# Mindanao State University
# General Santos City
# Introduction to R base commands
# Prepared by: Ancheta, Rose Ann
# Faculty
# Math Department
# April 03, 2023
# Processing of continuous data
# Using random number generator
# 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] 2.35515061 2.03784276 4.48406460 2.33469818 2.28778070 0.07512243
## [7] 0.24620434 0.09135989 3.73369035 2.44040058 3.09213733 0.16562831
## [13] 2.65014108 2.04954014 4.20662936 -0.08443052 4.03450523 1.04067316
## [19] 1.75746407 2.71914849
data[1:300] # display the first 300 elements
## [1] 2.35515061 2.03784276 4.48406460 2.33469818 2.28778070 0.07512243
## [7] 0.24620434 0.09135989 3.73369035 2.44040058 3.09213733 0.16562831
## [13] 2.65014108 2.04954014 4.20662936 -0.08443052 4.03450523 1.04067316
## [19] 1.75746407 2.71914849 2.48328238 0.38909565 2.04769039 2.66033505
## [25] 2.25877033 3.50824569 0.88389432 1.56043456 3.02613288 -0.93812646
## [31] 1.04872914 2.55068966 2.21895952 2.15956329 0.13285920 1.82359943
## [37] 1.51302690 5.64466788 0.65656697 2.56922037 4.65118933 1.94578859
## [43] 1.90167973 -0.11190364 3.48220261 0.64182019 3.06094867 -1.31938002
## [49] 1.39251400 1.93665095 3.72500254 2.28443498 3.56954452 3.70893496
## [55] 0.77563486 2.13636066 2.70127385 4.79739817 4.29412129 2.29164173
## [61] 4.10122671 2.07341361 5.00095972 2.81330435 1.36619255 3.76867261
## [67] 4.59242247 1.93722085 0.85088631 0.82800654 5.09289608 2.89073251
## [73] 3.42862766 2.61635435 1.08447838 2.00951686 0.77805371 5.48022270
## [79] 1.28090664 3.59344584 2.18920481 3.06645979 1.89064195 1.64921807
## [85] 4.51202507 3.59251420 0.64036584 2.47983829 2.67312867 2.99724581
## [91] 2.35532536 4.12805268 3.70915116 1.94072311 0.85281593 1.36161321
## [97] 2.43107658 1.99641704 1.62778571 0.57227230 1.48241532 0.03115562
## [103] 0.68764291 2.30130641 2.77024477 2.50576128 -1.33406554 3.02614094
## [109] 0.69587068 0.31273944 -1.33038561 0.03344238 3.78913611 2.04691494
## [115] 4.74626513 3.12588641 1.07437728 2.27269291 1.69907473 3.55841068
## [121] 2.00348198 2.56060818 2.66583691 2.91671536 1.24460813 1.84448846
## [127] 2.25963705 2.88608820 2.10800830 0.99367552 4.59542339 2.23903440
## [133] 0.26797534 1.95983495 5.29173663 0.71395839 1.45719232 2.21435680
## [139] 1.74523607 1.94125797 1.53908021 1.51258150 1.68844892 3.30855099
## [145] 2.46855981 3.39395057 2.33069404 2.17289937 0.69635263 1.29971384
## [151] -0.38224264 2.55754028 0.88614590 0.90970822 1.61480175 3.15898442
## [157] 3.14468844 1.43296918 1.02601351 2.92585918 1.74804206 3.03484524
## [163] 1.34585374 1.55113041 2.78249456 1.21592970 2.84894149 2.10984117
## [169] 1.94401527 0.85538371 3.11895256 -0.72693519 1.92573552 4.67793850
## [175] 2.79811979 2.68168373 0.89681319 2.87617925 2.56327231 2.49610497
## [181] 2.14158517 2.85865619 2.16529780 3.49724417 3.21406291 2.17678982
## [187] 1.77899248 2.00472888 2.91373201 1.79974062 3.45427861 1.97932486
## [193] 1.68231463 1.41363388 3.74986845 4.70679105 1.21022745 1.82097921
## [199] 2.36162544 2.67947565 2.90313915 1.32784683 1.30782111 3.27268588
## [205] 1.28847544 0.38654204 3.37796299 2.84897416 0.96939111 2.63135443
## [211] 2.38558057 3.70835530 -0.19187835 2.55937454 1.84615779 3.60487174
## [217] 4.13523814 2.46019280 3.33181466 0.88235729 -0.12913278 1.33961279
## [223] 1.39926908 1.68149629 3.57827983 -0.34423730 4.60365151 3.92204841
## [229] 3.13291995 2.49557168 3.45538272 1.12376745 0.75919140 1.86512235
## [235] 2.13503237 0.38135706 1.88328559 -0.15127975 -0.75704303 3.05488975
## [241] 1.03274913 2.34104865 1.14084445 1.79617054 2.25457266 2.96713893
## [247] 0.45651298 1.08766466 0.16768128 1.49472162 0.90654507 