Loaded Packages
library(dplyr)
library(datasets)
library(tibble)
mtcars <- tbl_df(mtcars)
mtcars
# A tibble: 32 x 11
mpg cyl disp hp drat wt qsec vs am gear carb
* <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
# ... with 22 more rows
Determining the Classifications of each Variable in MTCARS
mpg=continuous
cyl=discrete
disp=continuous
hp=continuous
drat=continuous
wt=continuous
qsec=continuous
vs=discrete
am=discrete
gear=discrete
carb=discrete
Distribution of Three Variables in MTCARS
summary(mtcars)
mpg cyl disp hp
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
Median :19.20 Median :6.000 Median :196.3 Median :123.0
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
drat wt qsec vs
Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
Median :3.695 Median :3.325 Median :17.71 Median :0.0000
Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
am gear carb
Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
Median :0.0000 Median :4.000 Median :2.000
Mean :0.4062 Mean :3.688 Mean :2.812
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :1.0000 Max. :5.000 Max. :8.000
mtcars$mpg
[1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2
[15] 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4
[29] 15.8 19.7 15.0 21.4
mtcars$cyl
[1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4
mtcars$disp
[1] 160.0 160.0 108.0 258.0 360.0 225.0 360.0 146.7 140.8 167.6 167.6
[12] 275.8 275.8 275.8 472.0 460.0 440.0 78.7 75.7 71.1 120.1 318.0
[23] 304.0 350.0 400.0 79.0 120.3 95.1 351.0 145.0 301.0 121.0
Data Type of Three Variables in Esoph
esoph<- tbl_df(esoph)
esoph
# A tibble: 88 x 5
agegp alcgp tobgp ncases ncontrols
<ord> <ord> <ord> <dbl> <dbl>
1 25-34 0-39g/day 0-9g/day 0 40
2 25-34 0-39g/day 10-19 0 10
3 25-34 0-39g/day 20-29 0 6
4 25-34 0-39g/day 30+ 0 5
5 25-34 40-79 0-9g/day 0 27
6 25-34 40-79 10-19 0 7
7 25-34 40-79 20-29 0 4
8 25-34 40-79 30+ 0 7
9 25-34 80-119 0-9g/day 0 2
10 25-34 80-119 10-19 0 1
# ... with 78 more rows
agegp=discrete
alcgp=discrete
tobgp=discrete
Frequency Distribution of agegp
esoph$agegp
[1] 25-34 25-34 25-34 25-34 25-34 25-34 25-34 25-34 25-34 25-34 25-34
[12] 25-34 25-34 25-34 25-34 35-44 35-44 35-44 35-44 35-44 35-44 35-44
[23] 35-44 35-44 35-44 35-44 35-44 35-44 35-44 35-44 45-54 45-54 45-54
[34] 45-54 45-54 45-54 45-54 45-54 45-54 45-54 45-54 45-54 45-54 45-54
[45] 45-54 45-54 55-64 55-64 55-64 55-64 55-64 55-64 55-64 55-64 55-64
[56] 55-64 55-64 55-64 55-64 55-64 55-64 55-64 65-74 65-74 65-74 65-74
[67] 65-74 65-74 65-74 65-74 65-74 65-74 65-74 65-74 65-74 65-74 65-74
[78] 75+ 75+ 75+ 75+ 75+ 75+ 75+ 75+ 75+ 75+ 75+
Levels: 25-34 < 35-44 < 45-54 < 55-64 < 65-74 < 75+
agegp.freq <- table (esoph$agegp)
agegp.freq
25-34 35-44 45-54 55-64 65-74 75+
15 15 16 16 15 11
cbind(agegp.freq)
agegp.freq
25-34 15
35-44 15
45-54 16
55-64 16
65-74 15
75+ 11
Relative Frequency Distribution of agegp
nrow(agegp.freq)
[1] 6
agegp.relfreq <- agegp.freq / nrow(agegp.freq)
agegp.relfreq
25-34 35-44 45-54 55-64 65-74 75+
2.500000 2.500000 2.666667 2.666667 2.500000 1.833333
