General
PromptBeginner5 minmarkdown
│ ├── dates-and-system.md # Date/POSIXct traps
options()
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Data
PromptBeginner5 minmarkdown
│ ├── visualization.md # par
layout
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General
PromptBeginner5 minmarkdown
│ ├── io-and-text.md # read.table
grep
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Data
PromptBeginner5 minmarkdown
│ ├── statistics.md # Hypothesis tests
distributions
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General
PromptBeginner5 minmarkdown
│ ├── modeling.md # Formula syntax
lm/glm/aov/nls
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Data
PromptBeginner5 minmarkdown
│ ├── data-wrangling.md # Subsetting traps
apply family
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General
PromptBeginner5 minmarkdown
If you're also someone who Googles `how to drop NA rows in R` every single time
this one's for you. 🫶
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Coding & Debugging
PromptBeginner5 minmarkdown
I'm a political science PhD candidate who uses R regularly but would never call myself *an R person*. I needed a Claude Code skill for base R — something without tidyverse
without ggplot2
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General
PromptBeginner5 minmarkdown
scaffold_analysis(project
outcome = outcome
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General
PromptBeginner5 minmarkdown
cat(sprintf(Created: %s\n
out_file))
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General
PromptBeginner5 minmarkdown
group
outcome
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General
PromptBeginner5 minmarkdown
project_name
project_name
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General
PromptBeginner5 minmarkdown
outcome
group
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General
PromptBeginner5 minmarkdown
group
group
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General
PromptBeginner5 minmarkdown
format(Sys.Date()
%%Y-%%m-%%d)
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Data
PromptBeginner5 minmarkdown
# boxplot(%s ~ %s
data = df
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General
PromptBeginner5 minmarkdown
par(mfrow = c(1
1))
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General
PromptBeginner5 minmarkdown
# png(%s_boxplot.png
width = 800
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General
PromptBeginner5 minmarkdown
plot(fit
which = 1) # residuals vs fitted
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General
PromptBeginner5 minmarkdown
par(mfrow = c(1
2))
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Data
PromptBeginner5 minmarkdown
# fit_aov <- aov(%s ~ %s
data = df)
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Data
PromptBeginner5 minmarkdown
# t.test(%s ~ %s
data = df)
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Data
PromptBeginner5 minmarkdown
fit <- lm(%s ~ %s
data = df)
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General
PromptBeginner5 minmarkdown
tapply(df$%s
df$%s
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