ex1

library(nlme)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following object is masked from 'package:nlme':
## 
##     collapse
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
pacman::p_load(lattice, nlme)
nlme::Oxboys %>% 
  as.data.frame() %>% 
mutate(Subject = reorder(Subject, height, mean)) %>%
  xyplot(height ~ age | Subject, data = ., type = c("p", "r"))

ex2

library(ggplot2)
dta <- read.csv("C:/Users/she22_000/Documents/income_tw.csv", header = T)
qplot( dta$Count,dta$Income)

ex3

3-1

library(tidyr)
read.table("brainsize.txt", header = T) %>%
  gather(IQtype, IQvalue, 3:5) %>% 
  ggplot(aes(IQtype, IQvalue, color = Gender)) +
  stat_summary(fun.data = mean_se, geom = "pointrange",
               size = rel(1.1), position = position_dodge(0.3))

以圖表來看，FSIQ和VIQ是有可能有性別差異的，而PIQ可能沒有。

3-2

read.table("brainsize.txt", header = T)%>%
ggplot(aes(Height, Weight)) +
  geom_point(aes(color = Gender)) +
  stat_smooth(aes(color = Gender), method = "lm", se = F)
## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).

身高和體重的關聯具有性別差異。

3-3

read.table("brainsize.txt", header = T) %>%
  gather(IQtype, IQvalue, 3:5) %>% 
  ggplot(aes(IQvalue, MRICount, color = Gender)) +
  geom_point() +
  stat_smooth(method = "lm", se = F) +
  facet_grid(. ~ IQtype) +
  labs(y = "Brain Size")

透過三種IQ分數來看，IQ和腦容量具有性別差異。