{
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  "Type": "Package",
  "Title": "Data Structures, Summaries, and Visualisations for Missing Data",
  "Version": "1.1.0.9000",
  "Authors@R": "c(\nperson(\"Nicholas\", \"Tierney\",\nrole = c(\"aut\", \"cre\"),\nemail = \"nicholas.tierney@gmail.com\",\ncomment = c(ORCID = \"https://orcid.org/0000-0003-1460-8722\")),\nperson(\"Di\", \"Cook\",\nrole = \"aut\",\nemail = \"dicook@monash.edu\",\ncomment = c(ORCID = \"https://orcid.org/0000-0002-3813-7155\")),\nperson(\"Miles\", \"McBain\",\nrole = \"aut\",\nemail = \"miles.mcbain@gmail.com\",\ncomment = c(ORCID = \"https://orcid.org/0000-0003-2865-2548\")),\nperson(\"Colin\", \"Fay\",\nrole = \"aut\",\nemail = \"contact@colinfay.me\",\ncomment = c(ORCID = \"https://orcid.org/0000-0001-7343-1846\")),\nperson(\"Mitchell\", \"O'Hara-Wild\",\nrole = \"ctb\"),\nperson(\"Jim\", \"Hester\",\nrole = \"ctb\",\nemail = \"james.f.hester@gmail.com\"),\nperson(\"Luke\", \"Smith\",\nrole = \"ctb\"),\nperson(\"Andrew\", \"Heiss\",\nrole = \"ctb\",\nemail = \"andrew@andrewheiss.com\",\ncomment = c(ORCID = \"https://orcid.org/0000-0002-3948-3914\"))\n)",
  "Description": "Missing values are ubiquitous in data and need to be\nexplored and handled in the initial stages of analysis.\n'naniar' provides data structures and functions that facilitate\nthe plotting of missing values and examination of imputations.\nThis allows missing data dependencies to be explored with\nminimal deviation from the common work patterns of 'ggplot2'\nand tidy data. The work is fully discussed at Tierney & Cook\n(2023) <doi:10.18637/jss.v105.i07>.",
  "License": "MIT + file LICENSE",
  "LazyData": "TRUE",
  "ByteCompile": "TRUE",
  "VignetteBuilder": "knitr",
  "Collate": "'add-cols.R' 'add-n-prop-miss.R' 'any-na-complete.R'\n'cast-shadows.R' 'data-common-na-numbers.R'\n'data-common-na-strings.R' 'data-oceanbuoys.R'\n'data-pedestrian.R' 'data-riskfactors.R' 'legend-draw.R'\n'geom-miss-point.R' 'geom2plotly.R' 'gg-miss-case-cumsum.R'\n'gg-miss-case.R' 'gg-miss-fct.R' 'gg-miss-span.R'\n'gg-miss-upset.R' 'gg-miss-var-cumsum.R' 'gg-miss-var.R'\n'gg-miss-which.R' 'impute-factor.R' 'impute-fixed.R'\n'impute-median.R' 'impute-mode.R' 'impute-zero.R'\n'impute_below.R' 'impute_mean.R' 'label-miss.R' 'mcar-test.R'\n'miss-complete-x-pct-prop.R' 'miss-prop-pct-summary.R'\n'miss-scan-count.R' 'miss-x-cumsum.R' 'miss-x-run.R'\n'miss-x-span.R' 'miss-x-summary.R' 'miss-x-table.R'\n'n-prop-miss-complete-rows.R' 'n-prop-miss-complete.R'\n'n-var-miss.R' 'nabular.R' 'naniar-ggproto.R'\n'naniar-package.R' 'prop-pct-var-case-miss-complete.R'\n'replace-to-na.R' 'replace-with-na.R' 'replace_na_with.R'\n'scoped-replace-with-na.R' 'set-n-prop-miss.R' 'shade.R'\n'shadow-recode.R' 'shadow-shifters.R' 'shadows.R'\n'stat-miss-point.R' 'utils.R' 'where-na.R'",
  "URL": "https://github.com/njtierney/naniar, https://naniar.njtierney.com/",
  "BugReports": "https://github.com/njtierney/naniar/issues",
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  "Repository": "https://njtierney.r-universe.dev",
  "Date/Publication": "2025-04-30 02:03:31 UTC",
  "RemoteUrl": "https://github.com/njtierney/naniar",
  "RemoteRef": "HEAD",
  "RemoteSha": "89f2a5df3743df02b6e601d403fa001fc8a79c79",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-25 09:52:05 UTC",
    "User": "root"
  },
  "Author": "Nicholas Tierney [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-1460-8722>),\nDi Cook [aut] (ORCID: <https://orcid.org/0000-0002-3813-7155>),\nMiles McBain [aut] (ORCID: <https://orcid.org/0000-0003-2865-2548>),\nColin Fay [aut] (ORCID: <https://orcid.org/0000-0001-7343-1846>),\nMitchell O'Hara-Wild [ctb],\nJim Hester [ctb],\nLuke Smith [ctb],\nAndrew Heiss [ctb] (ORCID: <https://orcid.org/0000-0002-3948-3914>)",
  "Maintainer": "Nicholas Tierney <nicholas.tierney@gmail.com>",
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  "_user": "njtierney",
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  "_published": "2026-05-25T09:56:15.527Z",
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    "ggplot2",
    "missing-data",
    "missingness",
    "tidy-data"
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/naniar"
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  "_devurl": "https://github.com/njtierney/naniar",
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  "_rbuild": "4.6.0",
