Reference

Tidyverse

Common dplyr, purrr, and tibble workflows.

1. dplyr::*_join()

   Join two data frames by matching rows based on key columns. Learn how to use dplyr join functions to combine datasets in R.

   left_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)
2. dplyr::across()

   Apply functions across multiple columns in dplyr: across() modifies columns in place while where() selects columns by type or condition.

   across(.cols = everything(), .fns = NULL, ..., .names = NULL)
3. dplyr::arrange()

   Sort rows of a data frame by column values in ascending or descending order using dplyr. arrange() reorder rows of a data frame based on column values.

   arrange(.data, ..., .by_group = FALSE)
4. dplyr::bind_cols

   dplyr's bind_cols() combines data frames column-wise, matching rows by position rather than by key. Fast for aligned data, but risky when row orders differ.

   bind_cols(..., .name_repair = "unique")
5. dplyr::bind_rows

   Stack two or more data frames by row with dplyr, matching columns by name and filling mismatches with NA. The optional .id column records each row's source.

   bind_rows(..., .id = NULL)
6. dplyr::bind_rows() / dplyr::bind_cols()

   Combine data frames by stacking rows or joining columns horizontally. bind_rows() and bind_cols() are dplyr functions for combining data frames.

   bind_rows(..., .id = NULL)
7. dplyr::case_when

   Create a new column using vectorized conditional logic with case_when(), handling multiple conditions in order, the dplyr equivalent of SQL's CASE WHEN.

   case_when(...)
8. dplyr::count()

   Count the number of observations in each group. These dplyr verbs provide a convenient way to summarise data by grouping variables.

   count(x, ..., wt = NULL, sort = FALSE, .drop = TRUE)
9. dplyr::distinct

   Remove duplicate rows from an R data frame with dplyr::distinct(), keeping only unique combinations of specified columns.

   distinct(.data, ..., .keep_all = FALSE)
10. dplyr::filter()

    Subset rows of a data frame based on logical conditions using expressive dplyr syntax. keeps rows where all conditions are .

    filter(.data, ...)
11. dplyr::group_by()

    Group data by one or more columns with dplyr::group_by() and compute summary statistics using summarise(). Adds grouping metadata to a data frame.

    group_by(.data, ..., .add = FALSE, .drop = group_by_drop_default(.data))
12. dplyr::if_else

    Type-strict vectorized if-else for R via dplyr::if_else(). Stricter than base ifelse(), handles NAs explicitly, and preserves factor types.

    if_else(condition, true, false, missing = NULL, ..., ptype = NULL, size = deprecated())
13. dplyr::mutate()

    Create new columns or modify existing ones in a tibble using dplyr::mutate(). Adds or modifies columns through vectorised operations.

    mutate(.data, ..., .by = NULL, .keep = c("all", "used", "unused", "none"), .before = NULL, .after = NULL)
14. dplyr::pull

    Extract a column from a data frame as a vector with dplyr::pull(). Defaults to the last column, supports negative indexing, and can produce named vectors.

    pull(.data, var = -1, name = NULL, ...)
15. dplyr::relocate

    Move data frame columns to new positions with dplyr::relocate() using tidy-select syntax, including .before, .after, and renaming during the move.

    relocate(.data, ..., .before = NULL, .after = NULL)
16. dplyr::select()

    Select columns from a data frame by name, position, or pattern with dplyr::select(). Supports helpers like starts_with(), ends_with(), and where().

    select(.data, ...)
17. dplyr::slice()

    Select rows by position, head, tail, random sampling, or rank using dplyr slice functions. - is very fast; it uses integer indexing.

    slice(.data, ...)
18. dplyr::summarise()

    Collapse a tibble to one row per group using summary functions. Use .by (dplyr 1.1+) or group_by(). It accepts bare column names directly: instead of .

    summarise(.data, ..., .by = NULL, .sort = FALSE, .na.rm = FALSE)
19. fct_lump

    Collapse uncommon factor levels into an Other category. Covers fct_lump_n, fct_lump_prop, fct_lump_min, and fct_lump_lowfreq.

20. fct_reorder

    Reorder factor levels with fct_reorder() by a summary statistic of a second variable. Reorders levels according to the summary without changing values.

    fct_reorder(.f, .x, .fun = median, ..., .na_rm = NULL, .default = Inf, .desc = FALSE)
21. ggplot2::aes()

    Map variables to visual aesthetics in ggplot2. Covers aes(), aes_string(), aes_quosures(), and how column names become plot labels.

    aes(x, y, ..., colour, fill, size, shape, alpha, linetype, linewidth)
22. ggplot2::coord_flip

    Flip horizontal and vertical axes in ggplot2. Swaps x and y so horizontal bar charts, boxplots, and histograms display cleanly without re-coding aesthetics.

