Can't run dplyr::summarize function with descriptive functions : "missing values and NaN's not allowed if 'na.rm' is FALSE"

Question

I am trying to run a few descriptive variables with dplyr's group by/ summarize_all, but can't find a way to pass "na.rm = TRUE".

The code is based on something I've found here to create Descriptive Statistics grouped by a variable:

df_grouped <-  df %>%  group_by (country, election) %>% 
  summarise_all(list(min=min, Q1=~quantile(., probs = 0.25),
                     median=median, Q3=~quantile(., probs = 0.75),
                     max=max), na.rm = TRUE))

The error:

Problem with `summarise()` input `cong_LH_all_Q1`.
x missing values and NaN's not allowed if 'na.rm' is FALSE
i Input `cong_LH_all_Q1` is `(structure(function (..., .x = ..1, .y = ..2, . = ..1) ...`.
i The error occurred in group 2: country = "Argentina", election = "ARG_2015".
Run `rlang::last_error()` to see where the error occurred.

There seems to be a solution here but I couldn't find a way to adapt it to my case.

A sample of the dataset I've used:

df<- structure(list(country = c("Norway", "Denmark", "Australia", 
"Mexico", "New Zealand", "Hong Kong", "Great Britain", "Taiwan", 
"Republic of Korea", "Hungary", "Peru", "Russian Federation", 
"Netherlands", "Sweden", "Austria", "Ireland", "France", "Japan", 
"Great Britain", "Canada", "Australia", "Denmark", "Australia", 
"Switzerland", "Brazil", "Serbia", "Switzerland", "Brazil", "Mexico", 
"Ireland", "Finland", "South Africa", "Peru", "Australia", "Romania", 
"Kenya", "Denmark", "New Zealand", "Philippines", "United States of America", 
"Spain", "Norway", "Ukraine", "Hungary", "Hungary", "Canada", 
"Japan", "Turkey", "Latvia", "Taiwan", "Ireland", "Chile", "New Zealand", 
"Philippines", "Czech Republic", "Japan", "Iceland", "Netherlands", 
"Netherlands", "Republic of Korea", "Hong Kong", "Peru", "Romania", 
"New Zealand", "Peru", "Uruguay", "United States of America", 
"Canada", "Albania", "Mexico", "Sweden", "Great Britain", "Spain", 
"Slovenia", "Switzerland", "Brazil", "Turkey", "Bulgaria", "Czech Republic", 
"Finland", "Norway", "Ireland", "Canada", "United States of America", 
"Iceland", "Argentina", "Switzerland", "Ireland", "Norway", "Uruguay", 
"United States of America", "New Zealand", "Russian Federation", 
"Israel", "Australia", "New Zealand", "Finland", "Canada", "Canada", 
"New Zealand", "Taiwan", "Mexico", "Hong Kong", "New Zealand", 
"Republic of Korea", "Japan", "Canada", "Hungary", "Mexico", 
"Czech Republic", "Bulgaria", "Portugal", "Australia", "Slovenia", 
"Sweden", "Belgium", "Norway", "Peru", "Ireland", "Iceland", 
"Argentina", "Republic of Korea", "Greece", "Thailand", "Czech Republic", 
"Great Britain", "Brazil", "Romania", "Canada", "Republic of Korea"
), election = c("NOR_1997", "DNK_2001", "AUS_2004", "MEX_2000", 
"NZL_2014", "HKG_1998", "GBR_2015", "TWN_1996", "KOR_2000", "HUN_1998", 
"PER_2016", "RUS_1999", "NLD_2010", "SWE_2014", "AUT_2008", "IRL_2002", 
"FRA_2007", "JPN_2004", "GBR_1997", "CAN_2015", "AUS_2007", "DNK_2001", 
"AUS_2007", "CHE_2011", "BRA_2014", "SRB_2012", "CHE_1999", "BRA_2002", 
