小白可上手，自制動圖展示連續(xù)數(shù)據(jù)

祥強(qiáng)6csdm0n3vs 2019-08-23

展開全文

這是ggplot中十分可愛的一個擴(kuò)增包，目的只有一個，就是讓你的圖動起來！就是醬紫??！

gganimate擴(kuò)展了ggplot2實現(xiàn)的圖形語法，包括動畫描述。它通過提供一系列新的語法類來實現(xiàn)這一點，這些類可以添加到繪圖對象中，以便自定義它應(yīng)該如何隨時間變化。

下面是他的parameter：

transition_*()定義了數(shù)據(jù)應(yīng)該如何展開以及它與時間的關(guān)系。
view_*()定義位置比例應(yīng)如何沿動畫更改。
shadow_*()定義如何在給定的時間點呈現(xiàn)來自其他時間點的數(shù)據(jù)。
enter_*()/ exit_*()定義新數(shù)據(jù)應(yīng)如何顯示以及舊數(shù)據(jù)在動畫過程中應(yīng)如何消失。
ease_aes()定義了在過渡期間應(yīng)該如何進(jìn)行過渡。

舉個栗子！

#安裝輔助包，該包有兩個版本，已經(jīng)更新為最新版本，老版本在未來將不再支持。install.packages('gganimate')

# 安裝開發(fā)版
# install.packages('devtools')
# devtools::install_github('thomasp85/gganimate')
library(ggplot2)
library(gganimate)

ggplot(mtcars, aes(factor(cyl), mpg)) +
  geom_boxplot() + geom_point() +
  # Here comes the gganimate code
  transition_states(
    gear,
    transition_length = 2,
    state_length = 1
  ) +
  enter_fade() +
  exit_shrink() +
  ease_aes('sine-in-out')

加載時間是比較長的，需要耐心等待哈！

Yet Another Example

首先查看一下數(shù)據(jù)格式吧，Gapminder是關(guān)于預(yù)期壽命，人均國內(nèi)生產(chǎn)總值和國家人口的數(shù)據(jù)摘錄。

library(gapminder)
head(gapminder)#我們看一下數(shù)據(jù)格式

ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
#點的大小和顏色分別由pop和country決定；geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +        #進(jìn)行數(shù)值之間的映射
  scale_size(range = c(2, 12)) +                    #設(shè)置繪圖符號大小
  scale_x_log10() +                              #連續(xù)數(shù)據(jù)位置的標(biāo)準(zhǔn)化
  facet_wrap(~continent) +                     #按照continent進(jìn)行分類
  # Here comes the gganimate specific bits
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')#指數(shù)據(jù)變化的狀態(tài)，線性發(fā)展比較緩慢

哈哈哈，現(xiàn)在我們以腫瘤數(shù)據(jù)為例進(jìn)行演示一下：

我編了一組測試數(shù)據(jù)，其中將腫瘤分為I,II,III型，IV型為control,然后分別顯示了再不同樣本中不同腫瘤分型下的部分基因的表達(dá)情況。

