# Mindanao State University
# General Santos City
# Introduction to R base commands
# Submitted by: Kient Rey L. Zuyco
# Submitted to: Prof. Carlito O. Daarol
# Faculty
# Math Department
# April 11, 2023
# 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] 2.4097335 0.3321920 0.7839341 1.7698435 2.4172169 -0.2880273
## [7] -0.1029885 1.0024039 0.5887879 1.3810644 0.5575648 2.2708779
## [13] 4.8632841 1.2130398 3.6698344 3.9640466 2.5262743 2.7764387
## [19] -1.5893364 4.4308899
data[1:300] # display the first 300 elements
## [1] 2.40973352 0.33219200 0.78393407 1.76984354 2.41721686 -0.28802732
## [7] -0.10298852 1.00240386 0.58878792 1.38106439 0.55756481 2.27087793
## [13] 4.86328407 1.21303975 3.66983437 3.96404662 2.52627426 2.77643870
## [19] -1.58933636 4.43088985 2.70539843 4.11490596 0.55423969 3.81854372
## [25] 2.13501940 0.44060387 2.57614930 2.73753837 1.88622227 4.07567355
## [31] 1.31443493 3.42241306 1.91554767 1.88597213 0.37579372 2.09315034
## [37] 4.85031285 2.19094999 1.19276263 0.78205487 3.10715388 2.08811621
## [43] 2.27463934 0.49073262 1.68886682 2.47228362 -0.28708295 5.03260153
## [49] 2.71576201 0.53660256 3.27843352 1.60913296 -0.21408686 0.96302116
## [55] -2.20910709 0.70014730 3.75048372 4.10720703 2.58290317 3.99982367
## [61] 3.82601325 2.21643961 2.98163771 3.60725514 0.77075585 0.22204339
## [67] 2.27316225 1.88930333 -0.39535398 -1.79404308 1.53633868 1.97154402
## [73] 2.12024565 2.02922629 1.08995854 2.59653596 0.38359317 3.08508110
## [79] 2.17511274 2.74683258 1.10646209 2.23854513 1.91636368 1.13984340
## [85] 1.27885906 3.55008695 0.26744308 2.78332195 -1.79579395 2.72485158
## [91] 2.03788951 1.13597853 2.18824146 2.47900136 0.09230804 1.91661932
## [97] 2.61878556 0.84205547 1.13402790 1.65865242 -0.64949936 1.53636122
## [103] 3.02789387 0.64293609 3.82902399 1.42439847 0.39072257 0.33662078
## [109] 2.13037046 2.41812421 4.02377921 0.73630013 0.69305132 2.21301483
## [115] 1.90143152 2.31949965 0.95057901 3.13857579 0.32386122 0.48007761
## [121] 1.69983878 2.43980368 2.44214484 3.23681878 3.73198787 0.42804902
## [127] 0.90855919 -2.33329467 -0.28312166 3.16753109 3.70421272 2.60758082
## [133] 0.10963556 4.40019464 1.03268648 4.10353477 6.62599965 2.72397381
## [139] 0.05799016 3.30032914 1.13228531 1.63592601 4.06770872 1.11826772
## [145] 0.31826391 1.16059822 5.04151796 -0.46877532 4.01389762 1.92081395
## [151] 4.98978920 3.34459206 1.89961037 0.05320513 -0.01343242 1.50419989
## [157] 0.91345601 0.49781205 3.52173026 3.37602527 1.19900211 1.25902808
## [163] 2.53621570 5.33208731 1.69242791 0.49600153 3.26678870 1.60459108
## [169] 2.32733740 4.05199921 1.47195047 1.00445020 1.13420029 0.76955293
## [175] 3.91596031 3.04593384 1.64726075 2.30520990 -0.63673946 1.44128147
## [181] 1.91640863 1.47094476 2.00034566 3.81085704 2.23196113 2.47385158
## [187] -1.65402798 1.92430390 1.38153280 1.81676417 5.93907279 2.03348994
## [193] 0.52282974 3.44522778 1.18562459 -0.27965306 -0.70613680 2.15910142
## [199] -0.01312405 6.32277186 2.76310243 3.37253123 0.71113640 2.77790704
## [205] 1.17721879 1.96794108 2.89704386 1.57743958 2.93909572 4.16929921
## [211] 2.73938845 2.24048065 3.45062909 3.22394848 3.28366377 0.76323903
## [217] 1.36451985 3.53475449 4.82968260 3.18304208 2.72585187 1.58033696
## [223] 2.00339522 1.99984109 2.03507217 1.67652090 1.75953312 4.59398357
## [229] 1.10646848 1.17397049 3.78613914 2.36637817 1.45924552 3.05084576
## [235] 2.85501805 2.75744429 4.01864906 