Group_by_at 和 summarise_ 在使用闪亮/降价可再现时产生值。 R答案

【问题标题】：Group_by_at and summarise_ producing values when using shiny/markdown rendetable. RGroup_by_at 和 summarise_ 在使用闪亮/降价可再现时产生值。 R
【发布时间】：2021-09-08 11:21:17
【问题描述】：

这是我的代码

```{r}
#Assign Variables
    
    
Categorical.Variables = c( "Race/Ethnicity" ,"Gender", "Education", "Intervention")
    
Numeric.Variables = c("Age", "Pre Weight", "Post Weight", "Follow-up Weight","Wieght Loss/Gain after Intervention","Wieght Loss/Gain on Follow Up" )
```

我有一张我正在尝试用 dplyr 制作的简单表格。

```{r}


renderTable({
   wl_data %>% group_by_at(input$categorical_variable) %>% 
    summarise("Average " = mean(wl_data[[input$numeric_variable]]),
      "Median " = median(wl_data[[input$numeric_variable]])
    ) 
})
```

这是我得到的输出，它对于整个数字输入具有相同的值，并且没有按类别分解。

我想要的输出是有一个反应表，它会给我根据输入变化的汇总统计信息。到目前为止，我已经非常接近，但我不知道如何让它工作。我做错了什么？

数据

> dput(wl_data)
structure(list(ID = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 
29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 
45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 
77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 
93, 94, 95, 96, 97, 98, 99, 100), Gender = c("Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male"), Age = c(34.1958081813646, 38.342817530618, 31.4255555318668, 
39.9174711825326, 30.4606180330738, 35.0906190911774, 33.4250614438206, 
32.4111385603901, 30.9181484377477, 25.1908965916373, 33.1378793967888, 
37.1778275772231, 33.7549078873126, 30.3750618664781, 40.9762685345486, 
27.3728471407667, 36.3470769267296, 33.8341794455191, 36.9817041397328, 
32.4507015799172, 31.736502675456, 29.6957238939358, 36.1113904697122, 
33.9414452506462, 36.9691713027423, 34.3095768736093, 32.6794349499396, 
32.5801726981881, 27.2223803373054, 34.2432519724825, 36.7139034247957, 
27.5831041259225, 41.5007923189551, 37.1082421375904, 30.3266722008702, 
33.9694667824078, 35.6325324142817, 35.2054973669583, 29.9515829434386, 
25.677076079417, 31.1902828949387, 32.4210338627454, 28.3575206745882, 
32.5940152075491, 31.9765592545155, 41.3789200289175, 36.1047693482833, 
41.7112493929453, 31.2430063028005, 34.3678300092579, 33.7714243110968, 
27.4467708701268, 31.7782484822674, 27.5600393402856, 33.2923297870439, 
38.9376543504186, 36.1805939215701, 36.2597199606244, 30.7401512296638, 
27.6097705105785, 37.7263165470213, 30.2310251905583, 32.0336846167338, 
30.4912316247355, 37.9383197620045, 29.5154438541504, 36.9983115129871, 
32.7947406882304, 33.9285486157751, 31.0574057190097, 26.5039522824809, 
31.8339186529629, 32.5527787177707, 31.8562467478914, 34.1271822586423, 
26.0893318378367, 32.6484211806091, 32.8446673998842, 35.6959423848893, 
38.4928932513576, 30.124668880133, 29.9166947266785, 35.4745850168983, 
31.8399849826237, 34.1574638847378, 33.9537143341731, 32.5872485669679, 
33.5270658220979, 31.9265234034974, 32.4627478372422, 29.9351938489126, 
34.0414714779472, 31.2413191901287, 27.0438647172414, 19.2909317016602, 
33.415520844399, 27.2705926514463, 42.3596463557333, 29.8770043778932, 
33.5144147507963), Education = c("Less than HS", "Less than HS", 
"Less than HS", "Less than HS", "Less than HS", "Less than HS", 
"Less than HS", "Less than HS", "Less than HS", "Less than HS", 
