Data
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│ ├── visualization.md # par
layout
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Showing 24 of 24,033Categories: Data & Insights, Cursor-rules, Research & Learning, Creative, Coding & Debugging, Data
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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Coding & Debugging
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I'm a political science PhD candidate who uses R regularly but would never call myself an R person....
without ggplot2
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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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fit <- lm(%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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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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Coding & Debugging
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- arrows: code = 1 (head at start)
code = 2 (head at end
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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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Creative
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- nstart > 1 recommended (e.g.
nstart = 25): runs algorithm from multiple random starts
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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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- Returns a matrix of p-values
not test statistics.
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Coding & Debugging
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- SANN (simulated annealing): convergence code is always 0 — it never fails. maxit = total function...
no other stopping criterion.
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