SPS_Bridge_Course_R

Import the data set “mtcars” from the wroking directory.

mtcars <- read.csv(file="mtcars.csv", header=TRUE, sep=",")

**This open source dataset is downloaded from http://vincentarelbundock.github.io/Rdatasets/

Description: The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

Format: A data frame with 32 observations on 11 (numeric) variables.
[, 1] mpg Miles/(US) gallon
[, 2] cyl Number of cylinders
[, 3] disp Displacement (cu.in.) [, 4] hp Gross horsepower
[, 5] drat Rear axle ratio
[, 6] wt Weight (1000 lbs)
[, 7] qsec 1/4 mile time
[, 8] vs Engine (0 = V-shaped, 1 = straight)
[, 9] am Transmission (0 = automatic, 1 = manual)
[,10] gear Number of forward gears
[,11] carb Number of carburetors

Use the summary function to gain an overview of the data set.Then displaythe mean and median for at least two attributes.

Summary:

summary(mtcars)

##                   X           mpg             cyl             disp      
##  AMC Javelin       : 1   Min.   :10.40   Min.   :4.000   Min.   : 71.1  
##  Cadillac Fleetwood: 1   1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8  
##  Camaro Z28        : 1   Median :19.20   Median :6.000   Median :196.3  
##  Chrysler Imperial : 1   Mean   :20.09   Mean   :6.188   Mean   :230.7  
##  Datsun 710        : 1   3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0  
##  Dodge Challenger  : 1   Max.   :33.90   Max.   :8.000   Max.   :472.0  
##  (Other)           :26                                                  
##        hp             drat             wt             qsec      
##  Min.   : 52.0   Min.   :2.760   Min.   :1.513   Min.   :14.50  
##  1st Qu.: 96.5   1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89  
##  Median :123.0   Median :3.695   Median :3.325   Median :17.71  
##  Mean   :146.7   Mean   :3.597   Mean   :3.217   Mean   :17.85  
##  3rd Qu.:180.0   3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90  
##  Max.   :335.0   Max.   :4.930   Max.   :5.424   Max.   :22.90  
##                                                                 
##        vs               am              gear            carb      
##  Min.   :0.0000   Min.   :0.0000   Min.   :3.000   Min.   :1.000  
##  1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
##  Median :0.0000   Median :0.0000   Median :4.000   Median :2.000  
##  Mean   :0.4375   Mean   :0.4062   Mean   :3.688   Mean   :2.812  
##  3rd Qu.:1.0000   3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
##  Max.   :1.0000   Max.   :1.0000   Max.   :5.000   Max.   :8.000  
##

The mean of mpg

mean(mtcars$mpg)

## [1] 20.09062

The median of mpg

median(mtcars$mpg)

## [1] 19.2

The mean of hp

mean(mtcars$hp)

## [1] 146.6875

The median of hp

median(mtcars$hp)

## [1] 123

The mean of wt

mean(mtcars$wt)

## [1] 3.21725

The median of wt

median(mtcars$wt)

## [1] 3.325

Create a new data frame with a subsetof the columnsand rows. Make sure to rename it.

newDataSet <- data.frame(mtcars$mpg, mtcars$hp, mtcars$wt, mtcars$gear)
row.names(newDataSet) <- row.names(mtcars)

Create new column names for the new data frame.

colnames(newDataSet) <- c("Miles_Per_Gallon","Gross_Horsepower", "Weight_In_1000_LBS", "Number_Of_Forward_Gears")

Use the summary function to create an overview of yournew data frame. The print the mean and median for the same two attributes. Please compare.

