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R DATASET
# Print the mtcars data set\
mtcars# Use the question mark to get information about the data set
?mtcars## starting httpd help server ... doneData_Cars <- mtcars
# Use dim() to find the dimension of the data set
dim(Data_Cars)## [1] 32 11# Use names() to find the names of the variables from the data set
names(Data_Cars)## [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
## [11] "carb"Data_Cars <- mtcars
rownames(Data_Cars)## [1] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710"
## [4] "Hornet 4 Drive" "Hornet Sportabout" "Valiant"
## [7] "Duster 360" "Merc 240D" "Merc 230"
## [10] "Merc 280" "Merc 280C" "Merc 450SE"
## [13] "Merc 450SL" "Merc 450SLC" "Cadillac Fleetwood"
## [16] "Lincoln Continental" "Chrysler Imperial" "Fiat 128"
## [19] "Honda Civic" "Toyota Corolla" "Toyota Corona"
## [22] "Dodge Challenger" "AMC Javelin" "Camaro Z28"
## [25] "Pontiac Firebird" "Fiat X1-9" "Porsche 914-2"
## [28] "Lotus Europa" "Ford Pantera L" "Ferrari Dino"
## [31] "Maserati Bora" "Volvo 142E"Data_Cars <- mtcars
Data_Cars$cyl## [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4Data_Cars <- mtcars
sort(Data_Cars$cyl)## [1] 4 4 4 4 4 4 4 4 4 4 4 6 6 6 6 6 6 6 8 8 8 8 8 8 8 8 8 8 8 8 8 8Data_Cars <- mtcars
summary(Data_Cars)## mpg cyl disp hp
## Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
## 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
## Median :19.20 Median :6.000 Median :196.3 Median :123.0
## Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
## 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
## Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
## drat wt qsec vs
## Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
## 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
## Median :3.695 Median :3.325 Median :17.71 Median :0.0000
## Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
## 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
## Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
## am gear carb
## Min. :0.0000 Min. :3.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
## Median :0.0000 Median :4.000 Median :2.000
## Mean :0.4062 Mean :3.688 Mean :2.812
## 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :1.0000 Max. :5.000 Max. :8.000R MAX AND MIN
Data_Cars <- mtcars
max(Data_Cars$hp)## [1] 335min(Data_Cars$hp)## [1] 52Data_Cars <- mtcars
which.max(Data_Cars$hp)## [1] 31which.min(Data_Cars$hp)## [1] 19Data_Cars <- mtcars
rownames(Data_Cars)[which.max(Data_Cars$hp)]## [1] "Maserati Bora"rownames(Data_Cars)[which.min(Data_Cars$hp)]## [1] "Honda Civic"MEAN, MEDIAN, AND MODE
Data_Cars <- mtcars
mean(Data_Cars$wt)## [1] 3.21725Data_Cars <- mtcars
median(Data_Cars$wt)## [1] 3.325Data_Cars <- mtcars
names(sort(-table(Data_Cars$wt)))## [1] "3.44" "3.57" "1.513" "1.615" "1.835" "1.935" "2.14" "2.2" "2.32"
## [10] "2.465" "2.62" "2.77" "2.78" "2.875" "3.15" "3.17" "3.19" "3.215"
## [19] "3.435" "3.46" "3.52" "3.73" "3.78" "3.84" "3.845" "4.07" "5.25"
## [28] "5.345" "5.424"R PERCENTILES
Data_Cars <- mtcars
# c() specifies which percentile you want
quantile(Data_Cars$wt, c(0.75))## 75%
## 3.61Data_Cars <- mtcars
quantile(Data_Cars$wt)## 0% 25% 50% 75% 100%
## 1.51300 2.58125 3.32500 3.61000 5.42400