"Less than HS", "Less than HS", "Less than HS", "Less than HS", 
"Less than HS", "Less than HS", "Less than HS", "Less than HS", 
"Less than HS", "Less than HS", "Less than HS", "Less than HS", 
"Less than HS", "Less than HS", "HS Diploma", "HS Diploma", "HS Diploma", 
"HS Diploma", "HS Diploma", "HS Diploma", "HS Diploma", "HS Diploma", 
"HS Diploma", "HS Diploma", "HS Diploma", "HS Diploma", "HS Diploma", 
"HS Diploma", "HS Diploma", "HS Diploma", "HS Diploma", "Some College", 
"Some College", "Some College", "Some College", "Some College", 
"Some College", "Some College", "Some College", "Some College", 
"Some College", "Some College", "Some College", "Some College", 
"Some College", "Some College", "Some College", "Some College", 
"Some College", "Some College", "Some College", "Some College", 
"Some College", "Some College", "Some College", "Some College", 
"Some College", "Some College", "Some College", "Some College", 
"Some College", "Some College", "Some College", "Some College", 
"Some College", "Some College", "Some College", "Some College", 
"Some College", "Some College", "Some College", "Some College", 
"Some College", "Some College", "Some College", "Some College", 
"Some College", "Some College", "Some College", "Some College", 
"Some College", "Some College", "Some College", "Some College", 
"Some College", "Some College", "Some College", "Some College", 
"Some College", "College Degree"), Ethnicity = c(0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2), `Pre Weight` = c(192.056576386152, 
199.200034264708, 206.876365089032, 195.881563220086, 200.023472577799, 
209.835274224519, 197.638006672991, 188.53439444449, 197.556054379151, 
195.261374235473, 183.834639690234, 206.592420843139, 201.388593429714, 
210.134243322886, 210.711872502929, 191.050579749048, 197.758412402822, 
194.271341504122, 198.923689983916, 198.967602661854, 192.920791894197, 
203.365595032141, 199.845179783937, 196.529509982996, 209.761359590106, 
195.030192050966, 191.221678439819, 188.893717611267, 205.644104360021, 
193.924385191553, 195.469716734282, 205.8901999839, 201.684244009288, 
199.956401891017, 202.022335022251, 204.845240593771, 201.497865807527, 
214.361511032097, 190.574097748526, 197.017952975715, 200.709252046567, 
196.371000972635, 185.010079737753, 197.991679603932, 201.731562744884, 
206.684686806897, 198.528149601247, 190.756862982584, 201.257889946122, 
201.911622232205, 202.823506631306, 192.12975887733, 196.74718730805, 
197.705610551042, 198.324502368836, 199.593768163526, 183.679870976834, 
195.266928974364, 195.89751916776, 209.72831764759, 198.465869677806, 
207.538604561181, 205.284033133852, 195.080391611031, 193.136535406142, 
202.807351731666, 194.04551510654, 201.363628937019, 200.066255208571, 
203.038557790409, 191.260291037383, 200.227987240782, 201.838208956746, 
196.743805124628, 195.478231878427, 202.583284069464, 191.771438428463, 
206.52146661398, 193.483762293385, 190.160595982365, 206.933106189739, 
206.326923423534, 205.130903784651, 198.517406195082, 202.249084556955, 
200.390387751599, 199.138963969221, 204.348338163458, 198.339567011702, 
197.763306621186, 198.144029061426, 191.630341694166, 202.501124865579, 
210.04412604036, 202.621188398029, 207.952265488915, 197.56589056435, 
197.411320802916, 199.215568434214, 195.430133251124), Intervention = c("Placebo Diet", 
"Placebo Diet", "Placebo Diet", "Placebo Diet", "Placebo Diet", 
"Placebo Diet", "Placebo Diet", "Placebo Diet", "Placebo Diet", 
"Placebo Diet", "Placebo Diet", "Placebo Diet", "Placebo Diet", 
"Placebo Diet", "Placebo Diet", "Placebo Diet", "Placebo Diet", 
"Placebo Diet", "Placebo Diet", "Placebo Diet", "Placebo Diet", 
"Placebo Diet", "Placebo Diet", "Placebo Diet", "Placebo Diet", 
"Placebo Diet", "Placebo Diet", "Placebo Diet", "Placebo Diet", 
"Placebo Diet", "Placebo Diet", "Placebo Diet", "Placebo Diet", 
"Adjusted Diet", "Adjusted Diet", "Adjusted Diet", "Adjusted Diet", 
"Adjusted Diet", "Adjusted Diet", "Adjusted Diet", "Adjusted Diet", 
"Adjusted Diet", "Adjusted Diet", "Adjusted Diet", "Adjusted Diet", 
"Adjusted Diet", "Adjusted Diet", "Adjusted Diet", "Adjusted Diet", 
"Adjusted Diet", "Adjusted Diet", "Adjusted Diet", "Adjusted Diet", 
"Adjusted Diet", "Adjusted Diet", "Adjusted Diet", "Adjusted Diet", 
"Adjusted Diet", "Adjusted Diet", "Adjusted Diet", "Adjusted Diet", 
"Adjusted Diet", "Adjusted Diet", "Adjusted Workout", "Adjusted Workout", 
"Adjusted Workout", "Adjusted Workout", "Adjusted Workout", "Adjusted Workout", 
"Adjusted Workout", "Adjusted Workout", "Adjusted Workout", "Adjusted Workout", 
"Adjusted Workout", "Adjusted Workout", "Adjusted Workout", "Adjusted Workout", 
"Adjusted Workout", "Adjusted Workout", "Adjusted Workout", "Adjusted Workout", 
"Adjusted Workout", "Adjusted Workout", "Adjusted Workout", "Adjusted Workout", 
"Adjusted Workout", "Adjusted Workout", "Adjusted Workout", "Adjusted Workout", 
"Adjusted Workout", "Adjusted Workout", "Adjusted Workout", "Adjusted Workout", 
"Adjusted Workout", "Adjusted Workout", "Adjusted Workout", "Adjusted Workout", 
"Adjusted Workout", "Adjusted Workout", "Adjusted Workout"), 
    `Post Weight` = c(183.884925652295, 199.800326915851, 194.671078067971, 
    203.366324901639, 200.073624389392, 198.747637957626, 197.077328200452, 
    205.294385457295, 203.63121523353, 202.319217967146, 194.309724454273, 
    204.37792175237, 201.774254426186, 199.169666236616, 196.435276100077, 
    188.147545420565, 192.339179127361, 197.267082901293, 193.759855326265, 
    194.147504705528, 210.371321306797, 198.317242327379, 193.102086020575, 
    186.002956913784, 187.153649610467, 197.444735638259, 199.397255917051, 
    199.896955043572, 200.289578221986, 200.359694579151, 194.154234966321, 
    201.103945462295, 198.381657289632, 174.026079230011, 195.176185252116, 
    192.193440534233, 189.540944711247, 195.402285634977, 190.26270833789, 
    188.311830833904, 193.327807801019, 186.754787273152, 189.741880512578, 
    184.221094195585, 194.408037395682, 187.552772240146, 178.570551406359, 
    199.402663974673, 189.554024381927, 195.699110599584, 196.260223355843, 
    184.848634171125, 194.972185590508, 186.89522462379, 195.963659870962, 
    189.975194325292, 185.117492173653, 188.429422814501, 191.845467861334, 
    184.460234458733, 181.766015566303, 195.604591362702, 185.29349076713, 
    185.749602360069, 186.493563341777, 194.774700184702, 170.250064507127, 
    192.264929460362, 194.279346537951, 194.095480815304, 192.09355792118, 
