Simulation-in-Histogram

Simulation-In-Histogram.
April James Palermo
2023-03-29
# Mindanao State University
# General Santos City
# Introduction to R base commands
# Submitted by: April James Palermo
# Submitted to: Prof. Carlito O. Daarol
# Processing of continuous data
# Using random number generators

# Exer1: generate data with mean 2 and standard deviation 1.5 using rno
rm() command
data <- rnorm(1000,2,1.5) # 1 thousand values
length(data) # count number of elements
## [1] 1000
data[1:20] # display first 20 elements
## [1] 6.7950501 2.9971858 0.5114664 3.7472543 -1.0568895 0.13443
03
## [7] 2.2099518 3.6546881 0.1161034 4.6784938 3.6113647 2.09991
32
## [13] 1.4003658 0.2159255 3.0074956 0.6181636 3.3564768 0.91454
59
## [19] -0.1743168 4.3279270
data[1:300] # display the first 300 elements
## [1] 6.795050140 2.997185793 0.511466423 3.747254252 -1.0568895
12
## [6] 0.134430261 2.209951813 3.654688092 0.116103382 4.6784938
05
## [11] 3.611364718 2.099913215 1.400365762 0.215925543 3.0074956
04
## [16] 0.618163649 3.356476832 0.914545900 -0.174316824 4.3279269
85
## [21] 0.151049885 0.279433234 1.273728401 1.893149093 -1.2263889
63
## [26] 1.602769253 2.472274938 1.707579631 0.421872923 1.5255188
28
## [31] 4.696390334 1.535573912 4.345618480 1.227820815 -0.6071010
52
## [36] 2.831963560 2.404229519 2.509695348 -0.583091111 5.5689491
99
## [41] 1.119368833 2.111837858 0.665506408 -0.620966233 1.9609263
19
## [46] 1.807994709 0.517271207 2.692412408 3.737708819 1.0195518
35
## [51] 0.353007120 1.628221232 -0.373567030 2.162220262 1.2564209
79
## [56] 3.230860975 0.527688137 3.430307311 1.599772884 1.9074367
61
## [61] -0.006102183 2.606483788 3.352963337 3.987781161 1.1797236
39
## [66] 3.002797128 1.061177927 1.800204756 0.384008538 1.1979587
20
## [71] 0.897165627 2.310465216 3.077940173 2.118584349 1.1412144
65
## [76] 1.016396574 -1.887644721 3.051540786 2.025936304 5.4993287
88
## [81] 2.663324021 1.246812266 2.280258064 1.616529449 -0.0576736
59
## [86] 2.429690661 -0.179536320 3.228365534 0.017335783 4.7766738
16
## [91] 1.798836542 0.215230399 -0.395454981 0.361934574 1.6274335
46
## [96] 3.786168664 4.151159393 -0.967524400 0.773678889 2.6171003
12
## [101] 2.185146308 2.499219574 2.216669853 0.334296188 3.3481633
66
## [106] -0.150050898 1.608062070 3.030498559 0.824991536 -0.1540341
50
## [111] 0.937714816 -0.466886077 2.256372290 0.669470894 2.9325254
62
## [116] 3.478287425 1.391616168 2.118326109 2.386965644 1.2284865
33
## [121] -0.413528032 3.300248906 1.875707863 2.554864587 2.8193410
62
## [126] 0.227161757 -1.452228004 4.025204696 2.168795746 1.9370254
03
## [131] 0.021424613 4.980938162 2.797625897 0.787023015 0.9641629
92
## [136] 1.241074510 1.285468412 1.977582910 0.167900297 0.2708950
64
## [141] 1.395779159 3.591536082 4.058971580 2.539896498 0.6471037
08
## [146] -0.205524325 -0.300788502 1.398177934 -0.122276472 2.2293000
24
## [151] 1.197424526 5.026313700 0.001975712 2.411165881 1.4081390
69
## [156] 3.549472740 -0.412302858 3.362745931 -0.998544580 2.3620314
29
## [161] 1.890737159 2.003650673 -0.664821990 1.878542912 -1.1958435
58
## [166] 2.339436716 1.739921476 4.194267582 2.056000110 4.1718896
72
## [171] 1.407019752 1.017248943 2.728342383 3.110178969 3.7086015
98