  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/naniar.html",
    "extra/NEWS.html",
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    "extra/readme.html",
    "extra/readme.md",
    "manual.pdf"
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  "_realowner": "njtierney",
  "_cranurl": true,
  "_releases": [
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      "date": "2017-08-09"
    },
    {
      "version": "0.2.0",
      "date": "2018-02-09"
    },
    {
      "version": "0.3.0",
      "date": "2018-06-07"
    },
    {
      "version": "0.3.1",
      "date": "2018-06-08"
    },
    {
      "version": "0.4.0.0",
      "date": "2018-09-10"
    },
    {
      "version": "0.4.1",
      "date": "2018-11-20"
    },
    {
      "version": "0.4.2",
      "date": "2019-02-15"
    },
    {
      "version": "0.5.0",
      "date": "2020-02-28"
    },
    {
      "version": "0.5.1",
      "date": "2020-05-01"
    },
    {
      "version": "0.5.2",
      "date": "2020-06-29"
    },
    {
      "version": "0.6.0",
      "date": "2020-09-02"
    },
    {
      "version": "0.6.1",
      "date": "2021-05-14"
    },
    {
      "version": "1.0.0",
      "date": "2023-02-02"
    },
    {
      "version": "1.1.0",
      "date": "2024-03-05"
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  "_exports": [
    ".where",
    "%>%",
    "add_any_miss",
    "add_label_missings",
    "add_label_shadow",
    "add_miss_cluster",
    "add_n_miss",
    "add_prop_miss",
    "add_shadow",
    "add_shadow_shift",
    "all_complete",
    "all_miss",
    "all_na",
    "any_complete",
    "any_miss",
    "any_na",
    "any_shade",
    "are_na",
    "are_shade",
    "as_shadow",
    "as_shadow_upset",
    "bind_shadow",
    "cast_shadow",
    "cast_shadow_shift",
    "cast_shadow_shift_label",
    "complete_case_pct",
    "complete_case_prop",
    "complete_var_pct",
    "complete_var_prop",
    "draw_key_missing_point",
    "gather_shadow",
    "geom_miss_point",
    "GeomMissPoint",
    "gg_miss_case",
    "gg_miss_case_cumsum",
    "gg_miss_fct",
    "gg_miss_span",
    "gg_miss_upset",
    "gg_miss_var",
    "gg_miss_var_cumsum",
    "gg_miss_which",
    "impute_below",
    "impute_below_all",
    "impute_below_at",
    "impute_below_if",
    "impute_factor",
    "impute_fixed",
    "impute_mean",
    "impute_mean_all",
    "impute_mean_at",
    "impute_mean_if",
    "impute_median",
    "impute_median_all",
    "impute_median_at",
    "impute_median_if",
    "impute_mode",
    "impute_zero",
    "is_na",
    "is_shade",
    "label_miss_1d",
    "label_miss_2d",
    "label_missings",
    "mcar_test",
    "miss_case_cumsum",
    "miss_case_pct",
    "miss_case_prop",
    "miss_case_summary",
    "miss_case_table",
    "miss_prop_summary",
    "miss_scan_count",
    "miss_summary",
    "miss_var_cumsum",
    "miss_var_pct",
    "miss_var_prop",
    "miss_var_run",
    "miss_var_span",
    "miss_var_summary",
    "miss_var_table",
    "miss_var_which",
    "n_case_complete",
    "n_case_miss",
    "n_complete",
    "n_complete_row",
    "n_miss",
    "n_miss_row",
    "n_var_complete",
    "n_var_miss",
    "nabular",
    "pct_complete",
    "pct_complete_case",
    "pct_complete_var",
    "pct_miss",
    "pct_miss_case",
    "pct_miss_var",
    "prop_complete",
    "prop_complete_case",
    "prop_complete_row",
    "prop_complete_var",
    "prop_miss",
    "prop_miss_case",
    "prop_miss_row",
    "prop_miss_var",
    "recode_shadow",
    "replace_na_with",
    "replace_to_na",
    "replace_with_na",
    "replace_with_na_all",
    "replace_with_na_at",
    "replace_with_na_if",
    "set_n_miss",
    "set_prop_miss",
    "shade",
    "shadow_long",
    "shadow_shift",
    "stat_miss_point",
    "StatMissPoint",
    "unbind_data",
    "unbind_shadow",
    "vis_miss",
    "where_na",
    "which_are_shade",
    "which_na"
  ],
  "_datasets": [
    {
      "name": "common_na_numbers",
      "title": "Common number values for NA",
      "object": "common_na_numbers",
      "class": [
        "numeric"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "common_na_strings",
      "title": "Common string values for NA",
      "object": "common_na_strings",
      "class": [
        "character"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "oceanbuoys",
      "title": "West Pacific Tropical Atmosphere Ocean Data, 1993 & 1997.",