Summary:

summary(newDataSet)

##  Miles_Per_Gallon Gross_Horsepower Weight_In_1000_LBS
##  Min.   :10.40    Min.   : 52.0    Min.   :1.513     
##  1st Qu.:15.43    1st Qu.: 96.5    1st Qu.:2.581     
##  Median :19.20    Median :123.0    Median :3.325     
##  Mean   :20.09    Mean   :146.7    Mean   :3.217     
##  3rd Qu.:22.80    3rd Qu.:180.0    3rd Qu.:3.610     
##  Max.   :33.90    Max.   :335.0    Max.   :5.424     
##  Number_Of_Forward_Gears
##  Min.   :3.000          
##  1st Qu.:3.000          
##  Median :4.000          
##  Mean   :3.688          
##  3rd Qu.:4.000          
##  Max.   :5.000

The mean of Miles_Per_Gallon

mean(newDataSet$Miles_Per_Gallon)

## [1] 20.09062

The median of Miles_Per_Gallon

median(newDataSet$Miles_Per_Gallon)

## [1] 19.2

The mean of Gross_Horsepower

mean(newDataSet$Gross_Horsepower)

## [1] 146.6875

The median of Gross_Horsepower

median(newDataSet$Gross_Horsepower)

## [1] 123

The mean of Weight_In_1000_LBS

mean(newDataSet$Weight_In_1000_LBS)

## [1] 3.21725

The median of Weight_In_1000_LBS

median(newDataSet$Weight_In_1000_LBS)

## [1] 3.325

The result show that the statistics (including the mean and median) of the attributes in the new data frame are the same as the corresponding attributes in the orginal data frame

For at least 3 values in a column please rename so that every value in that column is renamed. For example, suppose I have 20 values of the letter “e”in one column. Rename those values so that all 20 would show as “excellent”. The following codes replace the numbers in the Number_Of_Forward_Gears column with their corresponding word in English. For example, it will replace the number 1 with “One”

newDataSet$Number_Of_Forward_Gears[newDataSet$Number_Of_Forward_Gears == 1] <- "One"
newDataSet$Number_Of_Forward_Gears[newDataSet$Number_Of_Forward_Gears == 2] <- "Two"
newDataSet$Number_Of_Forward_Gears[newDataSet$Number_Of_Forward_Gears == 3] <- "Three"
newDataSet$Number_Of_Forward_Gears[newDataSet$Number_Of_Forward_Gears == 4] <- "Four"
newDataSet$Number_Of_Forward_Gears[newDataSet$Number_Of_Forward_Gears == 5] <- "Five"

Display enough rows to see examples of all of steps 1-5 above.

mtcars[1:20,]

##                      X  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## 1            Mazda RX4 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## 2        Mazda RX4 Wag 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## 3           Datsun 710 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## 4       Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## 5    Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## 6              Valiant 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## 7           Duster 360 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## 8            Merc 240D 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## 9             Merc 230 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## 10            Merc 280 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## 11           Merc 280C 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## 12          Merc 450SE 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## 13          Merc 450SL 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## 14         Merc 450SLC 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## 15  Cadillac Fleetwood 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## 16 Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## 17   Chrysler Imperial 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## 18            Fiat 128 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## 19         Honda Civic 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## 20      Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1

newDataSet[1:20,]

##    Miles_Per_Gallon Gross_Horsepower Weight_In_1000_LBS
## 1              21.0              110              2.620
## 2              21.0              110              2.875
## 3              22.8               93              2.320
## 4              21.4              110              3.215
## 5              18.7              175              3.440
## 6              18.1              105              3.460
## 7              14.3              245              3.570
## 8              24.4               62              3.190
## 9              22.8               95              3.150
## 10             19.2              123              3.440
## 11             17.8              123              3.440
## 12             16.4              180              4.070
## 13             17.3              180              3.730
## 14             15.2              180              3.780
## 15             10.4              205              5.250
## 16             10.4              215              5.424
## 17             14.7              230              5.345
## 18             32.4               66              2.200
## 19             30.4               52              1.615
## 20             33.9               65              1.835
##    Number_Of_Forward_Gears
## 1                     Four
## 2                     Four
## 3                     Four
## 4                    Three
## 5                    Three
## 6                    Three
## 7                    Three
## 8                     Four
## 9                     Four
## 10                    Four
## 11                    Four
## 12                   Three
## 13                   Three
## 14                   Three
## 15                   Three
## 16                   Three
## 17                   Three
## 18                    Four
## 19                    Four
## 20                    Four

place the original .csv in a github file and have R read from the link.

library(readr)
gitHubMtcars <- read.csv("https://raw.githubusercontent.com/ezaccountz/SPS_Bridge_R_HW2/master/mtcars.csv")

SPS_Bridge_Course_R_HW#2

Euclid Zhang

7/27/2019