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    Best R packages for data import, data wrangling, data visualization

    dplyr
    information wrangling, information evaluation
    The important data-munging R package deal when working with information frames. Especially helpful for working on information by classes. CRAN.
    See the intro vignette
    Hadley Wickham
    purrr
    information wrangling
    purrr makes it straightforward to use a perform to every merchandise in a listing and return ends in the format of your alternative. It’s extra complicated to be taught than the older plyr package deal, but in addition extra strong. And, its features are extra standardized than base R’s apply household — plus it is bought features for duties like error-checking. CRAN.
    map_df(mylist, myfunction)More: Charlotte Wickham’s purr tutorial video, the purrr cheat sheet PDF obtain, straightforward error checking with purrr’s presumably.
    Hadley Wickham
    readxl
    information import
    Fast strategy to learn Excel recordsdata in R, with out dependencies similar to Java. CRAN.
    read_excel(“my-spreadsheet.xls”, sheet = 1)
    Hadley Wickham
    readr and vroom
    information import
    Base R handles most of those features; however you probably have big recordsdata, these packages provide sooner and standardized strategy to learn CSVs and comparable recordsdata into R. readr has been round for awhile; vroom is a speedier different, helpful for bigger information units. Eventually the packages could merge. information.desk’s fread() is one other helpful different. CRAN.
    read_csv(myfile.csv) or vroom(myfile.csv)
    Hadley Wickham (readr), Jim Hester (vroom)
    rio
    information import, information export
    rio has a good suggestion: Pull plenty of separate data-reading packages into one, so that you simply want to recollect 2 features: import and export. CRAN.
    import(“myfile”)
    Thomas J. Leeper & others
    tidyxl
    information import, information wrangling
    If you’ve got ever wished to tear your hair out over an Excel file with merged cells, information in column headers, headers combined in information, and key data in shade coding, that is the package deal for you. Each cell is imported in its personal row, with details about information sort, place, and shade, not simply worth, permitting you to reshape the info from there. Super time saver for messy information. CRAN.
    xlsx_cells(“my_nightmare_file.xlsx”)
    Duncan Garmonsway
    Hmisc
    information evaluation
    There are various helpful features in right here. Two of my favorites: describe, a extra strong abstract perform, and Cs, which creates a vector of quoted character strings from unquoted comma-separated textual content. Cs(so, it, goes) creates c(“so”, “it”, “goes”). CRAN.
    describe(mydf)Cs(so, it, goes)
    Frank E Harrell Jr & others
    datapasta
    information import
    Data copy and paste: Meet reproducible analysis. If you’ve got copied information from the Web, a spreadsheet, or different supply into your clipboard, datapasta enables you to paste it into R as an R object, with the code to breed it. It consists of RStudio add-ins in addition to command-line features for transposing information, turning it into markdown format, and extra. CRAN.
    df_paste() to create an information body, vector_paste() to create a vector.
    Miles McBain
    sqldf
    information wrangling, information evaluation
    Do you already know an important SQL question you’d use in case your R information body had been in a SQL database? Run SQL queries in your information body with sqldf. CRAN.
    sqldf(“select * from mydf where mycol > 4”)
    G. Grothendieck
    jsonlite
    information import, information wrangling
    Parse json inside R or flip R information frames into json. CRAN.
    myjson <- toJSON(mydf, fairly=TRUE)mydf2 <- fromJSON(myjson)
    Jeroen Ooms & others
    XML
    information import, information wrangling
    Many features for elegantly coping with XML and HTML, similar to readHTMLTable. CRAN.
    mytables <- readHTMLTable(myurl)
    Duncan Temple Lang
    httr
    information import, information wrangling
    An R interface to http protocols; helpful for pulling information from APIs. See the httr quickstart information. CRAN.
    r <- GET(“http://httpbin.org/get”)content material(r, “text”)
    Hadley Wickham
    quantmod
    information import, information visualization, information evaluation
    Even in the event you’re not fascinated about analyzing and graphing monetary funding information, quantmod has easy-to-use features for importing financial in addition to monetary information from sources just like the Federal Reserve. CRAN.
    getSymbols(“AITINO”, src=”https://www.computerworld.com/article/2921176/FRED”)
    Jeffrey A. Ryan
    tidyquant
    information import, information visualization, information evaluation
    Another monetary package deal that is helpful for importing, analyzing and visualizing information, integrating elements of different fashionable finance packages in addition to tidyverse instruments. With thorough documentation. CRAN.
    aapl_key_ratios <- tq_get(“AAPL”, get = “key.ratios”)
    Matt Dancho
    rvest
    information import, internet scraping
    Web scraping: Extract information from HTML pages. Inspired by Python’s Beautiful Soup. Works properly with Selectorgadget. CRAN.
