在matplotlib或R中重现线图

时间:2016-11-09 15:03:10

标签: r matplotlib data-visualization reproducible-research

我遇到了精彩的figure,它总结了(科学)作者多年来的合作。这个数字贴在下面。

enter image description here

每条垂直线指的是单一作者。每条垂直线的开始对应于相关作者收到她的第一个合作者的年份(即,当她变得活跃并因此成为协作网络的一部分时)。作者根据他们去年(即2010年)的合作者总数进行排名。着色表示每个作者的合作者数量多年来(从活跃到2010年)的增加情况。

我有一个类似的数据集;而不是作者我在我的数据集中有关键字。每个数值表示特定年份的术语频率。数据如下:

Year Term1 Term2 Term3 Term4
1966     0     1     1     4
1967     1     5     0     0
1968     2     1     0     5
1969     5     0     0     2

例如,Term2首先出现在1967年,频率为1,而Term4首先出现在1966年,频率为4.完整数据集可用here

1 个答案:

答案 0 :(得分:2)

图表看起来很不错,所以我尝试重现它。事实证明它比我想象的要复杂一点。

df=read.table("test_data.txt",header=T,sep=",")
#turn O into NA until >0 then keep values
df2=data.frame(Year=df$Year,sapply(df[,!colnames(df)=="Year"],function(x) ifelse(cumsum(x)==0,NA,x)))
#turn dataframe to a long format 
library(reshape)
molten=melt(df2,id.vars = "Year")
#Create a new value to measure the increase over time: I used a log scale to avoid a few classes overshadowing the others.
#The "increase" is measured as the cumsum, ave() is used to get cumsum to work with NA's and tapply to group on "variable"
molten$inc=log(Reduce(c,tapply(molten$value,molten$variable,function(x) ave(x,is.na(x),FUN=cumsum)))+1)
#reordering of variable according to max increase
#this dataframe is sorted in descending order according to the maximum increase"
library(dplyr)
df_order=molten%>%group_by(variable)%>%summarise(max_inc=max(na.omit(inc)))%>%arrange(desc(max_inc))
#this allows to change the levels of variable so that variable is ranked in the plot according to the highest value of "increase"
molten$variable<-factor(molten$variable,levels=df_order$variable)
#plot
ggplot(molten)+
  theme_void()+ #removes axes, background, etc...
  geom_line(aes(x=variable,y=Year,colour=inc),size=2)+
  theme(axis.text.y = element_text())+
  scale_color_gradientn(colours=c("red","green","blue"),na.value = "white")# set the colour gradient

给予: enter image description here

不像报纸那么好,但那是一个开始。