    205.814604264742, 201.010346295516, 205.747699560743, 209.566269909556, 
    196.717178873951, 190.261858329177, 210.87666987587, 184.596615831833, 
    200.588227067055, 204.084495230927, 185.477325026295, 206.07568608757, 
    199.725719135313, 193.537903719698, 202.579156443971, 204.824664756597, 
    197.221812009229, 181.948258209741, 198.307839632616, 212.828922990593, 
    183.819634508109, 209.631753528083, 201.93635969481, 191.993820458447, 
    210.423491403344, 196.130759427615, 216.762351151556, 194.786605839181, 
    214.179886420607), `Follow-up Weight` = c(196.754451103698, 
    216.156309357029, 210.484507129149, 180.150482770114, 192.390769421036, 
    203.047307473025, 212.955081274413, 181.630344336736, 194.986069395964, 
    189.119214660604, 210.027861208073, 191.293032053363, 210.267319794366, 
    209.707298431749, 187.987575877924, 174.893785292807, 205.768015398644, 
    198.770255160707, 211.5933289635, 182.237056883605, 175.814034769428, 
    202.397501930318, 180.344375671848, 211.968205626545, 222.363337848219, 
    179.163385432912, 205.026072358305, 188.93023303026, 198.485623109445, 
    219.364188119653, 175.826722220518, 176.65140654135, 198.980842974561, 
    232.273055694532, 202.227960749224, 221.662799554178, 212.652799341595, 
    164.536254992709, 181.760834089073, 197.487373093463, 204.981188794773, 
    219.285266716906, 230.166802389431, 185.610656949575, 202.313856839464, 
    191.789445630275, 198.555966840286, 181.177552399458, 200.24041355573, 
    198.072104290259, 190.301193975029, 186.122213613271, 180.075892835885, 
    207.392077350232, 193.10916223294, 192.415564520343, 208.723270639166, 
    214.108275081526, 206.051982381905, 205.617556553261, 223.241295821208, 
    206.747063707735, 222.621179596172, 193.962939015546, 191.982042502059, 
    183.000486736273, 194.58624415638, 203.504464984871, 192.261302850238, 
    217.979959920922, 206.377081263054, 190.020876339258, 207.567484772153, 
    191.626350492879, 206.334397539831, 214.376621493284, 204.515487717072, 
    195.394387042325, 190.006551797705, 179.74960024294, 223.11257958354, 
    205.544995929085, 191.169158874982, 177.952415974869, 190.496962709585, 
    200.471106886835, 185.645138167456, 214.420277238969, 219.606989098975, 
    183.150109983399, 209.9404815046, 205.259732915874, 195.812441966336, 
    197.740712842642, 181.31540905888, 221.787286641484, 207.900393939053, 
    180.352527018113, 245.932392822579, 213.72185352011), `Race/Ethnicity` = c("Hispanic", 
    "Hispanic", "Hispanic", "Hispanic", "Hispanic", "Hispanic", 
    "Hispanic", "Hispanic", "Hispanic", "Hispanic", "Hispanic", 
    "Hispanic", "Hispanic", "Hispanic", "Hispanic", "Hispanic", 
    "Hispanic", "Hispanic", "Hispanic", "Hispanic", "Hispanic", 
    "Hispanic", "Hispanic", "Hispanic", "Hispanic", "Hispanic", 
    "Hispanic", "Hispanic", "Hispanic", "Hispanic", "Hispanic", 
    "Hispanic", "Hispanic", "Hispanic", "Hispanic", "Hispanic", 
    "Hispanic", "Hispanic", "Hispanic", "Hispanic", "Hispanic", 
    "Hispanic", "Hispanic", "Hispanic", "Hispanic", "Hispanic", 
    "Hispanic", "Hispanic", "Hispanic", "Hispanic", "Hispanic", 
    "Hispanic", "Hispanic", "Hispanic", "Hispanic", "Hispanic", 
