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base-R

name: base-r description: Provides base R programming guidance covering data structures, data wrangling, statistical modeling, visualization, and I/O, using only packages included in a standard R…

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Base R Programming Skill

A comprehensive reference for base R programming — covering data structures, control flow, functions, I/O, statistical computing, and plotting.

Quick Reference

Data Structures

# Vectors (atomic)
x <- c(1, 2, 3)              # numeric
y <- c("a", "b", "c")        # character
z <- c(TRUE, FALSE, TRUE)    # logical

# Factor
f <- factor(c("low", "med", "high"), levels = c("low", "med", "high"), ordered = TRUE)

# Matrix
m <- matrix(1:6, nrow = 2, ncol = 3)
m[1, ]       # first row
m[, 2]       # second column

# List
lst <- list(name = "ali", scores = c(90, 85), passed = TRUE)
lst$name      # access by name
lst[[2]]      # access by position

# Data frame
df <- data.frame(
  id = 1:3,
  name = c("a", "b", "c"),
  value = c(10.5, 20.3, 30.1),
  stringsAsFactors = FALSE
)
df[df$value > 15, ]    # filter rows
df$new_col <- df$value * 2  # add column

Subsetting

# Vectors
x[1:3]             # by position
x[c(TRUE, FALSE)]  # by logical
x[x > 5]           # by condition
x[-1]              # exclude first

# Data frames
df[1:5, ]                    # first 5 rows
df[, c("name", "value")]     # select columns
df[df$value > 10, "name"]    # filter + select
subset(df, value > 10, select = c(name, value))

# which() for index positions
idx <- which(df$value == max(df$value))

Control Flow

# if/else
if (x > 0) {
  "positive"
} else if (x == 0) {
  "zero"
} else {
  "negative"
}

# ifelse (vectorized)
ifelse(x > 0, "pos", "neg")

# for loop
for (i in seq_along(x)) {
  cat(i, x[i], "\n")
}

# while
while (condition) {
  # body
  if (stop_cond) break
}

# switch
switch(type,
  "a" = do_a(),
  "b" = do_b(),
  stop("Unknown type")
)

Functions

# Define
my_func <- function(x, y = 1, ...) {
  result <- x + y
  return(result)  # or just: result
}

# Anonymous functions
sapply(1:5, function(x) x^2)
# R 4.1+ shorthand:
sapply(1:5, \(x) x^2)

# Useful: do.call for calling with a list of args
do.call(paste, list("a", "b", sep = "-"))

Apply Family

# sapply — simplify result to vector/matrix
sapply(lst, length)

# lapply — always returns list
lapply(lst, function(x) x[1])

# vapply — like sapply but with type safety
vapply(lst, length, integer(1))

# apply — over matrix margins (1=rows, 2=cols)
apply(m, 2, sum)

# tapply — apply by groups
tapply(df$value, df$group, mean)

# mapply — multivariate
mapply(function(x, y) x + y, 1:3, 4:6)

# aggregate — like tapply for data frames
aggregate(value ~ group, data = df, FUN = mean)

String Operations

paste("a", "b", sep = "-")    # "a-b"
paste0("x", 1:3)              # "x1" "x2" "x3"
sprintf("%.2f%%", 3.14159)    # "3.14%"
nchar("hello")                # 5
substr("hello", 1, 3)         # "hel"
gsub("old", "new", text)      # replace all
grep("pattern", x)            # indices of matches
grepl("pattern", x)           # logical vector
strsplit("a,b,c", ",")        # list("a","b","c")
trimws("  hi  ")              # "hi"
tolower("ABC")                # "abc"

Data I/O

# CSV
df <- read.csv("data.csv", stringsAsFactors = FALSE)
write.csv(df, "output.csv", row.names = FALSE)

# Tab-delimited
df <- read.delim("data.tsv")

# General
df <- read.table("data.txt", header = TRUE, sep = "\t")

# RDS (single R object, preserves types)
saveRDS(obj, "data.rds")
obj <- readRDS("data.rds")

# RData (multiple objects)
save(df1, df2, file = "data.RData")
load("data.RData")

# Connections
con <- file("big.csv", "r")
chunk <- readLines(con, n = 100)
close(con)

Base Plotting

# Scatter
plot(x, y, main = "Title", xlab = "X", ylab = "Y",
     pch = 19, col = "steelblue", cex = 1.2)

# Line
plot(x, y, type = "l", lwd = 2, col = "red")
lines(x, y2, col = "blue", lty = 2)  # add line

# Bar
barplot(table(df$category), main = "Counts",
        col = "lightblue", las = 2)

# Histogram
hist(x, breaks = 30, col = "grey80",
     main = "Distribution", xlab = "Value")

# Box plot
boxplot(value ~ group, data = df,
        col = "lightyellow", main = "By Group")

# Multiple plots
par(mfrow = c(2, 2))  # 2x2 grid
# ... four plots ...
par(mfrow = c(1, 1))  # reset

# Save to file
png("plot.png", width = 800, height = 600)
plot(x, y)
dev.off()

# Add elements
legend("topright", legend = c("A", "B"),
       col = c("red", "blue"), lty = 1)
abline(h = 0, lty = 2, col = "grey")
text(x, y, labels = names, pos = 3, cex = 0.8)

Statistics

# Descriptive
mean(x); median(x); sd(x); var(x)
quantile(x, probs = c(0.25, 0.5, 0.75))
summary(df)
cor(x, y)
table(df$category)  # frequency table

# Linear model
fit <- lm(y ~ x1 + x2, data = df)
summary(fit)
coef(fit)
predict(fit, newdata = new_df)
confint(fit)

# t-test
t.test(x, y)                    # two-sample
t.test(x, mu = 0)               # one-sample
t.test(before, after, paired = TRUE)

# Chi-square
chisq.test(table(df$a, df$b))

# ANOVA
fit <- aov(value ~ group, data = df)
summary(fit)
TukeyHSD(fit)

# Correlation test
cor.test(x, y, method = "pearson")

Data Manipulation

# Merge (join)
merged <- merge(df1, df2, by = "id")                  # inner
merged <- merge(df1, df2, by = "id", all = TRUE)      # full outer
merged <- merge(df1, df2, by = "id", all.x = TRUE)    # left

# Reshape
wide <- reshape(long, direction = "wide",
                idvar = "id", timevar = "time", v.names = "value")
long <- reshape(wide, direction = "long",
                varying = list(c("v1", "v2")), v.names = "value")

# Sort
df[order(df$value), ]              # ascending
df[order(-df$value), ]             # descending
df[order(df$group, -df$value), ]   # multi-column

# Remove duplicates
df[!duplicated(df), ]
df[!duplicated(df$id), ]

# Stack / combine
rbind(df1, df2)    # stack rows (same columns)
cbind(df1, df2)    # bind columns (same rows)

# Transform columns
df$log_val <- log(df$value)
df$category <- cut(df$value, breaks = c(0, 10, 20, Inf),
                   labels = c("low", "med", "high"))

Environment & Debugging

ls()                  # list objects
rm(x)                 # remove object
rm(list = ls())       # clear all
str(obj)              # structure
class(obj)            # class
typeof(obj)           # internal type
is.na(x)              # check NA
complete.cases(df)    # rows without NA
traceback()           # after error
debug(my_func)        # step through
browser()             # breakpoint in code
system.time(expr)     # timing
Sys.time()            # current time

Reference Files

For deeper coverage, read the reference files in references/:

Function Gotchas & Quick Reference (condensed from R 4.5.3 Reference Manual)

Non-obvious behaviors, surprising defaults, and tricky interactions — only what Claude doesn't already know:

  • data-wrangling.md — Read when: subsetting returns wrong type, apply on data frame gives unexpected coercion, merge/split/cbind behaves oddly, factor levels persist after filtering, table/duplicated edge cases.
  • modeling.md — Read when: formula syntax is confusing (I(), * vs :, /), aov gives wrong SS type, glm silently fits OLS, nls won't converge, predict returns wrong scale, optim/optimize needs tuning.
  • statistics.md — Read when: hypothesis test gives surprising result, need to choose correct p.adjust method, clustering parameters seem wrong, distribution function naming is confusing (d/p/q/r prefixes).
  • visualization.md — Read when: par settings reset unexpectedly, layout/mfrow interaction is confusing, axis labels are clipped, colors don't look right, need specialty plots (contour, persp, mosaic, pairs).
  • io-and-text.md — Read when: read.table silently drops data or misparses columns, regex behaves differently than expected, sprintf formatting is tricky, write.table output has unwanted row names.
  • dates-and-system.md — Read when: Date/POSIXct conversion gives wrong day, time zones cause off-by-one, difftime units are unexpected, need to find/list/test files programmatically.
  • misc-utilities.md — Read when: do.call behaves differently than direct call, need Reduce/Filter/Map, tryCatch handler doesn't fire, all.equal returns string not logical, time series functions need setup.

