R ggplot2 教學：基本概念與 qplot 函數

library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.5.3
head(diamonds)
## # A tibble: 6 x 10
##   carat cut       color clarity depth table price     x     y     z
##   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23  Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
## 2 0.21  Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
## 3 0.23  Good      E     VS1      56.9    65   327  4.05  4.07  2.31
## 4 0.290 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
## 5 0.31  Good      J     SI2      63.3    58   335  4.34  4.35  2.75
## 6 0.24  Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
set.seed(5)
diamonds.subset <- diamonds[sample(nrow(diamonds), 100), ]

ggplot繪圖文法

安裝 ggplot2 套件

執行 ggplot2 套件

用head看diamonds資料

diamonds資料集的資料有五萬多筆

diamonds.subset <- diamonds[sample(nrow(diamonds), 100), ]

抽出100筆資料存在diamonds.subset中

qplot(diamonds$carat, diamonds$price)

qplot(carat, price, data = diamonds)

qplot(log(carat), log(price), data = diamonds)

qplot(carat, x * y * z, data = diamonds)

qplot函數的運用

qplot函數是ggplot中最基本的繪圖函數

qplot 的用法是x軸與y軸的座標資料為參數(ex：qplot(diamonds\(carat, diamonds\)price))

qplot(carat, price, data = diamonds) - 使用data參數指定資料的來源

price(價格)/carat(克拉數)

log(對數) - 可透過此轉換不是線性關係的圖表

qplot(log(carat), log(price), data = diamonds)鑽石的價格與克拉數有明顯的關係

qplot(carat, x * y * z, data = diamonds) - 使用多個變數進行運算，大部分鑽石的體積都與重量成正比(密度都差不多)，也有超出的

qplot(carat, price, data = diamonds.subset, color = color)

qplot(carat, price, data = diamonds.subset, shape = cut)
## Warning: Using shapes for an ordinal variable is not advised

qplot(carat, price, data = diamonds, alpha = I(1/10))

qplot(carat, price, data = diamonds, alpha = I(1/100))

圖形樣式

qplot一些參數可使更改資料點的顏色、大小等樣式

legend - 圖形上加入圖示說明

qplot(carat, price, data = diamonds.subset, color = color) - 用color替資料點著色區分不同顏色鑽石

shape - 以資料點的形狀區分資料

qplot(carat, price, data = diamonds, alpha = I(1/10))/qplot(carat, price, data = diamonds, alpha = I(1/100)) - 資料點加上透明度的參數使容易辨識實際的資料分佈情況(資料點大量重疊問題)

qplot(carat, price, data = diamonds.subset,
      geom = c("point", "smooth"))
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

qplot(carat, price, data = diamonds,
      geom = c("point", "smooth"))
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

在圖形上加入Smoother

point(點呈現資料)/smooth(平滑曲線與標準誤差)

同時指定多種幾何圖形(geom)可使用c函數

qplot(color, price / carat, data = diamonds, geom = "boxplot")

qplot(color, price / carat, data = diamonds, geom = "jitter")

qplot(color, price / carat, data = diamonds,
      geom = "jitter", alpha = I(1 / 5))

qplot(color, price / carat, data = diamonds, geom = "jitter",
      alpha = I(1 / 50))

qplot(color, price / carat, data = diamonds, geom = "jitter",
      alpha = I(1 / 200))

箱形圖與Jitter資料點

boxplot(箱形圖)

boxplot - 資料同時含一個類型的變數及一個或多個連續型變數時可使用

更細部的資訊可使用jitter

透明度 alpha = I(1 / 5)

調整圖形可使用size、color、shape

箱形圖可使用color、fill、size調整顏色與線條粗細

qplot(color, price / carat, data = diamonds, geom = "jitter",
      alpha = I(1 / 5), shape = cut)
## Warning: Using shapes for an ordinal variable is not advised

以資料點的形狀區分 cut 變數

qplot(color, price / carat, data = diamonds, geom = "jitter",
      alpha = I(1 / 5), color = color)

