将多行合并为一行并在 R 中删除 NA答案

【问题标题】：Collapse multiple rows into one and drop NAs in R将多行合并为一行并在 R 中删除 NA
【发布时间】：2020-09-28 18:15:54
【问题描述】：

这是我的 df 的负责人：

> head(waste3)
# A tibble: 6 x 76
  Period MaterialGroup council_name  year `Mixed glass` `Mixed paper & … `Co mingled mat… `Green waste on…
  <chr>  <chr>         <fct>        <dbl> <chr>         <chr>            <chr>            <chr>           
1 Apr 0… Glass         Adur          2006 243.39        NA               NA               NA              
2 Apr 0… Paper & Card  Adur          2006 NA            632.80999999999… NA               NA              
3 Apr 0… Co-mingled    Adur          2006 NA            NA               97.36            NA              
4 Apr 0… NA            Adur          2006 NA            NA               NA               24.68           
5 Apr 0… Bulky         Adur          2006 NA            NA               NA               NA              
6 Jul 0… Glass         Adur          2006 251.51        NA               NA               NA

这是输出

> dput(head(waste3))
structure(list(Period = c("Apr 06 - Jun 06", "Apr 06 - Jun 06", 
"Apr 06 - Jun 06", "Apr 06 - Jun 06", "Apr 06 - Jun 06", "Jul 06 - Sep 06"
), MaterialGroup = c("Glass", "Paper & Card", "Co-mingled", NA, 
"Bulky", "Glass"), council_name = structure(c(1L, 1L, 1L, 1L, 
1L, 1L), .Label = c("Adur", "Allerdale", "Alnwick", "Amber Valley", 
"Arun", "Ashfield", "Ashford", "Aylesbury Vale", "Babergh", "Barking and Dagenham", 
"Barnet", "Barnsley", "Barrow-in-Furness", "Basildon", "Basingstoke and Deane", 
"Bassetlaw", "Bath and North East Somerset", "Bedford", "Berwick-upon-Tweed", 
"Bexley", "Birmingham", "Blaby", "Blackburn with Darwen", "Blackpool", 
"Blyth Valley", "Bolsover", "Bolton", "Boston", "Bournemouth", 
"Bracknell Forest", "Bradford", "Braintree", "Breckland", "Brent", 
"Brentwood", "Bridgnorth", "Brighton and Hove", "Bristol", "Broadland", 
"Bromley", "Bromsgrove", "Broxbourne", "Broxtowe", "Burnley", 
"Bury", "Calderdale", "Cambridge", "Camden", "Cannock Chase", 
"Canterbury", "Caradon", "Carlisle", "Carrick", "Castle Morpeth", 
"Castle Point", "Central Bedfordshire", "Charnwood", "Chelmsford", 
"Cheltenham", "Cherwell", "Cheshire East", "Cheshire West and Chester", 
"Chester", "Chester-Le-Street", "Chesterfield", "Chichester", 
"Chiltern", "Chorley", "Christchurch", "Colchester", "Congleton", 
"Conventry", "Copeland", "Corby", "Cornwall", "Cotswold", "Craven", 
"Crawley", "Crewe and Nantwich", "Croydon", "Dacorum", "Darlington", 
"Dartford", "Daventry", "Derby", "Derbyshire Dales", "Derwentside", 
"Doncaster", "Dorset Waste Partnership", "Dover", "Dudley", "Durham", 
"Ealing", "Easington", "East Cambridgeshire", "East Devon", "East Dorset", 
"East Hampshire", "East Hertfordshire", "East Lindsey", "East Northamptonshire", 
"East Riding of Yorkshire", "East Staffordshire", "Eastbourne", 
"Eastleigh", "Eden", "Ellesmere Port and Neston", "Elmbridge", 
"Enfield", "Epping Forest", "Epsom and Ewell", "Erewash", "Exeter", 
"Fareham", "Fenland", "Folkestone and Hythe", "Forest Heath", 
"Forest of Dean", "Fylde", "Gateshead", "Gedling", "Gloucester", 
"Gosport", "Gravesham", "Great Yarmouth", "Greenwich", "Guildford", 
"Hackney", "Halton", "Hambleton", "Hammersmith and Fulham", "Harborough", 
"Haringey", "Harlow", "Harrogate", "Harrow", "Hart", "Hartlepool", 
"Hastings", "Havant", "Havering", "Herefordshire", "Hertsmere", 
"High Peak", "Hillingdon", "Hinckley and Bosworth", "Horsham", 
"Hounslow", "Huntingdonshire", "Hyndburn", "Ipswich", "Isle of Wight", 
"Isles of Scilly", "Islington", "Kennet", "Kerrier", "Kettering", 
"Kings Lynn and West Norfolk", "Kingston-upon-Hull", "Kirklees", 
"Knowsley", "Lambeth", "Lancaster", "Leeds", "Leicester", "Lewes", 
"Lewisham", "Lichfield", "Lincoln", "Liverpool", "London", "Luton", 
"Macclesfield", "Maidstone", "Maldon", "Malvern Hills", "Manchester", 
"Mansfield", "Medway", "Melton", "Mendip", "Merton", "Mid Bedfordshire", 
"Mid Devon", "Mid Suffolk", "Mid Sussex", "Middlesbrough", "Milton Keynes", 
"Mole Valley", "New Forest", "Newark and Sherwood", "Newcastle-under-Lyme", 
"Newcastle-upon-Tyne", "Newham", "North Cornwall", "North Devon", 
"North Dorset", "North East Derbyshire", "North East Lincolnshire", 
"North Hertfordshire", "North Kesteven", "North Lincolnshire", 
"North Norfolk", "North Shropshire", "North Somerset", "North Tyneside", 
"North Warwickshire", "North West Leicestershire", "North Wiltshire", 
"Northampton", "Northumberland", "Norwich", "Nottingham", "Nuneaton and Bedworth", 
"Oadby and Wigston", "Oldham", "Oswestry", "Oxford", "Pendle", 
"Penwith", "Peterborough", "Plymouth", "Poole", "Portsmouth", 
"Preston", "Purbeck", "Reading", "Redbridge", "Redcar and Cleveland", 
"Redditch", "Reigate and Banstead", "Restormel", "Ribble Valley", 
"Richmond upon Thames", "Richmondshire", "Rochdale", "Rochford", 
"Rossendale", "Rother", "Rotherham", "Royal Borough of Kensington and Chelsea", 
"Royal Borough of Kingston upon Thames", "Rugby", "Runnymede", 
"Rushcliffe", "Rushmoor", "Rutland County", "Ryedale", "Salford", 
"Salisbury", "Sandwell", "Scarborough", "Sedgefield", "Sedgemoor", 
"Sefton", "Selby", "Sevenoaks", "Sheffield", "Shrewsbury and Atcham", 
"Shropshire", "Slough", "Solihull", "South Bedfordshire", "South Bucks", 
"South Cambridgeshire", "South Derbyshire", "South Gloucestershire", 
"South Hams", "South Holland", "South Kesteven", "South Lakeland", 
"South Norfolk", "South Northamptonshire", "South Oxfordshire", 
"South Ribble", "South Shropshire", "South Somerset", "South Staffordshire", 
"South Tyneside", "Southampton", "Southend-on-Sea", "Southwark", 
"Spelthorne", "St Albans", "St Edmundsbury", "St Helens", "Stafford", 
"Staffordshire Moorlands", "Stevenage", "Stockport", "Stockton-on-Tees", 
"Stoke-on-Trent", "Stratford-on-Avon", "Stroud", "Suffolk Coastal", 
"Sunderland", "Surrey Heath", "Sutton", "Swale", "Swindon", "Tameside", 
"Tamworth", "Tandridge", "Taunton Deane", "Teesdale", "Teignbridge", 
"Telford and Wrekin", "Tendring", "Test Valley", "Tewkesbury", 
