0

I am working with the plot scripted below:


library(tidyverse)

stationary <- dat %>%
  dplyr::filter(predicted == "Stationary") %>%
  ggplot(aes(x = hour, y = Y, fill = data, group = data)) + 
  geom_col(position = position_dodge()) +  
  geom_errorbar(aes(ymin = YMIN, ymax = YMAX),
                position = position_dodge(0.9)) +
  scale_fill_manual(values = c("lgcanet" = "gray80", "snpnet" = "gray40" )) +
  labs(title = "a",
       x = "Hour of the day", 
       y = "Proportion",
       fill = "Behavioural\n category") +
  theme(axis.text=element_text(size=22),
        axis.title=element_text(size=22),
        legend.position = "none")  + 

  ylim(0, 0.9)+ theme(legend.position = "none")+  
  theme(plot.title = element_text(size=22))

stationary

My question is straighforward. When I visualize the dataset dat, I have no missing values. However, when I plot stationary, there's a missing bar in position 15 of the X axis.

I hope somebody is able to let me know the reason behind this? I suspect this is due to filter, but why does it only happen when plotting with "Stationary", and not with other categories like "Moving" in my dataset?

I hope there is a fix.

dputed dataset below:

> dput(dat)
structure(list(hour = c(0L, 0L, 0L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 
3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L, 
8L, 9L, 9L, 9L, 10L, 10L, 10L, 11L, 11L, 11L, 12L, 12L, 12L, 
13L, 13L, 13L, 14L, 14L, 14L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 
17L, 17L, 18L, 18L, 18L, 19L, 19L, 19L, 20L, 20L, 20L, 21L, 21L, 
21L, 22L, 22L, 22L, 23L, 23L, 23L, 0L, 0L, 0L, 1L, 1L, 1L, 2L, 
2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 
7L, 8L, 8L, 8L, 9L, 9L, 9L, 10L, 10L, 10L, 11L, 11L, 11L, 12L, 
12L, 12L, 13L, 13L, 13L, 14L, 14L, 14L, 15L, 15L, 15L, 16L, 16L, 
16L, 17L, 17L, 17L, 18L, 18L, 18L, 19L, 19L, 19L, 20L, 20L, 20L, 
21L, 21L, 21L, 22L, 22L, 22L, 23L, 23L, 23L), predicted = c("Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary", "Feeding", 
"Moving", "Stationary", "Feeding", "Moving", "Stationary"), Y = c(0.154681, 
0.674219792, 0.26018171, 0.123956169, 0.770734667, 0.196624589, 
0.100965488, 0.817486337, 0.150570688, 0.210191433, 0.279995373, 
0.522647236, 0.234739907, 0.139925966, 0.686217363, 0.24114481, 
0.184914944, 0.627808135, 0.234325872, 0.254871562, 0.546399513, 
0.248319131, 0.261282084, 0.508018619, 0.227005233, 0.251133647, 
0.549151992, 0.217179979, 0.226309486, 0.590163933, 0.225626231, 
0.228739084, 0.583517505, 0.219801659, 0.252196842, 0.554826957, 
0.213678598, 0.216436233, 0.592714024, 0.181927787, 0.176007486, 
0.673224042, 0.153096459, 0.103211711, 0.791985426, 0.097062644, 
0.076794171, 0.907589555, 0.173318656, 0.291811127, 0.55956284, 
0.114016498, 0.771948996, 0.18684528, 0.10592056, 0.758283747, 
0.196845864, 0.153399026, 0.637745394, 0.288967977, 0.154103192, 
0.545776799, 0.414282403, 0.17916971, 0.477163312, 0.428690807, 
0.188625532, 0.410643344, 0.460762284, 0.185673466, 0.556603437, 
0.360123553, 0.186468006, 0.619848556, 0.102946434, 0.117420501, 
0.710255217, 0.085296176, 0.122578887, 0.705326855, 0.082978251, 
0.282820082, 0.369538165, 0.225187701, 0.340835793, 0.290042658, 
0.254104054, 0.303040651, 0.361150303, 0.219680679, 0.281013065, 
0.38755169, 0.215007656, 0.267808505, 0.437847187, 0.188066206, 
0.256738674, 0.418523792, 0.191317998, 0.282095454, 0.378921649, 
0.192197946, 0.296221256, 0.343671498, 0.21204071, 0.264178641, 
0.37476084, 0.215286911, 0.285715162, 0.343315118, 0.225349727, 
0.274045769, 0.354524621, 0.233979875, 0.277640649, 0.2921432, 
0.289202238, 0.323114441, 0.221145203, 0.357155217, 0.240638977, 
0.470048865, 0.150795243, 0.146938436, 0.673705726, 0.071591529, 
0.143295364, 0.666298039, 0.079103593, 0.218088342, 0.568232731, 
0.110925072, 0.267305927, 0.482546431, 0.151432786, 0.306767296, 
0.476052652, 0.142276041, 0.264867497, 0.502361776, 0.148534296, 
0.238832836, 0.539938351, 0.14438792), YMIN = c(0.111622584, 
0.57558919, 0.178703401, 0.088143131, 0.687337528, 0.1217464, 
0.0733921, 0.755478814, 0.093856058, 0.1693268, 0.216235582, 
0.452992291, 0.189585149, 0.075280623, 0.621341306, 0.199077922, 
0.128682591, 0.559547066, 0.191776222, 0.188045367, 0.470199491, 
0.205963272, 0.201685928, 0.445531582, 0.186592457, 0.191004511, 
0.485288287, 0.18087768, 0.172783057, 0.524384284, 0.187255536, 
0.166292047, 0.511705373, 0.180626825, 0.196842022, 0.488278071, 
0.180383614, 0.170865092, 0.531781636, 0.147661097, 0.124588753, 
0.61272206, 0.119970955, 0.069121836, 0.738342094, 0.070756358, 
0.040137355, 0.861936686, 0.142587275, 0.228164929, 0.488514426, 
