使用ggplot和R绘制无数图层（条形图）

我试图重新创build一个条形图，我使用的数据列出了全年的库存和销售额。 这是我在Excel中的图表：

注意：平均销售率是条形图中13个月的总销售额/总库存。

我正在通过R和ggplot包进行此操作。 我很新，但这是我迄今为止所做的：

library(lubridate) library(ggplot2) library(scales) library(reshape2) COdata <- read.csv("C:/.../CenterOne.csv") # Grab related data # VIN refers to a unique inventory identifier for the item # First Launch Date is what I use to count my inventory for the month # Sale Date is what I use to count my sales for the month DFtest <- COdata[, c("VIN", "First.Launch.Date", "Sale.Date")] 

以下是数据外观的快照：

 > head(DFtest) VIN First.Launch.Date Sale.Date 1 4T1BF1FK4CU048373 22/04/2015 0:00 2 2T3KF4DVXCW108677 16/03/2015 0:00 3 4T1BF1FKXCU035935 19/03/2015 0:00 20/03/2015 0:00 4 JTDKN3DU3B1465796 16/04/2015 0:00 5 2T3YK4DV8CW015050 6 4T1BF1FK5CU599556 30/04/2015 0:00 

我将date转换为适当的格式，删除小时/秒，并将其分成月度间隔：

 DFtest$First.Launch.Date <- as.Date(DFtest$First.Launch.Date, format = "%d/%m/%Y") DFtest$Sale.Date <- as.Date(DFtest$Sale.Date, format = "%d/%m/%Y") DFtest$month.listings <- as.Date(cut(DFtest$First.Launch.Date, breaks = "month")) DFtest$month.sales <- as.Date(cut(DFtest$Sale.Date, breaks = "month")) > head(DFtest) VIN First.Launch.Date Sale.Date month.listings month.sales 1 4T1BF1FK4CU048373 2015-04-22 <NA> 2015-04-01 <NA> 2 2T3KF4DVXCW108677 2015-03-16 <NA> 2015-03-01 <NA> 3 4T1BF1FKXCU035935 2015-03-19 2015-03-20 2015-03-01 2015-03-01 4 JTDKN3DU3B1465796 2015-04-16 <NA> 2015-04-01 <NA> 5 2T3YK4DV8CW015050 <NA> <NA> <NA> <NA> 6 4T1BF1FK5CU599556 2015-04-30 <NA> 2015-04-01 <NA> 

平均线图 – 我尝试创build一个

 DF_Listings = data.frame(table(format(DFtest$month.listings))) DF_Sales = data.frame(table(format(DFtest$month.sales))) DF_Merge <- merge(DF_Listings, DF_Sales, by = "Var1", all = TRUE) > head(DF_Listings) Var1 Freq 1 2014-12-01 77 2 2015-01-01 886 3 2015-02-01 930 4 2015-03-01 1167 5 2015-04-01 1105 6 2015-05-01 1279 DF_Merge$Avg <- DF_Merge$Freq.y / DF_Merge$Freq.x > head(DF_Merge) Var1 Freq.x Freq.y Avg 1 2014-12-01 77 NA NA 2 2015-01-01 886 277 0.3126411 3 2015-02-01 930 383 0.4118280 4 2015-03-01 1167 510 0.4370180 5 2015-04-01 1105 309 0.2796380 6 2015-05-01 1279 319 0.2494136 ggplot(DF_Merge, aes(x=Var1, y=Avg, group = 1)) + stat_smooth(aes(x = seq(length(unique(Var1)))), se = F, method = "lm", formula = y ~ poly(x, 11)) 

条状图

 dfm <- melt(DFtest[ , c("VIN", "First.Launch.Date", "Sale.Date")], id.vars = 1) dfm$value <- as.Date(cut(dfm$value, breaks = "month")) ggplot(dfm, aes(x= value, width = 0.4)) + geom_bar(aes(fill = variable), position = "dodge") + scale_x_date(date_breaks = "months", labels = date_format("%m-%Y")) + theme(axis.text.x=element_text(hjust = 0.5)) + xlab("Date") + ylab("") 

所以我设法做了一些情节，给我带来几个问题：

1. 我如何将它们组合成使用ggplot的所有单个graphics？

2. 注意我的条形图在第一个月和最后一个月有空白吗？ 我如何删除（确切地说，我如何从X轴上删除11-2014和01-2016）？

3. 在我的条形图中，2014年1月没有销售，因此库存栏占据了更大的空间。 如何缩小尺寸以适合graphics的其余部分？

4. 我可以做些什么来改变X轴使用date作为数字（即12-2014）到使用月份（即2014年12月）。 我已经尝试使用as.yearmon但是这不适用于我的ggplot函数的scale_x_date部分。

5. 还有平均销售率线的问题，我可以安全地假设我将使用geom_hline()但我不知道如何处理这个问题。

Answer 1

使用mtoto关于使用googleVis的build议，我重新创build了一个图表：

 # Testing Google Vis mytest <- DF_Merge library(zoo) library(plyr) # to rename columns library(googleVis) mytest$Var1 <- as.yearmon(mytest$Var1) mytest$Var1 <- as.factor(mytest$Var1) # googleVis cannot understand yearmon "class" so change it to factor # Rename columns to ensure comprehension mytest <- rename(mytest, c("Var1"="Date", "Freq.x"="Listings", "Freq.y"="Sales", "Avg"="Sales Rate")) # Prepare for values to be displayed right on the plot mytest$Listings.annotation <- mytest$Listings mytest$Sales.annotation <- mytest$Sales mytest$`Sales Rate.annotation` <- percent(mytest$`Sales Rate`) #Googlevis automatically understands that .annotation is used to display values in the graph # Create average rate line mytest$`Sales Rate` <- as.numeric(mytest$`Sales Rate`) mytest$AvgRate <- (sum(mytest$Sales) / sum(mytest$Listings)) mytest <- rename(mytest, c("AvgRate"="Average Sales Rate")) # Create the annotation for the average line mytest$`Average Sales Rate.annotation` <- mytest$`Average Sales Rate` x = nrow(mytest) - 1 mytest$`Average Sales Rate.annotation`[1:x] = "" # Ensures only the last row in this column has a value mytest$`Average Sales Rate.annotation` <- as.numeric(mytest$`Average Sales Rate.annotation`, na.rm = TRUE) mytest$`Average Sales Rate.annotation`[nrow(mytest)] <- percent(mytest$`Average Sales Rate.annotation`[nrow(mytest)]) # Transforms only the last row to a proper percentage! # Plot the graph column <- gvisComboChart(mytest, xvar= "Date", yvar=c("Listings", "Listings.annotation", "Sales", "Sales.annotation", "Sales Rate", "Sales Rate.annotation", "Average Sales Rate", "Average Sales Rate.annotation"), options=list(seriesType="bars", series="[{type: 'bars', targetAxisIndex:0, color:'orange'}, {type: 'bars', targetAxisIndex:0, color:'green'}, {type: 'line', targetAxisIndex:1, color:'red'}, {type: 'line', targetAxisIndex:1, color:'purple', lineDashStyle:[2,2,20,2,20,2]}]", vAxes="[{format:'decimal', textPosition: 'out', viewWindow:{min:0, max:200}}, {format:'percent', textPosition: 'out', viewWindow:{min:0, max:1}}]", hAxes="[{textPosition: 'out'}]", legend = "bottom", curveType="function", width=1500, height=800)) plot(column) 

variables本来可以被命名得更好，但是我能够得到我最终的结果：