singularity error regressing lag in R - r

I am trying to run a forecasting model where I simply regress earnings on lagged earnings
$Y_{i,t+1}=a+bY_{i,t}$ (edit: the mathematical formulas do not seem to work in so?)
Y(i,t) = a + bY(i,t-1)
Doing so gives me however the following error:
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
NA/NaN/Inf in 'x'
What surprises me is that I was not getting an error message when my model was larger (where I included other variables such as X(i,t).
What can I do to solve this issue? I tried to copy my data with dput but it was too long.
How can i only paste the first 100 values of a vector? as in dput(A[1:100,])?
lm(I(inc.plus1/csout)~I(inc/csout), data=df)
where inc.plus1 is the lagged income (lag+1) and csout are the number of shares outstanding
A represents the first 100 entries of my vector I(inc.plus1/csout):
dput(head(A,600))
structure(c(NA, NA, 1.446, 0.9995, 1.999, 2.902, 3.657, 3.96875,
4.10175, 4.0565, 3.44475, 5.7205, 61.93475, -3.85725, -4.5245390070922,
5.62880429175694, 2.47738918119605, 2.96300124018189, 2.73025552646406,
8.07212115316016, 10.5535326434138, 15.9591327947488, 1.96747660017018,
2.22629738160507, 3.58404764906088, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4.37888779527559,
-1.41959644816633, 1.0763348800732, 2.68114979918346, 2.80325842125764,
2.43790873353744, -2.15764063396585, 1.4799008091882, NA, NA,
NA, 1.37544255734004, 1.9209810662425, 2.80281184257786, 4.19364769870183,
4.92729231874391, 2.12704602596336, 0.595823284929961, 2.11401303299297,
2.72348504284468, 2.52902356618847, 2.32582482425984, 0.346862332597876,
1.82379803991995, 2.08507363127918, 5.08163579455077, 5.94934078367083,
7.23445185266149, 5.8378412444986, 4.63755947383151, -2.17174787920901,
1.40756281631882, -4.05886251253207, -0.0212408027324169, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, -0.479369880443708, -0.293425018839488, 0.0929350592321281,
0.123835254518112, 0.12706259774046, 0.116354410972905, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3.44976515683205,
4.48091561842034, 4.96571829617233, 1.65772899046248, 0.680583048407414,
1.98560374302681, 4.02555335612741, 4.81968688141083, 0.762689075630252,
1.88469387755102, 3.33593220338983, 0.309443507588533, 3.51697478991597,
2.86402247341315, 2.03178679830559, 5.51008520836107, 3.87780527915746,
2.40556264606919, 5.07700077828744, 4.83989720998532, 4.53742961245445,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -1.98851777777778,
0.0187844444444444, 0.128071111111111, 0.699172682926829, 0.907074545454545,
0.795127607650579, 0.357956423686356, -0.463226859004222, 0.60042762366109,
0.855410812777128, 1.70155389507976, NA, NA, NA, 6.28810975609756,
5.65225290697674, 5.50109011627907, 5.46905424200278, 6.82666666666667,
9.63833333333333, 8.50433333333333, 7.02166666666667, 7.61833333333333,
8.26466666666667, 9.84033333333333, 9.239, 8.05, 6.783, 7.553,
8.29666724227249, 11.525749819631, 14.2648420619405, 11.2771348353078,
-14.1189900160153, -17.6900879135628, 14.6432951757972, 17.706462585034,
NA, NA, NA, 2.54016638089795, 2.28029063187611, -1.70405905251086,
-13.3982068926821, 1.08612445944267, 1.02272906761858, -4.90830977239342,
4.93192336244886, 2.71279872376048, 3.58730158730159, 2.67460317460317,
-14.7646639032994, -10.1970204908481, -2.43234807690238, 1.50090714167904,
0.777890139686844, 1.00461845428288, -0.577281452731578, 0.916700774289767,
0.693536467511796, 1.02936271521732, 0.885562723018554, 0.533956763933385,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 6.33737983599889, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 1.97118644067797, 2.74399323181049,
3.00780780780781, 3.58680659670165, 2.81889504170007, 2.50917809245523,
2.79962476547842, 2.3177358490566, 2.98221831785893, 3.53841135956795,
4.84714071243347, 4.42075856720104, 3.43539749078812, 2.10469553058082,
0.698847262247839, 1.15489967560666, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 16.48, 23.5307692307692, 26.2953846153846, 29.1553846153846,
34.8107692307692, 34.1, 31.7707692307692, 35.66, 42.6569230769231,
50.8294573643411, 60.6072371486256, 71.8920605209796, 73.1694942127795,
81.0144689246958, 101.277860326894, 84.9637442320369, 99.4780896110291,
NA, NA, NA, 3.21418291525857, 3.47403755478572, 2.82309202411355,
1.88046287028157, 2.54218477118243, 2.86466949503918, 2.7850953423992,
3.71485649429788, 3.57246064220529, 3.59200878943474, 3.39917935379717,
4.36734916933839, 3.47052845528455, 2.10217797766436, 2.50138966092273,
2.53216008085335, 5.6740159729317, 7.12959606279318, 3.0704500763493,
-9.36316494785294, 2.98453249387051, 0.15134546114962, -0.30269092229924,
NA, NA, NA, 2.5918904109589, 1.72054794520548, 3.37534246575342,
3.15616438356164, 2.44266666666667, 3.72987012987013, 4.66193853427896,
5.6548463356974, -6.70449172576832, 5.53191489361702, 7.55555555555556,
4.56737588652482, -0.699029126213592, 3.77435897435897, 5.6053580628542,
8.82009465467434, 9.75106700157242, 17.3575865849202, 6.79550870390565,
-10.0748330068806, 2.45326283957664, 12.7237136465324, 15.6632796497851,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 6.74737416450689, 3.73783734165596, 3.56847945896463,
4.01606499861509, 4.31513436031426, NA, NA, NA, 1.18216666666667,
1.15616666666667, 0.718833333333333, 0.846666666666667, 0.949833333333333,
0.855, 0.947, 1.362, 1.29283333333333, -0.4095, -1.71166666666667,
-6.73979933110368, -1.07073578595318, 0.439966555183946, 0.889597315436242,
1.29455445544554, 0.888846453280844, 2.07047744964451, 1.42014282853901,
-5.14692281267341, 2.76534467725315, 2.98839786207796, 2.61554688592088,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 9.3883876937312,
