# Mindanao State University
# General Santos City
# Introduction to R base commands
# Prepared by: Rojan Jhay A. Hamboy
# 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]
## [1] 1.9766174 0.6709881 1.2429795 0.7410721 2.4338327 3.0202006
## [7] 4.5593031 1.2700915 5.6380822 4.1924030 2.7809554 -0.1768700
## [13] -1.5817130 1.7259320 -1.3830634 1.9906991 1.5457988 1.8829988
## [19] 2.2068663 3.7536789
data[1:300]
## [1] 1.976617362 0.670988067 1.242979487 0.741072143 2.433832716
## [6] 3.020200627 4.559303076 1.270091488 5.638082214 4.192403034
## [11] 2.780955446 -0.176869975 -1.581712961 1.725931989 -1.383063428
## [16] 1.990699091 1.545798840 1.882998845 2.206866279 3.753678859
## [21] 2.363297107 1.903721155 -0.020098036 0.889416741 2.038250565
## [26] 0.442052580 3.192859009 2.501764462 -0.362962523 3.396479836
## [31] 0.901289033 1.560184945 -0.832020841 0.533137504 2.014772472
## [36] 2.522295810 2.202612031 3.907271237 0.001123262 2.520042121
## [41] 0.482188646 1.620107494 -0.400629769 1.958789791 0.266400674
## [46] 1.666526703 -0.978888572 1.029098988 4.041514259 2.666540793
## [51] 3.650832179 3.393345244 1.995707959 5.462257139 3.054928581
## [56] 3.344569548 0.974159113 2.074534435 0.210467076 2.137184548
## [61] 2.506474398 1.211363437 1.472968580 2.824038473 3.488725374
## [66] -0.729981585 4.716855924 1.718651714 2.605032366 2.952269675
## [71] 1.711370785 -1.162529638 3.168650888 0.719341689 2.349809325
## [76] 1.829048566 0.969257455 4.880601235 0.976557295 3.109209225
## [81] 3.020156565 3.422054558 2.867584176 1.585028987 5.447690119
## [86] 1.876037638 2.871322227 1.303053570 1.476137731 0.842436537
## [91] -0.474229248 1.826193552 2.763112917 2.202893043 1.620577162
## [96] 1.795560938 2.280072996 3.092332098 0.696928196 2.553011744
## [101] 3.541394832 3.843657343 0.784073999 2.192373140 2.810254395
## [106] 0.449158167 0.123485436 3.857810497 0.058930049 3.060489237
## [111] 2.170080877 -1.562683408 1.506824350 1.589287564 -0.812413552
## [116] 1.537370071 1.634001133 1.861454967 3.084969832 4.914140369
## [121] 0.911010508 4.005425420 0.584041790 1.001652833 0.677558582
## [126] 2.486102641 -1.497672074 1.192994366 0.141683915 1.541273063
## [131] 1.288461759 0.274541019 0.557936989 1.727693080 1.190469143
## [136] 3.292739837 -1.887048236 0.870453300 0.219411159 2.176145419
## [141] 2.395473606 2.045367794 -0.353850142 0.436044405 1.160303065
## [146] 4.679335628 3.539333060 2.843565645 2.328320232 1.408630676
## [151] 1.933464130 0.981716978 2.732416899 2.423610668 3.047915492
## [156] 4.025859749 3.336482903 2.698367958 1.157936170 0.937058148
## [161] 4.794463312 1.540255940 2.468962515 2.678214207 3.828976576
## [166] 0.344407437 0.948892946 -0.266944325 4.121880670 1.117708305
## [171] 3.462374928 0.268726695 1.185849314 2.858741555 1.265013499
## [176] 3.657304947 3.784699312 -0.688949751 4.458078498 1.484585712
## [181] 0.770270341 3.526701809 3.070972583 2.815454367 5.110781559
## [186] 2.399897123 2.500560139 0.660944854 2.041185131 -0.020306234
## [191] 0.625904135 2.944821625 5.749059197 1.978435525 3.500612481
## [196] 2.101764146 2.029814218 2.530082326 3.183376148 1.169875044
## [201] 2.097932499 7.755732852 1.587844335 -0.044705178 3.301611428
## [206] 2.848025935 0.933225307 1.793448077 4.667222000 -0.092719758
## [211] 2.203564868 0.971901990 2.032482565 -2.123263334 0.839470349
## [216] 2.411131379 3.430008363 -0.808656988 2.645613305 -0.459363358
## [221] 3.830918898 2.635959896 2.722722241 2.392796822 0.166973446
## [226] 2.668052648 3.141655082 3.099140372 1.738794209 1.748873313
## [231] 1.329228440 