4.62457605 0.87369293 1.40167939
## [241] 0.12606745 3.15379486 2.99983428 -0.34230926 3.06422320 -0.57095083
## [247] 2.41450089 4.30893768 3.12109108 1.20570038 3.18147660 3.09265860
## [253] -1.94630268 2.40303648 -0.15537265 -0.60965857 2.52576203 3.32349678
## [259] 3.49732666 2.97658172 3.06453192 2.42572039 3.02975007 2.76681790
## [265] 5.60963176 -0.33885413 1.85858326 0.42124402 1.45675994 0.66960938
## [271] 1.43341589 1.10203961 1.47786217 3.89582327 -0.52838169 2.71977974
## [277] 1.80062029 1.16961417 4.80219322 3.15392385 1.12101736 1.79628717
## [283] -0.12625739 3.15107621 3.25911726 -0.43574310 2.05372519 0.22535835
## [289] 3.09616174 2.66295094 1.97697518 2.47018151 1.64865122 1.65741510
## [295] 5.17096497 2.12749143 3.78485359 3.51386555 0.62395678 2.82464482
# 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] -2.66714749 -2.56594350 -2.46473950 -2.36353551 -2.26233152 -2.16112752
## [7] -2.05992353 -1.95871953 -1.85751554 -1.75631154 -1.65510755 -1.55390355
## [13] -1.45269956 -1.35149556 -1.25029157 -1.14908757 -1.04788358 -0.94667958
## [19] -0.84547559 -0.74427159 -0.64306760 -0.54186360 -0.44065961 -0.33945561
## [25] -0.23825162 -0.13704762 -0.03584363 0.06536037 0.16656436 0.26776836
## [31] 0.36897235 0.47017635 0.57138034 0.67258434 0.77378833 0.87499233
## [37] 0.97619632 1.07740031 1.17860431 1.27980830 1.38101230 1.48221629
## [43] 1.58342029 1.68462428 1.78582828 1.88703227 1.98823627 2.08944026
## [49] 2.19064426 2.29184825 2.39305225 2.49425624 2.59546024 2.69666423
## [55] 2.79786823 2.89907222 3.00027622 3.10148021 3.20268421 3.30388820
## [61] 3.40509220 3.50629619 3.60750019 3.70870418 3.80990818 3.91111217
## [67] 4.01231617 4.11352016 4.21472416 4.31592815 4.41713214 4.51833614
## [73] 4.61954013 4.72074413 4.82194812 4.92315212 5.02435611 5.12556011
## [79] 5.22676410 5.32796810 5.42917209 5.53037609 5.63158008 5.73278408
## [85] 5.83398807 5.93519207 6.03639606 6.13760006 6.23880405 6.34000805
## [91] 6.44121204 6.54241604 6.64362003 6.74482403 6.84602802 6.94723202
## [97] 7.04843601 7.14964001 7.25084400 7.35204800
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%
## -2.6671475 0.9608394 1.9163862 2.9658200 7.3520480
## 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] 2.409733521 0.332192001 0.783934071 1.769843542 2.417216859
## [6] -0.288027316 -0.102988518 1.002403859 0.588787920 1.381064386
## [11] 0.557564812 2.270877929 4.863284073 1.213039754 3.669834375
## [16] 3.964046620 2.526274257 2.776438696 -1.589336363 4.430889851
## [21] 2.705398434 4.114905961 0.554239689 3.818543718 2.135019403
## [26] 0.440603866 2.576149296 2.737538375 1.886222275 4.075673550
## [31] 1.314434930 3.422413061 1.915547671 1.885972127 0.375793720
## [36] 2.093150344 4.850312848 2.190949987 1.192762631 0.782054869
## [41] 3.107153883 2.088116214 2.274639339 0.490732615 1.688866819
## [46] 2.472283624 -0.287082952 5.032601530 2.715762007 0.536602557
## [51] 3.278433520 1.609132962 -0.214086863 0.963021164 -2.209107090
## [56] 0.700147305 3.750483720 4.107207032 2.582903173 3.999823667
## [61] 3.826013246 2.216439610 2.981637711 3.607255144 0.770755847
## [66] 0.222043387 2.273162252 1.889303326 -0.395353985 -1.794043085
## [71] 1.536338678 1.971544023 2.120245648 2.029226286 1.089958541
## [76] 2.596535963 0.383593173 3.085081096 2.175112736 2.746832584
## [81] 1.106462094 2.238545133 1.916363681 1.139843396 1.278859064
## [86] 3.550086953 0.267443082 2.783321947 -1.795793950 2.724851582
