ggplot2 - adding error bars around points in muti-facet plot of two variables?

Question

I have an R ggplot2 plot like so:

(It is the same plot that my other question is also based on)

As you can see, this plot has facets and plots points representing two variables (as seen in the legend).

Here is my ggplot call to make the plot:

ggplot(data = my_data) +
    geom_point(mapping = aes(x = frame_size, y = psi_hat_mean, colour = "psi_hat")) +
    geom_point(mapping = aes(x = frame_size, y = p_hat_mean, colour = "p_hat")) +
    facet_grid(facets = psi ~ p, labeller = label_both) +
    scale_x_continuous("Frame size") +
    scale_y_continuous("Parameter estimates") +
    scale_colour_manual("Estimated parameter",
                        values = c("psi_hat" = "grey", "p_hat" = "black"),
                        breaks = c("p_hat", "psi_hat"),
                        labels = c(expression(paste(hat(p))),
                                   expression(paste(hat(psi))))) +
    theme(legend.title=element_blank())

In my my_data data frame, I have additional columns representing the standard deviations around the points already plotted, but I don't know the correct/canonical way to plot those standard deviations as error bars around the points. I tried geom_errorbar and geom_pointrange to no avail.

Here is the glimpse() of my_data :

Observations: 99
Variables: 7
$ frame_size   <int> 1, 2, 3, 4, 5, 6, 10, 12, 15, 20, 30, 1, 2, 3, 4, 5, 6, 10, 12, 15, 20, 30, 1, 2, 3, 4, ...
$ psi          <dbl> 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1...
$ p            <dbl> 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.10, 0.10, 0.10, 0.10...
$ psi_hat_mean <dbl> 0.13435605, 0.13448677, 0.13873620, 0.13880599, 0.13870936, 0.13865080, 0.14379677, 0.13...
$ psi_hat_SD   <dbl> 1.535874e-01, 1.530718e-01, 1.643022e-01, 1.645519e-01, 1.646508e-01, 1.641912e-01, 1.74...
$ p_hat_mean   <dbl> 0.04938056, 0.09689240, 0.14150134, 0.18462472, 0.22528098, 0.26249276, 0.39490852, 0.45...
$ p_hat_SD     <dbl> 2.067107e-02, 4.059717e-02, 5.951006e-02, 7.724016e-02, 9.234872e-02, 1.065262e-01, 1.59...

I want to use psi_hat_SD and p_hat_SD which are the standard deviations to construct the error bars so that in the end the points would look something like this:

How do I do this correctly? Thank you!

UPDATE : Here is how I tried to do the above with geom_errorbar :

# doesn't work
ggplot(data = my_data) +
    geom_point(mapping = aes(x = frame_size, y = psi_hat_mean, colour = "psi_hat")) +
    geom_errorbar(aes(ymin = psi_hat_mean - psi_hat_SD, 
                      ymax = psi_hat_mean + psi_hat_SD)) +
    geom_point(mapping = aes(x = frame_size, y = p_hat_mean, colour = "p_hat")) +
    facet_grid(facets = psi ~ p, labeller = label_both) +
    scale_x_continuous("Frame size") +
    scale_y_continuous("Parameter estimates") +
    scale_colour_manual("Estimated parameter",
                        values = c("psi_hat" = "grey", "p_hat" = "black"),
                        breaks = c("p_hat", "psi_hat"),
                        labels = c(expression(paste(hat(p))),
                                   expression(paste(hat(psi))))) +
    theme(legend.title=element_blank())

It gives me this error:

Error in eval(substitute(list(...)), `_data`, parent.frame()) :    object 'x' not found In addition: Warning messages: 1: In min(x, na.rm
= na.rm) :   no non-missing arguments to min; returning Inf 2: In max(x, na.rm = na.rm) :   no non-missing arguments to max; returning
-Inf 3: In min(diff(sort(x))) : no non-missing arguments to min; returning Inf

If I replace geom_errorbar with geom_pointrange I get this error:

