[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-detail-573758-en":53,"doc-seo-573758-105":75},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":5,"data":54},{"doc_id":55,"user_id":56,"nickname":57,"user_avatar":58,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":60,"doc_content":61,"file_id":62,"file_url":63,"file_type":64,"file_size":65,"view_count":66,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":67,"language":68,"language_code":69,"site_id":70,"html_lang":69,"table_of_contents":71,"faqs":72,"seo_title":73,"seo_description":60,"update_tm":74,"read_time":9},573758,962093541613,"How to eat","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","tidyr Data Wrangling Cheat Sheet - Reshape, Tidy & Handle Missing Data","Cheat sheet for tidyr focused on tidying tabular data into a consistent structure: variables in separate columns and observations in separate rows. Covers tibbles (enhanced data frames) with clearer subsetting behavior, plus core reshaping tools including pivot_longer() and pivot_wider(). Includes expanding combinations, constructing tibbles, and cleaning workflows such as unite(), separate(), drop_na(), fill(), and replace_na(), with examples and tibble display options.","Data tidying with tidyr : : CHEAT SHEET  \nTidy data is a way to organize tabular data in a consistent data structure across packages. A table is tidy if:  \nEach variable is in its own column  \n&  \nEach observation, or case, is in its own row  \n\n|  | A |  |  | A |  | * B\u003Cbr>|  | |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n|  |  | B | C |  |  |  |  |  | C |  |\n|  |  |  |  |  |  |  |  |  |  |  |\n|  |  |  |  |  |  |  |  |  |  |  |\n|  |  |  |  |  |  |  |  |  |  |  |\n|  |  |  |  |  |  |  |  |  |  |  |\n| Access variables |  |  |  |  | Preserve cases in |  |  |  |  |  |\n| as vectors |  |  |  |  | vectorized operations |  |  |  |  |  |\n\nTibbles  \nAN ENHANCED DATA FRAME  \nTibbles are a table format provided by the tibble package. They inherit the data frame class, but have improved behaviors:  \n• Subset a new tibble with ], a vector with [[ and $.  \n• No partial matching when subsetting columns.  \n• Display concise views of the data on one screen.  \nReshape Data -Pivot data to reorganize values into a new layout.  \ntable4a  \n\n| country | 1999 | 2000 |\n| --- | --- | --- |\n| A | 0.7K | 2K |\n| B | 37K | 80K |\n| C | 212K | 213K |\n\n| country | year | cases |\n| --- | --- | --- |\n| A | 1999 | 0.7K |\n| B | 1999 | 37K |\n| C | 1999 | 212K |\n| A | 2000 | 2K |\n| B | 2000 | 80K |\n| C | 2000 | 213K |\n\npivot_longer(data, cols, names_to = \"name\", values_to = \"value\", values_drop_na = FALSE)\"Lengthen\" data by collapsing several columns into two. Column names move to a new names_to column and values to a new values_to column.  \npivot_longer(table4a, cols = 2:3, names_to =\"year\", values_to = \"cases\")  \n| country | year | cases | pop |\n| --- | --- | --- | --- |\n| A | 1999 | 0.7K | 19M |\n| A | 2000 | 2K | 20M |\n| B | 1999 | 37K | 172M |\n| B | 2000 | 80K | 174M |\n| C | 1999 | 212K | 1T |\n| C | 2000 | 213K | 1T |\n\npivot_wider(data, names_from = \"name\", values_from = \"value\")  \nThe inverse of pivot_longer(). \"Widen\" data by expanding two columns into several. One column provides the new column names, the other the values.  \npivot_wider(table2, names_from = type, values_from = count)  \nSplit Cells -Use these functions to split or combine cells into individual, isolated values.  \nExpand Tables  \nCreate new combinations of variables or identify implicit missing values (combinations of variables not present in the data) .  \nx  \nexpand(data,…) Create anew tibble with all possible combinations ofthe values of the variables listed in… Drop other variables. expand(mtcars, cyl, gear, carb)  \ncomplete(data,…, fill = list()) Add missing possible combinations of values of variables listed in…Fill remaining variables with NA. complete(mtcars, cyl, gear, carb)  \nHandle Missing Values  \noptions(tibble.print_max = n, tibble.print_min = m, tibble.width = Inf) Control default display settings. View() or glimpse() View the entire data set.  \nCONSTRUCT A TIBBLE  \n\n| A tibble: 3 × 2 |  |\n| --- | --- |\n| x \u003Cint>\u003Cbr>1 1 2 2\u003Cbr>3 3 | y \u003Cchr>\u003Cbr>a b\u003Cbr>c |\n\nBoth make this tibble  \ntibble(…) Construct by columns. tibble(x = 1:3, y = c(\"a\", \"b\", \"c\")) tribble(…) Construct by rows. tribble(~x, ~y,  \n1, \"a\",  \n2, \"b\",  \n3, \"c\")  \nas_tibble(x,…) Convert a data frame to a tibble. enframe(x, name = \"name\", value = \"value\") Convert a named vector to a tibble. Also deframe() . is_tibble(x) Test whether x is a tibble.  \ntable5  \n| country | year | cases | pop |\n| --- | --- | --- | --- |\n| A | 1999 | 0.7K | 19M |\n| A | 2000 | 2K | 20M |\n| B | 1999 | 37K | 172 |\n| B | 2000 | 80K | 174 |\n\nunite(data, col,…, sep = \"_ \", remove = TRUE, na. rm = FALSE) Collapse cells across several columns into a single column.  \nunite(table5, century, year, col = \"year\", sep = \"\")  \nseparate(data, col, into, sep = \"[^[:alnum:]]+\", remove = TRUE, convert = FALSE, extra = \"warn\", fill = \"warn\",…) Separate each cell in a column into several columns. Also extract() .  \nseparate(table3, rate, sep = \"/\", into = c(\"cases\", \"pop\"))  \nseparate_rows(data,…, sep = \"[^[:alnum:].]+\", convert = FAL","cbCaicttUkliJQcD","https://ap.wps.com/l/cbCaicttUkliJQcD","pdf",1293440,3,2,"English","en",105,"# Tidy Data & Tibbles\n# Reshaping Data (Pivot)\n# Split, Expand, and Construct Tibbles\n# Handling Missing Values (NA)\n# Nested Data Frames","[{\"question\":\"What is tidy data in tidyr terms?\",\"answer\":\"A table is tidy when each variable is in its own column and each observation (case) is in its own row.\"},{\"question\":\"How do pivot_longer() and pivot_wider() relate?\",\"answer\":\"pivot_longer() “lengthens” data by collapsing multiple columns into two columns (names_to and values_to). pivot_wider() is the inverse, “widening” data by expanding two columns into multiple columns.\"},{\"question\":\"Which functions help manage missing values (NA)?\",\"answer\":\"Use drop_na() to remove rows containing NA, fill() to propagate non-missing values into NA cells, and replace_na() to substitute a specified value for NA in selected columns.\"}]","tidyr Data Wrangling Cheat Sheet - Reshape, Tidy & Handle Missing Data | PDF",1791945046,{"code":4,"msg":76,"data":77},"ok",{"site_id":70,"language":69,"slug":78,"title":59,"keywords":79,"description":60,"schema_data":80,"social_meta":134,"head_meta":136,"extra_data":138,"updated_unix":139},"tidyr-data-wrangling-cheat-sheet-reshape-tidy-handle-missing-data","",{"@graph":81,"@context":133},[82,96,116],{"@type":83,"itemListElement":84},"BreadcrumbList",[85,89,91,93],{"item":86,"name":87,"@type":88,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":90,"name":10,"@type":88,"position":67},"https://docshare.wps.com/template/",{"item":92,"name":11,"@type":88,"position":66},"https://docshare.wps.com/template/presentations/",{"item":94,"name":59,"@type":88,"position":95},"https://docshare.wps.com/template/tidyr-data-wrangling-cheat-sheet-reshape-tidy-handle-missing-data/573758/",4,{"url":94,"name":59,"@type":97,"image":98,"author":103,"headline":59,"publisher":105,"fileFormat":108,"inLanguage":69,"description":60,"dateModified":109,"datePublished":110,"encodingFormat":108,"isAccessibleForFree":111,"interactionStatistic":112},"DigitalDocument",{"url":99,"@type":100,"width":101,"height":102},"https://docshare.wps.com/thumbnails/tidyr-data-wrangling-cheat-sheet-reshape-tidy-handle-missing-data/573758.png","ImageObject",442,249,{"name":57,"@type":104},"Person",{"url":86,"name":106,"@type":107},"DocShare","Organization","application/pdf","2026-10-16","2026-10-14",true,{"@type":113,"interactionType":114,"userInteractionCount":66},"InteractionCounter",{"@type":115},"ViewAction",{"@type":117,"mainEntity":118},"FAQPage",[119,125,129],{"name":120,"@type":121,"acceptedAnswer":122},"What is tidy data in tidyr terms?","Question",{"text":123,"@type":124},"A table is tidy when each variable is in its own column and each observation (case) is in its own row.","Answer",{"name":126,"@type":121,"acceptedAnswer":127},"How do pivot_longer() and pivot_wider() relate?",{"text":128,"@type":124},"pivot_longer() “lengthens” data by collapsing multiple columns into two columns (names_to and values_to). pivot_wider() is the inverse, “widening” data by expanding two columns into multiple columns.",{"name":130,"@type":121,"acceptedAnswer":131},"Which functions help manage missing values (NA)?",{"text":132,"@type":124},"Use drop_na() to remove rows containing NA, fill() to propagate non-missing values into NA cells, and replace_na() to substitute a specified value for NA in selected columns.","https://schema.org",{"og:url":94,"og:type":135,"og:title":59,"og:site_name":106,"og:description":60},"article",{"robots":137,"canonical":94},"index,follow",{"doc_id":55,"site_id":70},1792014502]