## [91] 2.037889513 1.135978532 2.188241458 2.479001363 0.092308045
## [96] 1.916619315 2.618785563 0.842055469 1.134027903 1.658652418
## [101] -0.649499361 1.536361215 3.027893871 0.642936090 3.829023993
## [106] 1.424398474 0.390722571 0.336620785 2.130370458 2.418124206
## [111] 4.023779207 0.736300128 0.693051324 2.213014827 1.901431517
## [116] 2.319499647 0.950579006 3.138575794 0.323861222 0.480077610
## [121] 1.699838780 2.439803678 2.442144841 3.236818783 3.731987871
## [126] 0.428049021 0.908559190 -2.333294671 -0.283121664 3.167531089
## [131] 3.704212720 2.607580822 0.109635563 4.400194644 1.032686485
## [136] 4.103534773 6.625999651 2.723973811 0.057990159 3.300329142
## [141] 1.132285308 1.635926006 4.067708721 1.118267724 0.318263913
## [146] 1.160598216 5.041517962 -0.468775317 4.013897616 1.920813955
## [151] 4.989789204 3.344592063 1.899610370 0.053205135 -0.013432420
## [156] 1.504199891 0.913456008 0.497812046 3.521730259 3.376025274
## [161] 1.199002112 1.259028078 2.536215695 5.332087306 1.692427910
## [166] 0.496001529 3.266788704 1.604591079 2.327337396 4.051999210
## [171] 1.471950472 1.004450198 1.134200295 0.769552927 3.915960308
## [176] 3.045933836 1.647260748 2.305209899 -0.636739459 1.441281469
## [181] 1.916408630 1.470944757 2.000345656 3.810857036 2.231961130
## [186] 2.473851581 -1.654027981 1.924303899 1.381532804 1.816764174
## [191] 5.939072791 2.033489937 0.522829735 3.445227784 1.185624595
## [196] -0.279653064 -0.706136804 2.159101417 -0.013124046 6.322771857
## [201] 2.763102430 3.372531232 0.711136401 2.777907043 1.177218792
## [206] 1.967941082 2.897043864 1.577439580 2.939095717 4.169299208
## [211] 2.739388448 2.240480652 3.450629092 3.223948478 3.283663770
## [216] 0.763239028 1.364519850 3.534754486 4.829682602 3.183042078
## [221] 2.725851872 1.580336961 2.003395219 1.999841094 2.035072165
## [226] 1.676520903 1.759533117 4.593983574 1.106468477 1.173970492
## [231] 3.786139138 2.366378175 1.459245520 3.050845760 2.855018053
## [236] 2.757444289 4.018649062 4.624576050 0.873692933 1.401679391
## [241] 0.126067451 3.153794862 2.999834278 -0.342309263 3.064223199
## [246] -0.570950832 2.414500891 4.308937684 3.121091075 1.205700378
## [251] 3.181476604 3.092658601 -1.946302680 2.403036479 -0.155372650
## [256] -0.609658565 2.525762027 3.323496780 3.497326659 2.976581717
## [261] 3.064531923 2.425720389 3.029750068 2.766817900 5.609631762
## [266] -0.338854129 1.858583257 0.421244024 1.456759944 0.669609377
## [271] 1.433415887 1.102039611 1.477862171 3.895823270 -0.528381690
## [276] 2.719779738 1.800620295 1.169614174 4.802193223 3.153923846
## [281] 1.121017364 1.796287167 -0.126257389 3.151076210 3.259117262
## [286] -0.435743098 2.053725190 0.225358352 3.096161743 2.662950940
## [291] 1.976975183 2.470181511 1.648651217 1.657415104 5.170964967
## [296] 2.127491425 3.784853593 3.513865553 0.623956784 2.824644825
## [301] 3.807542135 1.585239881 2.850672391 4.501696149 1.299937741
## [306] -0.204466314 3.393553892 0.882177935 3.173465155 1.854532698
## [311] 0.460125677 1.200523253 2.169282451 2.137338939 2.372392263
## [316] 3.461977090 0.086125773 3.602836274 1.819019434 3.798271129
## [321] 0.839160245 2.267737785 2.841513768 -0.381956628 -0.956130416
## [326] -0.747238603 3.969736125 0.192750375 1.811165049 2.966022536
## [331] 0.710969254 1.746685543 1.745228699 0.595021237 0.639745778
## [336] 1.313775120 1.564341473 0.491480089 2.185345325 3.033729290
## [341] 1.914886643 0.695696431 