R 16 basic exercises

> rnorm(4)
[1] -0.7074952  0.3645820  0.7685329 -0.1123462
> set.seed(10)# Seed a fixed random number , Random numbers with the same seed have certain same rules 
> rnorm(4)
[1]  0.01874617 -0.18425254 -1.37133055 -0.59916772
> set.seed(12)
> rnorm(3)
[1] -1.4805676  1.5771695 -0.9567445
> runif(3)
[1] 0.17878500 0.64166537 0.02287774
> rnorm(3)
[1] -2.3943544  0.8922874 -0.3033484
> set.seed(12)
> rnorm(3)
[1] -1.4805676  1.5771695 -0.9567445
> runif(3)
[1] 0.17878500 0.64166537 0.02287774
> rnorm(3)
[1] -2.3943544  0.8922874 -0.3033484
> sample(1:10,4)
[1] 9 6 4 2
> sample(1:10,8,replace=T)
[1] 10  7  8  4  8  9  8 10
> sample(letters,10,replace = T)
 [1] "k" "n" "g" "l" "e" "y" "b" "g" "n" "g"
> sample(1:10)
 [1] 10  4  9  3  8  6  1  5  2  7
> x=airquality# Data frame 
> index=sample(1:nrow(x),nrow(x)*0.7)# Random sampling line number 
> train=x[index,]# Get the training set 
> test=x[-index,]# Get the test set 
> rep(1:3,4)# Create duplicates 
 [1] 1 2 3 1 2 3 1 2 3 1 2 3
> rep(1:3,each=4)# repeat 4 Repeat the next number again 4 Time 
 [1] 1 1 1 1 2 2 2 2 3 3 3 3
> rep(c("a","b"),each=2,times=3)
 [1] "a" "a" "b" "b" "a" "a" "b" "b" "a" "a" "b" "b"
> seq(-10,10,2)# A sequence of equal differences 
 [1] -10  -8  -6  -4  -2   0   2   4   6   8  10
> seq(-10,by=3,length.out=10)
 [1] -10  -7  -4  -1   2   5   8  11  14  17
> sequence(3:5)# Sequential 3-5
 [1] 1 2 3 1 2 3 4 1 2 3 4 5
> rn=rnorm(100,mean = 5,sd=2)# establish 100 The average number is 5, The standard deviation is 2 Positive distribution random number 
> lt=sample(letters,100,replace = T)#100 Random English letters , Put it back 
> sa=sample(1:10,100,replace=T)#1-10,100 A random number , Put it back 
> rp=rep(1:5,each=2,times=10)# Create duplicates 1：5,1122334455,10 Time 
> se=seq(0,49.5,0.5)# establish 0-49.5 The difference is 0.5 Equal difference sequence of 
> a=data.frame(rn,lt,sa,rp,se)# Create a data frame 
> a
            rn lt sa rp   se
1    3.8970845  z  3  1  0.0
2    5.2141178  w  5  1  0.5
3    6.5782481  n  6  2  1.0
4    4.5114894  v 10  2  1.5
5    5.7683599  a  9  3  2.0
6    2.6615290  x  3  3  2.5
7    8.0365449  u  9  4  3.0
8    5.1459624  a  4  4  3.5
9    3.9446499  c  2  5  4.0
10   3.9509488  c  1  5  4.5
11   6.4938015  a  7  1  5.0
12   2.6351721  d  5  1  5.5
13   2.2905830  w  1  2  6.0
14   4.5754290  k  8  2  6.5
15   4.8210370  i  7  3  7.0
16   5.5140650  t 10  3  7.5
17   7.4085737  g  4  4  8.0
18   4.6519378  x  3  4  8.5
19   6.3804336  l  7  5  9.0
20   5.8267692  q  3  5  9.5
21   2.1390419  q  8  1 10.0
22   2.4006618  j  2  1 10.5
23   7.8066997  m  8  2 11.0
24   4.5612996  y 10  2 11.5
25   9.2649822  a  7  3 12.0
26   1.3330602  u  8  3 12.5
27   4.7789806  a  8  4 13.0
28   6.5314207  w  8  4 13.5
29   5.6470092  g  2  5 14.0
30   3.5292422  p  6  5 14.5
31   7.2914343  h  3  1 15.0
32   4.4435251  n 10  1 15.5
33   7.1136826  l  4  2 16.0
34   5.7350289  s  9  2 16.5
35   6.6950292  q  2  3 17.0
36   2.4743591  z  9  3 17.5
37   6.0564733  w  8  4 18.0
38   7.1788136  o  8  4 18.5
39   3.6010433  s  6  5 19.0
40   7.8420523  d  4  5 19.5
41   7.8780434  l  5  1 20.0
42   2.4093774  y  2  1 20.5
43   3.2058803  p  9  2 21.0
44   1.2304478  g  8  2 21.5
45   5.0549958  n  9  3 22.0
46   3.1851667  a 10  3 22.5
47   1.7048766  d  8  4 23.0
48   5.5046801  p  4  4 23.5
49   6.2846285  y  7  5 24.0
50   4.3061278  k  7  5 24.5
51   3.9694735  v  8  1 25.0