      "object": "oceanbuoys",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "year",
        "latitude",
        "longitude",
        "sea_temp_c",
        "air_temp_c",
        "humidity",
        "wind_ew",
        "wind_ns"
      ],
      "rows": 736,
      "table": true,
      "tojson": true
    },
    {
      "name": "pedestrian",
      "title": "Pedestrian count information around Melbourne for 2016",
      "object": "pedestrian",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "hourly_counts",
        "date_time",
        "year",
        "month",
        "month_day",
        "week_day",
        "hour",
        "sensor_id",
        "sensor_name"
      ],
      "rows": 37700,
      "table": true,
      "tojson": true
    },
    {
      "name": "riskfactors",
      "title": "The Behavioral Risk Factor Surveillance System (BRFSS) Survey Data, 2009.",
      "object": "riskfactors",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "state",
        "sex",
        "age",
        "weight_lbs",
        "height_inch",
        "bmi",
        "marital",
        "pregnant",
        "children",
        "education",
        "employment",
        "income",
        "veteran",
        "hispanic",
        "health_general",
        "health_physical",
        "health_mental",
        "health_poor",
        "health_cover",
        "provide_care",
        "activity_limited",
        "drink_any",
        "drink_days",
        "drink_average",
        "smoke_100",
        "smoke_days",
        "smoke_stop",
        "smoke_last",
        "diet_fruit",
        "diet_salad",
        "diet_potato",
        "diet_carrot",
        "diet_vegetable",
        "diet_juice"
      ],
      "rows": 245,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "add_any_miss",
      "title": "Add a column describing presence of any missing values",
      "topics": [
        "add_any_miss"
      ]
    },
    {
      "page": "add_label_missings",
      "title": "Add a column describing if there are any missings in the dataset",
      "topics": [
        "add_label_missings"
      ]
    },
    {
      "page": "add_label_shadow",
      "title": "Add a column describing whether there is a shadow",
      "topics": [
        "add_label_shadow"
      ]
    },
    {
      "page": "add_miss_cluster",
      "title": "Add a column that tells us which \"missingness cluster\" a row belongs to",
      "topics": [
        "add_miss_cluster"
      ]
    },
    {
      "page": "add_n_miss",
      "title": "Add column containing number of missing data values",
      "topics": [
        "add_n_miss"
      ]
    },
    {
      "page": "add_prop_miss",
      "title": "Add column containing proportion of missing data values",
      "topics": [
        "add_prop_miss"
      ]
    },
    {
      "page": "add_shadow",
      "title": "Add a shadow column to dataframe",
      "topics": [
        "add_shadow"
      ]
    },
    {
      "page": "add_shadow_shift",
      "title": "Add a shadow shifted column to a dataset",
      "topics": [
        "add_shadow_shift"
      ]
    },
    {
      "page": "add_span_counter",
      "title": "Add a counter variable for a span of dataframe",
      "topics": [
        "add_span_counter"
      ]
    },
    {
      "page": "any_row_miss",
      "title": "Helper function to determine whether there are any missings",
      "topics": [
        "any_row_miss"
      ]
    },
    {
      "page": "any-all-na-complete",
      "title": "Identify if there are any or all missing or complete values",
      "topics": [
        "all_complete",
        "all_miss",
        "all_na",
        "any-all-na-complete",
        "any_complete",
        "any_miss",
        "any_na"
      ]
    },
    {
      "page": "as_shadow",
      "title": "Create shadows",
      "topics": [
        "as_shadow"
      ]
    },
    {
      "page": "as_shadow_upset",
      "title": "Convert data into shadow format for doing an upset plot",
      "topics": [
        "as_shadow_upset"
      ]
    },
    {
      "page": "bind_shadow",
      "title": "Bind a shadow dataframe to original data",
      "topics": [
        "bind_shadow"
      ]
    },
    {
      "page": "cast_shadow",
      "title": "Add a shadow column to a dataset",
      "topics": [
        "cast_shadow"
      ]
    },
    {
      "page": "cast_shadow_shift",
      "title": "Add a shadow and a shadow_shift column to a dataset",