0.0920378175190796, 1.80967558338076, 4.26608220425823, 4.22229845626072,
8.08242880047986, -2.31627512828963, 7.1832993650083, 1.45599924522962,
-1.63071337035455, 2.12153047215424, -14.241522334182, -13.2118418381361,
NA, NA, NA, 115.623529411765, 210, 224.705882352941, 234.117647058824,
217.714285714286, 235.555555555556, 240, 244.878048780488, 305.853658536585,
316.818181818182, 346.363636363636, 419.724770642202, 467.43119266055,
561.467889908257, 683.48623853211, 792.201834862385, 953.902327704245,
1135.53943789665, 1172.64573991031, 854.803010181496, 1066.1084178052,
1080.66637439719, 1076.95690413369, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, -8.00416281221091, -10.8834412580944, 7.80989824236818,
-6.48704902867715, -27.7520814061055, 4.79648473635523, 8.26732673267327,
4.09595559080095, 4.86207928837189, -9.03804256745433, -3.14144062935344,
0.0326826252097871, -0.859022222222222, 2.15602936527451, NA,
NA, NA, NA, NA, NA, NA, Inf, 1.16978683393883, 1.51189430424169,
3.52039622666496, 3.18081603714028, 3.61737627328914, 3.10188513592364,
-7.09439807225699, -3.69752722803501, 0.707971643687819, 0.327086826731375,
1.23655832779557, 0.575014101013737, 0.473355975527496, -0.122513039981693,
-0.851168008718768, 0.840333176705788, 0.978143386038959, 0.767234041909876,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 3.9974572801517, 3.81505135065159, 3.59021071115013,
2.22077609062425, 1.36322005302483, 2.3571531896427, NA, NA,
NA, NA, NA, NA, NA, NA, 1.37809917355372, 5.96900826446281, 9.65909090909091,
8.60330578512397, 12.426, 21.7577639751553, 22.3953974895398,
12.6962809917355, 2.77066115702479, 5.46201232032854, 13.7374488657494,
4.1607203533809, 9.63248867259607, 12.3481116584565, -16.9937258042985,
9.91789166997749, 13.2756799577502, 7.46975276170437, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
0.564217391304348, -0.212196078431373, 0.0218077803203661, 1.11794583661086,
0.440747736316973, 0.363348739557059, 0.641371480161689, 1.61685900721797,
NA, NA, NA), class = "AsIs")
and B: I(inc/csout)
dput(head(B,600))
structure(c(NA, NA, 1.6175, 1.446, 0.9995, 1.999, 2.902, 3.657,
3.96875, 4.10175, 4.0565, 3.44475, 5.7205, 61.93475, -4.37702127659574,
-5.48471405481619, 5.62880429175694, 2.48174176657021, 2.85056176051437,
2.67146609592373, 7.95068353136579, 10.4269755112345, 15.9375059090479,
1.96710464127044, 2.22664817849489, 3.33193683532066, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2.46958661417323,
3.21177447300029, -1.28516110058166, 1.0933050087961, 2.73595610276559,
2.99856886650604, 2.66279506915451, -2.03313196852743, 1.54029613242514,
NA, NA, 1.00559289855816, 1.37544255734004, 1.9209810662425,
2.80281184257786, 4.19364769870183, 4.92729231874391, 2.12704602596336,
0.595823284929961, 2.11401303299297, 2.79331431759097, 2.4658012212017,
2.32582482425984, 0.346862332597876, 1.82379803991995, 2.08507363127918,
5.71682560206885, 5.63252814515248, 6.95230192325872, 5.36379165580937,
4.63755947383151, -2.23752373944775, 0.801826142613582, -4.05886251253207,
-0.0212111270744482, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, -0.0609118506239777, -0.46619168322258,
-0.273575584586673, 0.0868731271713617, 0.121252977886906, 0.126940380912401,
0.116359932757648, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -0.295874906555075,
NA, NA, NA, NA, 2.71176375317173, 3.44976515683205, 4.48091561842034,
1.65520964549217, 1.65772899046248, 0.680583048407414, 1.98560374302681,
4.02555335612741, 4.50134453781513, 0.771768707482993, 1.87830508474576,
3.31905564924115, 0.308403361344538, 3.49909705036452, 2.85647576798639,
2.02752629476767, 5.49992524792771, 3.86932091296348, 2.40325224792595,
4.50212922173275, 2.72935574693607, 4.27727254986144, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.189564444444444,
-1.98851777777778, 0.0187844444444444, 0.112452682926829, 0.651501818181818,
0.916795915967128, 0.794619441852948, 0.35890755018202, -0.473067607413648,
0.627831250675921, 0.749854780188053, 1.53659424970853, NA, NA,
5.64291158536585, 5.99563953488372, 5.65225290697674, 5.26390820584145,
5.243, 6.82666666666667, 9.63833333333333, 8.50433333333333,
7.02166666666667, 7.61833333333333, 8.26466666666667, 9.84033333333333,
9.239, 8.05, 6.783, 7.82559143498532, 8.25334112069262, 11.5059848406901,
14.2409094021776, 11.2229529423791, -13.903429300047, -17.9612973562279,
14.619387755102, 17.706462585034, NA, NA, 2.478958140989, 2.18773270954903,
2.18862560772722, -1.69130819883693, -13.3982068926821, 1.07011419816462,
1.02272906761858, -4.90830977239342, 4.93192336244886, 2.72619047619048,
3.58730158730159, 2.49220723035908, -14.3167028199566, -10.2029468004578,
-2.79564571994062, 1.0112571815929, 0.781369908886687, 1.00884137370567,
-0.577493454139731, 0.929452560968677, 0.701576822662082, 1.03605050601517,
0.8831933500736, 0.536452684873194, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 0.492431541310724, 6.37958532695375, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 1.18508474576271, 1.9678510998308, 2.43498498498498,
3.00329835082459, 3.58538211954771, 2.81853600059939, 2.51332082551595,
2.81547169811321, 2.32570467867258, 2.98696865802967, 3.53706989369607,
4.81367137790562, 4.3791117807647, 3.42566215683504, 2.10174099052259,
0.698447811272854, 1.15344409599431, NA, NA, NA, NA, NA, NA,
NA, NA, 14.96, 16.48, 23.5307692307692, 26.2953846153846, 29.1553846153846,
34.8107692307692, 34.1, 31.7707692307692, 35.66, 42.9875968992248,
50.9162913495884, 60.8719388550928, 73.0775328999524, 75.8763564616902,
81.3488525672775, 101.094264996704, 84.6151321188249, 99.3965234503116,
NA, NA, 2.3167169493513, 2.94633564772966, 3.08802835253036,
2.72227364294659, 1.88046287028157, 2.54218477118243, 2.86466949503918,
