# Mindanao State University
# General Santos City
# Introduction to R base commands
# Prepared by: Eugene D. Villaralbo
# March 20, 2023
# 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] 0.7156967 0.3582907 1.9697389 3.9388802 1.4399580 4.4733058
## [7] -1.4801422 1.7444057 1.1176271 4.9357245 2.6672891 0.9100458
## [13] 0.9983452 1.5694589 1.7354206 3.0860015 3.1083079 -0.4888019
## [19] -0.7781066 2.4725154
data[1:300] # display the first 300 elements
## [1] 0.71569675 0.35829073 1.96973891 3.93888015 1.43995796 4.47330576
## [7] -1.48014219 1.74440566 1.11762706 4.93572451 2.66728915 0.91004575
## [13] 0.99834525 1.56945890 1.73542055 3.08600152 3.10830794 -0.48880185
## [19] -0.77810657 2.47251541 1.88783766 -0.18572457 2.28981984 3.23372792
## [25] 3.35561760 2.10337768 1.35410931 2.35801224 2.56409993 3.76587342
## [31] 1.07444162 -0.11356258 1.54203812 -0.09369398 2.66182780 3.61136251
## [37] 1.98197575 2.96837580 4.13644679 2.08021170 2.30047663 2.56796932
## [43] 2.32359895 2.58630759 3.00465484 -1.70227591 3.46777579 -1.34827040
## [49] -0.78491132 2.74327561 1.02579470 2.70323667 3.03334599 3.02899938
## [55] 2.86889273 1.43271817 1.61923122 1.91506101 2.85491441 2.64594277
## [61] 1.94401832 1.65642746 1.69025364 -0.01668462 4.47779263 4.93240232
## [67] 1.43506438 1.25241592 2.09194537 1.21488766 1.89799342 1.23784867
## [73] 0.94415711 0.43642343 0.02745933 1.18584700 3.22266942 1.65389293
## [79] 2.62606072 4.43454054 2.72764071 4.40877993 1.98744773 3.87100588
## [85] 3.17528376 2.00514491 3.34288039 3.24103276 1.57723866 3.08886371
## [91] 1.25575980 0.93019879 3.95060608 3.11463755 2.73414281 2.42778109
## [97] 1.16328328 2.27928063 1.52937315 2.98208230 3.37903325 -0.41255852
## [103] -0.69337420 1.79538477 2.89030371 3.36663197 2.20185193 -0.74293398
## [109] 4.09816035 0.08160403 0.97657728 1.64676266 2.28769535 2.57407369
## [115] 3.12940447 3.56454008 3.85882842 2.00137739 0.42355719 1.09338418
## [121] 1.88860868 1.55860295 3.60800449 0.62611967 -0.52893103 3.13230145
## [127] 1.46619964 1.11359549 0.35050550 3.00818644 2.27268068 2.36770583
## [133] 2.10485113 3.82813969 2.54240133 4.23231033 -0.65235798 1.73135236
## [139] 2.73109881 1.53362689 3.91569752 1.15707137 2.65102615 3.55693477
## [145] 3.77027872 4.44300043 0.59777553 1.26276942 -2.03150252 2.06687746
## [151] 1.39781757 2.94422908 2.17376601 0.31295034 4.56117798 4.13183908
## [157] 3.18576044 2.12568283 1.09843572 3.13583694 3.26503788 4.66693249
## [163] -0.78094276 1.16074759 1.00096569 2.02781726 1.00361729 2.54565762
## [169] 2.26185644 0.84781244 0.35461146 3.22850495 0.74321199 1.10344527
## [175] 2.46189261 1.91570674 1.68184854 0.17900070 5.56434525 3.35028864
## [181] 3.64313176 0.62535682 2.38139629 1.35353226 1.06128839 2.02443210
## [187] 5.04472461 2.00954027 2.08687154 0.83695744 0.06672484 2.62167613
## [193] 4.00752357 1.35022077 4.14910731 1.34005146 1.99495784 3.78029510
## [199] 2.32889626 3.10892454 3.26914957 3.25948970 4.88925668 1.80526162
## [205] 3.20590286 -1.39643800 4.12955937 1.04472605 0.55709190 1.11547902
## [211] 4.79831158 4.06525082 1.55200954 5.07317651 3.53960586 1.61573837
## [217] 2.85186769 0.58873732 0.26443949 2.90699111 0.30465923 1.96821237
## [223] 2.02491188 4.20843744 2.32623915 2.33794648 3.01848382 4.55018594
## [229] 1.34028110 3.90976216 4.14705216 0.75171337 2.15902325 1.81707842
## [235] 0.92087472 2.48835896 4.56357286 0.92445486 2.53824603 1.00900216
## [241] 1.50294813 0.99326090 4.15884133 3.67107995 2.93177915 2.94933761
## [247] 2.19816945 2.15820061 2.61667364 