Tips for Writing Good R Code

  • Use vapply() over sapply() in production code — it enforces return types
  • Prefer seq_along(x) over 1:length(x) — the latter breaks when x is empty
  • Use stringsAsFactors = FALSE in read.csv() / data.frame() (default changed in R 4.0)
  • Vectorize operations instead of writing loops when possible
  • Use stop(), warning(), message() for error handling — not print()
  • <<- assigns to parent environment — use sparingly and intentionally
  • with(df, expr) avoids repeating df$ everywhere
  • Sys.setenv() and .Renviron for environment variables FILE:references/misc-utilities.md

Miscellaneous Utilities — Quick Reference

Non-obvious behaviors, gotchas, and tricky defaults for R functions. Only what Claude doesn't already know.

do.call

  • do.call(fun, args_list) — args must be a list, even for a single argument.
  • quote = TRUE prevents evaluation of arguments before the call — needed when passing expressions/symbols.
  • Behavior of substitute inside do.call differs from direct calls. Semantics are not fully defined for this case.
  • Useful pattern: do.call(rbind, list_of_dfs) to combine a list of data frames.

Reduce / Filter / Map / Find / Position

R's functional programming helpers from base — genuinely non-obvious.

  • Reduce(f, x) applies binary function f cumulatively: Reduce("+", 1:4) = ((1+2)+3)+4. Direction matters for non-commutative ops.
  • Reduce(f, x, accumulate = TRUE) returns all intermediate results — equivalent to Python's itertools.accumulate.
  • Reduce(f, x, right = TRUE) folds from the right: f(x1, f(x2, f(x3, x4))).
  • Reduce with init adds a starting value: Reduce(f, x, init = v) = f(f(f(v, x1), x2), x3).
  • Filter(f, x) keeps elements where f(elem) is TRUE. Unlike x[sapply(x, f)], handles NULL/empty correctly.
  • Map(f, ...) is a simple wrapper for mapply(f, ..., SIMPLIFY = FALSE) — always returns a list.
  • Find(f, x) returns the first element where f(elem) is TRUE. Find(f, x, right = TRUE) for last.
  • Position(f, x) returns the index of the first match (like Find but returns position, not value).

lengths

  • lengths(x) returns the length of each element of a list. Equivalent to sapply(x, length) but faster (implemented in C).
  • Works on any list-like object. Returns integer vector.

conditions (tryCatch / withCallingHandlers)

  • tryCatch unwinds the call stack — handler runs in the calling environment, not where the error occurred. Cannot resume execution.
  • withCallingHandlers does NOT unwind — handler runs where the condition was signaled. Can inspect/log then let the condition propagate.
  • tryCatch(expr, error = function(e) e) returns the error condition object.
  • tryCatch(expr, warning = function(w) {...}) catches the first warning and exits. Use withCallingHandlers + invokeRestart("muffleWarning") to suppress warnings but continue.
  • tryCatch finally clause always runs (like Java try/finally).
  • globalCallingHandlers() registers handlers that persist for the session (useful for logging).
  • Custom conditions: stop(errorCondition("msg", class = "myError")) then catch with tryCatch(..., myError = function(e) ...).

all.equal

  • Tests near equality with tolerance (default 1.5e-8, i.e., sqrt(.Machine$double.eps)).
  • Returns TRUE or a character string describing the difference — NOT FALSE. Use isTRUE(all.equal(x, y)) in conditionals.
  • tolerance argument controls numeric tolerance. scale for absolute vs relative comparison.
  • Checks attributes, names, dimensions — more thorough than ==.

combn

  • combn(n, m) or combn(x, m): generates all combinations of m items from x.
  • Returns a matrix with m rows; each column is one combination.
  • FUN argument applies a function to each combination: combn(5, 3, sum) returns sums of all 3-element subsets.
  • simplify = FALSE returns a list instead of a matrix.

modifyList

  • modifyList(x, val) replaces elements of list x with those in val by name.
  • Setting a value to NULL removes that element from the list.
  • Does add new names not in x — it uses x[names(val)] <- val internally, so any name in val gets added or replaced.

relist

  • Inverse of unlist: given a flat vector and a skeleton list, reconstructs the nested structure.
  • relist(flesh, skeleton) — flesh is the flat data, skeleton provides the shape.
  • Works with factors, matrices, and nested lists.

txtProgressBar

  • txtProgressBar(min, max, style = 3) — style 3 shows percentage + bar (most useful).
  • Update with setTxtProgressBar(pb, value). Close with close(pb).
  • Style 1: rotating |/-\, style 2: simple progress. Only style 3 shows percentage.

object.size

  • Returns an estimate of memory used by an object. Not always exact for shared references.
  • format(object.size(x), units = "MB") for human-readable output.
  • Does not count the size of environments or external pointers.

installed.packages / update.packages

  • installed.packages() can be slow (scans all packages). Use find.package() or requireNamespace() to check for a specific package.
  • update.packages(ask = FALSE) updates all packages without prompting.
  • lib.loc specifies which library to check/update.

vignette / demo

  • vignette() lists all vignettes; vignette("name", package = "pkg") opens a specific one.
  • demo() lists all demos; demo("topic") runs one interactively.
  • browseVignettes() opens vignette browser in HTML.

Time series: acf / arima / ts / stl / decompose

  • ts(data, start, frequency): frequency is observations per unit time (12 for monthly, 4 for quarterly).
  • acf default type = "correlation". Use type = "partial" for PACF. plot = FALSE to suppress auto-plotting.
  • arima(x, order = c(p,d,q)) for ARIMA models. seasonal = list(order = c(P,D,Q), period = S) for seasonal component.
  • arima handles NA values in the time series (via Kalman filter).
  • stl requires s.window (seasonal window) — must be specified, no default. s.window = "periodic" assumes fixed seasonality.
  • decompose: simpler than stl, uses moving averages. type = "additive" or "multiplicative".
  • stl result components: $time.series matrix with columns seasonal, trend, remainder. FILE:references/data-wrangling.md

Data Wrangling — Quick Reference

Non-obvious behaviors, gotchas, and tricky defaults for R functions. Only what Claude doesn't already know.

Extract / Extract.data.frame

Indexing pitfalls in base R.

  • m[j = 2, i = 1] is m[2, 1] not m[1, 2] — argument names are ignored in [, positional matching only. Never name index args.
  • Factor indexing: x[f] uses integer codes of factor f, not its character labels. Use x[as.character(f)] for label-based indexing.
  • x[[]] with no index is always an error. x$name does partial matching by default; x[["name"]] does not (exact by default).
  • Assigning NULL via x[[i]] <- NULL or x$name <- NULL deletes that list element.
  • Data frame [ with single column: df[, 1] returns a vector (drop=TRUE default for columns), but df[1, ] returns a data frame (drop=FALSE for rows). Use drop = FALSE explicitly.
  • Matrix indexing a data frame (df[cbind(i,j)]) coerces to matrix first — avoid.

subset

Use interactively only; unsafe for programming.