## [176] 3.798673768 3.603001115 -0.852651403 0.584732627 2.9633631
07
## [181] 0.707380439 2.777706189 2.141100853 4.608066706 1.2482046
87
## [186] 1.111529125 0.869009492 0.761559882 3.200158922 3.2645058
63
## [191] -1.079178002 2.963826608 3.280293544 0.867398583 1.1275362
68
## [196] 1.369006521 3.098055661 0.135720012 0.515333658 3.3961149
28
## [201] 5.247966333 2.688101575 0.842640514 3.265464254 2.5296347
91
## [206] 1.037064104 0.398775259 2.908166835 1.001596048 -0.5146671
94
## [211] 0.880574149 0.800895432 -0.224082422 2.544632159 1.8234559
71
## [216] -0.224335693 1.073669908 0.073960031 4.273908071 6.1964152
07
## [221] 5.676410225 3.363331284 -0.936296982 4.000668971 1.6229855
40
## [226] 0.068404191 1.176475142 3.429656055 3.416952572 2.2916118
36
## [231] 2.602141854 2.415200497 1.300414768 3.439331358 4.0200014
43
## [236] 1.106696812 -0.594560209 0.501360852 4.247218607 2.5237316
38
## [241] 2.707108680 3.752511370 2.894097741 2.647523784 3.8072793
54
## [246] 3.319387411 2.306003543 1.049012665 1.823571653 5.0946637
47
## [251] 1.893538112 2.136748072 1.291566619 0.765436698 1.3792772
81
## [256] 2.963045567 -0.695473564 2.566529941 4.273584784 3.4604527
02
## [261] 5.117364949 0.664109717 -1.020939135 1.087650152 3.3735151
65
## [266] 2.071374452 0.583851842 0.379234169 2.905616978 1.8738817
35
## [271] 5.171919307 -0.335591042 3.940047797 0.775103809 2.6351831
35
## [276] -0.459101548 3.281434927 2.826557111 -0.451146704 1.5758281
60
## [281] 0.925787880 3.674247135 1.858296997 1.771919716 0.7032241
32
## [286] 2.579438717 3.803453744 2.087316387 3.600449160 3.6793304
45
## [291] 4.270884357 2.989631244 3.952852862 3.559064935 2.3543997
96
## [296] 0.914442244 2.191114092 2.138425921 1.967064876 2.2737263
83
# Exer2: Draw histogram with one main title and different thickness
maintitle <- "Histogram and Density Plot"
hist(data, breaks=20,col="lightblue",main = maintitle)
hist(data, breaks=40,col="lightblue",main = maintitle)
hist(data, breaks=100,col="gray",main = maintitle)
hist(data, breaks=300,col="gray",main = maintitle)
# Exer3: Draw histogram with one main title and sub title
subtitle <- "This is my second title"
maintitle <- paste0("Histogram and Density Plot \n",subtitle)
hist(data, breaks=20,col="lightblue",main = maintitle)
hist(data, breaks=40,col="lightblue",main = maintitle)
hist(data, breaks=100,col="gray",main = maintitle)
# Question: What causes the subtitle to be in the second line?
# Exer4: Draw histogram with main title and sub title
subtitle <- "This is my second title"
maintitle <- paste0("Histogram and Density Plot \n",subtitle)
hist(data, breaks=20,col="lightblue",main = maintitle)
# # Add density curve. We define the range of the density curve
x = seq(from=min(data), to=max(data), length.out=100)
norm_dist = dnorm(x, mean=mean(data), sd=sd(data)) * (max(data)-min(dat
a))/
20*length(data)
lines(x, norm_dist, col='violet',lwd=4)
# Exer5: Draw histogram with main title and sub title
subtitle <- "This is my second title"
maintitle <- paste0("Histogram and Density Plot \n",subtitle)
hist(data, breaks=20,col="lightblue",main = maintitle)
# Add density curve and the location of the mean value
(x = seq(from=min(data), to=max(data), length.out=100))
## [1] -3.57541165 -3.47065951 -3.36590737 -3.26115523 -3.15640309 -3.
05165095
## [7] -2.94689881 -2.84214667 -2.73739453 -2.63264239 -2.52789025 -2.
42313811
## [13] -2.31838597 -2.21363384 -2.10888170 -2.00412956 -1.89937742 -1.
79462528
## [19] -1.68987314 -1.58512100 -1.48036886 -1.37561672 -1.27086458 -1.
16611244
## [25] -1.06136030 -0.95660816 -0.85185602 -0.74710389 -0.64235175 -0.
53759961
## [31] -0.43284747 -0.32809533 -0.22334319 -0.11859105 -0.01383891 0.
09091323
## [37] 0.19566537 0.30041751 0.40516965 0.50992179 0.61467392 0.
71942606
## [43] 0.82417820 0.92893034 1.03368248 1.13843462 1.24318676 1.
34793890
## [49] 1.45269104 1.55744318 1.66219532 1.76694746 1.87169960 1.
97645173
## [55] 2.08120387 2.18595601 2.29070815 2.39546029 2.50021243 2.
60496457
## [61] 2.70971671 2.81446885 2.91922099 3.02397313 3.12872527 3.
23347741
## [67] 3.33822955 3.44298168 3.54773382 3.65248596 3.75723810 3.
86199024
## [73] 3.96674238 4.07149452 4.17624666 4.28099880 4.38575094 4.
49050308
## [79] 4.59525522 4.70000736 4.80475949 4.90951163 5.01426377 5.
11901591
## [85] 5.22376805 5.32852019 5.43327233 5.53802447 5.64277661 5.
74752875
## [91] 5.85228089 5.95703303 6.06178517 6.16653730 6.27128944 6.
37604158
## [97] 6.48079372 6.58554586 6.69029800 6.79505014
norm_dist = dnorm(x, mean=mean(data), sd=sd(data)) * (max(data)-min(dat
a))/
20*length(data)
lines(x, norm_dist, col='violet',lwd=4)
abline(v = mean(data), col="black", lwd=3, lty=2)
# Add legend to top right position
legend("topright",
legend = c("Histogram", "density curve", "Average"),
col = c("lightblue", "violet", "black"),
lty = 1)
# Compute Quartile values Q1,Q2 and Q3
# These quantities divide the data into 4 equal parts
hist(data, breaks=20,col="lightblue",main = maintitle)
Quantiles = quantile(data)
Quantiles
## 0% 25% 50% 75% 100%
## -3.5754116 0.8513114 1.9366460 3.0049901 6.7950501
# locate the quartiles Q1, Q2, Q3
abline(v = Quantiles[2], col="black", lwd=3, lty=3)
abline(v = Quantiles[3], col="red", lwd=3, lty=3)
abline(v = Quantiles[4], col="blue", lwd=3, lty=3)
# Exer6: Locate the lowest 5% and the highest 5% of the distribution
data
## [1] 6.795050140 2.997185793 0.511466423 3.747254252 -1.056889
512
## [6] 0.134430261 2.209951813 3.654688092 0.116103382 4.678493
805
## [11] 3.611364718 2.099913215 1.400365762 0.215925543 3.007495
604
## [16] 0.618163649 3.356476832 0.914545900 -0.174316824 4.327926
985
## [21] 0.151049885 0.279433234 1.273728401 1.893149093 -1.226388
963
## [26] 1.602769253 2.472274938 1.707579631 0.421872923 1.525518
828
## [31] 4.696390334 1.535573912 4.345618480 1.227820815 -0.607101
052
## [36] 2.831963560 2.404229519 2.509695348 -0.583091111 5.568949
199
## [41] 1.119368833 2.111837858 0.665506408 -0.620966233 1.960926
319
## [46] 1.807994709 0.517271207 2.692412408 3.737708819 1.019551
835
## [51] 0.353007120 1.628221232 -0.373567030 2.162220262 1.256420
979
## [56] 3.230860975 0.527688137 3.430307311 1.599772884 1.907436
761
## [61] -0.006102183 2.606483788 3.352963337 3.987781161 1.179723
639
## [66] 3.002797128 1.061177927 1.800204756 0.384008538 1.197958
720
## [71] 0.897165627 2.310465216 3.077940173 2.118584349 1.141214
465
## [76] 1.016396574 -1.887644721 3.051540786 2.025936304 5.499328
788
## [81] 2.663324021 1.246812266 2.280258064 1.616529449 -0.057673
659
## [86] 2.429690661 -0.179536320 3.228365534 0.017335783 4.776673
816
## [91] 1.798836542 0.215230399 -0.395454981 0.361934574 1.627433
546
## [96] 3.786168664 4.151159393 -0.967524400 0.773678889 2.617100
312
## [101] 2.185146308 2.499219574 2.216669853 0.334296188 3.348163
366
## [106] -0.150050898 1.608062070 3.030498559 0.824991536 -0.154034
150
## [111] 0.937714816 -0.466886077 2.256372290 0.669470894 2.932525
462
## [116] 3.478287425 1.391616168 2.118326109 2.386965644 1.228486
533
## [121] -0.413528032 3.300248906 1.875707863 2.554864587 2.819341
062
## [126] 0.227161757 -1.452228004 4.025204696 2.168795746 1.937025
403
## [131] 0.021424613 4.980938162 2.797625897 0.787023015 0.964162
992
## [136] 1.241074510 1.285468412 1.977582910 0.167900297 0.270895
064
## [141] 1.395779159 3.591536082 4.058971580 2.539896498 0.647103
708
