Question

我搜索了几个选项，通常在cbind上尝试各种组合来完成此任务。基本上我想创建一个结合了不同数据透视表的数据框。到一个数据框中以便导出到csv / excel。有没有更好的方法来实现这一目标？

编辑：基本上我正在尝试学习创建一个函数的基础知识，该函数可以包装多个不同的数据透视表以创建准备导出的数据框，作为临时报告的模板。我遇到的问题是cbind产品采用对象B，它作为一个独立的将是一个日期为列的表，并强制它进入一个长表，其中日期转换为行。

数据帧：

State   FacilityName   Date
NY  Loew              June 2014
NY  Loew              June 2014
CA  Sunrise           May 2014
CA                    May 2014

代码：

volume <- function() {
    df$missing = ifelse(is.na(df$FacilityName), "Missing", df$FacilityName)
    df = subset(df, df$missing == "Missing")
    x <- function(){
      a <- as.data.frame(table(df$FacilityName))
      b <- table(df$FacilityName, df$date)
      cbind(a, b[,1], b[2])
}
}

Answer 1

当您为表函数提供一个因子时，它使用因子的级别来构建表。因此，通过添加＆＃34; Missing＆＃34;这是获得所需内容的好方法。达到＆＃34; FacilityName＆＃34;。

的水平

# loading data
ec <- read.csv(text=
'State, FacilityName, Date
NY,Loew,June 2014
NY,Loew,June 2014
CA,Sunrise,May 2014
CA,NA,May 2014', )

# Adding Missing to the possible levels of FacilityName
# note that we add it in front
new.levels <- c("Missing", levels(ec$FacilityName))
ec$FacilityName <- factor(ec$FacilityName, levels=new.levels)

# And replacing NAs by the new level "Missing"
ec$FacilityName[is.na(ec$FacilityName)] <- "Missing"
# the previous line would not have worked 
# if we had not added "Missing" explicitly to the levels


# table() uses the levels to generate the table
# the levels are displayed in order
# now there's a level "Missing" in first position
t <- table(ec$FacilityName, ec$Date)

你得到：

> t   
            June 2014  May 2014
  Missing           0         1
   Loew             2         0
   Sunrise          0         1

您可以添加这样的总计行（我不认为您的代码nrow按照您的说法执行）

# adding total line
rbind(t, TOTAL=colSums(as.matrix(t)))

          June 2014  May 2014
Missing           0         1
 Loew             2         0
 Sunrise          0         1
TOTAL             2         2

此时您有一个矩阵，因此您可能希望将其传递给as.data.frame。

如果您愿意，可以轻松将其实现为单独的功能。毕竟不需要绑定几个表：）

Answer 2

好吧，所以看起来我试图变得很酷并且使用一个函数来包装所有内容，希望它是学习编写灵活代码的开始。但是，我做了很长的路，结果得到了我想要的结果。虽然我将发布下面的代码，但我非常感兴趣的是有人指着我更好地解决这些问题，以便学习更好的编码。

# Label the empty cells as Missing 
ec$missing = ifelse(is.na(ec$FacilityName), "Missing", ec$FacilityName)
# Subset the dataframe to just missing values    
df = subset(ec, ec$missing == "Missing")
# Create table that is a row of frequency by month for missing values
a <- table(df$missing, df$date)

# Reload dataframe to exclude Missing values
df = subset(ec, ec$missing != "Missing")
# Create table that shows frequency of observations for each facility by Month
b <- table(df$FacilityName, df$date)

# Create a Total row that can go at the bottom of the final data frame
Total <- nrow(ec)

# Bind all three objects
rbind(a,b,Total)

以下是我正在寻找的最终产品的示例：

           May2014   June2014
Missing       2        0
Sunrise       0        0
Loew          1        2
Total         3        2

将表绑定到一个数据框

2 个答案: