General
PromptBeginner5 minmarkdown
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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
df[!duplicated(df$id)
]
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General
PromptBeginner5 minmarkdown
cbind(df1
df2) # bind columns (same rows)
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General
PromptBeginner5 minmarkdown
rbind(df1
df2) # stack rows (same columns)
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General
PromptBeginner5 minmarkdown
df[order(df$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
df[order(df$value)
] # ascending
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General
PromptBeginner5 minmarkdown
varying = list(c(v1
v2))
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General
PromptBeginner5 minmarkdown
long <- reshape(wide
direction = long
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General
PromptBeginner5 minmarkdown
wide <- reshape(long
direction = wide
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General
PromptBeginner5 minmarkdown
merged <- merge(df1
df2
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General
PromptBeginner5 minmarkdown
idvar = id
timevar = time
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General
PromptBeginner5 minmarkdown
cor.test(x
y
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General
PromptBeginner5 minmarkdown
merged <- merge(df1
df2
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General
PromptBeginner5 minmarkdown
merged <- merge(df1
df2
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Data
PromptBeginner5 minmarkdown
fit <- aov(value ~ group
data = df)
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General
PromptBeginner5 minmarkdown
t.test(before
after
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General
PromptBeginner5 minmarkdown
chisq.test(table(df$a
df$b))
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General
PromptBeginner5 minmarkdown
t.test(x
y) # two-sample
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General
PromptBeginner5 minmarkdown
t.test(x
mu = 0) # one-sample
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Data
PromptBeginner5 minmarkdown
predict(fit
newdata = new_df)
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