2.7850953423992, 3.71485649429788, 3.8251978743918, 3.59200878943474,
3.45198192946663, 4.8729674796748, 3.45141240082874, 2.10217797766436,
2.71891375276011, 2.53216008085335, 5.6740159729317, 7.12959606279318,
3.25700635087106, -9.72848624269758, 2.98453249387051, 0.15134546114962,
-0.30269092229924, NA, NA, 2.53928767123288, 2.5918904109589,
1.72054794520548, 3.37534246575342, 3.072, 2.37922077922078,
3.39479905437352, 4.66193853427896, 5.6548463356974, -6.70449172576832,
5.53191489361702, 7.55555555555556, 4.68932038834951, -0.738461538461539,
3.79185986604843, 5.57274273187322, 8.79127611345953, 9.6938568354379,
17.2664485641585, 6.76008236653106, -10.0433576656954, 2.44686800894855,
12.7916783806579, 15.9669198174039, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3.89339789933161,
4.54057279236277, 3.04295028677116, 3.52709814421568, 3.94625587429463,
0.843121058173297, NA, NA, 1.16633333333333, 1.18216666666667,
1.15616666666667, 0.718833333333333, 0.846666666666667, 0.949833333333333,
0.855, 0.947, 1.362, 1.29283333333333, -0.4095, -1.71739130434783,
-6.73979933110368, -1.07073578595318, 0.441442953020134, 0.874917491749175,
1.15237157924115, 0.816345803596724, 2.04858840018688, 1.40567608276299,
-5.14637884112347, 2.72871855038456, 2.91316668996454, 2.59330191769775,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 11.880985426787,
9.24615559858754, 0.0919749573136027, 1.81008766936127, 4.28461978273299,
4.25916740682639, 8.24690457134787, -2.3184854446709, 7.19083897445574,
1.46494850253928, -1.64066137408225, 2.12416777231924, -14.2285966776933,
-13.1682814768967, NA, NA, 121.211764705882, 115.623529411765,
210, 224.705882352941, 227.428571428571, 211.666666666667, 217.435897435897,
228.292682926829, 244.878048780488, 285, 316.818181818182, 349.54128440367,
419.724770642202, 467.43119266055, 561.467889908257, 683.48623853211,
788.224554997718, 947.416137805984, 1123.31838565022, 1157.59185480301,
851.035698545615, 1060.4997807979, 1083.99296394019, 1079.33010136624,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5.07169287696577,
-8.00416281221091, -10.8834412580944, 7.80989824236818, -6.48704902867715,
-27.7520814061055, 2.01320132013201, 4.82440240172199, 3.09241756831886,
4.76328138092844, -8.83881012865689, -3.3861849659924, 0.0263111111111111,
-0.84864238294285, 2.13807997770656, NA, NA, NA, NA, NA, NA,
Inf, 1.13667965939339, 1.16978683393883, 1.51189430424169, 1.93641281650334,
3.18037779732132, 3.60374959261304, 2.95168386947919, -7.1346652146591,
-3.73879239931005, 0.618398531789007, 0.302060049537848, 1.15002820202747,
0.574163266612055, 0.472550297072245, -0.122848990949101, -0.853532127125251,
0.849206303333824, 0.831485168572279, 0.722226135762859, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 4.87792789880837, 4.00254595667558, 3.88103599648815,
3.94239575801398, 2.22077609062425, 1.36361444621245, 2.33424853766265,
NA, NA, NA, NA, NA, NA, NA, 2.1797520661157, 1.37809917355372,
5.96900826446281, 9.65909090909091, 8.328, 12.8633540372671,
21.9853556485356, 22.1177685950413, 12.6962809917355, 2.75359342915811,
4.94607660840461, 12.5518178729188, 4.10974995804665, 8.56844305120167,
11.0425844346549, -16.8586942126871, 9.88777396355955, 13.2233035244608,
7.5351552135845, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, -1.47473913043478, 0.508901960784314,
-0.0619107551487414, 0.0142787579128741, 1.07581896303132, 0.445689416361701,
0.34090023049512, 0.649568445989942, 1.70371965372768, NA, NA
), class = "AsIs")

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UpsetR - choosing intersections to visualize

I would like to use Upset plot instead of venn diagram to show overlap between specific groups (20 in total). However, one of the group (number 10) is the most important for me and I would like to present how many unique values is in that specific fraction. I would like to present ~25-30 intersections in total on the graph but uniqueness of group 10 has to be also shown.
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EDIT:
Added reproducible example:
dput(rep_exp)
structure(list(Gr_4 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 24.4310955935393, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA), Gr_5 = c(NA, NA, NA, 21.8310535918369,
NA, NA, NA, NA, NA, 20.7254228450715, NA, 27.1619253143803, NA,
NA, NA, NA, NA, NA, NA, 26.6027203831498, NA, NA, NA, NA, NA,
NA, 30.8729830402671, NA, NA, NA), Gr_6 = c(28.8390902059829,
24.67734371881, 22.683139406727, 29.1546773298581, NA, NA, 21.9107159172821,
NA, 22.9230495998744, 26.9880437180908, NA, 32.391666051163,
NA, NA, NA, 21.6001415858001, 23.0239282537894, NA, 21.055168555216,
30.7121903523751, NA, NA, NA, NA, 22.0963548474675, NA, 32.513357598066,
NA, NA, 23.7976852708585), Gr_7 = c(21.4265985064224, NA, NA,
23.0695638371137, NA, NA, NA, NA, NA, 20.7903453146324, NA, 28.2499758022535,
NA, NA, NA, NA, NA, NA, NA, 25.9613085520105, NA, NA, NA, NA,
NA, NA, 29.355377815192, NA, NA, 21.1302512982254), Gr_8 = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 21.8730880931062, NA,
NA, NA, NA, NA, NA, NA, 22.4189005564519, NA, NA, NA, NA, NA,
NA, 30.2275670312356, NA, NA, NA), Gr_9 = c(22.195894810917,
25.9203441316619, NA, 23.5317193622031, NA, NA, NA, 20.4526193062251,
NA, 22.357699113594, 19.9767319209274, 29.0184743803346, NA,
NA, NA, NA, 22.1010446624755, NA, NA, 26.3118997535445, NA, NA,
NA, NA, NA, NA, 29.9658173049532, NA, NA, 22.7388204380555),
Gr_10 = c(24.9716280984187, 26.6702013159945, NA, 26.0197313721615,
NA, NA, NA, 22.1233522815746, NA, 24.0516716332837, 22.4063679987568,
30.256761573029, NA, NA, NA, NA, 26.4434318913431, NA, NA,
27.9654320211905, NA, NA, NA, NA, NA, NA, 29.8212398361126,