  • subset argument uses non-standard evaluation — column names are resolved in the data frame, which can silently pick up wrong variables in programmatic use. Use [ with explicit logic in functions.
  • NAs in the logical condition are treated as FALSE (rows silently dropped).
  • Factors may retain unused levels after subsetting; call droplevels().

match / %in%

  • %in% never returns NA — this makes it safe for if() conditions unlike ==.
  • match() returns position of first match only; duplicates in table are ignored.
  • Factors, raw vectors, and lists are all converted to character before matching.
  • NaN matches NaN but not NA; NA matches NA only.

apply

  • On a data frame, apply coerces to matrix via as.matrix first — mixed types become character.
  • Return value orientation is transposed: if FUN returns length-n vector, result has dim c(n, dim(X)[MARGIN]). Row results become columns.
  • Factor results are coerced to character in the output array.
  • ... args cannot share names with X, MARGIN, or FUN (partial matching risk).

lapply / sapply / vapply

  • sapply can return a vector, matrix, or list unpredictably — use vapply in non-interactive code with explicit FUN.VALUE template.
  • Calling primitives directly in lapply can cause dispatch issues; wrap in function(x) is.numeric(x) rather than bare is.numeric.
  • sapply with simplify = "array" can produce higher-rank arrays (not just matrices).

tapply

  • Returns an array (not a data frame). Class info on return values is discarded (e.g., Date objects become numeric).
  • ... args to FUN are not divided into cells — they apply globally, so FUN should not expect additional args with same length as X.
  • default = NA fills empty cells; set default = 0 for sum-like operations. Before R 3.4.0 this was hard-coded to NA.
  • Use array2DF() to convert result to a data frame.

mapply

  • Argument name is SIMPLIFY (all caps) not simplify — inconsistent with sapply.
  • MoreArgs must be a list of args not vectorized over.
  • Recycles shorter args to common length; zero-length arg gives zero-length result.

merge

  • Default by is intersect(names(x), names(y)) — can silently merge on unintended columns if data frames share column names.
  • by = 0 or by = "row.names" merges on row names, adding a "Row.names" column.
  • by = NULL (or both by.x/by.y length 0) produces Cartesian product.
  • Result is sorted on by columns by default (sort = TRUE). For unsorted output use sort = FALSE.
  • Duplicate key matches produce all combinations (one row per match pair).

split

  • If f is a list of factors, interaction is used; levels containing "." can cause unexpected splits unless sep is changed.
  • drop = FALSE (default) retains empty factor levels as empty list elements.
  • Supports formula syntax: split(df, ~ Month).

cbind / rbind

  • cbind on data frames calls data.frame(...), not cbind.matrix. Mixing matrices and data frames can give unexpected results.
  • rbind on data frames matches columns by name, not position. Missing columns get NA.
  • cbind(NULL) returns NULL (not a matrix). For consistency, rbind(NULL) also returns NULL.

table

  • By default excludes NA (useNA = "no"). Use useNA = "ifany" or exclude = NULL to count NAs.
  • Setting exclude non-empty and non-default implies useNA = "ifany".
  • Result is always an array (even 1D), class "table". Convert to data frame with as.data.frame(tbl).
  • Two kinds of NA (factor-level NA vs actual NA) are treated differently depending on useNA/exclude.

duplicated / unique

  • duplicated marks the second and later occurrences as TRUE, not the first. Use fromLast = TRUE to reverse.
  • For data frames, operates on whole rows. For lists, compares recursively.
  • unique keeps the first occurrence of each value.

data.frame (gotchas)

  • stringsAsFactors = FALSE is the default since R 4.0.0 (was TRUE before).
  • Atomic vectors recycle to match longest column, but only if exact multiple. Protect with I() to prevent conversion.
  • Duplicate column names allowed only with check.names = FALSE, but many operations will de-dup them silently.
  • Matrix arguments are expanded to multiple columns unless protected by I().

factor (gotchas)

  • as.numeric(f) returns integer codes, not original values. Use as.numeric(levels(f))[f] or as.numeric(as.character(f)).
  • Only == and != work between factors; factors must have identical level sets. Ordered factors support <, >.
  • c() on factors unions level sets (since R 4.1.0), but earlier versions converted to integer.
  • Levels are sorted by default, but sort order is locale-dependent at creation time.

aggregate

  • Formula interface (aggregate(y ~ x, data, FUN)) drops NA groups by default.
  • The data frame method requires by as a list (not a vector).
  • Returns columns named after the grouping variables, with result column keeping the original name.
  • If FUN returns multiple values, result column is a matrix column inside the data frame.

complete.cases

  • Returns a logical vector: TRUE for rows with no NAs across all columns/arguments.
  • Works on multiple arguments (e.g., complete.cases(x, y) checks both).

order

  • Returns a permutation vector of indices, not the sorted values. Use x[order(x)] to sort.
  • Default is ascending; use -x for descending numeric, or decreasing = TRUE.
  • For character sorting, depends on locale. Use method = "radix" for locale-independent fast sorting.
  • sort.int() with method = "radix" is much faster for large integer/character vectors. FILE:references/dates-and-system.md

Dates and System — Quick Reference

Non-obvious behaviors, gotchas, and tricky defaults for R functions. Only what Claude doesn't already know.

Dates (Date class)

  • Date objects are stored as integer days since 1970-01-01. Arithmetic works in days.
  • Sys.Date() returns current date as Date object.
  • seq.Date(from, to, by = "month") — "month" increments can produce varying-length intervals. Adding 1 month to Jan 31 gives Mar 3 (not Feb 28).
  • diff(dates) returns a difftime object in days.
  • format(date, "%Y") for year, "%m" for month, "%d" for day, "%A" for weekday name (locale-dependent).
  • Years before 1CE may not be handled correctly.
  • length(date_vector) <- n pads with NAs if extended.

DateTimeClasses (POSIXct / POSIXlt)

  • POSIXct: seconds since 1970-01-01 UTC (compact, a numeric vector).
  • POSIXlt: list with components $sec, $min, $hour, $mday, $mon (0-11!), $year (since 1900!), $wday (0-6, Sunday=0), $yday (0-365).
  • Converting between POSIXct and Date: as.Date(posixct_obj) uses tz = "UTC" by default — may give different date than intended if original was in another timezone.
  • Sys.time() returns POSIXct in current timezone.
  • strptime returns POSIXlt; as.POSIXct(strptime(...)) to get POSIXct.
  • difftime arithmetic: subtracting POSIXct objects gives difftime. Units auto-selected ("secs", "mins", "hours", "days", "weeks").

difftime

  • difftime(time1, time2, units = "auto") — auto-selects smallest sensible unit.
  • Explicit units: "secs", "mins", "hours", "days", "weeks". No "months" or "years" (variable length).
  • as.numeric(diff, units = "hours") to extract numeric value in specific units.
  • units(diff_obj) <- "hours" changes the unit in place.

system.time / proc.time

  • system.time(expr) returns user, system, and elapsed time.
  • gcFirst = TRUE (default): runs garbage collection before timing for more consistent results.
  • proc.time() returns cumulative time since R started — take differences for intervals.
  • elapsed (wall clock) can be less than user (multi-threaded BLAS) or more (I/O waits).

Sys.sleep

  • Sys.sleep(seconds) — allows fractional seconds. Actual sleep may be longer (OS scheduling).
  • The process yields to the OS during sleep (does not busy-wait).

options (key options)

Selected non-obvious options:

  • options(scipen = n): positive biases toward fixed notation, negative toward scientific. Default 0. Applies to print/format/cat but not sprintf.
  • options(digits = n): significant digits for printing (1-22, default 7). Suggestion only.
  • options(digits.secs = n): max decimal digits for seconds in time formatting (0-6, default 0).
  • options(warn = n): -1 = ignore warnings, 0 = collect (default), 1 = immediate, 2 = convert to errors.
  • options(error = recover): drop into debugger on error. options(error = NULL) resets to default.
  • options(OutDec = ","): change decimal separator in output (affects format, print, NOT sprintf).
  • options(stringsAsFactors = FALSE): global default for data.frame (moot since R 4.0.0 where it's already FALSE).
  • options(expressions = 5000): max nested evaluations. Increase for deep recursion.
  • options(max.print = 99999): controls truncation in print output.
  • options(na.action = "na.omit"): default NA handling in model functions.
  • options(contrasts = c("contr.treatment", "contr.poly")): default contrasts for unordered/ordered factors.

file.path / basename / dirname

  • file.path("a", "b", "c.txt") → "a/b/c.txt" (platform-appropriate separator).
  • basename("/a/b/c.txt") → "c.txt". dirname("/a/b/c.txt") → "/a/b".
  • file.path does NOT normalize paths (no .. resolution); use normalizePath() for that.

list.files

  • list.files(pattern = "*.csv") — pattern is a regex, not a glob! Use glob2rx("*.csv") or "\\.csv$".
  • full.names = FALSE (default) returns basenames only. Use full.names = TRUE for complete paths.
  • recursive = TRUE to search subdirectories.
  • all.files = TRUE to include hidden files (starting with .).