3.80000074
## [253] 1.55864827 0.37010129 0.44461967 -0.19406676 2.51789848 2.66001437
## [259] -0.44919453 2.38675983 0.01116665 2.18534801 0.46476382 0.87442404
## [265] 1.35559978 4.08734429 4.99785863 1.74389633 3.78091413 2.88176841
## [271] 3.51217710 1.16953394 3.36791110 2.98506166 0.86307659 1.68986727
## [277] 1.66661775 0.82646236 -0.18296022 -0.30660790 -1.57096039 2.52950315
## [283] 2.48209636 2.04962171 1.12820744 0.49525717 3.52433781 3.03065919
## [289] 0.43568730 2.88136364 0.79351574 1.54089976 2.19407813 2.93052285
## [295] 2.65489775 -0.80477697 2.18107157 1.27741555 1.63286301 2.42564155
# 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=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)
# 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] -3.03210392 -2.93913499 -2.84616606 -2.75319713 -2.66022820 -2.56725927
## [7] -2.47429034 -2.38132141 -2.28835248 -2.19538355 -2.10241462 -2.00944569
## [13] -1.91647676 -1.82350784 -1.73053891 -1.63756998 -1.54460105 -1.45163212
## [19] -1.35866319 -1.26569426 -1.17272533 -1.07975640 -0.98678747 -0.89381854
## [25] -0.80084961 -0.70788069 -0.61491176 -0.52194283 -0.42897390 -0.33600497
## [31] -0.24303604 -0.15006711 -0.05709818 0.03587075 0.12883968 0.22180861
## [37] 0.31477754 0.40774646 0.50071539 0.59368432 0.68665325 0.77962218
## [43] 0.87259111 0.96556004 1.05852897 1.15149790 1.24446683 1.33743576
## [49] 1.43040469 1.52337362 1.61634254 1.70931147 1.80228040 1.89524933
## [55] 1.98821826 2.08118719 2.17415612 2.26712505 2.36009398 2.45306291
## [61] 2.54603184 2.63900077 2.73196969 2.82493862 2.91790755 3.01087648
## [67] 3.10384541 3.19681434 3.28978327 3.38275220 3.47572113 3.56869006
## [73] 3.66165899 3.75462792 3.84759685 3.94056577 4.03353470 4.12650363
## [79] 4.21947256 4.31244149 4.40541042 4.49837935 4.59134828 4.68431721
## [85] 4.77728614 4.87025507 4.96322400 5.05619292 5.14916185 5.24213078
## [91] 5.33509971 5.42806864 5.52103757 5.61400650 5.70697543 5.79994436
## [97] 5.89291329 5.98588222 6.07885115 6.17182008
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%
## -3.0321039 0.9682219 2.0912618 3.0629656 6.1718201
# locate the quartiles Q1, Q2, Q3
abline(v = Quantiles[2], col="black", lwd=3, lty=3)
11
## [1] 11
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] 2.355150606 2.037842755 4.484064601 2.334698179 2.287780703
## [6] 0.075122430 0.246204337 0.091359888 3.733690345 2.440400579
## [11] 3.092137328 0.165628307 2.650141076 2.049540138 4.206629360
## [16] -0.084430522 4.034505232 1.040673159 1.757464067 2.719148486
## [21] 2.483282379 0.389095645 2.047690385 2.660335052 2.258770331
## [26] 3.508245688 0.883894321 1.560434559 3.026132880 -0.938126464
## [31] 1.048729142 2.550689658 2.218959522 2.159563291 0.132859196
## [36] 1.823599432 1.513026897 5.644667883 0.656566968 2.569220366
## [41] 4.651189331 1.945788591 1.901679735 -0.111903638 3.482202612
## [46] 0.641820193 3.060948671 -1.319380018 1.392513998 1.936650954
## [51] 3.725002541 2.284434983 3.569544521 3.708934961 0.775634863
## [56] 2.136360660 2.701273846 4.797398169 4.294121293 2.291641727
## [61] 4.101226706 2.073413613 5.000959718 2.813304353 1.366192547
## [66] 3.768672611 4.592422468 1.937220850 0.850886310 0.828006537
## [71] 5.092896076 2.890732510 3.428627655 2.616354349 1.084478378
## [76] 2.009516856 0.778053712 5.480222696 1.280906642 3.593445839
## [81] 2.189204807 3.066459788 1.890641946 1.649218073 4.512025072
## [86] 3.592514196 0.640365839 2.479838287 2.673128671 2.997245812
## [91] 2.355325359 4.128052680 3.709151156 1.940723114 0.852815935
## [96] 1.361613212 2.431076582 1.996417040 1.627785708 0.572272296
## [101] 1.482415319 0.031155623 0.687642913 