以資料點的顏色區分 color 變數

qplot(color, price / carat, data = diamonds, geom = "jitter",
      alpha = I(1 / 5), color = cut)

#以資料點的顏色區分 cut 變數

qplot(color, price / carat, data = diamonds, geom = "boxplot",
      color = color)

以箱形圖的外框顏色區分 color 變數

qplot(color, price / carat, data = diamonds, geom = "boxplot",
      fill = color)

以箱形圖的內部顏色區分 color 變數

qplot(color, price / carat, data = diamonds, geom = "boxplot",
      size = I(2))

調整箱形圖的外框粗細

qplot(carat, data = diamonds, geom = "histogram")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

qplot(carat, data = diamonds, geom = "histogram",
      binwidth = 0.5, xlim = c(0, 3))
## Warning: Removed 32 rows containing non-finite values (stat_bin).
## Warning: Removed 2 rows containing missing values (geom_bar).

qplot(carat, data = diamonds, geom = "histogram",
      binwidth = 0.01, xlim = c(0, 3))
## Warning: Removed 32 rows containing non-finite values (stat_bin).

## Warning: Removed 2 rows containing missing values (geom_bar).

qplot(carat, data = diamonds, geom = "histogram",
      fill = color)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

qplot(carat, data = diamonds, geom = "density")

qplot(carat, data = diamonds, geom = "density", adjust = 3)

qplot(carat, data = diamonds, geom = "density",
      color = color)

qplot(carat, ..density.., data = diamonds,
      geom = c("histogram", "density"))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

直方圖與密度函數

histogram(直方圖0

bin 寬度可使用binwidth調整

qplot(carat, data = diamonds, geom = “histogram”,binwidth = 0.01, xlim = c(0, 3))(細部資訊可能要在bin寬度非常小的情況下才會顯現)

qplot(carat, data = diamonds, geom = “histogram”,fill = color) - 不同顏色畫出不同鑽石顏色的資料產生堆疊的直方圖呈現多組資料

adjust調整密度圖的平滑程度(值越大曲線越平滑)

qplot(carat, data = diamonds, geom = “density”,color = color) - 呈現多組資料

qplot(carat, ..density.., data = diamonds,geom = c(“histogram”, “density”)) - 直方圖密度函數圖畫在一起

qplot(color, data = diamonds, geom = "bar")

qplot(color, data = diamonds, geom = "bar", weight = carat) +
  ylab("carat")

長條圖

bar(長條圖)

weight - 指定數值的來源

qplot(date, unemploy / pop, data = economics, geom = "line")

qplot(date, uempmed, data = economics, geom = "line")

qplot(unemploy / pop, uempmed, data = economics,
      geom = c("point", "path"))

year <- function(x) as.POSIXlt(x)$year + 1900
qplot(unemploy / pop, uempmed, data = economics,
      geom = "path", colour = year(date))

時間序列與路徑

line與path可用來繪製時間序列與路徑類型的資料

path會依資料中的順序將每個點以線段連接，path是在比較兩個變數隨時間變化的關係

line會將點的順序依x軸排序，line 圖形的x軸是時間的資訊，來呈現某個變數隨時間的變化

unemploy(失業人口)/ pop(人口總數)/uempmed(失業時間的中位數)

year <- function(x) as.POSIXlt(x)$year + 1900 qplot(unemploy / pop, uempmed, data = economics, geom = “path”, colour = year(date))加上顏色來表示不同的年份

qplot(carat, data = diamonds, facets = color ~ cut,
      geom = "histogram", binwidth = 0.1, xlim = c(0, 3))
## Warning: Removed 32 rows containing non-finite values (stat_bin).
## Warning: Removed 70 rows containing missing values (geom_bar).

qplot(carat, data = diamonds, facets = color ~ .,
      geom = "histogram", binwidth = 0.1, xlim = c(0, 3))
## Warning: Removed 32 rows containing non-finite values (stat_bin).
## Warning: Removed 14 rows containing missing values (geom_bar).

