了解日期并在 R 中使用 ggplot2 绘制直方图

更新

版本 2：使用 Date 类

我更新了示例以演示对齐标签并在图上设置限制。我还证明了 as.Date 在一致使用时确实有效（实际上它可能比我之前的示例更适合您的数据）。

目标情节 v2

https://i.stack.imgur.com/exn5E.png

守则 v2

这是（有点过分）注释代码：

library("ggplot2")
library("scales")

dates <- read.csv("http://pastebin.com/raw.php?i=sDzXKFxJ", sep=",", header=T)
dates$Date <- as.Date(dates$Date)

# convert the Date to its numeric equivalent
# Note that Dates are stored as number of days internally,
# hence it is easy to convert back and forth mentally
dates$num <- as.numeric(dates$Date)

bin <- 60 # used for aggregating the data and aligning the labels

p <- ggplot(dates, aes(num, ..count..))
p <- p + geom_histogram(binwidth = bin, colour="white")

# The numeric data is treated as a date,
# breaks are set to an interval equal to the binwidth,
# and a set of labels is generated and adjusted in order to align with bars
p <- p + scale_x_date(breaks = seq(min(dates$num)-20, # change -20 term to taste
                                   max(dates$num), 
                                   bin),
                      labels = date_format("%Y-%b"),
                      limits = c(as.Date("2009-01-01"), 
                                 as.Date("2011-12-01")))

# from here, format at ease
p <- p + theme_bw() + xlab(NULL) + opts(axis.text.x  = theme_text(angle=45,
                                                                  hjust = 1,
                                                                  vjust = 1))
p

版本 1：使用 POSIXct

我尝试了一个解决方案，该解决方案在 ggplot2 中完成所有操作，在没有聚合的情况下进行绘制，并在 2009 年初和 2011 年底之间设置 x 轴上的限制。

目标情节 v1

https://i.stack.imgur.com/ia0Hd.png

代码 v1

library("ggplot2")
library("scales")

dates <- read.csv("http://pastebin.com/raw.php?i=sDzXKFxJ", sep=",", header=T)
dates$Date <- as.POSIXct(dates$Date)

p <- ggplot(dates, aes(Date, ..count..)) + 
    geom_histogram() +
    theme_bw() + xlab(NULL) +
    scale_x_datetime(breaks = date_breaks("3 months"),
                     labels = date_format("%Y-%b"),
                     limits = c(as.POSIXct("2009-01-01"), 
                                as.POSIXct("2011-12-01")) )

p

当然，它可以通过使用轴上的标签选项来完成，但这是为了在绘图包中使用一个干净的简短例程来完成绘图。

我知道这是一个老问题，但对于任何在 2021 年（或之后）提出的人来说，使用 geom_histogram() 的 breaks= 参数并创建一个小快捷函数来制作所需的序列，这可以更容易地完成。

dates <- read.csv("http://pastebin.com/raw.php?i=sDzXKFxJ", sep=",", header=T)

dates$Date <- lubridate::ymd(dates$Date)

by_month <- function(x,n=1){
  seq(min(x,na.rm=T),max(x,na.rm=T),by=paste0(n," months"))
}

ggplot(dates,aes(Date)) +
  geom_histogram(breaks = by_month(dates$Date)) +
  scale_x_date(labels = scales::date_format("%Y-%b"),
               breaks = by_month(dates$Date,2)) + 
  theme(axis.text.x = element_text(angle=90))

https://i.stack.imgur.com/uWCVC.png

我认为关键是您需要在 ggplot 之外进行频率计算。将 aggregate() 与 geom_bar(stat="identity") 一起使用以获得没有重新排序因子的直方图。这是一些示例代码：

require(ggplot2)

# scales goes with ggplot and adds the needed scale* functions
require(scales)

# need the month() function for the extra plot
require(lubridate)

# original data
#df<-read.csv("http://pastebin.com/download.php?i=sDzXKFxJ", header=TRUE)

# simulated data
years=sample(seq(2008,2012),681,replace=TRUE,prob=c(0.0176211453744493,0.302496328928047,0.323054331864905,0.237885462555066,0.118942731277533))
months=sample(seq(1,12),681,replace=TRUE)
my.dates=as.Date(paste(years,months,01,sep="-"))
df=data.frame(YM=strftime(my.dates, format="%Y-%b"),Date=my.dates,Year=years,Month=months)
# end simulated data creation

# sort the list just to make it pretty. It makes no difference in the final results
df=df[do.call(order, df[c("Date")]), ]

# add a dummy column for clarity in processing
df$Count=1

# compute the frequencies ourselves
freqs=aggregate(Count ~ Year + Month, data=df, FUN=length)

# rebuild the Date column so that ggplot works
freqs$Date=as.Date(paste(freqs$Year,freqs$Month,"01",sep="-"))

# I set the breaks for 2 months to reduce clutter
g<-ggplot(data=freqs,aes(x=Date,y=Count))+ geom_bar(stat="identity") + scale_x_date(labels=date_format("%Y-%b"),breaks="2 months") + theme_bw() + opts(axis.text.x = theme_text(angle=90))
print(g)

# don't overwrite the previous graph
dev.new()

# just for grins, here is a faceted view by year
# Add the Month.name factor to have things work. month() keeps the factor levels in order
freqs$Month.name=month(freqs$Date,label=TRUE, abbr=TRUE)
g2<-ggplot(data=freqs,aes(x=Month.name,y=Count))+ geom_bar(stat="identity") + facet_grid(Year~.) + theme_bw()
print(g2)

标题“基于 Gauden 方法的绘图”下的错误图是由于 binwidth 参数： ... + Geom_histogram (binwidth = 30, color = "white") + ... 如果我们将 30 的值更改为值小于 20，例如 10，您将获得所有频率。

在统计数据中，数值比显示更重要，平淡的图形对非常漂亮的图片但有错误。