"Thanet", "Three Rivers", "Thurrock", "Tonbridge and Malling", 
"Torbay", "Torridge", "Tower Hamlets", "Trafford", "Tunbridge Wells", 
"Tynedale", "Uttlesford", "Vale of White Horse", "Vale Royal", 
"Wakefield", "Walsall", "Waltham Forest", "Wandsworth", "Wansbeck", 
"Warrington", "Warwick", "Watford", "Waveney", "Waverley", "Wealden", 
"Wear Valley", "Wellingborough", "Welwyn Hatfield", "West Berkshire", 
"West Devon", "West Dorset", "West Lancashire", "West Lindsey", 
"West Oxfordshire", "West Somerset", "West Wiltshire", "Westminster", 
"Weymouth and Portland", "Wigan", "Wiltshire", "Winchester", 
"Windsor and Maidenhead", "Wirral", "Woking", "Wokingham", "Wolverhampton", 
"Worcester", "Worthing", "Wychavon", "Wycombe", "Wyre", "Wyre Forest", 
"York"), class = "factor"), year = c(2006, 2006, 2006, 2006, 
2006, 2006), `Mixed glass` = c("243.39", NA, NA, NA, NA, "251.51"
), `Mixed paper &  card` = c(NA, "632.80999999999995", NA, NA, 
NA, NA), `Co mingled materials` = c(NA, NA, "97.36", NA, NA, 
NA), `Green waste only` = c(NA, NA, NA, "24.68", NA, NA), `Fridges & Freezers` = c(NA, 
NA, NA, NA, "2.76", NA), no.donors = c(NA_character_, NA_character_, 
NA_character_, NA_character_, NA_character_, NA_character_), 
    `Green garden waste only` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `WEEE - Fridges & Freezers` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), Paper = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), `Other electrical goods` = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), `Other White Goods` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Mixed cans` = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), Plastics = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), `WEEE - Large Domestic App` = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), `WEEE - Small Domestic App` = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), `Other Scrap metal` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Other materials` = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), Card = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), `Green glass` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `WEEE - TVs & Monitors` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Car tyres` = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), `Other compostable waste` = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), `Textiles & footwear` = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), Furniture = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Brown glass` = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), `Clear glass` = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), `Waste food only` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Textiles only` = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), `Video tapes, DVDs and CDs` = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), Wood = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), Rubble = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), `Fluorescent tubes` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Automotive batteries` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Mineral Oil` = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), `WEEE - Flourescent tubes and other light bulbs` = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), `Mixed tyres` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Post consumer, non automotive batteries` = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), `Vegetable Oil` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Wood for composting` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Mixed Plastic Bottles` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), Books = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), `Steel cans` = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), `Aluminium cans` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Aluminium foil` = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), Paint = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), `PET [1]` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `HDPE [2]` = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), `PVC [3]` = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), `LDPE [4]` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `PP [5]` = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), `PS [6]` = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), `OTHER PLASTICS [7]` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Mixed garden and food waste` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Chipboard and mdf` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Composite wood materials` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Van tyres` = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), `Large vehicle tyres` = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), Soil = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), Plasterboard = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), Aerosols = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Bric-a-brac` = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), `Composite food and beverage cartons` = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), `Fire extinguishers` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Gas bottles` = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), `Ink & toner cartridges` = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), Mattresses = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Yellow Pages` = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), Bicycles = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), `Footwear only` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), Carpets = c(NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_), `Absorbent Hygiene Products (AHP)` = c(NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_), Aggregates = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    )), row.names = c(NA, -6L), class = c("tbl_df", "tbl", "data.frame"
))

