GGPLOT2 summarySE繪製小的數據集 - 使用geom_point

Question

我正與一個小數據集作爲工作失分如下：

Thermo Chl  Month 
1.53 1.53 May 
3.63 1.53 June 
3.83 2.33 July 
3.84 2.33 July 
5.08   August 
5.50   August 
5.04 1.55 September 
5.90 1.70 September 
3.75   September 
3.63 1.70 September 
5.90 1.70 September 
9.00 2.50 October 
11.03 1.03 October 
11.00 1.00 October 
9.00 2.37 October 
9.00 2.03 November 
14.96 2.04 November 
7.13 15.95 January 
13.03 9.00 February 
9.00 1.03 April 
9.00 2.50 May 
8.97 2.50 May 
4.88 2.50 June 
4.88 2.50 June 
3.63 2.50 July 
4.88 1.03 July 
3.83 2.33 August 
5.53 2.30 August 
7.20 2.37 September 
7.17 2.33 September 
7.17 2.33 September 
7.20 2.37 September 
7.17 2.33 September 
7.17 2.33 September 
7.17 1.08 September 
7.17 2.33 September 
7.17 1.03 September 
7.17 2.33 September 
7.17 2.33 September 
7.17 1.03 September 
9.00 1.03 October 
9.00 2.37 October 
8.97 5.50 October 
11.00 5.50 October 
13.00 10.00 November 
13.00 10.00 December 
11.00 15.17 January 
11.00 1.03 February 
9.00 2.33 March 
5.50 1.03 April 
3.83 2.37 May 
3.87 2.33 June 
5.50 3.83 June 
3.83 2.33 July 
5.50 3.37 July 
7.17 2.37 July 
7.17 2.30 August 
7.17 2.33 September 
9.00 2.33 September 
9.03 3.83 September 
9.00 3.83 September 
9.00 2.33 September 
9.00 2.37 September 
7.17 2.33 September 
7.17 4.00 September 
7.17 3.50 September 
7.17 2.33 September 
7.20 2.33 September 
7.17 3.83 September 
7.17 2.33 October 
7.17 2.30 October 
7.17 2.33 October 
7.17 2.33 October 
8.68 2.33 October 
7.20 2.33 October 
9.00 3.87 October 
9.00 5.50 October 
11.00 5.50 November 
13.00 1.03 November 
11.00 3.83 November 
9.00 3.83 December 
6.03 13.00 March 
8.00 15.67 March 
5.97 0.70 April 
5.57 2.40 May 
7.25 1.03 May 
5.50 2.33 June 
5.50 3.00 July 
4.70 2.00 July 
5.50 2.33 August 
5.50 2.33 August 
9.00 2.33 September 
9.00 1.03 September 
9.00 2.33 September 
7.13 2.37 September 
7.17 2.33 September 
7.17 2.33 September 
7.17 2.33 September 
7.17 2.43 September 
7.20 1.07 September 
7.17 2.00 September 
7.17 2.33 September 
7.17 2.00 September 
7.17 1.03 September 
7.13 2.33 October 
7.17 2.33 October 
7.17 2.30 October 
7.17 2.33 October 
9.00 3.83 October 
13.00 2.33 October 
14.97 3.83 October 
14.93 2.03 October 

當我與summarySE和GGPLOT2功能繪製如下：

phyto.maxchl$Month <- factor(phyto.maxchl$Month) 

phyto.se <- summarySE(phyto.maxchl, measurevar="Thermocline", groupvars=c("Month")) 

ggplot(phyto.se, aes(x=phyto.se$Month, y=phyto.se$Thermocline)) + 
    geom_point(position=position_dodge(),stat="identity") + 
    geom_errorbar(aes(ymin=phyto.se$Thermocline-se, ymax=phyto.se$Thermocline+se), 
       width=.2,    
       position=position_dodge(.9))+ 
    ylab("Thermocline Depth (m)")+ 
    xlab("Month")+ 
    theme_bw()+ 
    theme(axis.text.x=element_text(angle=70,size=15,vjust=0.60))+ 
    labs(title = "Thermocline Depth for 2013 - 2016")+ 
    scale_y_continuous(trans='reverse')+ 
    theme(legend.title=element_blank()) 