NA, NA, 24.1442935303143), Gr_11 = c(22.9856008507804, 25.1691705265075,
NA, 26.0689081411402, NA, NA, NA, 21.1400234004731, NA, 24.5711480491199,
23.5402595534611, 29.329649538014, NA, NA, NA, NA, 28.6076666902364,
NA, NA, 26.5597151498881, NA, NA, 25.8334491330428, NA, NA,
NA, 29.7854239060885, NA, NA, 21.751849665826), Gr_12 = c(28.2942576160509,
28.4109042369708, NA, 30.2938411874268, 28.1159976488766,
26.6893919055319, NA, 20.2236435193017, NA, 31.1236147481775,
27.1394614209655, 33.7497512742728, NA, NA, NA, 22.1620175455317,
32.740995072413, NA, 23.2685659859292, 31.9204662366898,
NA, NA, 30.7601811119423, NA, NA, 22.8704941623247, 31.3416488641037,
NA, NA, 28.6773773257387), Gr_13 = c(27.9415091276483, 27.0299165363222,
NA, 30.7110417097659, 28.7379570773404, 25.5882365428802,
NA, NA, NA, 32.2667076588073, 27.2933369287433, 34.7079501935325,
NA, 22.8206916170467, NA, NA, 32.5779472688676, NA, NA, 32.6317048040664,
NA, NA, 30.1389490092958, 23.8308408919424, 23.0679896658325,
26.164689687244, 30.2006952484736, NA, 24.447772868487, 29.5606883639626
), Gr_14 = c(27.4616237005853, 26.7750499947566, NA, 30.3932526396929,
31.1062446290124, 27.2595253359549, NA, NA, NA, 33.6656430607522,
27.734214173453, 35.0800848727354, NA, NA, NA, 23.151279208873,
33.2366327906614, NA, NA, 33.4932145181405, 22.9608977649923,
NA, 31.8193222893087, 24.7850652730265, NA, 24.9920915833786,
29.0239557410047, 25.2744788247811, 26.6821750741598, 29.7891764054099
), Gr_15 = c(27.2029382158867, 25.3112934881725, NA, 29.1103329989503,
29.514275096105, NA, NA, NA, NA, 31.6854120776358, 28.5249970429603,
35.9001903675862, 22.4465240056921, NA, NA, NA, 31.8450938083269,
25.5788830788713, NA, 34.7663358707296, 25.6549086895753,
26.2291635318221, 31.9466351025545, 26.715548983008, NA,
25.6752720211283, 28.4457302899793, 27.2647239196348, 25.0412216502086,
31.6489022687779), Gr_16 = c(25.1843096821522, NA, NA, 26.444459119903,
23.8302606418847, NA, NA, NA, NA, 27.987230611469, 27.8591095189136,
32.8816869988268, NA, NA, 24.8165571469754, NA, 28.7689442058935,
25.2395434664377, NA, 32.829999906694, 23.6787411063596,
NA, 27.8325560998723, 25.9582137297807, NA, NA, 25.6769403745901,
25.3048339598422, 23.7070405817542, 29.8423911570548), Gr_17 = c(23.2209751780558,
NA, NA, 24.6434488773652, 22.5225058653221, NA, NA, NA, NA,
27.0216809889885, 26.6607134339159, 31.099676534797, NA,
NA, 26.93077937966, NA, 27.8090060912948, 26.7795654758791,
NA, 32.3731900255852, 24.9494014193233, NA, 24.5609834789349,
26.086325932043, NA, NA, 25.5082418618407, 23.6504233402429,
23.8014399755019, 28.7791270904749), Gr_18 = c(NA, NA, NA,
NA, NA, NA, 26.0401348427439, NA, NA, 24.3341543275568, 24.7556235529872,
30.4889365348298, NA, NA, 26.9888022043666, NA, 25.7387173773674,
27.1316334308385, NA, 31.571451882524, NA, NA, NA, 25.745888266175,
NA, NA, 23.2997781674234, NA, NA, 23.2402643606836), Gr_19 = c(NA,
NA, NA, NA, NA, NA, 24.3940790216008, NA, NA, NA, 21.4222413790374,
25.7991932672173, NA, NA, 25.9372380266141, NA, 22.9217973627502,
20.5334552143032, NA, 28.7776543930148, NA, NA, NA, 23.9298543509444,
NA, NA, 24.3614522942989, NA, NA, NA), Gr_20 = c(NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 25.6961748124338, NA,
NA, NA, NA, NA, NA, NA, 26.4582321196234, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA), Gr_21 = c(NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 22.8258042878256, NA, NA, NA, NA, NA,
NA, NA, 25.1317511650203, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA)), row.names = c(NA, 30L), class = "data.frame")
It is a code which has been used to generate a plot:
for (i in 1:nrow(rep_exp)) { rep_exp[i, ][is.na(rep_exp[i, ]) == F] = rownames(rep_exp)[i]}
n_sizescale=nrow(rep_exp)*1.2
p1 <- { upset( fromList(rep_exp),
nsets = ncol(rep_exp), nintersects = 35, order.by = "freq", #degree, freq
empty.intersections = "on", number.angles = 0, mb.ratio = c(0.55, 0.45), point.size = 2.5, line.size = 0.8,
text.scale = c(1.3, 1.3, 1, 1, 1, 1), mainbar.y.label = "Number of groups",
sets.x.label = "Groups",
show.numbers = "yes", keep.order = TRUE,
set_size.show = TRUE,
set_size.scale_max = n_sizescale)
}
If I understood correctly the idea of dots below barplot they indicate how many groups can be found in specific intersections and a single dot gives a number for that specific Gr ? Am I right ?
How to force and algorithm to show couple of "interesting" groups as a single dot (show uniqueness of this group) and other as intersections.
Can you (#krassowski) rewrite a code for the package you mentioned ?

Counting observations and adding them to data frame in R

I have the return og 108 mutual funds and over from 1987 to 2019. I want to count the number of observations in total (excluding NA) over the existence of the funds.
I have been able to get the kurtosis, skewness, etc using the following codes:
kurt <- apply(funds, 2, kurtosis, na.rm = TRUE)
skew <- apply(funds, 2, skewness, na.rm = TRUE)
max <- apply(funds, 2, max, na.rm = TRUE)
min <- apply(funds, 2, min, na.rm = TRUE)
sd <- apply(funds, 2, sd, na.rm = TRUE)
m <- apply(funds, 2, mean, na.rm = TRUE)
Then trying to the same with the number of observations and not succeeding:
obs <- apply(funds, 2, count, na.rm = TRUE)
Getting this error:
Error in UseMethod("group_by_") :
no applicable method for 'group_by_' applied to an object of class "c('double', 'numeric')"
The first 10 lines from the data set is here (funds). It is much longer but this should be sufficiently illustrative. As you can see there are a lot of NA in the first lines. Number of observations here would result in 0, and if one looks at the fund "DK.NORGE" the number of observations in the first 10 lines would be 10.