Error: geom_pointrange requires the following missing aesthetics: x, y

Here is the actual dput() output of the data frame:

structure(list(frame_size = c(1L, 2L, 3L, 4L, 5L, 6L, 10L, 12L, 
15L, 20L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 10L, 12L, 15L, 20L, 30L, 
1L, 2L, 3L, 4L, 5L, 6L, 10L, 12L, 15L, 20L, 30L, 1L, 2L, 3L, 
4L, 5L, 6L, 10L, 12L, 15L, 20L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 
10L, 12L, 15L, 20L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 10L, 12L, 15L, 
20L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 10L, 12L, 15L, 20L, 30L, 1L, 
2L, 3L, 4L, 5L, 6L, 10L, 12L, 15L, 20L, 30L, 1L, 2L, 3L, 4L, 
5L, 6L, 10L, 12L, 15L, 20L, 30L), psi = c(0.1, 0.1, 0.1, 0.1, 
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 
0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.4, 0.4, 
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 
0.4, 0.4, 0.4, 0.4), p = c(0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 
0.05, 0.05, 0.05, 0.05, 0.05, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 
0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
0.2, 0.2, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 
0.05, 0.05, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 
0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.05, 
0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.1, 
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2), psi_hat_mean = c(0.134356048382177, 
0.134486773572389, 0.13873620166692, 0.138805987584267, 0.13870935995785, 
0.138650800195541, 0.143796766183063, 0.13916560213237, 0.143315740756538, 
0.169921216208113, 0.197659424623347, 0.100613069549282, 0.100635588260763, 
0.100597940900849, 0.100694725369573, 0.100675516255325, 0.100745841907948, 
0.100815988120923, 0.100979695039558, 0.101142533641973, 0.101443977996904, 
0.115373528295039, 0.0999755153064313, 0.0999979752698177, 0.0998962157963402, 
0.100005898203748, 0.100003047766876, 0.100019378162055, 0.100011289299875, 
0.100012663699438, 0.100017130290604, 0.100019688944185, 0.100002352877339, 
0.250280871706711, 0.20814054675288, 0.207769046277594, 0.208090970914499, 
0.208532916287244, 0.208859991544016, 0.209252480710842, 0.210811715962647, 
0.211537486757659, 0.212108292997122, 0.221164186075537, 0.200847254811565, 
0.200882290085097, 0.200895194559799, 0.200963134246294, 0.200935377663199, 
0.200986349050379, 0.201026021866445, 0.20133815021031, 0.201285819726138, 
0.201771848718818, 0.202132030705017, 0.200000863248174, 0.200013219984772, 
0.20001223270007, 0.199994520753463, 0.200008287620153, 0.200004330046502, 
0.199998072041364, 0.20000498181904, 0.199884681785292, 0.199925907752792, 
0.200030218291904, 0.46909003266665, 0.404543079367836, 0.405542037009819, 
0.405473359663344, 0.406450221494248, 0.407016892401095, 0.407804741068077, 
0.408998802872545, 0.409059879710074, 0.412281980391505, 0.417686374689605, 
0.40047111166792, 0.400465155003593, 0.4004738934807, 0.400494312554866, 
0.400470923112942, 0.400528527625076, 0.400573788345039, 0.400647311390814, 
0.400818156817758, 0.400819981298249, 0.401099396871977, 0.399988878019095, 
0.400032636565743, 0.400001592936094, 0.400001283207262, 0.40000165174745, 
0.399995790514786, 0.400004582819455, 0.400021723478689, 0.400009633788358, 
0.400003950520113, 0.400026826367252), psi_hat_SD = c(0.153587439714877, 
0.153071750905185, 0.16430223183556, 0.164551851214726, 0.164650834475015, 
0.164191178124105, 0.174995748335211, 0.163948969973203, 0.174791156358891, 
0.227232415724057, 0.272815709195867, 0.00416873072294715, 0.00418449905149223, 
0.00408281532783013, 0.00413066011578574, 0.00411030244674003, 
0.00415794082443098, 0.00428705188869137, 0.00447385303686605, 
0.00512309080419838, 0.00538878394819733, 0.109059513881393, 
8.10448978287042e-05, 3.25412047710771e-05, 0.000229019038524637, 
1.32554791740984e-05, 3.28644816689232e-05, 2.89433268844505e-05, 
2.95023799151163e-05, 4.38564452461257e-05, 6.39815108451877e-05, 
9.1881375010522e-05, 4.78209458970029e-13, 0.183002870467444, 
0.0293818799338775, 0.0288402656743286, 0.0288165177620824, 0.0297634852739387, 
0.0299527800584211, 0.0347846993392747, 0.0372990730196757, 0.035669047956326, 
0.0368368101376486, 0.0705751689361306, 0.00203121177367882, 
0.00192575329570197, 0.00184269195613544, 0.00240720598274821, 
0.00214770293122925, 0.00226807525752776, 0.00188169304328542, 
0.00340586369727475, 0.00345333835319253, 0.00394664917443553, 
0.00872196151289955, 8.6094454437387e-06, 1.71106169362863e-05, 
3.23287049318278e-05, 3.53049297358841e-05, 2.1492246131839e-05, 
6.2288439810166e-06, 1.44667033948848e-05, 1.2515352244187e-05, 
