ggpubr002 繪制一個(gè)連續(xù)變量

1.密度圖 ggdensity

1.1 用法
# 密度圖 ggdensity
ggdensity(
# 數(shù)據(jù)，數(shù)據(jù)框  
  data, 
# 橫坐標(biāo)，繪制的變量
  x,   
# 縱坐標(biāo)，引用數(shù)據(jù)需""
  y = "..density..", 
# 邏輯值。默認(rèn)值為FALSE。僅當(dāng)y是包含要繪制的多個(gè)變量的向量時(shí)使用。
# 如果為T(mén)RUE，則通過(guò)組合y變量的圖來(lái)創(chuàng)建多面板圖
  combine = FALSE, 
# 邏輯或字符值。默認(rèn)值為FALSE。僅當(dāng)y是包含要繪制的多個(gè)變量的向量時(shí)使用。
# 如果為T(mén)RUE，則在同一繪圖區(qū)域中合并多個(gè)y變量。允許的值還包括"asis”（TRUE）和“ flip”。如果merge =“ flip”，則y變量用作x刻度標(biāo)簽，而x變量用作分組變量。
  merge = FALSE,  
# 密度線(xiàn)顏色和填充顏色
  color = "black",                                      
  fill = NA,
# 用于按組著色或填充的調(diào)色板
  palette = NULL,
# 更改點(diǎn)和輪廓的大小
  size = NULL,
  linetype = "solid",
# 透明度
  alpha = 0.5,
# 標(biāo)題及標(biāo)簽
  title = NULL,
  xlab = NULL,
  ylab = NULL,
# 指定用于將圖面劃分為多個(gè)面板的分組變量
  facet.by = NULL,
  panel.labs = NULL,
  short.panel.labs = TRUE,
# 允許的值是“平均值”或“中位數(shù)”之一（分別用于添加平均值或中位數(shù)線(xiàn)）
  add = c("none", "mean", "median"),
  add.params = list(linetype = "dashed"),
# 輯值值，如果為T(mén)RUE，則添加邊緣地毯。
  rug = FALSE,
  label = NULL,
# 字體標(biāo)簽，一個(gè)可以包含以下元素的組合的列表：
# 大?。ɡ纾?4），樣式（例如：“純”，“粗體”，“斜體”，“ bold.italic”）
# 顏色（例如：“紅色”）的標(biāo)簽。例如font.label = list（size = 14，face =“ bold”，顏色=“ red”）。
# 若要僅指定大小和樣式，請(qǐng)使用font.label = list（size = 14，face =“ plain”）
  font.label = list(size = 11, color = "black"),
  label.select = NULL,
# 是否使用ggrepel避免過(guò)度繪制文本標(biāo)簽的邏輯值
  repel = FALSE,
  label.rectangle = FALSE,
  ggtheme = theme_pubr(),
  ...
)
1.2 自定義參數(shù)ggpar()

使用函數(shù)ggpar()可以輕松自定義繪圖, 閱讀?ggpar進(jìn)行更改：

  • 主標(biāo)題和軸標(biāo)簽：main，xlab，ylab
  • 軸區(qū)間：xlim，ylim(例如：ylim = c(0，30))
  • 軸比例尺：xscale，yscale(例如：yscale ="log2")
  • 調(diào)色板：palette="Dark2"或調(diào)色板= c("gray"，"blue"，"red")
  • 圖例標(biāo)題，標(biāo)簽和位置：legend="right"
  • 繪圖方向：orientation = c("vertical", "horizontal", "reverse")
1.3 實(shí)例
# Create some data format
set.seed(1234)
wdata = data.frame(
  sex = factor(rep(c("F", "M"), each=200)),
  weight = c(rnorm(200, 55), rnorm(200, 58)))

