R & Tools | Chen Xing

How to Use GPT in R/Python

Sun, 28 Jan 2024 00:00:00 +0000

To read

8 ChatGPT tools for R programming

Google Search Operator

Tue, 05 Dec 2023 00:00:00 +0000

Here’s an cheatsheet with examples for each Google search operator:

site: - Restricts your search to a specific website or domain.
- Example: site:wikipedia.org quantum physics searches for information about quantum physics only on Wikipedia.
filetype: - Limits results to a specific file type.
- Example: filetype:pdf statistics textbook searches for PDF documents related to statistics textbooks.
intext: - Searches for specific keywords within the body of a webpage.
- Example: intext:marketing strategies finds pages containing the phrase “marketing strategies.”
intitle: - Searches for specific keywords in the titles of webpages.
- Example: intitle:economics research methods finds pages with “economics research methods” in their titles.
inurl: - Searches for specific keywords in the URLs of webpages.
- Example: inurl:statistics data finds pages with “statistics data” in their URLs.
related: - Finds websites related to a specified URL.
- Example: related:nytimes.com lists sites related to The New York Times.
OR: - Used to search for pages that contain either of two terms.
- Example: marketing OR advertising finds pages containing either “marketing” or “advertising.”
-“word” - Excludes a specific word from search results.
- Example: data analysis -Excel finds pages about data analysis but excludes those mentioning Excel.
“phrase search” - Enclose phrases in double quotation marks for exact phrase matching.
- Example: "consumer behavior" finds pages containing that exact phrase.
$*$ - Use it as a wildcard to replace one or more words in a search query.
- Example: quantitative * techniques returns results with various terms in place of the asterisk.
define: - Provides definitions for a specific term.
- Example: define:marketing gives you the definition of marketing.
AROUND(x): - Finds words that are within a certain number of words apart.
- Example: economics AROUND(5) statistics searches for pages where “economics” and “statistics” are within five words of each other.
daterange: - Search for results within a specific date range.
- Example: marketing strategies daterange:2022-01-01..2022-12-31

Awesome ChatGPT Prompts for PhD students

Sun, 26 Mar 2023 00:00:00 +0000

ChatGPT stands for “Chat Generative Pre-trained Transformer” and is built on the transformer architecture, which is a neural network architecture that has been shown to be highly effective in natural language processing tasks.

As a PhD student, how can I make the most of ChatGPT? Here, I summarize some useful tips and prompts.

Tips for great prompts

Start prompts with a description of the AI’s role.
- e.g., “I want you to act as {x}”.
Use it for common tasks.
Offer context.
Be precise.

For example,

Act as a university professor in Bayesian Statistics, explain the concept of MCMC in simple terms to a 2nd year undergraduate student with a statistics and probability background.

Collection of Useful Prompts

Prompts for understanding concepts

Act as a university professor in [insert field of studies], explain the concept of [the term] in simple terms
“What is the history of Y?” – This prompt can be used to get a brief overview of the history of a specific subject, such as a particular invention, historical figure, or cultural tradition.
“Summarize the main points of Z” – This prompt can be used to get a concise overview of a longer piece of text, such as a book, article, or research paper.
“What are the key characteristics of X?” – This prompt can be used to get a list of the key characteristics of a specific subject, such as a species of animal, a type of plant, or a historical period.
“How does X work?” – This prompt can be used to get a detailed explanation of how a specific process or system works, such as a machine, a technology, or a social system.
“List the pros and cons of Y” – This prompt can be used to get a balanced overview of the advantages and disadvantages of a specific subject, such as a policy, a technology, or a method of teaching.
“What are the similarities and differences between X and Y?” – This prompt can be used to compare and contrast two subjects, such as two historical events, two scientific theories, or two teaching methods.
“What are the causes of Z?” – This prompt can be used to get a list of the underlying causes of a specific phenomenon, such as a historical event, a natural disaster, or a social problem.
“What are the effects of X?” – This prompt can be used to get a list of the outcomes or consequences of a specific subject, such as a policy, a technology, or a natural event.
“What are the solutions to Y?” – This prompt can be used to get a list of potential solutions to a specific problem or challenge, such as a social issue, a technical challenge, or a scientific question.
“Provide examples of lesson plans for [subject]” – This prompt can generate a list of detailed lesson plans for a specific subject, including objectives, activities, and assessments.
Write a song in the style of [Artist] that Explains [Topic]

