# Mindanao State University
# General Santos City
# Introduction to R base commands
# Submitted by:ANGGA,PRINCESS JOY C.
# Mat108
# Processing of continuous data
# Using random number generators
# Exer1: generate data with mean 2 and standard deviation 1.5 using rnorm() 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] 0.4817050 1.7748488 0.4185488 0.0938448 0.2204109 5.4289669 2.3465584
## [8] 2.7493398 2.1488291 2.6874033 5.7012437 1.7341444 1.3355748 3.2351546
## [15] 2.0714138 0.3560184 2.1761205 3.0915067 2.7838319 1.5235322
data[1:300] # display the first 300 elements
## [1] 0.48170498 1.77484879 0.41854877 0.09384480 0.22041086 5.42896692
## [7] 2.34655841 2.74933984 2.14882915 2.68740333 5.70124368 1.73414441
## [13] 1.33557479 3.23515458 2.07141379 0.35601837 2.17612048 3.09150671
## [19] 2.78383189 1.52353223 2.83299546 1.57318595 1.52299520 0.76398933
## [25] 1.48738688 1.44663268 1.52021096 1.66398787 1.29545700 3.46911811
## [31] 4.36178306 0.21920191 -0.16661837 1.42697689 1.65290795 3.77251418
## [37] 0.84518391 2.11996676 5.33489925 -0.87657204 2.33179981 0.62537081
## [43] 0.04612595 1.35046850 3.26485985 2.80227657 -0.50235235 1.90486459
## [49] 4.61075074 -0.78119649 1.67051839 0.95319468 0.82344464 4.15881704
## [55] 1.15653194 3.54766661 0.74170573 3.56183483 1.54323273 2.65352690
## [61] -0.33403946 2.16160583 -0.13222141 1.74631232 1.35931433 2.47067268
## [67] 5.64479832 2.57799659 0.52834699 3.73273829 -1.36890662 3.94046113
## [73] 3.33963719 -0.41688211 5.12841478 1.39961297 0.51626361 3.05984648
## [79] 4.26562368 3.62038041 2.10304047 0.68679149 0.69720609 2.91259664
## [85] 1.69744281 2.02166059 2.09982739 2.78913054 1.62891447 3.03484981
## [91] 2.88278422 0.65903291 0.81979666 0.62423048 3.93027768 0.33408208
## [97] 0.58160311 1.93479517 1.85107771 1.26413704 1.70040093 4.98741159
## [103] 1.90370026 1.72927391 0.34862855 -1.44051501 -1.84722989 0.57809664
## [109] 2.13602578 1.13742333 0.96429292 2.45658854 5.78444071 1.92481922
## [115] 1.96289696 0.15388045 2.71998189 1.11424378 2.20491383 0.82845129
## [121] 0.01858452 3.16130947 4.10287931 1.47622496 4.58689712 2.71916659
## [127] 1.95286600 2.29910721 1.98876421 3.19318324 1.79852430 1.21445840
## [133] 2.08754821 1.96478286 3.24498019 2.50082232 2.48587560 0.44650969
## [139] 0.78595734 3.05926702 1.58585501 1.18257762 2.44505895 2.90655843
## [145] 2.65638763 2.16201511 -0.66593240 3.29061568 1.67301067 1.62154405
## [151] 0.21645085 3.77333314 1.05025756 3.42293983 3.03258033 4.38548148
## [157] 2.67540198 0.97183311 1.10976865 2.65042120 1.02340908 4.15115698
## [163] -0.28542072 1.51748061 2.22160494 1.50393168 2.72323747 3.33319798
## [169] 0.32529349 4.43562547 4.18510219 -0.05260419 3.52466416 -0.39606630
## [175] 1.54739640 1.54886404 0.90792107 3.26221314 2.31992486 5.01442099
## [181] 2.83924292 3.20653503 3.19664534 -0.74940019 4.48421896 1.14650527
## [187] 1.80880142 0.87722266 1.08532637 1.93884302 0.43506124 2.07720414
## [193] -0.13565361 2.59959321 2.15945970 2.28336584 0.32037006 1.54636057
## [199] 1.66406760 1.98957991 2.43552884 1.80373700 1.32776220 1.61179871
## [205] 2.17258924 0.63883684 3.12684691 0.54963264 3.05180440 2.16575698
## [211] 1.13270369 0.69702324 1.77047924 4.68451108 3.09927388 -0.49141265
## [217] 4.44014422 1.65276733 3.36316511 3.56081433 1.24897780 2.92734146
## [223] 5.46395671 0.31914034 4.32261635 1.72575286 2.64351352 0.62722633
## [229] -0.56653794 2.53676523 1.45279196 -0.51116407 1.92753396 2.51201808
## [235] 1.94971492 1.06821067 0.26044263 3.12321285 4.90633681 0.19048035
## [241] 4.37970472 -0.22203361 3.30374363 1.80885073 2.32185691 0.05788919
## [247] 2.96334015 2.10590985 2.58309697 0.32274486 1.94045180 0.26880333
## [253] 1.34436585 -0.12681517 1.82677388 2.31431188 2.23681792 1.89030464
## [259] 1.77119517 0.22159893 1.71414107 1.98397962 -0.33646017 1.21776218
## [265] 0.86031035 2.09053772 2.01691564 2.21204085 4.15142869 1.89966790
## [271] 3.05701038 0.07849038 2.90216920 3.99552299 0.18990334 0.19705333
## [277] 2.80173006 1.56123623 0.77494205 0.15619080 3.79664031 0.48803891
## [283] 1.62344166 -0.04826490 3.13576431 0.81641774 1.60778859 1.72363248
## [289] 4.84600942 3.07600896 1.38467027 1.52529492 2.23988417 1.94799801
## [295] 0.92068393 5.35712295 -0.52422822 0.87493692 5.11732962 2.66823334
# 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(data))/
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] -3.65036584 -3.52543612 -3.40050641 -3.27557669 -3.15064698 -3.02571726
## [7] -2.90078755 -2.77585784 -2.65092812 -2.52599841 -2.40106869 -2.27613898
## [13] -2.15120926 -2.02627955 -1.90134984 -1.77642012 -1.65149041 -1.52656069
## [19] -1.40163098 -1.27670127 -1.15177155 -1.02684184 -0.90191212 -0.77698241
## [25] -0.65205269 -0.52712298 -0.40219327 -0.27726355 -0.15233384 -0.02740412
## [31] 0.09752559 0.22245531 0.34738502 0.47231473 0.59724445 0.72217416
## [37] 0.84710388 0.97203359 1.09696331 1.22189302 1.34682273 1.47175245
## [43] 1.59668216 1.72161188 1.84654159 1.97147131 2.09640102 2.22133073
## [49] 2.34626045 2.47119016 2.59611988 2.72104959 2.84597931 2.97090902
## [55] 3.09583873 3.22076845 3.34569816 3.47062788 3.59555759 3.72048731
## [61] 3.84541702 3.97034673 4.09527645 4.22020616 4.34513588 4.47006559
## [67] 4.59499530 4.71992502 4.84485473 4.96978445 5.09471416 5.21964388
## [73] 5.34457359 5.46950330 5.59443302 5.71936273 5.84429245 5.96922216
## [79] 6.09415188 6.21908159 6.34401130 6.46894102 6.59387073 6.71880045
## [85] 6.84373016 6.96865988 7.09358959 7.21851930 7.34344902 7.46837873
## [91] 7.59330845 7.71823816 7.84316788 7.96809759 8.09302730 8.21795702
## [97] 8.34288673 8.46781645 8.59274616 8.71767588
norm_dist = dnorm(x, mean=mean(data), sd=sd(data)) * (max(data)-min(data))/
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%
## -3.650366 1.023408 2.008921 2.966392 8.717676
# 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] 0.481704981 1.774848787 0.418548767 0.093844799 0.220410856
## [6] 5.428966917 2.346558411 2.749339837 2.148829146 2.687403333
## [11] 5.701243676 1.734144413 1.335574789 3.235154584 2.071413788
## [16] 0.356018373 2.176120484 3.091506706 2.783831894 1.523532227
## [21] 2.832995461 1.573185953 1.522995203 0.763989331 1.487386879
## [26] 1.446632684 1.520210957 1.663987870 1.295457005 3.469118113
## [31] 4.361783057 0.219201910 -0.166618370 1.426976888 1.652907950
## [36] 3.772514179 0.845183908 2.119966762 5.334899251 -0.876572043
## [41] 2.331799813 0.625370807 0.046125946 1.350468498 3.264859849
## [46] 2.802276568 -0.502352352 1.904864593 4.610750743 -0.781196485
## [51] 1.670518386 0.953194678 0.823444641 4.158817035 1.156531941
## [56] 3.547666606 0.741705733 3.561834829 1.543232733 2.653526897
## [61] -0.334039465 2.161605827 -0.132221410 1.746312315 1.359314333
## [66] 2.470672682 5.644798317 2.577996594 0.528346991 3.732738293
## [71] -1.368906615 3.940461132 3.339637186 -0.416882111 5.128414782
## [76] 1.399612975 0.516263612 3.059846477 4.265623675 3.620380412
## [81] 2.103040466 0.686791494 0.697206092 2.912596637 1.697442810
## [86] 2.021660594 2.099827389 2.789130542 1.628914472 3.034849809
## [91] 2.882784218 0.659032907 0.819796660 0.624230477 3.930277679
## [96] 0.334082076 0.581603114 1.934795172 1.851077706 1.264137035
## [101] 1.700400932 4.987411590 1.903700257 1.729273908 0.348628549
## [106] -1.440515010 -1.847229893 0.578096640 2.136025775 1.137423325
## [111] 0.964292924 2.456588543 5.784440710 1.924819221 1.962896960
## [116] 0.153880451 2.719981892 1.114243780 2.204913829 0.828451285
## [121] 0.018584517 3.161309469 4.102879310 1.476224962 4.586897118
## [126] 2.719166592 1.952866001 2.299107209 1.988764205 3.193183242
## [131] 1.798524300 1.214458401 2.087548205 1.964782857 3.244980194
## [136] 2.500822323 2.485875595 0.446509691 0.785957342 3.059267017
## [141] 1.585855015 1.182577620 2.445058948 2.906558433 2.656387629
## [146] 2.162015112 -0.665932404 3.290615680 1.673010670 1.621544050
## [151] 0.216450851 3.773333135 1.050257560 3.422939830 3.032580332
## [156] 4.385481479 2.675401978 0.971833110 1.109768648 2.650421205
## [161] 1.023409076 4.151156977 -0.285420719 1.517480610 2.221604935
## [166] 1.503931677 2.723237474 3.333197982 0.325293493 4.435625472
## [171] 4.185102189 -0.052604193 3.524664159 -0.396066296 1.547396397
## [176] 1.548864036 0.907921075 3.262213137 2.319924855 5.014420988
## [181] 2.839242919 3.206535032 3.196645335 -0.749400191 4.484218965
## [186] 1.146505273 1.808801418 0.877222657 1.085326371 1.938843019
## [191] 0.435061236 2.077204136 -0.135653611 2.599593211 2.159459699
## [196] 2.283365840 0.320370063 1.546360566 1.664067600 1.989579910
## [201] 2.435528841 1.803737003 1.327762202 1.611798705 2.172589243
## [206] 0.638836844 3.126846907 0.549632636 3.051804405 2.165756979
## [211] 1.132703694 0.697023240 1.770479237 4.684511075 3.099273876
## [216] -0.491412650 4.440144220 1.652767328 3.363165111 3.560814329
## [221] 1.248977798 2.927341465 5.463956713 0.319140342 4.322616347
## [226] 1.725752864 2.643513519 0.627226330 -0.566537940 2.536765231
## [231] 1.452791960 -0.511164067 1.927533956 2.512018084 1.949714922
## [236] 1.068210674 0.260442626 3.123212850 4.906336812 0.190480350
## [241] 4.379704720 -0.222033610 3.303743627 1.808850726 2.321856905
## [246] 0.057889194 2.963340146 2.105909851 2.583096965 0.322744855
## [251] 1.940451797 0.268803330 1.344365855 -0.126815171 1.826773876
## [256] 2.314311884 2.236817915 1.890304638 1.771195168 0.221598927
## [261] 1.714141074 1.983979618 -0.336460169 1.217762183 0.860310347
## [266] 2.090537720 2.016915639 2.212040854 4.151428694 1.899667902
## [271] 3.057010383 0.078490381 2.902169198 3.995522988 0.189903338
## [276] 0.197053331 2.801730061 1.561236226 0.774942048 0.156190800
## [281] 3.796640312 0.488038915 1.623441656 -0.048264899 3.135764307
## [286] 0.816417735 1.607788586 1.723632484 4.846009418 3.076008959
## [291] 1.384670267 1.525294918 2.239884171 1.947998010 0.920683926
## [296] 5.357122954 -0.524228222 0.874936917 5.117329615 2.668233342
## [301] 3.208675303 -0.965178206 0.693962871 2.710457614 2.235866331
## [306] 1.183155750 2.609132034 2.828117162 3.568707747 0.209384784
## [311] 3.457839719 0.127108228 1.440192795 3.317777037 3.260930583
## [316] 2.868618221 3.581580696 2.197592013 1.656170029 3.854710490
## [321] 2.927615985 1.747069080 2.006212473 2.894450669 4.384279270
