一些情节不能在Rstudio,knitr,Rmarkdown中呈现

Question

我正在使用:Ubuntu 12.04 64位,R 3.0.2,RStudio 0.98.312,knitr 1.5,markdown 0.6.3,mgcv1.​​7-27

我有一个带有多个代码块的Rmarkdown文档.在一个块的中间有一些代码,我适合GAM,总结拟合并绘制拟合.问题是第一个绘图渲染到输出文件,但第二个绘图没有.这是来自块的已清理代码片段:

fit <- gam(y ~ s(x), data=j0, subset= !is.na(x))
summary(fit) # look at non-missing only
plot(fit)

fit <- gam(y ~ s(sqrt(x)), data=j0, subset= !is.na(x))
summary(fit)
plot(fit)

mean(y[is.na(x)]) - mean(y[!is.na(x)])

所有内容都按预期呈现,除了输出直接从回显第二个绘图语句到回显下面的均值计算.均值计算的结果正确呈现.

如果我注释掉另一个图块,稍后会在块中调用7行,则会正确呈现丢失的图.

有没有人对这里发生的事情有任何建议？

更新下面

总结 - 在调用Plot 2之后的几行中有一些R代码生成一个执行错误(未找到变量),之后有几行代表Plot 3.如果代码错误被修复,那么渲染Plot 2.如果代码错误不固定并且对Plot 3的调用被注释掉,则渲染Plot 2.问题取决于用于存储不同拟合结果的相同变量"拟合".如果我将每个拟合分配给不同的变量,则绘图2呈现正常.

我不明白在多行成功执行的代码之后进行的更改如何(显然是回顾性地)阻止了绘图2的渲染.

可重复的例子:

Some text.

```{r setup}
require(mgcv)

mkdata <- function(n=100) {
  x <- rnorm(n) + 5
  y <- x + 0.3 * rnorm(n)
  x[sample(ceiling(n/2), ceiling(n/10))] <- NA
  x <- x^2
  data.frame(x, y)  
} 
```

Example 1
=========

Plot 2 fails to render. (Using the same fit object for each fit.)

```{r example_1}
j0 <- mkdata()
attach(j0)
mx <- min(x, na.rm=TRUE)

fit <- gam(y ~ s(x), data=j0, subset= !is.na(x))
summary(fit)
plot(fit) # plot 1

fit <- gam(y ~ s(sqrt(x)), data=j0, subset= !is.na(x))
summary(fit)
plot(fit) #plot 2

mean(y[is.na(x)]) - mean(y[!is.na(x)]) # means calculation

# recode the missing values
j0$x.na <- is.na(x)
j0$x.c <- ifelse(x.na, mx, x) # ERROR in recode
detach()

attach(j0)
fit <- gam(y ~ s(sqrt(x.c)) + x.na, data=j0) # doesn't run because of error in recode
summary(fit) # this is actually fit 2
plot(fit) # plot 3 (this is actually fit 2)
detach()
```

Example 2
=========

Use separate fit objects for each fit. Plot 2 renders OK.

```{r example_2}
j0 <- mkdata()
attach(j0)
mx <- min(x, na.rm=TRUE)

fit1 <- gam(y ~ s(x), data=j0, subset= !is.na(x))
summary(fit1)
plot(fit1) # plot 1

fit2 <- gam(y ~ s(sqrt(x)), data=j0, subset= !is.na(x))
summary(fit2)
plot(fit2) #plot 2

mean(y[is.na(x)]) - mean(y[!is.na(x)]) # means calculation

# recode the missing values
j0$x.na <- is.na(x)
j0$x.c <- ifelse(x.na, mx, x) # ERROR in recode
detach()

attach(j0)
fit3 <- gam(y ~ s(sqrt(x.c)) + x.na, data=j0) # doesn't run because of error in recode
summary(fit3)
plot(fit3) # plot 3
detach()
```

Example 3
=========

Revert to using the same fit object for each fit. Plot 2 renders because plot 3 is commented out.

```{r example_3}
j0 <- mkdata()
attach(j0)
mx <- min(x, na.rm=TRUE)

fit <- gam(y ~ s(x), data=j0, subset= !is.na(x))
summary(fit)
plot(fit) # plot 1

fit <- gam(y ~ s(sqrt(x)), data=j0, subset= !is.na(x))
summary(fit)
plot(fit) #plot 2

mean(y[is.na(x)]) - mean(y[!is.na(x)]) # means calculation

# recode the missing values
j0$x.na <- is.na(x)
j0$x.c <- ifelse(x.na, mx, x) # ERROR in recode
detach()

attach(j0)
fit <- gam(y ~ s(sqrt(x.c)) + x.na, data=j0)
summary(fit) # this is actually fit 2
# plot(fit) # plot 3 (this is actually fit 2)
detach()
```

Example 4
=========

Plot 2 renders because later recode error is fixed.

```{r example_4}
j0 <- mkdata()
attach(j0)
mx <- min(x, na.rm=TRUE)

fit <- gam(y ~ s(x), data=j0, subset= !is.na(x))
summary(fit)
plot(fit) # plot 1

fit <- gam(y ~ s(sqrt(x)), data=j0, subset= !is.na(x))
summary(fit)
plot(fit) #plot 2

mean(y[is.na(x)]) - mean(y[!is.na(x)]) # means calculation

# recode the missing values
j0$x.na <- is.na(x)
j0$x.c <- ifelse(j0$x.na, mx, x) # error in recode fixed
detach()

attach(j0)
fit <- gam(y ~ s(sqrt(x.c)) + x.na, data=j0)
summary(fit)
plot(fit) # plot 3
detach()
```

日志文件:

> require(knitr); knit('reproduce.Rmd', encoding='UTF-8');
Loading required package: knitr


processing file: reproduce.Rmd
  |......                                                           |   9%
  ordinary text without R code

  |............                                                     |  18%
label: setup
  |..................                                               |  27%
  ordinary text without R code

  |........................                                         |  36%
label: example_1
  |..............................                                   |  45%
  ordinary text without R code

  |...................................                              |  55%
label: example_2
  |.........................................                        |  64%
  ordinary text without R code

  |...............................................                  |  73%
label: example_3
  |.....................................................            |  82%
  ordinary text without R code

  |...........................................................      |  91%
label: example_4
  |.................................................................| 100%
  ordinary text without R code


output file: reproduce.md

[1] "reproduce.md"

Answer 1

你只是另一个受害者attach(),尽管人们一直警告不要使用attach().搞砸太容易了attach().你这样做之后attach(j0):

j0$x.na <- is.na(x)
j0$x.c <- ifelse(x.na, mx, x) # ERROR in recode

当然,R找不到对象,x.na因为它不存在于任何地方.是的,它j0现在已经存在,但除非您拆卸j0并重新安装,否则它不会暴露给R. 换句话说,attach()当您添加更多变量时,不会自动刷新自身j0.所以简单的解决方法是:

j0$x.c <- ifelse(j0$x.na, mx, x)

我理解你为什么要使用attach()- 你可以避免在j0$任何地方使用笨拙的前缀,但你需要非常小心.除了我提到的问题,detach()也是不好的,因为你没有指定脱离其环境,默认情况下,搜索路径上第二个被分离,这是不是一定是你连接的一个,例如,你可能已经加载其他包到搜索路径上.因此,你必须明确:detach('j0').

回到knitr:如果你想知道,我可以解释发生了什么,但首先,你必须确保你的代码在传递之前确实有效knitr.当错误被消除时,你观察到的奇怪现象也会消失.