round.agegp.relfreq <- round(agegp.relfreq, digits = 2)
round.agegp.relfreq
25-34 35-44 45-54 55-64 65-74 75+
2.50 2.50 2.67 2.67 2.50 1.83
Frequency Distribution of alcgp
esoph$alcgp
[1] 0-39g/day 0-39g/day 0-39g/day 0-39g/day 40-79 40-79 40-79
[8] 40-79 80-119 80-119 80-119 120+ 120+ 120+
[15] 120+ 0-39g/day 0-39g/day 0-39g/day 0-39g/day 40-79 40-79
[22] 40-79 40-79 80-119 80-119 80-119 80-119 120+
[29] 120+ 120+ 0-39g/day 0-39g/day 0-39g/day 0-39g/day 40-79
[36] 40-79 40-79 40-79 80-119 80-119 80-119 80-119
[43] 120+ 120+ 120+ 120+ 0-39g/day 0-39g/day 0-39g/day
[50] 0-39g/day 40-79 40-79 40-79 40-79 80-119 80-119
[57] 80-119 80-119 120+ 120+ 120+ 120+ 0-39g/day
[64] 0-39g/day 0-39g/day 0-39g/day 40-79 40-79 40-79 80-119
[71] 80-119 80-119 80-119 120+ 120+ 120+ 120+
[78] 0-39g/day 0-39g/day 0-39g/day 40-79 40-79 40-79 40-79
[85] 80-119 80-119 120+ 120+
Levels: 0-39g/day < 40-79 < 80-119 < 120+
alcgp.freq <- table(esoph$alcgp)
alcgp.freq
0-39g/day 40-79 80-119 120+
23 23 21 21
cbind(alcgp.freq)
alcgp.freq
0-39g/day 23
40-79 23
80-119 21
120+ 21
Relative Frequency Distribution of alcgp
nrow(alcgp.freq)
[1] 4
alchgp.relfreq <- alcgp.freq/ nrow(alcgp.freq)
alchgp.relfreq
0-39g/day 40-79 80-119 120+
5.75 5.75 5.25 5.25
Frequency Distribution of tobgp
esoph$tobgp
[1] 0-9g/day 10-19 20-29 30+ 0-9g/day 10-19 20-29
[8] 30+ 0-9g/day 10-19 30+ 0-9g/day 10-19 20-29
[15] 30+ 0-9g/day 10-19 20-29 30+ 0-9g/day 10-19
[22] 20-29 30+ 0-9g/day 10-19 20-29 30+ 0-9g/day
[29] 10-19 20-29 0-9g/day 10-19 20-29 30+ 0-9g/day
[36] 10-19 20-29 30+ 0-9g/day 10-19 20-29 30+
[43] 0-9g/day 10-19 20-29 30+ 0-9g/day 10-19 20-29
[50] 30+ 0-9g/day 10-19 20-29 30+ 0-9g/day 10-19
[57] 20-29 30+ 0-9g/day 10-19 20-29 30+ 0-9g/day
[64] 10-19 20-29 30+ 0-9g/day 10-19 20-29 0-9g/day
[71] 10-19 20-29 30+ 0-9g/day 10-19 20-29 30+
[78] 0-9g/day 10-19 30+ 0-9g/day 10-19 20-29 30+
[85] 0-9g/day 10-19 0-9g/day 10-19
Levels: 0-9g/day < 10-19 < 20-29 < 30+
tobgp.freq <- table (esoph$tobgp)
tobgp.freq
0-9g/day 10-19 20-29 30+
24 24 20 20
cbind(tobgp.freq)
tobgp.freq
0-9g/day 24
10-19 24
20-29 20
30+ 20
Relative Frequency Distribution of tobgp
nrow(tobgp.freq)
[1] 4
tobgp.relfreq <- tobgp.freq/ nrow(tobgp.freq)
tobgp.relfreq
0-9g/day 10-19 20-29 30+
6 6 5 5
Joint Frequency of agegp and alcgp
y <- c(esoph$alcgp)
y
[1] 1 1 1 1 2 2 2 2 3 3 3 4 4 4 4 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 1 1 1 1 2
[36] 2 2 2 3 3 3 3 4 4 4 4 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 1 1 1 1 2 2 2 3
[71] 3 3 3 4 4 4 4 1 1 1 2 2 2 2 3 3 4 4
x <- c(esoph$agegp)
x
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3
[36] 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5
[71] 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6
esophtab <- table(x,y)
esophtab
y
x 1 2 3 4
1 4 4 3 4
2 4 4 4 3
3 4 4 4 4
4 4 4 4 4
5 4 3 4 4
6 3 4 2 2
ftable (esophtab)
y 1 2 3 4
x
1 4 4 3 4
2 4 4 4 3
3 4 4 4 4
4 4 4 4 4
5 4 3 4 4
6 3 4 2 2
Joint Frequency of alcgp and tobgp
y <- c(esoph$tobgp)
y
[1] 1 2 3 4 1 2 3 4 1 2 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 1 2 3 4 1
