側由側R Barplot用誤差棒

Question

說我有兩個數據集的裝置，我要繪製在GGPLOT2與誤差條barplots彼此相鄰，或鹼

每個數據集包括數字矩陣的

10 20 12 
10 20 12 
10 20 12 

，然後將其轉化爲例如3個元素的平均值矢量

10 20 12 

我想要做的是採取兩個均值向量並繪製它們作爲一個條形圖，其中t他的第一個元素除了另一個的第一個元素外

Dataset1Element1Bar-Dataset2Element1Bar Dataset1Element2Bar-Dataset2Element2Bar etc 

給每個條形成一個錯誤欄，例如標準偏差。我知道我可以通過SD計算，但我不知道怎麼用元數它粘成的圖形適當形式

最後一點顏色他們（即元1）

我的代碼做一個數據集，但我不知道該從哪裏去。

result<-barplot(bardata, main="Mean Coverage", names.arg=namePosTargetGroup, ylab="mean Magnitude", cex.names=.4,col=c("red","blue","green")) 
      legend(10,legend=c("Group1","Group2","Group3"),fill = c("red","blue","green")) 

很多我仰望的東西給出了這個或那個東西的答案，但很難弄清楚如何將它們結合在一起。

Answer 1

我通常不會推薦繪製帶有誤差條的條形圖。還有許多其他方式來繪製您的數據，這些數據及其結構顯示得更好。

特別是如果您只有極少數情況下，繪圖方式與酒吧並不好。一個很好的解釋可以在這裏找到：Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm

我覺得很難給你一個很好的解決方案，因爲我不知道你的研究問題。知道你真正想要展示或強調會讓事情變得更容易。

我會給你兩個建議，一個是小數據集，一個是大數據集。所有這些都是用ggplot2創建的。我沒有用他們的「元素編號」，而是以他們的起源（「數據集1/2」）爲他們着色，因爲我發現用這種方法來完成一個合適的圖形更容易。

小數據集

使用geom_jitter來顯示所有的情況下，避免overplotting。

# import hadleyverse 
library(magrittr) 
library(dplyr) 
library(tidyr) 
library(ggplot2) 

# generate small amount of data 
set.seed(1234) 
df1 <- data.frame(v1 = rnorm(5, 4, 1), 
        v2 = rnorm(5, 5, 1), 
        v3 = rnorm(5, 6, 1), 
        origin = rep(factor("df1", levels = c("df1", "df2")), 5)) 

df2 <- data.frame(v1 = rnorm(5, 4.5, 1), 
        v2 = rnorm(5, 5.5, 1), 
        v3 = rnorm(5, 6.5, 1), 
        origin = rep(factor("df2", levels = c("df1", "df2")), 5)) 

# merge dataframes and gather in long format 
pdata <- bind_rows(df1, df2) %>% 
    gather(id, variable, -origin) 

# plot data 
ggplot(pdata, aes(x = id, y = variable, fill = origin, colour = origin)) + 
    stat_summary(fun.y = mean, geom = "point", position = position_dodge(width = .5), 
       size = 30, shape = "-", show_guide = F, alpha = .7) + # plot mean as "-" 
    geom_jitter(position = position_jitterdodge(jitter.width = .3, jitter.height = .1, 
               dodge.width = .5), 
       size = 4, alpha = .85) + 
    labs(x = "Variable", y = NULL) + # adjust legend 
    theme_light() # nicer theme 

「大」 數據集

如果您有更多的數據點，就可以使用geom_violin來概括他們。

set.seed(12345) 
df1 <- data.frame(v1 = rnorm(50, 4, 1), 
        v2 = rnorm(50, 5, 1), 
        v3 = rnorm(50, 6, 1), 
        origin = rep(factor("df1", levels = c("df1", "df2")), 50)) 

df2 <- data.frame(v1 = rnorm(50, 4.5, 1), 
        v2 = rnorm(50, 5.5, 1), 
        v3 = rnorm(50, 6.5, 1), 
        origin = rep(factor("df2", levels = c("df1", "df2")), 50)) 

# merge dataframes 
pdata <- bind_rows(df1, df2) %>% 
    gather(id, variable, -origin) 

# plot with violin plot 
ggplot(pdata, aes(x = id, y = variable, fill = origin)) + 
    geom_violin(adjust = .6) + 
    stat_summary(fun.y = mean, geom = "point", position = position_dodge(width = .9), 
       size = 6, shape = 4, show_guide = F) + 
    guides(fill = guide_legend(override.aes = list(colour = NULL))) + 
    labs(x = "Variable", y = NULL) + 
    theme_light() 

版本均值和標繪與標準差的均值SD

如果你堅持，在這裏是如何可以做到。

# merge dataframes and compute limits for sd 
pdata <- bind_rows(df1, df2) %>% 
    gather(id, variable, -origin) %>% 
    group_by(origin, id) %>%   # group data for limit calculation 
    mutate(upper = mean(variable) + sd(variable), # upper limit for error bar 
     lower = mean(variable) - sd(variable)) # lower limit for error bar 

# plot 
ggplot(pdata, aes(x = id, y = variable, fill = origin)) + 
    stat_summary(fun.y = mean, geom = "bar", position = position_dodge(width = .9), 
       size = 3) + 
    geom_errorbar(aes(ymin = lower, ymax = upper), 
       width = .2,     # Width of the error bars 
       position = position_dodge(.9))