file.info

  • Returns data frame with size, isdir, mode, mtime, ctime, atime, uid, gid.
  • mtime: modification time (POSIXct). Useful for file.info(f)$mtime.
  • On some filesystems, ctime is status-change time, not creation time.

file_test

  • file_test("-f", path): TRUE if regular file exists.
  • file_test("-d", path): TRUE if directory exists.
  • file_test("-nt", f1, f2): TRUE if f1 is newer than f2.
  • More reliable than file.exists() for distinguishing files from directories. FILE:references/io-and-text.md

I/O and Text Processing — Quick Reference

Non-obvious behaviors, gotchas, and tricky defaults for R functions. Only what Claude doesn't already know.

read.table (gotchas)

  • sep = "" (default) means any whitespace (spaces, tabs, newlines) — not a literal empty string.
  • comment.char = "#" by default — lines with # are truncated. Use comment.char = "" to disable (also faster).
  • header auto-detection: set to TRUE if first row has one fewer field than subsequent rows (the missing field is assumed to be row names).
  • colClasses = "NULL" skips that column entirely — very useful for speed.
  • read.csv defaults differ from read.table: header = TRUE, sep = ",", fill = TRUE, comment.char = "".
  • For large files: specifying colClasses and nrows dramatically reduces memory usage. read.table is slow for wide data frames (hundreds of columns); use scan or data.table::fread for matrices.
  • stringsAsFactors = FALSE since R 4.0.0 (was TRUE before).

write.table (gotchas)

  • row.names = TRUE by default — produces an unnamed first column that confuses re-reading. Use row.names = FALSE or col.names = NA for Excel-compatible CSV.
  • write.csv fixes sep = ",", dec = ".", and uses qmethod = "double" — cannot override these via ....
  • quote = TRUE (default) quotes character/factor columns. Numeric columns are never quoted.
  • Matrix-like columns in data frames expand to multiple columns silently.
  • Slow for data frames with many columns (hundreds+); each column processed separately by class.

read.fwf

  • Reads fixed-width format files. widths is a vector of field widths.
  • Negative widths skip that many characters (useful for ignoring fields).
  • buffersize controls how many lines are read at a time; increase for large files.
  • Uses read.table internally after splitting fields.

count.fields

  • Counts fields per line in a file — useful for diagnosing read errors.
  • sep and quote arguments match those of read.table.

grep / grepl / sub / gsub (gotchas)

  • Three regex modes: POSIX extended (default), perl = TRUE, fixed = TRUE. They behave differently for edge cases.
  • Name arguments explicitly — unnamed args after x/pattern are matched positionally to ignore.case, perl, etc. Common source of silent bugs.
  • sub replaces first match only; gsub replaces all matches.
  • Backreferences: "\\1" in replacement (double backslash in R strings). With perl = TRUE: "\\U\\1" for uppercase conversion.
  • grep(value = TRUE) returns matching elements; grep(value = FALSE) (default) returns indices.
  • grepl returns logical vector — preferred for filtering.
  • regexpr returns first match position + length (as attributes); gregexpr returns all matches as a list.
  • regexec returns match + capture group positions; gregexec does this for all matches.
  • Character classes like [:alpha:] must be inside [[:alpha:]] (double brackets) in POSIX mode.

strsplit

  • Returns a list (one element per input string), even for a single string.
  • split = "" or split = character(0) splits into individual characters.
  • Match at beginning of string: first element of result is "". Match at end: no trailing "".
  • fixed = TRUE is faster and avoids regex interpretation.
  • Common mistake: unnamed arguments silently match fixed, perl, etc.

substr / substring

  • substr(x, start, stop): extracts/replaces substring. 1-indexed, inclusive on both ends.
  • substring(x, first, last): same but last defaults to 1000000L (effectively "to end"). Vectorized over first/last.
  • Assignment form: substr(x, 1, 3) <- "abc" replaces in place (must be same length replacement).

trimws

  • which = "both" (default), "left", or "right".
  • whitespace = "[ \\t\\r\\n]" — customizable regex for what counts as whitespace.

nchar

  • type = "bytes" counts bytes; type = "chars" (default) counts characters; type = "width" counts display width.
  • nchar(NA) returns NA (not 2). nchar(factor) works on the level labels.
  • keepNA = TRUE (default since R 3.3.0); set to FALSE to count "NA" as 2 characters.

format / formatC

  • format(x, digits, nsmall): nsmall forces minimum decimal places. big.mark = "," adds thousands separator.
  • formatC(x, format = "f", digits = 2): C-style formatting. format = "e" for scientific, "g" for general.
  • format returns character vector; always right-justified by default (justify = "right").

type.convert

  • Converts character vectors to appropriate types (logical, integer, double, complex, character).
  • as.is = TRUE (recommended): keeps characters as character, not factor.
  • Applied column-wise on data frames. tryLogical = TRUE (R 4.3+) converts "TRUE"/"FALSE" columns.

Rscript

  • commandArgs(trailingOnly = TRUE) gets script arguments (excluding R/Rscript flags).
  • #! line on Unix: /usr/bin/env Rscript or full path.
  • --vanilla or --no-init-file to skip .Rprofile loading.
  • Exit code: quit(status = 1) for error exit.

capture.output

  • Captures output from cat, print, or any expression that writes to stdout.
  • file = NULL (default) returns character vector. file = "out.txt" writes directly to file.
  • type = "message" captures stderr instead.

URLencode / URLdecode

  • URLencode(url, reserved = FALSE) by default does NOT encode reserved chars (/, ?, &, etc.).
  • Set reserved = TRUE to encode a URL component (query parameter value).

glob2rx

  • Converts shell glob patterns to regex: glob2rx("*.csv") → "^.*\\.csv$".
  • Useful with list.files(pattern = glob2rx("data_*.RDS")). FILE:references/modeling.md

Modeling — Quick Reference

Non-obvious behaviors, gotchas, and tricky defaults for R functions. Only what Claude doesn't already know.

formula

Symbolic model specification gotchas.

  • I() is required to use arithmetic operators literally: y ~ x + I(x^2). Without I(), ^ means interaction crossing.
  • * = main effects + interaction: a*b expands to a + b + a:b.
  • (a+b+c)^2 = all main effects + all 2-way interactions (not squaring).
  • - removes terms: (a+b+c)^2 - a:b drops only the a:b interaction.
  • / means nesting: a/b = a + b %in% a = a + a:b.
  • . in formula means "all other columns in data" (in terms.formula context) or "previous contents" (in update.formula).
  • Formula objects carry an environment used for variable lookup; as.formula("y ~ x") uses parent.frame().

terms / model.matrix

  • model.matrix creates the design matrix including dummy coding. Default contrasts: contr.treatment for unordered factors, contr.poly for ordered.
  • terms object attributes: order (interaction order per term), intercept, factors matrix.
  • Column names from model.matrix can be surprising: e.g., factorLevelName concatenation.

glm

  • Default family = gaussian(link = "identity") — glm() with no family silently fits OLS (same as lm, but slower and with deviance-based output).
  • Common families: binomial(link = "logit"), poisson(link = "log"), Gamma(link = "inverse"), inverse.gaussian().
  • binomial accepts response as: 0/1 vector, logical, factor (second level = success), or 2-column matrix cbind(success, failure).
  • weights in glm means prior weights (not frequency weights) — for frequency weights, use the cbind trick or offset.
  • predict.glm(type = "response") for predicted probabilities; default type = "link" returns log-odds (for logistic) or log-rate (for Poisson).
  • anova(glm_obj, test = "Chisq") for deviance-based tests; "F" is invalid for non-Gaussian families.
  • Quasi-families (quasibinomial, quasipoisson) allow overdispersion — no AIC is computed.
  • Convergence: control = glm.control(maxit = 100) if default 25 iterations isn't enough.

aov

  • aov is a wrapper around lm that stores extra info for balanced ANOVA. For unbalanced designs, Type I SS (sequential) are computed — order of terms matters.
  • For Type III SS, use car::Anova() or set contrasts to contr.sum/contr.helmert.
  • Error strata for repeated measures: aov(y ~ A*B + Error(Subject/B)).
  • summary.aov gives ANOVA table; summary.lm(aov_obj) gives regression-style summary.