Prompt for Academic Research

Literature Review Assistance: “Can you summarize the key findings and arguments of [insert academic paper title]?”
Brainstorming Ideas: “I’m working on a research project about [insert topic]. Can you suggest some potential research questions or hypotheses?”
Methodology Suggestions: “What are some common research methods used in [insert field of study]?”
Statistical Analysis: “Can you explain the difference between a t-test and an ANOVA?”
Writing Assistance: “Can you help me rephrase this sentence/paragraph in a more academic tone?”
Citation Guidance: “What is the correct way to cite a book in APA style?”
Historical Context: “Can you provide a brief overview of the history of [insert topic]?”
Concept Explanation: “Can you explain the concept of [insert complex concept] in simple terms?”
Paper Structuring: “What is the typical structure of a research paper in [insert field of study]?”
Data Interpretation: “What conclusions might be drawn from a dataset where [insert observed trend]?”
Ethical Considerations: “What are some ethical considerations when conducting research on [insert topic]?”
Theory Application: “How might [insert theory] apply to a situation where [insert scenario]?”
Research Proposal Feedback: “Here’s a brief outline of my research proposal on [insert topic]. Can you provide some feedback?”
Language Translation: “Can you translate this [insert language] phrase to English?”
Proofreading: “Can you check this paragraph for grammatical errors?”

Prompt Engineering

We can use the following to improve a prompt :)

You are a prompt generator that generates the best prompts to get the best possible answers from an AI. Generate a prompt that does the following: <insert>

Fact-Checking ChatGPT

Of course, just because ChatGPT is powerful, it doesn’t mean that it will always provide correct information, which can lead to confusion in your studies. So what can you do to confirm that the information that you are getting is correct? Well, there are 3 great ways to do so:

Ask ChatGPT to cite the sources: After getting an answer, simply giving the prompt “Cite your sources for this information with website links” will have ChatGPT provide the sources. You can check the links of the sources to be sure. In my case, it gave sources that didn’t contain false information.
Do a Google Search for the information that you are not sure of: If you are not sure about the answer you are getting, or you are new to the topic, doing a quick search on Google to make sure it is correct is a helpful way of making sure you are learning the right information.
Providing the information yourself: You can ensure you are not getting anything wrong by just providing a part of your study material (if you don’t have an E-book version, get an OCR reader) and asking ChatGPT to summarise it or just asking related questions.

Reference

The GitHub repo Awesome ChatGPT Prompts is a good resource to check.

How to use DT table in blogdown?

Fri, 08 Jul 2022 00:00:00 +0000

The DT::datatable object often does not work appropriately in the blogdown post. How to solve this issue?

It seems no good answers for this problem. DT + widgetframe 📦 is a potential solution. I will do some experiment in this post.

library(tidyverse)
library(DT)
# this is the package to solve above issue
library(widgetframe)

After loading the packages, let’s make some tables.

Knit File Format

Here I am using Rmarkdown rather than Rmd!

Tibble

iris %>%
  head(10) %>%
  as_tibble()

## # A tibble: 10 × 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
##  1          5.1         3.5          1.4         0.2 setosa 
##  2          4.9         3            1.4         0.2 setosa 
##  3          4.7         3.2          1.3         0.2 setosa 
##  4          4.6         3.1          1.5         0.2 setosa 
##  5          5           3.6          1.4         0.2 setosa 
##  6          5.4         3.9          1.7         0.4 setosa 
##  7          4.6         3.4          1.4         0.3 setosa 
##  8          5           3.4          1.5         0.2 setosa 
##  9          4.4         2.9          1.4         0.2 setosa 
## 10          4.9         3.1          1.5         0.1 setosa

kableExtra

iris %>%
  head(5) %>%
  # this is a wrapping function for kbl in kableExtra
  zetaEDA::print_kbl(
    cap = "Test kableExtra"
  )

Table 1: Test kableExtra
Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
5.1	3.5	1.4	0.2	setosa
4.9	3.0	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5.0	3.6	1.4	0.2	setosa

gt

Note that gt table has some displaying problem in Rmarkdown, but it works fine in Rmd. Really drives me crazy…

iris %>%
  head(5) %>%
  gt::gt()

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
5.1	3.5	1.4	0.2	setosa
4.9	3.0	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5.0	3.6	1.4	0.2	setosa

DT

If you need to sort, search or add url link in the table, you will find the DT::datatable super helpful.