## [146] -0.205524325 -0.300788502 1.398177934 -0.122276472 2.229300
024
## [151] 1.197424526 5.026313700 0.001975712 2.411165881 1.408139
069
## [156] 3.549472740 -0.412302858 3.362745931 -0.998544580 2.362031
429
## [161] 1.890737159 2.003650673 -0.664821990 1.878542912 -1.195843
558
## [166] 2.339436716 1.739921476 4.194267582 2.056000110 4.171889
672
## [171] 1.407019752 1.017248943 2.728342383 3.110178969 3.708601
598
## [176] 3.798673768 3.603001115 -0.852651403 0.584732627 2.963363
107
## [181] 0.707380439 2.777706189 2.141100853 4.608066706 1.248204
687
## [186] 1.111529125 0.869009492 0.761559882 3.200158922 3.264505
863
## [191] -1.079178002 2.963826608 3.280293544 0.867398583 1.127536
268
## [196] 1.369006521 3.098055661 0.135720012 0.515333658 3.396114
928
## [201] 5.247966333 2.688101575 0.842640514 3.265464254 2.529634
791
## [206] 1.037064104 0.398775259 2.908166835 1.001596048 -0.514667
194
## [211] 0.880574149 0.800895432 -0.224082422 2.544632159 1.823455
971
## [216] -0.224335693 1.073669908 0.073960031 4.273908071 6.196415
207
## [221] 5.676410225 3.363331284 -0.936296982 4.000668971 1.622985
540
## [226] 0.068404191 1.176475142 3.429656055 3.416952572 2.291611
836
## [231] 2.602141854 2.415200497 1.300414768 3.439331358 4.020001
443
## [236] 1.106696812 -0.594560209 0.501360852 4.247218607 2.523731
638
## [241] 2.707108680 3.752511370 2.894097741 2.647523784 3.807279
354
## [246] 3.319387411 2.306003543 1.049012665 1.823571653 5.094663
747
## [251] 1.893538112 2.136748072 1.291566619 0.765436698 1.379277
281
## [256] 2.963045567 -0.695473564 2.566529941 4.273584784 3.460452
702
## [261] 5.117364949 0.664109717 -1.020939135 1.087650152 3.373515
165
## [266] 2.071374452 0.583851842 0.379234169 2.905616978 1.873881
735
## [271] 5.171919307 -0.335591042 3.940047797 0.775103809 2.635183
135
## [276] -0.459101548 3.281434927 2.826557111 -0.451146704 1.575828
160
## [281] 0.925787880 3.674247135 1.858296997 1.771919716 0.703224
132
## [286] 2.579438717 3.803453744 2.087316387 3.600449160 3.679330
445
## [291] 4.270884357 2.989631244 3.952852862 3.559064935 2.354399
796
## [296] 0.914442244 2.191114092 2.138425921 1.967064876 2.273726
383
## [301] 0.220879177 4.082999903 1.671456432 3.195961515 1.207592
942
## [306] 2.875189212 1.857190718 1.084614217 4.906765391 1.343667
168
## [311] 2.032315523 2.476480179 -1.070792622 2.020971032 6.186236
925
## [316] 1.138593439 0.043309479 2.643123040 0.994689548 1.888622
999
## [321] 1.080057360 1.644525065 2.153317970 2.442957840 4.798494
667
## [326] 1.163670125 1.348058061 4.164677285 3.826269969 1.925113
405
## [331] 3.231813577 1.088646383 2.024858178 0.547109467 -0.489607
061
## [336] -0.309901376 -1.581730735 4.949248212 0.068491227 2.908237
304
## [341] 2.178363044 2.459361173 2.178638877 1.740774488 3.698397
464
## [346] 1.533467903 0.896601845 2.302783062 3.875482802 1.267143
494
## [351] -1.275594605 1.423002596 3.992722900 1.603951948 1.318473
683
## [356] 2.619657820 4.629449224 2.563049528 2.630892816 2.743201
800
## [361] 4.115788134 1.459013505 2.729805458 0.949905576 2.425971
915
## [366] 3.769413950 1.439217960 -0.107846992 -1.316618571 1.622461
164
## [371] 0.050267712 1.005908204 1.013830687 2.356625013 2.949415
017
## [376] 2.419453385 3.555284265 2.281199490 1.225283126 -0.585133
676
## [381] 0.945452145 4.582033460 0.542360140 3.911150176 1.372389
495
## [386] 0.446155013 0.966667218 4.579783303 0.743109326 0.801345
528
## [391] 1.171484752 1.874731349 2.129289797 0.519047519 2.485590
596
## [396] 0.430349118 2.398755145 3.223449261 0.351903472 1.676357
318
## [401] 1.694215402 3.213743486 1.477893515 2.050495511 0.826578
822
## [406] 3.052117622 0.768667441 2.682874309 2.263838812 2.159695
842
## [411] 2.423292793 1.668600112 1.655704478 1.952707918 2.830999
537
## [416] 1.289467147 2.069350208 3.981261607 4.461629303 0.885217
324
## [421] -1.000522134 4.727642410 3.203293240 -0.119996675 3.485061
128
## [426] 2.875660433 1.884146311 2.187822607 1.293867247 1.604578
698
## [431] 1.214136516 0.076391492 -1.375950312 4.905165395 0.151170
923
## [436] 2.807384753 1.655845750 3.038619123 5.173248210 2.450901
088