head(wdata, 4)#>   sex   weight
#> 1   F 53.79293
#> 2   F 55.27743
#> 3   F 56.08444
#> 4   F 52.65430
# Basic density plot
# Add mean line and marginal rug
p1 <- ggdensity(wdata, x = "weight", fill = "lightgray",
          add = "mean", rug = TRUE)
p1
#> Warning: geom_vline(): Ignoring `mapping` because `xintercept` was provided.#> Warning: geom_vline(): Ignoring `data` because `xintercept` was provided.
# Change outline colors by groups ("sex")
# Use custom palette
p2 <- ggdensity(wdata, x = "weight",
          add = "mean", rug = TRUE,
          color = "sex", palette = c("#00AFBB", "#E7B800"))
p2
# Change outline and fill colors by groups ("sex")
# Use custom palette
p3 <- ggdensity(wdata, x = "weight",
          add = "mean", rug = TRUE,
          color = "sex", fill = "sex",
          palette = c("#00AFBB", "#E7B800"))
p3
p4 <- ggdensity(wdata, x = "weight",
                add = "mean", rug = TRUE,
                color = "sex", fill = "sex",
                palette = c("#00AFBB", "#E7B800"),
                facet.by = "sex")
p4
p1

p2

p3

p4

2. 疊加正常密度圖 stat_overlay_normal_density

在視覺(jué)上檢查偏離正常程度的程度

2.1 用法
stat_overlay_normal_density(
# 由aes()或aes_()創(chuàng)建的美學(xué)映射集。如果指定且繼承.aes = TRUE（默認(rèn)值），它將與繪圖頂層的默認(rèn)映射結(jié)合。如果沒(méi)有繪圖映射，則必須提供映射。  
mapping = NULL,
  data = NULL,
  geom = "line",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)
2.2 舉例
# Simpledensity plot
data("mtcars")
p1 <- ggdensity(mtcars, x = "mpg", fill = "red") +
  scale_x_continuous(limits = c(-1, 50)) +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
p1
# Color by groups
data(iris)
p2 <- ggdensity(iris, "Sepal.Length", color = "Species") +
  stat_overlay_normal_density(aes(color = Species), linetype = "dashed")
p2
# Facet
p3 <- ggdensity(iris, "Sepal.Length", facet.by = "Species") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
p3
p1

p2

p3

3.經(jīng)驗(yàn)累積密度函數(shù) Empirical cumulative density function

樣本分布函數(shù)(sample distribution function)亦稱(chēng)經(jīng)驗(yàn)分布函數(shù)，統(tǒng)計(jì)學(xué)中的基本概念之一。樣本分布函數(shù)Fn(x)具有分布函數(shù)的性質(zhì)，我們可以將其看成是以等概率1/n 取值X1，X2，…，Xn的離散型隨機(jī)變量的分布函數(shù)，且該函數(shù)的圖形呈跳躍式一條臺(tái)階形折線(xiàn)，如觀(guān)測(cè)值不重復(fù)，則每一跳躍為1/n ，如有重復(fù)，則按1/n的倍數(shù)跳躍上升。

3.1 用法
ggecdf(
  data,
  x,
  combine = FALSE,
  merge = FALSE,
  color = "black",
  palette = NULL,
  size = NULL,
  linetype = "solid",
  title = NULL,
  xlab = NULL,
  ylab = NULL,
  facet.by = NULL,
  panel.labs = NULL,
  short.panel.labs = TRUE,
  ggtheme = theme_pubr(),
  ...
)
3.2 舉例
# Create some data format
set.seed(1234)
wdata = data.frame(
  sex = factor(rep(c("F", "M"), each=200)),
  weight = c(rnorm(200, 55), rnorm(200, 58)))

head(wdata, 4)#>   sex   weight
#> 1   F 53.79293
#> 2   F 55.27743
#> 3   F 56.08444
#> 4   F 52.65430
# Basic ECDF plot
p1 <- ggecdf(wdata, x = "weight")
p1
# Change colors and linetype by groups ("sex")
# Use custom palette
p2 <- ggecdf(wdata, x = "weight",
       color = "sex", linetype = "sex",
       palette = c("#00AFBB", "#E7B800"))
p2
p1