3.611574573 3.099062731 2.462030285
## [346] 3.174250822 2.720116655 3.296723600 4.087920705 2.147611899
## [351] 2.497317402 2.281712878 0.053296638 2.634476101 2.665619957
## [356] 0.895959521 4.335791004 2.463786916 1.256191372 3.253500388
## [361] 1.120717694 3.976659456 5.084484722 3.840856417 1.938072718
## [366] 1.261562060 5.397735041 -1.629521605 1.863675112 2.544003603
## [371] 1.941516827 2.756042132 -0.491227225 2.192622034 1.959663920
## [376] -0.346661728 1.170204855 4.024087749 3.672445839 4.121382348
## [381] 1.187145058 2.592879995 0.969146512 1.736872918 0.306057680
## [386] 1.120199401 2.691857452 4.880325109 1.807489301 3.958792926
## [391] -0.968405812 2.192349565 0.121587941 2.943978515 3.088592669
## [396] -0.117798513 1.412370767 -0.850454975 2.610281659 1.323175092
## [401] 2.601527332 2.492896113 3.653891258 1.615218408 4.078472765
## [406] 2.421278777 3.332591014 1.865228729 2.986546262 0.417754429
## [411] 4.885423222 2.558474593 1.222472193 4.193817501 0.657374322
## [416] 3.469189316 -0.529670186 1.924592915 2.193368299 4.568320495
## [421] 0.767299391 2.545446694 1.948578235 1.730199310 1.409214021
## [426] 2.263169268 1.725964961 2.939728866 2.629501848 2.108960169
## [431] 0.676759970 2.289559233 2.315942837 2.524292023 0.474493304
## [436] 2.343776424 4.729965905 -0.216186396 1.778286041 1.513905532
## [441] 4.185337749 3.268688245 0.138315576 2.269392473 0.966694299
## [446] 0.443587450 1.361343832 -0.879784057 2.124044066 2.627198247
## [451] 1.898591058 3.798074357 3.355693276 -0.193597000 1.018479010
## [456] 0.317948044 1.557396318 2.837198129 -0.037302489 3.015942472
## [461] 1.898461213 3.743415057 2.187388109 1.823430740 2.178068619
## [466] 3.764937688 2.746205197 -0.169461671 2.969541432 3.129681816
## [471] 2.928387610 0.173687639 0.104813486 2.119531712 1.196981880
## [476] 1.947308934 4.129008241 4.743045019 1.837168565 2.553593445
## [481] 3.887727150 1.671046627 4.142732194 1.162233860 -0.037321599
## [486] 4.195952106 2.302880186 1.745756807 3.016762361 -0.790424460
## [491] 3.082487175 1.894341430 0.886851894 0.769061777 1.860318260
## [496] 0.434682887 0.914526231 0.276596349 1.583937108 2.962635339
## [501] 2.018069622 0.409438528 2.629436714 2.727238500 2.609397313
## [506] -1.398907420 4.627010951 2.659537579 2.400231662 0.424480324
## [511] 1.653363474 2.820303981 7.352047996 1.282337830 1.976281919
## [516] 0.670466533 3.644619967 0.628037658 0.372887460 4.211464916
## [521] 1.792137918 1.434659847 -1.064926986 0.859999365 2.734146259
## [526] 3.366507646 1.802268080 2.543546707 1.461469410 4.550971090
## [531] 2.122421396 2.823885268 1.178875204 3.758271557 0.203754987
## [536] 0.828663789 0.319625554 -0.097740299 3.946675232 2.049099183
## [541] -0.668478589 -0.175295958 0.533915193 2.175968344 1.663640269
## [546] -0.022780134 0.609037656 1.171879013 2.428704761 2.478493855
## [551] 1.220901539 2.323096951 0.310942927 1.689482258 0.837180792
## [556] 0.012609115 2.357004754 1.387602166 0.812009364 2.102181488
## [561] 3.080948992 0.073698799 3.145811033 -1.822458894 1.550250976
## [566] 1.204726068 0.933677805 4.215771294 0.730323127 4.315148632
## [571] 2.380057700 1.524682572 1.457443021 3.461455240 2.238028018
## [576] 3.497873688 1.326625604 2.381988879 0.713532167 3.721304297
## [581] 1.722217072 1.151380954 -0.859868522 2.646299444 2.870877815
## [586] -0.523877781 2.778781360 4.329833174 0.386087566 3.450143368
## [591] 1.336037454 1.646520657 1.619911420 0.164920824 