[英]ggplot2 summarySE plotting small data set - missing points using geom_point
我正在使用以下小型數據集：
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
當我使用summarySE和ggplot2函數進行繪制時，如下所示：
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-4月的分數。 這是因為我沒有足夠的數據點來滿足計算標准誤差所需的要求嗎？
如果在組內遇到任何NA summarySE()將返回NA ，除非提供na.rm = TRUE作為參數。
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
相對於：
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
這就是為什么您會得到兩個缺失值的原因。 NA是相對於1月和4月而不是正確的8月和9月的事實，可能是由於Month因子值與其標簽之間存在差異。
levels(df$Month) <- month.name
要么
df$Month <- factor(df$Month, levels = month.name)
應該修復它。
以下是您的確切代碼，沒有NA以及gggplot調用中的一些修復程序。 特別是，永遠不要在aes()內使用df$Var ，變量的名稱就足夠了。
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 = ?)`
為了完整ggplot ，我將向您展示什至不需要預先計算摘要，我們可以將計算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)
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