    How to import information into R or see the SelectorGadget vignette
    Hadley Wickham
    tidyr
    information wrangling
    tidyr initially gained me over with specialised features like fill (fill in lacking columns from information above) and replace_na. But now I additionally use it for its primary function too: serving to you modify information row and column codecs from “wide” to “long”. CRAN.
    See my YouTube video How to reshape information with tidyr’s new pivot features.
    Hadley Wickham
    splitstackshape
    information wrangling
    The package deal’s cSplit() perform solves a fairly complicated shaping downside in an astonishingly straightforward means. If you could have an information body column with a number of comma-separated values (suppose a survey query with “select all that apply”), that is value an set up if you wish to separate every merchandise into its personal new information body row.. CRAN.
    cSplit(mydata, “multi_val_column”, sep = “,”, course = “long”).
    Ananda Mahto
    magrittr
    information wrangling
    This package deal gave us the %>% image for chaining R operations, nevertheless it’s bought different helpful operators similar to %<>% for mutating an information body in place and and . as a placeholder for the unique object being operated upon. CRAN.
    mydf %<>% mutate(newcol = myfun(colname))
    Stefan Milton Bache & Hadley Wickham
    validate
    information wrangling
    Intuitive information validation primarily based on guidelines you may outline, save and re-use. CRAN.
    See the introductory vignette.
    Mark van der Loo & Edwin de Jonge
    testthat
    programming
    Package that makes it straightforward to write down unit checks to your R code. CRAN.
    See the testing chapter of Hadley Wickham’s ebook on R packages.
    Hadley Wickham
    information.desk
    information wrangling, information evaluation
    Popular package deal for heavy-duty information wrangling and computation. While I typically desire dplyr for fundamental evaluation, information.desk has grow to be my go-to for giant information units or when velocity is important (similar to in Shiny apps). CRAN.
    information.desk in 5 minutes video, The final information.desk cheat sheet, Intro vignette
    Matt Dowle & others
    stringr
    information wrangling
    Numerous features for textual content manipulation. Some are much like present base R features however in a extra commonplace format, together with working with common expressions. Some of my favorites: str_pad and str_trim. CRAN.
    str_pad(myzipcodevector, 5, “left”, “0”)
    Hadley Wickham
    lubridate
    information wrangling
    Everything you ever wished to do with date arithmetic, though understanding & utilizing accessible performance will be considerably complicated. CRAN.
    mdy(“05/06/2015″) + months(1)More examples within the package deal vignette
    Garrett Grolemund, Hadley Wickham & others
    DataExplorer
    information evaluation
    Not certain the place to get began taking a look at an information set? Want to get a fundamental deal with on that information with out working a number of instructions like str() and plot()? DataExplorer makes an attempt to supply one-click report technology to point out and visualize fundamentals a few information set, similar to distributions and lacking information. CRAN.
    create_report(mydataframe)
    Boxuan Cui
    zoo
    information wrangling, information evaluation
    Robust package deal with a slew of features for coping with time sequence information; I just like the useful rollmean perform with its align=proper and fill=NA choices for calculating shifting averages. CRAN.
    rollmean(mydf, 7)
    Achim Zeileis & others
    tsbox
    information wrangling, information evaluation
    Super straightforward strategy to convert information between totally different R time-series information codecs: xts, information body, zoo, tsibble, and extra. Plus some fundamental evaluation features. CRAN.
    ts_zoo(mydf)
    Christoph Sax
    knitr and rmarkdown
    information show
    Add R to a markdown doc and simply generate reviews in HTML, Word and different codecs. Vital in the event you’re fascinated about reproducible analysis and automating the journey from information evaluation to report creation. CRAN.