繪圖面(Facet)

facets(資料區隔方式)

row(列)/column(行)

只需對一個變數分組將公式另一側指定為一個句點（.）

qplot(
  carat, price, data = diamonds.subset,
  xlab = "Price ($)", ylab = "Weight (carats)",
  main = "Price-weight relationship"
)

qplot(
  carat, price/carat, data = diamonds.subset,
  ylab = expression(frac(price,carat)),
  xlab = "Weight (carats)",
  main="Small diamonds",
  xlim = c(.2,1)
)
## Warning: Removed 29 rows containing missing values (geom_point).

qplot(carat, price, data = diamonds.subset, log = "xy")

其他用法

xlim(設定x軸繪圖範圍)/ylim(設定y軸的繪圖範圍)

log - 指定對數轉換的座標軸(log =“xy”是讓x與y軸都經過對數轉換)

main - 指定圖形的標題

xlab(指定X軸名稱)/ylab(指定y軸名稱)

R ggplot2 教學：圖層式繪圖

my.plot <- ggplot(diamonds, aes(carat, price, colour = cut))

建立圖形物件

對應參數用aes函數包裝

aes下的範例是將carat變數對應到x軸、price數對應到y軸

以顏色區隔cut變數

my.plot <- my.plot + layer(
  geom = "point",
  stat = "identity",
  position = "identity",
  params = list(na.rm = FALSE)
)
my.plot

my.plot2 <- ggplot(diamonds, aes(x = carat))
my.plot2 <- my.plot2 + layer(
  geom = "bar",
  stat = "bin",
  position = "identity",
  params = list(
    fill = "steelblue",
    binwidth = 0.2,
    na.rm = FALSE
  )
)
my.plot2

my.plot3 <- ggplot(diamonds, aes(x = carat))
my.plot3 <- my.plot3 +
  geom_histogram(binwidth = 0.2, fill = "steelblue")
my.plot3

圖層

以上的參數都可省略，不指定就會使用內部預設值

+(疊加至既有的繪圖物件上)

identity - 不需要特別的轉換與調整就指定為此

params - 用來指定geom與stat所需的參數

my.plot - 顯示圖形

繪製直方圖需用bin統計轉換直方圖每個bin的數值

position - 指定細部的資料的位置調整

geom_histogram繪製直方圖

簡化圖層函數都是以geom_與stat_的方式命名

… - geom與stat所使用的參數

ggplot與qplot的寫法比較

ggplot(msleep, aes(sleep_rem / sleep_total, awake)) +
  geom_point()
## Warning: Removed 22 rows containing missing values (geom_point).

qplot(sleep_rem / sleep_total, awake, data = msleep)
## Warning: Removed 22 rows containing missing values (geom_point).

有2種作法分別為

做法一 / # 做法二

加入平滑曲線

qplot(sleep_rem / sleep_total, awake, data = msleep) +
  geom_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 22 rows containing non-finite values (stat_smooth).
## Warning: Removed 22 rows containing missing values (geom_point).

qplot(sleep_rem / sleep_total, awake, data = msleep,
      geom = c("point", "smooth"))
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 22 rows containing non-finite values (stat_smooth).

## Warning: Removed 22 rows containing missing values (geom_point).

ggplot(msleep, aes(sleep_rem / sleep_total, awake)) +
  geom_point() + geom_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 22 rows containing non-finite values (stat_smooth).