1.92130503 4.05144649 2.22394257
## [253] 0.89607355 3.26583894 -2.08366875 2.89201338 1.41285417 1.22520082
## [259] 3.57320438 -0.46104599 2.76843446 -0.75766581 -1.61956594 1.34886400
## [265] 3.15088448 0.18057281 1.42308247 1.52402231 0.38317082 1.45589028
## [271] 2.64148138 2.87160266 2.62680052 2.34253254 3.06913694 3.40245723
## [277] 1.65488254 1.83519475 2.00267115 2.55688015 -0.64122644 1.56154280
## [283] 2.95705464 2.26126411 1.54962352 -1.40564181 2.19641883 1.86960012
## [289] -1.18968667 3.31541296 2.14842885 -0.86151116 1.30863739 -1.00075734
## [295] -0.32391683 6.45324311 3.14687952 0.50521955 3.54293839 0.17677285
# 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] -2.25948562 -2.17147825 -2.08347089 -1.99546353 -1.90745617 -1.81944881
## [7] -1.73144145 -1.64343409 -1.55542673 -1.46741937 -1.37941201 -1.29140465
## [13] -1.20339728 -1.11538992 -1.02738256 -0.93937520 -0.85136784 -0.76336048
## [19] -0.67535312 -0.58734576 -0.49933840 -0.41133104 -0.32332368 -0.23531631
## [25] -0.14730895 -0.05930159 0.02870577 0.11671313 0.20472049 0.29272785
## [31] 0.38073521 0.46874257 0.55674993 0.64475729 0.73276466 0.82077202
## [37] 0.90877938 0.99678674 1.08479410 1.17280146 1.26080882 1.34881618
## [43] 1.43682354 1.52483090 1.61283826 1.70084562 1.78885299 1.87686035
## [49] 1.96486771 2.05287507 2.14088243 2.22888979 2.31689715 2.40490451
## [55] 2.49291187 2.58091923 2.66892659 2.75693396 2.84494132 2.93294868
## [61] 3.02095604 3.10896340 3.19697076 3.28497812 3.37298548 3.46099284
## [67] 3.54900020 3.63700756 3.72501493 3.81302229 3.90102965 3.98903701
## [73] 4.07704437 4.16505173 4.25305909 4.34106645 4.42907381 4.51708117
## [79] 4.60508853 4.69309589 4.78110326 4.86911062 4.95711798 5.04512534
## [85] 5.13313270 5.22114006 5.30914742 5.39715478 5.48516214 5.57316950
## [91] 5.66117686 5.74918423 5.83719159 5.92519895 6.01320631 6.10121367
## [97] 6.18922103 6.27722839 6.36523575 6.45324311
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%
## -2.2594856 0.9644725 1.9874866 3.0121048 6.4532431
# 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] 0.715696747 0.358290728 1.969738907 3.938880151 1.439957965
## [6] 4.473305761 -1.480142195 1.744405662 1.117627064 4.935724511
## [11] 2.667289149 0.910045750 0.998345247 1.569458901 1.735420554
## [16] 3.086001517 3.108307937 -0.488801854 -0.778106570 2.472515405
## [21] 1.887837657 -0.185724571 2.289819841 3.233727925 3.355617600
## [26] 2.103377683 1.354109312 2.358012244 2.564099933 3.765873423
## [31] 1.074441622 -0.113562582 1.542038117 -0.093693976 2.661827796
## [36] 3.611362510 1.981975748 2.968375804 4.136446791 2.080211704
## [41] 2.300476630 2.567969322 2.323598954 2.586307592 3.004654841
## [46] -1.702275907 3.467775787 -1.348270396 -0.784911317 2.743275615
## [51] 1.025794699 2.703236667 3.033345993 3.028999384 2.868892727
## [56] 1.432718171 1.619231220 1.915061013 2.854914413 2.645942767
## [61] 1.944018319 1.656427464 1.690253644 -0.016684617 4.477792635
## [66] 4.932402318 1.435064380 1.252415916 2.091945366 1.214887665
## [71] 1.897993417 1.237848670 0.944157114 0.436423426 0.027459331
## [76] 1.185847003 3.222669424 1.653892930 2.626060719 4.434540545
## [81] 2.727640708 4.408779934 1.987447732 3.871005877 3.175283757
## [86] 2.005144910 3.342880390 3.241032765 1.577238657 3.088863710
## [91] 1.255759802 0.930198790 3.950606075 3.114637545 2.734142808
## [96] 2.427781091 1.163283278 2.279280634 1.529373146 2.982082300
## [101] 3.379033249 -0.412558519 -0.693374203 1.795384769 2.890303712
## [106] 3.366631967 