    See the Minimal Examples knitr web page and RStudio’s R Markdown web page.
    Yihui Xie & others (knitr), RStudio (rmarkdown)
    treatment
    information show
    RStudio add-in provides a menu for R Markdown formatting instructions, so that you now not want to recollect and/or sort code for issues like making an HTML record or embedding a YouTube video. While WYSIWYG modifying is now accessible for R Markdown, this add-in nonetheless has advantages: Its instructions will be assigned customized keyboard shortcuts, so you may create your personal shortcuts for duties like bolding textual content. GitHub.
    See the package deal web site.
    Colin Fay & others
    ymlthis
    information show
    Another helpful RStudio add-in for R Markdown, this helps you generate YML headers with correct format. GitHub.
    See the package deal web site.
    Malcolm Barrett & Richard Iannone
    officeR
    information show
    Import and edit Microsoft Word and PowerPoint paperwork, making it straightforward so as to add R-generated evaluation and visualizations to present in addition to new reviews and shows. CRAN.
    my_doc <- read_docx() %>% body_add_img(src = myplot)The package deal web site has many extra examples.
    David Gohel
    listviewer
    information show, information wrangling
    While RStudio has since added a list-viewing choice, this HTML widget nonetheless provides a chic strategy to view complicated nested lists inside R. GitHub timelyportfolio/listviewer.
    jsonedit(mylist)
    Kent Russell
    DT and reactable
    information show
    Create a sortable, searchable desk in a single line of code with both of those R packages CRAN.
    DT::datatable(mydf)reactable::reactable(mydf): Quick interactive HTML tablesreactable: reactable: Create tables with expandable rows
    DT: RStudioreactable: Gregg Lin
    ggplot2
    information visualization
    Powerful, versatile and well-thought-out dataviz package deal following ‘grammar of graphics’ syntax to create static graphics, however be ready for a steep studying curve. CRAN.
    qplot(issue(myfactor), information=mydf, geom=”bar”, fill=issue(myfactor))See my searchable ggplot2 cheat sheet andtime-saving code snippets.
    Hadley Wickham
    patchwork
    information visualization
    Easily mix ggplot2 plots and preserve the brand new, merged plot a ggplot2 object. plot_layout() provides capability to set columns, rows, and relative sizes of every element graphic. GitHub.
    plot1 + plot2 + plot_layout(ncol=1)
    Thomas Lin Pedersen
    ggforce
    information visualization
    Adds some design performance to base ggplot2 together with straightforward labeling of plot teams. CRAN.
    See this weblog put up by RStudio’s Edgar Ruiz for a number of helpful examples.
    Thomas Lin Pedersen
    plotly
    information visualization
    R interface to the Plotly JavaScript library that was open-sourced in late 2015. Basic graphs have a particular look which will not be for everybody, nevertheless it’s full-featured, comparatively straightforward to be taught (particularly if you already know ggplot2) and features a ggplotly() perform to show graphs created with ggplot2 interactive. CRAN.
    d <- diamonds[sample(nrow(diamonds), 1000), ] plot_ly(d, x = carat, y = worth, textual content = paste(“Clarity: “, readability), mode = “markers”, shade = carat, dimension = carat)
    Carson Sievert & others
    ggiraph
    information visualization
    Another strategy to make ggplot2 plots interactive utilizing geom features such geom_bar_interactive() that embody arguments for tooltips and JavaScript onclick occasions. CRAN.
    g <- ggplot(mpg, aes( x = displ, y = cty, shade = drv) )my_gg <- g + geom_point_interactive(aes(tooltip = mannequin), dimension = 2) %>%ggiraph(code = print(my_gg), width = .7).Easy interactive ggplot graphs in R with ggiraph
    David Gohel
    esquisse
    information visualization
    This RStudio add-in provides a drag-and-drop interface for ggplot2. And, it generates codes for the graph you create with the GUI. It’s a useful gizmo for exploring totally different shade palettes and themes, even in the event you’re snug creating your visualizations instantly in R. CRAN.
    See examples on the challenge’s web site .