2.807511935
## [596] 3.091148002 0.540970797 4.354028211 3.646536213 0.049990845
## [601] 1.531586773 2.376080435 3.908052936 0.309892425 0.842974088
## [606] 0.972060215 0.360823303 0.919925529 1.176239286 1.403384361
## [611] 3.793190587 1.799083770 2.255889924 3.360574939 1.741975161
## [616] 0.440170579 -0.204148601 3.363022115 1.532607288 1.176298259
## [621] 0.954294203 -0.219363991 2.691842603 -0.265611453 3.372295086
## [626] 0.141681596 1.087404630 -0.365594543 1.967651786 3.318308424
## [631] 3.357705013 1.125610987 2.263308623 2.775177703 0.572128971
## [636] 1.175739308 2.791749918 3.127476161 2.666336082 0.075259310
## [641] 2.154946046 2.733062512 4.951143789 0.992407340 4.065740207
## [646] 0.358812814 1.637703697 3.038259402 -0.339855691 -2.667147495
## [651] 2.594522186 0.931946244 2.760251594 -0.246816545 0.308260532
## [656] 4.331462767 1.305879288 2.155599181 1.102820194 -0.424433115
## [661] 2.922212286 3.071583831 2.529265212 -1.842525945 -0.839174116
## [666] 1.800248358 3.018263277 0.749151499 2.460490385 0.229984383
## [671] 2.251645607 2.060415320 1.784831107 4.214944839 3.164888015
## [676] 2.706703336 2.276605316 3.014252873 1.484981855 1.542377006
## [681] 4.037660679 1.759346547 3.442674004 0.942056094 2.562422621
## [686] 2.354122937 1.543031835 0.628348638 0.595078256 0.797917815
## [691] -0.559577947 3.915066047 2.040351221 1.601901033 1.730278235
## [696] 0.314417235 1.405566479 2.913124519 1.594214510 3.799069128
## [701] 1.770785038 0.166474421 1.417558047 1.222017908 3.929795568
## [706] 6.248222254 3.933360832 3.459840629 3.002576721 4.730841922
## [711] 2.413776751 3.933345826 1.267367486 -1.253551894 0.129121559
## [716] 4.134829892 1.052129022 0.413382950 4.066262833 1.952051904
## [721] 0.300575747 1.907849657 1.578645052 2.629283105 3.845566699
## [726] -0.546487776 1.471025710 -0.125948011 3.670348226 0.754660902
## [731] 1.377047832 2.510275405 1.803785183 3.148740225 3.020821429
## [736] 4.057875190 0.314970309 1.331940623 2.352994585 4.649430941
## [741] 3.651103348 1.623553432 3.852033570 -0.295358694 -0.469742099
## [746] 1.352464563 0.568990552 1.575824543 1.824051867 -0.367747904
## [751] 2.860429703 1.775406242 -0.853645457 1.033335522 1.797515604
## [756] 2.384119150 6.279854017 2.490109509 1.558242872 2.159808061
## [761] 2.489147675 3.147709633 -0.320554496 1.763080598 4.056590173
## [766] 3.058868234 1.620563375 2.329638307 4.245282547 2.236548897
## [771] 1.397926952 3.002485749 2.852427991 1.840481958 -0.823422027
## [776] 1.806697192 3.017448128 0.255350886 2.943840624 0.667563717
## [781] 1.009214529 2.350264811 1.448513790 2.300297762 2.490319379
## [786] 2.185886327 3.554900561 1.564868234 2.435716031 1.165630766
## [791] 0.923608795 2.776214538 1.361506922 -0.030554378 0.631627370
## [796] 0.194498028 4.121806042 5.065959775 1.050694726 1.110820798
## [801] 1.836774610 2.001974939 1.580240253 1.475924826 1.551370454
## [806] 0.826127472 0.107900238 2.382205145 3.253804910 4.278012317
## [811] 2.511760951 2.899370336 2.470717647 4.160769381 1.646553800
## [816] 2.314528127 -0.019617632 0.359688895 1.682506824 2.176066464
## [821] 1.390299152 1.552846597 2.238004999 4.121752916 4.830082767
## [826] 1.814893297 1.201752564 2.210027506 3.858659346 0.864838495
## [831] 3.793577046 0.427260932 3.531144835 0.009382786 3.773720946
## [836] 2.387664951 2.434169233 -0.732110570 1.180064459 1.040549633
## [841] 1.187950849 2.965752456 -0.425230054 1.831740338 0.035888723
## [846] 