## [441] 5.871866602 1.485345748 1.513342255 1.460967255 2.162043
500
## [446] 3.453910073 2.824242021 2.006432226 0.219350190 1.947260
781
## [451] 3.904376096 3.076547385 0.895933964 2.885090663 3.432678
389
## [456] 2.467654190 -0.309343140 0.411438610 0.668180670 -0.249866
193
## [461] 4.125051888 0.406004489 -1.518594308 3.076504685 3.461789
479
## [466] 2.141568571 2.519208042 1.530850258 2.068421482 3.495426
912
## [471] 3.522961496 1.326560892 1.615080922 1.188746739 4.549208
443
## [476] 2.600803242 2.206499565 2.678741728 3.709414226 2.360060
202
## [481] -0.447019627 1.218309391 -0.105587315 0.170505443 1.504964
369
## [486] -1.298939150 3.404916953 0.803177473 -0.298411375 2.434916
767
## [491] 5.141913206 3.724655757 0.601177526 3.169820501 0.883370
835
## [496] 0.727378670 0.498719820 0.480091499 -0.631062634 2.432687
598
## [501] 3.706545074 3.677513578 1.224027274 3.113150202 0.750769
038
## [506] -0.301181061 2.250694938 2.219217935 1.977742786 2.597814
723
## [511] -0.131138480 1.623764959 1.395725615 1.656500801 1.491236
585
## [516] 3.486321194 2.971195807 4.198588644 3.771777750 3.774450
382
## [521] 0.692904857 2.471132795 2.934745524 0.064860154 1.742114
586
## [526] 2.926401793 4.437657223 3.020597844 0.337597041 4.189059
180
## [531] 1.711568139 -0.425900220 0.801755266 0.186310709 3.705099
926
## [536] 1.435737117 1.185118073 2.257769481 3.407767771 2.849783
013
## [541] 2.992534017 1.178175770 2.361699770 2.056208437 1.963282
502
## [546] 2.674198081 1.413175111 1.171207926 0.961810184 3.562846
689
## [551] 0.001426836 3.596475227 -1.655739050 0.427573403 1.795434
721
## [556] 4.762212076 0.991994118 3.277619888 0.995769527 4.980927
125
## [561] -0.452281620 0.648884615 2.079987530 2.076727813 1.182485
741
## [566] 1.873811502 2.007571021 1.882434511 3.254327739 -0.170096
009
## [571] 3.044520782 0.549099392 0.793326706 -0.324500209 -0.374584
060
## [576] 2.771876306 4.088093419 1.564986777 1.577381866 1.853214
755
## [581] 0.777356561 2.637903644 0.747007199 -0.339107305 2.979966
846
## [586] 2.127998150 2.192451448 2.193779509 2.022839501 0.085484
092
## [591] 0.559737910 4.109813321 -2.095299906 4.601292342 0.315577
419
## [596] 0.449076192 0.809628474 1.586657322 2.124452955 3.275283
094
## [601] 1.849713514 1.002829277 1.823378994 -0.624164340 3.829338
711
## [606] -0.613596411 1.674633812 0.834201865 2.466366711 -0.655043
180
## [611] 0.564251186 4.789306612 0.445343387 5.696914808 3.627024
607
## [616] 2.835437773 1.131054338 3.682889822 2.856937115 1.758801
284
## [621] 4.312855495 1.460316592 3.223820266 1.309562290 3.389119
803
## [626] 1.908329477 0.846784673 2.357666541 -0.192927748 2.538130
654
## [631] 4.197891741 1.413608844 -0.525329086 1.499710994 2.906618
869
## [636] 1.717995855 0.538062527 1.960436077 1.646467474 1.850500
170
## [641] 1.604813063 4.190557323 0.811225173 1.588543547 4.173425
897
## [646] 2.618060289 4.171309623 2.614054244 1.865698725 3.365848
288
## [651] 2.226845191 3.264591113 -0.325397333 1.055424752 0.375334
260
## [656] 3.020184514 2.521406704 1.543412285 2.550969607 -0.606162
613
## [661] -0.656644329 2.462253208 -1.666150393 1.386371287 3.290618
392
## [666] 1.095412110 1.400150333 1.244357560 0.831328579 4.520641
709
## [671] -1.000364433 3.788260599 0.722632048 1.531094160 1.726640
457
## [676] 1.340683855 1.183896990 2.919382193 1.102226846 4.162641
720
## [681] 3.491886943 2.261490517 4.798242119 1.971833866 3.902422
510
## [686] 1.482955956 -0.644399751 1.972067776 4.063046313 0.196186
999
## [691] 2.626947098 -0.984860714 3.321227718 1.936266560 2.075171
541
## [696] 1.354901244 1.933936205 0.252627061 2.953882468 1.070892
082
## [701] 2.172754280 3.405652172 1.487034369 2.394833090 2.350080
757
## [706] 3.598330220 -0.188118404 1.717818434 2.301609934 1.610906
636
## [711] 2.497365191 0.944813637 1.578107242 -1.391068822 2.363070
687
## [716] 2.754318331 1.963866550 0.522656479 -0.932336963 4.529820
567
## [721] 4.535804796 5.789012033 1.823586017 0.599283398 1.087248
063
## [726] -1.739174232 1.742781179 -0.134534184 -0.014040832 -0.646363
199
## [731] 2.289784523 2.891792144 3.174212576 2.007737082 1.331272
998