However, usually DT table cannot appropriately display in blogdown environment.

For example,

iris %>%
  head(5) %>%
  DT::datatable(caption = "DOES NOT WORK Table")

We have to use widgetframe package. See more details for this package here.

dt <- iris %>%
  head(20) %>%
  DT::datatable(
    caption = "It Works Table",
    rownames = FALSE,
    selection = "none",
    escape = FALSE,
    class = "cell-border hover compact stripe",
    fillContainer = T,
    options = list(
      dom = "frtip",
      pageLength = 10,
      scrollX = TRUE
    )
  )

widgetframe::frameWidget(dt, height = 300, width = "100%")

Now it works fine.

I am also curious about the bootstrap in DT::datatable .

dt2 <- iris %>%
  head(20) %>%
  DT::datatable(
    caption = "Bootstrap Table",
    style = "bootstrap",
    rownames = FALSE,
    selection = "none",
    escape = FALSE,
    class = "cell-border hover compact stripe",
    fillContainer = T,
    options = list(
      dom = "frtip",
      pageLength = 10,
      scrollX = TRUE
    )
  )

widgetframe::frameWidget(dt2, height = 300, width = "100%")

Well, DOES NOT WORK…

Summary

When using DT in blogdown post, do 2 step:

Save DT::datatable to an object.
Use widgetframe::frameWidget .

Add Floating TOC in Blogdown

Thu, 30 Jun 2022 00:00:00 +0000

Motivation

Question: How to add a floating TOC in my blogdown post?

Answer: It is not straightforward. I try to follow the Mickael’s approach and this one, then do the experiment.

Potential Approach

Step 1

Create <root>/layouts/_default/single.html.

{{- define "main" -}}
{{ if .Params.toc }}
<div class="container-fluid docs">
    <div class="row flex-xl-nowrap">
      <div class="d-none d-xl-block col-xl-2 docs-toc">
        <ul class="nav toc-top">
          <li><a href="#" id="back_to_top" class="docs-toc-title">{{ i18n "on_this_page" }}</a></li>
        </ul>
        {{ .TableOfContents }}
      </div>
      <main class="col-12 col-md-0 col-xl-10 py-md-3 pl-md-5 docs-content" role="main">
{{ end }}
        <article class="article">
            {{ partial "page_header" . }}
            <div class="article-container">
              <div class="article-style">
                {{ .Content }}
              </div>
              {{ partial "page_footer" . }}
            </div>
        </article>
  {{ if .Params.toc }}
      </main>
    </div>
  </div>
  {{ end }}
{{- end -}}

Step 2.

DONT USE .Rmd , using .Rmarkdown instead.

Further discussion

Further discussion and issues is here (Add a Floating Table of Contents in Blogdown \\\\| R & MC).

More Application

Activate the TOC

In every post you make where you would like a Table of Contents, you will need to ensure that you add toc: true in your front matter.

We can also use this code \{\{% toc %\}\} to show the table of content anyway in the post. For example,

Table of Contents

For LaTeX

For the $\LaTeX$, we have to use the shortcode, and put the latex code inside, check here.

$$ \gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2} $$

Summary

需要floating TOC
- Step1 + Step2 设置好
- Use .Rmarkdown , set toc:true in the yaml of the post.
- .Rmarkdown 默认生成markdown，如果需要$\LaTeX$代码的话，必须使用shortcode
  - {{< math >}} … {{< /math >}}
  - 给它们包起来，这个是wowchemy academic theme要求的
需要TOC，在最上方就可以
- Use .Rmd
- Add this into the yaml part of the post
```
output:
  blogdown::html_page:
    toc: true
```
- LaTeX code works fine, NO Need to use shortcode.
所以，如果不需要浮动的TOC的话，直接选.Rmd 就好了，最方便简单也不会出什么问题。如果选择了.Rmarkdown 的话，latex 需要shortcode，也可能出现其他的问题。

Tidy Evaluation in R

Mon, 16 May 2022 00:00:00 +0000

1 Motivation

为什么需要tidy evaluation?