2.352330981 0.403789017 1.750226768 2.667225067
## [236] 1.412862092 2.922582679 1.956311634 2.632479004 4.016981226
## [241] 2.609356842 3.533669424 1.576290443 3.731828047 4.332033201
## [246] 2.559063290 4.343874024 3.513310501 4.298154094 3.977061413
## [251] 1.208098750 -0.449342482 0.705985032 0.910425553 0.687063792
## [256] 1.773056518 3.530037728 -0.413006784 3.189051980 3.609606893
## [261] 1.498851371 -0.267408615 1.177157684 -0.830001174 1.554200108
## [266] 1.971736897 2.749220764 3.978258969 5.463662791 3.500235366
## [271] 4.134503001 2.216758794 3.780618247 0.641513780 3.425413217
## [276] 0.204882101 -0.427520941 2.885177316 4.956966104 2.497274952
## [281] 0.515655959 3.959921003 2.882175247 3.200648952 5.246681878
## [286] 1.043981168 3.023883702 3.863060954 1.680383463 1.315715163
## [291] 3.714709256 1.631085233 1.348643302 2.022016180 1.802528742
## [296] 2.093597043 6.251665927 0.508324052 1.122790042 0.279416236
# 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)
# 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] -2.74650432 -2.64042112 -2.53433791 -2.42825471 -2.32217151 -2.21608830
## [7] -2.11000510 -2.00392189 -1.89783869 -1.79175549 -1.68567228 -1.57958908
## [13] -1.47350588 -1.36742267 -1.26133947 -1.15525626 -1.04917306 -0.94308986
## [19] -0.83700665 -0.73092345 -0.62484025 -0.51875704 -0.41267384 -0.30659063
## [25] -0.20050743 -0.09442423 0.01165898 0.11774218 0.22382538 0.32990859
## [31] 0.43599179 0.54207500 0.64815820 0.75424140 0.86032461 0.96640781
## [37] 1.07249101 1.17857422 1.28465742 1.39074063 1.49682383 1.60290703
## [43] 1.70899024 1.81507344 1.92115665 2.02723985 2.13332305 2.23940626
## [49] 2.34548946 2.45157266 2.55765587 2.66373907 2.76982228 2.87590548
## [55] 2.98198868 3.08807189 3.19415509 3.30023829 3.40632150 3.51240470
## [61] 3.61848791 3.72457111 3.83065431 3.93673752 4.04282072 4.14890392
## [67] 4.25498713 4.36107033 4.46715354 4.57323674 4.67931994 4.78540315
## [73] 4.89148635 4.99756955 5.10365276 5.20973596 5.31581917 5.42190237
## [79] 5.52798557 5.63406878 5.74015198 5.84623518 5.95231839 6.05840159
## [85] 6.16448480 6.27056800 6.37665120 6.48273441 6.58881761 6.69490081
## [91] 6.80098402 6.90706722 7.01315043 7.11923363 7.22531683 7.33140004
## [97] 7.43748324 7.54356644 7.64964965 7.75573285
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%
## -2.746504 1.008342 1.955394 3.001230 7.755733
# 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] 1.976617362 0.670988067 1.242979487 0.741072143 2.433832716
## [6] 3.020200627 4.559303076 1.270091488 5.638082214 4.192403034
## [11] 2.780955446 -0.176869975 -1.581712961 1.725931989 -1.383063428
## [16] 1.990699091 1.545798840 1.882998845 2.206866279 3.753678859
## [21] 2.363297107 1.903721155 -0.020098036 0.889416741 2.038250565
## [26] 0.442052580 3.192859009 2.501764462 -0.362962523 3.396479836
## [31] 0.901289033 1.560184945 -0.832020841 0.533137504 2.014772472
## [36] 2.522295810 2.202612031 3.907271237 0.001123262 2.520042121
## [41] 0.482188646 1.620107494 -0.400629769 1.958789791 0.266400674
## [46] 1.666526703 -0.978888572 1.029098988 4.041514259 2.666540793
## [51] 3.650832179 3.393345244 1.995707959 5.462257139 3.054928581
## [56] 3.344569548 0.974159113 2.074534435 0.210467076 2.137184548
## [61] 2.506474398 1.211363437 1.472968580 2.824038473 3.488725374
## [66] -0.729981585 4.716855924 1.718651714 2.605032366 2.952269675
## [71] 1.711370785 -1.162529638 3.168650888 0.719341689 2.349809325
## [76] 1.829048566 0.969257455 4.880601235 0.976557295 3.109209225
## [81] 3.020156565 3.422054558 2.867584176 1.585028987 