R 多轴海图

我创建了一个R脚本，供学生学习OLAP操作。到目前为止一切正常。 我想将结果可视化为多轴图表。我相信帕库德适合这样做。 但是我不能为轴和图例插入标签。有人有什么建议吗？ 这也可能是另一种绘图方式。 提前谢谢

### OLAP-Operations in R ###

# This R-script is essentially based on the coding of Ricky Ho (2013).
# https://dzone.com/articles/olap-operation-r

# I have made minor corrections and additions.
# S. Horn, 30.05.2021

# Parts:
# (1) Generate three dimension tables
# (2) Generate the central fact table
# (3) Build the multi-dimensional cube
# (4) OLAP-operations

## (1) Generate three dimension tables (state_table, month_table, prod_table)

state_table <- 
  data.frame(key=c("CA", "NY", "WA", "ON", "QU"),
             name=c("California", "new York", "Washington", "Ontario", "Quebec"),
             country=c("USA", "USA", "USA", "Canada", "Canada"))

month_table <- 
  data.frame(key=1:12,
             desc=c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"),
             quarter=c("Q1","Q1","Q1","Q2","Q2","Q2","Q3","Q3","Q3","Q4","Q4","Q4"))

prod_table <- 
  data.frame(key=c("Printer", "Tablet", "Laptop"),
             price=c(200, 300, 1000))

## Generate the central fact table (sales_fact)

gen_sales <- function(no_of_recs) {
  
  # create transaction data randomly
  loc <- sample(state_table$key, no_of_recs, 
                replace=T, prob=c(2,2,1,1,1))
  time_month <- sample(month_table$key, no_of_recs, replace=T)
  time_year <- sample(c(2012, 2013), no_of_recs, replace=T)
  prod <- sample(prod_table$key, no_of_recs, replace=T, prob=c(1, 3, 2))
  unit <- sample(c(1,2), no_of_recs, replace=T, prob=c(10, 3))
  # delete: amount <- unit*prod_table[prod,]$price # original
  
  sales <- data.frame(month=time_month,
                      year=time_year,
                      loc=loc,
                      prod=prod,
                      unit=unit)
  
  # get price for calculation, new
  sales <- merge(sales, prod_table, by.x="prod", by.y="key")
  sales$amount <- sales$unit * sales$price
  
  # Sort the records by time order
  sales <- sales[order(sales$year, sales$month),]
  row.names(sales) <- NULL
  return(sales)
}

# Create the sales_fact table
sales_fact <- gen_sales(500)

# Look at a few records
head(sales_fact)


## (3) Build the multi-dimensional cube (revenue_cube)
  revenue_cube <- 
    tapply(sales_fact$amount, 
           sales_fact[,c("prod", "month", "year", "loc")], 
           FUN=function(x){return(sum(x))})
  
  # Showing the cells of the cube
  revenue_cube


## (4) OLAP Operations

# Slice
# Cutting one slice of the cube by choosing a single value for one dimension (here year = 2012).
# Scheme: ["prod", "month", "year", "loc"]
slice1 <- revenue_cube[, , "2012", ]
slice1


# Dice
# Creating a smaller subcube by choosing specific values for more dimensions.
# Scheme: ["prod", "month", "year", "loc"]
dice1 <- revenue_cube[
             c("Tablet","Laptop"), 
             c("1","2","3"), 
             c("2012"),
             c("CA","NY")]
dice1


# Rollup
# Aggregation of data along a dimension by collapsing dimensions (here time and location). 
# Differentiation by location and month are not important now.
# It corresponds to a navigation along a hierarchy from down (more details) to top (few details).
rollup1 <- apply(revenue_cube, c("prod", "year"),
      FUN=function(x) {return(sum(x, na.rm=TRUE))})
rollup1


# Drilldown
# It is the reverse operation of rollup. In our example we unfold the time dimension now (year plus months). 
# We are still not interested in differentiating location.
# It corresponds to a navigation along a hierarchy from top (few details) to down (more details).
drilldown1 <- apply(revenue_cube, c("prod", "month", "year"), 
      FUN=function(x) {return(sum(x, na.rm=TRUE))})
drilldown1


# Pivot
# Rotation of the cube to get another view (compare pivot1 and pivot2).
pivot1 <- apply(revenue_cube, c("prod", "month", "year"), 
                FUN=function(x) {return(sum(x, na.rm=TRUE))})
pivot1

pivot2 <- apply(revenue_cube, c("month", "year", "prod"), 
      FUN=function(x) {return(sum(x, na.rm=TRUE))})
pivot2

### TEST ###
library(MASS)
mycol = palette("R3")
parcoord(dice1,            # data
         col = mycol,      # color
         var.label = TRUE, # show min. and max. values
         lty = 1,          # linetype
         main = "Test")    # title 

R中的OLAP操作### #该R脚本基本上基于Ricky Ho（2013）的编码。 # https://dzone.com/articles/olap-operation-r #我做了一些小的修改和补充。 #美国霍恩，2021年5月30日 #部分： #（1）生成三维表格 #（2）生成中心事实表 #（3）构建多维立方体 #（4）OLAP操作 ##（1）生成三维表（状态表、月份表、生产表）

---

state_table对于上面的示例，最简单的选择可能是手动添加项目。我复制了代码的最后一部分来说明。轴可以标记为

mtext（）

（或

text（）

）。图例也可以通过

legend（）

功能添加

我已经包含了一个函数，如果数组具有一致的格式，它应该自动生成图例条目。它提取第一个和最后一个维度名称，并根据需要重复它们

---

getlegnames>#从摘要中按顺序复制看起来很棒。您使用什么类型的摘要函数来获取图例参数的信息？我没有使用任何函数。我的意思是当你计算完骰子1后输出骰子1谢谢。我认为可能有一个函数可以进一步自动化绘图，例如图例的参数。我添加了一个函数，该函数可以自动处理图例名称，前提是数组的格式是一致的。很好，我甚至不知道R可以实现什么。
getlegnames <- function(array){
  legend_names <- c() # will become vector of names
  list_of_names <- list(dimnames(array)) # turn dimension names into list
  
  for(x in 1:dim(array)[3]){ # loop over 3rd dimension
    for(y in 1:dim(array)[1]){ # loop over first dimension
      # make a legend name
      legend_names <- c(legend_names,paste(list_of_names[[1]][[3]][[x]],list_of_names[[1]][[1]][[y]],sep=', '))
    }
  }
  
  return(legend_names)
}

library(MASS)
mycol = palette("R3")
parcoord(dice1,            # data
         col = mycol,      # color
         var.label = TRUE, # show min. and max. values
         lty = 1,          # linetype
         main = "Test")    # title 
mtext('Month',side=1,line=3) # x axis as text
mtext('Sales',side=2,line=3) # y axis as text
legend('left', # location
       legend = getlegnames(dice1), # copied in order from summary
       col=mycol, # same colors
       lty=1, # line type
       lwd=1, # line width
       cex=0.8 # legend size
       )