Productivity
PromptBeginner5 minmarkdownQuality: 22
- Append-only to these files: task.md
implementation-plan.md
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Showing 24 of 9,591Categories: Productivity, Cursor-rules, Data
Data
PromptBeginner5 minmarkdownQuality: 26
Functional Analyst
Act as a Senior Functional Analyst. Your role prioritizes correctness, clarity, traceability, and controlled scope, following UML2, Gherkin, and Agile/Scrum methodologies. Below are your core principl...
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Data
PromptBeginner5 minmarkdownQuality: 26
Probably the most useful standalone thing here. Source it and run check_data(df) on any data frame t...
NA counts
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Data
PromptBeginner5 minmarkdownQuality: 22
│ ├── visualization.md # par
layout
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Data
PromptBeginner5 minmarkdownQuality: 22
│ ├── statistics.md # Hypothesis tests
distributions
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Data
PromptBeginner5 minmarkdownQuality: 22
│ ├── data-wrangling.md # Subsetting traps
apply family
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Data
PromptBeginner5 minmarkdownQuality: 22
boxplot(%s ~ %s
data = df
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Data
PromptBeginner5 minmarkdownQuality: 22
fit_aov <- aov(%s ~ %s
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 22
fit <- lm(%s ~ %s
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 22
t.test(%s ~ %s
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 22
if (is.null(datafile)) datafile <- paste0(project_name
.csv)
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Data
PromptBeginner5 minmarkdownQuality: 22
check_data <- function(df
topnlevels = 8) {
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Data
PromptBeginner5 minmarkdownQuality: 22
saveRDS(df
data_clean.rds)
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Data
PromptBeginner5 minmarkdownQuality: 22
fitaov <- aov(outcomevar ~ group_var
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 22
t.test(outcomevar ~ groupvar
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 22
abline(lm(outcome_var ~ predictor
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 22
df <- read.csv(your_data.csv
stringsAsFactors = FALSE)
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Data
PromptBeginner5 minmarkdownQuality: 22
- Formula interface: `pairs(~ var1 + var2 + var3
data = df)`.
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Data
PromptBeginner5 minmarkdownQuality: 22
- Formula interface: `cor.test(~ x + y
data = df) — note the ~` with no LHS.
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Data
PromptBeginner5 minmarkdownQuality: 26
- Default kernel: gaussian. Range of density extends beyond data range (controlled by cut
default 3 bandwidths).
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Data
PromptBeginner5 minmarkdownQuality: 26
- Default bandwidth: bw = nrd0 (Silverman's rule of thumb). For multimodal data
consider SJ or bcv.
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Data
PromptBeginner5 minmarkdownQuality: 26
- 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
PromptBeginner5 minmarkdownQuality: 22
- Returns a matrix of p-values
not test statistics.
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Data
PromptBeginner5 minmarkdownQuality: 26
- If data fits too exactly (no residual noise)
convergence check fails — use control = list(scaleOffset = 1) or jitter data.
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