## Warning: Removed 22 rows containing missing values (geom_point).

有3種作法分別為

做法一 / # 做法二 / # 做法三

my.plot4 <- ggplot(msleep, aes(sleep_rem / sleep_total, awake))
summary(my.plot4)
## data: name, genus, vore, order, conservation, sleep_total,
##   sleep_rem, sleep_cycle, awake, brainwt, bodywt [83x11]
## mapping:  x = ~sleep_rem/sleep_total, y = ~awake
## faceting: <ggproto object: Class FacetNull, Facet, gg>
##     compute_layout: function
##     draw_back: function
##     draw_front: function
##     draw_labels: function
##     draw_panels: function
##     finish_data: function
##     init_scales: function
##     map_data: function
##     params: list
##     setup_data: function
##     setup_params: function
##     shrink: TRUE
##     train_scales: function
##     vars: function
##     super:  <ggproto object: Class FacetNull, Facet, gg>
my.plot4 <- my.plot4 + geom_point()
summary(my.plot4)
## data: name, genus, vore, order, conservation, sleep_total,
##   sleep_rem, sleep_cycle, awake, brainwt, bodywt [83x11]
## mapping:  x = ~sleep_rem/sleep_total, y = ~awake
## faceting: <ggproto object: Class FacetNull, Facet, gg>
##     compute_layout: function
##     draw_back: function
##     draw_front: function
##     draw_labels: function
##     draw_panels: function
##     finish_data: function
##     init_scales: function
##     map_data: function
##     params: list
##     setup_data: function
##     setup_params: function
##     shrink: TRUE
##     train_scales: function
##     vars: function
##     super:  <ggproto object: Class FacetNull, Facet, gg>
## -----------------------------------
## geom_point: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity
bestfit <- geom_smooth(method = "lm", se = F,
                       color = alpha("steelblue", 0.5), size = 2)
qplot(sleep_rem, sleep_total, data = msleep) + bestfit
## Warning: Removed 22 rows containing non-finite values (stat_smooth).
## Warning: Removed 22 rows containing missing values (geom_point).

qplot(awake, brainwt, data = msleep, log = "y") + bestfit
## Warning: Removed 27 rows containing non-finite values (stat_smooth).
## Warning: Removed 27 rows containing missing values (geom_point).

qplot(bodywt, brainwt, data = msleep, log = "xy") + bestfit
## Warning: Removed 27 rows containing non-finite values (stat_smooth).

## Warning: Removed 27 rows containing missing values (geom_point).

繪圖物件可用summary查看內容

建立一個圖層套用在不同繪圖物件上

my.plot5 <- ggplot(mtcars, aes(mpg, wt, colour = 1)) + geom_point()
my.plot5

mtcars.trans <- transform(mtcars, mpg = mpg ^ 2)
my.plot5 %+% mtcars.trans

資料來源

%+% - 改變ggplot繪圖物件的資料來源可使用(抽換資料來源)

my.plot5 <- ggplot(mtcars, aes(mpg, wt, colour = cyl)) + geom_point() - 當中的colour = cyl要改成數值1才可執行

aes(x = weight, y = height, color = age)
## Aesthetic mapping: 
## * `x`      -> `weight`
## * `y`      -> `height`
## * `colour` -> `age`
aes(weight, height, colour = sqrt(age))
## Aesthetic mapping: 
## * `x`      -> `weight`
## * `y`      -> `height`
## * `colour` -> `sqrt(age)`

美學對應

aes(x = weight, y = height, color = age) - 將x軸指定為weight、y軸指定為height，顏色(color)指定為age