nls

  • Requires good starting values in start = list(...) or convergence fails.
  • Self-starting models (SSlogis, SSasymp, etc.) auto-compute starting values.
  • Algorithm "port" allows bounds on parameters (lower/upper).
  • If data fits too exactly (no residual noise), convergence check fails — use control = list(scaleOffset = 1) or jitter data.
  • weights argument for weighted NLS; na.action for missing value handling.

step / add1

  • step does stepwise model selection by AIC (default). Use k = log(n) for BIC.
  • Direction: direction = "both" (default), "forward", or "backward".
  • add1/drop1 evaluate single-term additions/deletions; step calls these iteratively.
  • scope argument defines the upper/lower model bounds for search.
  • step modifies the model object in place — can be slow for large models with many candidate terms.

predict.lm / predict.glm

  • predict.lm with interval = "confidence" gives CI for mean response; interval = "prediction" gives PI for new observation (wider).
  • newdata must have columns matching the original formula variables — factors must have the same levels.
  • predict.glm with type = "response" gives predictions on the response scale (e.g., probabilities for logistic); type = "link" (default) gives on the link scale.
  • se.fit = TRUE returns standard errors; for predict.glm these are on the link scale regardless of type.
  • predict.lm with type = "terms" returns the contribution of each term.

loess

  • span controls smoothness (default 0.75). Span < 1 uses that proportion of points; span > 1 uses all points with adjusted distance.
  • Maximum 4 predictors. Memory usage is roughly quadratic in n (1000 points ~ 10MB).
  • degree = 0 (local constant) is allowed but poorly tested — use with caution.
  • Not identical to S's loess; conditioning is not implemented.
  • normalize = TRUE (default) standardizes predictors to common scale; set FALSE for spatial coords.

lowess vs loess

  • lowess is the older function; returns list(x, y) — cannot predict at new points.
  • loess is the newer formula interface with predict method.
  • lowess parameter is f (span, default 2/3); loess parameter is span (default 0.75).
  • lowess iter default is 3 (robustifying iterations); loess default family = "gaussian" (no robustness).

smooth.spline

  • Default smoothing parameter selected by GCV (generalized cross-validation).
  • cv = TRUE uses ordinary leave-one-out CV instead — do not use with duplicate x values.
  • spar and lambda control smoothness; df can specify equivalent degrees of freedom.
  • Returns object with predict, print, plot methods. The fit component has knots and coefficients.

optim

  • Minimizes by default. To maximize: set control = list(fnscale = -1).
  • Default method is Nelder-Mead (no gradients, robust but slow). Poor for 1D — use "Brent" or optimize().
  • "L-BFGS-B" is the only method supporting box constraints (lower/upper). Bounds auto-select this method with a warning.
  • "SANN" (simulated annealing): convergence code is always 0 — it never "fails". maxit = total function evals (default 10000), no other stopping criterion.
  • parscale: scale parameters so unit change in each produces comparable objective change. Critical for mixed-scale problems.
  • hessian = TRUE: returns numerical Hessian of the unconstrained problem even if box constraints are active.
  • fn can return NA/Inf (except "L-BFGS-B" which requires finite values always). Initial value must be finite.

optimize / uniroot

  • optimize: 1D minimization on a bounded interval. Returns minimum and objective.
  • uniroot: finds a root of f in [lower, upper]. Requires f(lower) and f(upper) to have opposite signs.
  • uniroot with extendInt = "yes" can auto-extend the interval to find sign change — but can find spurious roots for functions that don't actually cross zero.
  • nlm: Newton-type minimizer. Gradient/Hessian as attributes of the return value from fn (unusual interface).

TukeyHSD

  • Requires a fitted aov object (not lm).
  • Default conf.level = 0.95. Returns adjusted p-values and confidence intervals for all pairwise comparisons.
  • Only meaningful for balanced or near-balanced designs; can be liberal for very unbalanced data.

anova (for lm)

  • anova(model): sequential (Type I) SS — order of terms matters.
  • anova(model1, model2): F-test comparing nested models.
  • For Type II or III SS use car::Anova(). FILE:references/statistics.md

Statistics — Quick Reference

Non-obvious behaviors, gotchas, and tricky defaults for R functions. Only what Claude doesn't already know.

chisq.test

  • correct = TRUE (default) applies Yates continuity correction for 2x2 tables only.
  • simulate.p.value = TRUE: Monte Carlo with B = 2000 replicates (min p ~ 0.0005). Simulation assumes fixed marginals (Fisher-style sampling, not the chi-sq assumption).
  • For goodness-of-fit: pass a vector, not a matrix. p must sum to 1 (or set rescale.p = TRUE).
  • Return object includes $expected, $residuals (Pearson), and $stdres (standardized).

wilcox.test

  • exact = TRUE by default for small samples with no ties. With ties, normal approximation used.
  • correct = TRUE applies continuity correction to normal approximation.
  • conf.int = TRUE computes Hodges-Lehmann estimator and confidence interval (not just the p-value).
  • Paired test: paired = TRUE uses signed-rank test (Wilcoxon), not rank-sum (Mann-Whitney).

fisher.test

  • For tables larger than 2x2, uses simulation (simulate.p.value = TRUE) or network algorithm.
  • workspace controls memory for the network algorithm; increase if you get errors on large tables.
  • or argument tests a specific odds ratio (default 1) — only for 2x2 tables.

ks.test

  • Two-sample test or one-sample against a reference distribution.
  • Does not handle ties well — warns and uses asymptotic approximation.
  • For composite hypotheses (parameters estimated from data), p-values are conservative (too large). Use dgof or ks.test with exact = NULL for discrete distributions.

p.adjust

  • Methods: "holm" (default), "BH" (Benjamini-Hochberg FDR), "bonferroni", "BY", "hochberg", "hommel", "fdr" (alias for BH), "none".
  • n argument: total number of hypotheses (can be larger than length(p) if some p-values are excluded).
  • Handles NAs: adjusted p-values are NA where input is NA.

pairwise.t.test / pairwise.wilcox.test

  • p.adjust.method defaults to "holm". Change to "BH" for FDR control.
  • pool.sd = TRUE (default for t-test): uses pooled SD across all groups (assumes equal variances).
  • Returns a matrix of p-values, not test statistics.

shapiro.test

  • Sample size must be between 3 and 5000.
  • Tests normality; low p-value = evidence against normality.

kmeans

  • nstart > 1 recommended (e.g., nstart = 25): runs algorithm from multiple random starts, returns best.
  • Default iter.max = 10 — may be too low for convergence. Increase for large/complex data.
  • Default algorithm is "Hartigan-Wong" (generally best). Very close points may cause non-convergence (warning with ifault = 4).
  • Cluster numbering is arbitrary; ordering may differ across platforms.
  • Always returns k clusters when k is specified (except Lloyd-Forgy may return fewer).

hclust

  • method = "ward.D2" implements Ward's criterion correctly (using squared distances). The older "ward.D" did not square distances (retained for back-compatibility).
  • Input must be a dist object. Use as.dist() to convert a symmetric matrix.
  • hang = -1 in plot() aligns all labels at the bottom.

dist

  • method = "euclidean" (default). Other options: "manhattan", "maximum", "canberra", "binary", "minkowski".
  • Returns a dist object (lower triangle only). Use as.matrix() to get full matrix.
  • "canberra": terms with zero numerator and denominator are omitted from the sum (not treated as 0/0).
  • Inf values: Euclidean distance involving Inf is Inf. Multiple Infs in same obs give NaN for some methods.

prcomp vs princomp

  • prcomp uses SVD (numerically superior); princomp uses eigen on covariance (less stable, N-1 vs N scaling).
  • scale. = TRUE in prcomp standardizes variables; important when variables have very different scales.
  • princomp standard deviations differ from prcomp by factor sqrt((n-1)/n).
  • Both return $rotation (loadings) and $x (scores); sign of components may differ between runs.

density

  • Default bandwidth: bw = "nrd0" (Silverman's rule of thumb). For multimodal data, consider "SJ" or "bcv".
  • adjust: multiplicative factor on bandwidth. adjust = 0.5 halves the bandwidth (less smooth).
  • Default kernel: "gaussian". Range of density extends beyond data range (controlled by cut, default 3 bandwidths).
  • n = 512: number of evaluation points. Increase for smoother plotting.
  • from/to: explicitly bound the evaluation range.

quantile

  • Nine type options (1-9). Default type = 7 (R default, linear interpolation). Type 1 = inverse of empirical CDF (SAS default). Types 4-9 are continuous; 1-3 are discontinuous.
  • na.rm = FALSE by default — returns NA if any NAs present.
  • names = TRUE by default, adding "0%", "25%", etc. as names.

