x <- 1000 #
typeof(x) # type in R is diffirent class() function
## [1] "double"
x <- 1
print(x)
## [1] 1
class(x)
## [1] "numeric"
x <-c("n","m","w")
if(is.character(x)){print(x)} # if statement
## [1] "n" "m" "w"
if(is.integer(x)){print(x)} else {print("x is not integer")} # if else statement
## [1] "x is not integer"
x <- 1000
class(x) # [1] "numeric"
## [1] "numeric"
typeof(x) # [1] "double"
## [1] "double"
x <- "toi la an"
class(x)
## [1] "character"
count <- 4
while (count < 11) {print(x)
count <-count+1 }
## [1] "toi la an"
## [1] "toi la an"
## [1] "toi la an"
## [1] "toi la an"
## [1] "toi la an"
## [1] "toi la an"
## [1] "toi la an"
# neu dieu kien cua while con, thi countinue run
fruit <- c("cam","tao","luu")
for (i in fruit) {print(i)} # chua hieu lam, tim hieu them
## [1] "cam"
## [1] "tao"
## [1] "luu"
df <- mtcars # co san mtcars
df # view
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
df['mpg'] # xem cot of bien mpg
## mpg
## Mazda RX4 21.0
## Mazda RX4 Wag 21.0
## Datsun 710 22.8
## Hornet 4 Drive 21.4
## Hornet Sportabout 18.7
## Valiant 18.1
## Duster 360 14.3
## Merc 240D 24.4
## Merc 230 22.8
## Merc 280 19.2
## Merc 280C 17.8
## Merc 450SE 16.4
## Merc 450SL 17.3
## Merc 450SLC 15.2
## Cadillac Fleetwood 10.4
## Lincoln Continental 10.4
## Chrysler Imperial 14.7
## Fiat 128 32.4
## Honda Civic 30.4
## Toyota Corolla 33.9
## Toyota Corona 21.5
## Dodge Challenger 15.5
## AMC Javelin 15.2
## Camaro Z28 13.3
## Pontiac Firebird 19.2
## Fiat X1-9 27.3
## Porsche 914-2 26.0
## Lotus Europa 30.4
## Ford Pantera L 15.8
## Ferrari Dino 19.7
## Maserati Bora 15.0
## Volvo 142E 21.4
df[df['mpg'] >=30,] # chua hieu dau ","
## mpg cyl disp hp drat wt qsec vs am gear carb
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
dim(df) # xem bao nhieu hang, bao nhieu cot
## [1] 32 11
subset(df,mpg>30) # lay toan bo cot va hang nao co mpg > 30
## mpg cyl disp hp drat wt qsec vs am gear carb
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#class("mpg", data.frame(df)) # xem class cua "mpg" trong df <lenh sai>
class(df[,2]) # <lenh dung> xem class cua cot so 2 cua df
## [1] "numeric"
print(df[1,1]) # in df hang 1, cot 1
## [1] 21
a <- df[,1] # lay a la numberic cot so 1 trong data frame df
df[(df["mpg"]>=20)& (df['hp']>=100) ] # dau 'mpg' = "mpg"
## [1] 21.000 21.000 21.400 30.400 21.400 6.000 6.000 6.000 4.000
## [10] 4.000 160.000 160.000 258.000 95.100 121.000 110.000 110.000 110.000
## [19] 113.000 109.000 3.900 3.900 3.080 3.770 4.110 2.620 2.875
## [28] 3.215 1.513 2.780 16.460 17.020 19.440 16.900 18.600 0.000
## [37] 0.000 1.000 1.000 1.000 1.000 1.000 0.000 1.000 1.000
## [46] 4.000 4.000 3.000 5.000 4.000 4.000 4.000 1.000 2.000
## [55] 2.000
hot<-FALSE
temp <- 49 # co nhieu loai sai <de y>
if(temp>50){hot<-TRUE} else {print(hot)}
## [1] FALSE
score <- 1 # gia trị thay đổi đc # co the test voi cac moc >100, 80-100, 50-80,30-50, >30
if(score >=80 & score<100){print("this is good score")
}else if(score<80&score>=50) {print("this is a normal score")
}else if(score<50&score>=30){print('nearly fail')
}else if(score<30){print('already fail')
}else if(score>100){print('dcmm dien ah')}
## [1] "already fail"