很多时候，我们需要建立一些wrapping functions，这些函数是建立在dplyr或者ggplot2之上的，函数中的参数 function(data, xvar, yvar, ...) 往往是data 中的某个列。我们来举个例子吧，

library(zetaEDA)
enable_zeta_ggplot_theme()

The data is diamonds data set,

dat <- diamonds %>% head(1000)

print_kbl(head(dat))

carat	cut	color	clarity	depth	table	price	x	y	z
0.23	Ideal	E	SI2	61.5	55	326	3.95	3.98	2.43
0.21	Premium	E	SI1	59.8	61	326	3.89	3.84	2.31
0.23	Good	E	VS1	56.9	65	327	4.05	4.07	2.31
0.29	Premium	I	VS2	62.4	58	334	4.20	4.23	2.63
0.31	Good	J	SI2	63.3	58	335	4.34	4.35	2.75
0.24	Very Good	J	VVS2	62.8	57	336	3.94	3.96	2.48

The common task is to compute group mean,

dat %>%
  group_by(color) %>%
  summarise(mean_price = mean(price, na.rm = TRUE))

## # A tibble: 7 × 2
##   color mean_price
##   <ord>      <dbl>
## 1 D          2594.
## 2 E          2543.
## 3 F          2692.
## 4 G          2576.
## 5 H          2308.
## 6 I          1948.
## 7 J          1990.

How to build a function to compute the group mean?

Maybe you would like to try this:

# define the function
cal_group_mean <- function(data, group_var, mean_var) {
  data %>%
    group_by(group_var) %>%
    summarize(mean = mean(mean_var, na.rm = TRUE))
}

# first try
cal_group_mean(dat, group_var = color, mean_var = price)

## Error in `group_by()`:
## ! Must group by variables found in `.data`.
## ✖ Column `group_var` is not found.

NOT Work! OK, let’s try this,

# second try
cal_group_mean(dat, group_var = "color", mean_var = "price")

## Error in `group_by()`:
## ! Must group by variables found in `.data`.
## ✖ Column `group_var` is not found.

In order to make it work, what you need is the Tidy Evaluation!

# define the function again, use tidy evaluation
cal_group_mean <- function(data, group_var, mean_var) {
  group_var <- enquo(group_var)
  mean_var <- enquo(mean_var)

  data %>%
    group_by(!!group_var) %>%
    summarize(mean = mean(!!mean_var, na.rm = TRUE))
}

# the correct result
cal_group_mean(dat, group_var = color, mean_var = price)

## # A tibble: 7 × 2
##   color  mean
##   <ord> <dbl>
## 1 D     2594.
## 2 E     2543.
## 3 F     2692.
## 4 G     2576.
## 5 H     2308.
## 6 I     1948.
## 7 J     1990.

2 `quo()` function & “bang bang” `!!`

In the example below we use the quo function and the “bang-bang” !! operator to set color as our group by variable.

The “quo” function allows us to store the variable name in our “group_var” variable
“!!” extracts the stored variable name.

# store the variable name in 'group_var'
group_var <- quo(color)

class(group_var)

## [1] "quosure" "formula"

dat %>%
  # extract the stored variable name using 'bang bang'
  group_by(!!group_var) %>%
  summarize(mean = mean(price, na.rm = TRUE))

## # A tibble: 7 × 2
##   color  mean
##   <ord> <dbl>
## 1 D     2594.
## 2 E     2543.
## 3 F     2692.
## 4 G     2576.
## 5 H     2308.
## 6 I     1948.
## 7 J     1990.

注意：上面的代码并没有写在函数的内部，如果我们需要使用tidy evaluation的方式来写函数的话，不要用quo ，需要使用enquo !

