2

下面的最小可重现示例 (RE)是我试图弄清楚如何使用它knitr来生成复杂的动态文档，这里的“复杂”不是指文档的元素及其布局，而是指底层 R 代码的非线性逻辑块。虽然提供的 RE 及其结果表明基于这种方法的解决方案可能效果很好，但我想知道：1）这是用于这种情况的正确方法吗？2）是否可以进行knitr任何优化以改进该方法；3）什么是替代方法，可以降低粒度的代码块。

EDA 源代码（文件“reEDA.R”）：

## @knitr CleanEnv
rm(list = ls(all.names = TRUE))

## @knitr LoadPackages
library(psych)
library(ggplot2)

## @knitr PrepareData

set.seed(100) # for reproducibility
data(diamonds, package='ggplot2')  # use built-in data


## @knitr PerformEDA

generatePlot <- function (df, colName) {

  df <- df
  df$var <- df[[colName]]

  g <- ggplot(data.frame(df)) +
    scale_fill_continuous("Density", low="#56B1F7", high="#132B43") +
    scale_x_log10("Diamond Price [log10]") +
    scale_y_continuous("Density") +
    geom_histogram(aes(x = var, y = ..density..,
                       fill = ..density..),
                   binwidth = 0.01)
  return (g)
}

performEDA <- function (data) {

  d_var <- paste0("d_", deparse(substitute(data)))
  assign(d_var, describe(data), envir = .GlobalEnv)

  for (colName in names(data)) {
    if (is.numeric(data[[colName]]) || is.factor(data[[colName]])) {
      t_var <- paste0("t_", colName)
      assign(t_var, summary(data[[colName]]), envir = .GlobalEnv)

      g_var <- paste0("g_", colName)
      assign(g_var, generatePlot(data, colName), envir = .GlobalEnv)
    }
  }
}

performEDA(diamonds)

EDA 报告 R Markdown 文档（文件“reEDA.Rmd”）：

```{r KnitrSetup, echo=FALSE, include=FALSE}
library(knitr)
opts_knit$set(progress = TRUE, verbose = TRUE)
opts_chunk$set(
  echo = FALSE,
  include = FALSE,
  tidy = FALSE,
  warning = FALSE,
  comment=NA
)
```

```{r ReadChunksEDA, cache=FALSE}
read_chunk('reEDA.R')
```

```{r CleanEnv}
```

```{r LoadPackages}
```

```{r PrepareData}
```

Narrative: Data description

```{r PerformEDA}
```

Narrative: Intro to EDA results

Let's look at summary descriptive statistics for our dataset

```{r DescriptiveDataset, include=TRUE}
print(d_diamonds)
```

Now, let's examine each variable of interest individually.

Varible Price is ... Decriptive statistics for 'Price':

```{r DescriptivePrice, include=TRUE}
print(t_price)
```

Finally, let's examine price distribution across the dataset visually:

```{r VisualPrice, include=TRUE, fig.align='center'}
print(g_price)
```

结果可以在这里找到：

http://rpubs.com/abrpubs/eda1

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1 回答 1

2

我不明白这段代码有什么非线性；也许是因为这个例子（顺便说一句谢谢）小到足以展示代码，但又不够大，无法展示关注点。

特别是，我不明白该performEDA功能的原因。为什么不将该功能放入降价中？阅读起来似乎更简单、更清晰。（这是未经测试的......）

Let's look at summary descriptive statistics for our dataset

```{r DescriptiveDataset, include=TRUE}
print(describe(diamonds))
```

Now, let's examine each variable of interest individually.

Varible Price is ... Decriptive statistics for 'Price':

```{r DescriptivePrice, include=TRUE}
print(summary(data[["Price"]]))
```

Finally, let's examine price distribution across the dataset visually:

```{r VisualPrice, include=TRUE, fig.align='center'}
print(generatePlot(data, "Price"))
```

看起来您要显示所有变量的图；你可能想在那里循环吗？

此外，这不会改变功能，但在 R 惯用语中更多的是performEDA返回一个包含它创建的东西的列表，而不是分配到全局环境中。我花了一段时间才弄清楚代码做了什么，因为这些新变量似乎没有在任何地方定义。

于 2014-09-08T20:03:31.840 回答