structure(c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, -0.0090002245623988, 0.00232763847063611,
0.0666744669374286, 0.0541982646590207, 0.0357777115456177, 0.0112375620619904,
0.0517733147448458, 0.0553272554088993, 0.0964919466161833, -0.183504972082187,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, -0.0132758821474321, 0.0246370824973443,
0.0436835891381346, 0.0356472795497187, 0.000293052003410121,
-0.0158201720510295, 0.0677617514139583, 0.0710647033479483,
0.0996190340976313, -0.26700522906759, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 0.0609178826615828, 0.0330911715918167,
0.0246199591154059, 0.0387559218497211, -0.0219724959665873,
0.00576292730999128, 0.0607497869923317, 0.0968700634555142,
0.118662582078258, -0.149187455335955, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -0.00338009126246408,
0.0625741902662371, -0.0197095435684648, 0.0235653235653237,
-0.0205574774344905, 0.0211513478402079, 0.0440504114817319,
0.0713605727123872, 0.122338724009241, -0.193811951737024, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 0.0182819486204802, 0.052568368712947,
0.0223478709564888, 0.0430931528662419, 0.00418444259680784,
0.0149102804245731, 0.0891504229496138, 0.101929676995524, 0.0713342508037151,
-0.184479046400599, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA), .Dim = c(10L, 108L), .Dimnames = list(
NULL, c("AI.NORS2", "AI.AKSJN", "AB.AKSPR", "AI.AKTIV", "GA.KAPIT",
"GA.GAMB", "BF.HUMAN", "AB.NOPEN", "VL.AKNOR", "AI.NORGS",
"AI.NORG", "BF.NORGE", "AI.NORGI", "AI.VEKST", "AC.NWECA",
"AC.NEQCB", "AC.NWECD", "AC.NWECI", "NR.NORGE", "BF.NORG",
"CA.AKSJE", "CL.AKNOR", "FF.AKFOR", "FF.NOIII", "FF.NOAI2",
"FF.NORGE", "FF.NORII", "FF.VEKST", "DF.NORGE", "DF.VEKST",
"DK.PBNOR", "DK.NORGE", "DK.NORII", "DI.RINV", "DK.NORG3",
"DK.NORIV", "DK.NOIVR", "DK.NSEL1", "DK.NSEL2", "DK.NSEL3",
"DI.RVKST", "DI.SMB", "EK.NORGE", "NF.PLUSS", "FT.GNRTR",
"FT.NOFOK", "FF.BARNE", "FK.AKTI2", "FK.SPAR", "FV.NORGE",
"FV.TRNDR", "GA.OPPKJ", "GF.AKSJE", "GF.INVES", "SU.AKTIV",
"SU.GLNO", "SU.NORGE", "HF.NORGE", "HB.HNORG", "HO.NORGE",
"KF.IPA", "KL.AKSJE", "KL.AKSNO", "IS.NORGE", "IS.UTBYT",
"IS.UTBYI", "NF.AKSJE", "KF.AVKAS", "KF.BARNE", "KF.KAP",
"KF.KAPIT", "KF.KAIII", "KF.NOPLS", "KF.AKPEN", "KF.SMB",
"KF.SMBII", "KF.VEKST", "OD.NORGE", "OD.NORGA", "OD.NORGB",
"OD.NORGD", "OD.NORII", "OR.FIN30", "PO.AKTIV", "FO.AKSJE",
"FO.INDX", "PV.VEKST", "NF.RFAKS", "NF.RFPLU", "AI.SKAFS",
"SE.NORGE", "SK.HORIS", "SK.SMB", "SR.NORGA", "SR.NORGB",
"SP.INNLA", "SP.AKSJS", "SP.NORGE", "SP.NORGA", "SP.STNOP",
"SP.NORGI", "SP.NOINS", "SP.OPTIM", "SP.VEKST", "SP.VERDI",
"SP.STVEN", "TF.NORGE", "OD.VÅRAK")))
Any feedback is appreciated. Thank you.

count is not the right function here. To count number of non-NA value in each column use is.na with sum.
obs <- apply(funds, 2, function(x) sum(!is.na(x)))
However, a better option is colSums which can take input as complete dataframe or matrix.
colSums(!is.na(funds))

Ignoring NA (missing values)

I want to calculate the mean of each fund in the dataset below, but I am not able to exclude the na's I put in in XL as an indication of missing values.
I have tried using na.rm and na.omit without getting it working as intended. Does anyone have any suggestions on how to perceed?
dput(funds[1:50,]) from dataset.
structure(c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, -0.0371203551212511, -0.0556553503130705,
0.114407946115376, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, -0.0307016857361987, -0.0325795717342283,
-0.0224503689467165, 0.0258370136198041, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
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-0.0238208182014747, -0.0339683325366178, -0.0373842599362443,
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NA, NA, NA, -0.0090002245623988, 0.00232763847063611, 0.0666744669374286,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -0.0924237534290786,
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NA, NA, NA, NA, NA, NA, NA, 0.0609178826615828, 0.0330911715918167,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, -0.00338009126246408, 0.0625741902662371,
-0.0197095435684648, 0.0235653235653237, -0.0205574774344905,
0.0211513478402079, 0.0440504114817319, 0.0713605727123872, 0.122338724009241,
-0.193811951737024, -0.23541658482932, -0.0560521989313605, 0.0041909323463778,
0.0892682926829269, 0.0763298004677315, -0.0193703481115066,
-0.0609560626060721, 0.0787690145087605, 0.0327624720774384,
-0.0813356164383561, 0.0331583852454995, 0.0658500688410957,
0.0821826280623608, 0.134060506277012, 0.195049361207898, 0.034926805563992,
0.0763587275501818, 0.0808659141719832, 0.055796589647866, -0.0574827981651376,
0.0425095057034219, 0.019500571400783, 0.0867895728493404, -0.108760533707865,
0.0118930365409238, 0.0694001703370242, 0.0441213279632284, 0.0339101250926208,
0.0286665823531891, -0.0765747305438302, 0.0718739598357929,
-0.0335168930109307, 0.0607261433008459, -0.0744547657512117,
-0.136473719808871, -0.0317348021628175, -0.0269818902980012,
-0.0809375670456983, -0.0628246279544791, 0.107575427343774,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 0.0182819486204802, 0.052568368712947,
0.0223478709564888, 0.0430931528662419, 0.00418444259680784,
0.0149102804245731, 0.0891504229496138, 0.101929676995524, 0.0713342508037151,
-0.184479046400599, -0.0538613608825967, -0.0555593000997546,
-0.0649010498069525, 0.0740915994413365, 0.0624862329910825,
0.00650709895739299, -0.0423449522265485, 0.044737700254633,
0.0252296167409365, -0.0954811628750583, 0.0179609842733965,
-0.000429142278253081, 0.0345924565218922, 0.132750105443612,
0.172954501777649, 0.0535453712284513, 0.103130209117992, 0.102176307326314,
0.0768477474488676, -0.0796743984053985, 0.070035975865518, 0.0698581653845138,