my.plot6 <- ggplot(mtcars, aes(x = mpg, y = wt))
my.plot6 <- my.plot6 + geom_point()
summary(my.plot6)
## data: mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb
##   [32x11]
## mapping:  x = ~mpg, y = ~wt
## faceting: <ggproto object: Class FacetNull, Facet, gg>
##     compute_layout: function
##     draw_back: function
##     draw_front: function
##     draw_labels: function
##     draw_panels: function
##     finish_data: function
##     init_scales: function
##     map_data: function
##     params: list
##     setup_data: function
##     setup_params: function
##     shrink: TRUE
##     train_scales: function
##     vars: function
##     super:  <ggproto object: Class FacetNull, Facet, gg>
## -----------------------------------
## geom_point: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity
my.plot7 <- ggplot(mtcars)
my.plot7 <- my.plot7 + aes(x = mpg, y = wt)
my.plot7 <- my.plot7 + geom_point()
summary(my.plot7)
## data: mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb
##   [32x11]
## mapping:  x = ~mpg, y = ~wt
## faceting: <ggproto object: Class FacetNull, Facet, gg>
##     compute_layout: function
##     draw_back: function
##     draw_front: function
##     draw_labels: function
##     draw_panels: function
##     finish_data: function
##     init_scales: function
##     map_data: function
##     params: list
##     setup_data: function
##     setup_params: function
##     shrink: TRUE
##     train_scales: function
##     vars: function
##     super:  <ggproto object: Class FacetNull, Facet, gg>
## -----------------------------------
## geom_point: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity
my.plot7 + geom_point(aes(colour = factor(1)))

my.plot7 + geom_point(aes(y = disp))

aes(y = NULL)
## Aesthetic mapping: 
## * `y` -> NULL

繪圖與圖層

預設可在繪圖物件建立時就指定

也可在繪圖物件建立後再加上aes來指定

在繪圖物件中設定可在圖層中增加、修改或刪除

aes(y = NULL) - 刪除某一參數把參數設定為NULL

my.plot7 + geom_point(aes(colour = factor(cyl)) - 當中的colour = factor(cyl)要改成數值1才可執行

my.plot8 <- ggplot(mtcars, aes(mpg, wt))
my.plot8 + geom_point(colour = "blue")

my.plot8 + geom_point(aes(colour = "blue"))

設定與對應

colour = “blue” - 資料點的顏色指定為藍色

aes函數 - 代表對應關係

加上aes函數內部產生的blue對應呈粉紅色

qplot函數中要設定固定參數要額外用I函數(color = I(“blue”))

my.df <- data.frame(x = 1:3, y = 1:3, z = 1:3)
qplot(x, y, data = my.df, color = factor(z), size = I(5)) +
  geom_line(size = 3, group = 1)

qplot(color, data = diamonds, geom = "bar",
      fill = cut)

美學對應與幾何圖形

繪製line或path的線段時，第一條線段會用第一個點的顏色，第二條線段會用第二個點的顏色，依序下去

ggplot會自動將資料依變數分組

ggplot(diamonds, aes(carat)) +
  geom_histogram(aes(y = ..density..), binwidth = 0.1)

qplot(carat, ..density.., data = diamonds,
      geom="histogram", binwidth = 0.1)

統計轉換(Stat)

stat_bin用於建立直方圖的統計轉換會產生count(每個bin的資料點數)、density(每個bin的資料點數比例)、ncount(將count標準化最大值為1)、ndensity(將density標準化最大值為1)

density(密度為單位可使用)

統計轉換所產生的變數時必須前後加上 ..

ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar(position="dodge")

ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar(position="fill")

ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar(position="stack")

ggplot(diamonds, aes(clarity, group = cut)) +
  geom_line(aes(color = cut), position="identity", stat = "count")

set.seed(5)
diamonds.subset <- diamonds[sample(nrow(diamonds), 500), ]
ggplot(diamonds.subset, aes(clarity, cut)) +
  geom_point(aes(color = color), position="jitter")

位置調整(Position)

主要用於調整圖層中幾何圖形比較細微的位置選項

可用的位置調整選項：dodge(並列顯示)、fill(標準化堆疊顯示)、identity(不調整位置)、jitter(使用jitter方式避免資料點重疊)、stack(堆疊顯示)

identity適合用在一般的折線圖

jitter適用於以資料點繪製類型資料的情況

my.plot <- ggplot(diamonds, aes(carat)) + xlim(0, 3)
my.plot + stat_bin(aes(ymax = ..count..), binwidth = 0.1, geom = "area")
## Warning: Ignoring unknown aesthetics: ymax
## Warning: Removed 32 rows containing non-finite values (stat_bin).

my.plot + stat_bin(
  aes(size = ..density..), binwidth = 0.1,
  geom = "point", position="identity"
)
## Warning: Removed 32 rows containing non-finite values (stat_bin).

