So I have a data frame column that looks like:
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-3.87188005447388, -3.65591430664062, -3.87001085281372, -3.82757210731506,
-3.16145372390747, -3.97476768493652, -3.59298753738403, -3.73642778396606,
-3.79994654655457, -3.89668774604797, -4.01786231994629, -3.99708604812622,
-3.94176983833313, -3.8647096157074, -3.89766311645508, -3.80278873443604,
-3.99250793457031, -4.02426767349243, -4.10021066665649, -3.39703774452209,
-3.26359176635742, -3.82671117782593, -3.81313323974609, -3.43604493141174,
-3.31377339363098, -3.80798125267029, -3.81451535224915, -4.07718801498413,
-3.72391510009766, -3.08661699295044, -3.76619458198547, -3.83467435836792,
-3.84827923774719, -3.88853645324707, -3.79065752029419, -3.82326292991638,
-3.34796190261841, -4.02198314666748, -3.92778158187866, -3.62070775032043,
-3.82512187957764, -3.70889115333557, -3.81021595001221, -3.85158038139343,
-3.79376363754272, -3.875408411026, -3.75661754608154, -3.96288800239563,
-3.94140219688416, -3.74917697906494, -3.68129587173462, -3.78641748428345,
-3.52331447601318, -3.36141896247864, -3.88113570213318, -2.99620270729065,
-3.86633539199829, -3.68766975402832, -3.65976810455322, -3.70610952377319,
-3.38187885284424, -3.70525312423706, -3.81387853622437, -3.88207864761353,
-3.60756802558899, -3.75014662742615, -3.33153533935547, -3.18031525611877,
-3.72099876403809, -3.8076856136322, -3.67741799354553, -3.71635913848877,
-4.04951238632202, -3.74342226982117, -3.93503856658936, -3.99668455123901,
-3.83283662796021, -3.96427869796753, -3.91281938552856, -4.01104164123535,
-4.12361478805542, -3.49834251403809, -3.69158387184143, -3.70991778373718,
-3.77035188674927, -3.73654866218567, -3.58738207817078, -3.93428349494934,
-4.09196758270264, -3.68657469749451, -3.55566501617432, -3.50684690475464,
-3.59922695159912, -3.82266092300415, -3.61335706710815, -3.65858793258667,
-3.84981679916382, -3.73356938362122, -3.87916350364685, -3.77270436286926,
-3.89338707923889, -3.67522549629211, -3.80304479598999, -3.92915940284729,
-3.62608790397644, -3.7146475315094, -3.19878935813904, -3.84145474433899,
-3.67336130142212, -3.75900483131409, -3.77894949913025, -3.7319495677948,
-3.97335457801819, -4.0653977394104, -3.94989037513733, -4.03973150253296,
-3.75971102714539, -3.6455090045929, -3.64915871620178, -3.8141827583313,
-3.90111184120178, -3.75651788711548, -3.62716436386108, -3.54678297042847,
-3.75648069381714, -3.89319491386414, -4.01765012741089, -3.68124341964722,
-3.53644680976868, -3.75795292854309, -3.62451767921448, -3.80958104133606,
-3.5502712726593, -3.83708763122559, -3.556560754776, -3.69092893600464,
-2.76485157012939, -3.21907234191895, -3.67799139022827, -3.87315726280212,
-3.91908407211304, -3.83191752433777, -3.98365211486816, -3.89440631866455,
-3.76949167251587, -3.77768683433533, -3.83335590362549, -3.8156430721283,
-3.96391773223877, -3.85391211509705, -3.96095585823059, -3.92816495895386,
-3.73137664794922, -3.62495374679565, -3.89675331115723, -3.81962633132935,
-4.00135278701782, -3.75711274147034, -3.45405077934265, -3.47705769538879,
-3.66829466819763, -3.203857421875, -4.1542181968689, -3.79695463180542,
-3.67982244491577, -3.97755575180054, -3.70538926124573, -3.73775625228882,
-3.63174986839294, -3.82852816581726, -3.81266474723816, -3.57111001014709,
-3.69380807876587, -3.76427030563354, -3.72001457214355, -3.72431659698486,
-3.75455355644226, -3.61129784584045, -3.74940323829651, -3.80048871040344,
-3.8577663898468, -3.85973215103149, -3.80843257904053, -3.9712381362915,
-3.66903376579285, -4.0623836517334, -3.51091074943542, -3.98390460014343,
-3.96623015403748, -3.90212512016296, -3.80060911178589, -3.84315299987793,
-3.50798034667969, -3.73342847824097, -3.48506736755371, -3.94630765914917,
-3.19087338447571, -3.47729897499084, -3.16243934631348, -3.86419224739075,
-3.76706266403198, -3.94075512886047, -3.97516179084778, -3.74776673316956,
-3.84644770622253, -3.88299155235291, -3.89002251625061, -3.87353229522705,
-3.93982410430908, -3.62951350212097, -3.70244455337524, -3.5008864402771,
-3.59969925880432, -3.68025517463684, -3.23367619514465, -3.44335341453552,