      "topics": [
        "cast_shadow_shift"
      ]
    },
    {
      "page": "cast_shadow_shift_label",
      "title": "Add a shadow column and a shadow shifted column to a dataset",
      "topics": [
        "cast_shadow_shift_label"
      ]
    },
    {
      "page": "common_na_numbers",
      "title": "Common number values for NA",
      "topics": [
        "common_na_numbers"
      ]
    },
    {
      "page": "common_na_strings",
      "title": "Common string values for NA",
      "topics": [
        "common_na_strings"
      ]
    },
    {
      "page": "gather_shadow",
      "title": "Long form representation of a shadow matrix",
      "topics": [
        "gather_shadow"
      ]
    },
    {
      "page": "geom_miss_point",
      "title": "Plot Missing Data Points",
      "topics": [
        "geom_miss_point"
      ]
    },
    {
      "page": "naniar-ggproto",
      "title": "naniar-ggproto",
      "topics": [
        "GeomMissPoint",
        "naniar-ggproto",
        "StatMissPoint"
      ]
    },
    {
      "page": "gg_miss_case",
      "title": "Plot the number of missings per case (row)",
      "topics": [
        "gg_miss_case"
      ]
    },
    {
      "page": "gg_miss_case_cumsum",
      "title": "Plot of cumulative sum of missing for cases",
      "topics": [
        "gg_miss_case_cumsum"
      ]
    },
    {
      "page": "gg_miss_fct",
      "title": "Plot the number of missings for each variable, broken down by a factor",
      "topics": [
        "gg_miss_fct"
      ]
    },
    {
      "page": "gg_miss_span",
      "title": "Plot the number of missings in a given repeating span",
      "topics": [
        "gg_miss_span"
      ]
    },
    {
      "page": "gg_miss_upset",
      "title": "Plot the pattern of missingness using an upset plot.",
      "topics": [
        "gg_miss_upset"
      ]
    },
    {
      "page": "gg_miss_var",
      "title": "Plot the number of missings for each variable",
      "topics": [
        "gg_miss_var"
      ]
    },
    {
      "page": "gg_miss_var_cumsum",
      "title": "Plot of cumulative sum of missing value for each variable",
      "topics": [
        "gg_miss_var_cumsum"
      ]
    },
    {
      "page": "gg_miss_which",
      "title": "Plot which variables contain a missing value",
      "topics": [
        "gg_miss_which"
      ]
    },
    {
      "page": "impute_below",
      "title": "Impute data with values shifted 10 percent below range.",
      "topics": [
        "impute_below"
      ]
    },
    {
      "page": "impute_below_all",
      "title": "Impute data with values shifted 10 percent below range.",
      "topics": [
        "impute_below_all"
      ]
    },
    {
      "page": "impute_below_at",
      "title": "Scoped variants of 'impute_below'",
      "topics": [
        "impute_below_at"
      ]
    },
    {
      "page": "impute_below_if",
      "title": "Scoped variants of 'impute_below'",
      "topics": [
        "impute_below_if"
      ]
    },
    {
      "page": "impute_below.numeric",
      "title": "Impute numeric values below a range for graphical exploration",
      "topics": [
        "impute_below.numeric"
      ]
    },
    {
      "page": "impute_factor",
      "title": "Impute a factor value into a vector with missing values",
      "topics": [
        "impute_factor",
        "impute_factor.character",
        "impute_factor.default",
        "impute_factor.factor",
        "impute_factor.shade"
      ]
    },
    {
      "page": "impute_fixed",
      "title": "Impute a fixed value into a vector with missing values",
      "topics": [
        "impute_fixed",
        "impute_fixed.default"
      ]
    },
    {
      "page": "impute_mean",
      "title": "Impute the mean value into a vector with missing values",
      "topics": [
        "impute_mean",
        "impute_mean.default",
        "impute_mean.factor"
      ]
    },
    {
      "page": "impute_median",
      "title": "Impute the median value into a vector with missing values",
      "topics": [
        "impute_median",
        "impute_median.default",
        "impute_median.factor"
      ]
    },
    {
      "page": "impute_mode",
      "title": "Impute the mode value into a vector with missing values",
      "topics": [
        "impute_mode",
        "impute_mode.default",
        "impute_mode.factor",
        "impute_mode.integer"
      ]
    },
    {
      "page": "impute_zero",
