TDTU.Day2
Paul Vo
January 6, 2020
Input data
pisa = read.csv("C:\\Users\\Paul Vo\\Desktop\\Textbook\\PISA Data Vietnam 2015.csv")
head(pisa)## School SchoolSize ClassSize STratio SchoolType Area Region Age Gender
## 1 70400001 883 18 22.075 3 URBAN SOUTH 15.58 Boys
## 2 70400001 883 18 22.075 3 URBAN SOUTH 15.92 Boys
## 3 70400001 883 18 22.075 3 URBAN SOUTH 15.42 Girls
## 4 70400001 883 18 22.075 3 URBAN SOUTH 15.58 Girls
## 5 70400001 883 18 22.075 3 URBAN SOUTH 15.92 Girls
## 6 70400001 883 18 22.075 3 URBAN SOUTH 16.25 Girls
## PARED HISCED WEALTH INSTSCIE JOYSCIE ICTRES Math Read Science
## 1 9 2 -2.0697 0.9798 2.1635 -1.5244 439.923 412.290 475.612
## 2 12 4 -1.7903 1.7359 2.1635 -1.9305 406.251 409.598 450.320
## 3 9 2 -2.1942 -0.2063 -0.1808 -1.6093 414.369 384.307 405.787
## 4 5 1 -2.0301 -0.3115 -0.4318 -1.6250 468.801 459.104 462.968
## 5 9 2 -1.0522 0.7648 1.3031 -0.5305 355.432 402.435 453.736
## 6 5 1 -3.0570 0.3708 0.5094 -2.5873 458.955 483.885 529.866Many chart
Link đánh tiếng Việthttps://www.freeformatter.com/
hist(pisa$Math, col="navy", main = "Distribution of Math score-\u0111a\u0301nh ti\u00EA\u0301ng Vi\u00EA\u0323t \u0111\u01B0\u01A1\u0323c ne\u0300 ch\u00F4\u0303 na\u0300y h\u01A1\u0303i israel", xlab = "Science score", ylab="Student")
Phân nhóm xíu:
p1=hist(pisa$Science[pisa$Gender=="Boys"], plot=F)
p2=hist(pisa$Science[pisa$Gender=="Girls"], plot=F)
plot(p1, col="skyblue", border="white")
plot(p2,add=T,col=scales::alpha("yellow",0.4),border="aliceblue")
## dòng lệnh add phía trên nó ghép vài vô trai nói cách kháclà nằm đè lên
Giờ thì đổi gói sang lattice (mà chắc cung ko vui đâu)
library(lattice)
densityplot(~ Science, group = Gender, data = pisa)
densityplot(~Science|Gender, data=pisa)
Use this link to call the color http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf
now you got a lot of colors
Time to Play with ggplot2
lipit = read.csv("C:\\Users\\Paul Vo\\Desktop\\Textbook\\Obesity data.csv")
library(ggplot2)
library(ggthemes)Now this
p= ggplot(data=lipit, aes(x=lipit$bmi, y=lipit$pcfat, col= gender, fill = gender)) + geom_point() + labs(x="BMI", y="pcfat")+ geom_smooth (method ="lm", formula=y~x+I(x^2))
p