0.082217165, 0.689415552, 0.114563802, 0.077481332, 0.682770968, 
0.13755071, 0.113970229, 0.536293985, 0.20134563, 0.112932108, 
0.431542955, 0.318313417, 0.139101201, 0.377559378, 0.339132576, 
0.142921212, 0.308316469, 0.368743688, 0.132494556, 0.452613721, 
0.277533897, 0.128852501, 0.512838079, 0.068314237, 0.07729792, 
0.602868608, 0.048590748, 0.075040937, 0.604890563, 0.013469543, 
0.227039126, 0.290705926, 0.153093633, 0.278753614, 0.216026099, 
0.184689256, 0.233381948, 0.285383003, 0.165621149, 0.209569197, 
0.304152031, 0.150927274, 0.206967076, 0.356326729, 0.133252296, 
0.201372373, 0.337442795, 0.144078897, 0.222104493, 0.301436677, 
0.145942262, 0.234941302, 0.272937178, 0.165504544, 0.207677165, 
0.300390926, 0.159293156, 0.227882598, 0.281692727, 0.167766199, 
0.210148377, 0.278131075, 0.176383174, 0.215768541, 0.212191421, 
0.225708559, 0.252433463, 0.15739698, 0.299506967, 0.190150005, 
0.387794877, 0.114610831, 0.106533711, 0.58181318, 0.047868642, 
0.100032326, 0.580337524, 0.041469188, 0.149052706, 0.476381247, 
0.069468115, 0.196848657, 0.403074576, 0.100976953, 0.223306634, 
0.39000654, 0.0915594, 0.195878551, 0.409341697, 0.083305359, 
0.172234465, 0.439986563, 0.08055391), YMAX = c(0.197739415, 
0.772850394, 0.341660019, 0.159769208, 0.854131807, 0.271502778, 
0.128538875, 0.87949386, 0.207285318, 0.251056066, 0.343755165, 
0.592302181, 0.279894666, 0.204571309, 0.751093421, 0.283211698, 
0.241147296, 0.696069203, 0.276875523, 0.321697757, 0.622599535, 
0.29067499, 0.320878239, 0.570505655, 0.267418009, 0.311262784, 
0.613015698, 0.253482278, 0.279835915, 0.655943582, 0.263996927, 
0.291186121, 0.655329638, 0.258976493, 0.307551662, 0.621375843, 
0.246973583, 0.262007374, 0.653646412, 0.216194476, 0.22742622, 
0.733726024, 0.186221963, 0.137301586, 0.845628758, 0.12336893, 
0.113450987, 0.953242423, 0.204050036, 0.355457325, 0.630611254, 
0.145815831, 0.854482441, 0.259126758, 0.134359788, 0.833796525, 
0.256141017, 0.192827823, 0.739196803, 0.376590324, 0.195274276, 
0.660010642, 0.510251389, 0.219238218, 0.576767247, 0.518249038, 
0.234329851, 0.51297022, 0.552780881, 0.238852376, 0.660593152, 
0.442713209, 0.24408351, 0.726859034, 0.137578631, 0.157543082, 
0.817641826, 0.122001604, 0.170116836, 0.805763147, 0.15248696, 
0.338601039, 0.448370404, 0.297281769, 0.402917971, 0.364059216, 
0.323518852, 0.372699353, 0.436917603, 0.27374021, 0.352456934, 
0.47095135, 0.279088038, 0.328649933, 0.519367645, 0.242880117, 
0.312104975, 0.499604789, 0.238557099, 0.342086415, 0.456406621, 
0.23845363, 0.357501211, 0.414405819, 0.258576876, 0.320680116, 
0.449130755, 0.271280665, 0.343547727, 0.404937508, 0.282933255, 
0.337943161, 0.430918168, 0.291576576, 0.339512757, 0.372094979, 
0.352695917, 0.39379542, 0.284893427, 0.414803466, 0.29112795, 
0.552302853, 0.186979655, 0.187343161, 0.765598272, 0.095314417, 
0.186558401, 0.752258555, 0.116737998, 0.287123977, 0.660084216, 
0.152382029, 0.337763196, 0.562018286, 0.20188862, 0.390227957, 
0.562098763, 0.192992682, 0.333856444, 0.595381856, 0.213763233, 
0.305431207, 0.639890139, 0.20822193), data = c("lgcanet", "lgcanet", 
"lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", 
"lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", 
"lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", 
"lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", 
"lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", 
"lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", 
"lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", 
"lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", 
"lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", 
"lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", 
"lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", "lgcanet", 
"lgcanet", "lgcanet", "lgcanet", "lgcanet", "snpnet", "snpnet", 
"snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", 
"snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", 
"snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", 
"snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", 
"snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", 
"snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", 
"snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", 
"snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", 
"snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", 
"snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet", "snpnet"
)), class = "data.frame", row.names = c(NA, -144L))
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1 Answer 1