23. ggplot2::facet_wrap

    Wrap a 1D sequence of panels into a 2D grid with ggplot2. Control nrow, ncol, scales, strip position, and which axes are displayed.

24. ggplot2::geom_bar()

    Draw bars with ggplot2::geom_bar() where height is proportional to count or value. Uses stat_count by default, mapping x to categories and y to frequencies.

    geom_bar(mapping = NULL, data = NULL, stat = "count", position = "stack", ...)
25. ggplot2::geom_boxplot

    Create box and whiskers plots in ggplot2 to visualise the distribution of a continuous variable across groups. **Returns:** A Layer object.

26. ggplot2::geom_histogram()

    Use ggplot2 geom_histogram to show the distribution of a continuous variable. Bins the data and draws bars proportional to the count in each bin.

    geom_histogram(mapping = NULL, data = NULL, stat = "bin", position = "stack", ...)
27. ggplot2::geom_line()

    Use ggplot2 geom_line to connect observations in order with a line. Draws a line through data in sequence, suitable for time series.

    geom_line(mapping = NULL, data = NULL, stat = "identity", position = "identity", ...)
28. ggplot2::geom_point()

    Use ggplot2 geom_point to add a scatter plot layer. Covers position, size, colour, shape, alpha aesthetics, and position_jitter for overplotting.

    geom_point(mapping = NULL, data = NULL, stat = "identity", position = "identity", ...)
29. ggplot2::labs

    Use ggplot2 labs to set axis labels, legend title, plot title, subtitle, caption, and tag for a ggplot all in one place.

30. ggplot2::scale_color_manual

    Define your own colour mappings in ggplot2 for discrete variables. Map factor levels to exact colours using a named or unnamed vector.

31. ggplot2::theme

    Control non-data plot elements in ggplot2: titles, axis labels, legend, panel background, grid lines, and more.

32. glue()

    Use the glue() function in R to format and interpolate strings with expressions inside braces. Evaluates R expressions inside braces for templating.

33. interval

    Create an Interval object in lubridate, representing a time span between two specific datetime endpoints with calendar awareness.

34. keep() / discard() / compact()

    Use purrr keep, discard, and compact to filter list or vector elements. Keep those matching a predicate, discard ones that don't, or remove NULL and empty.

    keep(.x, .p, ...) discard(.x, .p, ...) compact(.x, ...)
35. lubridate::ymd

    Parse dates in lubridate using ymd() for year-month-day format from character or numeric input, with automatic separator detection and truncation support.

    ymd(..., quiet = FALSE, tz = NULL, locale = Sys.getlocale('LC_TIME'), truncated = 0)
36. now

    Use lubridate now() to get the current system time as a POSIXct object. Controls timezone via the tzone argument with IANA strings like 'UTC'.

37. purrr::discard

    Use purrr discard() to drop elements from a list or vector that match a predicate. Removes items where predicate is TRUE, the opposite of keep().

38. purrr::map()

    Use purrr map() to apply a function to each element of a list or vector, returning a list. Typed variants like map_chr() return atomic vectors.

    map(.x, .f, ...)
39. purrr::map2

    Use purrr map2 to iterate over two vectors in parallel, applying a function pairwise. Type-specific variants return atomic vectors.

40. purrr::pmap

    Use purrr pmap to iterate over multiple inputs simultaneously with a list of parameters. Feeds corresponding elements from each list as arguments.

41. purrr::possibly

    Use purrr possibly to wrap any function and return a default value instead of crashing. Suppress errors or let them surface. The counterpart to safely().

42. purrr::reduce

    Apply a binary function cumulatively to a list or vector with purrr reduce. Fold left, fold right, provide an initial value, and inspect intermediate results.