"MEX_2009", "IRL_2011", "FIN_2015", "ZAF_2014", "PER_2016", "AUS_2004", 
"ROU_2004", "KEN_2013", "DNK_1998", "NZL_1996", "PHL_2016", "USA_2008", 
"ESP_1996", "NOR_2001", "UKR_1998", "HUN_1998", "HUN_1998", "CAN_2011", 
"JPN_2004", "TUR_2015", "LVA_2011", "TWN_2001", "IRL_2002", "CHL_2005", 
"NZL_2014", "PHL_2016", "CZE_2010", "JPN_2004", "ISL_2013", "NLD_2006", 
"NLD_2010", "KOR_2000", "HKG_2000", "PER_2001", "ROU_2004", "NZL_2002", 
"PER_2016", "URY_2009", "USA_2004", "CAN_2015", "ALB_2005", "MEX_2012", 
"SWE_2006", "GBR_2015", "ESP_2000", "SVN_2008", "CHE_2007", "BRA_2002", 
"TUR_2011", "BGR_2014", "CZE_2013", "FIN_2011", "NOR_1997", "IRL_2011", 
"CAN_2004", "USA_2008", "ISL_2009", "ARG_2015", "CHE_2011", "IRL_2002", 
"NOR_2005", "URY_2009", "USA_2004", "NZL_1996", "RUS_1999", "ISR_2013", 
"AUS_2004", "NZL_2008", "FIN_2007", "CAN_2015", "CAN_2011", "NZL_2011", 
"TWN_2001", "MEX_2009", "HKG_2008", "NZL_1996", "KOR_2000", "JPN_2007", 
"CAN_2008", "HUN_1998", "MEX_2006", "CZE_2010", "BGR_2014", "PRT_2005", 
"AUS_2013", "SVN_2004", "SWE_2014", "BEL_2003", "NOR_2001", "PER_2011", 
"IRL_2007", "ISL_2009", "ARG_2015", "KOR_2000", "GRC_2012", "THA_2011", 
"CZE_2006", "GBR_1997", "BRA_2006", "ROU_2004", "CAN_2015", "KOR_2004"
), cong_LH_all = c(-4999996.5, -4999996, -9999990, NA, -1, -4999995, 
NA, -4999995.5, -4999996.5, -2, -4999995, 0, -4999997, -4999997.5, 
-9999991, -9999987, -4999996, NA, -4999997, NA, -4999996.5, -4999997, 
-4999995, NA, NA, -9999995, -4999997.5, NA, -4999996.5, -4999993, 
-9999993, -4999997, -4999995, -9999988, NA, NA, -4999996.5, 0, 
-9999993, -9999985, -4999995.5, -4999998, NA, NA, NA, -9999989, 
NA, -4999995, -4999995.5, NA, -4999994.5, -4999995, -7, -9999983, 
-9999991, NA, -4999995, -4999994.5, -4999994, -4999997.5, -4999994.5, 
-4999995, -9999986.5, NA, -9999990, -4999995.5, -4999994.5, -4999997.5, 
0, -4999994.5, NA, -4999995.5, -9999990, -9999992, -4999996, 
NA, -4999997, -4999997.5, -9999992, -4999993.5, -7889997.5, -9999989.5, 
-4999993.5, -4999997.5, -9999989, NA, NA, -4999996, -9999989.5, 
-4999992.5, -4999995, -2, -8, -4999996, -4999995, -2, -4999996.5, 
NA, NA, NA, NA, NA, -4999996, -2.5, NA, -9999990, -4999995, -1, 
-4999997.5, -4999994, -4999997.5, -9999991, -4999995, -9999991.5, 
-4999996, -9999985, -4999996.5, -4999997, -9999992, -9999991, 
-9999990, -4999994, NA, NA, -4999997.5, -9999990, NA, -8210003.5, 
NA, NA), exp_cong_LH_all = c(-9999991, -4999996, -4999995.5, 
NA, -2, -4999995.5, NA, -4999995.5, -4999996.5, -2.5, -4999996, 
-5, -4999996.5, -4999996.5, -9999991, -9999987, -4999997, NA, 
-4999996.5, NA, -4999996, -4999997.5, -4999997, NA, NA, -9999995, 
-4999998, NA, -4999995.5, -4999994.5, -9999993, -4999996, -4999994.5, 
-9999988, NA, NA, -4999997, -1, -9999993, -9999985, -4999994.5, 
-4999995.5, -7, NA, NA, -4999996, NA, -4999995.5, -4999994.5, 
NA, -4999994.5, -4999994, -1, -9999983, -9999991, NA, -4999995.5, 
-4999994, -4999994, -4999999, -4999996, -9999990, -9999986.5, 
NA, -4999996.5, -4999996, -4999996, -4999996, -3.5, -4999994.5, 
NA, -4999996, -9999990, -9999992, -4999996.5, NA, -4999998, -4999999, 
