Teste de BI

In [1]:

    

data(mtcars)
dim(mtcars)


    

    






	
32
	
11

In [2]:

    

head(mtcars, 32)


    

    






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    

In [3]:

    

x <- mean(mtcars$mpg[substr(rownames(mtcars), 0, 4) == 'Merc'])
x


    

    





19.0142857142857

In [4]:

    

mtcars[c('mpg', 'wt')]


    

    






mpg
wt

	
Mazda RX4
21.0 
2.620
	
Mazda RX4 Wag
21.0 
2.875
	
Datsun 710
22.8 
2.320
	
Hornet 4 Drive
21.4 
3.215
	
Hornet Sportabout
18.7 
3.440
	
Valiant
18.1 
3.460
	
Duster 360
14.3 
3.570
	
Merc 240D
24.4 
3.190
	
Merc 230
22.8 
3.150
	
Merc 280
19.2 
3.440
	
Merc 280C
17.8 
3.440
	
Merc 450SE
16.4 
4.070
	
Merc 450SL
17.3 
3.730
	
Merc 450SLC
15.2 
3.780
	
Cadillac Fleetwood
10.4 
5.250
	
Lincoln Continental
10.4 
5.424
	
Chrysler Imperial
14.7 
5.345
	
Fiat 128
32.4 
2.200
	
Honda Civic
30.4 
1.615
	
Toyota Corolla
33.9 
1.835
	
Toyota Corona
21.5 
2.465
	
Dodge Challenger
15.5 
3.520
	
AMC Javelin
15.2 
3.435
	
Camaro Z28
13.3 
3.840
	
Pontiac Firebird
19.2 
3.845
	
Fiat X1-9
27.3 
1.935
	
Porsche 914-2
26.0 
2.140
	
Lotus Europa
30.4 
1.513
	
Ford Pantera L
15.8 
3.170
	
Ferrari Dino
19.7 
2.770
	
Maserati Bora
15.0 
3.570
	
Volvo 142E
21.4 
2.780

In [5]:

    

cor(mtcars[c('mpg', 'wt')])


    

    






mpg
wt

	
mpg
 1.0000000
-0.8676594
	
wt
-0.8676594
 1.0000000

In [6]:

    

library(ggplot2)

# Basic scatter plot with regression line:
ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point(size = 3) + geom_smooth(method=lm)


    

In [7]:

    

data(quakes)
dim(quakes)


    

    






	
1000
	
5

In [8]:

    

head(quakes)


    

    






lat
long
depth
mag
stations

	
-20.42
181.62
562   
4.8   
41    
	
-20.62
181.03
650   
4.2   
15    
	
-26.00
184.10
 42   
5.4   
43    
	
-17.97
181.66
626   
4.1   
19    
	
-20.42
181.96
649   
4.0   
11    
	
-19.68
184.31
195   
4.0   
12    

In [9]:

    

cat('A maior magnitude de um terremoto é', max(quakes$mag), '!')


    

    




A maior magnitude de um terremoto é 6.4 !

In [10]:

    

cat('A magnitude média é', mean(quakes$mag))


    

    




A magnitude média é 4.6204

In [11]:

    

cat('O desvio padrão das magnitudes é de', sd(quakes$mag))


    

    




O desvio padrão das magnitudes é de 0.402773

In [12]:

    

data(cars)
dim(cars)


    

    






	
50
	
2

In [13]:

    

tail(cars)


    

    






speed
dist

	
45
23 
 54
	
46
24 
 70
	
47
24 
 92
	
48
24 
 93
	
49
24 
120
	
50
25 
 85

In [14]:

    

max(cars$speed)


    

    





25

In [15]:

    

# Basic scatter plot with regression line:
ggplot(cars, aes(x=speed, y=dist)) + geom_point(size = 3) + geom_smooth(method=lm)


    

In [16]:

    

cor(cars)


    

    






speed
dist

	
speed
1.0000000
0.8068949
	
dist
0.8068949
1.0000000

In [17]:

    

fit <- lm('dist ~ speed', data = cars)
summary(fit)


    

    






Call:
lm(formula = "dist ~ speed", data = cars)

Residuals:
    Min      1Q  Median      3Q     Max 
-29.069  -9.525  -2.272   9.215  43.201 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -17.5791     6.7584  -2.601   0.0123 *  
speed         3.9324     0.4155   9.464 1.49e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.38 on 48 degrees of freedom
Multiple R-squared:  0.6511,	Adjusted R-squared:  0.6438 
F-statistic: 89.57 on 1 and 48 DF,  p-value: 1.49e-12

In [18]:

    

newvalue <- data.frame(speed = 90)
cat('A distância de frenagem é de', predict.lm(fit, newvalue), 'ft caso o carro esteja a 90 mph')


    

    




A distância de frenagem é de 336.3377 ft caso o carro esteja a 90 mph