Data
PromptBeginner5 minmarkdown
- **visualization.md** — Read when: par settings reset unexpectedly
layout/mfrow interaction is confusing
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Find agent skills by outcome
115,786 skills indexed with the new KISS metadata standard.
Showing 24 of 115,786Categories: Data, Coding & Debugging, Cursor-rules, Communication, General
Data
PromptBeginner5 minmarkdown
- **statistics.md** — Read when: hypothesis test gives surprising result
need to choose correct p.adjust method
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General
PromptBeginner5 minmarkdown
- **modeling.md** — Read when: formula syntax is confusing (`I()`
`*` vs `:`
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Data
PromptBeginner5 minmarkdown
- **data-wrangling.md** — Read when: subsetting returns wrong type
apply on data frame gives unexpected coercion
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General
PromptBeginner5 minmarkdown
Non-obvious behaviors
surprising defaults
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General
PromptBeginner5 minmarkdown
For deeper coverage
read the reference files in `references/`:
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General
PromptBeginner5 minmarkdown
labels = c(low
med
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General
PromptBeginner5 minmarkdown
df$category <- cut(df$value
breaks = c(0
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General
PromptBeginner5 minmarkdown
cbind(df1
df2) # bind columns (same rows)
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General
PromptBeginner5 minmarkdown
rbind(df1
df2) # stack rows (same columns)
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General
PromptBeginner5 minmarkdown
df[!duplicated(df$id)
]
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General
PromptBeginner5 minmarkdown
df[order(df$group
-df$value)
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General
PromptBeginner5 minmarkdown
df[!duplicated(df)
]
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General
PromptBeginner5 minmarkdown
df[order(-df$value)
] # descending
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General
PromptBeginner5 minmarkdown
varying = list(c(v1
v2))
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General
PromptBeginner5 minmarkdown
df[order(df$value)
] # ascending
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General
PromptBeginner5 minmarkdown
long <- reshape(wide
direction = long
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General
PromptBeginner5 minmarkdown
merged <- merge(df1
df2
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General
PromptBeginner5 minmarkdown
wide <- reshape(long
direction = wide
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General
PromptBeginner5 minmarkdown
idvar = id
timevar = time
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General
PromptBeginner5 minmarkdown
merged <- merge(df1
df2
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General
PromptBeginner5 minmarkdown
cor.test(x
y
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General
PromptBeginner5 minmarkdown
merged <- merge(df1
df2
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Data
PromptBeginner5 minmarkdown
fit <- aov(value ~ group
data = df)
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