## [736] 3.101431107 2.727126352 2.201573495 3.042505391 1.699238
585
## [741] 3.695679969 3.846874301 3.916611540 3.067523911 3.459379
087
## [746] 5.372152587 2.128862802 1.942682407 -0.957937451 1.533144
227
## [751] 1.131658858 1.111289619 0.712329765 3.449536040 3.165923
211
## [756] -1.749404788 -0.928863720 2.820332950 2.487001075 2.922057
555
## [761] 1.887473492 0.671591446 2.099569531 2.016107197 2.643881
568
## [766] 3.173742111 0.617294677 0.382916623 0.781165170 -1.054597
602
## [771] -0.528957192 3.835674559 4.674451289 2.886821211 4.368170
914
## [776] 0.341337659 1.863294963 1.629965115 4.024879480 -0.532005
051
## [781] 1.117950828 1.069379422 1.767143171 1.267085341 2.593013
428
## [786] 2.583741464 1.622874771 0.143041715 3.919337728 -0.134919
585
## [791] 3.478836016 1.373205836 2.587106412 2.837114035 2.661576
789
## [796] 0.505212812 2.179293390 4.411258156 2.312644063 3.535971
708
## [801] 2.121551782 1.304466954 1.873287149 0.971330545 0.877567
585
## [806] 3.694901529 -0.244056140 3.812443598 -0.511530803 1.259393
899
## [811] 2.314123334 2.761530557 -1.491407671 -0.109694871 0.590782
341
## [816] 0.102176057 1.441105349 0.542550785 3.051805899 1.974227
870
## [821] 0.541514026 3.576460355 0.425754130 1.746449142 2.224129
970
## [826] 3.999578143 2.023381700 2.664380717 2.540384336 2.353327
711
## [831] 1.647424439 1.824024295 2.643755799 2.385605497 4.715161
185
## [836] 3.386408332 2.242501216 4.982648701 1.109108743 -0.235646
483
## [841] 1.266297539 1.973624192 0.482516476 -1.019954204 4.545408
473
## [846] 1.037887728 3.882095058 0.988240390 2.494925146 0.112544
344
## [851] 5.568664642 3.659286935 0.484807165 3.379451382 2.800361
173
## [856] 5.917450215 1.008408112 2.462593682 0.754330731 0.438003
150
## [861] -1.005298691 1.671907747 2.673073499 4.386200916 2.341044
447
## [866] 0.333835368 4.811512152 -0.087761439 3.351551395 3.375684
391
## [871] 0.867658787 2.134196947 4.522708000 3.646964053 3.428545
312
## [876] 0.474797506 2.383965154 0.687332284 0.222278834 1.714769
824
## [881] 1.990791457 1.499365842 0.617535213 2.445947144 4.073301
202
## [886] 5.191535378 1.850631510 2.530039885 3.342985648 -1.854672
217
## [891] 3.404274671 1.496633075 2.484258215 2.718972935 -1.319544
397
## [896] 0.092780585 1.481163698 3.394798518 2.418212617 3.302158
314
## [901] 1.953194260 1.310317509 2.034332240 2.068468522 2.973729
094
## [906] 4.480464078 1.259660759 1.269998669 3.796272942 1.631736
041
## [911] 2.850235095 1.435978899 2.121162379 4.117027175 3.076959
428
## [916] -0.400745134 0.009857423 2.596362300 1.384884009 1.322564
780
## [921] 4.137896853 2.956121536 0.240440974 -0.274277026 2.304079
014
## [926] 0.319907793 1.561897735 -0.514680213 1.171866307 0.729465
814
## [931] 3.589326431 1.535755486 4.199680177 0.093265292 2.441985
930
## [936] 3.279735723 3.004154917 6.252110140 0.540363036 0.153472
880
## [941] 4.447217194 3.130428054 0.284222771 1.538933607 3.893861
088
## [946] 0.835160106 3.361086444 1.807312009 0.916601053 0.344144
407
## [951] 2.953598813 0.262050009 4.038659150 3.561131903 3.661436
811
## [956] 2.479456041 0.939512202 3.369168859 1.926401680 2.233581
522
## [961] 3.233826831 0.844089870 3.238901640 5.083396890 0.768924
952
## [966] 3.648814610 4.186953209 0.638222878 1.322371097 4.377999
827
## [971] 0.171002110 1.637723642 3.810481239 1.613217592 2.674855
341
## [976] 3.648562743 2.034957532 0.852820346 1.469414786 3.483417
841
## [981] 0.494425023 2.269006233 4.901418352 2.069647629 2.200726
851
## [986] 0.898501513 4.753990827 1.163342293 1.981031874 2.754153
385
## [991] 4.030990209 2.269479027 0.822790758 1.436812408 0.586205
384
## [996] -3.575411645 2.893262542 2.756701543 0.525447365 1.441007
718
summary(data)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.5754 0.8513 1.9366 1.9371 3.0050 6.7950
subtitle <- "This is my second title"
maintitle <- paste0("Histogram and Density Plot \n",subtitle)
hist(data, breaks=20,col="lightblue",main = maintitle)
# Add density curve (define the range of the density curve)
x = seq(from=min(data), to=max(data), length.out=100)
norm_dist = dnorm(x, mean=mean(data), sd=sd(data)) * (max(data)-min(dat
a))/