-9999992, -4999994.5, -7889997.5, -9999989.5, -4999995, -4999996.5, 
-9999989, NA, NA, -4999996.5, -9999989.5, -4999993.5, -4999994, 
-4, -3, -4999996, -4999995.5, -1, -4999996.5, NA, NA, -1, NA, 
NA, -4999996.5, -2.5, NA, -9999990, -4999996, 0, -4999996, -4999994, 
-4999999, -9999991, -4999995.5, -9999991.5, -4999996.5, -4999994.5, 
-4999996.5, -4999997, -9999992, -9999991, -4999995.5, -4999994.5, 
NA, NA, -4999998, -9999990, NA, -8210003.5, NA, NA), meanv_cong_LH_all = c(-9999991, 
-4999995.80586283, -4999996.01771117, NA, -1.35323866282033, 
-4999995.50472973, NA, -4999995.27892562, -4999996.22057143, 
-1.80247213606773, -4999994.89092873, -3.12671232876712, -4999996.73983096, 
-4999996.592711, -9999991, -9999987, -4999996.59302922, NA, -4999996.48278034, 
NA, -4999995.67434421, -4999997.98937865, -4999995.6932351, NaN, 
NA, -9999995, -4999998.14614499, NA, -4999995.9920279, -4999994.09825175, 
-9999993, -4999996.26961679, -4999994.98146718, -9999988, NA, 
NA, -4999996.820478, -0.825446568473833, -9999993, -9999985, 
-4999995.41772152, -4999995.72854134, NaN, NaN, NaN, -4999995.78947368, 
NA, -4999996.48796389, -4999994.64504881, NA, -4999994.53212851, 
-4999993.34686347, -2.08479532163743, -9999983, -9999991, NA, 
-4999995.38550247, -4999994.09418932, -4999993.83119219, -4999998.33127109, 
-4999994.83909574, -9999990, -9999986.5, NA, NaN, -4999996.33812155, 
-4999995.45712695, -4999996.19116398, -1.70591908091908, -4999994.74283207, 
NA, -4999994.86858407, -9999990, -9999992, -4999996.55981541, 
NA, -4999997.44994438, -4999998.0552546, -9999992, -4999994.48720137, 
-7889997.5, -9999989.5, -4999994.96545455, -4999995.10490463, 
-9999989, NA, NaN, -4999995.68173015, -9999989.5, -4999993.66206897, 
-4999994.15760266, -3.02943425076453, -4.87328767123288, -4999996.23019518, 
-4999995.34304775, -1.33935018050542, -4999996.56598361, NA, 
NA, -0.709143968871595, NA, NA, -4999995.17271157, -2.24531186956095, 
NA, -9999990, -4999995.65860401, -0.523975588491718, -4999996.05606061, 
-4999993.63230951, -4999998.0552546, -9999991, -4999995.46795612, 
-9999991.5, -4999996.36323155, NaN, -4999996.11256544, -4999995.59288194, 
-9999992, -9999991, NaN, -4999994.77942857, NA, NA, -4999998.02211435, 
-9999990, NA, -8210003.5, NA, NaN), voter_exp_dif_LH_all = c(0, 
0, 0, 0, 1, 0.5, 0, 2, 0, 4.5, 1, 5, 0.5, 1, 0, 0, 1, 0, 0.5, 
0, 0.5, 0.5, 2, NA, 0, 0, 0.5, 0, 2, 1.5, 0, 1, 0.5, 0, 0, 0, 
0.5, 1, 0, 0, 1, 2.5, NA, NA, NA, 0, 0, 0.5, 2, 0, 0, 1, 6, 0, 
0, 0, 0.5, 0.5, 1, 1.5, 1.5, 0, 0, 4, 0, 0.5, 2.5, 1.5, 3.5, 
2, 0, 0.5, 0, 0, 0.5, 0, 1, 1.5, 0, 1, 0, 0, 1.5, 4, 0, 0, NA, 
1.5, 0, 1, 1, 2, 5, 0, 0.5, 3, 0, 0, 0, NA, 0, 0, 2.5, 1, 2.5, 
0, 1, 1, 1.5, 0, 1.5, 0, 0.5, 0, 0.5, 0, 0, 0, 0, 0, 0, 0.5, 
0, NA, 0.5, 0, 0, 0, 0, NA), voter_meanv_dif_LH_PL = c(0.234183673469388, 
0.388274336283186, 0, 0, 0.179138321995465, 1.00945945945946, 
0, 0, 0, 4.12891986062718, 0.218142548596112, 3.12671232876712, 
1.4796619123085, 1.81457800511509, NaN, 0, 0, 0, 0, 0, 0, 1.97875730217738, 
0, NaN, NA, NaN, 1.29228998849252, 0, 0, 0, NaN, 4.53923357664234, 
0.0370656370656368, 0, 0, 0, 0.640956002172732, 0.825446568473833, 