    "Hispanic", "Hispanic", "Hispanic", "Hispanic", "Hispanic", 
    "Hispanic", "Hispanic", "Hispanic", "Hispanic", "Hispanic", 
    "Hispanic", "Hispanic", "Hispanic", "Hispanic", "Hispanic", 
    "Hispanic", "Hispanic", "Hispanic", "Hispanic", "Hispanic", 
    "Hispanic", "Hispanic", "Hispanic", "Hispanic", "White", 
    "White", "White", "White", "White", "White", "White", "White", 
    "Black", "Black", "Black", "Black", "Black", "Black", "Black", 
    "Black", "Black", "Black", "Black", "Black"), `Wieght Loss/Gain after Intervention` = c(8.17165073385695, 
    -0.600292651142809, 12.2052870210609, -7.48476168155321, 
    -0.0501518115925137, 11.0876362668932, 0.560678472538712, 
    -16.7599910128047, -6.07516085437965, -7.05784373167262, 
    -10.4750847640389, 2.2144990907691, -0.385660996471415, 10.9645770862699, 
    14.2765964028513, 2.90303432848305, 5.41923327546101, -2.99574139717151, 
    5.16383465765102, 4.82009795632621, -17.4505294125993, 5.04835270476178, 
    6.74309376336168, 10.5265530692122, 22.6077099796385, -2.41454358729243, 
    -8.17557747723185, -11.0032374323055, 5.354526138035, -6.43530938759795, 
    1.31548176796059, 4.78625452160486, 3.30258671965566, 25.9303226610064, 
    6.84614977013553, 12.6518000595388, 11.95692109628, 18.9592253971205, 
    0.311389410635456, 8.70612214181165, 7.38144424554775, 9.61621369948261, 
    -4.73180077482539, 13.770585408347, 7.3235253492021, 19.1319145667512, 
    19.9575981948874, -8.64580099208979, 11.7038655641954, 6.21251163262059, 
    6.56328327546362, 7.28112470620545, 1.77500171754218, 10.8103859272524, 
    2.36084249787382, 9.61857383823371, -1.43762119681924, 6.83750615986355, 
    4.05205130642571, 25.2680831888574, 16.6998541115026, 11.9340131984791, 
    19.9905423667224, 9.33078925096197, 6.64297206436459, 8.03265154696419, 
    23.795450599413, 9.0986994766572, 5.7869086706196, 8.94307697510521, 
    -0.833266883797478, -5.58661702396057, 0.827862661230029, 
    -9.00389443611493, -14.0880380311282, 5.86610519551323, 1.50958009928581, 
    -4.35520326188998, 8.88714646155131, -10.4276310846908, 2.84861095881206, 
    20.8495983972389, -0.944782302918611, -1.20831294023083, 
    8.71118083725742, -2.18876869237283, -5.68570078737685, 7.12652615422849, 
    16.3913088019617, -0.544533011430758, -14.6848939291667, 
    7.8107071860577, -7.13062866250402, 8.10776634555077, 10.6273679395817, 
    -2.47122591442894, 1.43513113673544, -19.3510303486401, 4.42896259503323, 
    -18.7497531694826), `Wieght Loss/Gain on Follow Up` = c(-12.8695254514023, 
    -16.3559824411786, -15.8134290611779, 23.2158421315253, 7.68285496835597, 
    -4.29966951539973, -15.8777530739608, 23.664041120559, 8.64514583756682, 
    13.2000033065415, -15.7181367537996, 13.0848896990065, -8.4930653681804, 
    -10.5376321951335, 8.4477002221538, 13.2537601277581, -13.4288362712832, 
    -1.50317225941399, -17.8334736372344, 11.9104478219233, 34.5572865373688, 
    -4.0802596029389, 12.7577103487274, -25.9652487127605, -35.2096882377518, 
    18.2813502053468, -5.62881644125446, 10.9667220133124, 1.80395511254028, 
    -19.0044935405022, 18.3275127458037, 24.4525389209448, -0.599185684928671, 
    -58.2469764645211, -7.05177549710788, -29.4693590199458, 