20*length(data)
lines(x, norm_dist, col='violet',lwd=4)
abline(v = mean(data), col="black", lwd=3, lty=2)
# Add legend
legend("topright", # Add legend to plot
legend = c("Histogram", "density curve", "Average"),
col = c("lightblue", "violet", "black"),
lty = 1)
# Locate lowest 5% and highest 5% of the data
quantile(data,prob = 0.05)
## 5%
## -0.585605
abline(v = quantile(data,prob = 0.05), col="red", lwd=3, lty=3)
# Area to the left of the red line is lowest 5% of the data
quantile(data,prob = 0.95)
## 95%
## 4.523064
abline(v = quantile(data,prob = 0.95), col="blue", lwd=3, lty=3)
# Area to the right of the blue line is top 5% of the data
# Area in between the red line and the blue line is 90%
# of the data
# How to get the values (lowest 5%)
(Cutoff <- quantile(data,prob = 0.05))
## 5%
## -0.585605
# mark those values that is lower than -.42 as true
# and higher than -.42 as false
(Low5Percent <- (data < Cutoff))
## [1] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [25] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
TRUE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE F
ALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [73] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [97] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE F
ALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [157] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE F
ALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE F
ALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
TRUE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE F
ALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE F
ALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [253] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
TRUE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [313] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [337] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [349] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE F
ALSE FALSE
## [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [421] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [433] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [457] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE F
ALSE FALSE
## [469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE F
ALSE FALSE
## [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [553] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [589] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [601] FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE F
ALSE FALSE
## [613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE TRUE
## [661] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
TRUE FALSE
## [673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [685] FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE F
ALSE FALSE
## [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [709] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
TRUE FALSE
## [721] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE F
ALSE FALSE
## [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [745] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE F
ALSE TRUE
## [757] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [769] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE F
ALSE FALSE
## [817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [829] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [841] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE F
ALSE FALSE
## [865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [877] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [889] FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE F
ALSE FALSE
## [901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [925] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [937] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [949] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [961] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [973] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE TRUE
## [997] FALSE FALSE FALSE FALSE
# count all true values using the sum command
sum(Low5Percent[TRUE]) # there are 50 of them