0, 0, 0.164556962025316, 4.54291732490785, NaN, NaN, NaN, 0, 
0, 2.97592778335005, 1.70990237099024, 0, 0, 3.30627306273063, 
4.91520467836257, 0, 0, 0, 0.771004942339374, 0.811621368322399, 
1.66238437821172, 0, 0.678191489361702, 0.101661779081134, 0, 
6.04143862392494, NaN, 1.67624309392265, 0, 0, 2.25, 0, 0, 0, 
NaN, NaN, 1.11963081292155, 0, 0.899888765294772, 1.11050920910076, 
0, 1.97440273037543, 0, 0, 0, 0, 0, NA, NaN, 0, 0, 2.32413793103448, 
0, 1.02943425076453, 3.12671232876712, 0.460390355912744, 0, 
3.33935018050542, 0.131967213114754, 0, 0, NA, 0, 0, 0, 0.316070307595731, 
0, 0, 0, 1.52397558849172, 0, 0.735380980507975, 1.11050920910076, 
0, 0, 0, 0.7264631043257, NaN, 0.774869109947644, 2.81423611111111, 
0, 0, NaN, 0, NaN, NA, 1.04422869471413, 0, 0, 0, 0, NaN), cong_closest = c(-1, 
-1, NA, NA, 0, 0, NA, -1, -2, -2, 0, 0, 0, 0, -2, 0, 0, NA, -2, 
NA, -2, 0, 0, 0, NA, -1, 0, NA, 0, 0, -1, -3, 0, 0, NA, NA, -1, 
0, 0, 0, -1, 0, 0, 0, -1, NA, NA, 0, 0, NA, 0, 0, 0, 0, 0, NA, 
0, 0, -1, -2, 0, 0, -1, NA, 0, 0, -1, -2, 0, -2, NA, -1, -1, 
0, 0, NA, 0, 0, 0, 0, 0, -1, 0, -5, 0, NA, NA, -1, -1, 0, -4, 
0, 0, -1, 0, 0, 0, NA, NA, NA, NA, NA, 0, 0, NA, 0, 0, -1, -5, 
-1, 0, NA, 0, -1, 0, NA, 0, 0, -1, NA, 0, 0, NA, NA, 0, -1, NA, 
0, NA, 0), cong_closest_exp = c(NA, 0, -1, NA, -1, -1, NA, -1, 
-1, 0, 0, -2, 0, -1, 0, -1, 0, NA, 0, NA, -1, -1, -2, 0, NA, 
-3, -1, NA, -1, -3, 0, 0, -1, -1, NA, NA, 0, 0, 0, -2, 0, 0, 
-1, -2, 0, -1, NA, -1, 0, NA, 0, -1, -1, -3, 0, NA, 0, 0, -1, 
-1, 0, NA, -1, NA, 0, 0, 0, 0, -3, -1, NA, -1, NA, 0, 0, NA, 
-2, -3, -1, -1, NA, -1, -1, -3, 0, NA, 0, 0, -1, -2, -2, -1, 
-1, 0, 0, 0, 0, NA, NA, -1, NA, NA, 0, -1, NA, 0, 0, 0, 0, -1, 
-3, 0, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, NA, NA, -1, -1, NA, -1, 
NA, 0), cong_closest_meanv = c(-0.146556188503366, -0.378645833333334, 
-0.594634873323398, NaN, -0.5273390036452, -0.106280193236715, 
NA, -0.202380952380953, -0.213114754098361, -1.01231310466139, 
-0.679217958001448, -1.90327613104524, -0.365552699228792, -1.14507772020725, 
-0.200808625336927, -1.55851358846367, -0.429164504411002, NaN, 
-0.965560688786224, NaN, -0.348688419705694, -0.222043010752688, 
-0.651311580294306, -0.297255706591434, NA, -3.60599078341014, 
-2.02916666666667, NaN, -0.394220283533261, -2.1965034965035, 
-0.217857142857143, -0.306077348066298, -0.218142548596112, -0.0354223433242504, 
NaN, NA, -0.535480349344978, -0.323232323232323, -0.33271719038817, 
-4.34629929767693, -0.237392373923739, -0.438716465018412, -1.80363636363636, 
-0.930591259640103, -1.01231310466139, -0.949536560247168, NaN, 
-2.12789415656009, -0.0535475234270413, NaN, -0.0642570281124497, 
-0.965105601469238, -2.08479532163743, -3.90884955752212, -0.355878634639697, 
NaN, -0.506517690875233, -0.188378631677601, -0.2328125, -2.21311475409836, 
-0.0964705882352943, -0.0314341846758346, -0.135678391959799, 
NA, -0.0370656370656368, -0.98132183908046, -0.315205327413985, 
-0.149914821124361, -1.16183816183816, -0.949636261891438, NA, 
-0.73716814159292, -0.539143279172821, -0.0751104565537557, -0.202718006795017, 
NA, -0.899888765294772, -1.11050920910076, -0.841677943166441, 