      "title": "Impute zero into a vector with missing values",
      "topics": [
        "impute_zero"
      ]
    },
    {
      "page": "is_shade",
      "title": "Detect if this is a shade",
      "topics": [
        "any_shade",
        "are_shade",
        "is_shade"
      ]
    },
    {
      "page": "label_miss_1d",
      "title": "Label a missing from one column",
      "topics": [
        "label_miss_1d"
      ]
    },
    {
      "page": "label_miss_2d",
      "title": "label_miss_2d",
      "topics": [
        "label_miss_2d"
      ]
    },
    {
      "page": "label_missings",
      "title": "Is there a missing value in the row of a dataframe?",
      "topics": [
        "label_missings"
      ]
    },
    {
      "page": "mcar_test",
      "title": "Little's missing completely at random (MCAR) test",
      "topics": [
        "mcar_test"
      ]
    },
    {
      "page": "miss_case_cumsum",
      "title": "Summarise the missingness in each case",
      "topics": [
        "miss_case_cumsum"
      ]
    },
    {
      "page": "miss_case_summary",
      "title": "Summarise the missingness in each case",
      "topics": [
        "miss_case_summary"
      ]
    },
    {
      "page": "miss_case_table",
      "title": "Tabulate missings in cases.",
      "topics": [
        "miss_case_table"
      ]
    },
    {
      "page": "miss_prop_summary",
      "title": "Proportions of missings in data, variables, and cases.",
      "topics": [
        "miss_prop_summary"
      ]
    },
    {
      "page": "miss_scan_count",
      "title": "Search and present different kinds of missing values",
      "topics": [
        "miss_scan_count"
      ]
    },
    {
      "page": "miss_summary",
      "title": "Collate summary measures from naniar into one tibble",
      "topics": [
        "miss_summary"
      ]
    },
    {
      "page": "miss_var_cumsum",
      "title": "Cumulative sum of the number of missings in each variable",
      "topics": [
        "miss_var_cumsum"
      ]
    },
    {
      "page": "miss_var_run",
      "title": "Find the number of missing and complete values in a single run",
      "topics": [
        "miss_var_run"
      ]
    },
    {
      "page": "miss_var_span",
      "title": "Summarise the number of missings for a given repeating span on a variable",
      "topics": [
        "miss_var_span"
      ]
    },
    {
      "page": "miss_var_summary",
      "title": "Summarise the missingness in each variable",
      "topics": [
        "miss_var_summary"
      ]
    },
    {
      "page": "miss_var_table",
      "title": "Tabulate the missings in the variables",
      "topics": [
        "miss_var_table"
      ]
    },
    {
      "page": "miss_var_which",
      "title": "Which variables contain missing values?",
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        "miss_var_which"
      ]
    },
    {
      "page": "miss-pct-prop-defunct",
      "title": "Proportion of variables containing missings or complete values",
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        "complete_case_prop",
        "complete_var_pct",
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        "miss-pct-prop-defunct",
        "miss_case_pct",
        "miss_case_prop",
        "miss_var_pct",
        "miss_var_prop"
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    },
    {
      "page": "n_complete",
      "title": "Return the number of complete values",
      "topics": [
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    {
      "page": "n_complete_row",
      "title": "Return a vector of the number of complete values in each row",
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      "page": "n_miss",
      "title": "Return the number of missing values",
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    },
    {
      "page": "n_miss_row",
      "title": "Return a vector of the number of missing values in each row",
      "topics": [
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    },
    {
      "page": "n-var-case-complete",
      "title": "The number of variables with complete values",
      "topics": [
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        "n_case_complete",
        "n_var_complete"
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    },
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      "page": "n-var-case-miss",