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It's because of the setting your pass on ylim.

Because for x = 15 and data = lgcanet y is 0.90758956. So, when you pass ylim(0,0.9) ggplot do not plot anything that is out of this value and so it remove your column (it's like if you were doing filter(y >=0 & y <= 0.9) with dplyr before plotting).

When you plot stationary, you can notice the warning message in the console indicating that ggplot2 remove one column:

Warning messages:

1: Removed 1 rows containing missing values (geom_col).

2: Removed 1 rows containing missing values (geom_errorbar).

To fix it, you can plot it using ylim(0,1) as I did below:

dat %>% filter(predicted == "Stationary") %>%
  ggplot(aes(x = hour, y = Y, fill = data)) +
  geom_col(position = position_dodge())+
  geom_errorbar(aes(ymin = YMIN, ymax = YMAX),
                position = position_dodge(0.9))+
  scale_fill_manual(values = c("lgcanet" = "gray80", "snpnet" = "gray40" )) +
  labs(title = "a",
       x = "Hour of the day", 
       y = "Proportion",
       fill = "Behavioural\n category") +
  theme(axis.text=element_text(size=22),
        axis.title=element_text(size=22),
        legend.position = "none")  + 
  
  ylim(0, 1)+ theme(legend.position = "none")+  
  theme(plot.title = element_text(size=22))

enter image description here

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