2.201851933 -0.742933984 4.098160346 0.081604026
## [111] 0.976577276 1.646762660 2.287695353 2.574073685 3.129404473
## [116] 3.564540083 3.858828424 2.001377393 0.423557186 1.093384183
## [121] 1.888608683 1.558602946 3.608004485 0.626119665 -0.528931029
## [126] 3.132301453 1.466199637 1.113595495 0.350505505 3.008186444
## [131] 2.272680679 2.367705826 2.104851130 3.828139685 2.542401330
## [136] 4.232310327 -0.652357982 1.731352362 2.731098811 1.533626888
## [141] 3.915697520 1.157071374 2.651026149 3.556934768 3.770278721
## [146] 4.443000430 0.597775526 1.262769422 -2.031502519 2.066877456
## [151] 1.397817570 2.944229082 2.173766006 0.312950344 4.561177977
## [156] 4.131839076 3.185760442 2.125682828 1.098435722 3.135836943
## [161] 3.265037882 4.666932485 -0.780942761 1.160747591 1.000965695
## [166] 2.027817264 1.003617290 2.545657622 2.261856437 0.847812441
## [171] 0.354611457 3.228504954 0.743211991 1.103445268 2.461892608
## [176] 1.915706736 1.681848538 0.179000695 5.564345254 3.350288641
## [181] 3.643131757 0.625356825 2.381396286 1.353532265 1.061288388
## [186] 2.024432105 5.044724614 2.009540269 2.086871542 0.836957436
## [191] 0.066724842 2.621676126 4.007523567 1.350220766 4.149107309
## [196] 1.340051456 1.994957842 3.780295102 2.328896261 3.108924536
## [201] 3.269149571 3.259489705 4.889256677 1.805261615 3.205902857
## [206] -1.396437997 4.129559367 1.044726047 0.557091905 1.115479021
## [211] 4.798311583 4.065250818 1.552009543 5.073176511 3.539605859
## [216] 1.615738371 2.851867692 0.588737318 0.264439489 2.906991113
## [221] 0.304659230 1.968212370 2.024911878 4.208437441 2.326239152
## [226] 2.337946475 3.018483818 4.550185944 1.340281095 3.909762156
## [231] 4.147052157 0.751713368 2.159023247 1.817078425 0.920874723
## [236] 2.488358956 4.563572863 0.924454857 2.538246026 1.009002156
## [241] 1.502948135 0.993260904 4.158841331 3.671079949 2.931779150
## [246] 2.949337610 2.198169453 2.158200615 2.616673636 1.921305035
## [251] 4.051446488 2.223942565 0.896073553 3.265838944 -2.083668751
## [256] 2.892013376 1.412854175 1.225200823 3.573204382 -0.461045993
## [261] 2.768434459 -0.757665813 -1.619565939 1.348864000 3.150884484
## [266] 0.180572808 1.423082471 1.524022311 0.383170817 1.455890280
## [271] 2.641481377 2.871602661 2.626800518 2.342532539 3.069136939
## [276] 3.402457229 1.654882543 1.835194746 2.002671148 2.556880150
## [281] -0.641226444 1.561542802 2.957054639 2.261264110 1.549623518
## [286] -1.405641805 2.196418830 1.869600122 -1.189686667 3.315412960
## [291] 2.148428849 -0.861511163 1.308637387 -1.000757344 -0.323916834
## [296] 6.453243113 3.146879517 0.505219553 3.542938389 0.176772851
## [301] 3.042998155 5.567151784 2.845745943 0.957825397 3.202580038
## [306] 1.144323823 1.865174983 -0.293437390 2.349881002 3.379885623
## [311] 3.952083634 1.464959036 0.602838092 3.934709037 1.646376439
## [316] 2.237127573 -0.778498914 2.404789554 1.720306530 3.517112250
## [321] 0.538465163 -1.204520229 3.469848233 0.790112442 4.108448174
## [326] 3.809062721 -0.571729373 2.602717759 0.881741226 2.236106266
## [331] 3.831336407 1.167042416 2.104829276 2.451358950 2.951296069
## [336] 2.205880402 3.446612115 3.052519579 2.185462801 3.338802359
## [341] -1.056332329 3.539970960 5.683136937 1.638635820 2.631774304
## [346] 1.642863480 4.346470320 2.601280978 0.839019426 3.887551995
## [351] 0.346762993 0.301497448 2.259032047 2.372974159 2.927282980
## [356] 3.151165016 0.506460174 2.827570839 2.895793802 1.598614525
## [361] 1.086425267 2.337274048 3.190278217 2.305546357 4.297243361