2.301306406 2.770244766
## [106] 2.505761281 -1.334065540 3.026140940 0.695870683 0.312739437
## [111] -1.330385611 0.033442380 3.789136114 2.046914940 4.746265132
## [116] 3.125886413 1.074377277 2.272692915 1.699074735 3.558410683
## [121] 2.003481984 2.560608181 2.665836913 2.916715358 1.244608129
## [126] 1.844488461 2.259637053 2.886088196 2.108008305 0.993675520
## [131] 4.595423389 2.239034398 0.267975340 1.959834948 5.291736625
## [136] 0.713958388 1.457192318 2.214356804 1.745236074 1.941257968
## [141] 1.539080213 1.512581504 1.688448915 3.308550985 2.468559810
## [146] 3.393950573 2.330694041 2.172899372 0.696352625 1.299713838
## [151] -0.382242642 2.557540278 0.886145903 0.909708223 1.614801751
## [156] 3.158984424 3.144688442 1.432969184 1.026013507 2.925859181
## [161] 1.748042064 3.034845243 1.345853744 1.551130414 2.782494562
## [166] 1.215929698 2.848941491 2.109841174 1.944015270 0.855383708
## [171] 3.118952556 -0.726935185 1.925735520 4.677938501 2.798119786
## [176] 2.681683727 0.896813195 2.876179249 2.563272311 2.496104970
## [181] 2.141585172 2.858656187 2.165297803 3.497244175 3.214062912
## [186] 2.176789823 1.778992477 2.004728878 2.913732009 1.799740624
## [191] 3.454278613 1.979324862 1.682314628 1.413633885 3.749868448
## [196] 4.706791046 1.210227447 1.820979211 2.361625438 2.679475652
## [201] 2.903139149 1.327846826 1.307821114 3.272685878 1.288475436
## [206] 0.386542041 3.377962993 2.848974158 0.969391111 2.631354432
## [211] 2.385580571 3.708355304 -0.191878346 2.559374537 1.846157790
## [216] 3.604871742 4.135238137 2.460192795 3.331814661 0.882357288
## [221] -0.129132781 1.339612786 1.399269081 1.681496293 3.578279834
## [226] -0.344237298 4.603651505 3.922048411 3.132919947 2.495571681
## [231] 3.455382720 1.123767447 0.759191400 1.865122347 2.135032369
## [236] 0.381357055 1.883285586 -0.151279755 -0.757043028 3.054889751
## [241] 1.032749131 2.341048654 1.140844448 1.796170538 2.254572656
## [246] 2.967138929 0.456512980 1.087664655 0.167681279 1.494721617
## [251] 0.906545072 3.800000735 1.558648266 0.370101292 0.444619668
## [256] -0.194066761 2.517898481 2.660014367 -0.449194525 2.386759828
## [261] 0.011166652 2.185348014 0.464763816 0.874424036 1.355599780
## [266] 4.087344291 4.997858626 1.743896332 3.780914128 2.881768415
## [271] 3.512177096 1.169533942 3.367911104 2.985061656 0.863076595
## [276] 1.689867265 1.666617751 0.826462356 -0.182960219 -0.306607897
## [281] -1.570960393 2.529503154 2.482096357 2.049621712 1.128207445
## [286] 0.495257169 3.524337808 3.030659185 0.435687297 2.881363639
## [291] 0.793515740 1.540899759 2.194078130 2.930522846 2.654897754
## [296] -0.804776971 2.181071568 1.277415549 1.632863008 2.425641547
## [301] 0.834862288 0.867317659 1.417117056 -1.465767851 3.189287748
## [306] 2.218335610 2.385605742 0.745596294 3.846844514 -0.293381707
## [311] 3.549456502 -0.938651823 2.422045156 0.633884670 1.234301884
## [316] 5.377325215 1.889350594 3.638136790 -0.552912653 2.127272122
## [321] 2.935746536 0.880031059 -0.748029080 6.171820075 4.499856615
## [326] -1.931323615 4.327854771 2.541508395 5.312239912 2.250146922
## [331] 4.523247362 0.340278684 0.060596409 1.148533770 2.056697967
## [336] 2.221620580 3.249830262 -0.058297231 2.167555547 -0.202726186
## [341] 3.477293812 0.518690902 1.293467179 5.607258747 2.628624487
## [346] 4.523853957 3.438987547 3.214987061 3.568588946 1.330787489
## [351] 1.057882615 0.958245866 2.208839113 1.504112182 1.511911877
## [356] 0.233947797 -0.046471909 4.647026086 3.369564942 3.700364812
## [361] 3.560674306 2.953636836 0.080946757 1.852942614 1.803336820
## [366] 3.860592067 1.107694776 2.907235251 0.925754866 3.131695324