library(ggplot2)
library(gganimate)
#首先我們進(jìn)行數(shù)據(jù)的讀入

data <- 'subgroup,sample,gene,expression
I,Tumor,p53,12.725952
II,Tumor,p53,11.914176
III,Tumor,p53,12.315768
IV,Normal,p53,12.978894
I,Tumor,p53,11.93924
II,Tumor,p53,12.262185
III,Tumor,p53,11.538924
IV,Normal,p53,12.016589
I,Tumor,p53,12.302574
II,Tumor,p53,11.939233
III,Tumor,p53,12.803992
IV,Normal,p53,10.674506
I,Tumor,p53,12.569142
II,Tumor,p53,12.088496
III,Tumor,p53,9.971951
IV,Normal,p53,13.008554
I,Tumor,p53,12.804154
II,Tumor,p53,11.847107
III,Tumor,p53,12.081261
IV,Normal,p53,12.158431
I,Tumor,p53,11.096693
II,Tumor,p53,12.655811
III,Tumor,p53,11.509067
IV,Normal,p53,12.523573
I,Tumor,p53,11.3554
II,Tumor,p53,11.560566
III,Tumor,p53,10.969046
IV,Normal,p53,11.169892
I,Tumor,p53,12.884054
II,Tumor,p53,12.284268
III,Tumor,her2,9.575523
IV,Normal,her2,12.409381
I,Tumor,her2,12.114364
II,Tumor,her2,11.493997
III,Tumor,her2,10.987106
IV,Normal,her2,11.943991
I,Tumor,her2,11.171378
II,Tumor,her2,13.120248
III,Tumor,her2,12.628872
IV,Normal,her2,11.91914
I,Tumor,her2,12.36504
II,Tumor,her2,12.707354
III,Tumor,her2,12.54517
IV,Normal,her2,12.199749
I,Tumor,her2,13.184496
II,Tumor,her2,12.640412
III,Tumor,her2,12.716897
IV,Normal,her2,13.359091
I,Tumor,her2,11.760945
II,Tumor,her2,11.406367
III,Tumor,her2,11.984382
IV,Normal,her2,12.254977
I,Tumor,her2,11.579763
II,Tumor,her2,11.983042
III,Tumor,her2,12.566317
IV,Normal,her2,10.869331
I,Tumor,her2,10.910963
II,Tumor,her2,11.948207
III,Tumor,myc,12.363072
IV,Normal,myc,12.755182
I,Tumor,myc,11.922223
II,Tumor,myc,9.618839
III,Tumor,myc,12.693868
IV,Normal,myc,13.40685
I,Tumor,myc,11.871609
II,Tumor,myc,11.783704
III,Tumor,myc,12.485053
IV,Normal,myc,12.669123
I,Tumor,myc,11.653691
II,Tumor,myc,11.675768
III,Tumor,myc,12.744605
IV,Normal,myc,12.911619
I,Tumor,myc,12.008307
II,Tumor,myc,11.838161
III,Tumor,myc,12.590989
IV,Normal,myc,11.680278
I,Tumor,myc,11.719241
II,Tumor,myc,10.156746
III,Tumor,myc,11.84406
IV,Normal,myc,12.975393
I,Tumor,myc,10.963332
II,Tumor,myc,12.338216
III,Tumor,myc,12.030859
IV,Normal,myc,11.119114
I,Tumor,myc,12.661349
II,Tumor,myc,13.168166
III,Tumor,myc,11.707595
IV,Normal,myc,12.06719
I,Tumor,myc,12.463962
II,Tumor,myc,12.288819
III,Tumor,myc,12.036757
IV,Normal,myc,12.98055
I,Tumor,myc,11.343075
II,Tumor,myc,12.565481
III,Tumor,myc,12.279996
IV,Normal,myc,12.965189
I,Tumor,myc,12.946155
II,Tumor,myc,11.688462
III,Tumor,sox4,11.944477
IV,Normal,sox4,12.128177
I,Tumor,sox4,11.116105
II,Tumor,sox4,11.148871
III,Tumor,sox4,13.139244
IV,Normal,sox4,10.043207
I,Tumor,sox4,12.043914
II,Tumor,sox4,9.990576
III,Tumor,sox4,11.624263
IV,Normal,sox4,11.647402
I,Tumor,sox4,12.502176
II,Tumor,sox4,12.291812
III,Tumor,sox4,11.421913
IV,Normal,sox4,12.282511
I,Tumor,sox4,12.511991
II,Tumor,sox4,12.285322
III,Tumor,sox4,11.7884
IV,Normal,sox4,13.747552
I,Tumor,sox4,11.212993
II,Tumor,sox4,12.936845
III,Tumor,sox4,12.442484
IV,Normal,sox4,10.324288
I,Tumor,sox4,12.436421
II,Tumor,sox4,11.923122
III,Tumor,sox4,12.831474
IV,Normal,sox4,12.271537
I,Tumor,sox4,12.208086
II,Tumor,sox4,11.830799
III,Tumor,sox4,12.410238
IV,Normal,sox4,12.13912
I,Tumor,sox4,12.47'

test <- read.csv(text=data,header=T)
head(test)

library(ggplot2)
ggplot(test,aes(x=subgroup,y=expression,fill=subgroup))+
  geom_boxplot()+
  geom_jitter()+
  theme_bw()                     #按照subgroup進(jìn)行分型，并畫出箱式圖

同樣對不同gene在各組中的分布情況進(jìn)行描述：

library(ggplot2)
p <- ggplot(test,aes(x=subgroup,y=expression,fill=subgroup))+
  geom_boxplot()+
  geom_jitter()+
  theme_bw()
p +facet_grid(.~gene)#按照gene對各個小組進(jìn)行分類

library(ggplot2)
library(gganimate)
p <- ggplot(test,aes(x=subgroup,y=expression,fill=subgroup))+
  geom_boxplot()+
  geom_jitter()+
  theme_bw()
p +transition_states(gene, state_length = 0)+
  labs(title = '{closest_state} expression')