我缺少1月至4月的積分。這是因爲我沒有足夠的數據點來滿足計算標準誤差所需的要求嗎？

Answer 1

summarySE()如果在組內遇到任何NA，則返回NA，除非提供na.rm = TRUE作爲參數。

Rmisc::summarySE(df, "Chl", groupvars="Month") 
#>  Month N  Chl  sd  se   ci 
#> 1  April 3 0.920000 0.1905256 0.1100000 0.4732918 
#> 2  August 7  NA  NA  NA   NA 
#> 3 December 2 6.915000 4.3628488 3.0850000 39.1986416 
#> 4 February 2 5.015000 5.6356410 3.9850000 50.6342259 
#> 5 January 2 15.560000 0.5515433 0.3900000 4.9554198 
#> 6  July 9 2.362222 0.6471626 0.2157209 0.4974532 
#> 7  June 6 2.503333 0.7439803 0.3037287 0.7807594 
#> 8  March 3 10.333333 7.0584866 4.0752191 17.5342528 
#> 9  May 6 2.055000 0.6229848 0.2543325 0.6537824 
#> 10 November 6 4.071667 3.3103318 1.3514373 3.4739802 
#> 11 October 24 2.747083 1.2873312 0.2627754 0.5435923 
#> 12 September 42  NA  NA  NA   NA 

至於反對：

Rmisc::summarySE(df, "Chl", groupvars="Month", na.rm = T) 
#>  Month N  Chl   sd   se   ci 
#> 1  April 3 0.920000 0.19052559 0.110000000 0.47329180 
#> 2  August 5 2.318000 0.01643168 0.007348469 0.02040262 
#> 3 December 2 6.915000 4.36284884 3.085000000 39.19864161 
#> 4 February 2 5.015000 5.63564105 3.985000000 50.63422587 
#> 5 January 2 15.560000 0.55154329 0.390000000 4.95541985 
#> 6  July 9 2.362222 0.64716261 0.215720870 0.49745322 
#> 7  June 6 2.503333 0.74398029 0.303728680 0.78075943 
#> 8  March 3 10.333333 7.05848662 4.075219149 17.53425280 
#> 9  May 6 2.055000 0.62298475 0.254332460 0.65378240 
#> 10 November 6 4.071667 3.31033181 1.351437301 3.47398018 
#> 11 October 24 2.747083 1.28733118 0.262775376 0.54359228 
#> 12 September 41 2.246098 0.75104220 0.117293086 0.23705817 

這就是爲什麼你得到兩個缺失值。 NA與1月和4月相對，而不是正確的8月和9月，這可能是因爲Month因子值與其標籤之間的差異。

levels(df$Month) <- month.name 

或

df$Month <- factor(df$Month, levels = month.name) 

應該修復它。

以下是您的確切代碼，不包括NA s和gggplot調用中的一些修補程序。特別是你不應該在aes()裏面使用df$Var，這個變量的名字就夠了。

df2 <- Rmisc::summarySE(df, "Chl", groupvars="Month", na.rm = T) 

library(ggplot2) 

ggplot(df2, aes(x=factor(Month, levels = month.name), y=Chl)) + 
    geom_point(position=position_dodge(),stat="identity") + 
    geom_errorbar(aes(ymin=Chl-se, ymax=Chl+se), 
       width=.2,    
       position=position_dodge(.9))+ 
    ylab("Thermocline Depth (m)")+ 
    xlab("Month")+ 
    theme_bw()+ 
    theme(axis.text.x=element_text(angle=70,size=15,vjust=0.60))+ 
    labs(title = "Thermocline Depth for 2013 - 2016")+ 
    scale_y_continuous(trans='reverse')+ 
    theme(legend.title=element_blank()) 
#> Warning: Width not defined. Set with `position_dodge(width = ?)` 