## [371] 2.961769827 1.607747193 1.000778476 -0.587831244 1.435040453
## [376] -0.410771524 -1.569148185 1.995837292 3.545443715 1.818171825
## [381] 0.978969557 2.871893249 -2.206846028 4.228216416 3.729382704
## [386] 3.015991437 4.035289365 -0.127668897 2.739061542 -0.862073537
## [391] 1.574009139 2.993038897 -0.806419899 3.578044781 2.379476345
## [396] 1.542734984 2.859282409 -0.016931752 4.609057161 3.125745259
## [401] -0.612877989 1.758565325 4.526386379 4.865329530 4.604677240
## [406] 1.181497195 0.908821090 -0.780073750 3.377281098 2.901794234
## [411] 2.477845386 2.603962316 2.176764873 0.440190337 2.117713831
## [416] 1.021562360 0.698167977 2.793280876 -0.280407670 1.404543790
## [421] 2.131678600 4.976641041 1.748579698 3.327428735 4.996529388
## [426] 3.684569558 3.883329898 3.094632222 1.899708354 2.362732602
## [431] 2.091775276 1.502873771 2.415499000 1.236500306 3.319895414
## [436] 0.406080575 2.502279914 2.775391312 0.427425463 0.642407122
## [441] 2.194521973 1.590472685 0.900818257 4.261912232 0.512064283
## [446] 3.682470052 5.190277891 2.438799248 2.156885844 4.113827686
## [451] 0.881111507 1.760659508 1.863012463 2.380053693 0.940952689
## [456] 1.662256115 5.056488927 2.090748348 0.695894016 2.016282896
## [461] 1.392682250 1.206051299 1.609686601 2.667437686 0.041488750
## [466] -0.496294446 1.460513553 2.095065070 1.090947359 1.201415801
## [471] 0.603368368 3.612917721 2.516166865 3.525292314 3.200073646
## [476] 0.711614614 3.462177038 0.081926905 1.557969532 -0.041376406
## [481] 1.382459493 3.990845828 3.698306781 3.261057416 -0.115064953
## [486] 3.631331429 2.258793365 4.839719502 1.808337152 0.385830771
## [491] -0.559890903 -0.069016176 0.070711444 2.990980195 1.654535954
## [496] 1.235918885 1.403164661 1.917360020 2.826475537 -0.834204359
## [501] 0.779800621 1.611117596 2.048654830 0.580877979 3.026427862
## [506] 0.842043801 2.623589108 0.844410636 -0.685968825 1.114074591
## [511] 1.701702581 5.067613879 4.314816570 4.175804388 1.548534208
## [516] 2.866514727 0.646550549 4.012731897 -1.017398295 3.641650109
## [521] 3.461067669 3.173118045 0.385334751 1.904036687 1.330792899
## [526] -0.126368757 1.686652380 0.054269871 2.687181948 -1.248502914
## [531] 0.948732588 3.509273859 4.194702761 3.807183093 3.990041171
## [536] 0.566196493 2.321997313 1.560641696 3.292109946 5.064179158
## [541] 2.385009751 1.050325979 2.217017618 2.798292375 4.274637180
## [546] 2.043169342 2.896547954 -0.231854903 1.595249561 0.498654747
## [551] 4.157274079 1.543097289 3.730660539 5.102465079 1.263503205
## [556] 3.240144399 2.152032556 2.309929103 2.960325928 2.237081022
## [561] 1.884649978 2.171957945 2.611740672 1.427387166 3.136034155
## [566] 2.682420928 1.775648580 0.113756562 3.034178233 3.126653540
## [571] 3.227582464 -0.854388649 2.722073018 1.958376430 2.262606274
## [576] -0.121435503 2.801981002 2.020598306 2.817741795 1.613274682
## [581] 3.142733325 0.484272993 3.208649410 3.035083282 1.479167395
## [586] 1.150790759 3.701087768 1.085508002 0.648694871 1.672787574
## [591] -0.030538133 -0.329367811 3.614505846 2.895135526 -1.117699726
## [596] -0.257930268 1.560001235 4.226804028 0.580031301 3.589155364
## [601] 2.924109030 3.509968140 0.382780650 2.768471804 1.146847999
## [606] 1.199226841 1.199200701 1.545234131 2.627762914 -1.005531485
## [611] 2.675528345 0.942385619 2.236110796 1.666614613 2.466047144
## [616] -0.075316639 1.222873683 2.891571440 2.675414137 3.624419512
## [621] 1.646911785 2.658578779 -0.031688641 0.735050494 0.451091386
## [626] 2.451128923 0.949997149 3.514244854 1.863204944 3.836204974
## [631] -0.603021480 -0.319337024 1.257422422 2.394351043 0.766818194
## [636] 