-3.70403838157654, -3.67157292366028, -3.45795941352844, -3.56970477104187,
-3.6645519733429, -3.38615775108337, -3.59955024719238, -3.54332137107849,
-3.55048751831055, -3.59047031402588, -3.48982715606689, -3.71279692649841,
-3.89534473419189, -3.49593186378479, -3.22738265991211, -3.6751070022583,
-3.48276782035828, -3.52480053901672, -3.53807759284973, -3.7566659450531,
-3.59930944442749, -3.43871521949768, -3.28517746925354, -3.55587458610535,
-3.54328560829163, -3.26904964447021, -3.27183699607849, -3.41104817390442,
-3.48867750167847, -3.7642650604248, -3.84013152122498, -3.80252146720886,
-3.83257961273193, -3.95601034164429, -3.80873441696167, -3.94621109962463,
-3.92282319068909, -3.71384572982788, -3.98516297340393, -3.86579823493958,
-3.72224903106689, -3.5329053401947, -3.95190286636353, -3.6790075302124,
-3.03383803367615, -3.88659405708313, -3.74973678588867, -3.41382384300232,
-3.39654207229614, -2.76125597953796, -4.1404070854187, -3.8703727722168,
-3.80810523033142, -3.80035138130188, -3.67795181274414, -3.92546987533569,
-3.82394599914551, -3.84174799919128, -3.7097020149231, -3.62672233581543,
-3.69019556045532, -3.4281063079834, -3.23178672790527, -3.37821459770203,
-3.90212202072144, -4.04663467407227, -3.65541696548462, -4.01369285583496,
-3.61924171447754, -3.91963672637939, -3.82316422462463, -3.82684969902039,
-3.44636225700378, -3.43918633460999, -3.46366119384766, -3.66606950759888,
-3.47395133972168, -3.70814967155457, -3.25252461433411, -3.42652940750122,
-3.37687730789185, -3.57012510299683, -3.79865074157715, -3.56782031059265,
-3.55748176574707, -3.79533123970032, -3.51380968093872, -3.8156909942627,
-3.61043334007263, -3.84270167350769, -3.67433190345764, -3.81427645683289,
-3.80643391609192, -3.56018972396851, -3.83699107170105, -3.62709188461304,
-3.75777626037598, -3.84473013877869, -3.64597678184509, -3.51665759086609,
-3.70861983299255, -3.61876273155212, -3.72858834266663, -4.06253290176392,
-3.97517681121826, -3.77920961380005, -3.25707650184631, -3.53454208374023,
-3.14036655426025, -3.76010417938232, -3.53100967407227, -3.79575705528259,
-3.77562570571899, -3.71199631690979, -3.63523483276367, -3.55303573608398,
-3.73708510398865, -3.8654191493988, -3.75371766090393, -3.39529395103455,
-3.5087423324585, -3.76858234405518, -3.27294230461121, -3.51291465759277,
-3.5897159576416, -3.59522104263306, -3.72381043434143, -3.65958666801453,
-4.01140213012695, -3.89993095397949, -3.80350923538208, -3.79413294792175,
-4.02849149703979, -3.79621315002441, -3.09956812858582, -3.81814932823181,
-3.73365259170532, -3.30368709564209, -3.42570853233337, -3.36531376838684,
-3.57714676856995, -3.99519300460815, -3.8405442237854, -3.80736303329468,
-4.11514949798584, -3.8206262588501, -3.81878995895386, -3.39630246162415,
-3.81526851654053, -3.34133982658386, -3.47912859916687, -3.75328516960144,
-3.06075572967529, -3.58890318870544, -3.70843410491943, -3.34376883506775,
-3.84624218940735, -3.5749146938324, -4.03315925598145, -3.66028523445129,
-3.2373788356781,
-3.93130111694336, -4.11096096038818, -3.84883522987366, -3.90472340583801,
-3.86706209182739, -4.03439426422119, -4.12989568710327, -4.01405668258667,
-3.99284100532532, -3.86394143104553, -3.92312788963318,
And so on, with the quantiles looking like
0% 25% 50% 75% 100%
-4.268446 -3.845898 -3.703559 -3.471152 -1.423046
And the mean being
[1] -3.624417
And the Stdev being
[1] 0.317673
I want to standardize this number, with the mean being 100, and the best value (the max) being some above 100. And any value should be 100-x% better/worse than the mean.
So if the value is 110, it should be 10% greater than the mean, if it is 90, it should be 10% lower than the mean.
I've tried the way I usually do to standardize this way by doing
value / mean * 100
But that makes the worse values valued higher.
CodePudding user response：
Try with a normal distribution.
# your data
head(vec)
[1] -3.630924 -3.465714 -3.239579 -3.089863 -3.842719 -3.636238
mean(rnorm( 1000, 100-(mean(vec)), 5) vec)
[1] 100.0151
CodePudding user response：
new_vec = (vec - mean(vec) * 100 100)