5.447690119
## [86] 1.876037638 2.871322227 1.303053570 1.476137731 0.842436537
## [91] -0.474229248 1.826193552 2.763112917 2.202893043 1.620577162
## [96] 1.795560938 2.280072996 3.092332098 0.696928196 2.553011744
## [101] 3.541394832 3.843657343 0.784073999 2.192373140 2.810254395
## [106] 0.449158167 0.123485436 3.857810497 0.058930049 3.060489237
## [111] 2.170080877 -1.562683408 1.506824350 1.589287564 -0.812413552
## [116] 1.537370071 1.634001133 1.861454967 3.084969832 4.914140369
## [121] 0.911010508 4.005425420 0.584041790 1.001652833 0.677558582
## [126] 2.486102641 -1.497672074 1.192994366 0.141683915 1.541273063
## [131] 1.288461759 0.274541019 0.557936989 1.727693080 1.190469143
## [136] 3.292739837 -1.887048236 0.870453300 0.219411159 2.176145419
## [141] 2.395473606 2.045367794 -0.353850142 0.436044405 1.160303065
## [146] 4.679335628 3.539333060 2.843565645 2.328320232 1.408630676
## [151] 1.933464130 0.981716978 2.732416899 2.423610668 3.047915492
## [156] 4.025859749 3.336482903 2.698367958 1.157936170 0.937058148
## [161] 4.794463312 1.540255940 2.468962515 2.678214207 3.828976576
## [166] 0.344407437 0.948892946 -0.266944325 4.121880670 1.117708305
## [171] 3.462374928 0.268726695 1.185849314 2.858741555 1.265013499
## [176] 3.657304947 3.784699312 -0.688949751 4.458078498 1.484585712
## [181] 0.770270341 3.526701809 3.070972583 2.815454367 5.110781559
## [186] 2.399897123 2.500560139 0.660944854 2.041185131 -0.020306234
## [191] 0.625904135 2.944821625 5.749059197 1.978435525 3.500612481
## [196] 2.101764146 2.029814218 2.530082326 3.183376148 1.169875044
## [201] 2.097932499 7.755732852 1.587844335 -0.044705178 3.301611428
## [206] 2.848025935 0.933225307 1.793448077 4.667222000 -0.092719758
## [211] 2.203564868 0.971901990 2.032482565 -2.123263334 0.839470349
## [216] 2.411131379 3.430008363 -0.808656988 2.645613305 -0.459363358
## [221] 3.830918898 2.635959896 2.722722241 2.392796822 0.166973446
## [226] 2.668052648 3.141655082 3.099140372 1.738794209 1.748873313
## [231] 1.329228440 2.352330981 0.403789017 1.750226768 2.667225067
## [236] 1.412862092 2.922582679 1.956311634 2.632479004 4.016981226
## [241] 2.609356842 3.533669424 1.576290443 3.731828047 4.332033201
## [246] 2.559063290 4.343874024 3.513310501 4.298154094 3.977061413
## [251] 1.208098750 -0.449342482 0.705985032 0.910425553 0.687063792
## [256] 1.773056518 3.530037728 -0.413006784 3.189051980 3.609606893
## [261] 1.498851371 -0.267408615 1.177157684 -0.830001174 1.554200108
## [266] 1.971736897 2.749220764 3.978258969 5.463662791 3.500235366
## [271] 4.134503001 2.216758794 3.780618247 0.641513780 3.425413217
## [276] 0.204882101 -0.427520941 2.885177316 4.956966104 2.497274952
## [281] 0.515655959 3.959921003 2.882175247 3.200648952 5.246681878
## [286] 1.043981168 3.023883702 3.863060954 1.680383463 1.315715163
## [291] 3.714709256 1.631085233 1.348643302 2.022016180 1.802528742
## [296] 2.093597043 6.251665927 0.508324052 1.122790042 0.279416236
## [301] 0.610249743 1.145459886 2.458470799 1.878471258 1.616553073
## [306] 1.632691836 4.804131016 2.756519358 2.253545782 4.634940123
## [311] 3.866973384 0.709894622 3.265464242 0.448731486 -1.087827496
## [316] -0.121103595 2.059935779 3.215095787 2.569544019 1.220910014
## [321] -0.836538487 0.479549671 4.612323515 2.049255362 2.706574074
## [326] 3.269029940 4.671845354 -0.171636693 2.872663106 2.371885505
## [331] 1.069925153 0.334481352 2.542659637 1.427358145 3.932583503
## [336] 2.110724422 2.710410116 1.362667141 0.929646302 3.271076607
## [341] 4.503430910 2.650839377 1.600873889 -1.039801205 0.970040471
## [346] 