Find agent skills by outcome

I'm a political science PhD candidate who uses R regularly but would never call myself an R person....

without ggplot2

Writing & Content

writeLines(template

out_file)

boxplot(%s ~ %s

data = df

Writing & Content

write.csv(df

%s_clean.csv

fit_aov <- aov(%s ~ %s

data = df)

fit <- lm(%s ~ %s

data = df)

t.test(%s ~ %s

data = df)

if (is.null(datafile)) datafile <- paste0(project_name

.csv)

check_data <- function(df

topnlevels = 8) {

Writing & Content

write.csv(df

data_clean.csv

saveRDS(df

data_clean.rds)

fitaov <- aov(outcomevar ~ group_var

data = df)

t.test(outcomevar ~ groupvar

data = df)

abline(lm(outcome_var ~ predictor

data = df)

df <- read.csv(your_data.csv

stringsAsFactors = FALSE)

Coding & Debugging

- arrows: code = 1 (head at start)

code = 2 (head at end

- Formula interface: `pairs(~ var1 + var2 + var3

data = df)`.

- Formula interface: `cor.test(~ x + y

data = df) — note the ~` with no LHS.

- Default bandwidth: bw = nrd0 (Silverman's rule of thumb). For multimodal data

consider SJ or bcv.

- Default kernel: gaussian. Range of density extends beyond data range (controlled by cut

default 3 bandwidths).

- Returns a matrix of p-values

not test statistics.

- For composite hypotheses (parameters estimated from data)

p-values are conservative (too large). Use dgof or ks.test with exact = NULL for discrete distributions.

Coding & Debugging

- SANN (simulated annealing): convergence code is always 0 — it never fails. maxit = total function...

no other stopping criterion.

- If data fits too exactly (no residual noise)

convergence check fails — use control = list(scaleOffset = 1) or jitter data.