52   5.7963378  j  5  1 25.5
53   6.9071144  k  5  2 26.0
54   5.5203578  r 10  2 26.5
55   0.8997312  u  1  3 27.0
56   5.5759414  i  7  3 27.5
57   5.7238908  w  3  4 28.0
58   1.6265180  u  2  4 28.5
59   7.4943396  u 10  5 29.0
60   1.4596142  a  1  5 29.5
61   5.4456717  b  2  1 30.0
62   3.8789591  q  9  1 30.5
63   3.9837409  q  1  2 31.0
64   3.1223338  i  7  2 31.5
65   8.2712988  l  1  3 32.0
66   3.6943662  d  7  3 32.5
67   6.6784272  e 10  4 33.0
68   3.7768094  d  4  4 33.5
69   4.2565661  t  8  5 34.0
70   3.9695321  u  9  5 34.5
71   4.3164628  r  4  1 35.0
72   6.3678376  r  4  1 35.5
73   6.5966849  b  3  2 36.0
74   3.4003917  o  5  2 36.5
75   5.4592605  l  2  3 37.0
76   8.3870959  z  3  3 37.5
77   3.4630581  d  1  4 38.0
78   1.9136946  z  3  4 38.5
79   2.3587982  g  4  5 39.0
80   3.6483570  x  1  5 39.5
81   4.2659891  m  6  1 40.0
82   3.9722556  n  7  1 40.5
83   3.0814921  u  3  2 41.0
84   4.7566727  h 10  2 41.5
85   6.3318743  f  9  3 42.0
86   7.4774396  q  8  3 42.5
87   7.3678816  v  5  4 43.0
88   7.9967230  w  3  4 43.5
89   4.4217913  k 10  5 44.0
90   8.8473744  g  6  5 44.5
91   6.0370249  f  9  1 45.0
92   5.9531819  h  3  1 45.5
93   5.6052075  r  7  2 46.0
94   6.6424654  v  4  2 46.5
95   5.7851585  g  5  3 47.0
96   2.1190982  s  4  3 47.5
97   3.2622050  m  1  4 48.0
98  -0.9729121  a  9  4 48.5
99   4.8989054  d  5  5 49.0
100  7.5990258  z  6  5 49.5
> r1=mean(rn)# The average 
> r2=sd(rn)# Standard deviation 
> r3=var(rn)# variance 
> r4=median(rn)# Median 
> r5=sum(rn)# Sum up 
> rowMeans(a[,-2])# Row average 
  [1]  1.974271  2.928529  3.894562  4.502872  4.942090  2.790382  6.009136  4.161491  3.736162  3.612737  4.873450  3.533793  2.822646
 [14]  5.268857  5.455259  6.503516  5.852143  5.037984  6.845108  5.831692  5.284760  3.975165  7.201675  7.015325  7.816246  6.208265
 [27]  7.444745  8.007855  6.661752  7.257311  6.572859  7.735881  7.278421  8.308757  7.173757  7.993590  9.014118  9.419703  8.400261
 [40]  9.085513  8.469511  6.477344  8.801470  8.182612  9.763749  9.671292  9.176219  9.251170 10.571157 10.201532  9.492368  9.324084
 [53]  9.976779 11.005089  7.974933 10.768985 10.180973  9.031629 12.873585  9.239904  9.611418 11.094740  9.495935 10.905583 11.067825
 [66] 11.548592 13.419607 11.319202 12.814142 13.117383 11.079116 11.716959 11.899171 11.725098 11.864815 12.971774 11.615765 11.853424
 [79] 12.589700 12.287089 12.816497 13.118064 12.270373 14.564168 15.082969 15.244360 14.841970 14.624181 15.855448 16.086844 15.259256
 [92] 13.863295 15.151302 14.785616 15.196290 14.154775 14.065551 15.131772 15.974726 17.024756
> colSums(a[,-2])# Column sum 
       rn        sa        rp        se 
 490.9063  566.0000  300.0000 2475.0000 
> a$logrn=log(a$rn)# The new column takes logarithm 
Warning message:
In log(a$rn) : NaNs produced
> a
            rn lt sa rp   se      logrn
1    3.8970845  z  3  1  0.0  1.3602287
2    5.2141178  w  5  1  0.5  1.6513699
3    6.5782481  n  6  2  1.0  1.8837685
4    4.5114894  v 10  2  1.5  1.5066273
5    5.7683599  a  9  3  2.0  1.7523878
6    2.6615290  x  3  3  2.5  0.9789008
7    8.0365449  u  9  4  3.0  2.0839992
8    5.1459624  a  4  4  3.5  1.6382124
9    3.9446499  c  2  5  4.0  1.3723602
10   3.9509488  c  1  5  4.5  1.3739558
11   6.4938015  a  7  1  5.0  1.8708481
12   2.6351721  d  5  1  5.5  0.9689485
13   2.2905830  w  1  2  6.0  0.8288064
14   4.5754290  k  8  2  6.5  1.5207005
