Exercise_0330
yuwenchen
4/12/2020
0330_Exercise 1
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 data
dta <- Ecdat::Caschool
head(dta)## 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 605.7 605.4names(dta)## [1] "distcod" "county" "district" "grspan" "enrltot" "teachers"
## [7] "calwpct" "mealpct" "computer" "testscr" "compstu" "expnstu"
## [13] "str" "avginc" "elpct" "readscr" "mathscr"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#'set.seed' to confirm the reproducibilty
set.seed(1001)
#extract 1 school from each county
dta_r <- dta %>% group_by(.,county) %>% sample_n(1)
plot(dta_r$mathscr, dta_r$readscr, xlab= "Average math score", ylab="Average reading score")
0330_Exercise 2
Find 133 class-level 95%-confidence intervals for language test score means of the nlschools{MASS} data set by using the tidy approach.
# load data
dta2 <- MASS::nlschools
head(dta2)## 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 0str(dta2)## 'data.frame': 2287 obs. of 6 variables:
## $ lang : int 46 45 33 46 20 30 30 57 36 36 ...
## $ IQ : num 15 14.5 9.5 11 8 9.5 9.5 13 9.5 11 ...
## $ class: Factor w/ 133 levels "180","280","1082",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ GS : int 29 29 29 29 29 29 29 29 29 29 ...
## $ SES : int 23 10 15 23 10 10 23 10 13 15 ...
## $ COMB : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...dta2 %>%
mutate(classID = factor(class, levels = levels(class), labels = c(1:length(levels(.$class))))) %>%
group_by(classID) %>%
summarize(language_mean= mean(lang, na.rm=T),
language_SE=sd(lang, na.rm=T)/sqrt(n() ),
language_lb= language_mean - 1.96*language_SE,
language_ub= language_mean + 1.96*language_SE) %>%
tail(.,3)## # A tibble: 3 x 5
## classID language_mean language_SE language_lb language_ub
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 131 38.1 1.71 34.7 41.4
## 2 132 29.3 4.20 21.1 37.5
## 3 133 28.4 2.64 23.3 33.60330_Exercise 3
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
library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recodehead(Prestige)## 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 profstr(Prestige)## '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 ...# summarise median for each occupation
dta3 <- Prestige %>%
group_by(type)%>%
mutate(pmedian = median(prestige),
plevel = case_when(prestige > pmedian ~ "High",
prestige < pmedian ~ "Low")) ## Warning: Factor `type` contains implicit NA, consider using
## `forcats::fct_explicit_na`
## Warning: Factor `type` contains implicit NA, consider using
## `forcats::fct_explicit_na`#xyplot by type
library(lattice)
dta3 %>%
xyplot(income ~ education | type, groups =plevel, data = ., type = c("g","p","r"))
0330_Exercise 4
Reverse the order of input to the series of dplyr::*_join examples using data from the Nobel laureates in literature and explain the resulting output.
dta41 <- read.table("nobel_countries.txt", header = T)
str(dta41)## 'data.frame': 8 obs. of 2 variables:
## $ Country: Factor w/ 7 levels "Canada","China",..: 3 6 6 7 1 2 4 5
## $ Year : int 2014 1950 2017 2016 2013 2012 2015 2011dta42 <- read.table("nobel_winners.txt", header = T)
str(dta42)## 'data.frame': 7 obs. of 3 variables:
## $ Name : Factor w/ 7 levels "Alice Munro",..: 6 2 4 3 1 5 7
## $ Gender: Factor w/ 2 levels "Female","Male": 2 2 2 2 1 2 1
## $ Year : int 2014 1950 2017 2016 2013 2012 1938#merge data by `left_join`, Joining, by = "Year"
library(dplyr)
dta4 <- left_join(dta41, dta42)## Joining, by = "Year"arrange(dta4, by= Year) #arranged by year## Country Year Name Gender
## 1 UK 1950 Bertrand Russell Male
## 2 Sweden 2011 <NA> <NA>
## 3 China 2012 Mo Yan Male
## 4 Canada 2013 Alice Munro Female
## 5 France 2014 Patrick Modiano Male
## 6 Russia 2015 <NA> <NA>
## 7 US 2016 Bob Dylan Male
## 8 UK 2017 Kazuo Ishiguro Male0330_Exercise 5
Augment the data object in the ‘SAT’ lecture note with state.division{datasets}. For each of the 9 divisions, find the slope estimate for regressing average SAT scores onto average teacher’s salary. How many of them are of negative signs?
str(state.division)## Factor w/ 9 levels "New England",..: 4 9 8 5 9 8 1 3 3 3 ...fL <- "http://www.amstat.org/publications/jse/datasets/sat.dat.txt"
dta5 <- read.table(fL, row.names=1)
names(dta5) <- c("Spending", "PTR", "Salary", "PE", "Verbal", "Math", "SAT")
dta5$Region <- state.division
head(dta5)## Spending PTR Salary PE Verbal Math SAT Region
## Alabama 4.405 17.2 31.144 8 491 538 1029 East South Central
## Alaska 8.963 17.6 47.951 47 445 489 934 Pacific
## Arizona 4.778 19.3 32.175 27 448 496 944 Mountain
## Arkansas 4.459 17.1 28.934 6 482 523 1005 West South Central
## California 4.992 24.0 41.078 45 417 485 902 Pacific
## Colorado 5.443 18.4 34.571 29 462 518 980 Mountaindta5 %>% xyplot(SAT~Salary | Region, type=c("g", "r", "p"), data=.)
#**Negative signs:
#1. Mountain, West South Central(*strong*)
#2. West North Central, East South Centraol(*slightly*)**