## [326] -0.316671640 0.336442902 1.917589189 4.360937810 2.549282777
## [331] 1.145634474 1.592357786 3.700987043 1.947893307 3.291059670
## [336] 3.299159697 3.258390888 3.652947779 5.101956478 2.070214216
## [341] 2.895354947 3.032150322 3.049624365 -0.740099027 1.690672371
## [346] 0.452418826 1.969943697 1.446590515 -1.086354576 2.712271782
## [351] -0.397916143 0.732985603 1.602576384 2.272355113 2.837611755
## [356] -0.262399090 1.722892415 1.219710772 2.924332534 3.831376453
## [361] 1.881309913 3.401524316 1.134307231 2.939725143 3.409858670
## [366] 1.094921571 2.609804777 4.744106947 1.172122039 2.414759771
## [371] 3.672599772 2.812625165 0.691587614 2.753272768 2.081118651
## [376] 2.253220884 3.942227548 0.866211607 2.597957547 0.961990394
## [381] -0.156342963 2.555456665 0.469673821 3.853568388 2.320300793
## [386] -1.158693613 4.910360107 -0.024048518 2.409013802 3.275235034
## [391] 2.533259238 4.080417438 2.774244788 0.591463414 2.383408418
## [396] 3.930924923 1.810239620 2.840328050 2.865135417 3.507259439
## [401] 2.864109788 0.466325168 2.188396051 2.562652696 2.878878336
## [406] 0.759895553 2.328774974 -2.013443034 3.202399906 1.734792301
## [411] 2.724593002 3.465437902 0.497044002 3.402413102 2.599705912
## [416] 2.739616266 0.603170172 -1.209659612 1.327068968 1.481902263
## [421] 0.588607862 1.310786985 -0.820302963 0.833202501 0.856362411
## [426] 0.892668976 1.413956946 2.994008940 3.830921073 1.856519470
## [431] 2.140998226 1.842699118 0.571658178 3.524214748 2.795711588
## [436] 2.379067812 2.444299211 1.428808565 1.058774703 0.474307009
## [441] 0.391050657 0.898551661 -1.308483161 0.468647024 3.045122723
## [446] 3.176978827 1.404171273 2.753144887 2.638695269 0.248168046
## [451] -0.078438868 8.717675875 2.933141634 2.699633457 3.196942566
## [456] 0.489233379 2.469674370 -0.836406681 2.385899302 0.311716477
## [461] 1.656136593 2.341431223 0.601007664 1.960113883 1.549799140
## [466] 5.015235928 5.205189572 1.155224396 1.660006659 0.028148254
## [471] -0.933097827 2.259424790 5.038925963 0.070471763 1.146912064
## [476] 1.955445212 1.970158006 1.290222353 3.402649863 3.152610908
## [481] 2.690219567 -0.222420191 0.954561774 1.823288035 4.036386223
## [486] 3.042784570 3.021661619 2.814302851 2.419163231 -2.027389587
## [491] 2.284190256 0.910599818 1.817961831 0.284106532 3.200408225
## [496] -0.165248948 0.822842034 2.433229043 2.694270651 2.985532830
## [501] 0.340499853 3.924383960 0.754322074 0.420091267 1.580158047
## [506] 2.620399138 5.540570976 -0.107804456 2.433607423 1.632720777
## [511] 2.646818266 2.147113818 1.592707146 4.082155030 0.777009644
## [516] 0.247107103 1.306631902 1.534347557 1.033016640 0.603632343
## [521] -0.053075434 2.186952713 1.292971860 4.228694702 1.328852252
## [526] 2.033767534 3.893240730 2.063052610 -0.006492528 2.017041266
## [531] 1.581946357 1.834364454 -0.327173934 2.562845865 3.411909212
## [536] 2.788484008 3.339835859 3.041470428 2.459336646 2.161170855
## [541] 1.673897398 1.591045897 -0.155815165 3.100115848 2.481048526
## [546] 2.726377730 -0.597047250 2.000877671 2.536134731 3.945670532
## [551] 0.175760886 -0.740779836 1.229167866 3.417480909 0.132326446
## [556] 2.042850357 0.691584065 1.818133654 -0.276068889 0.004039847
## [561] -0.059681804 2.713293400 2.222248433 -1.443668559 1.757711030
## [566] 3.692907153 2.719174855 2.676965433 2.799031584 2.514023587
## [571] 2.699081146 1.897021254 0.899732432 2.980184497 -0.604501293