    -23.1118546303478, 30.866030642268, 8.50187424881733, -9.17554225955973, 
    -11.6533809937537, -32.5304794437543, -40.4249218768527, 
    -1.38956275398959, -7.90581944378209, -4.236673390129, -19.9854154339264, 
    18.2251115752151, -10.686389173803, -2.37299369067478, 5.95902938081417, 
    -1.273579442146, 14.8962927546236, -20.4968527264427, 2.85449763802171, 
    -2.44037019505049, -23.6057784655131, -25.6788522670249, 
    -14.2065145205706, -21.1573220945284, -41.4752802549046, 
    -11.1424723450327, -37.327688829042, -8.21333665547718, -5.48847916028171, 
    11.7742134484288, -24.3361796492536, -11.2395355245098, 2.01804368771263, 
    -23.8844791056181, -14.2835233418737, 15.7937279254838, -6.55713847663719, 
    14.1213490678638, 3.23187236972444, -17.6594426193333, -14.2536293878948, 
    15.4822828335455, -5.40993596587214, 20.8386268241156, -19.0280843526125, 
    -20.0676709027903, 14.9065272125881, 21.7733031604439, 3.04094101011287, 
    2.10804955713684, 19.1795265891415, -17.1984652297397, -37.658730889234, 
    15.1577296492178, 2.8884414859931, -21.4400984077656, 13.8193115617469, 
    4.19564685216756, 10.6784113995673, -11.3637952381396, -11.7696345114382, 
    36.4098241334432, -51.1457869833976, 0.458032900496619)), row.names = c(NA, 
-100L), class = c("tbl_df", "tbl", "data.frame"))

附加代码块错误照片： ```{r}

renderTable({
   wl_data %>% group_by(across(all_of(input$categorical_variable))) %>% 
    summarise("Average " = mean(.data[[input$numeric_variable]]),
      "Median " = median(.data[[input$numeric_variable]]),
      "Freq " = count(.data[[input$categorical_variable]])
    ) 
})
```

【问题讨论】：

标签： r dplyr shiny r-markdown rshiny

【解决方案1】：

mean(wl_data[[input$numeric_variable]]) 正在从整个数据帧中提取平均值，并且不考虑分组。尝试改用.data。

另外group_by_at 已被取代，我们可以使用across。

renderTable({
  wl_data %>%
    group_by(across(all_of(input$categorical_variable))) %>% 
    summarise(Average = mean(.data[[input$numeric_variable]]),
              Median = median(.data[[input$numeric_variable]])
    )
})

【讨论】：

感谢您的回答 - 效果很好 - 你知道为什么我会在上面提供的方法中使用 tally() 或 count() 时出错吗？
你是如何使用它的？ wl_data %>% count(.data[[input$categorical_variable]]) 应该可以工作。
由于某种原因，我无法让它与摘要一起使用。我添加了一段新的代码和我的错误截图。
count 不是那里的正确功能。对于频率，您可以使用n()。

【解决方案2】：

使用您的数据，这是一个简单的 Shiny 应用程序，它可以满足您的需求：

library(shiny)
library(DT)

categorical_variables = c( "Race/Ethnicity" ,"Gender", "Education", "Intervention")
numeric_variables = c("Age", "Pre Weight", "Post Weight", "Follow-up Weight","Wieght Loss/Gain after Intervention","Wieght Loss/Gain on Follow Up" )

ui <- {
    fluidPage(
        selectInput("categorical_var",
            "Select categorical variable",
            choices = categorical_variables
        ),
        selectInput("numeric_var",
            "Select numeric variable",
            choices = numeric_variables
        ),
        DTOutput("tbl")
    )
}

server <- function(input, output, session) {
    
    output$tbl <- renderDT({
        dat %>%
            group_by(!!sym(input$categorical_var)) %>%
            summarise(
                mean = mean(!!sym(input$numeric_var)),
                median = median(!!sym(input$numeric_var))
            )
    })
}

shinyApp(ui, server)

几点：

group_by_at 已弃用，不要再使用它了
你让你的group_by/summarise 变得比它需要的更复杂——它可以为你完成工作:)

【讨论】：