#Ho van ten: Pham The Hung
#Bai 1
##a kiem tra matrix A = matrix A^3
a <- c(1, 1, 3)
b <- c(5, 2, 6)
c <- c(-2, -1, -3)
matrix <- rbind(a,b,c)
matrix
## [,1] [,2] [,3]
## a 1 1 3
## b 5 2 6
## c -2 -1 -3
matrix3 <- matrix %*% matrix %*% matrix
matrix3
## [,1] [,2] [,3]
## a 0 0 0
## b 0 0 0
## c 0 0 0
##b thay cot 3 cua matrix a = tong cot thu 1 va cot thu 2 matrix a
d <- a + b
d
## [1] 6 3 9
matrix <- rbind(a,b,d)
matrix
## [,1] [,2] [,3]
## a 1 1 3
## b 5 2 6
## d 6 3 9
#Bai 2
x <- c(0,1,2,3,4)
y <- c(0,1,2,3,4)
outer(x,y,FUN = "+")
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0 1 2 3 4
## [2,] 1 2 3 4 5
## [3,] 2 3 4 5 6
## [4,] 3 4 5 6 7
## [5,] 4 5 6 7 8
#Bai 3
a <- c(1, 2, 3, 4, 5)
b <- c(2, 1, 2, 3, 4)
c <- c(3, 2, 1, 2, 3)
d <- c(4, 3, 2, 1, 2)
e <- c(5, 4, 3, 2, 1)
kq <- c(7, -1, -3, 5, 15)
matrix <- rbind(a,b,c,d,e)
solve(matrix,kq)
## [1] -2.166667 3.000000 5.000000 1.000000 -3.166667
#Bai 4
count <- 0
for(i in 1:20)
for(j in 1:5)
count <- count + (1^4 / (3+j))
count
## [1] 17.69048
#Bai 5
#install.packages(c("dplyr","ggplot2","gapminder"))
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(ggplot2)
library(gapminder)
data("gapminder")
##y 1
filter(gapminder,country == "Vietnam") -> vietnamdata
vietnamdata
## # A tibble: 12 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Vietnam Asia 1952 40.4 26246839 605.
## 2 Vietnam Asia 1957 42.9 28998543 676.
## 3 Vietnam Asia 1962 45.4 33796140 772.
## 4 Vietnam Asia 1967 47.8 39463910 637.
## 5 Vietnam Asia 1972 50.3 44655014 700.
## 6 Vietnam Asia 1977 55.8 50533506 714.
## 7 Vietnam Asia 1982 58.8 56142181 707.
## 8 Vietnam Asia 1987 62.8 62826491 821.
## 9 Vietnam Asia 1992 67.7 69940728 989.
## 10 Vietnam Asia 1997 70.7 76048996 1386.
## 11 Vietnam Asia 2002 73.0 80908147 1764.
## 12 Vietnam Asia 2007 74.2 85262356 2442.
##y 2
gapminder %>%
filter(country == "Vietnam") -> data
summarise(data, meanAge = mean(lifeExp))
## # A tibble: 1 × 1
## meanAge
## <dbl>
## 1 57.5
##y 3
#install.packages("gridExtra")
library(gridExtra)
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
gapminder %>%
filter(continent == "Europe", year == 2007) -> chauAu
gapminder %>%
filter(continent == "Asia", year == 2007) -> chauA
chauAu
## # A tibble: 30 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Albania Europe 2007 76.4 3600523 5937.
## 2 Austria Europe 2007 79.8 8199783 36126.
## 3 Belgium Europe 2007 79.4 10392226 33693.
## 4 Bosnia and Herzegovina Europe 2007 74.9 4552198 7446.
## 5 Bulgaria Europe 2007 73.0 7322858 10681.
## 6 Croatia Europe 2007 75.7 4493312 14619.
## 7 Czech Republic Europe 2007 76.5 10228744 22833.
## 8 Denmark Europe 2007 78.3 5468120 35278.
## 9 Finland Europe 2007 79.3 5238460 33207.
## 10 France Europe 2007 80.7 61083916 30470.
## # ℹ 20 more rows
chauA
## # A tibble: 33 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 2007 43.8 31889923 975.
## 2 Bahrain Asia 2007 75.6 708573 29796.
## 3 Bangladesh Asia 2007 64.1 150448339 1391.
## 4 Cambodia Asia 2007 59.7 14131858 1714.
## 5 China Asia 2007 73.0 1318683096 4959.
## 6 Hong Kong, China Asia 2007 82.2 6980412 39725.
## 7 India Asia 2007 64.7 1110396331 2452.
## 8 Indonesia Asia 2007 70.6 223547000 3541.
## 9 Iran Asia 2007 71.0 69453570 11606.
## 10 Iraq Asia 2007 59.5 27499638 4471.
## # ℹ 23 more rows
ggplot(data = chauAu, mapping = aes(x = reorder(country,gdpPercap),
y = gdpPercap, fill = country)) +
geom_bar(stat = "identity") +
coord_flip() +
theme(legend.position = "none") -> gdpEurope
ggplot(data = chauA, mapping = aes(x = reorder(country,gdpPercap),
y = gdpPercap, fill = country)) +
geom_bar(stat = "identity") +
coord_flip() +
theme(legend.position = "none") -> gdpAsian
gdpEurope
gdpAsian
