generate-statistical-tables
关于
This skill generates publication-ready statistical tables in R using gt, kableExtra, or flextable. It creates descriptive statistics, regression results, ANOVA tables, and correlation matrices with APA formatting. Use it when preparing tables for academic papers, reports, or Quarto/R Markdown documents.
快速安装
Claude Code
推荐npx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/generate-statistical-tables在 Claude Code 中复制并粘贴此命令以安装该技能
技能文档
統計表の生成
Create publication-ready statistical tables for reports and manuscripts.
使用タイミング
- Creating descriptive statistics tables
- Formatting regression or ANOVA output
- Building correlation matrices
- Producing APA-style tables for academic papers
- Generating tables for Quarto/R Markdown documents
入力
- 必須: Statistical analysis results (model objects, summary data)
- 必須: Output format (HTML, PDF, Word)
- 任意: Style guide (APA, journal-specific)
- 任意: Table numbering scheme
手順
ステップ1: Choose Table Package
| Package | Best for | Formats |
|---|---|---|
gt | HTML, general-purpose | HTML, PDF, Word |
kableExtra | LaTeX/PDF documents | PDF, HTML |
flextable | Word documents | Word, PDF, HTML |
gtsummary | Clinical/statistical summaries | All via gt/flextable |
期待結果: A table package selected based on the output format and use case. The chosen package is installed and loadable.
失敗時: If the required package is not installed, run install.packages("gt") (or the appropriate package). For gtsummary, both gt and gtsummary must be installed.
ステップ2: Descriptive Statistics Table
library(gt)
descriptives <- data |>
group_by(group) |>
summarise(
n = n(),
M = mean(score, na.rm = TRUE),
SD = sd(score, na.rm = TRUE),
Min = min(score, na.rm = TRUE),
Max = max(score, na.rm = TRUE)
)
gt(descriptives) |>
tab_header(
title = "Table 1",
subtitle = "Descriptive Statistics by Group"
) |>
fmt_number(columns = c(M, SD), decimals = 2) |>
fmt_number(columns = c(Min, Max), decimals = 1) |>
cols_label(
group = "Group",
n = md("*n*"),
M = md("*M*"),
SD = md("*SD*")
)
期待結果: A gt table object with formatted means, SDs, and counts grouped by category. Column headers use proper statistical notation (italicized M, SD, n).
失敗時: If group_by() produces unexpected results, verify the grouping variable exists and has the expected levels. If fmt_number() throws an error, ensure the target columns contain numeric data.
ステップ3: Regression Results Table
model <- lm(outcome ~ predictor1 + predictor2 + predictor3, data = data)
library(gtsummary)
tbl_regression(model) |>
bold_p() |>
add_glance_source_note(
include = c(r.squared, adj.r.squared, nobs)
) |>
modify_header(label = "**Predictor**") |>
modify_caption("Table 2: Regression Results")
期待結果: A gtsummary regression table with bolded p-values, model fit statistics (R-squared, N) in a source note, and a descriptive caption.
失敗時: If tbl_regression() fails, verify the input is a model object (e.g., lm, glm). If add_glance_source_note() errors, check that broom can tidy the model: broom::glance(model).
ステップ4: Correlation Matrix
library(gt)
cor_matrix <- cor(data[, c("var1", "var2", "var3", "var4")],
use = "pairwise.complete.obs")
# Format lower triangle
cor_matrix[upper.tri(cor_matrix)] <- NA
as.data.frame(cor_matrix) |>
tibble::rownames_to_column("Variable") |>
gt() |>
fmt_number(decimals = 2) |>
sub_missing(missing_text = "") |>
tab_header(title = "Table 3", subtitle = "Correlation Matrix")
期待結果: A lower-triangle correlation matrix rendered as a gt table with blanked upper triangle, two decimal places, and a clear caption.
失敗時: If sub_missing() does not blank the upper triangle, verify that NA values were set correctly with cor_matrix[upper.tri(cor_matrix)] <- NA. If variables are non-numeric, cor() will fail; filter to numeric columns first.