## [366] 3.420264624 0.379621586 2.522925894 1.643627535 2.428388027
## [371] 3.494512133 0.441331980 4.067824458 2.009868147 1.395053853
## [376] 1.251383672 3.969256594 3.000603597 5.399896408 3.902953116
## [381] 3.310360381 3.061429798 3.212318599 2.669712705 1.199902445
## [386] 3.681367880 3.538513478 1.366900097 1.827237781 2.247436102
## [391] 2.320904582 4.592348892 1.880991307 -2.259485615 2.171331525
## [396] 2.274377856 2.508988926 2.917268288 -0.647195060 0.792508640
## [401] 2.822077332 4.357129895 0.676388205 0.981689778 1.212344551
## [406] 2.368971356 0.082110818 -0.467608114 0.196874132 4.920193108
## [411] 0.454814979 3.695291752 0.960161850 2.413222164 2.237344816
## [416] 0.363045467 1.067808109 3.522747629 2.373460390 -1.076657929
## [421] 2.766365936 2.810674894 1.592241618 1.159156084 0.016709898
## [426] 1.394163142 -0.439586194 -0.738252048 2.155234955 0.281450264
## [431] 1.403576417 2.831055168 3.755639510 0.537542256 3.500825218
## [436] 1.096985632 2.430445279 1.687724175 1.362524484 3.595386185
## [441] 0.542384558 3.491973625 0.559452777 1.205861649 4.017966849
## [446] 1.489780913 -0.027819917 0.943888897 1.565503210 1.366538907
## [451] 1.802143939 2.850045115 1.778882853 4.315125261 2.406667988
## [456] 4.761508990 1.947156182 0.014866351 3.099026876 4.133921979
## [461] 2.398129482 3.980120834 3.746184310 2.705538764 1.938110208
## [466] 0.913284585 0.731611955 3.058542309 0.862947351 1.809026584
## [471] 0.652819959 0.445506268 1.428561587 0.149776818 2.955725371
## [476] -0.331483618 1.719798650 3.949502493 1.824438075 0.825761979
## [481] -0.008897354 5.021263275 2.863055250 1.618509811 1.974902981
## [486] -0.426625524 1.505704599 4.802635921 0.395009111 1.386687017
## [491] -0.128682890 2.055806447 2.979068535 0.414845411 1.708296481
## [496] 2.008346819 2.717110281 1.738177694 0.680676172 1.040409276
## [501] 2.373479181 3.634249460 0.939725912 0.705826544 2.982258368
## [506] 2.242020955 1.840627779 3.203388102 0.502629659 -0.822554009
## [511] 2.462485681 -1.330989512 2.222348263 3.224179339 0.503955978
## [516] 4.109114978 -0.921830577 1.931747313 0.687429626 1.286968084
## [521] 3.207479315 2.067533728 1.996131261 -0.965475600 -0.289650281
## [526] 3.599267504 3.802563240 2.266099117 4.248120094 0.174566733
## [531] 2.106501757 0.385197319 3.479664764 0.711390069 1.565648634
## [536] 4.621342966 -0.545354448 0.835649680 -1.147130988 3.004830381
## [541] 1.965576793 0.009083165 2.284729655 2.001470781 1.793550728
## [546] 2.097168240 1.921592999 0.526181231 1.928574836 0.204487875
## [551] 1.180321215 3.485128161 0.591563195 0.458290904 2.429481656
## [556] 1.637908589 2.789529065 3.831399721 2.463028169 3.103618538
## [561] 5.965380131 2.746061480 3.107765719 3.207541386 -0.312967388
## [566] -1.990491959 1.315290409 2.096568116 1.890523151 0.817103246
## [571] 1.738385040 2.486252512 1.877008614 3.605276247 2.274369453
## [576] 2.720765610 1.455459949 1.959560845 3.898332120 0.140751877
## [581] 2.692251672 3.035850164 1.639256614 1.297220886 2.405678407
## [586] 2.394026862 0.790679692 -0.452682185 0.051269691 -0.444154340
## [591] 1.324696771 1.837859598 3.011333441 0.665421973 0.329540858
## [596] 2.405338937 3.496076040 2.177583158 2.893308801 3.325072161
## [601] 1.736183928 5.137742998 0.673399762 -0.596069213 1.730065570
## [606] 0.173369803 3.107335613 -1.547223964 3.838162889 1.310096955
## [611] 3.321858571 2.256849655 2.098488802 -0.323547670 1.184496467
## [616] 1.836716890 2.313158754 2.679320744 -1.063592027 1.535387732
## [621] 2.851754481 0.512949851 