0.00012029349894011, 0.000171331269685535, 0.000293960412588456, 
0.195750328230943, 0.0432980747152544, 0.0448046192056449, 0.0440846647061932, 
0.0485042955171366, 0.0482979217319834, 0.0522379989209262, 0.0579890416481406, 
0.0531009498688177, 0.0630117285310315, 0.0713821778815143, 0.00623289291854068, 
0.006221899243366, 0.00624538209117939, 0.00628328531688065, 
0.00627909851780257, 0.00634871536458593, 0.00637069626931915, 
0.00644027718251307, 0.00670664538203358, 0.00674945522402437, 
0.00672507992540975, 1.28897443961884e-05, 4.4027910050059e-05, 
2.80088266440853e-06, 4.4937052942413e-06, 3.89369731696281e-06, 
1.90379259756568e-05, 1.15262840907433e-05, 3.12553218039823e-05, 
2.25050991263921e-05, 5.46276800711688e-05, 0.00028230060200018
), p_hat_mean = c(0.0493805572043857, 0.0968923979425734, 0.141501343959094, 
0.184624723464391, 0.225280979633726, 0.262492758502991, 0.39490851538299, 
0.45154142965489, 0.525853334795089, 0.622498582290085, 0.744750596464598, 
0.0951102054450773, 0.181157836797368, 0.258547631613774, 0.329852164417611, 
0.392734430296655, 0.452845308468353, 0.634656661079906, 0.700411111167911, 
0.775401595177942, 0.852473557888133, 0.927365458017404, 0.201075086977168, 
0.361245627511475, 0.490362348231505, 0.593823984188126, 0.675404456152514, 
0.741475393843094, 0.896180595647868, 0.930911156580922, 0.969228130789834, 
0.993980061494683, 0.999933401786537, 0.0476141340798173, 0.0949703678299418, 
0.139111305167333, 0.179581265249894, 0.218955133012378, 0.255643703659721, 
0.390317309763656, 0.442577946011462, 0.517324051736444, 0.616089655559368, 
0.742460394002412, 0.0991474592154088, 0.188345493237174, 0.268690852765111, 
0.340630181377103, 0.404700993857244, 0.466171060122403, 0.646753481555216, 
0.710048127892561, 0.790236156784217, 0.86402129317977, 0.953873990924048, 
0.202356758942191, 0.363873473395577, 0.493936650336295, 0.597901898041952, 
0.680296139765208, 0.747366703193019, 0.903322005155783, 0.939981567846486, 
0.966801836293303, 0.988586306636356, 0.999243451951223, 0.0463364925613695, 
0.096186827643031, 0.13997233747503, 0.181749771931483, 0.222523649736396, 
0.259499892174649, 0.394045900835822, 0.45083186052406, 0.527845241576527, 
0.625777508556727, 0.765427947503516, 0.0993534376856661, 0.189237207621871, 
0.269758186956382, 0.342693314139248, 0.409295462208863, 0.467030278808092, 
0.651622635973668, 0.718795315510802, 0.793143513356861, 0.879655463405415, 
0.957032974716723, 0.201367021664948, 0.362466628523926, 0.490623284961033, 
0.595997765341907, 0.676872347808209, 0.73980714214723, 0.892075883852205, 
0.930114063779856, 0.963579844299096, 0.985988067825672, 0.99830794696651
), p_hat_SD = c(0.020671073048708, 0.0405971695814201, 0.0595100648300367, 
0.077240162880486, 0.0923487236898059, 0.106526196932577, 0.159729823310882, 
0.176228877737896, 0.200751656004054, 0.254670828390244, 0.287381559859864, 
0.0253741193781226, 0.0471638768455461, 0.0655493558111445, 0.0839136353773245, 
0.100142912148987, 0.110911181108964, 0.140636755877925, 0.15125797466698, 
0.1555013613506, 0.149459455618193, 0.166850371924508, 0.0344233871624597, 
0.0575241859626709, 0.0765279598077096, 0.088251660637436, 0.0907468071979005, 
0.09555580032782, 0.0910651633810324, 0.0787075972576783, 0.0610760339901177, 
0.0314642360500455, 1.07277201318732e-11, 0.0174068050890887, 
0.0296542146793441, 0.0420243108930666, 0.0538557925028335, 0.0650751964538859, 
0.0748944574050911, 0.107149795097664, 0.126045003227699, 0.145148214248163, 
0.161268452161263, 0.192544810714351, 0.0189081918124477, 0.0358817044204188, 
0.0483995702453099, 0.0615944579033814, 0.0706741412337433, 0.0806577966830984, 
0.101766144853803, 0.111253971769697, 0.108010267370669, 0.109406145445318, 
0.0844663953398124, 0.0237717398485985, 0.0415592337691319, 0.0508739981443487, 
0.055065018088554, 0.0607402227151579, 0.0644644110987824, 0.0599412871159953, 
0.0537879467634287, 0.0473614192478991, 0.0335711724058262, 0.0100985164006129, 
0.0148757945028784, 0.0223727872205404, 0.0328124061087913, 0.0416862513830743, 
0.051522313637991, 0.0599641576237172, 0.0870524394338348, 0.100814653541668, 
0.115655991730271, 0.131950868996117, 0.159736280003032, 0.0152671641785822, 
0.0282221543538266, 0.0388769807365557, 0.0478383382638392, 0.0552111224929799, 
0.0611073915392696, 0.0758076715431021, 0.0818244295408088, 0.0820682293454554, 
0.0729034999789475, 0.0524286593814453, 0.0175508745752872, 0.0293973130349527, 
0.0405068273531901, 0.0454857976255542, 0.0469496795934104, 0.0509564122496874, 
0.0469363426205197, 0.0448122577291855, 0.0334480891840883, 0.0234468477023789, 
0.0104300159826976)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -99L))