3.578301293 -0.414411958 2.454020524 0.850870445 3.606511957
## [351] 2.342283301 2.228009449 2.059140189 3.603009700 0.677350525
## [356] 1.997826203 3.935554418 1.656260980 0.558930890 1.784118480
## [361] 1.879082934 0.814648326 1.293570374 3.895061461 1.293918785
## [366] 2.459049905 4.437457029 2.500296566 1.218823334 1.960034648
## [371] 4.746506392 1.547538100 2.404800720 1.987981545 -0.036285524
## [376] 2.938973528 2.590749274 1.422596555 1.068997307 1.162386480
## [381] 3.144835300 3.235915217 3.679393375 0.438648334 0.265728947
## [386] 3.879237407 1.914038353 1.173051582 0.564817074 1.724694886
## [391] 1.932185358 1.745654261 3.428402739 3.193905366 3.672660414
## [396] 0.889342103 4.106056911 5.380552104 3.422744277 -1.923633911
## [401] 1.881708547 1.793207057 4.435919200 -2.176846414 -0.173326581
## [406] 1.631015037 2.039363312 1.220724727 2.381798542 1.811152944
## [411] 0.973894788 1.181405213 3.994420893 2.591137530 3.315452801
## [416] 4.637500918 3.808405853 2.221924259 3.962852596 1.744928668
## [421] 3.811805734 -0.790134142 2.391022610 1.740368256 2.383400041
## [426] 1.882999639 2.399154614 0.698922517 3.949417048 0.220656901
## [431] 2.247351336 2.985746370 2.234990070 0.556788469 0.855573606
## [436] 0.513793778 1.457893689 1.755485177 2.113279442 0.590594497
## [441] 2.024996567 0.298994260 2.554196294 1.986647032 0.832390955
## [446] 1.583614203 -0.061522791 3.658173457 2.778975857 3.061485306
## [451] 5.239969307 3.473252894 0.806469164 2.182617170 0.452925295
## [456] -1.822415485 4.650750040 1.418011981 2.372248765 3.281415218
## [461] 5.022859272 2.362850420 3.595401228 2.427802140 0.973104767
## [466] -2.153852347 2.474633653 2.271079043 -1.176968044 1.962274431
## [471] 1.414469224 5.075928308 2.020066085 5.276896008 -0.073952302
## [476] 0.437378716 1.332446435 -0.181569941 2.989406236 -0.088769521
## [481] 1.378226574 0.864260738 0.222503278 0.559698982 2.176002444
## [486] 0.287337690 3.121365178 0.627107699 1.999855303 5.148240692
## [491] 1.439529946 3.361596701 3.440176764 3.984591889 1.547085708
## [496] 2.571004441 1.398925777 2.174288779 3.122201383 1.598899996
## [501] 2.301751945 0.772914721 0.957413827 5.191176485 2.112569121
## [506] 0.349733751 -0.244006517 1.074691313 3.929997502 -0.001855612
## [511] 3.964119442 2.364334683 2.435805525 5.746543444 3.177966307
## [516] -0.561831664 2.491088889 3.919835464 1.749051756 3.304033342
## [521] 2.158095731 1.104492323 0.927591092 1.985420508 3.619592591
## [526] 3.305623223 2.069272624 0.428615190 3.170013038 1.768446336
## [531] 1.888911645 0.733496502 3.465846986 1.587666958 -0.044375717
## [536] 2.458701257 2.137626770 5.243730078 2.402503739 1.249316312
## [541] 2.693456122 2.685552550 1.112382194 -2.047649872 3.831901482
## [546] 1.357499246 -0.153386253 2.591922739 3.001861872 3.744671351
## [551] 2.448233606 4.020262157 2.082916828 3.446343286 3.450998337
## [556] 2.337912157 1.460824599 2.068118409 4.060409733 3.317161634
## [561] 2.619578942 4.229865681 1.340314843 3.009834702 1.416148665
## [566] 2.259145928 0.192389752 3.680447672 -0.088791286 3.880409583
## [571] 3.628138549 1.730507118 4.485334382 1.165204738 0.583864897
## [576] -0.127209437 2.006352962 -0.118295813 1.721009835 2.620562351
## [581] -0.929019033 1.672555745 1.483210998 3.258539621 4.976818376
## [586] 1.793223261 1.895117157 1.451887906 2.481185432 3.112930023
## [591] 3.447057937 4.109540055 0.768942187 2.205713542 1.994434429
## [596] 2.500387962 1.070976063 3.449294025 1.844158639 1.389838414
## [601] -0.051752899 5.095842976 5.811650364 2.517769417 1.853795754
## [606] 5.207146869 