p2

4.直方圖 Histogram plot

4.1 用法
gghistogram(
# 數(shù)據(jù)，數(shù)據(jù)框  
  data, 
# 橫坐標(biāo)，繪制的變量
  x,   
# 縱坐標(biāo)，引用數(shù)據(jù)需" "
  y = "..count..",
# 邏輯值。默認(rèn)值為FALSE。僅當(dāng)y是包含要繪制的多個(gè)變量的向量時(shí)使用。
# 如果為T(mén)RUE，則通過(guò)組合y變量的圖來(lái)創(chuàng)建多面板圖
  combine = FALSE,
# 邏輯或字符值。默認(rèn)值為FALSE。僅當(dāng)y是包含要繪制的多個(gè)變量的向量時(shí)使用。
# 如果為T(mén)RUE，則在同一繪圖區(qū)域中合并多個(gè)y變量。允許的值還包括"asis”（TRUE）和“ flip”。如果merge =“ flip”，則y變量用作x刻度標(biāo)簽，而x變量用作分組變量。
  merge = FALSE,
  weight = NULL,
  color = "black",
  fill = NA,
# 著色板，用于按組著色或填充的調(diào)色板
  palette = NULL,
  size = NULL,
  linetype = "solid",
  alpha = 0.5,
# bin數(shù)默認(rèn)為30。
  bins = NULL,
# 指定箱寬的數(shù)值。當(dāng)您有很強(qiáng)的密集點(diǎn)圖時(shí)，請(qǐng)使用介于0和1之間的值。例如binwidth = 0.2。
  binwidth = NULL,
  title = NULL,
  xlab = NULL,
  ylab = NULL,
# 長(zhǎng)度為1或2的字符向量，指定用于將圖面劃分為多個(gè)面板的分組變量。應(yīng)該在數(shù)據(jù)中
  facet.by = NULL,
# 用于修改構(gòu)面面板標(biāo)簽的一個(gè)或兩個(gè)字符向量的列表。例如，panel.labs = list（sex = c（“ Male”，“ Female”））指定“ sex”變量的標(biāo)簽。對(duì)于兩個(gè)分組變量，您可以使用例如panel.labs = list（sex = c（“ Male”，“ Female”），rx = c（“ Obs”，“ Lev”，“ Lev2”）））。
  panel.labs = NULL,
  short.panel.labs = TRUE,
  add = c("none", "mean", "median"),
# 參數(shù)'add'的參數(shù)（顏色，大小，線(xiàn)型）；例如：add.params = list（顏色=“紅色”）。
  add.params = list(linetype = "dashed"),
  rug = FALSE,
  add_density = FALSE,
  label = NULL,
  font.label = list(size = 11, color = "black"),
  label.select = NULL,
  repel = FALSE,
  label.rectangle = FALSE,
  position = position_identity(),
  ggtheme = theme_pubr(),
  ...
)
4.2 自定義參數(shù)ggpar()

使用函數(shù)ggpar()可以輕松自定義繪圖, 閱讀？ggpar進(jìn)行更改：

  • 主標(biāo)題和軸標(biāo)簽：main，xlab，ylab
  • 軸區(qū)間：xlim，ylim(例如：ylim = c(0，30))
  • 軸比例尺：xscale，yscale(例如：yscale ="log2")
  • 調(diào)色板：palette="Dark2"或調(diào)色板= c("gray"，"blue"，"red")
  • 圖例標(biāo)題，標(biāo)簽和位置：legend="right"
  • 繪圖方向：orientation = c("vertical", "horizontal", "reverse")
4.3 實(shí)例
# Create some data format
set.seed(1234)
wdata = data.frame(
  sex = factor(rep(c("F", "M"), each=200)),
  weight = c(rnorm(200, 55), rnorm(200, 58)))

head(wdata, 4)
#>   sex   weight
#> 1   F 53.79293
#> 2   F 55.27743
#> 3   F 56.08444
#> 4   F 52.65430
# Basic density plot
# Add mean line and marginal rug
p1 <- gghistogram(wdata, x = "weight", fill = "lightgray",
            add = "mean", rug = TRUE)
p1
#> Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.#> Warning: geom_vline(): Ignoring `mapping` because `xintercept` was provided.#> Warning: geom_vline(): Ignoring `data` because `xintercept` was provided.
# Change outline colors by groups ("sex")
# Use custom color palette
p2 <- gghistogram(wdata, x = "weight",
            add = "mean", rug = TRUE,
            color = "sex", palette = c("#00AFBB", "#E7B800"))
p2
#> Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
# Change outline and fill colors by groups ("sex")
# Use custom color palette
p3 <- gghistogram(wdata, x = "weight",
            add = "mean", rug = TRUE,
            color = "sex", fill = "sex",
            palette = c("#00AFBB", "#E7B800"))
p3
#> Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.