0.0996889125326326, -0.148626092862475, 0.0098176084772359, 0.0926008734665891,
0.0541087153741737, 0.0483966467251862, 0.0115390906369492, -0.0557620023919358,
0.0673137506421835, -0.027245976677444, 0.0564134642914937, -0.0897508035033038,
-0.0964286292214832, -0.0122604677817973, 0.0015012673742687,
-0.0554419022307834, -0.0730251582211762, 0.111314660965433,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA), .Dim = c(50L, 108L), .Dimnames = list(NULL,
c("AI.NORS2", "AI.AKSJN", "AB.AKSPR", "AI.AKTIV", "GA.KAPIT",
"GA.GAMB", "BF.HUMAN", "AB.NOPEN", "VL.AKNOR", "AI.NORGS",
"AI.NORG", "BF.NORGE", "AI.NORGI", "AI.VEKST", "AC.NWECA",
"AC.NEQCB", "AC.NWECD", "AC.NWECI", "NR.NORGE", "BF.NORG",
"CA.AKSJE", "CL.AKNOR", "FF.AKFOR", "FF.NOIII", "FF.NOAI2",
"FF.NORGE", "FF.NORII", "FF.VEKST", "DF.NORGE", "DF.VEKST",
"DK.PBNOR", "DK.NORGE", "DK.NORII", "DI.RINV", "DK.NORG3",
"DK.NORIV", "DK.NOIVR", "DK.NSEL1", "DK.NSEL2", "DK.NSEL3",
"DI.RVKST", "DI.SMB", "EK.NORGE", "NF.PLUSS", "FT.GNRTR",
"FT.NOFOK", "FF.BARNE", "FK.AKTI2", "FK.SPAR", "FV.NORGE",
"FV.TRNDR", "GA.OPPKJ", "GF.AKSJE", "GF.INVES", "SU.AKTIV",
"SU.GLNO", "SU.NORGE", "HF.NORGE", "HB.HNORG", "HO.NORGE",
"KF.IPA", "KL.AKSJE", "KL.AKSNO", "IS.NORGE", "IS.UTBYT",
"IS.UTBYI", "NF.AKSJE", "KF.AVKAS", "KF.BARNE", "KF.KAP",
"KF.KAPIT", "KF.KAIII", "KF.NOPLS", "KF.AKPEN", "KF.SMB",
"KF.SMBII", "KF.VEKST", "OD.NORGE", "OD.NORGA", "OD.NORGB",
"OD.NORGD", "OD.NORII", "OR.FIN30", "PO.AKTIV", "FO.AKSJE",
"FO.INDX", "PV.VEKST", "NF.RFAKS", "NF.RFPLU", "AI.SKAFS",
"SE.NORGE", "SK.HORIS", "SK.SMB", "SR.NORGA", "SR.NORGB",
"SP.INNLA", "SP.AKSJS", "SP.NORGE", "SP.NORGA", "SP.STNOP",
"SP.NORGI", "SP.NOINS", "SP.OPTIM", "SP.VEKST", "SP.VERDI",
"SP.STVEN", "TF.NORGE", "OD.VÅRAK")))
````

We can also use sapply with mean
sapply(funds, mean, na.rm = TRUE)
Or with apply
apply(funds, 1, mean, na.rm = TRUE)

This way you can get means for every column:
library(tidyverse)
means <- data %>% as_tibble() %>% mutate_all(as.numeric) %>% summarise_all(sum, na.rm = TRUE)

Maybe you can try colMeans with option na.rm = TRUE, e.g.,
res <- colMeans(funds,na.rm = TRUE)

Boxplot/ Box & Whisker help in ggplot2 (R) Need to remove duplicates while also plotting on one plot

So I would like to create a plot of 6 different boxplots (so all in one graphic). I am however running into some trouble.
First, there are many NA's and they are randomly admist the dataset so I am unsure how to plot all the variables on one plot with this issue.
Secondly, ggplot requires a y variable.... I am fairly new to the tidyverse but am unsure how to create a y variable that will allow for each plot to be represented.
Here is the code I have thus far as well as some sample data, just getting some nonsense...:
library(readr)
library(ggplot2)
library(tidyr)
Box_Whiskers_Plot <- read_delim("C:/Users/johnt/Downloads/Box & Whiskers Plot.txt",
"\t", escape_double = FALSE, trim_ws = TRUE)
box_tidy <- gather(data = Box_Whiskers_Plot,
key = Concern,
value = Value)
ggplot(data = box_tidy, mapping = aes(x = Concern, y = Value)) +
geom_boxplot()
Sample data:
structure(list(`1 concern` = c(NA, NA, NA, NA, "4.7051072361071977E-2",
"0.19811079686050914", "0.15241809445883892", "9.3784616216209704E-2",
NA, NA, "0.12902642667986841", NA, NA, NA, "-2.7995766112836051E-3",
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "0.16257072914439274",
NA, NA, NA, NA, NA, NA, NA, "-0.32189822523240785", NA, NA, "8.8779492146409344E-2",
NA, NA, NA, NA, "0.25876167614411516", NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, "0.12140341771652063", NA,
NA, NA, NA, NA, NA, NA, "7.8315099203373872E-2", NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, "-2.4887790087301243E-2", NA, NA, "0.17817816702345479",
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "-0.45715764794257374",
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "0.1727380710391988",
NA, NA, NA, NA, NA, "-", NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, "0.11810158539718096", NA, NA, NA,
"0.27340288238873622", NA, NA, "0.31222498045287939", NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "1 concern", "8.3059150913641758E-2"
), `2 concerns` = c(NA, "0.30750786698302712", NA, NA, NA, NA,
NA, NA, "0.19491094633578943", "0.14347068243793348", NA, NA,
NA, NA, NA, "9.4003202704330935E-2", NA, NA, NA, NA, NA, NA,
"5.8682039323707746E-2", NA, NA, NA, NA, NA, NA, "0.38837474884084",
NA, NA, "9.9772158663856914E-2", NA, NA, NA, NA, "0.15369966808838376",
NA, "-9.7591933707396827E-2", "7.5799891559719335E-2", NA, "0.74069094176638783",
"0.18455079764897997", "0.35878241180217119", NA, NA, "9.7671065222774578E-2",
NA, "-1", "1.9762661406333537E-2", NA, NA, NA, NA, NA, NA, "0.12110279127050561",
"-8.8073972864920469E-2", NA, NA, "-5.3063552654085022E-2", "-0.19524178703281547",
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "0.19324044960582598",
NA, NA, NA, NA, NA, "0.19558769095890249", "9.8480653359761305E-2",
"-2.7258845509566809E-2", NA, NA, "4.2377241471322602E-2", NA,
NA, NA, "-0.31089100169922018", NA, NA, "9.4259642624681561E-2",
NA, NA, NA, NA, NA, NA, NA, NA, "2.9465956237787916E-2", "0.36028868638565514",
"0.28696166852692623", "0.16026874768911181", NA, NA, NA, NA,
NA, NA, NA, "0.17495242646710829", NA, NA, NA, "8.0174590835183634E-2",
NA, NA, NA, NA, NA, "0.3741514609038552", NA, NA, NA, NA, NA,
NA, NA, NA, NA, "0.18203421025448296", NA, NA, NA, "0.18032116561517086",
NA, NA, "-0.24673024063961035", "8.3759133449436751E-2", NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "-0.12597421585178167",
NA, NA, NA, "0.19902495793991903", NA, NA, NA, NA, "-9.2408051470944635E-2",
NA, NA, NA, NA, NA, "1.3515493037121162E-2", NA, NA, "-2.2110562098653141E-2",
NA, NA, NA, NA, "3.5029098159769845E-2", NA, NA, NA, NA, NA,