    Victor Perrier and Fanny Meyer, dreamRs
    dygraphs
    information visualization
    Create HTML/JavaScript graphs of time sequence – one-line command in case your information is an xts object. CRAN.
    dygraph(myxtsobject)
    JJ Allaire & RStudio
    echarts4r
    information visualization
    Robust R wrapper for the echarts JavaScript library. CRAN.
    mydata %>%e_charts(xcol)%>% e_line(ycol)Plot in R with echarts4r or the package deal website
    John Coene
    taucharts
    information visualization
    This html widget library is particularly helpful for scatterplots the place you need to view a number of regression choices. However, it does rather more than that, together with line and bar charts with legends and tooltips. GitHub hrbrmstr/taucharts.
    See the writer’s put up on RPubs
    Bob Rudis
    RColorBrewer
    information visualization
    Not a designer? RColorBrewer helps you choose shade palettes to your visualizations. CRAN.
    See Jennifer Bryan’s tutorial
    Erich Neuwirth
    paletteer
    information visualization
    This package deal is a set of dozens of R shade palettes, all with a standard interface. Helpful if you wish to transfer past built-in and RColorBrewer choices.
    Make essentially the most of R colours and palettes or see the paletteer package deal website for examples on accessing palettes and utilizing them with ggplot2.
    Emil Hvitfeldt
    sf
    mapping, information wrangling
    This package deal makes it a lot simpler to do GIS work in R. Simple options protocols make geospatial information look lots like common information frames, whereas numerous features enable for evaluation similar to figuring out whether or not factors are in a polygons. A GIS game-changer for R. CRAN.
    See the package deal vignettes, beginning with the introduction, Simple Features for R.
    Edzer Pebesma & others
    leaflet
    mapping
    Map information utilizing the Leaflet JavaScript library inside R. GitHub rstudio/leaflet.
    See my tutorial or the package deal web site
    RStudio
    tidygeocoder
    mapping
    This is my new geocoding go-to. It helps greater than a dozen totally different geocoding companies and returns ends in an instantly usable tibble format. Plus it provides reverse and batch geocoding in addition to getting lat/lengthy for an tackle.
    mydata %>%; geocode(address_col, technique = ‘osm’, lat = latitude , lengthy = longitude)See the getting began vignette
    Jesse Cambon & others
    tmap & tmaptools
    mapping
    This package deal provide a simple strategy to learn in form recordsdata and be a part of information recordsdata with geographic information, in addition to do some exploratory mapping. Recent performance provides assist for easy options, interactive maps and creating leaflet objects. Plus, tmaptools::palette_explorer() is a superb device for choosing ColorBrewer palettes. CRAN.
    See the package deal vignette or my mapping in R tutorial
    Martijn Tennekes
    colourpicker
    information visualization
    The package deal’s RStudio add-in makes it straightforward to flick thru and choose R’s built-in colours, or get hex codes for customized colours not accessible by identify. The plotHelper() perform lets you choose colours and see how they’d look on a scatter plot. CRAN.
    See the GitHub repo.
    Dean Attali
    mapsapi
    mapping, information wrangling
    This interface to the Google Maps Direction and Distance Matrix APIs allow you to analyze and map distances and driving routes. CRAN.
    google_directions( origin = c(my_longitude, my_latitude),vacation spot = c(my_address),alternate options = TRUEAlso see the vignette
    Michael Dorman
    tidycensus
    mapping, information wrangling
    Want to research and map U.S. Census Bureau information from 5-year American Community Surveys or 10-year censuses? This makes it straightforward to obtain numerical and geospatial information in R-ready format. You may also use it to obtain US, state, or native shapefiles for different use. CRAN.
    See Basic utilization of tidycensus and the writer’s on-line ebook Analyzing US Census Data.
    Kyle E. Walker
    albersusa
    mapping
    Do it is advisable make a map of the US with Alaska and Hawaii insets? This package deal provides one of many easiest methods to get a well-designed shapefile. GitHub hrbrmstr/albersusa.
    us_sf <- usa_sf(“lcc”) %>%mutate(State = as.character(identify))See the package deal GitHub repo.
    Bob Rudis
    glue
    information wrangling
    Main perform, additionally glue, evaluates variables and R expressions inside a quoted string, so long as they’re enclosed by {} braces. This makes for a chic paste() substitute. CRAN.
    glue(“Today is {Sys.Date()}”)
    Jim Hester
    googleanalyticsR
    Web analytics
    Pull information from Google Analytics, together with GA’s model 4 API. Also has anti-sampling choices. CRAN.