-0.136026380873867, -0.2, -0.196503496503497, -1.7219730941704, 
-0.209809264305177, -0.528112449799197, NA, -0.698352344740178, 
-0.363460296965784, -0.852008456659619, -2.20134983127109, -2.31520532741398, 
-0.437139952558455, -0.107908351810791, -0.0410958904109586, 
-0.0642750373692076, -0.255223880597015, -0.131967213114754, 
NA, NA, -0.709143968871595, NA, NA, -0.260299625468165, -0.0294342507645258, 
NaN, -0.514563106796117, -0.000625390869293696, -0.128919860627177, 
-0.276595744680851, -0.106004901960785, -1.11050920910076, -0.321428571428571, 
-0.935912240184757, -0.343023255813954, -0.625348189415042, NaN, 
-0.225130890052356, -0.279111111111111, -0.130154639175258, -0.528112449799197, 
-0.087478559176672, -0.442857142857143, NA, NA, -1.04422869471413, 
-0.28648892905558, NA, -0.135678391959799, NA, -0.161317567567568
), dif_cls_LH_all = c(4999995.5, 4999995, NA, NA, 1, 4999995, 
NA, 4999994.5, 4999994.5, 0, 4999995, 0, 4999997, 4999997.5, 
9999989, 9999987, 4999996, NA, 4999995, NA, 4999994.5, 4999997, 
4999995, NA, NA, 9999994, 4999997.5, NA, 4999996.5, 4999993, 
9999992, 4999994, 4999995, 9999988, NA, NA, 4999995.5, 0, 9999993, 
9999985, 4999994.5, 4999998, NA, NA, NA, NA, NA, 4999995, 4999995.5, 
NA, 4999994.5, 4999995, 7, 9999983, 9999991, NA, 4999995, 4999994.5, 
4999993, 4999995.5, 4999994.5, 4999995, 9999985.5, NA, 9999990, 
4999995.5, 4999993.5, 4999995.5, 0, 4999992.5, NA, 4999994.5, 
9999989, 9999992, 4999996, NA, 4999997, 4999997.5, 9999992, 4999993.5, 
7889997.5, 9999988.5, 4999993.5, 4999992.5, 9999989, NA, NA, 
4999995, 9999988.5, 4999992.5, 4999991, 2, 8, 4999995, 4999995, 
2, 4999996.5, NA, NA, NA, NA, NA, 4999996, 2.5, NA, 9999990, 
4999995, 0, 4999992.5, 4999993, 4999997.5, NA, 4999995, 9999990.5, 
4999996, NA, 4999996.5, 4999997, 9999991, NA, 9999990, 4999994, 
NA, NA, 4999997.5, 9999989, NA, 8210003.5, NA, NA), knowledge_adj = c(1, 
NaN, 1, 0, 0.75, 1, 0.75, 1, NaN, 0.666666666666667, NaN, NaN, 
0.333333333333333, 0.5, 0.333333333333333, 0.333333333333333, 
1, 0.333333333333333, 1, NaN, 1, NaN, 0.333333333333333, 0.25, 
0.5, 0.5, 0.666666666666667, 0, 0.666666666666667, 1, 0.75, 0, 
NaN, 0.333333333333333, 0.666666666666667, 0, NaN, 1, 0, 0, 0.333333333333333, 
0.666666666666667, 0.333333333333333, 0.666666666666667, 0, 1, 
0, 0.75, 0.666666666666667, 0.333333333333333, 1, 0.5, 0.5, 0, 
0.666666666666667, 1, 0.5, 0.333333333333333, 0.333333333333333, 
NaN, 0.333333333333333, NaN, 1, 0.333333333333333, NaN, NaN, 
0, 1, 0.666666666666667, 0.25, NaN, 1, 0.333333333333333, NaN, 
1, 0.333333333333333, NaN, 0.25, 0.5, 0.333333333333333, 1, 0.666666666666667, 
0.666666666666667, 0, 0.666666666666667, 0, 0, 1, 0, NaN, 1, 
1, NaN, 0.5, 0.666666666666667, 0, 1, NaN, NaN, 1, 0, 0.666666666666667, 
0.333333333333333, 0.666666666666667, NaN, 0.666666666666667, 
NaN, 0.666666666666667, 1, 0.333333333333333, 0.25, 0.666666666666667, 
0, 1, 0.75, 0.666666666666667, 0.666666666666667, 0.333333333333333, 
1, 0.666666666666667, 0.5, NaN, 0.5, 0, 0.666666666666667, 0, 
0.666666666666667, 1, NaN, 1)), row.names = c(NA, -130L), class = "data.frame")