NA, NA, "-0.38542680544884389", "0.2673937214255111", NA, NA,
NA, NA, NA, NA, "0.1514695655588354", NA, "-0.19290183848262321",
"0.19830470195985717", "0.25974088161209186", "0", "0.12635072134014091",
"4.3529572197642308E-2", NA, "-2.811733193779542E-2", "5.2999441490886978E-2",
"-1.5294438792050502E-2", "-0.1092036064257218", NA, NA, NA,
NA, NA, NA, NA, "-8.4682877918448418E-2", NA, NA, NA, NA, NA,
NA, "0.33060935555613358", "-0.26950721703104663", NA, NA, NA,
NA, NA, NA, NA, NA, NA, "2 concerns", "6.5143247152538983E-2"
), `3 concerns` = c(NA, NA, NA, "-6.2005384615384615E-2", NA,
NA, NA, NA, NA, NA, NA, NA, "0.16466373149154445", "0.14529429748819767",
NA, NA, "3.3101080910433733E-3", NA, NA, "-0.14716333286324612",
NA, NA, NA, "0.101855405108354", NA, NA, NA, "1.5624661137794593E-2",
"4.089776650666388E-2", NA, "-", "0.14868399697158718", NA, NA,
"8.4936940656134663E-2", "-7.3275278911856751E-5", NA, NA, "0.16209406140402915",
NA, NA, NA, NA, NA, NA, "7.5733790938149026E-2", "7.7802906849214093E-2",
NA, "0.29092905402715896", NA, NA, NA, "0.2433591777340911",
NA, NA, "0.16878584978409417", "0.23450765393402495", NA, NA,
NA, NA, NA, NA, "1.4972641033242029E-2", NA, NA, "0.15914858376902719",
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "0.23167642917280462",
"-0.12014200114033269", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, "0.23296355648900113", "9.2737675544091028E-2",
"-1.135676252608786E-2", "-2.5231545331790839E-3", "-1.831276418414618E-2",
NA, "3.700270212564627E-2", NA, NA, NA, NA, NA, NA, NA, "0.12864133565206637",
NA, "0.2713309994071611", NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, "0.33081997450170131", NA, NA, NA, NA, NA, NA, "0.10916148370698719",
NA, NA, NA, NA, NA, NA, NA, NA, "-1", "-0.10648202245319915",
NA, NA, NA, NA, NA, NA, "7.6583001375218229E-2", "0.11923826063359644",
"0.1382325704168097", "4.411629139778972E-2", NA, NA, "-2.7571494462436785E-2",
"-8.1186210791162505E-2", "0.36815766123347382", "0.21997253864625099",
"9.5269593575127098E-2", NA, "0.40386694165317971", "0.1317061317077115",
"8.4533840305895946E-2", NA, NA, NA, "7.064976326243011E-2",
"8.2533081202996961E-2", NA, NA, NA, NA, "-6.5935523766861404E-2",
"0.15935278497831473", NA, "0.1159060020401923", NA, NA, "0.11817005685670501",
"6.1029901139001863E-2", "0.12692362698225845", "3.4415424790262605E-2",
NA, NA, "0.23155179134453707", "0.14332216947092591", "7.4795242229677816E-2",
"0", NA, NA, NA, NA, NA, NA, "0.14534924839754271", "0.27815354547396853",
"0.19493428600637031", "0.1283055485269069", NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "-2.3773331360783301E-2",
NA, "0.20660830748524073", NA, "0.23154616465669209", NA, "-0.80937062468163068",
NA, NA, "0.41853447897377194", NA, NA, NA, "9.4089917760579844E-2",
NA, NA, "6.3552512454774224E-3", "-0.43971479670164443", "0.15974143122420936",
"-0.16029537031373786", NA, "3 concerns", "7.198593957320798E-2"
), `4 concerns` = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, "6.4352691220779912E-2", NA, NA,
"0.21279729530946834", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, "-9.3690492677869663E-2", NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, "-7.1185121289991993E-2", NA, NA, "6.569732863463229E-2",
NA, NA, "9.7222332805540157E-2", NA, NA, NA, NA, NA, NA, NA,
"8.5074456366478923E-2", NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, "1.7663351832379881E-3", NA, NA, NA, "0.20859715043286409",
"-4.0588246304824382E-2", NA, NA, NA, NA, NA, NA, NA, NA, "3.1799587621662351E-2",
NA, "8.6166092731043253E-2", NA, NA, NA, NA, NA, NA, NA, "4.869038187032948E-2",
"0.18071545075957585", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "4.2986578596766911E-2",
NA, NA, NA, NA, "9.4277092317434086E-3", NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, "3.5496813684543493E-2", "-8.1501862554191895E-2",
NA, NA, "9.9940934380241986E-2", NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, "4 concerns", "5.1777127158001604E-2"), `5 concerns` = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, "8.9612836579635591E-2", "8.1063923186028175E-2",
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, "8.4668999169687842E-2", NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, "6.0739595493825904E-2", NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, "9.0340384993987888E-2", NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, "6.1693228854984072E-2", NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
"-0.20631919750140182", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, "5 concerns", "3.7399967253821095E-2"), `6 concerns` = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, "0.32874505543754307", NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, "0.15408216010209475", NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "-4.8467807432570065E-2",
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, "6 concerns", "0.14478646936902259"
)), row.names = c(NA, -238L), class = c("tbl_df", "tbl", "data.frame"
))

ggplot requires your data to be "tidy" (see here for details https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html).
In your case just use the gather function from tidyr package. As your numeric values are actually character and not numeric you also ned to convert them to numeric before plotting them. ggplot will drop NA values (with a warning) so you don't need to worry about them.
So the code is as follows:
library(tidyverse)
Box_Whiskers_Plot <- Box_Whiskers_Plot %>%
gather(key = "concerns", "value") %>%
mutate(value = as.numeric(value))
ggplot(Box_Whiskers_Plot, mapping = aes(x = concerns, y = value)) +
geom_boxplot()
Resulting in:

How to further format forest Plots in R, from the metafor package?

I'm quite new to R and have been struggling with properly formatting a forest plot I've created.