Distributions (gotchas across all)

All distribution functions follow the d/p/q/r pattern. Common non-obvious points:

  • n argument in r*() functions: if length(n) > 1, uses length(n) as the count, not n itself. So rnorm(c(1,2,3)) generates 3 values, not 1+2+3.
  • log = TRUE / log.p = TRUE: compute on log scale for numerical stability in tails.
  • lower.tail = FALSE gives survival function P(X > x) directly (more accurate than 1 - pnorm() in tails).
  • Gamma: parameterized by shape and rate (= 1/scale). Default rate = 1. Specifying both rate and scale is an error.
  • Beta: shape1 (alpha), shape2 (beta) — no mean/sd parameterization.
  • Poisson dpois: x can be non-integer (returns 0 with a warning for non-integer values if log = FALSE).
  • Weibull: shape and scale (no rate). R's parameterization: f(x) = (shape/scale)(x/scale)^(shape-1) exp(-(x/scale)^shape).
  • Lognormal: meanlog and sdlog are mean/sd of the log, not of the distribution itself.

cor.test

  • Default method: "pearson". Also "kendall" and "spearman".
  • Returns $estimate, $p.value, $conf.int (CI only for Pearson).
  • Formula interface: cor.test(~ x + y, data = df) — note the ~ with no LHS.

ecdf

  • Returns a function (step function). Call it on new values: Fn <- ecdf(x); Fn(3.5).
  • plot(ecdf(x)) gives the empirical CDF plot.
  • The returned function is right-continuous with left limits (cadlag).

weighted.mean

  • Handles NA in weights: observation is dropped if weight is NA.
  • Weights do not need to sum to 1; they are normalized internally. FILE:references/visualization.md

Visualization — Quick Reference

Non-obvious behaviors, gotchas, and tricky defaults for R functions. Only what Claude doesn't already know.

par (gotchas)

  • par() settings are per-device. Opening a new device resets everything.
  • Setting mfrow/mfcol resets cex to 1 and mex to 1. With 2x2 layout, base cex is multiplied by 0.83; with 3+ rows/columns, by 0.66.
  • mai (inches), mar (lines), pin, plt, pty all interact. Restoring all saved parameters after device resize can produce inconsistent results — last-alphabetically wins.
  • bg set via par() also sets new = FALSE. Setting fg via par() also sets col.
  • xpd = NA clips to device region (allows drawing in outer margins); xpd = TRUE clips to figure region; xpd = FALSE (default) clips to plot region.
  • mgp = c(3, 1, 0): controls title line (mgp[1]), label line (mgp[2]), axis line (mgp[3]). All in mex units.
  • las: 0 = parallel to axis, 1 = horizontal, 2 = perpendicular, 3 = vertical. Does not respond to srt.
  • tck = 1 draws grid lines across the plot. tcl = -0.5 (default) gives outward ticks.
  • usr with log scale: contains log10 of the coordinate limits, not the raw values.
  • Read-only parameters: cin, cra, csi, cxy, din, page.

layout

  • layout(mat) where mat is a matrix of integers specifying figure arrangement.
  • widths/heights accept lcm() for absolute sizes mixed with relative sizes.
  • More flexible than mfrow/mfcol but cannot be queried once set (unlike par("mfrow")).
  • layout.show(n) visualizes the layout for debugging.

axis / mtext

  • axis(side, at, labels): side 1=bottom, 2=left, 3=top, 4=right.
  • Default gap between axis labels controlled by par("mgp"). Labels can overlap if not managed.
  • mtext: line argument positions text in margin lines (0 = adjacent to plot, positive = outward). adj controls horizontal position (0-1).
  • mtext with outer = TRUE writes in the outer margin (set by par(oma = ...)).

curve

  • First argument can be an expression in x or a function: curve(sin, 0, 2*pi) or curve(x^2 + 1, 0, 10).
  • add = TRUE to overlay on existing plot. Default n = 101 evaluation points.
  • xname = "x" by default; change if your expression uses a different variable name.

pairs

  • panel function receives (x, y, ...) for each pair. lower.panel, upper.panel, diag.panel for different regions.
  • gap controls spacing between panels (default 1).
  • Formula interface: pairs(~ var1 + var2 + var3, data = df).

coplot

  • Conditioning plots: coplot(y ~ x | a) or coplot(y ~ x | a * b) for two conditioning variables.
  • panel function can be customized; rows/columns control layout.
  • Default panel draws points; use panel = panel.smooth for loess overlay.

matplot / matlines / matpoints

  • Plots columns of one matrix against columns of another. Recycles col, lty, pch across columns.
  • type = "l" by default (unlike plot which defaults to "p").
  • Useful for plotting multiple time series or fitted curves simultaneously.

contour / filled.contour / image

  • contour(x, y, z): z must be a matrix with dim = c(length(x), length(y)).
  • filled.contour has a non-standard layout — it creates its own plot region for the color key. Cannot use par(mfrow) with it. Adding elements requires the plot.axes argument.
  • image: plots z-values as colored rectangles. Default color scheme may be misleading; set col explicitly.
  • For image, x and y specify cell boundaries or midpoints depending on context.

persp

  • persp(x, y, z, theta, phi): theta = azimuthal angle, phi = colatitude.
  • Returns a transformation matrix (invisible) for projecting 3D to 2D — use trans3d() to add points/lines to the perspective plot.
  • shade and col control surface shading. border = NA removes grid lines.

segments / arrows / rect / polygon

  • All take vectorized coordinates; recycle as needed.
  • arrows: code = 1 (head at start), code = 2 (head at end, default), code = 3 (both).
  • polygon: last point auto-connects to first. Fill with col; border controls outline.
  • rect(xleft, ybottom, xright, ytop) — note argument order is not the same as other systems.

dev / dev.off / dev.copy

  • dev.new() opens a new device. dev.off() closes current device (and flushes output for file devices like pdf).
  • dev.off() on the last open device reverts to null device.
  • dev.copy(pdf, file = "plot.pdf") followed by dev.off() to save current plot.
  • dev.list() returns all open devices; dev.cur() the active one.

pdf

  • Must call dev.off() to finalize the file. Without it, file may be empty/corrupt.
  • onefile = TRUE (default): multiple pages in one PDF. onefile = FALSE: one file per page (uses %d in filename for numbering).
  • useDingbats = FALSE recommended to avoid issues with certain PDF viewers and pch symbols.
  • Default size: 7x7 inches. family controls font family.

png / bitmap devices

  • res controls DPI (default 72). For publication: res = 300 with appropriate width/height in pixels or inches (with units = "in").
  • type = "cairo" (on systems with cairo) gives better antialiasing than default.
  • bg = "transparent" for transparent background (PNG supports alpha).

colors / rgb / hcl / col2rgb

  • colors() returns all 657 named colors. col2rgb("color") returns RGB matrix.
  • rgb(r, g, b, alpha, maxColorValue = 255) — note maxColorValue default is 1, not 255.
  • hcl(h, c, l): perceptually uniform color space. Preferred for color scales.
  • adjustcolor(col, alpha.f = 0.5): easy way to add transparency.

colorRamp / colorRampPalette

  • colorRamp returns a function mapping [0,1] to RGB matrix.
  • colorRampPalette returns a function taking n and returning n interpolated colors.
  • space = "Lab" gives more perceptually uniform interpolation than "rgb".

palette / recordPlot

  • palette() returns current palette (default 8 colors). palette("Set1") sets a built-in palette.
  • Integer colors in plots index into the palette (with wrapping). Index 0 = background color.
  • recordPlot() / replayPlot(): save and restore a complete plot — device-dependent and fragile across sessions. FILE:assets/analysis_template.R

============================================================

Analysis Template — Base R

Copy this file, rename it, and fill in your details.