    See package deal web site.
    Mark Edmonson
    roxygen2
    package deal improvement
    Useful instruments for documenting features inside R packages. CRAN.
    How to write down an R package deal or the roxygen2 introductory vignette.
    Hadley Wickham & others
    shiny
    information visualization
    Turn R information into interactive Web functions with this framework for R. CRAN.
    See the tutorial or my Create a Shiny app to look Twitter
    RStudio
    shinyjs
    information visualization
    Includes a number of features so as to add sophistication to your UI similar to hiding and exhibiting inputs and reset enter values. Was initially revealed with industrial use restrictions however now could be beneath an MIT license.
    See the get began information
    Dean Attali
    flexdashboard
    information visualization
    If Shiny is simply too complicated and concerned to your wants, this package deal provides a less complicated (if considerably much less strong) resolution primarily based on R Markdown. CRAN.
    More information in Using flexdashboard
    JJ Allaire, RStudio & others
    openxlsx
    misc
    If it is advisable write to an Excel file in addition to learn, this package deal is simple to make use of and provides plenty of choices for formatting your spreadsheet. CRAN.
    write.xlsx(mydf, “myfile.xlsx”)
    Alexander Walker
    gmodels
    information wrangling, information evaluation
    There are a number of features for modeling information right here, however the one I exploit, CrossTable, merely creates cross-tabs with a great deal of choices — totals, proprotions and a number of other statistical checks. CRAN.
    CrossTable(myxvector, myyvector, prop.t=FALSE, prop.chisq = FALSE)
    Gregory R. Warnes
    janitor
    information wrangling, information evaluation
    Basic information cleansing made straightforward, similar to discovering duplicates by a number of columns, making R-friendly column names and eradicating empty columns. It additionally has some good tabulating instruments, like including a complete row, in addition to producing tables with percentages and straightforward crosstabs. And, its get_dupes() perform is a chic means of discovering duplicate rows in information frames, both primarily based on one column, a number of columns, or complete rows. CRAN.
    tabyl(mydf, type = TRUE) %>% adorn_totals(“row”)
    Samuel Firke
    scales
    information wrangling
    While this package deal has many extra subtle methods that will help you format information for graphing, it is value a obtain only for the comma(), %() and greenback() features. CRAN.
    comma(mynumvec)
    Hadley Wickham
    profvis
    programming
    Is your R code sluggish? This package deal offers you a visible consultant of your code line by line so you will discover the velocity bottlenecks. CRAN.
    profvis({ your code right here })
    Winston Chang & others
    tidytext
    textual content mining
    Elegant implementation of textual content mining features utilizing Hadley Wickham’s “tidy data” rules. CRAN.
    See tidytextmining.com for quite a few examples.
    Julia Silge & David Robinson
    diffobj
    information evaluation
    Base R’s equivalent() perform tells you whether or not or not two objects are the identical; but when they don’t seem to be, it will not let you know why. diffobj offers you a visible illustration of how two R objects differ. CRAN.
    diffObj(x,y)
    Brodie Gaslam & Michael B. Allen
    prophet
    forecasting
    I do not do a lot forecasting evaluation; but when I did, I’d begin with this package deal. CRAN.
    See the Quick begin information.
    Sean Taylor & Ben Letham at Facebook
    tidymodels
    forecasting
    For significantly extra strong forecasting, try this suite of modeling packages. Somewhat steep studying curve. CRAN.
    See the Getting began information or this workshop repo from Thomas Mock.
    Numerous
    arrow
    information import, information export
    R implementation of the cross-language platform for in-memory information with columns. Includes features to learn and write Parquet and Feather recordsdata in addition to CSV and JSON. CRAN.
    write_feather(mydf, tempfile())
    Neal Richardson & others
    fst
    information import, information export
    Another different for binary file storage (R-only), fst was constructed for quick storage and retrieval, with entry speeds above 1 GB/sec. It additionally provides compression that does not gradual information entry an excessive amount of, in addition to the power to import a selected vary of rows (by row quantity). CRAN.
    write.fst(mydf, “myfile.fst”, 100)
    Mark Klik
    googleAuthR
    information import
    If you need to use information from a Google API in an R challenge and there is not but a selected package deal for that API, that is the place to show for authenticating CRAN.
