They are numeric, character, and logical
## Q2. What are R’s five most common types of objects? Character, Numeric, Logical, Advanced, Integer
Upload the cars.csv file; save it in the data folder; and upload it into the data folder. Revise the read.csv code line so that the code matches both the name and address of the data file. The row represent cars and the column characteristics of the cars.
cars <- read.csv("/resources/rstudio/Business Statistics/Data/cars.csv")
str(cars)
## 'data.frame': 428 obs. of 19 variables:
## $ name : Factor w/ 425 levels "Acura 3.5 RL 4dr",..: 66 67 68 69 70 114 115 133 129 130 ...
## $ sports_car : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ suv : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ wagon : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ minivan : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ pickup : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ all_wheel : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ rear_wheel : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ msrp : int 11690 12585 14610 14810 16385 13670 15040 13270 13730 15460 ...
## $ dealer_cost: int 10965 11802 13697 13884 15357 12849 14086 12482 12906 14496 ...
## $ eng_size : num 1.6 1.6 2.2 2.2 2.2 2 2 2 2 2 ...
## $ ncyl : int 4 4 4 4 4 4 4 4 4 4 ...
## $ horsepwr : int 103 103 140 140 140 132 132 130 110 130 ...
## $ city_mpg : int 28 28 26 26 26 29 29 26 27 26 ...
## $ hwy_mpg : int 34 34 37 37 37 36 36 33 36 33 ...
## $ weight : int 2370 2348 2617 2676 2617 2581 2626 2612 2606 2606 ...
## $ wheel_base : int 98 98 104 104 104 105 105 103 103 103 ...
## $ length : int 167 153 183 183 183 174 174 168 168 168 ...
## $ width : int 66 66 69 68 69 67 67 67 67 67 ...
summary(cars)
## name sports_car suv
## Infiniti G35 4dr : 2 Mode :logical Mode :logical
## Mercedes-Benz C240 4dr : 2 FALSE:379 FALSE:368
## Mercedes-Benz C320 4dr : 2 TRUE :49 TRUE :60
## Acura 3.5 RL 4dr : 1
## Acura 3.5 RL w/Navigation 4dr: 1
## Acura MDX : 1
## (Other) :419
## wagon minivan pickup all_wheel
## Mode :logical Mode :logical Mode :logical Mode :logical
## FALSE:398 FALSE:408 FALSE:404 FALSE:336
## TRUE :30 TRUE :20 TRUE :24 TRUE :92
##
##
##
##
## rear_wheel msrp dealer_cost eng_size
## Mode :logical Min. : 10280 Min. : 9875 Min. :1.300
## FALSE:318 1st Qu.: 20334 1st Qu.: 18866 1st Qu.:2.375
## TRUE :110 Median : 27635 Median : 25294 Median :3.000
## Mean : 32775 Mean : 30015 Mean :3.197
## 3rd Qu.: 39205 3rd Qu.: 35710 3rd Qu.:3.900
## Max. :192465 Max. :173560 Max. :8.300
##
## ncyl horsepwr city_mpg hwy_mpg
## Min. :-1.000 Min. : 73.0 Min. :10.00 Min. :12.00
## 1st Qu.: 4.000 1st Qu.:165.0 1st Qu.:17.00 1st Qu.:24.00
## Median : 6.000 Median :210.0 Median :19.00 Median :26.00
## Mean : 5.776 Mean :215.9 Mean :20.09 Mean :26.91
## 3rd Qu.: 6.000 3rd Qu.:255.0 3rd Qu.:21.00 3rd Qu.:29.00
## Max. :12.000 Max. :500.0 Max. :60.00 Max. :66.00
## NA's :14 NA's :14
## weight wheel_base length width
## Min. :1850 Min. : 89.0 Min. :143.0 Min. :64.00
## 1st Qu.:3102 1st Qu.:103.0 1st Qu.:177.0 1st Qu.:69.00
## Median :3474 Median :107.0 Median :186.0 Median :71.00
## Mean :3577 Mean :108.2 Mean :185.1 Mean :71.29
## 3rd Qu.:3974 3rd Qu.:112.0 3rd Qu.:193.0 3rd Qu.:73.00
## Max. :7190 Max. :144.0 Max. :227.0 Max. :81.00
## NA's :2 NA's :2 NA's :26 NA's :28428 Cars
19 Columns
7800 LBs