1.168200851 3.887332325 0.378681153
## [626] 2.940611454 1.974227249 0.818457386 3.088830650 1.037726726
## [631] 2.053563841 0.950286091 3.127060927 2.136088573 5.136069421
## [636] 1.939176042 3.085376930 0.565588424 1.993527343 1.570721475
## [641] 4.368229582 0.914178622 2.152937836 2.217527695 -0.618971782
## [646] -1.532722694 -0.430350054 -0.154346958 1.135719219 2.037972782
## [651] 1.332950782 3.281580391 -0.924828344 1.336263804 2.695966450
## [656] 2.246108722 -1.545197962 1.374777864 2.863863902 4.374940527
## [661] 0.487938578 1.240082510 2.464081245 1.793642760 0.798434318
## [666] 2.173642204 4.194390073 4.170673896 -0.485592884 3.085386261
## [671] 0.133798249 1.057742740 3.137950728 0.124095574 4.023508550
## [676] 0.295421413 -0.919849847 -0.282983943 1.877046767 -0.408689348
## [681] 2.626056182 4.861801308 0.901005348 0.564917057 0.386936171
## [686] 1.432473045 1.115541204 3.098971854 0.901991822 1.117574412
## [691] 0.114343562 3.742737964 3.298768849 2.254529346 1.046105756
## [696] 0.181969774 0.359255821 -0.746316283 0.265680413 1.815767535
## [701] 1.416893331 4.478475770 2.991604702 3.014418829 4.450643205
## [706] 2.574209347 1.120365285 3.079143791 2.815258909 1.689015641
## [711] -0.903532178 0.248783776 3.962402886 3.412001570 4.331001931
## [716] -1.115324520 3.254985019 -0.039382141 1.088562837 4.878662357
## [721] 5.253766751 0.826643080 0.047333907 2.173483445 2.479431198
## [726] 3.946876486 4.247154436 0.807984792 1.523955902 2.419252187
## [731] 0.603340352 1.801356050 3.169158025 -1.024628397 3.427137432
## [736] 2.478409403 -0.131785609 4.196995254 2.525108554 2.424733946
## [741] 3.276374280 2.485972283 0.868511643 1.726528154 2.052576493
## [746] 2.662564447 3.313899398 2.119564702 4.296915069 1.907340030
## [751] 2.015395355 1.459006590 4.148465660 0.689507751 1.425514936
## [756] 1.936655702 2.405130181 2.288327975 1.497691519 2.224359159
## [761] 2.150172232 1.944794209 2.383498365 4.329770187 2.825157331
## [766] 1.271088913 0.470409045 4.562176113 0.474244339 2.317443794
## [771] 1.509112033 0.857827953 2.637962656 1.561996935 0.263980910
## [776] 0.318796477 1.811946515 1.380558801 1.850835449 0.256604893
## [781] -2.038318228 1.794427632 2.839951908 -0.722059047 3.458620939
## [786] 3.093315987 1.416034813 2.081385627 5.394037500 1.802780349
## [791] -1.863770255 1.960329460 1.690674292 1.983217104 1.706736763
## [796] -0.391636297 3.419480594 1.511186104 1.519074459 -0.194312010
## [801] 3.106062020 0.051332073 4.662089472 1.595797207 1.675011439
## [806] 4.538068035 2.102449549 2.551110028 1.463026953 2.772580778
## [811] 0.786888640 2.846062720 1.255721654 1.931569816 -0.803567871
## [816] 3.448249081 -1.249060930 -0.685845581 2.727205163 1.408663374
## [821] -0.204822636 -0.527573309 2.512439458 2.526455982 -0.569522160
## [826] 2.364791682 1.514933380 3.459688122 3.490914877 4.363474546
## [831] 1.358890512 1.808221744 4.235568254 1.175572520 4.589620144
## [836] 3.197636996 1.987525393 1.986963563 1.818944344 -0.555697001
## [841] -0.664118863 1.223071927 -0.673862584 3.933447057 3.660268163
## [846] 4.708023963 0.165660127 3.706492636 2.464651823 2.085827472
## [851] 1.293965578 1.314389341 2.446032942 3.015812455 1.485293049
## [856] 3.892020243 2.026471379 2.127366773 1.698760666 1.065633485
## [861] 3.277137104 -1.427950343 0.783176284 5.122996704 0.376731861
## [866] 3.455889634 1.935211037 0.234359383 1.715080512 1.111413176
## [871] 2.405964804 2.312069089 0.162285294 -0.809233987 3.349164149
## [876] 3.142844219 2.234417853 3.136246949 2.855334798 