只是爲了保持完整性，我會告訴你如何甚至沒有必要預先計算的總結，我們可以離開計算ggplot：

library(ggplot2) 


ggplot(df, aes(factor(Month, levels = month.name), Chl)) + 
    stat_summary(geom = 'point', position = position_dodge(.9)) + 
    stat_summary(geom = 'errorbar', width=.2,    
       position=position_dodge(.9))+ 
    ylab("Thermocline Depth (m)")+ 
    xlab("Month")+ 
    theme_bw()+ 
    theme(axis.text.x=element_text(angle=70,size=15,vjust=0.60))+ 
    labs(title = "Thermocline Depth for 2013 - 2016")+ 
    scale_y_continuous(trans='reverse')+ 
    theme(legend.title=element_blank()) 

數據：

df <- read.table(text = 'Thermo Chl  Month 
1.53 1.53 May 
      3.63 1.53 June 
      3.83 2.33 July 
      3.84 2.33 July 
      5.08  NA  August 
      5.50  NA  August 
      5.04 1.55 September 
      5.90 1.70 September 
      3.75  NA  September 
      3.63 1.70 September 
      5.90 1.70 September 
      9.00 2.50 October 
      11.03 1.03 October 
      11.00 1.00 October 
      9.00 2.37 October 
      9.00 2.03 November 
      14.96 2.04 November 
      7.13 15.95 January 
      13.03 9.00 February 
      9.00 1.03 April 
      9.00 2.50 May 
      8.97 2.50 May 
      4.88 2.50 June 
      4.88 2.50 June 
      3.63 2.50 July 
      4.88 1.03 July 
      3.83 2.33 August 
      5.53 2.30 August 
      7.20 2.37 September 
      7.17 2.33 September 
      7.17 2.33 September 
      7.20 2.37 September 
      7.17 2.33 September 
      7.17 2.33 September 
      7.17 1.08 September 
      7.17 2.33 September 
      7.17 1.03 September 
      7.17 2.33 September 
      7.17 2.33 September 
      7.17 1.03 September 
      9.00 1.03 October 
      9.00 2.37 October 
      8.97 5.50 October 
      11.00 5.50 October 
      13.00 10.00 November 
      13.00 10.00 December 
      11.00 15.17 January 
      11.00 1.03 February 
      9.00 2.33 March 
      5.50 1.03 April 
      3.83 2.37 May 
      3.87 2.33 June 
      5.50 3.83 June 
      3.83 2.33 July 
      5.50 3.37 July 
      7.17 2.37 July 
      7.17 2.30 August 
      7.17 2.33 September 
      9.00 2.33 September 
      9.03 3.83 September 
      9.00 3.83 September 
      9.00 2.33 September 
      9.00 2.37 September 
      7.17 2.33 September 
      7.17 4.00 September 
      7.17 3.50 September 
      7.17 2.33 September 
      7.20 2.33 September 
      7.17 3.83 September 
      7.17 2.33 October 
      7.17 2.30 October 
      7.17 2.33 October 
      7.17 2.33 October 
      8.68 2.33 October 
      7.20 2.33 October 
      9.00 3.87 October 
      9.00 5.50 October 
      11.00 5.50 November 
      13.00 1.03 November 
      11.00 3.83 November 
      9.00 3.83 December 
      6.03 13.00 March 
      8.00 15.67 March 
      5.97 0.70 April 
      5.57 2.40 May 
      7.25 1.03 May 
      5.50 2.33 June 
      5.50 3.00 July 
      4.70 2.00 July 
      5.50 2.33 August 
      5.50 2.33 August 
      9.00 2.33 September 
      9.00 1.03 September 
      9.00 2.33 September 
      7.13 2.37 September 
      7.17 2.33 September 
      7.17 2.33 September 
      7.17 2.33 September 
      7.17 2.43 September 
      7.20 1.07 September 
      7.17 2.00 September 
      7.17 2.33 September 
      7.17 2.00 September 
      7.17 1.03 September 
      7.13 2.33 October 
      7.17 2.33 October 
      7.17 2.30 October 
      7.17 2.33 October 
      9.00 3.83 October 
      13.00 2.33 October 
      14.97 3.83 October 
      14.93 2.03 October', h = TRUE)