7. BONUS – place the original .csv in a github file and have R read from the link. This will be a very useful skill as you progress in your data science education and career.

data <- read.csv(url("https://raw.githubusercontent.com/hovig/MSDS_CUNY/master/mtcars.csv"))

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

summary(data)
##                   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  
## 
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
data %>%
summarise(avg_mpg = mean(mpg), median_mpg = median(mpg), avg_qsec = mean(qsec), median_qsec = median(qsec))
##    avg_mpg median_mpg avg_qsec median_qsec
## 1 20.09062       19.2 17.84875       17.71
                                                                      :921

2. Create a new data frame with a subset of the columns and rows. Make sure to rename it

d1<-subset(data, mpg > 20 & qsec > 15 & cyl > 4, c('mpg','qsec','cyl','hp','wt'))
d1
##    mpg  qsec cyl  hp    wt
## 1 21.0 16.46   6 110 2.620
## 2 21.0 17.02   6 110 2.875
## 4 21.4 19.44   6 110 3.215

3. Create new column names for the new data frame

d2<-rename(d1, Miles_Per_gallon = mpg, Quarter_Mile_Time = qsec, Number_Of_Cylinders = cyl, Horsepower = hp, Weight = wt)
d2
##   Miles_Per_gallon Quarter_Mile_Time Number_Of_Cylinders Horsepower Weight
## 1             21.0             16.46                   6        110  2.620
## 2             21.0             17.02                   6        110  2.875
## 4             21.4             19.44                   6        110  3.215

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

summary(d2)
##  Miles_Per_gallon Quarter_Mile_Time Number_Of_Cylinders   Horsepower 
##  Min.   :21.00    Min.   :16.46     Min.   :6           Min.   :110  
##  1st Qu.:21.00    1st Qu.:16.74     1st Qu.:6           1st Qu.:110  
##  Median :21.00    Median :17.02     Median :6           Median :110  
##  Mean   :21.13    Mean   :17.64     Mean   :6           Mean   :110  
##  3rd Qu.:21.20    3rd Qu.:18.23     3rd Qu.:6           3rd Qu.:110  
##  Max.   :21.40    Max.   :19.44     Max.   :6           Max.   :110  
##      Weight     
##  Min.   :2.620  
##  1st Qu.:2.748  
##  Median :2.875  
##  Mean   :2.903  
##  3rd Qu.:3.045  
##  Max.   :3.215
d2 %>%
summarise(avg_mpg = mean(Miles_Per_gallon), median_mpg = median(Miles_Per_gallon), avg_qsec = mean(Quarter_Mile_Time), median_qsec = median(Quarter_Mile_Time))
##    avg_mpg median_mpg avg_qsec median_qsec
## 1 21.13333         21    17.64       17.02
# Comparison
nrow(data)  
## [1] 32
nrow(d2)
## [1] 3

nrow(data) > nrow(d2) => the list of data in data is higher than d2 (d2 went under a conditional selection of its list). The result of this difference in the data amount will effect in the output of the mean and median.

5. 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”.

# Mutation is an option:

d2 %>%
mutate(Number_Of_Cylinders=replace(Number_Of_Cylinders, Miles_Per_gallon>21, NA)) %>%
as.data.frame()
##   Miles_Per_gallon Quarter_Mile_Time Number_Of_Cylinders Horsepower Weight
## 1             21.0             16.46                   6        110  2.620
## 2             21.0             17.02                   6        110  2.875
## 3             21.4             19.44                  NA        110  3.215
# Or another way is:

count <- 1
for (val in d2["Number_Of_Cylinders"]){
  if (val[count] == 6){
    d2["Number_Of_Cylinders"][count] <- "six"
  }
  count=count+1
}
d2
##   Miles_Per_gallon Quarter_Mile_Time Number_Of_Cylinders Horsepower Weight
## 1             21.0             16.46                 six        110  2.620
## 2             21.0             17.02                 six        110  2.875
## 4             21.4             19.44                 six        110  3.215

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

library(knitr)
kable(data)
X 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 
library(knitr)
kable(d2)
Miles_Per_gallon Quarter_Mile_Time Number_Of_Cylinders Horsepower Weight
1 21.0 16.46 six 110 2.620
2 21.0 17.02 six 110 2.875
4 21.4 19.44 six 110 3.215