3 `enquo()` function & “bang bang” `!!`

To use tidy evaluation in a function, we will still use the “!!” operator as we did above, but instead of “quo” we will use the enquo function.

cal_group_stats <- function(data, group_var, target_var) {
  # store the variable name using `enquo`
  group_var <- enquo(group_var)
  target_var <- enquo(target_var)

  data %>%
    # extract the stored variable using bang-bang !!
    group_by(!!group_var) %>%
    summarize(across(!!target_var, list(
      mean = ~ mean(.x, na.rm = TRUE),
      sd = ~ sd(.x, na.rm = TRUE)
    )))
}

# try
cal_group_stats(dat, group_var = clarity, target_var = price)

## # A tibble: 8 × 3
##   clarity price_mean price_sd
##   <ord>        <dbl>    <dbl>
## 1 I1           2674.    618. 
## 2 SI2          2489.    840. 
## 3 SI1          2440.    873. 
## 4 VS2          2616.    669. 
## 5 VS1          2383.    928. 
## 6 VVS2         2497.    828. 
## 7 VVS1         2159.   1051. 
## 8 IF           2829.     38.0

cal_group_stats(dat, group_var = NULL, target_var = price)

## # A tibble: 1 × 2
##   price_mean price_sd
##        <dbl>    <dbl>
## 1      2477.     840.

4 The “Curly-Curly” Shortcut `{{}}`

The “curly-curly” {{ }} operator directly extracts a stored variable name from “target_var” in the example below. In the prior example we needed both “enquo” and “!!” to evaluate a variable like this so the “curly-curly” operator is a convenient shortcut.

cal_group_stats2 <- function(data, group_var, target_var) {
  # store the variable name using `enquo`
  group_var <- enquo(group_var)

  data %>%
    group_by(!!group_var) %>%
    # using `{{}}` as shortcut
    summarize(across({{ target_var }}, list(
      mean = ~ mean(.x, na.rm = TRUE),
      sd = ~ sd(.x, na.rm = TRUE)
    )))
}

# check
cal_group_stats2(dat, group_var = clarity, target_var = price)

## # A tibble: 8 × 3
##   clarity price_mean price_sd
##   <ord>        <dbl>    <dbl>
## 1 I1           2674.    618. 
## 2 SI2          2489.    840. 
## 3 SI1          2440.    873. 
## 4 VS2          2616.    669. 
## 5 VS1          2383.    928. 
## 6 VVS2         2497.    828. 
## 7 VVS1         2159.   1051. 
## 8 IF           2829.     38.0

identical(
  cal_group_stats(dat, group_var = clarity, target_var = price),
  cal_group_stats2(dat, group_var = clarity, target_var = price)
)

## [1] TRUE

Remark: {{}} is the shortcut of enquo() and !! .

5 `as_label()` function and `:=` operator

How can I use the variable name? The answer is as_label() function.

print_class <- function(data, myvar) {
  myvar1 <- enquo(myvar)
  message("Understand the class of `enquo(myvar)`:")
  print(class(myvar1))

  myvar2 <- as_label(enquo(myvar))
  message("Understand the class of `as_label(enquo(myvar))`:")
  print(class(myvar2))
}

# check
print_class(dat, myvar = color)

## Understand the class of `enquo(myvar)`:

## [1] "quosure" "formula"

## Understand the class of `as_label(enquo(myvar))`:

## [1] "character"

We will illustrate the as_label() using the below example:

The as_label function extracts the string value of the target_var variable (price in this case). We use this to set the value of the target_var column.
If you want to extract the string variable name with the as_label function, you will still need to use enquo and !!. You CANNOT use the {{}} shortcut!

cal_group_stats3 <- function(data, group_var, target_var) {
  # store the variable name using `enquo`
  group_var <- enquo(group_var)
  target_var <- enquo(target_var)

  data %>%
    group_by(!!group_var) %>%
    summarize(across(!!target_var, list(
      mean = ~ mean(.x, na.rm = TRUE),
      sd = ~ sd(.x, na.rm = TRUE)
    ))) %>%
    mutate(varName = as_label(target_var))
}

# check
cal_group_stats3(dat, group_var = color, target_var = price)

## # A tibble: 7 × 4
##   color price_mean price_sd varName
##   <ord>      <dbl>    <dbl> <chr>  
## 1 D          2594.     712. price  
## 2 E          2543.     769. price  
## 3 F          2692.     553. price  
## 4 G          2576.     715. price  
## 5 H          2308.     974. price  
## 6 I          1948.    1141. price  
## 7 J          1990.    1181. price

Let’s see another example: using the variable name in ggplot title.

plotxy <- function(data, xvar, yvar) {
  xvar <- enquo(xvar)
  yvar <- enquo(yvar)
  data_name <- as_label(enquo(data))

  data %>%
    ggplot(aes(x = !!xvar, y = !!yvar)) +
    geom_point() +
    geom_smooth() +
    # using variable name in the title
    ggtitle(str_c(
      as_label(yvar), " vs. ", as_label(xvar),
      " in ", data_name, " data"
    ))
}

# check
plotxy(iris, xvar = Sepal.Length, yvar = Sepal.Width)

Remark: If you need to use/extract the variable name as a string, please use enquo() and as_label() functions.