1.271152407 0.728117418 2.187625723 1.052877588
## [611] 2.504915993 0.680526990 -0.338287319 2.114996965 1.576194779
## [616] 2.297622590 0.450108578 2.285636419 4.246300942 3.996370924
## [621] 2.161816395 1.022125074 1.212492809 1.655660359 2.196201102
## [626] -0.154344866 3.298715998 1.161902791 2.491202940 0.821697307
## [631] 2.775192038 0.691580597 2.085499736 1.937572346 4.546776048
## [636] 4.020489142 1.398946603 1.971837268 1.437748334 3.890869135
## [641] 2.936626011 1.915677109 -0.374315854 2.515823950 2.969922450
## [646] 1.080799984 1.653635174 3.647858342 2.716362060 2.450313123
## [651] 1.616522268 3.369835499 -1.753310271 -0.137306454 1.623655164
## [656] 2.278815268 -0.225163641 1.924630355 2.319095762 3.436182345
## [661] 1.130066509 0.120002255 1.255300533 1.056525652 4.001855487
## [666] 4.267797390 0.996657616 -0.229379673 0.803035960 1.794376731
## [671] 2.570667299 1.423459732 1.706801277 1.771626383 0.978163470
## [676] 1.739782252 4.852679240 3.177488438 -0.561492456 1.238532336
## [681] 5.428839735 1.259379969 1.947724167 1.537504896 1.150095293
## [686] 1.873886406 0.848517916 2.414932535 1.887093299 1.147410594
## [691] -2.746504321 0.544000343 5.187108013 1.229136447 1.202100718
## [696] 1.591079299 -1.813179753 3.120703608 1.031499867 4.187839770
## [701] 2.271641755 3.968688447 -0.838825698 2.149898663 0.752744225
## [706] -1.224482404 -0.131721243 1.941029835 0.676105952 0.372236525
## [711] 4.831338716 1.647713755 1.686603360 5.373926795 1.297446671
## [716] 0.477673619 2.838152716 1.375935326 1.388019174 0.456676520
## [721] 2.978967628 2.344018450 -1.330895129 1.020111520 2.233027368
## [726] 0.403862874 3.720806532 2.401739176 1.112869660 1.668041804
## [731] 1.561614735 2.810963363 2.594925020 3.197676369 0.011631354
## [736] 4.151048206 3.639926282 4.229961731 1.846983358 4.229946931
## [741] 2.732162983 3.121785532 -0.481460671 7.378712660 0.881119394
## [746] 3.764159888 1.259122563 1.265239778 1.956938178 1.159550560
## [751] 3.109696658 2.692295051 0.572475887 2.946409037 0.134606458
## [756] 0.665492989 3.876476582 1.964842388 1.524752210 3.228934855
## [761] 3.533603441 2.703420446 2.882802754 2.242034815 2.383614980
## [766] 1.298533723 1.592276661 1.125352383 -1.311322654 2.046118893
## [771] 2.572664207 2.516481278 0.582069163 1.441730257 1.045042314
## [776] 0.972702001 2.573654513 2.204360606 -0.582690963 2.118806770
## [781] 1.943990996 5.790411097 3.913404611 1.499285672 0.109062289
## [786] 2.359646986 1.622486789 4.265189018 2.688447024 -0.903264473
## [791] 4.483657673 2.769854147 0.278663688 0.406903596 0.107399686
## [796] -0.860794521 0.479045105 0.976183132 2.262910142 1.921286141
## [801] 0.652034293 3.845995384 0.418943078 3.088944116 3.963440919
## [806] 0.161140369 0.824834309 2.578733879 1.949265115 2.407461854
## [811] 2.857520596 1.829055027 2.991353630 2.470945549 1.132021106
## [816] 3.838770457 1.007632703 2.442620876 -1.135282987 1.954475529
## [821] 1.460091441 2.057166996 1.049603155 1.546615594 2.976200514
## [826] 1.771398615 1.618977869 1.914747796 4.601141947 3.262035387
## [831] 1.504205782 0.516337666 0.122412778 1.705507870 2.083159298
## [836] 0.640783852 3.019380475 1.889086214 4.595367118 1.916682672
## [841] 1.758569715 1.825042744 3.145884248 0.240632002 -0.391262254
## [846] 1.106755053 3.971462660 2.481832431 2.546708870 2.600126784
## [851] 2.800102977 2.839642313 1.322452656 2.882282709 3.505581934
## [856] 1.764827567 3.915144662 2.134765014 2.302115238 5.301141849
## [861] 2.381907465 0.986660129 1.813936393 1.367151519 0.077494859
## [866] 2.444003510 1.528023396 