2.052109024
## [881] -0.841078654 0.048015499 2.073934845 2.110279315 5.136147271
## [886] 2.646106836 0.115800862 2.511613595 0.356624463 0.296497561
## [891] 1.321096404 1.033098570 1.772446588 0.458460557 2.569189736
## [896] 1.913885775 2.133396063 -0.509448654 0.631034092 2.417431327
## [901] 3.325273829 2.321005284 2.438843517 1.632434930 0.659065169
## [906] 2.857380800 -1.760750482 0.274063217 3.949921153 3.006760212
## [911] 2.381230772 1.309640028 0.933040442 5.676483240 3.369650744
## [916] 1.076169290 3.117952382 3.877190943 1.932992849 -0.935691893
## [921] 1.764034426 1.909485580 2.454813871 -0.078376328 2.994125098
## [926] 1.173160426 0.914461416 2.668283078 2.020659291 3.455120017
## [931] 2.346445447 1.746624102 3.998722087 -0.854959474 0.689708407
## [936] 3.604668246 2.112081191 1.407507793 1.063106002 3.335396285
## [941] 0.613686721 1.510057127 3.816803105 1.706480853 2.159988174
## [946] 0.675210097 2.054569534 4.397200579 3.591190757 3.084537319
## [951] -0.203263252 2.756510982 2.048041540 4.943151603 3.303393244
## [956] 0.546194296 4.014422544 3.744924677 1.344842678 1.764820437
## [961] 1.050852273 1.968506100 1.750576742 0.953720455 0.496188809
## [966] 2.908749420 1.656939346 3.050287152 3.617554054 -0.208334494
## [971] -1.843464727 0.960070497 3.418316886 2.811812911 1.631114565
## [976] 1.242641053 3.211975788 2.165781921 1.167637904 3.993149308
## [981] 1.484027948 3.976860653 0.294468509 -0.711321146 1.306172026
## [986] 1.868805737 2.418254673 0.727560298 1.564872837 2.233110849
## [991] 4.006206671 1.249655524 4.416001529 1.020372116 0.386905349
## [996] 0.608555523 3.694822089 0.965909391 2.678482016 2.213520266
summary(data)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.2595 0.9645 1.9875 1.9425 3.0121 6.4532
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%
## -0.711858
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%
## 4.347003
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%
## -0.711858
# 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 TRUE 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 TRUE FALSE TRUE
## [49] TRUE 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 TRUE
## [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 FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE 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 FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE TRUE 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 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [289] TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE FALSE FALSE TRUE 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 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 TRUE
## [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 FALSE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [505] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
## [517] TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 TRUE 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 TRUE FALSE FALSE FALSE FALSE
## [613] FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 TRUE 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 TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [697] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [709] FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [733] FALSE TRUE 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] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 TRUE FALSE
## [817] TRUE 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 FALSE FALSE