5.1 `:=` 用于在 `summarize` 或 `mutate` 中声明新变量

In the following example:

The “walrus operator” := is used to create a column named after the variable name stored in the target_var argument (“price” in the example).
The := allows you to use strings and evaluated variables (such as target_name and target_var in our example) on the left hand side of an assignment operation (where there would normally be a “=” operator) in functions such as “mutate” and “summarize”.

cal_group_stats4 <- function(data, group_var, target_var) {
  target_name <- as_label(enquo(target_var))

  data %>%
    group_by({{ group_var }}) %>%
    summarize(
      # use := to declare new column
      !!paste0("mean_", target_name) := mean({{ target_var }}),
      !!paste0("sd_", target_name) := sd({{ target_var }})
    )
}

# check
cal_group_stats4(dat, group_var = color, target_var = price)

## # A tibble: 7 × 3
##   color mean_price sd_price
##   <ord>      <dbl>    <dbl>
## 1 D          2594.     712.
## 2 E          2543.     769.
## 3 F          2692.     553.
## 4 G          2576.     715.
## 5 H          2308.     974.
## 6 I          1948.    1141.
## 7 J          1990.    1181.

Remark: 在 summarize 或者 mutate 函数里声明新变量时，如果等号到左边包含 as_label(enquo(var)) ，需要使用 := ，而非传统的🟰

6 `sysms()` function and `!!!`

The syms function and the !!! operator are used for passing a list of variables as a function argument.

In prior examples !! was used to evaluate a single group-by variable. We now use !!! to evaluate a list of group-by variables. One quirk is that to use the syms function we will need to pass the variable names in quotes.

cal_group_stats5 <- function(data, group_vars, target_var) {
  group_vars <- syms(group_vars)
  target_name <- as_label(enquo(target_var))

  data %>%
    group_by(!!!group_vars) %>%
    summarize(
      !!paste0("minVal_", target_name) := min({{ target_var }}),
      !!paste0("maxVal_", target_name) := max({{ target_var }})
    )
}

# check
# single group-by variable
cal_group_stats5(
  data = dat,
  group_vars = "color",
  target_var = price
)

## # A tibble: 7 × 3
##   color minVal_price maxVal_price
##   <ord>        <int>        <int>
## 1 D              357         2897
## 2 E              326         2897
## 3 F              342         2897
## 4 G              354         2896
## 5 H              337         2893
## 6 I              334         2896
## 7 J              335         2898

# a list of group-by variables
cal_group_stats5(
  data = dat,
  group_vars = c("color", "clarity"),
  target_var = price
)

## # A tibble: 53 × 4
## # Groups:   color [7]
##    color clarity minVal_price maxVal_price
##    <ord> <ord>          <int>        <int>
##  1 D     I1              2869         2869
##  2 D     SI2             2762         2896
##  3 D     SI1              552         2896
##  4 D     VS2              357         2897
##  5 D     VS1              402         2861
##  6 D     VVS2             554         2897
##  7 D     VVS1             553         2866
##  8 E     I1               345         2862
##  9 E     SI2              326         2896
## 10 E     SI1              326         2897
## # … with 43 more rows

# a NULL group-by variable
cal_group_stats5(
  data = dat,
  group_vars = NULL,
  target_var = price
)

## # A tibble: 1 × 2
##   minVal_price maxVal_price
##          <int>        <int>
## 1          326         2898

Remark: 如果有多个group-by variables，那么需要使用 syms 和 !!! .

7 Summary

The variable I am referring here is the variable in the function argument:

{{}} operator is a shortcut for using enquo() and !! .
If you need to use the variable name string, eg. use it in the ggplot title, please use enquo() and as_label .
If you need to declare the new variable in summarize / mutate , DO NOT use = , please use := instead.
If you have multiple group-by variables, the tidy evaluation requires to use syms() and !!! operator.