1.052838544 0.515610056 3.746165928 1.617630643 2.651236998
## [641] 1.134091136 5.020876670 3.071165818 2.492117405 1.817997713
## [646] 0.574436500 0.911932446 5.210762114 0.596416402 2.332503344
## [651] 0.190461556 0.949514332 -2.770135288 1.819223138 3.102507774
## [656] 2.035814623 0.971425828 1.086469368 2.465583617 3.502209592
## [661] 3.111489920 0.997750018 0.808803748 1.734104825 4.070072017
## [666] 1.172146903 3.981510493 0.640949390 4.214419136 0.953116807
## [671] 3.623449570 2.028355115 -0.710511579 -0.938922115 2.358686369
## [676] 1.901396131 1.854185094 -1.533894307 0.283680000 2.688017375
## [681] 0.687639989 -0.277678155 2.343456428 0.157321335 3.313735230
## [686] -0.001917727 3.104180695 -1.367541495 2.117766945 2.491595483
## [691] 3.680403659 3.016596668 1.502181854 0.672837105 2.434811529
## [696] -0.769261133 1.620867105 2.573075480 1.812328080 2.949681366
## [701] 1.137050707 0.834973784 1.024347582 2.579634797 3.784555886
## [706] 4.814488856 2.532590948 -1.616980266 3.317233668 0.158837240
## [711] 3.309842897 0.483705893 -0.311526133 -0.160252647 0.579607843
## [716] 2.524513868 1.763920835 3.547768474 2.629925806 1.446274683
## [721] 3.139622787 -1.076106312 4.209918891 1.971537779 3.883913854
## [726] 3.290520667 -1.279874790 2.811665112 0.878562834 -0.758010997
## [731] 4.922818682 1.548811158 0.494741434 2.180618966 2.864407536
## [736] 0.731104652 2.724412443 0.254555588 0.884565159 1.549724337
## [741] 1.736007887 4.449264483 2.472529017 1.848016504 4.388478713
## [746] 3.127112122 0.583950410 2.172654230 3.458216292 1.438083433
## [751] 4.162619109 3.014258907 4.010867467 1.921455123 1.579531263
## [756] 2.452007478 3.430592621 4.058468777 4.044830485 3.753169527
## [761] 1.338781785 5.294234803 3.998838443 4.701717786 2.621254734
## [766] -0.556415556 1.616064095 2.213889366 1.595847208 4.916869844
## [771] 1.433936301 3.507015065 0.525193398 0.943896794 4.729544317
## [776] 2.621739869 2.191350081 2.548088521 5.663356067 3.321834371
## [781] 3.852457886 1.466556031 4.348955377 2.085881452 1.753753959
## [786] 1.821295814 1.081896849 3.061800861 -0.536425837 2.095775083
## [791] 0.445717207 2.586493260 0.964714438 3.861183008 0.744046046
## [796] 1.720898812 0.910057380 4.059577619 1.236213694 3.783689667
## [801] 3.689437687 1.189495131 3.357646302 3.492574443 -0.503454645
## [806] 1.407870753 3.343108270 2.986354097 3.354839962 0.345949115
## [811] 0.331047807 2.122137315 4.523316682 -0.701958913 4.137776039
## [816] 3.639109781 3.088262728 1.214752689 2.999429967 0.799842070
## [821] 2.981307972 2.212414048 -1.843412861 1.986753854 1.963792069
## [826] 0.537522800 3.431876325 4.073115785 3.562710086 1.311904743
## [831] 0.607830476 2.313241123 2.420648651 1.758383243 2.170864172
## [836] 4.566958367 1.923662787 3.859770508 1.089309542 3.283251142
## [841] 3.777681162 -0.042870618 0.228009452 2.983255926 3.376326400
## [846] 2.918136510 -0.271718132 1.819263474 3.307831732 2.155793387
## [851] 4.777552606 1.094635281 -0.213748082 -0.075303888 3.617827967
## [856] 5.189498755 1.874028703 1.709349330 -1.339822423 2.471665915
## [861] 1.723266216 2.144190837 -0.402399458 4.278697142 0.117220935
## [866] 2.948487184 3.207420630 2.517850022 2.284099311 2.950868117
## [871] 0.321583885 2.453889330 3.283546374 2.139452847 3.920844279
## [876] 2.597185557 1.243518522 3.700372388 0.472013158 2.566415927
## [881] 1.979615293 3.203406812 1.142641745 -0.728362693 5.160650976
## [886] 4.296301492 2.331556495 1.479219374 3.999919988 1.587084675
## [891] 3.584323715 1.792678198 2.782970876 2.571085555 4.776146009
## [896] -0.913720143 -0.013607980 1.384850709 3.478512342 -0.577342708
## [901] 3.175825349 1.647118310 