ステップ5: ANOVA Table
aov_result <- aov(score ~ group * condition, data = data)
library(gtsummary)
tbl_anova <- broom::tidy(aov_result) |>
gt() |>
fmt_number(columns = c(sumsq, meansq, statistic), decimals = 2) |>
fmt_number(columns = p.value, decimals = 3) |>
cols_label(
term = "Source",
df = md("*df*"),
sumsq = md("*SS*"),
meansq = md("*MS*"),
statistic = md("*F*"),
p.value = md("*p*")
) |>
tab_header(title = "Table 4", subtitle = "ANOVA Results")
期待結果: A formatted ANOVA table with Source, df, SS, MS, F, and p columns. Interaction terms are clearly labeled and p-values are formatted to three decimal places.
失敗時: If broom::tidy(aov_result) produces unexpected columns, verify the model is an aov object. For Type III sums of squares, use car::Anova(model, type = 3) instead of base aov().
ステップ6: Save Tables
# Save as HTML
gtsave(my_table, "table1.html")
# Save as Word
gtsave(my_table, "table1.docx")
# Save as PNG image
gtsave(my_table, "table1.png")
# For LaTeX/PDF (kableExtra)
kableExtra::save_kable(kable_table, "table1.pdf")
期待結果: Table saved to the specified file format (HTML, Word, PNG, or PDF). The output file opens correctly in the appropriate application.
失敗時: If gtsave() fails for Word format, ensure the webshot2 package is installed. For PDF output via kableExtra, ensure a LaTeX distribution (TinyTeX or MiKTeX) is installed.
ステップ7: Embed in Quarto Document
```{r}
#| label: tbl-descriptives
#| tbl-cap: "Descriptive Statistics by Group"
gt(descriptives) |>
fmt_number(columns = c(M, SD), decimals = 2)
```
See @tbl-descriptives for summary statistics.
期待結果: The table renders inline in the Quarto document with a cross-referenceable label (@tbl-*) and a proper caption. The table adapts to the document's output format automatically.
失敗時: If the table does not render, verify the chunk label starts with tbl- for Quarto cross-referencing. If formatting is lost in PDF, switch from gt to kableExtra for LaTeX-based output.
バリデーション
- Table renders correctly in target format (HTML, PDF, Word)
- Numbers are formatted consistently (decimal places, alignment)
- Statistical notation follows the style guide (italicized, proper symbols)
- Table has a clear caption and numbering
- Column headers are meaningful
- Notes/footnotes explain abbreviations or significance markers
よくある落とし穴
- gt in PDF: gt has limited PDF support. Use kableExtra for LaTeX-heavy documents.
- Rounding inconsistency: Always use
fmt_number()(gt) orformat()rather thanround()for display - Missing values display: Configure with
sub_missing()in gt oroptions(knitr.kable.NA = "") - Wide tables in PDF: Tables exceeding page width need
landscape()or font size reduction - APA number formatting: No leading zero for values bounded by 1 (p-values, correlations): ".03" not "0.03"
関連スキル
format-apa-report- tables within APA manuscriptscreate-quarto-report- embedding tables in reportsbuild-parameterized-report- tables that adapt to parameters
GitHub 仓库
相关推荐技能
content-collections
元Content Collections 是一个 TypeScript 优先的构建工具,可将本地 Markdown/MDX 文件转换为类型安全的数据集合。它专为构建博客、文档站和内容密集型 Vite+React 应用而设计,提供基于 Zod 的自动模式验证。该工具涵盖从 Vite 插件配置、MDX 编译到生产环境部署的完整工作流。
polymarket
元这个Claude Skill为开发者提供完整的Polymarket预测市场开发支持,涵盖API调用、交易执行和市场数据分析。关键特性包括实时WebSocket数据流,可监控实时交易、订单和市场动态。开发者可用它构建预测市场应用、实施交易策略并集成实时市场预测功能。
creating-opencode-plugins
元该Skill帮助开发者创建OpenCode插件,用于接入命令、文件、LSP等25+种事件。它提供了插件结构、事件API规范和JavaScript/TypeScript实现模式,适合需要拦截操作、扩展功能或自定义事件处理的场景。开发者可通过它快速构建响应式模块来增强OpenCode AI助手的能力。
sglang
元SGLang是一个专为LLM设计的高性能推理框架,特别适用于需要结构化输出的场景。它通过RadixAttention前缀缓存技术,在处理JSON、正则表达式、工具调用等具有重复前缀的复杂工作流时,能实现极速生成。如果你正在构建智能体或多轮对话系统,并追求远超vLLM的推理性能,SGLang是理想选择。
