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r_for_data_science's Issues

关于中文R语言书

你好,

我自己使用R 已经超过十年了,偶然看到了你的这本书。翻了一下,非常喜欢。

全书整体性强,结构合理,逻辑清晰,语言简练,排版清新,图片设计尤其用心。这样的书籍在中文届太难得了。

所以特地来感谢一下。如果将来出版,我一定会买一本支持一下。

Dong

关于28.16 stat_summar的一点疑问

王老师您好,请问# ggplot2之从图层到几何形状 {#tidyverse-ggplot2-from-layer-to-geom}

penguins %>% 
  ggplot(aes(x = species, y = bill_length_mm)) +
  geom_point() +
  stat_summary(
    geom = "pointrange",
    fun.data = ~ mean_se(., mult = 5),
    color = "red", 
    size = 1
  ) +
  stat_summary(
    fun = "mean",
    geom = "text",
    mapping = aes(y = stage(bill_length_mm, after_stat = 30), 
                  label = round(after_stat(y), 2)),
    color = "blue", 
    size = 5
  ) +
  stat_summary(
    fun = "length",
    geom = "text",
    mapping = aes(y = stage(bill_length_mm, after_stat = 62), 
                  label = after_stat(y)
                  ),
    color = "black", 
    size = 5
  )

为何after_stat 要设置为30 和62?

谢谢!

Chapter 32 question

Hi Dr. Wang, it was great to have a chance to read your book. In chapter 32.1, you mentioned "在构建的虚拟(Δ=0)的平行世界里,出现实际观察值(0.5412)的概率很小,这里是(0.1644)。 如果以(p< 0.05)为标准,那我们有足够的证据证明,H0不成立,即爱情电影和动作电影的评分均值存在显著差异,具体来说,动作电影的平均评分要比爱情电影低些". Since p value is 0.1644, which is large than 0.05, should we not reject the null hypothesis?

感谢信

Wang 老师好!

我是R语言的初学者。

今天偶然在搜索引擎中搜索到了您的电子书,非常喜欢,特来感谢!

能否将其生成一个pdf或者epub的文件方便下载

能否将其生成一个pdf或者epub的文件方便下载,昨天试图将其pull下来,自己用
rstudio build ,折腾了一下午都没有成功,所以想提交一个issue给您,希望您在方便的时候生成一个。

有些函数过时了,要是能换成最新的就更好了

有个朋友想入门R语言,我推荐了这本书给她。粗略看下来是这本书非常棒,对新手十分友好,是一本很适合用来入门的书,更难得的是作者有随着tidyverse包的迭代同步更新着本书。个人觉得,对于 top_n()summarise_if 等已经被代替的函数,没有必要专门去学习了,可以在解释为什么要引入 across() 函数时作为补充说明,就如书中 across() 之美的部分那样。当然,可能作者从教学上有所考量才选择保留这些内容,为我的无理提前说一声抱歉。

Potential typo in the proportional odds model

There is a potential typo in the proportional odds model in Chapter 40. The formula in the polr function of the MASS library is:

logit(P(Y≤j))=αj - β1 * x1 - β2 * x2

So, when interpreting the result of the following function, the sign of beta should be reversed. For example, the coefficient of edu_m should be -0.461 in the equation.

polr(formula = edu ~ edu_f + edu_m + sex + num_siblings + urban, 
##     data = df1, method = c("logistic"))

References on the formula of polr:

  • See documentation on polr
  • Venables, W.N. and Ripley, B.D. (2003) Modern Applied Statistics with S, Fourth. Springer, New York

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