# Combine histogram and density plots
p4 <- gghistogram(wdata, x = "weight",
            add = "mean", rug = TRUE,
            fill = "sex", palette = c("#00AFBB", "#E7B800"),
            add_density = TRUE)
p4
#> Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
# Weighted histogram
p5 <- gghistogram(iris, x = "Sepal.Length", weight = "Petal.Length")
p5
#> Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
p1

p2

p3

p4

p5

5.分位數(shù)圖示法 Quantile Quantile Plot

統(tǒng)計(jì)學(xué)里Q-Q圖（Q代表分位數(shù)）是一個(gè)概率圖，用圖形的方式比較兩個(gè)概率分布，把他們的兩個(gè)分位數(shù)放在一起比較。首先選好分位數(shù)間隔。圖上的點(diǎn)（x,y）反映出其中一個(gè)第二個(gè)分布（y坐標(biāo)）的分位數(shù)和與之對(duì)應(yīng)的第一分布（x坐標(biāo)）的相同分位數(shù)。因此，這條線(xiàn)是一條以分位數(shù)間隔為參數(shù)的曲線(xiàn)。如果兩個(gè)分布相似，則該Q-Q圖趨近于落在y=x線(xiàn)上。如果兩分布線(xiàn)性相關(guān)，則點(diǎn)在Q-Q圖上趨近于落在一條直線(xiàn)上，但不一定在y=x線(xiàn)上。Q-Q圖可以用來(lái)可在分布的位置-尺度范疇上可視化的評(píng)估參數(shù)。
從定義中可以看出Q-Q圖主要用于檢驗(yàn)數(shù)據(jù)分布的相似性，如果要利用Q-Q圖來(lái)對(duì)數(shù)據(jù)進(jìn)行正態(tài)分布的檢驗(yàn)，則可以令x軸為正態(tài)分布的分位數(shù)，y軸為樣本分位數(shù)，如果這兩者構(gòu)成的點(diǎn)分布在一條直線(xiàn)上，就證明樣本數(shù)據(jù)與正態(tài)分布存在線(xiàn)性相關(guān)性，即服從正態(tài)分布。

5.1 用法
ggqqplot(
  data,
  x,
  combine = FALSE,
  merge = FALSE,
  color = "black",
  palette = NULL,
  size = NULL,
  shape = NULL,
  add = c("qqline", "none"),
  add.params = list(linetype = "solid"),
  conf.int = TRUE,
  conf.int.level = 0.95,
  title = NULL,
  xlab = NULL,
  ylab = NULL,
  facet.by = NULL,
  panel.labs = NULL,
  short.panel.labs = TRUE,
  ggtheme = theme_pubr(),
  ...
)
5.2 自定義參數(shù)ggpar()

使用函數(shù)ggpar()可以輕松自定義繪圖, 閱讀？ggpar進(jìn)行更改：

  • 主標(biāo)題和軸標(biāo)簽：main，xlab，ylab
  • 軸區(qū)間：xlim，ylim(例如：ylim = c(0，30))
  • 軸比例尺：xscale，yscale(例如：yscale ="log2")
  • 調(diào)色板：palette="Dark2"或調(diào)色板= c("gray"，"blue"，"red")
  • 圖例標(biāo)題，標(biāo)簽和位置：legend="right"
  • 繪圖方向：orientation = c("vertical", "horizontal", "reverse")
5.3 實(shí)例
# Create some data format
set.seed(1234)
wdata = data.frame(
  sex = factor(rep(c("F", "M"), each=200)),
  weight = c(rnorm(200, 55), rnorm(200, 58)))

head(wdata, 4)#>   sex   weight
#> 1   F 53.79293
#> 2   F 55.27743
#> 3   F 56.08444
#> 4   F 52.65430
# Basic QQ plot
p1 <- ggqqplot(wdata, x = "weight")
p1
# Change colors and shape by groups ("sex")
# Use custom palette
p2 <- ggqqplot(wdata, x = "weight",
         color = "sex", palette = c("#00AFBB", "#E7B800"))
p2
p1

p2

Reference

https://rpkgs.datanovia.com/ggpubr/reference/index.html
http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/

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