0.252997322 1.242159984 2.515658829
## [906] 3.194430692 2.880583464 2.076392752 2.548392495 0.314345880
## [911] 2.499442465 2.438108674 -0.447928654 3.020947989 2.264994095
## [916] 3.185023015 3.527484012 -0.204099460 2.730253717 0.241468427
## [921] 2.452241248 1.425350978 1.652593004 3.127966958 1.604610741
## [926] 4.881506774 0.193686055 1.735364638 0.277461297 1.725632266
## [931] 0.328657728 1.978512011 2.170329949 1.474591967 2.348535919
## [936] 2.829876749 0.733224212 4.885930368 0.417359737 -0.343943002
## [941] 3.347158588 2.378416211 -0.349784716 2.412862549 2.168250581
## [946] -3.032103915 3.171089363 3.685878294 2.876424832 3.933584123
## [951] 5.142913027 -0.487878019 4.157600676 1.922997351 4.703886760
## [956] 2.637116083 0.150006877 0.994843708 -0.647925626 -0.355732656
## [961] 2.310294921 1.490850755 3.785227191 1.148375541 0.959298381
## [966] 2.596610196 0.236544118 4.759620964 2.047609185 1.770965072
## [971] 2.263903064 -0.479213348 3.110442212 3.860047987 0.690683803
## [976] 4.785014664 2.983873647 1.001276602 0.351728563 4.868495400
## [981] 0.723121894 2.697136340 1.700250041 1.427060090 2.964862526
## [986] 3.516249716 0.744672394 1.520555126 1.922225671 2.273491312
## [991] 2.338850542 0.824869100 3.188331658 0.616821194 2.161412908
## [996] 2.533024941 -0.191815314 1.986450673 -0.016335122 2.175432632
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%
## -0.4966525
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
16
## [1] 16
quantile(data,prob = 0.95)
## 95%
## 4.568232
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%
## -0.4966525
# 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 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 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 FALSE TRUE FALSE
## [109] FALSE FALSE TRUE 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 FALSE 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 TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE 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 FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE 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 TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE
## [325] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE FALSE 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 FALSE FALSE FALSE FALSE FALSE
## [373] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [385] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [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 FALSE FALSE FALSE FALSE FALSE
## [457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [505] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [517] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [529] FALSE TRUE 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 TRUE FALSE FALSE FALSE FALSE
## [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [589] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [625] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [649] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [673] TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [685] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [721] FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE 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 TRUE FALSE FALSE
## [769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [805] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [817] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [829] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [853] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [877] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE
## [901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 