4.284761067 2.140957791 1.584185868
## [871] 0.591906511 1.382588699 1.513750324 1.740716459 2.155978945
## [876] 2.363685662 1.680479773 0.333493110 2.614318220 0.925148188
## [881] 0.318764681 3.862203067 0.513261964 0.627861947 2.619197911
## [886] 0.033762945 3.103459191 1.206883289 1.369231524 3.502876260
## [891] 1.247090338 3.282579007 2.638046123 3.606252192 2.096842027
## [896] 1.450861563 3.575236979 1.401992360 1.559096439 5.133925937
## [901] 0.399072756 5.162801476 3.228892000 -1.491500342 1.009591745
## [906] 1.757481070 4.489003160 1.285772587 3.468641299 0.576815910
## [911] 3.355253500 1.493676327 0.039420994 2.472891732 1.287528586
## [916] -1.692583951 5.756476602 1.468814672 0.256225930 0.855821366
## [921] 2.692625227 2.280050312 1.633647478 3.909022504 3.109420758
## [926] 1.404969746 1.008578142 4.498159507 2.966660404 0.411239642
## [931] 1.646287381 2.916355018 1.489809265 3.210915573 3.630659739
## [936] 5.512560235 2.943333967 -1.406343948 0.333742307 1.509523569
## [941] 2.152010943 2.216497984 1.501464243 -0.168068778 2.752427842
## [946] -0.056920508 1.368255728 -0.131893254 3.971201287 0.951147193
## [951] 1.219003668 4.075730512 0.876520440 0.775975018 2.627326843
## [956] 0.602546340 2.634855194 3.110844750 0.928947503 -0.326426127
## [961] 1.685900840 3.871003390 1.979338503 1.230467996 3.277814985
## [966] 2.916408056 0.289189272 2.355836274 2.995791526 1.680102791
## [971] 1.541393807 0.816808339 2.239554063 0.724497672 0.140383366
## [976] 1.277607652 4.324151915 1.449575210 1.677734275 4.523531790
## [981] 1.085785372 2.627356148 -1.180127422 2.691760069 1.193685383
## [986] 3.400717749 3.414771923 3.681231404 0.912465824 3.219902647
## [991] 3.001019595 1.425852021 1.689879466 1.898037636 -0.077930340
## [996] 0.083530150 0.878068680 3.794159831 0.891511559 0.223889493
summary(data)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.747 1.008 1.955 2.004 3.001 7.756
subtitle <- "This is my second title"
maintitle <- paste0("Histogram and Density Plot \n",subtitle)
hist(data, breaks=20,col="lightblue",main = maintitle)
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%
## -0.3917306
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%
## 4.561106
abline(v = quantile(data,prob = 0.95), col="blue", lwd=3, lty=3)
# How to get the values (lowest 5%)
(Cutoff <- quantile(data,prob = 0.05))
## 5%
## -0.3917306
(Low5Percent <- (data < Cutoff))
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE TRUE 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 FALSE FALSE FALSE FALSE TRUE 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 TRUE FALSE FALSE
## [217] FALSE TRUE FALSE TRUE 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 TRUE
## [253] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] TRUE 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 TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE 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 FALSE FALSE FALSE
## [397] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [421] FALSE TRUE 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 TRUE FALSE FALSE
## [469] TRUE 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 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [541] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [577] FALSE FALSE FALSE FALSE TRUE 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 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] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [685] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [697] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE
