Data
PromptBeginner5 minmarkdownQuality: 28
- statistics.md — Read when: hypothesis test gives surprising result
need to choose correct p.adjust method
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Data
PromptBeginner5 minmarkdownQuality: 28
- data-wrangling.md — Read when: subsetting returns wrong type
apply on data frame gives unexpected coercion
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General
PromptBeginner5 minmarkdownQuality: 24
- modeling.md — Read when: formula syntax is confusing (I()
* vs :
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General
PromptBeginner5 minmarkdownQuality: 24
For deeper coverage
read the reference files in references/:
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General
PromptBeginner5 minmarkdownQuality: 24
Non-obvious behaviors
surprising defaults
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General
PromptBeginner5 minmarkdownQuality: 24
labels = c(low
med
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General
PromptBeginner5 minmarkdownQuality: 24
cbind(df1
df2) # bind columns (same rows)
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General
PromptBeginner5 minmarkdownQuality: 24
df$category <- cut(df$value
breaks = c(0
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General
PromptBeginner5 minmarkdownQuality: 24
rbind(df1
df2) # stack rows (same columns)
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General
PromptBeginner5 minmarkdownQuality: 24
df[!duplicated(df$id)
]
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General
PromptBeginner5 minmarkdownQuality: 24
df[!duplicated(df)
]
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General
PromptBeginner5 minmarkdownQuality: 24
df[order(df$group
-df$value)
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General
PromptBeginner5 minmarkdownQuality: 24
varying = list(c(v1
v2))
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General
PromptBeginner5 minmarkdownQuality: 24
df[order(-df$value)
] # descending
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General
PromptBeginner5 minmarkdownQuality: 24
df[order(df$value)
] # ascending
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General
PromptBeginner5 minmarkdownQuality: 24
long <- reshape(wide
direction = long
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General
PromptBeginner5 minmarkdownQuality: 24
idvar = id
timevar = time
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General
PromptBeginner5 minmarkdownQuality: 24
wide <- reshape(long
direction = wide
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General
PromptBeginner5 minmarkdownQuality: 24
merged <- merge(df1
df2
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General
PromptBeginner5 minmarkdownQuality: 24
merged <- merge(df1
df2
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General
PromptBeginner5 minmarkdownQuality: 24
merged <- merge(df1
df2
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General
PromptBeginner5 minmarkdownQuality: 24
chisq.test(table(df$a
df$b))
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General
PromptBeginner5 minmarkdownQuality: 24
cor.test(x
y
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Data
PromptBeginner5 minmarkdownQuality: 24
fit <- aov(value ~ group
data = df)
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