使用geom_bar作为因子变量中NA为NA的数据帧-Java 学习之路

我试图绘制日期明智的多变量数据以及顶轴上的独立变量 . 为此 - 我将多变量（响应变量）数据帧与单个输入（自变量）合并为一个数据帧 . 结果数据帧现在在行和列中具有几个NA值（对于两个数据集） .

我的问题：

为什么我用当前代码丢失宽度/闪避？
这是否与我数据中的因式变量中的NA值有关？
如何使用因子变量中的NA值？我的一半数据集是一个完全不同的变量，只需要一列 . 我合并它们的唯一原因是因为我想将所有数据放在同一个图上（计划是在此之后使用grob，但我被困在这里）

在此之前，我使用此代码使用响应变量绘制geom_bar与数据帧，并且它有效 .

Previuosly plotted geom_bar (this is how I expect it to look)

Geom_bar plot with the merged dataframe and same code

数据框名称为final，因子变量为TYPE，其中Open，Shrub和Lowland为类别，NA为仅具有自变量的日期（在本例中为Rain）

final$TYPE<-factor(final$TYPE, levels = c("Open", "Shrub","Lowland"))      

 limits <- aes(ymax = final$Max, ymin = final$Min, ysd= final$SD)
 rhg_cols <- c("brown","forestgreen", "cyan4")


 p <- ggplot(final, aes(Date, MeanTWC, fill=TYPE), na.rm=F )+
   geom_bar(stat="identity", position = "dodge")+
   scale_fill_manual(values = rhg_cols)+
   scale_x_date(breaks = seq(as.Date("2016-08-15"), as.Date("2017-10-15"), by="30 days"),labels=date_format("%b-%Y")) 


 p<-p + labs(x="DATE", y ="Total Water in mm")


 p<-p + geom_bar(stat = "Identity",
                 position = "dodge")+
   geom_errorbar(limits, position = "dodge", size =0.2)+ 
   ggtitle("Total Water Storage-60cm")+
   scale_y_continuous(limits = c(0,100))

p<-p+theme_bw() +theme(axis.text.x = element_text(angle = 270, vjust = 1, 

size =15),axis.text.y = element_text(vjust = 1, hjust = 1, size =20),
                            panel.grid.major.x = element_blank(),
                            panel.grid.minor.x = element_line(linetype="longdash"),
                            panel.grid.major.y = element_line(linetype = "longdash"))
     print(p)

样本数据：

Date    TYPE   MeanTWC       Max       Min   Rain
1   2016-08-13    <NA>        NA        NA        NA 27.686
2   2016-08-14    <NA>        NA        NA        NA 79.248
3   2016-08-15    <NA>        NA        NA        NA  9.398
4   2016-08-16    <NA>        NA        NA        NA  9.906
5   2016-08-17    <NA>        NA        NA        NA 26.670
6   2016-08-21    <NA>        NA        NA        NA 52.324
7   2016-08-27    <NA>        NA        NA        NA 13.200
8   2016-08-28    <NA>        NA        NA        NA  0.200
9   2016-08-29    <NA>        NA        NA        NA  3.000
10  2016-08-30    <NA>        NA        NA        NA  0.400
11  2016-09-02    <NA>        NA        NA        NA  5.400
12  2016-09-04    <NA>        NA        NA        NA 22.200
13  2016-09-05    <NA>        NA        NA        NA  0.400
14  2016-09-06    <NA>        NA        NA        NA  0.400
15  2016-09-11    <NA>        NA        NA        NA  0.200
16  2016-09-19    Open  82.40583  94.13074  71.95022     NA
17  2016-09-19   Shrub  75.25720  81.09062  66.31633     NA
18  2016-09-19 Lowland  79.78265  91.46637  71.42791     NA
19  2016-09-24    <NA>        NA        NA        NA  1.200
20  2016-09-28    Open 107.00762 128.82301  87.78908     NA
21  2016-09-28   Shrub 102.29717 114.59530  93.02085     NA
22  2016-09-28 Lowland 100.62097 108.65464  93.06479     NA
23  2016-10-04    Open  94.35146 119.11809  80.80844     NA
24  2016-10-04   Shrub  89.78960 106.59891  77.91514     NA
25  2016-10-04 Lowland  87.66499  98.93036  77.44905     NA
26  2016-10-07    <NA>        NA        NA        NA 15.200
27  2016-10-24    Open  77.75282  90.99799  60.89542     NA
28  2016-10-24   Shrub  73.13549  84.68082  64.38086     NA
29  2016-10-24 Lowland  77.54505  89.20983  68.77503     NA
30  2016-11-04    Open  75.79262  84.63392  61.17391     NA