Answer 1

It is the first part of the error message that tells you what went wrong (but the additional warning messages were definitely distracting :-) ).

Error in eval(substitute(list(...)), _data , parent.frame()) :
object 'x' not found

You didn't define the x axis in geom_errorbar() , so ggplot doesn't know where to put the bars along the x axis.

You can add x specifically to this layer to plot the error bars:

 geom_errorbar(aes(x = frame_size, 
                   ymin = psi_hat_mean - psi_hat_SD, 
                   ymax = psi_hat_mean + psi_hat_SD))

Another alternative is to map x globally by doing the mapping within ggplot() . This is useful in your case because you use the same x throughout the plot. You can then remove x from all geom layers, since anything set in ggplot() affects all the layers of the plot.

That would look like:

ggplot(data = my_data, aes(x = frame_size)) +
     geom_point(mapping = aes(y = psi_hat_mean, colour = "psi_hat")) +
     geom_errorbar(aes(ymin = psi_hat_mean - psi_hat_SD, 
                       ymax = psi_hat_mean + psi_hat_SD)) +
     geom_point(mapping = aes(y = p_hat_mean, colour = "p_hat")) +
     facet_grid(facets = psi ~ p, labeller = label_both) +
     scale_x_continuous("Frame size") +
     scale_y_continuous("Parameter estimates") +
     scale_colour_manual("Estimated parameter",
                         values = c("psi_hat" = "grey", "p_hat" = "black"),
                         breaks = c("p_hat", "psi_hat"),
                         labels = c(expression(paste(hat(p))),
                                    expression(paste(hat(psi))))) +
     theme(legend.title=element_blank())