Find agent skills by outcome

write.csv(df

%s_clean.csv

fit <- lm(%s ~ %s

data = df)

fit_aov <- aov(%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) {

saveRDS(df

data_clean.rds)

Writing & Content

write.csv(df

data_clean.csv

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)

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

data = df)`.

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

data = df) — note the ~` with no LHS.

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

default 3 bandwidths).

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

consider SJ or bcv.

- 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.

- If data fits too exactly (no residual noise)

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

Writing & Content

- write.csv fixes `sep =

- Returns data frame with size

isdir

- If FUN returns multiple values

result column is a matrix column inside the data frame.

- Formula interface (`aggregate(y ~ x

data

- For data frames

operates on whole rows. For lists