Translate Column Names back and forth

Fri, 11 Mar 2022 00:00:00 +0000

在处理中文数据中，第一步就是翻译列名到英语。当我们分析完数据后，可能还需要将结果与之前的中文列名对应回去，这样比较麻烦。今天来总结一个小技巧。

# loading package
library(zetaEDA)

# raw data
dat <- tibble(姓名 = c("张三", "李四"), 年龄 = c(20, 30))

# check
dat %>% 
  print_kbl()

姓名	年龄
张三	20
李四	30

首先，建立一个数据集储存中英文对照表。

# build a column name translation data set
column_names_translation <- tibble(
  "姓名" = "name",
  "年龄" = "age"
) %>% 
  pivot_longer(1:2, names_to = "chinese", values_to = "english") 
  
# check
column_names_translation %>% 
  print_kbl()

chinese	english
姓名	name
年龄	age

接下来就是trick啦：

using deframe(), make a named vector
using rename(!!! your_variable)

# translate to English
tmp <- column_names_translation %>% 
  select(english, chinese) %>% 
  deframe()

tmp

##   name    age 
## "姓名" "年龄"

# `!!!` in rename is important
dat %>% 
  rename(!!!tmp) %>% 
  print_kbl()

name	age
张三	20
李四	30

# translate back to Chinese
tmp2 <- column_names_translation %>% 
  select(chinese, english) %>% 
  deframe()

dat %>% 
  rename(!!!tmp) %>% 
  rename(!!!tmp2) %>% 
  print_kbl()

姓名	年龄
张三	20
李四	30

R Plotly Tutorial

Fri, 07 Jan 2022 00:00:00 +0000

Loading Package

library(tidyverse)
library(plotly)
library(zetaEDA)
enable_zeta_ggplot_theme()

ggplotly

If you are very familiar with ggplot, then you can easily convert the ggplot object to plotly object, which is able to interactively communicate the plots with the end-user.

set.seed(123)

dat <- tibble(
  date = seq.Date(from = as.Date("2010-01-01"), length.out = 48, by = "month"),
  actual = rnorm(48, mean = 10, sd = 3),
  fc = rnorm(48, mean = 9.8, sd = 2.4)
)

dat <- dat %>%
  mutate(
    diff = actual - fc,
    RMSE = sqrt(mean((diff)^2)),
    MAE = mean(abs(diff))
  )

Take a look at our data:

dat %>%
  head() %>%
  knitr::kable()

date	actual	fc	diff	RMSE	MAE
2010-01-01	8.318573	11.671916	-3.3533432	3.462068	2.836012
2010-02-01	9.309468	9.599914	-0.2904467	3.462068	2.836012
2010-03-01	14.676125	10.407964	4.2681605	3.462068	2.836012
2010-04-01	10.211525	9.731488	0.4800374	3.462068	2.836012
2010-05-01	10.387863	9.697111	0.6907523	3.462068	2.836012
2010-06-01	15.145195	13.084646	2.0605495	3.462068	2.836012

ggplot code

plt_dat <- dat %>%
  select(actual, fc, date) %>%
  pivot_longer(cols = -date, names_to = "type", values_to = "value") %>%
  mutate(type = factor(type, levels = c("actual", "fc"))) %>%
  filter(date <= as.Date("2012-06-01"))

p <- plt_dat %>%
  ggplot(aes(x = date, y = value, fill = type)) +
  geom_col(position = "dodge", alpha = .8) +
  scale_fill_manual(values = c("steelblue", "orange")) +
  scale_x_date(
    date_breaks = "6 month",
    date_labels = "%Y-%m"
  ) +
  labs(x = "month", y = "value", title = "Compare Actual with Forecast Value")

p

Convert to plotly object using `ggplotly`

When you have a ggplot object, you can apply ggplotly function directly to get the result.

Note that:

This approach is the easiest way to do conversion!
However, this approach lacks some customization for plotly object.

# ?ggplotly

ggplotly(p)

Plotly

For advanced usage, please use plotly package function directly!

Reference: plotly r official website

Quickly learn use cheat sheet for plotly.

