Data
PromptBeginner5 minmarkdownQuality: 26
- Returns an array (not a data frame). Class info on return values is discarded (e.g.
Date objects become numeric).
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Showing 24 of 9,751Categories: Data, Writing & Content
Data
PromptBeginner5 minmarkdownQuality: 26
- On a data frame
apply coerces to matrix via as.matrix first — mixed types become character.
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Data
PromptBeginner5 minmarkdownQuality: 22
- Matrix indexing a data frame (`df[cbind(i
j)]`) coerces to matrix first — avoid.
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Data
PromptBeginner5 minmarkdownQuality: 26
- Data frame [ with single column: `df[
1]` returns a vector (drop=TRUE default for columns)
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Data
PromptBeginner5 minmarkdownQuality: 26
- subset argument uses non-standard evaluation — column names are resolved in the data frame
which can silently pick up wrong variables in programmatic use. Use [ with explicit logic in functions.
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Data
PromptBeginner5 minmarkdownQuality: 22
- `ts(data
start
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Data
PromptBeginner5 minmarkdownQuality: 22
- `relist(flesh
skeleton) — flesh` is the flat data
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Data
PromptBeginner5 minmarkdownQuality: 22
- Useful pattern: `do.call(rbind
listofdfs)` to combine a list of data frames.
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Data
PromptBeginner5 minmarkdownQuality: 26
- io-and-text.md — Read when: read.table silently drops data or misparses columns
regex behaves differently than expected
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Data
PromptBeginner5 minmarkdownQuality: 26
- visualization.md — Read when: par settings reset unexpectedly
layout/mfrow interaction is confusing
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Data
PromptBeginner5 minmarkdownQuality: 26
- statistics.md — Read when: hypothesis test gives surprising result
need to choose correct p.adjust method
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Data
PromptBeginner5 minmarkdownQuality: 26
- data-wrangling.md — Read when: subsetting returns wrong type
apply on data frame gives unexpected coercion
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Data
PromptBeginner5 minmarkdownQuality: 22
fit <- aov(value ~ group
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 22
predict(fit
newdata = new_df)
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Data
PromptBeginner5 minmarkdownQuality: 22
fit <- lm(y ~ x1 + x2
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 22
boxplot(value ~ group
data = df
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Data
PromptBeginner5 minmarkdownQuality: 22
saveRDS(obj
data.rds)
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Data
PromptBeginner5 minmarkdownQuality: 22
df <- read.table(data.txt
header = TRUE
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Writing & Content
PromptBeginner5 minmarkdownQuality: 22
write.csv(df
output.csv
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Data
PromptBeginner5 minmarkdownQuality: 22
df <- read.csv(data.csv
stringsAsFactors = FALSE)
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Data
PromptBeginner5 minmarkdownQuality: 22
aggregate(value ~ group
data = df
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Data
PromptBeginner5 minmarkdownQuality: 22
- “What exactly changes with EU data residency
and what must we verify for auditors?”
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Writing & Content
PromptBeginner5 minmarkdownQuality: 22
- Explain audit log usage and contents for compliance and investigations (actor
context
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Data
PromptBeginner5 minmarkdownQuality: 26
- Truthfulness rule for residency questions: if asked whether “all data stays in the EU
” the agent states only what’s documented and outlines how to verify scope in official docs and tenant configuration.
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