Data
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│ ├── visualization.md # par
layout
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Data
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│ ├── statistics.md # Hypothesis tests
distributions
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Data
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│ ├── data-wrangling.md # Subsetting traps
apply family
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Data
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boxplot(%s ~ %s
data = df
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Data
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fit_aov <- aov(%s ~ %s
data = df)
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Data
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t.test(%s ~ %s
data = df)
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Data
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fit <- lm(%s ~ %s
data = df)
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Data
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if (is.null(datafile)) datafile <- paste0(project_name
.csv)
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Data
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check_data <- function(df
topnlevels = 8) {
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Data
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saveRDS(df
data_clean.rds)
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Data
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fitaov <- aov(outcomevar ~ group_var
data = df)
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Data
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t.test(outcomevar ~ groupvar
data = df)
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Data
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abline(lm(outcome_var ~ predictor
data = df)
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Data
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df <- read.csv(your_data.csv
stringsAsFactors = FALSE)
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Data
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- Formula interface: `pairs(~ var1 + var2 + var3
data = df)`.
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Data
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- Formula interface: `cor.test(~ x + y
data = df) — note the ~` with no LHS.
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Data
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- Default kernel: gaussian. Range of density extends beyond data range (controlled by cut
default 3 bandwidths).
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Data
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- Default bandwidth: bw = nrd0 (Silverman's rule of thumb). For multimodal data
consider SJ or bcv.
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Data
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- Returns a matrix of p-values
not test statistics.
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Data
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- For composite hypotheses (parameters estimated from data)
p-values are conservative (too large). Use dgof or ks.test with exact = NULL for discrete distributions.
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Data
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- If data fits too exactly (no residual noise)
convergence check fails — use control = list(scaleOffset = 1) or jitter data.
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Data
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- Returns data frame with size
isdir
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Data
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- If FUN returns multiple values
result column is a matrix column inside the data frame.
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Data
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- Formula interface (`aggregate(y ~ x
data
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