# step 1: make a plotly object
p2 <- plot_ly()

# step 2: add trace
p2 <- p2 %>%
  plotly::add_trace(
    data = plt_dat,
    x = ~date,
    y = ~value,
    color = ~type,
    type = "bar",
    colors = c("steelblue", "orange"),
    text = ~date,
    hovertemplate = "%{text}<br>%{y:.2f}"
  )

# step 3: set layout, i.e. x-axis, y-axis, title, etc.
p2 <- p2 %>%
  plotly::layout(
    title = "Compare Actual with Forecast Value",
    xaxis = list(
      tickformat = "%m\n%Y\n",
      title = "month",
      showline = TRUE,
      mirror = TRUE
    ),
    yaxis = list(
      showline = TRUE,
      mirror = TRUE,
      title = "value"
    )
  )

print your plotly object :)

p2

Plotly Reference

Shell Tutorial For R User

Fri, 05 Feb 2021 00:00:00 +0000

Let’s go over some basic shell commands.

Get started

Command	Description	In R
whoami	Who is the user?	Sys.info() / whoami::whoami()
date	Get date & time	Sys.date() / Sys.time()
cal	Display Calendar
clear	Clear the screen	Ctrl + L

whoami # who is the user

date # get data & time

cal # display canlendar

clear # clear the screen, crtl + l

Help/Documentation

Command	Description	In R
whatis	Single line description of a command	? / ?? / help()
man	Display manual pages for a command	? / ?? / help()
help	Display manual pages for a command	? / ?? / help()
/?	Display manual pages for a command	? / ?? / help()

man ls # help & documentation

whatis ls # single line description of a command

# help命令是bash内建命令，用于显示bash内建命令的帮助信息

Navigating file system

Command	Description	In R
pwd	Print working directory	getwd() / here::here()
ls	List directory contents	dir() / list.files() / list.dirs() / dir_info() / fs::dir_ls()
cd	Change current working directory	setwd()
mkdir	Create directory	dir.create() / fs::dir_create()
rmdir	Remove/delete directory	fs::dir_delete()

pwd # print working directory

ls # list files & folders

mkdir test # create directory called test

change working directory

Command	Description
cd	Navigate into directory
cd ..	Go up one level
cd -	Go to previous working directory
cd ~	Change directory to home directory
cd /	Change directory to root directory

cd test # navigate into test directory

cd .. # go up to one level

cd - # go to previous working directory
  
cd ~ # change directory to home directory

cd / # change directory to root directory
  
rmdir test # remove empty directory, note that the folder must be empty

list the file and folders

Command	Description
ls	List directory contents
ls -l	List files one per line
ls -a	List all files, including hidden files
ls - la	Display permission, ownership, size & modification date
ls -lh	Long format with size displayed in human readable units
ls -lS	Long format list sorted by size
ls -ltr	Long format list sorted by modification date

R & Tools | Chen Xing

How to Use GPT in R/Python

To read

Google Search Operator

Awesome ChatGPT Prompts for PhD students

Tips for great prompts

Collection of Useful Prompts

Prompts for understanding concepts

Prompt for Academic Research

Prompt Engineering

Fact-Checking ChatGPT

Reference

How to use DT table in blogdown?

Knit File Format

Tibble

kableExtra

gt

DT

Summary

Add Floating TOC in Blogdown

Motivation

Potential Approach

Step 1

Step 2.

Further discussion

More Application

Activate the TOC

For LaTeX

Summary

Tidy Evaluation in R

1 Motivation

2 quo() function & “bang bang” !!

3 enquo() function & “bang bang” !!

4 The “Curly-Curly” Shortcut {{}}

5 as_label() function and := operator

5.1 := 用于在 summarize 或 mutate 中声明新变量

6 sysms() function and !!!

7 Summary

Translate Column Names back and forth

R Plotly Tutorial

Loading Package

ggplotly

ggplot code

Convert to plotly object using ggplotly

Plotly

Plotly Reference

Shell Tutorial For R User

Get started

Help/Documentation

Navigating file system

change working directory

list the file and folders

2 `quo()` function & “bang bang” `!!`

3 `enquo()` function & “bang bang” `!!`

4 The “Curly-Curly” Shortcut `{{}}`

5 `as_label()` function and `:=` operator

5.1 `:=` 用于在 `summarize` 或 `mutate` 中声明新变量

6 `sysms()` function and `!!!`

Convert to plotly object using `ggplotly`