[36] 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 1
[71] 2 3 4 1 2 3 4 1 2 4 1 2 3 4 1 2 1 2
x <- c(esoph$alcgp)
x
[1] 1 1 1 1 2 2 2 2 3 3 3 4 4 4 4 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 1 1 1 1 2
[36] 2 2 2 3 3 3 3 4 4 4 4 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 1 1 1 1 2 2 2 3
[71] 3 3 3 4 4 4 4 1 1 1 2 2 2 2 3 3 4 4
esophtab2 <- table(x,y)
esophtab2
y
x 1 2 3 4
1 6 6 5 6
2 6 6 6 5
3 6 6 4 5
4 6 6 5 4
ftable(esophtab2)
y 1 2 3 4
x
1 6 6 5 6
2 6 6 6 5
3 6 6 4 5
4 6 6 5 4
Diamonds Dataset
library(ggplot2)
diamonds
# A tibble: 53,940 x 10
carat cut color clarity depth table price x y z
<dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
# ... with 53,930 more rows
Range of Price
range(diamonds$price)
[1] 326 18823
Range of Carat
range(diamonds$carat)
[1] 0.20 5.01
Range of Depth
range(diamonds$depth)
[1] 43 79
Range of Table
range(diamonds$table)
[1] 43 95
Group Frequency of Depth in Diamonds
diamonds <- tbl_df(diamonds)
diamonds
# A tibble: 53,940 x 10
carat cut color clarity depth table price x y z
<dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
# ... with 53,930 more rows
summary(diamonds)
carat cut color clarity
Min. :0.2000 Fair : 1610 D: 6775 SI1 :13065
1st Qu.:0.4000 Good : 4906 E: 9797 VS2 :12258
Median :0.7000 Very Good:12082 F: 9542 SI2 : 9194
Mean :0.7979 Premium :13791 G:11292 VS1 : 8171
3rd Qu.:1.0400 Ideal :21551 H: 8304 VVS2 : 5066
Max. :5.0100 I: 5422 VVS1 : 3655
J: 2808 (Other): 2531
depth table price x
Min. :43.00 Min. :43.00 Min. : 326 Min. : 0.000
1st Qu.:61.00 1st Qu.:56.00 1st Qu.: 950 1st Qu.: 4.710
Median :61.80 Median :57.00 Median : 2401 Median : 5.700
Mean :61.75 Mean :57.46 Mean : 3933 Mean : 5.731
3rd Qu.:62.50 3rd Qu.:59.00 3rd Qu.: 5324 3rd Qu.: 6.540
Max. :79.00 Max. :95.00 Max. :18823 Max. :10.740
y z
Min. : 0.000 Min. : 0.000
1st Qu.: 4.720 1st Qu.: 2.910
Median : 5.710 Median : 3.530
Mean : 5.735 Mean : 3.539
3rd Qu.: 6.540 3rd Qu.: 4.040
Max. :58.900 Max. :31.800
breaks.depth=seq(40,80, by=5)
breaks.depth
[1] 40 45 50 55 60 65 70 75 80
depth.cut=cut(diamonds$depth, breaks.depth, right=FALSE)
depth.freq=table(depth.cut)
depth.freq
depth.cut
[40,45) [45,50) [50,55) [55,60) [60,65) [65,70) [70,75) [75,80)
3 0 19 5092 47932 870 21 3
cbind(depth.freq)
depth.freq
[40,45) 3
[45,50) 0
[50,55) 19
[55,60) 5092
[60,65) 47932
[65,70) 870
[70,75) 21
[75,80) 3
Grouped Frequency of Table in Diamonds
breaks.table=seq(40,95, by =5)
breaks.table
[1] 40 45 50 55 60 65 70 75 80 85 90 95
table.cut=cut(diamonds$table, breaks.table, right=FALSE)
table.freq=table(table.cut)
table.freq
table.cut
[40,45) [45,50) [50,55) [55,60) [60,65) [65,70) [70,75) [75,80) [80,85)
2 2 3571 41278 8760 310 14 2 0
[85,90) [90,95)
0 0
cbind(table.freq)
table.freq
[40,45) 2
[45,50) 2
[50,55) 3571
[55,60) 41278
[60,65) 8760
[65,70) 310
[70,75) 14
[75,80) 2
[80,85) 0
[85,90) 0
[90,95) 0
Plotting Histogram of Price
hist(diamonds$price)
Plotting Histogram of Carat
hist(diamonds$carat)
Plotting Histogram of Depth
hist(diamonds$depth)
Plotting Histogram of Table
hist(diamonds$table)