43. purrr::safely() / purrr::possibly() / purrr::quietly()

    Use purrr safely to wrap functions, handle errors gracefully, and continue execution without failing. Returns list with result and error.

    safely(.f, otherwise = NULL, quiet = TRUE) possibly(.f, otherwise = NULL, quiet = TRUE) quietly(.f)
44. purrr::walk()

    Use purrr walk to apply a function for its side effects, returning the input invisibly. For single vectors and walk2 for parallel iteration over two vectors.

    walk(.x, .f, ...)
45. read_csv

    read_csv() reads a CSV file into a tibble with automatic type inference. Part of the readr package, supporting local files, URLs, and inline data.

    read_csv(file, col_names = TRUE, col_types = NULL, na = c("", "NA"), skip = 0, n_max = Inf, guess_max = min(1000, n_max), .name_repair = "unique", trim_ws = TRUE, progress = show_progress(), show_col_types = should_show_types())
46. readr::write_csv()

    Write a data frame to a CSV file with readr. Covers parameters, NA handling, quoting, appending, compression, and common gotchas.

    write_csv(x, file, na = "NA", append = FALSE, col_names = !append, quote = "needed", escape = "double", eol = "\n", num_threads = readr_threads(), progress = show_progress())
47. rename() / relocate()

    Rename and reorder columns in a tibble or data frame using dplyr's rename() and relocate() functions. Both always return a new tibble.

    rename(.data, ...) relocate(.data, ..., .before = NULL, .after = NULL)
48. replace_na

    Replace NA values in R vectors and data frames with tidyr::replace_na(). replaces missing values () with a specified replacement value.

    replace_na(data, replace, ...)
49. separate()

    Split a character column into multiple columns using separate() by splitting on a delimiter pattern. Part of the tidyr package in the tidyverse.

50. str_sub

    Extract a substring from a character vector using str_sub with inclusive start/end positions, with support for negative indexing from the end of the string.

51. stringr::str_c()

    Join multiple strings into one string with optional separators. The function from stringr combines multiple strings into one.

    str_c(..., sep = "", collapse = NULL)
52. stringr::str_detect()

    Detect the presence or absence of a pattern in a string. The function from stringr detects whether a pattern exists within a string.

    str_detect(string, pattern, regex = TRUE)
53. stringr::str_extract()

    Extract the first matching pattern from a string. The function from stringr extracts the first matching pattern from a string.

    str_extract(string, pattern, regex = TRUE)
54. stringr::str_length()

    Get the length of a string in characters. The function from stringr returns the number of characters in a string.

    str_length(string)
55. stringr::str_pad()

    Pad a string to a specified width by adding characters. The function from stringr pads a string to a specified width by adding filler characters.

    str_pad(string, width, side = c("left", "right", "both"), pad = " ")
56. stringr::str_replace()

    Replace the first occurrence or all occurrences of a pattern in a string. The function from stringr replaces the first occurrence of a pattern in a string.

    str_replace(string, pattern, replacement)
57. stringr::str_trim()

    Remove leading and trailing whitespace from strings. The function from stringr removes leading and trailing whitespace from strings.

    str_trim(string, side = "both")
58. tibble

    Create a tibble, a modern reimagining of the data frame in R, with better printing, stricter subsetting, and consistent behavior.

    tibble(..., .rows = NULL, .name_repair = c('check_unique', 'unique', 'universal', 'minimal'))
59. tidyr::complete()

    Complete missing combinations with tidyr::complete(). Expands data to include all variable combinations, turning implicit NAs into explicit ones.

60. tidyr::drop_na()

    Drop rows containing any missing values from a data frame using tidyr::drop_na(). Use drop_na() to quickly clean data before analysis or modelling.

61. tidyr::fill

    Fill missing values in selected columns using the previous or next value with tidyr::fill(). Supports down, up, and bidirectional filling within groups.

    fill(data, ..., .by = NULL, .direction = c("down", "up", "downup", "updown"))
62. tidyr::nest()

    Nest data frames into list-columns with tidyr. Groups rows into nested data frames stored within a single row per group.

63. tidyr::pivot_longer()

    Pivot data from wide to long format with tidyr. Stacks multiple columns into key-value pairs for tidy data analysis.

64. tidyr::pivot_longer() / tidyr::pivot_wider()

    Reshape data between long and wide formats using tidyr's pivot functions. is the inverse: it spreads a key-value pair across multiple columns.

    pivot_longer(data, cols, names_to = "name", values_to = "value")
65. tidyr::pivot_wider()

    Pivot data from long to wide format with tidyr. Spreads key-value pairs across multiple columns for presentation or analysis.

66. tidyr::unnest()

    Unnest list-columns in data frames with tidyr. Expands nested data frames stored in list-columns back into regular rows and columns.

67. tribble

    Create a tibble using a readable row-by-row layout with tribble(), the tidyverse alternative to data.frame() for small, human-readable tables.

    tribble(...)
68. unite

    Unite multiple columns into one by pasting strings together. combines multiple columns into a single new column by pasting the values together as strings.