      "title": "The number of variables or cases with missing values",
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        "n-var-case-miss",
        "n_case_miss",
        "n_var_miss"
      ]
    },
    {
      "page": "nabular",
      "title": "Convert data into nabular form by binding shade to it",
      "topics": [
        "nabular"
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    {
      "page": "naniar",
      "title": "naniar: Data Structures, Summaries, and Visualisations for Missing Data",
      "topics": [
        "naniar-package",
        "naniar"
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    },
    {
      "page": "oceanbuoys",
      "title": "West Pacific Tropical Atmosphere Ocean Data, 1993 & 1997.",
      "topics": [
        "oceanbuoys"
      ]
    },
    {
      "page": "pct_complete",
      "title": "Return the percent of complete values",
      "topics": [
        "pct_complete"
      ]
    },
    {
      "page": "pct_miss",
      "title": "Return the percent of missing values",
      "topics": [
        "pct_miss"
      ]
    },
    {
      "page": "pct-miss-complete-case",
      "title": "Percentage of cases that contain a missing or complete values.",
      "topics": [
        "pct-miss-complete-case",
        "pct_complete_case",
        "pct_miss_case"
      ]
    },
    {
      "page": "pct-miss-complete-var",
      "title": "Percentage of variables containing missings or complete values",
      "topics": [
        "pct-miss-complete-var",
        "pct_complete_var",
        "pct_miss_var"
      ]
    },
    {
      "page": "pedestrian",
      "title": "Pedestrian count information around Melbourne for 2016",
      "topics": [
        "pedestrian"
      ]
    },
    {
      "page": "prop_complete",
      "title": "Return the proportion of complete values",
      "topics": [
        "prop_complete"
      ]
    },
    {
      "page": "prop_complete_row",
      "title": "Return a vector of the proportion of missing values in each row",
      "topics": [
        "prop_complete_row"
      ]
    },
    {
      "page": "prop_miss",
      "title": "Return the proportion of missing values",
      "topics": [
        "prop_miss"
      ]
    },
    {
      "page": "prop_miss_row",
      "title": "Return a vector of the proportion of missing values in each row",
      "topics": [
        "prop_miss_row"
      ]
    },
    {
      "page": "prop-miss-complete-case",
      "title": "Proportion of cases that contain a missing or complete values.",
      "topics": [
        "prop-miss-complete-case",
        "prop_complete_case",
        "prop_miss_case"
      ]
    },
    {
      "page": "prop-miss-complete-var",
      "title": "Proportion of variables containing missings or complete values",
      "topics": [
        "prop-miss-complete-var",
        "prop_complete_var",
        "prop_miss_var"
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    },
    {
      "page": "recode_shadow",
      "title": "Add special missing values to the shadow matrix",
      "topics": [
        "recode_shadow",
        "recode_shadow.data.frame",
        "recode_shadow.grouped_df"
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    {
      "page": "replace_na_with",
      "title": "Replace NA value with provided value",
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      "page": "replace_to_na",
      "title": "Replace values with missings",
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      "page": "replace_with_na",
      "title": "Replace values with missings",
      "topics": [
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      "page": "replace_with_na_all",
      "title": "Replace all values with NA where a certain condition is met",
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      "page": "replace_with_na_at",
      "title": "Replace specified variables with NA where a certain condition is met",
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      "page": "replace_with_na_if",
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    {
      "page": "riskfactors",
      "title": "The Behavioral Risk Factor Surveillance System (BRFSS) Survey Data, 2009.",
      "topics": [
        "riskfactors"
      ]
    },
    {
      "page": "scoped-impute_mean",
      "title": "Scoped variants of 'impute_mean'",
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      "page": "scoped-impute_median",