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 TRUE FALSE FALSE
## [949] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [961] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [973] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 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] -0.9381265 -1.3193800 -1.3340655 -1.3303856 -0.7269352 -0.7570430
## [7] -1.5709604 -0.8047770 -1.4657679 -0.9386518 -0.5529127 -0.7480291
## [13] -1.9313236 -0.5878312 -1.5691482 -2.2068460 -0.8620735 -0.8064199
## [19] -0.6128780 -0.7800738 -0.5598909 -0.8342044 -0.6859688 -1.0173983
## [25] -1.2485029 -0.8543886 -1.1176997 -1.0055315 -0.6030215 -2.7701353
## [31] -0.7105116 -0.9389221 -1.5338943 -1.3675415 -0.7692611 -1.6169803
## [37] -1.0761063 -1.2798748 -0.7580110 -0.5564156 -0.5364258 -0.5034546
## [43] -0.7019589 -1.8434129 -1.3398224 -0.7283627 -0.9137201 -0.5773427
## [49] -3.0321039 -0.6479256
# How to get the top 5%
(Cutoff <- quantile(data,prob = 0.95))
## 95%
## 4.568232
(Top5Percent <- (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 TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [61] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE TRUE 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 FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [133] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE 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 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [229] FALSE FALSE FALSE FALSE 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 TRUE 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 FALSE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [325] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [421] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [445] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [457] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 TRUE
## [541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [553] FALSE TRUE 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 FALSE 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 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 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
## [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 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 TRUE FALSE TRUE FALSE FALSE FALSE FALSE
## [769] FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE
## [781] FALSE FALSE FALSE FALSE 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 FALSE
## [817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [829] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [853] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [877] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [889] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [925] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [937] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [949] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [961] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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
sum(Top5Percent[TRUE]) # counts all the true values
## [1] 50
data[Top5Percent==TRUE]
## [1] 5.644668 4.651189 4.797398 5.000960 4.592422 5.092896 5.480223 4.746265
## [9] 4.595423 5.291737 4.677939 4.706791 4.603652 4.997859 5.377325 6.171820
## [17] 5.312240 5.607259 4.647026 4.609057 4.865330 4.604677 4.976641 4.996529
## [25] 5.190278 5.056489 4.839720 5.067614 5.064179 5.102465 5.020877 5.210762
## [33] 4.814489 4.922819 5.294235 4.701718 4.916870 4.729544 5.663356 4.777553
## [41] 5.189499 5.160651 4.776146 4.881507 4.885930 5.142913 4.703887 4.759621
## [49] 4.785015 4.868495