幾何圖形與統計轉換

以count統計轉換

my.plot + stat_bin( aes(size = ..density..), binwidth = 0.1, geom = “point”, position=“identity”)配合點的幾何圖形的結果

用stat_identity統計轉換會依原始資料的類型直接選擇適合的幾何圖形來呈現

以下都跑出錯誤訊息，是不知道如何修改的程式與說明

qplot範例

mtcars\(gear <- factor(mtcars\)gear,levels=c(3, 4, 5), labels=c(“3gears”, “4gears”, “5gears”)) mtcars\(am <- factor(mtcars\)am,levels=c(0, 1), labels=c(“Automatic”, “Manual”)) mtcars\(cyl <- factor(mtcars\)cyl,levels=c(4, 6, 8), labels=c(“4cyl”, “6cyl”, “8cyl”)) # 產生繪圖用的因子變數

qplot(mpg, data = mtcars, geom = “density”, fill = gear, alpha = I(.5), main=“Distribution of Gas Milage”, xlab=“Miles Per Gallon”, ylab=“Density”) # 以 gear 分組畫出 mpg 密度函數圖

qplot(hp, mpg, data = mtcars, shape = am, color = am, facets = gear~cyl, size = I(3), xlab = “Horsepower”, ylab = “Miles per Gallon”) # 將資料以 gear 與 cylinder 分組 # 畫出 mpg 與 hp 的散佈圖 # 並且以資料點的顏色與形狀標示 am

qplot(gear, mpg, data = mtcars, geom = c(“boxplot”, “jitter”), fill = gear, main = “Mileage by Gear Number”, xlab = “”, ylab = “Miles per Gallon”) # 畫出箱形圖， # 並且在上面用 jitter 資料點畫出實際資料的位置

以下是跑得出圖但是不能連上knit的程式與說明

my.plot9 <- ggplot(Oxboys, aes(age, height)) + geom_line() my.plot9 <- ggplot(Oxboys, aes(age, height, group = Subject)) + geom_line()

my.plot9 + geom_smooth(method=“lm”, se = F) my.plot9 + geom_smooth(aes(group = 1), method=“lm”, size = 2, se = F)

boysbox <- ggplot(Oxboys, aes(Occasion, height)) + geom_boxplot() boysbox + geom_line(aes(group = Subject), color = “blue”) #群組(Grouping)/基本群組 以nlme套件中Oxboys資料集繪圖

畫出多條時間序列的線條可加上group並指定為個體的變數

圖層與群組

加上一條線性迴歸線可用geom_smooth配合method = “lm”，加上group可對每個各體畫出各自的迴歸線

group設為一個固定的值(取消群組的功能)

將height依Occasion畫出箱形圖

Occasion - 離散型變數，不需特別指定group

geom_line依Subject區分資料畫出每個的資料並另外加color參數設定線條顏色

library(nlme) model <- lme(height ~ age, data = Oxboys, random = ~ 1 + age | Subject) age_grid <- seq(-1, 1, length = 10) subjects <- unique(Oxboys\(Subject) preds <- expand.grid(age = age_grid, Subject = subjects) preds\)height <- predict(model, preds)

my.plot <- ggplot(Oxboys, aes(age, height, group = Subject)) + geom_line() my.plot + geom_line(data = preds, colour = “#3366FF”, size= 0.4)

Oxboys\(fitted <- predict(model) Oxboys\)resid <- with(Oxboys, fitted - height) my.plot %+% Oxboys + aes(y = resid) + geom_smooth(aes(group=1)) #不同的資料來源 執行nlme

不同的圖層有不同的資料來源，可將相關幾個資料集畫在一起(例如用模型預測資料時繪製預測值)

畫出模型的預測值

畫出模型的殘差值並加入平滑曲線