我希望一行是一对 Council_name/Period 值。可以看出，每对 Council_name/Period 出现 5 次。我想做的我不太明白的是，将每对 Council_name/Period 折叠在一行中，每个变量（混合玻璃、混合纸等）的值也都在同一行中，而不仅仅是每行一个。所以基本上按委员会名称和时期对其进行分组，并且只有一行，并保留现在每列的不同行中的值。

基本上是这样的：

> head(df.desired)
  council_name     period mixed.glass mixed.paper green.waste comingled.materials
1         Adur April 2006      243.39      632.81       24.68               97.36
2         Adur  July 2006      251.51      540.00       33.00               99.10
3       Barnet April 2006      560.00      599.00       88.00               83.50

我在这里尝试了解决方案：R collapse multiple rows into 1 row - same columns 并且无法完成。

谢谢！

【问题讨论】：

标签： r dplyr row tidyr plyr

【解决方案1】：

您可以按组为选定列选择每列中的第一个非 NA 值。

library(dplyr)

waste3 %>%
  group_by(council_name, Period) %>%
  summarise(across(`Mixed glass`:`Green waste on`, ~first(na.omit(.))))
  #In older version of dplyr
  #summarise_at(vars(`Mixed glass`:`Green waste on`), ~first(na.omit(.)))

【讨论】：

【解决方案2】：

您的预期输出和一些示例数据将大大有助于获得高质量的答案。分享dput(head(waste3))的输出

也许这样的事情可能会起作用

apply(waste3[,5:ncol(waste3)], 1 , max,na.rm=T)

这假设不存在所有NAs 的行。

【讨论】：

感谢 Daniel O。现在对其进行了编辑，并将两者都包括在内，有帮助吗？