============================================================

Author :

Date :

Data :

Purpose :

============================================================

── 0. Setup ─────────────────────────────────────────────────

Clear environment (optional — comment out if loading into existing session)

rm(list = ls())

Set working directory if needed

setwd("/path/to/your/project")

Reproducibility

set.seed(42)

Libraries — uncomment what you need

library(haven) # read .dta / .sav / .sas

library(readxl) # read Excel files

library(openxlsx) # write Excel files

library(foreign) # older Stata / SPSS formats

library(survey) # survey-weighted analysis

library(lmtest) # Breusch-Pagan, Durbin-Watson etc.

library(sandwich) # robust standard errors

library(car) # Type II/III ANOVA, VIF

── 1. Load Data ─────────────────────────────────────────────

df <- read.csv("your_data.csv", stringsAsFactors = FALSE)

df <- readRDS("your_data.rds")

df <- haven::read_dta("your_data.dta")

First look — always run these

dim(df) str(df) head(df, 10) summary(df)

── 2. Data Quality Check ────────────────────────────────────

Missing values

na_report <- data.frame( column = names(df), n_miss = colSums(is.na(df)), pct_miss = round(colMeans(is.na(df)) * 100, 1), row.names = NULL ) print(na_report[na_report$n_miss > 0, ])

Duplicates

n_dup <- sum(duplicated(df)) cat(sprintf("Duplicate rows: %d\n", n_dup))

Unique values for categorical columns

cat_cols <- names(df)[sapply(df, function(x) is.character(x) | is.factor(x))] for (col in cat_cols) { cat(sprintf("\n%s (%d unique):\n", col, length(unique(df[[col]])))) print(table(df[[col]], useNA = "ifany")) }

── 3. Clean & Transform ─────────────────────────────────────

Rename columns (example)

names(df)[names(df) == "old_name"] <- "new_name"

Convert types

df$group <- as.factor(df$group)

df$date <- as.Date(df$date, format = "%Y-%m-%d")

Recode values (example)

df$gender <- ifelse(df$gender == 1, "Male", "Female")

Create new variables (example)

df$log_income <- log(df$income + 1)

df$age_group <- cut(df$age,

breaks = c(0, 25, 45, 65, Inf),

labels = c("18-25", "26-45", "46-65", "65+"))

Filter rows (example)

df <- df[df$year >= 2010, ]

df <- df[complete.cases(df[, c("outcome", "predictor")]), ]

Drop unused factor levels

df <- droplevels(df)

── 4. Descriptive Statistics ────────────────────────────────

Numeric summary

num_cols <- names(df)[sapply(df, is.numeric)] round(sapply(df[num_cols], function(x) c( n = sum(!is.na(x)), mean = mean(x, na.rm = TRUE), sd = sd(x, na.rm = TRUE), median = median(x, na.rm = TRUE), min = min(x, na.rm = TRUE), max = max(x, na.rm = TRUE) )), 3)

Cross-tabulation

table(df$group, df$category, useNA = "ifany")

prop.table(table(df$group, df$category), margin = 1) # row proportions

── 5. Visualization (EDA) ───────────────────────────────────

par(mfrow = c(2, 2))

Histogram of main outcome

hist(df$outcome_var, main = "Distribution of Outcome", xlab = "Outcome", col = "steelblue", border = "white", breaks = 30)

Boxplot by group

boxplot(outcome_var ~ group_var, data = df, main = "Outcome by Group", col = "lightyellow", las = 2)

Scatter plot

plot(df$predictor, df$outcome_var, main = "Predictor vs Outcome", xlab = "Predictor", ylab = "Outcome", pch = 19, col = adjustcolor("steelblue", alpha.f = 0.5), cex = 0.8) abline(lm(outcome_var ~ predictor, data = df), col = "red", lwd = 2)

Correlation matrix (numeric columns only)

cor_mat <- cor(df[num_cols], use = "complete.obs") image(cor_mat, main = "Correlation Matrix", col = hcl.colors(20, "RdBu", rev = TRUE))

par(mfrow = c(1, 1))

── 6. Analysis ───────────────────────────────────────────────

·· 6a. Comparison of means ··

t.test(outcome_var ~ group_var, data = df)

·· 6b. Linear regression ··

fit <- lm(outcome_var ~ predictor1 + predictor2 + group_var, data = df) summary(fit) confint(fit)

Check VIF for multicollinearity (requires car)

car::vif(fit)

Robust standard errors (requires lmtest + sandwich)

lmtest::coeftest(fit, vcov = sandwich::vcovHC(fit, type = "HC3"))

·· 6c. ANOVA ··

fit_aov <- aov(outcome_var ~ group_var, data = df)

summary(fit_aov)

TukeyHSD(fit_aov)

·· 6d. Logistic regression (binary outcome) ··

fit_logit <- glm(binary_outcome ~ x1 + x2,

data = df,

family = binomial(link = "logit"))

summary(fit_logit)

exp(coef(fit_logit)) # odds ratios

exp(confint(fit_logit)) # OR confidence intervals

── 7. Model Diagnostics ─────────────────────────────────────

par(mfrow = c(2, 2)) plot(fit) par(mfrow = c(1, 1))

Residual normality

shapiro.test(residuals(fit))

Homoscedasticity (requires lmtest)

lmtest::bptest(fit)

── 8. Save Output ────────────────────────────────────────────

Cleaned data

write.csv(df, "data_clean.csv", row.names = FALSE)

saveRDS(df, "data_clean.rds")

Model results to text file

sink("results.txt")

cat("=== Linear Model ===\n")

print(summary(fit))

cat("\n=== Confidence Intervals ===\n")

print(confint(fit))

sink()

Plots to file

png("figure1_distributions.png", width = 1200, height = 900, res = 150)

par(mfrow = c(2, 2))

# ... your plots ...

par(mfrow = c(1, 1))

dev.off()

============================================================

END OF TEMPLATE

============================================================

FILE:scripts/check_data.R

check_data.R — Quick data quality report for any R data frame

Usage: source("check_data.R") then call check_data(df)

Or: source("check_data.R"); check_data(read.csv("yourfile.csv"))

check_data <- function(df, top_n_levels = 8) {

if (!is.data.frame(df)) stop("Input must be a data frame.")

n_row <- nrow(df) n_col <- ncol(df)

cat("══════════════════════════════════════════\n") cat(" DATA QUALITY REPORT\n") cat("══════════════════════════════════════════\n") cat(sprintf(" Rows: %d Columns: %d\n", n_row, n_col)) cat("══════════════════════════════════════════\n\n")

── 1. Column overview ──────────────────────

cat("── COLUMN OVERVIEW ────────────────────────\n")

for (col in names(df)) { x <- df[[col]] cls <- class(x)[1] n_na <- sum(is.na(x)) pct <- round(n_na / n_row * 100, 1) n_uniq <- length(unique(x[!is.na(x)]))

na_flag <- if (n_na == 0) "" else sprintf("  *** %d NAs (%.1f%%)", n_na, pct)
cat(sprintf("  %-20s  %-12s  %d unique%s\n",
            col, cls, n_uniq, na_flag))

}

── 2. NA summary ────────────────────────────

cat("\n── NA SUMMARY ─────────────────────────────\n")

na_counts <- sapply(df, function(x) sum(is.na(x))) cols_with_na <- na_counts[na_counts > 0]

if (length(cols_with_na) == 0) { cat(" No missing values. \n") } else { cat(sprintf(" Columns with NAs: %d of %d\n\n", length(cols_with_na), n_col)) for (col in names(cols_with_na)) { bar_len <- round(cols_with_na[col] / n_row * 20) bar <- paste0(rep("█", bar_len), collapse = "") pct_na <- round(cols_with_na[col] / n_row * 100, 1) cat(sprintf(" %-20s [%-20s] %d (%.1f%%)\n", col, bar, cols_with_na[col], pct_na)) } }

── 3. Numeric columns ───────────────────────

num_cols <- names(df)[sapply(df, is.numeric)]

if (length(num_cols) > 0) { cat("\n── NUMERIC COLUMNS ────────────────────────\n") cat(sprintf(" %-20s %8s %8s %8s %8s %8s\n", "Column", "Min", "Mean", "Median", "Max", "SD")) cat(sprintf(" %-20s %8s %8s %8s %8s %8s\n", "──────", "───", "────", "──────", "───", "──"))

for (col in num_cols) {
  x  <- df[[col]][!is.na(df[[col]])]
  if (length(x) == 0) next
  cat(sprintf("  %-20s  %8.3g  %8.3g  %8.3g  %8.3g  %8.3g\n",
              col,
              min(x), mean(x), median(x), max(x), sd(x)))
}