## [709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [721] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [793] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [817] FALSE FALSE TRUE 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 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 FALSE FALSE FALSE FALSE
## [889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [901] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [913] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [925] FALSE FALSE 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 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 TRUE 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] -1.5817130 -1.3830634 -0.8320208 -0.4006298 -0.9788886 -0.7299816
## [7] -1.1625296 -0.4742292 -1.5626834 -0.8124136 -1.4976721 -1.8870482
## [13] -0.6889498 -2.1232633 -0.8086570 -0.4593634 -0.4493425 -0.4130068
## [19] -0.8300012 -0.4275209 -1.0878275 -0.8365385 -1.0398012 -0.4144120
## [25] -1.9236339 -2.1768464 -0.7901341 -1.8224155 -2.1538523 -1.1769680
## [31] -0.5618317 -2.0476499 -0.9290190 -1.7533103 -0.5614925 -2.7465043
## [37] -1.8131798 -0.8388257 -1.2244824 -1.3308951 -0.4814607 -1.3113227
## [43] -0.5826910 -0.9032645 -0.8607945 -1.1352830 -1.4915003 -1.6925840
## [49] -1.4063439 -1.1801274
# How to get the top 5%
(Cutoff <- quantile(data,prob = 0.95))
## 95%
## 4.561106
(Top5Percent <- (data >= Cutoff))
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] TRUE 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 TRUE
## [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 FALSE FALSE
## [157] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE TRUE 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 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [325] FALSE FALSE TRUE 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 TRUE 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 TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 TRUE FALSE FALSE FALSE FALSE FALSE
## [457] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [469] FALSE FALSE FALSE TRUE FALSE TRUE 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 TRUE
## [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 TRUE FALSE FALSE
## [541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [601] FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [673] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [709] FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [781] FALSE TRUE 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] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE 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 FALSE FALSE FALSE FALSE
## [889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [901] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [913] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [925] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [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 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [997] FALSE FALSE FALSE FALSE
sum(Top5Percent[TRUE])
## [1] 50
data[Top5Percent==TRUE]
## [1] 5.638082 5.462257 4.716856 4.880601 5.447690 4.914140 4.679336 4.794463
## [9] 5.110782 5.749059 7.755733 4.667222 5.463663 4.956966 5.246682 6.251666
## [17] 4.804131 4.634940 4.612324 4.671845 4.746506 5.380552 4.637501 5.239969
## [25] 4.650750 5.022859 5.075928 5.276896 5.148241 5.191176 5.746543 5.243730
## [33] 4.976818 5.095843 5.811650 5.207147 4.852679 5.428840 5.187108 4.831339
## [41] 5.373927 7.378713 5.790411 4.601142 4.595367 5.301142 5.133926 5.162801
## [49] 5.756477 5.512560