## [853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [877] FALSE FALSE FALSE FALSE TRUE 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 TRUE FALSE FALSE FALSE FALSE FALSE
## [913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [925] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 TRUE 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] -1.4801422 -0.7781066 -1.7022759 -1.3482704 -0.7849113 -0.7429340
## [7] -2.0315025 -0.7809428 -1.3964380 -2.0836688 -0.7576658 -1.6195659
## [13] -1.4056418 -1.1896867 -0.8615112 -1.0007573 -0.7784989 -1.2045202
## [19] -1.0563323 -2.2594856 -1.0766579 -0.7382520 -0.8225540 -1.3309895
## [25] -0.9218306 -0.9654756 -1.1471310 -1.9904920 -1.5472240 -1.0635920
## [31] -1.5327227 -0.9248283 -1.5451980 -0.9198498 -0.7463163 -0.9035322
## [37] -1.1153245 -1.0246284 -2.0383182 -0.7220590 -1.8637703 -0.8035679
## [43] -1.2490609 -1.4279503 -0.8092340 -0.8410787 -1.7607505 -0.9356919
## [49] -0.8549595 -1.8434647
# How to get the top 5%
(Cutoff <- quantile(data,prob = 0.95))
## 95%
## 4.347003
(Top5Percent <- (data >= Cutoff))
## [1] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE 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 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE 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 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE TRUE 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 FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 TRUE FALSE 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 TRUE 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 FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [409] FALSE TRUE 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 TRUE
## [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 TRUE 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 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 TRUE 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 TRUE 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 TRUE 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 TRUE FALSE
## [637] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [697] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
## [709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [721] TRUE 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 TRUE
## [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 TRUE FALSE
## [805] FALSE TRUE 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 TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [841] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [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 FALSE FALSE FALSE FALSE FALSE FALSE
## [901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [913] FALSE TRUE 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 TRUE
## [949] FALSE FALSE FALSE FALSE FALSE TRUE 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 FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [997] FALSE FALSE FALSE FALSE
sum(Top5Percent[TRUE]) # counts all the true values
## [1] 50
data[Top5Percent==TRUE]
## [1] 4.473306 4.935725 4.477793 4.932402 4.434541 4.408780 4.443000 4.561178
## [9] 4.666932 5.564345 5.044725 4.889257 4.798312 5.073177 4.550186 4.563573
## [17] 6.453243 5.567152 5.683137 5.399896 4.592349 4.357130 4.920193 4.761509
## [25] 5.021263 4.802636 4.621343 5.965380 5.137743 5.136069 4.368230 4.374941
## [33] 4.861801 4.478476 4.450643 4.878662 5.253767 4.562176 5.394037 4.662089
## [41] 4.538068 4.363475 4.589620 4.708024 5.122997 5.136147 5.676483 4.397201
## [49] 4.943152 4.416002