## [1] 50
# filter those 50 values that are smaller than the cutoff
data[Low5Percent==TRUE]
## [1] -1.0568895 -1.2263890 -0.6071011 -0.6209662 -1.8876447 -0.96752
44
## [7] -1.4522280 -0.9985446 -0.6648220 -1.1958436 -0.8526514 -1.07917
80
## [13] -0.9362970 -0.5945602 -0.6954736 -1.0209391 -1.0707926 -1.58173
07
## [19] -1.2755946 -1.3166186 -1.0005221 -1.3759503 -1.5185943 -1.29893
91
## [25] -0.6310626 -1.6557390 -2.0952999 -0.6241643 -0.6135964 -0.65504
32
## [31] -0.6061626 -0.6566443 -1.6661504 -1.0003644 -0.6443998 -0.98486
07
## [37] -1.3910688 -0.9323370 -1.7391742 -0.6463632 -0.9579375 -1.74940
48
## [43] -0.9288637 -1.0545976 -1.4914077 -1.0199542 -1.0052987 -1.85467
22
## [49] -1.3195444 -3.5754116
# How to get the top 5%
(Cutoff <- quantile(data,prob = 0.95))
## 95%
## 4.523064
(Top5Percent <- (data >= Cutoff))
## [1] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE F
ALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE F
ALSE FALSE
## [37] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE F
ALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE TRUE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE F
ALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [181] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE F
ALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [217] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE F
ALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE F
ALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE F
ALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE F
ALSE FALSE
## [313] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [325] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [337] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE F
ALSE FALSE
## [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE F
ALSE FALSE
## [385] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [421] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [433] FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE F
ALSE FALSE
## [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [469] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE F
ALSE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
TRUE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [553] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE F
ALSE FALSE
## [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [589] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE TRUE
## [613] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
TRUE FALSE
## [685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE TRUE
## [721] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [745] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [769] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [829] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE F
ALSE FALSE
## [841] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
TRUE FALSE
## [853] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [865] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [877] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE F
ALSE FALSE
## [889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [925] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [937] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [949] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [961] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [973] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
TRUE FALSE
## [985] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
ALSE FALSE
## [997] FALSE FALSE FALSE FALSE
sum(Top5Percent[TRUE]) # counts all the true values
## [1] 50
data[Top5Percent==TRUE]
## [1] 6.795050 4.678494 4.696390 5.568949 5.499329 4.776674 4.980938
5.026314
## [9] 4.608067 5.247966 6.196415 5.676410 5.094664 5.117365 5.171919
4.906765
## [17] 6.186237 4.798495 4.949248 4.629449 4.582033 4.579783 4.727642
4.905165
## [25] 5.173248 5.871867 4.549208 5.141913 4.762212 4.980927 4.601292
4.789307
## [33] 5.696915 4.798242 4.529821 4.535805 5.789012 5.372153 4.674451
4.715161
## [41] 4.982649 4.545408 5.568665 5.917450 4.811512 5.191535 6.252110
5.083397
## [49] 4.901418 4.753991