243 14.00
## 3 68379 San Diego San Ysidro Elementary KK-08 4142 201.00
## 4 65870 Merced Winton Elementary KK-08 1657 90.40
## 5 69369 Santa Clara Alum Rock Union Elementary KK-08 16244 766.65
## 6 70409 Siskiyou McCloud Union Elementary KK-08 175 12.50
## calwpct mealpct computer testscr compstu expnstu str avginc
## 1 0.5102 2.0408 67 690.80 0.34358975 6384.911 17.88991 22.69000
## 2 36.4754 77.0492 85 647.70 0.34979424 7101.831 17.35714 8.97800
## 3 35.5625 81.5065 569 624.55 0.13737325 5342.233 20.60697 6.61300
## 4 28.8473 84.7314 204 626.90 0.12311406 5225.719 18.32965 9.59800
## 5 17.4531 69.7399 1423 630.35 0.08760157 5645.496 21.18829 12.58158
## 6 21.7647 68.8235 31 635.60 0.17714286 6653.031 14.00000 10.65600
## elpct readscr mathscr
## 1 0.00000 691.6 690.0
## 2 0.00000 651.9 643.5
## 3 80.42009 620.4 628.7
## 4 56.12553 623.6 630.2
## 5 49.86457 629.5 631.2
## 6 0.00000 634.6 636.6scatter plot
HW 2
Find 133 class-level 95%-confidence intervals for language test score means of the nlschools{MASS} data set by using the tidy approach. The tail end of the data object should looks as follows:
load data and check data structure
## lang IQ class GS SES COMB
## 1 46 15.0 180 29 23 0
## 2 45 14.5 180 29 10 0
## 3 33 9.5 180 29 15 0
## 4 46 11.0 180 29 23 0
## 5 20 8.0 180 29 10 0
## 6 30 9.5 180 29 10 0tidy approach
dta1<-dta%>%group_by(class)%>%
dplyr::summarize(n=n(), # using dplyr:: to avoid conflict
IQ_mean=mean(IQ),
language_mean=mean(lang),
language_se=sd(lang)/sqrt(n),
language_lb=language_mean-1.96*language_se,
language_ub=language_mean+1.96*language_se) %>%
mutate(classID= 1:length(levels(dta$class))) %>%
select(classID, IQ_mean, language_mean, language_lb, language_ub)
tail(dta1)## # A tibble: 6 x 5
## classID IQ_mean language_mean language_lb language_ub
## <int> <dbl> <dbl> <dbl> <dbl>
## 1 128 12.8 45.5 41.1 49.9
## 2 129 12.2 41.5 39.0 44.0
## 3 130 11.9 40.3 35.2 45.3
## 4 131 11.3 38.1 34.7 41.4
## 5 132 10.6 29.3 21.1 37.5
## 6 133 10.6 28.4 23.3 33.61.group by class.
2.calculate the means, 95%CI.
3.create new variable “classID”.
4.select variables to save in dta1.
5.display last 6 rows of dta1.
HW3
Use the Prestige{car} data set for this problem. Find the median prestige score for each of the three types of occupation, respectively. Use the median score in each type of occupation to define two levels of prestige: High and low, for each occupation, respectively. Summarize the relationship between income and education for each category generated from crossing the factor prestige with the type of occupation.
load data and check data structure
## 'data.frame': 102 obs. of 6 variables:
## $ education: num 13.1 12.3 12.8 11.4 14.6 ...
## $ income : int 12351 25879 9271 8865 8403 11030 8258 14163 11377 11023 ...
## $ women : num 11.16 4.02 15.7 9.11 11.68 ...
## $ prestige : num 68.8 69.1 63.4 56.8 73.5 77.6 72.6 78.1 73.1 68.8 ...
## $ census : int 1113 1130 1171 1175 2111 2113 2133 2141 2143 2153 ...
## $ type : Factor w/ 3 levels "bc","prof","wc": 2 2 2 2 2 2 2 2 2 2 ...## education income women prestige census type
## gov.administrators 13.11 12351 11.16 68.8 1113 prof
## general.managers 12.26 25879 4.02 69.1 1130 prof
## accountants 12.77 9271 15.70 63.4 1171 prof
## purchasing.officers 11.42 8865 9.11 56.8 1175 prof
## chemists 14.62 8403 11.68 73.5 2111 prof
## physicists 15.64 11030 5.13 77.6 2113 profmedian prestige score for each of the three types of occupation, respectively.
## type prestige
## 1 bc 35.9
## 2 prof 68.4
## 3 wc 41.5Use the median score in each type of occupation to define two levels of prestige: High and low, for each occupation
dta<-Prestige%>%mutate(occup=rownames(Prestige))
dta_bc<-dta%>%filter(dta$type=="bc")%>%mutate(level_prestige=case_when(prestige<=m[1, 2]~"Low", prestige>m[1,2]~"High"))
dta_prof<-dta%>%filter(dta$type=="prof")%>%mutate(level_prestige=case_when(prestige<=m[2, 2]~"Low", prestige>m[2,2]~"High"))
dta_wc<-dta%>%filter(dta$type=="wc")%>%mutate(level_prestige=case_when(prestige<=m[3, 2]~"Low", prestige>m[3,2]~"High"))
dta_1<-rbind(dta_bc, dta_prof, dta_wc)
str(dta_1)## 'data.frame': 98 obs. of 8 variables:
## $ education : num 9.45 9.93 9.47 10.93 7.74 ...
## $ income : int 3485 2370 8895 8891 3116 3930 7869 3000 3472 3582 ...
## $ women : num 76.14 3.69 0 1.65 52 ...
## $ prestige : num 34.9 23.3 43.5 51.6 29.7 20.2 54.9 20.8 17.3 20.1 ...
## $ census : int 3135 5145 6111 6112 6121 6123 6141 6162 6191 6193 ...
## $ type : Factor w/ 3 levels "bc","prof","wc": 1 1 1 1 1 1 1 1 1 1 ...
## $ occup : chr "nursing.aides" "service.station.attendant" "firefighters" "policemen" ...
## $ level_prestige: chr "Low" "Low" "High" "High" ...## education income women prestige census type occup
## 1 9.45 3485 76.14 34.9 3135 bc nursing.aides
## 2 9.93 2370 3.69 23.3 5145 bc service.station.attendant
## 3 9.47 8895 0.00 43.5 6111 bc firefighters
## 4 10.93 8891 1.65 51.6 6112 bc policemen
## 5 7.74 3116 52.00 29.7 6121 bc cooks
## 6 8.50 3930 15.51 20.2 6123 bc bartenders
## level_prestige
## 1 Low
## 2 Low
## 3 High
## 4 High
## 5 Low
## 6 LowSummarize the relationship between income and education for each category generated from crossing the factor prestige with the type of occupation.
ggplot2::qplot(income, education, data=dta_1, geom = c('point', 'abline'),
col=level_prestige, facets = level_prestige ~ type)
correlation between education and income in types of occupation
## Warning: package 'plyr' was built under R version 3.6.3## type corr
## 1 bc 0.5032838
## 2 prof 0.2827775
## 3 wc 0.1409227correlation between education and income between two levels of prestige score across types of occupation
## level_prestige corr
## 1 High 0.44956311
## 2 Low 0.05737649## level_prestige corr
## 1 High 1.661953e-06
## 2 Low 3.728961e-01## level_prestige corr
## 1 High 0.4901340
## 2 Low 0.6467921