 structure(list(Date = structure(c(17026, 17027, 17028, 17029, 
    17030, 17034, 17040, 17041, 17042, 17043, 17046, 17048, 17049, 
    17050, 17055, 17063, 17063, 17063, 17068, 17072, 17072, 17072, 
    17078, 17078, 17078, 17081, 17098, 17098, 17098, 17109), class = "Date"), 
        TYPE = structure(c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
        NA, NA, NA, NA, NA, 1L, 2L, 3L, NA, 1L, 2L, 3L, 1L, 2L, 3L, 
        NA, 1L, 2L, 3L, 1L), .Label = c("Open", "Shrub", "Lowland"
        ), class = "factor"), MeanTWC = c(NA, NA, NA, NA, NA, NA, 
        NA, NA, NA, NA, NA, NA, NA, NA, NA, 82.4058263935714, 75.2571964744444, 
        79.782649985, NA, 107.0076241875, 102.297170442857, 100.620970785, 
        94.3514631776471, 89.7895999577778, 87.664985085, NA, 77.75281636125, 
        73.135492118, 77.54505326, 75.792624628125), Max = c(NA, 
        NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 94.13073642, 
        81.09062269, 91.46637475, NA, 128.8230145, 114.5952995, 108.6546353, 
        119.1180866, 106.5989092, 98.93036216, NA, 90.99798892, 84.68081807, 
        89.20983383, 84.63391564), Min = c(NA, NA, NA, NA, NA, NA, 
        NA, NA, NA, NA, NA, NA, NA, NA, NA, 71.95021894, 66.31632641, 
        71.42791015, NA, 87.78907749, 93.02084587, 93.06478569, 80.8084363, 
        77.91514274, 77.44904985, NA, 60.89542395, 64.38086067, 68.77503196, 
        61.17390712), SD = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, 
        NA, NA, NA, NA, NA, NA, 6.52668534466645, 5.31370742998586, 
        8.40565594980702, NA, 10.3287869191442, 8.45785409063748, 
        6.49446280465913, 9.73718805734734, 10.5575933779477, 9.35169762923353, 
        NA, 8.27219492616507, 6.75450870627616, 8.51146778459709, 
        6.75447037137946), N = c(NA, NA, NA, NA, NA, NA, NA, NA, 
        NA, NA, NA, NA, NA, NA, NA, 14, 9, 4, NA, 12, 7, 4, 17, 9, 
        4, NA, 16, 10, 4, 16), SE = c(NA, NA, NA, NA, NA, NA, NA, 
        NA, NA, NA, NA, NA, NA, NA, NA, 1.74433003078718, 1.77123580999529, 
        4.20282797490351, NA, 2.9816639540851, 3.19676836415673, 
        3.24723140232956, 2.36161499158505, 3.51919779264923, 4.67584881461676, 
        NA, 2.06804873154127, 2.13596319872699, 4.25573389229854, 
        1.68861759284486), Rain = c(27.686, 79.248, 9.398, 9.906, 
        26.67, 52.324, 13.2, 0.2, 3, 0.4, 5.4, 22.2, 0.4, 0.4, 0.2, 
        NA, NA, NA, 1.2, NA, NA, NA, NA, NA, NA, 15.2, NA, NA, NA, 
        NA)), .Names = c("Date", "TYPE", "MeanTWC", "Max", "Min", 
    "SD", "N", "SE", "Rain"), row.names = c(NA, 30L), class = "data.frame")

使用geom_bar作为因子变量中NA为NA的数据帧

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