library(dplyr)##
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsmtcars## 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 2str(mtcars)## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...Our rows represent a different style of car with some repeating cars. One of our variabes is currrently how many miles per gallon(mpg) each car gets. almost all variables are numeric but to name two would be horsepower (hp) and cylinders (cyl) ## Cleaning the Data
cars=mtcars %>%
mutate(cyl=as.factor(cyl),am=as.factor(am))
str(cars)## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : Factor w/ 3 levels "4","6","8": 2 2 1 2 3 2 3 1 1 2 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...cars_clean=cars %>%
select(mpg, hp, wt, cyl, am)cars_hp=cars_clean %>%
filter(hp>100)
cars_hp## mpg hp wt cyl am
## Mazda RX4 21.0 110 2.620 6 1
## Mazda RX4 Wag 21.0 110 2.875 6 1
## Hornet 4 Drive 21.4 110 3.215 6 0
## Hornet Sportabout 18.7 175 3.440 8 0
## Valiant 18.1 105 3.460 6 0
## Duster 360 14.3 245 3.570 8 0
## Merc 280 19.2 123 3.440 6 0
## Merc 280C 17.8 123 3.440 6 0
## Merc 450SE 16.4 180 4.070 8 0
## Merc 450SL 17.3 180 3.730 8 0
## Merc 450SLC 15.2 180 3.780 8 0
## Cadillac Fleetwood 10.4 205 5.250 8 0
## Lincoln Continental 10.4 215 5.424 8 0
## Chrysler Imperial 14.7 230 5.345 8 0
## Dodge Challenger 15.5 150 3.520 8 0
## AMC Javelin 15.2 150 3.435 8 0
## Camaro Z28 13.3 245 3.840 8 0
## Pontiac Firebird 19.2 175 3.845 8 0
## Lotus Europa 30.4 113 1.513 4 1
## Ford Pantera L 15.8 264 3.170 8 1
## Ferrari Dino 19.7 175 2.770 6 1
## Maserati Bora 15.0 335 3.570 8 1
## Volvo 142E 21.4 109 2.780 4 1nrow(cars_hp)## [1] 23cars_hp=cars_hp %>%
mutate(power_to_weight=hp/wt)
cars_hp## mpg hp wt cyl am power_to_weight
## Mazda RX4 21.0 110 2.620 6 1 41.98473
## Mazda RX4 Wag 21.0 110 2.875 6 1 38.26087
## Hornet 4 Drive 21.4 110 3.215 6 0 34.21462
## Hornet Sportabout 18.7 175 3.440 8 0 50.87209
## Valiant 18.1 105 3.460 6 0 30.34682
## Duster 360 14.3 245 3.570 8 0 68.62745
## Merc 280 19.2 123 3.440 6 0 35.75581
## Merc 280C 17.8 123 3.440 6 0 35.75581
## Merc 450SE 16.4 180 4.070 8 0 44.22604
## Merc 450SL 17.3 180 3.730 8 0 48.25737
## Merc 450SLC 15.2 180 3.780 8 0 47.61905
## Cadillac Fleetwood 10.4 205 5.250 8 0 39.04762
## Lincoln Continental 10.4 215 5.424 8 0 39.63864
## Chrysler Imperial 14.7 230 5.345 8 0 43.03087
## Dodge Challenger 15.5 150 3.520 8 0 42.61364
## AMC Javelin 15.2 150 3.435 8 0 43.66812
## Camaro Z28 13.3 245 3.840 8 0 63.80208
## Pontiac Firebird 19.2 175 3.845 8 0 45.51365
## Lotus Europa 30.4 113 1.513 4 1 74.68605
## Ford Pantera L 15.8 264 3.170 8 1 83.28076
## Ferrari Dino 19.7 175 2.770 6 1 63.17690
## Maserati Bora 15.0 335 3.570 8 1 93.83754
## Volvo 142E 21.4 109 2.780 4 1 39.20863summary_cyl=cars_hp %>%
group_by(cyl) %>%
summarise(mean_mpg=mean(mpg, na.rm=TRUE), mean_hp=mean(hp, na.rm=TRUE), count= n())
summary_cyl## # A tibble: 3 × 4
## cyl mean_mpg mean_hp count
## <fct> <dbl> <dbl> <int>
## 1 4 25.9 111 2
## 2 6 19.7 122. 7
## 3 8 15.1 209. 14summary_transmission=cars_hp %>%
group_by(am) %>%
summarise(mean_mpg=mean(mpg, na.rm=TRUE), mean_power_to_weight_ratio=mean(power_to_weight, na.rm=TRUE))
summary_transmission## # A tibble: 2 × 3
## am mean_mpg mean_power_to_weight_ratio
## <fct> <dbl> <dbl>
## 1 0 16.1 44.6
## 2 1 20.6 62.1Looking at our car groups efficiency for mpg, it would appear that 4 cylinder cars have the best fuel efficiency, as they have the best mean miles per gallon and the worst mean horsepower.
If we wanted to create a bar plot our summary cylinder table would be the better option as we could have each type of cylinder be its own chart, and compare things like mean horsepower or mean miles per gallon. The same is true if we wanted to make a box plot later on as the is simply more information was can use to make our plots in our summary cylinder.
During the time in writing the code, I had a lot of trouble with the summarise function. it is much lengthier than the other functions and requires a bit more of knowledge about R language than the other, especially the “na.rm=TRUE” part as I had to look at resources to find out I needed to add that line of code. However something that does make more sense to me know is filtering data. I have no doubt that I can filter any data as the code is pretty straight forward for it.
Cleaning and summarizing data is important because it helps the reader focus on the important parts of the data that the author wants the reader to see. If an author does not clean their data, figures and tables will have too much information making it much harder to read and interpret.