## [576] 2.880387056 1.169032822 2.702041259 1.031192680 1.349298960
## [581] 1.036193510 -1.685333268 3.429272552 5.250581012 -0.305430084
## [586] 2.734288433 2.244977173 3.109739842 -0.990452581 1.630436416
## [591] 2.435776302 2.769207464 2.151439766 0.054057049 1.114511417
## [596] 3.184450692 3.626313705 3.744044881 0.593138458 2.486237157
## [601] 1.986764869 2.878445392 1.181925082 1.465597151 3.178048579
## [606] 1.780926197 1.353936855 2.626541049 -0.210229442 0.409366886
## [611] 4.569840440 1.588948556 1.126521095 1.260618667 1.574978424
## [616] 0.650188563 2.981264317 1.386372702 1.874972002 2.997951391
## [621] 1.689414716 2.201854425 4.421663944 1.920964046 2.483975047
## [626] 0.791963881 0.849913586 2.520326718 1.277386460 2.436421448
## [631] 0.488317389 3.969452922 2.567360219 2.191250691 1.123343549
## [636] 1.355882220 2.039586469 1.207307197 3.248347235 2.843421173
## [641] 4.883275672 3.836975818 2.879834994 2.909200300 0.802173234
## [646] 3.941343597 2.569894641 2.025571677 3.547630472 0.994486975
## [651] 1.876207186 0.782986642 4.797838283 1.044577595 1.360179425
## [656] 1.352965504 5.598876683 0.329727432 3.876130777 2.878865572
## [661] 3.938080400 0.929772520 2.987219057 2.938079190 1.399896776
## [666] 3.661777473 0.545638145 3.893653868 1.504764466 3.494051360
## [671] 1.326398816 1.684193424 2.593765240 4.349836602 4.263872365
## [676] 2.484949024 1.886207550 2.312192086 3.781489319 1.964986277
## [681] 3.305850643 2.101999759 3.252553486 4.030208027 1.180625569
## [686] 1.808635794 -0.225961099 -0.305167700 4.039419814 -2.213391337
## [691] -0.555483410 2.570878173 0.143683132 0.905315889 -0.725846035
## [696] 1.309723724 2.204932876 2.963376302 2.049540561 1.514257437
## [701] 2.277993156 2.295817479 3.024133225 -1.260149677 4.375088937
## [706] 1.692260333 1.391706758 2.480481038 1.758539094 3.789658208
## [711] 1.513894763 1.277243612 7.324345176 1.879972101 1.890738418
## [716] 4.038855534 4.341459848 1.877346408 2.134600637 -1.634200890
## [721] 0.626890772 0.290072884 2.420175855 4.012363554 -0.374430980
## [726] 1.551029971 2.455346206 0.773572945 0.124223213 4.086731054
## [731] 0.586948757 1.000560097 -3.650365836 2.480656508 3.719356911
## [736] 1.161647770 -1.176246207 2.331148697 3.987433040 2.668766618
## [741] 1.528779664 3.800781633 2.503586968 2.011629494 1.426210603
## [746] 0.391703167 3.096234720 3.254614722 1.289492466 0.820673627
## [751] 2.516027493 3.078940459 -0.377305918 1.174888391 3.004456574
## [756] 1.275157232 2.575427722 3.301046455 2.562249218 0.551134929
## [761] -1.090269203 3.704515797 1.428116752 2.363684065 0.548252210
## [766] 1.738683679 2.388503769 2.413372676 3.765078904 2.969813094
## [771] 0.412720568 2.708066407 0.860633627 2.505029663 2.573083169
## [776] 4.073693844 1.695092412 3.043379768 3.265387721 4.328701899
## [781] 0.779072094 1.756953492 3.704496079 -1.694484130 1.290636251
## [786] 2.979381500 1.157915913 4.434888493 3.254805095 -0.069245304
## [791] 2.112471799 3.734471834 4.306718784 5.349542242 0.898445548
## [796] 4.584465548 1.544658240 4.338398649 2.125273711 0.878613131
## [801] 3.957941349 0.853649091 2.376422934 3.471395103 -0.214285600
## [806] 0.664977373 2.002328299 2.734979178 1.279646817 1.219165855
## [811] 2.296221353 2.769735864 1.805297948 2.227617455 1.108903159
## [816] -0.669508408 2.747807011 1.721510113 3.651537148 2.907265926
## [821] 3.186140108 1.991282304 2.801830475 3.357885312 1.701506830
