这里是我的DF的头。
> 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
这里是DUTU的头
> 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"
))
我希望每一行都有1对council_namePeriod的值，可以看到每对council_namePeriod都有5次。我想做的是，我不能完全做到的是将每对 council_namePeriod 只折叠在一行，每个变量（Mixed glass，Mixed paper等）的值也都在同一行，而不是每行只有一个。所以基本上是按 council_name & period 来分组，并且只有一行，保留现在每列在不同行的值。
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
我试过这里的解决方案： R将多行折叠成1行--同样的列。却无法完成。
谢谢!
你的预期输出和一些样本数据会对获得高质量的答案有很大帮助。分享输出的 dput(head(waste3))
也许这样的东西会有用
apply(waste3[,5:ncol(waste3)], 1 , max,na.rm=T)
这假定没有任何行是全为 NAs.
您可以按组选择每一列中的第一个非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(.)))