15   4.8210370  i  7  3  7.0  1.5729891
16   5.5140650  t 10  3  7.5  1.7073021
17   7.4085737  g  4  4  8.0  2.0026379
18   4.6519378  x  3  4  8.5  1.5372839
19   6.3804336  l  7  5  9.0  1.8532361
20   5.8267692  q  3  5  9.5  1.7624627
21   2.1390419  q  8  1 10.0  0.7603580
22   2.4006618  j  2  1 10.5  0.8757444
23   7.8066997  m  8  2 11.0  2.0549823
24   4.5612996  y 10  2 11.5  1.5176076
25   9.2649822  a  7  3 12.0  2.2262419
26   1.3330602  u  8  3 12.5  0.2874772
27   4.7789806  a  8  4 13.0  1.5642273
28   6.5314207  w  8  4 13.5  1.8766245
29   5.6470092  g  2  5 14.0  1.7311261
30   3.5292422  p  6  5 14.5  1.2610832
31   7.2914343  h  3  1 15.0  1.9867003
32   4.4435251  n 10  1 15.5  1.4914480
33   7.1136826  l  4  2 16.0  1.9620201
34   5.7350289  s  9  2 16.5  1.7465928
35   6.6950292  q  2  3 17.0  1.9013653
36   2.4743591  z  9  3 17.5  0.9059814
37   6.0564733  w  8  4 18.0  1.8011277
38   7.1788136  o  8  4 18.5  1.9711341
39   3.6010433  s  6  5 19.0  1.2812236
40   7.8420523  d  4  5 19.5  2.0595006
41   7.8780434  l  5  1 20.0  2.0640796
42   2.4093774  y  2  1 20.5  0.8793684
43   3.2058803  p  9  2 21.0  1.1649867
44   1.2304478  g  8  2 21.5  0.2073781
45   5.0549958  n  9  3 22.0  1.6203770
46   3.1851667  a 10  3 22.5  1.1585046
47   1.7048766  d  8  4 23.0  0.5334927
48   5.5046801  p  4  4 23.5  1.7055987
49   6.2846285  y  7  5 24.0  1.8381067
50   4.3061278  k  7  5 24.5  1.4600391
51   3.9694735  v  8  1 25.0  1.3786335
52   5.7963378  j  5  1 25.5  1.7572263
53   6.9071144  k  5  2 26.0  1.9325520
54   5.5203578  r 10  2 26.5  1.7084427
55   0.8997312  u  1  3 27.0 -0.1056592
56   5.5759414  i  7  3 27.5  1.7184612
57   5.7238908  w  3  4 28.0  1.7446488
58   1.6265180  u  2  4 28.5  0.4864415
59   7.4943396  u 10  5 29.0  2.0141480
60   1.4596142  a  1  5 29.5  0.3781722
61   5.4456717  b  2  1 30.0  1.6948211
62   3.8789591  q  9  1 30.5  1.3555669
63   3.9837409  q  1  2 31.0  1.3822213
64   3.1223338  i  7  2 31.5  1.1385807
65   8.2712988  l  1  3 32.0  2.1127915
66   3.6943662  d  7  3 32.5  1.3068090
67   6.6784272  e 10  4 33.0  1.8988825
68   3.7768094  d  4  4 33.5  1.3288796
69   4.2565661  t  8  5 34.0  1.4484627
70   3.9695321  u  9  5 34.5  1.3786482
71   4.3164628  r  4  1 35.0  1.4624363
72   6.3678376  r  4  1 35.5  1.8512599
73   6.5966849  b  3  2 36.0  1.8865672
74   3.4003917  o  5  2 36.5  1.2238906
75   5.4592605  l  2  3 37.0  1.6973133
76   8.3870959  z  3  3 37.5  2.1266943
77   3.4630581  d  1  4 38.0  1.2421520
78   1.9136946  z  3  4 38.5  0.6490357
79   2.3587982  g  4  5 39.0  0.8581522
80   3.6483570  x  1  5 39.5  1.2942769
81   4.2659891  m  6  1 40.0  1.4506741
82   3.9722556  n  7  1 40.5  1.3793341
83   3.0814921  u  3  2 41.0  1.1254139
84   4.7566727  h 10  2 41.5  1.5595484
85   6.3318743  f  9  3 42.0  1.8455963
86   7.4774396  q  8  3 42.5  2.0118904
87   7.3678816  v  5  4 43.0  1.9971302
88   7.9967230  w  3  4 43.5  2.0790318
89   4.4217913  k 10  5 44.0  1.4865449
90   8.8473744  g  6  5 44.5  2.1801207
91   6.0370249  f  9  1 45.0  1.7979113
92   5.9531819  h  3  1 45.5  1.7839259
93   5.6052075  r  7  2 46.0  1.7236961
94   6.6424654  v  4  2 46.5  1.8934832
95   5.7851585  g  5  3 47.0  1.7552958
96   2.1190982  s  4  3 47.5  0.7509906
97   3.2622050  m  1  4 48.0  1.1824034
98  -0.9729121  a  9  4 48.5        NaN
99   4.8989054  d  5  5 49.0  1.5890118
100  7.5990258  z  6  5 49.5  2.0280200