When I click the "zoom" option in R to open the graph in a new window, it looks as such:
Forest Plot Currently
My main goal is to get the forest plot as compact as possible, i.e. publication quality/style. I currently have wayyyy too much white space in my plot. I think it has something to do with me messing around with the par() function, and now have no clue how to revert to defaults.
#Metafor library
library(metafor)
#ReadXL library to import excel sheet
library(readxl)
#Name the data sheet from the excel file
ACDF<- read_excel("outpatient_ACDF_meta_analysis.xlsx")
#View the data sheet with view(ACDF)
par(mar=c(20,1,1,1))
#This below measures with risk ratios. If you want to measure odds ratios, use argument measure=OR
returnop <- escalc(measure="OR", ai=op_return_OR, bi=op_no_return_OR, ci=ip_return_OR, di=ip_no_return_OR, data=ACDF)
#Generate a Random Effects Model
REmodel<-rma(yi=yi, vi=vi, data=returnop, slab=paste(Author, Year, sep=", "), method="REML")
#Generate a forest plot of the data
forest(REmodel, xlim=c(-17, 6),
ilab=cbind(ACDF$op_return_OR, ACDF$op_no_return_OR, ACDF$ip_return_OR, ACDF$ip_no_return_OR),
ilab.xpos=c(-9.5,-8,-6,-4.5), cex=.75, ylim=c(-1, 27),
psize=1)
### add column headings to the plot
text(c(-9.5,-8,-6,-4.5), 26, c("Return+", "Return-", "Return+", "Return-"))
text(c(-8.75,-5.25), 27, c("Outpatient", "Inpatient"))
text(-16, 26, "Study", pos=4)
text(6, 26, "Log Odds Ratio [95% CI]", pos=2)
I'm not 100% as to how to provide my data otherwise, but I used the dput function to provide as follows. Apologies for the N/As, still fleshing out the data for the future.
structure(list(Study = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA), Author = c("Stieber", "Villavicencio",
"Lied", "Liu", "Garringer", "Joseffer", "Trahan", "Lied", "Sheperd",
"Talley", "Martin", "McGirt", "Adamson", "Fu", "Arshi", "Khanna",
"McClelland", "Purger", "McLellend2", NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Year = c(2005, 2007,
2007, 2009, 2010, 2010, 2011, 2012, 2012, 2013, 2015, 2015, 2016,
2017, 2017, 2017, 2017, 2017, 2017, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA), op_return_OR = c(NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 1, 3, 2, 16, 257, 7, NA, 5, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), op_no_return_OR = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
596, 769, 992, 4581, 958, 1749, NA, 3120, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), ip_return_OR = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 8, 9, 2, 257, 2034, 12, NA,
200, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA), ip_no_return_OR = c(NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 589, 641, 482, 16171, 8930, 1744, NA, 46312, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), op_death = c(NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, 1, NA,
1, 0, NA, 2, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA), op_no_death = c(NA, NA, NA, 45, NA,
NA, NA, NA, NA, NA, 596, NA, 993, 4597, NA, 1754, NA, 3125, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), ip_death = c(NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, 0, NA,
0, 42, NA, 2, NA, 20, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA), ip_no_death = c(NA, NA, NA, 64,
NA, NA, NA, NA, NA, NA, 597, NA, 484, 16386, NA, 1754, NA, 46492,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
2979.79797979798), op_thrombo = c(NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 0, NA, NA, 8, 20, 4, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), op_no_thrombo = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 597, NA, NA, 4589, 1195,
1752, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA), ip_thrombo = c(NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 2, NA, NA, 67, 150, 4, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), ip_no_thrombo = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 595, NA, NA, 16361, 10814,
1752, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA), op_stroke = c(NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 0, NA, NA, 2, 12, 0, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), op_no_stroke = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 597, NA, NA, 4595, 1203,
1756, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA), ip_stroke = c(NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 2, NA, NA, 14, 132, 0, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), ip_no_stroke = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 595, NA, NA, 16414, 10832,
1756, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA), op_dysphagia = c(NA, NA, NA, 0, NA, NA,
NA, NA, NA, NA, NA, NA, 11, NA, NA, NA, NA, 2, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), op_no_dysphagia = c(NA,
NA, NA, 45, NA, NA, NA, NA, NA, NA, NA, NA, 618, NA, NA, NA,
NA, 49, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA), ip_dysphagia = c(NA, NA, NA, 1, NA, NA, NA, NA,
NA, NA, NA, NA, 1, NA, NA, NA, NA, 59, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), ip_no_dysphagia = c(NA,
NA, NA, 63, NA, NA, NA, NA, NA, NA, NA, NA, 273, NA, NA, NA,
NA, 2917, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA), op_hematoma = c(NA, NA, NA, 0, NA, NA, NA, NA,
NA, NA, NA, NA, 1, NA, NA, NA, 1, 4, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), op_no_hematoma = c(NA,
NA, NA, 45, NA, NA, NA, NA, NA, NA, NA, NA, 629, NA, NA, NA,
2015, 47, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA), ip_hematoma = c(NA, NA, NA, 1, NA, NA, NA, NA,
NA, NA, NA, NA, 1, NA, NA, NA, 273, 65, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), ip_no_hematoma = c(NA,
NA, NA, 63, NA, NA, NA, NA, NA, NA, NA, NA, 273, NA, NA, NA,
7791, 1713, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA)), .Names = c("Study", "Author", "Year", "op_return_OR",
"op_no_return_OR", "ip_return_OR", "ip_no_return_OR", "op_death",
"op_no_death", "ip_death", "ip_no_death", "op_thrombo", "op_no_thrombo",
"ip_thrombo", "ip_no_thrombo", "op_stroke", "op_no_stroke", "ip_stroke",
"ip_no_stroke", "op_dysphagia", "op_no_dysphagia", "ip_dysphagia",
"ip_no_dysphagia", "op_hematoma", "op_no_hematoma", "ip_hematoma",
"ip_no_hematoma"), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-35L))

The par option looks ok to me. I changed the ylim option and modified the y location and size of some of the header text as below:
#Generate a forest plot of the data
forest(REmodel, xlim=c(-17, 6),
ylim=c(-1, 10),
ilab=cbind(ACDF$op_return_OR, ACDF$op_no_return_OR, ACDF$ip_return_OR,
ACDF$ip_no_return_OR),
ilab.xpos=c(-9.5,-8,-6,-4.5), cex=.75,
psize=1)
### add column headings to the plot
text(c(-9.5,-8,-6,-4.5), 8.5, c("Return+", "Return-", "Return+", "Return-"),
cex = 0.65)
text(c(-8.75,-5.25), 9.5, c("Outpatient", "Inpatient"))
text(-17, 8.5, "Study", pos=4)
text(6, 8.5, "Log Odds Ratio [95% CI]", pos=2)
This gives the following plot:

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