EDIT: took off an excerpt for "selecting variables" that was pointless here, and corrected group variable names.

Sorry, I was going to use generic names but then I've put a sample from my real dataset and forgot to change it. Please see edited version. — Guilherme Pires Arbache, May 02 '21 at 16:54

score 2 · Answer 1 · answered May 02 '21 at 03:04

2

The variables used in your code and data do not match but in general you can use :

library(dplyr)
  
df %>% 
  select(var1:var10)  %>%  
  group_by(var1, var2) %>% 
  summarise(across(.fns = list(min=~min(., na.rm = TRUE), 
                               Q1=~quantile(., probs = 0.25),
                               median=median(., na.rm = TRUE), 
                               Q3=~quantile(., probs = 0.75),
                               max=~max(., na.rm = TRUE))))

answered May 02 '21 at 03:04

Ronak Shah

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Oh, sorry, I wrote generic var names but then decided it was better to have a real sample of my dataset. Forgot to change the names. I will edit this right now – Guilherme Pires Arbache May 02 '21 at 16:51
@GuilhermePiresArbache btw, `summarise_all` has been deprecated and it is now recommended to use `across`, hence I used that in my answer. You can achieve the same result with `summarise_all`. – Ronak Shah May 03 '21 at 00:16

pete_a_dunham · Answer 2 · 2021-05-02T17:23:30.070

1

Inclusion of dataset in your question was very helpful. The na.rm = TRUE needs to go in the quantile function.

This code worked for me against the sample set:

df_grouped <-  df %>% select (country:cong_closest_meanv)  %>%  
  group_by (country, election) %>% 
  select_if(is.numeric) %>%
  summarise_all(list(
                  min=min, 
                  Q1=~quantile(., probs = 0.25, na.rm = TRUE),
                  median=median, 
                  Q3=~quantile(., probs = 0.75, na.rm = TRUE),
                  max=max
                  ))

edited May 02 '21 at 17:23

answered May 02 '21 at 03:11

pete_a_dunham

63
7

It works fine but min, median and max have too many NAs, I guess I should find a way to run na.rm for them as well. – Guilherme Pires Arbache May 02 '21 at 17:00
@GuilhermePiresArbache see updated post. Does adding select_if help? – pete_a_dunham May 02 '21 at 17:23
Didn't work for me. What seems to work is a solution that was based on your but mixed with the one by Ronak Shak below: I've assigned "na.rm = T" individually to all functions, like this: `summarise_all(list( min=~min(., na.rm = TRUE), Q1=~quantile(., probs = 0.25, na.rm = TRUE), median=~median(., na.rm = TRUE), Q3=~quantile(., probs = 0.75, na.rm = TRUE), max=~max(., na.rm = TRUE) ))` – Guilherme Pires Arbache May 02 '21 at 23:15

Can't run dplyr::summarize function with descriptive functions : "missing values and NaN's not allowed if 'na.rm' is FALSE"

2 Answers2