}

── 4. Factor / character columns ───────────

cat_cols <- names(df)[sapply(df, function(x) is.factor(x) | is.character(x))]

if (length(cat_cols) > 0) { cat("\n── CATEGORICAL COLUMNS ────────────────────\n")

for (col in cat_cols) {
  x    <- df[[col]]
  tbl  <- sort(table(x, useNA = "no"), decreasing = TRUE)
  n_lv <- length(tbl)
  cat(sprintf("\n  %s  (%d unique values)\n", col, n_lv))
  
  show <- min(top_n_levels, n_lv)
  for (i in seq_len(show)) {
    lbl <- names(tbl)[i]
    cnt <- tbl[i]
    pct <- round(cnt / n_row * 100, 1)
    cat(sprintf("    %-25s  %5d  (%.1f%%)\n", lbl, cnt, pct))
  }
  if (n_lv > top_n_levels) {
    cat(sprintf("    ... and %d more levels\n", n_lv - top_n_levels))
  }
}

}

── 5. Duplicate rows ────────────────────────

cat("\n── DUPLICATES ─────────────────────────────\n") n_dup <- sum(duplicated(df)) if (n_dup == 0) { cat(" No duplicate rows.\n") } else { cat(sprintf(" %d duplicate row(s) found (%.1f%% of data)\n", n_dup, n_dup / n_row * 100)) }

cat("\n══════════════════════════════════════════\n") cat(" END OF REPORT\n") cat("══════════════════════════════════════════\n")

Return invisibly for programmatic use

invisible(list( dims = c(rows = n_row, cols = n_col), na_counts = na_counts, n_dupes = n_dup )) } FILE:scripts/scaffold_analysis.R #!/usr/bin/env Rscript

scaffold_analysis.R — Generates a starter analysis script

Usage (from terminal):

Rscript scaffold_analysis.R myproject

Rscript scaffold_analysis.R myproject outcome_var group_var

Usage (from R console):

source("scaffold_analysis.R")

scaffold_analysis("myproject", outcome = "score", group = "treatment")

Output: myproject_analysis.R (ready to edit)

scaffold_analysis <- function(project_name, outcome = "outcome", group = "group", data_file = NULL) {

if (is.null(data_file)) data_file <- paste0(project_name, ".csv") out_file <- paste0(project_name, "_analysis.R")

template <- sprintf( '# ============================================================

Project : %s

Created : %s

============================================================

── 0. Libraries ─────────────────────────────────────────────

Add packages you need here

library(ggplot2)

library(haven) # for .dta files

library(openxlsx) # for Excel output

── 1. Load Data ─────────────────────────────────────────────

df <- read.csv("%s", stringsAsFactors = FALSE)

Quick check — always do this first

cat("Dimensions:", dim(df), "\n") str(df) head(df)

── 2. Explore / EDA ─────────────────────────────────────────

summary(df)

NA check

na_counts <- colSums(is.na(df)) na_counts[na_counts > 0]

Key variable distributions

hist(df$%s, main = "Distribution of %s", xlab = "%s")

if ("%s" %%in%% names(df)) { table(df$%s) barplot(table(df$%s), main = "Counts by %s", col = "steelblue", las = 2) }

── 3. Clean / Transform ──────────────────────────────────────

df <- df[complete.cases(df), ] # drop rows with any NA

df$%s <- as.factor(df$%s) # convert to factor

── 4. Analysis ───────────────────────────────────────────────

Descriptive stats by group

tapply(df$%s, df$%s, mean, na.rm = TRUE) tapply(df$%s, df$%s, sd, na.rm = TRUE)

t-test (two groups)

t.test(%s ~ %s, data = df)

Linear model

fit <- lm(%s ~ %s, data = df) summary(fit) confint(fit)

ANOVA (multiple groups)

fit_aov <- aov(%s ~ %s, data = df)

summary(fit_aov)

TukeyHSD(fit_aov)

── 5. Visualize Results ──────────────────────────────────────

par(mfrow = c(1, 2))

Boxplot by group

boxplot(%s ~ %s, data = df, main = "%s by %s", xlab = "%s", ylab = "%s", col = "lightyellow")

Model diagnostics

plot(fit, which = 1) # residuals vs fitted

par(mfrow = c(1, 1))

── 6. Save Output ────────────────────────────────────────────

Save cleaned data

write.csv(df, "%s_clean.csv", row.names = FALSE)

Save model summary to text

sink("%s_results.txt")

summary(fit)

sink()

Save plot to file

png("%s_boxplot.png", width = 800, height = 600, res = 150)

boxplot(%s ~ %s, data = df, col = "lightyellow")

dev.off()

', project_name, format(Sys.Date(), "%%Y-%%m-%%d"), data_file, # Section 2 — EDA outcome, outcome, outcome, group, group, group, group, # Section 3 group, group, # Section 4 outcome, group, outcome, group, outcome, group, outcome, group, outcome, group, outcome, group, # Section 5 outcome, group, outcome, group, group, outcome, # Section 6 project_name, project_name, project_name, outcome, group )

writeLines(template, out_file) cat(sprintf("Created: %s\n", out_file)) invisible(out_file) }

── Run from command line ─────────────────────────────────────

if (!interactive()) { args <- commandArgs(trailingOnly = TRUE)

if (length(args) == 0) { cat("Usage: Rscript scaffold_analysis.R <project_name> [outcome_var] [group_var]\n") cat("Example: Rscript scaffold_analysis.R myproject score treatment\n") quit(status = 1) }

project <- args[1] outcome <- if (length(args) >= 2) args[2] else "outcome" group <- if (length(args) >= 3) args[3] else "group"

scaffold_analysis(project, outcome = outcome, group = group) } FILE:README.md

base-r-skill

GitHub: https://github.com/iremaydas/base-r-skill

A Claude Code skill for base R programming.

The Story

I'm a political science PhD candidate who uses R regularly but would never call myself an R person. I needed a Claude Code skill for base R — something without tidyverse, without ggplot2, just plain R — and I couldn't find one anywhere.

So I made one myself. At 11pm. Asking Claude to help me build a skill for Claude.

If you're also someone who Googles how to drop NA rows in R every single time, this one's for you. 🫶

What's Inside

base-r/
├── SKILL.md                    # Main skill file
├── references/                 # Gotchas & non-obvious behaviors
│   ├── data-wrangling.md       # Subsetting traps, apply family, merge, factor quirks
│   ├── modeling.md             # Formula syntax, lm/glm/aov/nls, optim
│   ├── statistics.md           # Hypothesis tests, distributions, clustering
│   ├── visualization.md        # par, layout, devices, colors
│   ├── io-and-text.md          # read.table, grep, regex, format
│   ├── dates-and-system.md     # Date/POSIXct traps, options(), file ops
│   └── misc-utilities.md       # tryCatch, do.call, time series, utilities
├── scripts/
│   ├── check_data.R            # Quick data quality report for any data frame
│   └── scaffold_analysis.R     # Generates a starter analysis script
└── assets/
    └── analysis_template.R     # Copy-paste analysis template

The reference files were condensed from the official R 4.5.3 manual — 19,518 lines → 945 lines (95% reduction). Only the non-obvious stuff survived: gotchas, surprising defaults, tricky interactions. The things Claude already knows well got cut.

How to Use

Add this skill to your Claude Code setup by pointing to this repo. Then Claude will automatically load the relevant reference files when you're working on R tasks.

Works best for:

  • Base R data manipulation (no tidyverse)
  • Statistical modeling with lm, glm, aov
  • Base graphics with plot, par, barplot
  • Understanding why your R code is doing that weird thing

Not for: tidyverse, ggplot2, Shiny, or R package development.

The check_data.R Script

Probably the most useful standalone thing here. Source it and run check_data(df) on any data frame to get a formatted report of dimensions, NA counts, numeric summaries, and categorical breakdowns.

source("scripts/check_data.R")
check_data(your_df)

Built With Help From

  • Claude (obviously)
  • The official R manuals (all 19,518 lines of them)
  • Mild frustration and several cups of coffee

Contributing

If you spot a missing gotcha, a wrong default, or something that should be in the references — PRs are very welcome. I'm learning too.

Made by @iremaydas — PhD candidate, occasional R user, full-time Googler of things I should probably know by now.