## [826] 1.578655891 1.895899450 2.646712168 2.168825549 1.955032570
## [831] 0.148245581 5.403237845 0.282184838 1.023775082 3.949690477
## [836] -1.869745371 -0.785884630 3.181907434 2.310108149 0.770205806
## [841] 0.865870981 4.624448224 3.348948181 1.484740188 1.205909291
## [846] 4.646365177 0.354024511 4.578596612 2.497189952 4.919359649
## [851] 1.320075150 1.357483634 5.736561937 0.446134270 -0.998528842
## [856] 1.252462414 2.079751653 -0.247930996 2.930157093 1.910789220
## [861] 2.854421815 2.949108153 3.823482900 2.170359000 2.497458302
## [866] 3.314098882 2.221187190 -0.238842447 1.031022744 1.421862322
## [871] 4.585707044 3.224990271 2.161258761 0.510155379 3.610311729
## [876] -0.456109448 2.028856803 4.039257927 -0.814774462 4.420576081
## [881] 1.178174032 1.697801631 4.213550025 3.090124483 2.422063566
## [886] 2.235024393 1.822343186 3.066069219 1.319151196 3.388020703
## [891] 1.279392887 2.336140852 2.603744088 2.157106406 -0.076405452
## [896] 3.056663877 0.667506798 3.211589506 -0.012882765 2.700563206
## [901] 3.485001542 1.881906670 -1.477055352 3.593204745 2.526739140
## [906] 0.855586271 2.243515217 1.422238767 2.366832996 4.232504769
## [911] 4.701712342 0.771923244 1.953312192 2.240127209 1.488703438
## [916] 3.404344217 3.920273752 2.005064766 0.007363017 1.479508107
## [921] 1.434895428 2.276126474 -0.663920764 1.187163412 0.443145509
## [926] 1.038604808 -0.591458980 3.757836496 0.793375460 2.241262691
## [931] 2.537942305 0.585810277 0.131034279 4.008565257 3.123246283
## [936] 2.227205374 2.338544817 3.198695703 2.211906141 3.877464735
## [941] 2.020126135 3.594105658 3.895033228 1.035001769 -0.474339600
## [946] 1.802607993 3.530059894 1.915958278 2.849380646 2.977864774
## [951] 4.206639773 1.570444650 3.015448430 4.032597768 0.716052082
## [956] 4.859310207 2.527527259 3.945303431 1.269011605 -0.865522803
## [961] 4.592089465 3.418654402 3.101006007 1.311853182 -0.762333952
## [966] 0.187658129 1.171907336 -0.421006248 -0.265997427 2.106369575
## [971] 2.965251224 -0.466287121 3.029978674 3.027622558 2.862610006
## [976] -1.035257357 2.294223139 1.250090967 2.777991453 -0.505948545
## [981] 3.456980084 1.711861353 5.216751108 2.509504562 2.620892539
## [986] 1.023404633 3.973272197 0.643620214 1.567598912 1.240017884
## [991] 2.570448268 2.117565534 3.817445022 1.089595460 -0.146013460
## [996] 2.469293101 1.865330022 2.182833937 0.460975515 2.112281619
summary(data)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.650 1.023 2.009 1.990 2.966 8.718
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(data))/
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%
## -0.4751933
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%
## 4.384339
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%
## -0.4751933
# mark those values that is lower than -.42 as true
# and higher than -.42 as false
(Low5Percent <- (data < Cutoff))
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [49] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [301] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [349] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [421] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [457] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [469] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [541] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