      "title": "Scoped variants of 'impute_median'",
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        "impute_median_at",
        "impute_median_if",
        "scoped-impute_median"
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    },
    {
      "page": "set-prop-n-miss",
      "title": "Set a proportion or number of missing values",
      "topics": [
        "set-prop-n-miss",
        "set_n_miss",
        "set_prop_miss"
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    },
    {
      "page": "shade",
      "title": "Create new levels of missing",
      "topics": [
        "shade"
      ]
    },
    {
      "page": "shadow_long",
      "title": "Reshape shadow data into a long format",
      "topics": [
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    },
    {
      "page": "shadow_shift",
      "title": "Shift missing values to facilitate missing data exploration/visualisation",
      "topics": [
        "shadow_shift"
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    },
    {
      "page": "stat_miss_point",
      "title": "stat_miss_point",
      "topics": [
        "stat_miss_point"
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    },
    {
      "page": "unbinders",
      "title": "Unbind (remove) shadow from data, and vice versa",
      "topics": [
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        "unbind_shadow"
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    },
    {
      "page": "where",
      "title": "Split a call into two components with a useful verb name",
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        ".where",
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    },
    {
      "page": "where_na",
      "title": "Which rows and cols contain missings?",
      "topics": [
        "where_na"
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    },
    {
      "page": "which_are_shade",
      "title": "Which variables are shades?",
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        "which_are_shade"
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    {
      "page": "which_na",
      "title": "Which elements contain missings?",
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      "source": "exploring-imputed-values.Rmd",
      "filename": "exploring-imputed-values.html",
      "title": "Exploring Imputed Values",
      "author": "Nicholas Tierney",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Imputing and tracking missing values",
        "Using impute_below",
        "Using impute_mean",
        "Track imputed values using nabular data",
        "Imputing values using simputation",
        "Improving imputations",
        "Other imputation approaches",
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      "created": "2018-08-21 08:30:50",
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      "source": "naniar-visualisation.Rmd",
      "filename": "naniar-visualisation.html",
      "title": "Gallery of Missing Data Visualisations",
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      "headings": [
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        "Exploring patterns with UpSetR",
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      "title": "Getting Started with naniar",
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      "headings": [
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        "How do we start looking at missing data?",
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        "Visualising missings in variables",
        "Replacing existing values with NA",
        "Tidy Missing Data: The Shadow Matrix",
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        "Numerical summaries of missing values",
        "Using group_by with naniar",
        "Modelling missingness",
        "Summary",
        "Future development",
        "Thank you",
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        "replace_with_na_if",
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      "created": "2018-01-18 23:41:41",
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      "headings": [
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        "Recoding missing values"
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      "modified": "2024-03-16 02:58:03",
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