5.186935661 0.143727153 2.637797895 0.471220822 3.264526346
## [851] 4.984076353 0.707310507 2.599645202 1.314807901 4.145517259
## [856] 0.650541420 1.422609471 1.320522699 2.351754763 2.307503654
## [861] 0.786982576 -0.327364169 1.608751342 1.216237985 1.609971354
## [866] 4.452384191 2.249949434 1.444434340 3.386695051 4.963511494
## [871] 3.230146657 1.403565733 1.515544291 2.347481492 1.505700597
## [876] 2.532635690 1.363649305 1.458275142 2.391654127 3.199851201
## [881] 3.501038299 -0.685885710 2.036561070 1.878566323 2.167331605
## [886] 2.244693718 0.895158833 -0.121368186 0.551141908 3.410643846
## [891] -0.509951446 1.107221271 0.883849961 1.969698341 2.576266542
## [896] 3.926029443 3.460743910 3.071174799 1.796422896 1.667648712
## [901] 4.081755955 1.143708460 1.798462606 3.549371316 5.600434657
## [906] 4.847032345 -0.460871857 0.399146341 4.730911855 0.760972057
## [911] 2.244972440 1.602781876 -0.312907093 4.066040681 2.189980804
## [916] 1.546072628 -0.114025145 0.218848542 2.271857556 2.748676834
## [921] 4.994913708 0.857591667 -1.196567828 0.744922179 0.429175052
## [926] 0.102581843 1.828601841 3.715699350 -0.499449527 1.625529381
## [931] 4.346394412 2.355968365 1.152101314 1.522269654 2.243115511
## [936] 1.563911707 3.031550420 0.165552610 1.339480321 2.429264696
## [941] 1.580868016 0.699102827 4.266348712 0.991841735 1.195551672
## [946] 1.883251938 3.292292401 2.335756576 2.738239665 0.979734693
## [951] 2.059551679 0.886836882 3.896710708 -0.112625895 2.711203648
## [956] 2.290331214 2.534216945 3.991794791 -1.629233386 3.411162963
## [961] 1.040816286 2.868239964 3.638714819 1.137183260 3.621515952
## [966] 0.160396104 3.553410866 3.126999901 4.044167855 2.181877167
## [971] 1.757206186 1.797851640 1.106853095 2.302110284 1.682208566
## [976] 2.487865460 1.094877855 1.075846191 -0.078747936 3.994978554
## [981] 2.397999509 2.431656141 2.469277651 0.304212752 2.445496183
## [986] 4.845357501 4.017150074 -0.576732779 1.467422640 0.583444025
## [991] 2.330948658 0.096892598 0.637135238 1.781248898 1.928767398
## [996] 0.614571757 3.356406328 1.606145754 1.829028550 1.760010146
summary(data)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.6671 0.9608 1.9164 1.9574 2.9658 7.3520
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%
## -0.3826265
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%
## 4.309248
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%
## -0.3826265
# 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 TRUE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE TRUE 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 TRUE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE TRUE 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 FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [325] TRUE 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 TRUE FALSE FALSE FALSE FALSE
## [373] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 TRUE 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 TRUE FALSE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [505] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [517] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [541] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [577] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE 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 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [649] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [661] FALSE FALSE