## [553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [577] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [589] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [685] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE FALSE
## [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [733] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [757] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [781] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [829] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
## [841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [853] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [877] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [901] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [925] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [937] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [949] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [961] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [973] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [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] -0.8765720 -0.5023524 -0.7811965 -1.3689066 -1.4405150 -1.8472299
## [7] -0.6659324 -0.7494002 -0.4914127 -0.5665379 -0.5111641 -0.5242282
## [13] -0.9651782 -0.7400990 -1.0863546 -1.1586936 -2.0134430 -1.2096596
## [19] -0.8203030 -1.3084832 -0.8364067 -0.9330978 -2.0273896 -0.5970473
## [25] -0.7407798 -1.4436686 -0.6045013 -1.6853333 -0.9904526 -2.2133913
## [31] -0.5554834 -0.7258460 -1.2601497 -1.6342009 -3.6503658 -1.1762462
## [37] -1.0902692 -1.6944841 -0.6695084 -1.8697454 -0.7858846 -0.9985288
## [43] -0.8147745 -1.4770554 -0.6639208 -0.5914590 -0.8655228 -0.7623340
## [49] -1.0352574 -0.5059485
# How to get the top 5%
(Cutoff <- quantile(data,prob = 0.95))
## 95%
## 4.384339
(Top5Percent <- (data >= Cutoff))
## [1] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [181] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [217] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
## [469] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [505] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [637] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [649] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [709] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [793] FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [829] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [841] FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE
## [853] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [865] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [877] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [925] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [937] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [949] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [961] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [973] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [997] FALSE FALSE FALSE FALSE
sum(Top5Percent[TRUE]) # counts all the true values
## [1] 50
data[Top5Percent==TRUE]
## [1] 5.428967 5.701244 5.334899 4.610751 5.644798 5.128415 4.987412 5.784441
## [9] 4.586897 4.385481 4.435625 5.014421 4.484219 4.684511 4.440144 5.463957
## [17] 4.906337 4.846009 5.357123 5.117330 5.101956 4.744107 4.910360 8.717676
## [25] 5.015236 5.205190 5.038926 5.540571 5.250581 4.569840 4.421664 4.883276
## [33] 4.797838 5.598877 7.324345 4.434888 5.349542 4.584466 5.403238 4.624448
## [41] 4.646365 4.578597 4.919360 5.736562 4.585707 4.420576 4.701712 4.859310
## [49] 4.592089 5.216751