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 with dplyr::arrange(), dplyr's row-ordering verb.

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

   Sort rows of a data frame by column values in ascending or descending order using dplyr.

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

   Combine data frames by adding columns side by side. Matches rows by position, not by key.

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

   Combine data frames by stacking rows or joining columns horizontally.

   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, 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.

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

    Subset rows of a data frame or tibble using logical conditions with dplyr's filter() function.

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

    Group a data frame by one or more columns for per-group operations with summarise() and mutate().

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

    Group data by one or more columns and compute summary statistics using summarise().

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

    Type-strict vectorized if-else for R. Stricter than base ifelse(), handles NAs explicitly, preserves types. Used inside mutate() for conditional columns.

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

    Add or modify columns in a tibble or data frame with mutate, dplyr's column-wise transformation function.

    mutate(.data, ..., .by = NULL, .keep = c('all','used','unused','none'), .before = NULL, .after = NULL)
16. dplyr::mutate()

    Create new columns or modify existing ones in a tibble or data frame using vectorised operations.

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

    Extract a single column from a data frame as a vector. Defaults to the last column, supports negative indexing from the right, and can produce named vectors.

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

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

    relocate(.data, ..., .before = NULL, .after = NULL)
19. dplyr::rename

    Rename columns in a data frame using new_name = old_name syntax. Also covers rename_with() for batch renaming with functions.

    rename(.data, ...)
20. dplyr::rename() / dplyr::relocate()

    Rename and reorder columns in a tibble or data frame using syntactic naming.

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

    Select specific columns from a data frame using flexible tidyselect helpers and syntax.

22. dplyr::select()

    Select columns from a data frame by name, position, or pattern.

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

    Select rows by position, head, tail, random sampling, or rank using dplyr slice functions.

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

    Collapse a tibble to one row per group using summary functions. Use .by (dplyr 1.1+) or group_by().

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

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

26. fct_reorder

    Reorder factor levels by a summary statistic of a second variable.

    fct_reorder(.f, .x, .fun = median, ..., .na_rm = NULL, .default = Inf, .desc = FALSE)
27. 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)
28. 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.

29. 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.

30. ggplot2::geom_bar()

    Draw bars with height proportional to count or value. geom_bar uses stat_count by default, mapping x to categories and y to frequencies.

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

    Create box and whiskers plots to visualise the distribution of a continuous variable across groups.

32. ggplot2::geom_histogram()

    Draw histograms to show the distribution of a continuous variable. geom_histogram bins the data and draws bars proportional to the count in each bin.

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

    Connect observations in order with a line. geom_line draws a line through the data in the sequence it appears, suitable for time series and sequential data.

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

    Add a scatter plot layer with geom_point(). Covers position, size, colour, shape, alpha aesthetics, and position_jitter for overplotting.

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

    Set axis labels, legend title, plot title, subtitle, caption, and tag for a ggplot. All in one place.

36. ggplot2::scale_color_manual

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

37. ggplot2::theme

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

38. glue()

    Format and interpolate strings in R with expressions inside braces.

39. interval

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

40. lubridate::ymd

    Parse dates in year-month-day format from character or numeric input with automatic separator detection and flexible truncation support.

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

    Get the current system time in R as a POSIXct object. Control the timezone with the tzone argument using IANA timezone strings like 'UTC' or 'America/New_York'.

42. purrr::discard

    Drop elements from a list or vector that don't match a predicate. discard() removes items where predicate is TRUE — opposite of keep(). Works with pipes.

43. purrr::keep

    Keep elements of a list or vector that satisfy a predicate. Discard drops the rest. Compact removes empty elements. All three work with the pipe.

44. purrr::keep() / purrr::discard() / purrr::compact()

    Filter elements of a list or vector by keeping those matching a predicate, discarding those that don't, or removing NULL and empty elements.

    keep(.x, .p, ...) discard(.x, .p, ...) compact(.x, ...)
45. purrr::map

    Apply a function to each element of a vector or list. map returns a list; type-specific variants return atomic vectors directly.

46. purrr::map()

    Apply a function to each element of a list or vector, returning a list, vector, or other type.

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

    Iterate over two vectors in parallel, applying a function pairwise. Type-specific variants return atomic vectors of the corresponding type.

48. purrr::pmap

    Iterate over multiple inputs simultaneously using a list of parameters. pmap feeds corresponding elements from each list as arguments to your function.

49. purrr::possibly

    Wrap any function with possibly() to return a default value instead of crashing. quietly suppress errors or let them surface. The counterpart to safely().

50. 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.

51. 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.

52. purrr::safely

    Wrap any function with safely() to return a list with result and error components. Inspect errors without crashing. The counterpart to possibly().

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

    Wrap functions to handle errors gracefully, capture them, and continue execution without failing.

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

    Use purrr walk to perform side effects — save files, print output, plot — while keeping the pipe flowing. Returns input invisibly.

55. purrr::walk()

    Apply a function for its side effects, returning the input invisibly. Use walk() for single vectors and walk2() for parallel iteration over two vectors.

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

    Read a CSV file into a tibble with automatic type inference. Part of the readr package in the tidyverse.

    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())
57. 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())
58. replace_na

    Replace NA values in R vectors and data frames with tidyr::replace_na().

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

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

60. str_sub

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

61. stringr::str_c()

    Join multiple strings into one string with optional separators.

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

    Detect the presence or absence of a pattern in a string.

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

    Extract the first matching pattern from a string.

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

    Get the length of a string in characters.

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

    Pad a string to a specified width by adding characters.

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

    Replace the first occurrence or all occurrences of a pattern in a string.

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

    Remove leading and trailing whitespace from strings.

    str_trim(string, side = "both")
68. 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'))
69. tidyr::complete()

    Fill in missing combinations of values in a data frame. Use complete() to expose implicit gaps in your data and turn them into explicit rows.

70. tidyr::drop_na()

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

71. tidyr::fill

    Fill missing values in selected columns using the previous or next value. Supports down, up, and bidirectional filling within groups.

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

    Nest columns into a list-column of data frames. Use nest() to create nested tidy data for per-group operations.

73. tidyr::pivot_longer()

    Lengthen data by pivoting columns into rows. Transform wide data into tidy format where each row is a single observation.

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

    Reshape data between long and wide formats using tidyr's pivot functions.

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

    Widen data by spreading key-value pairs across columns. The inverse of pivot_longer().

76. tidyr::unnest()

    Expand list-columns back into rows and regular columns. Use unnest() to flatten nested tidy data for analysis.

77. 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(...)
78. unite

    Unite multiple columns into one by pasting strings together.