FALSE TRUE TRUE 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 TRUE FALSE FALSE FALSE FALSE FALSE
## [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [709] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [721] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [745] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [769] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE 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 TRUE FALSE FALSE
## [841] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [853] FALSE FALSE FALSE 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 FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [889] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [901] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [925] FALSE FALSE FALSE FALSE TRUE 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 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 TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [997] FALSE FALSE FALSE FALSE
# How to get the top 5%
(Cutoff <- quantile(data,prob = 0.95))
## 95%
## 4.309248
(Top5Percent <- (data >= Cutoff))
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] TRUE 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 FALSE 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 TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE TRUE 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 FALSE FALSE FALSE FALSE FALSE
## [349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [361] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [409] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [433] FALSE FALSE FALSE FALSE TRUE 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 TRUE 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 TRUE FALSE FALSE FALSE
## [517] FALSE FALSE FALSE 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 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 TRUE FALSE FALSE FALSE FALSE FALSE
## [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 TRUE FALSE FALSE FALSE 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 TRUE FALSE FALSE FALSE FALSE
## [745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [757] TRUE 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 FALSE FALSE FALSE FALSE FALSE
## [793] FALSE FALSE FALSE FALSE FALSE TRUE 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 TRUE FALSE FALSE FALSE
## [829] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [841] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE
## [853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [865] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [877] FALSE FALSE FALSE 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 FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
## [913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [925] FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 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 TRUE 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] 4.863284 4.430890 4.850313 5.032602 4.400195 6.626000 5.041518 4.989789
## [9] 5.332087 5.939073 6.322772 4.829683 4.593984 4.624576 5.609632 4.802193
## [17] 5.170965 4.501696 4.335791 5.084485 5.397735 4.880325 4.885423 4.568320
## [25] 4.729966 4.743045 4.627011 7.352048 4.550971 4.315149 4.329833 4.354028
## [33] 4.951144 4.331463 6.248222 4.730842 4.649431 6.279854 5.065960 4.830083
## [41] 5.186936 4.984076 4.452384 4.963511 5.600435 4.847032 4.730912 4.994914
## [49] 4.346394 4.845358