    See examples on the package deal web site and this gist to be used with Google Calendars. CRAN.
    Mark Edmondson
    devtools
    package deal improvement, package deal set up
    devtools has a slew of features aimed toward serving to you create your personal R packages, similar to routinely working all instance code in your assist recordsdata to ensure every little thing works. Requires Rtools on Windows and XCode on a Mac. CRAN.
    run_examples()
    Hadley Wickham & others
    remotes
    package deal set up
    remotes is a lighter-weight different to devtools if all you need is to put in packages from GitHub, Bitbucket and another sources. CRAN.
    install_github(“mangothecat/franc”)
    Gabor Csardi & others
    githubinstall
    package deal set up
    Do you need to set up a package deal from GitHub however cannot bear in mind the creator’s identify — or simply do not feel like typing it out? With githubinstall, merely run githubinstall(“packagename”) and the perform will recommend an account; you simply reply Y to put in or n if it is the mistaken one. It even consists of fuzzy matching in the event you misspell a package deal identify!
    githubinstall(“AnomalyDetection”)
    Koji Makiyama
    installr
    misc
    Windows solely: Update your put in model of R from inside R. On CRAN.
    updateR()
    Tal Galili & others
    usethis
    package deal improvement, programming
    Initially aimed toward package deal improvement, usethis now consists of helpful features for any coding challenge. Among its useful options are an edit household that allows you to simply replace your .Renvironment and .Rprofile recordsdata. On CRAN, however set up GitHub model from “r-lib/usethis” for contemporary updates.
    edit_r_environ()
    Hadley Wickham, Jennifer Bryan & RStudio
    right here
    misc
    This package deal has one perform with a single, helpful function: discover your challenge’s working listing. Surprisingly useful if you would like your code to run on multiple system. CRAN.
    my_project_directory <- right here()
    Kirill Müller
    pacman
    misc, package deal set up
    This package deal is one other that goals to unravel one downside, and clear up it properly: package deal set up. The primary features will loadi a package deal that is already put in or putting in it first if it is not accessible. While that is definitely potential to do with base R’s require() and an if assertion, p_load() is a lot extra elegant for CRAN packages, or p_load_gh() for GitHub. Other helpful choices embody p_temp(), which permits for a short lived, this-session-only package deal set up. CRAN.
    p_load(dplyr, right here, tidycensus)
    Tyler Rinker
    plumber
    information export, programming
    Turn any R perform right into a host-able API with a line or two of code. This well-thought-out package deal makes it straightforward to make use of R for information dealing with in different, non-R coding initiatives. CRAN.
    See the documentation or my article Create your personal Slack bots — and Web APIs — with R
    Jeff Allen, Trestle Technology & others
    dataCompareR
    information wrangling
    A fast and chic strategy to examine two information frames, both row by row or by a specified key. CRAN.
    rCompare(mydf1, mydf2)
    Rob Noble-Eddy at CapitalOne & others
    cloudyR challenge
    information import, information export
    This is a set of packages aimed toward making it simpler for R to work with cloud platforms similar to Amazon Web Services, Google and Travis-CI. Some are already on CRAN, some will be discovered on GitHub.
    See the record of packages.
    Various
    flyio
    information import, information export
    This is a bit like rio, however for the cloud: It provides a standard set of features whether or not you are utilizing Amazon’s S3 or Google Cloud. Set your information supply, authenticate along with your credentials (which will be saved in an R environmental variable), set a bucket identify, and off you go. GitHub.
    See the GitHub repo or YouTube video of a demo on the Delhi useR meetup.
    SocialCops
    geofacet
    information visualization, mapping
    While I hardly ever have to create “geofacets” — maps with same-sized blocks in geospatially acceptable areas — this package deal is so cool that I needed to embody it. The package deal enables you to create your personal geofacet visualizations utilizing ggplot2 and built-in grids similar to US states and EU nations. And, it comes with design-your-own geofacet grid capabilities. CRAN.
    grid_design()
    Ryan Hafen
    reticulate
    programming
    If you already know Python in addition to R, this package deal provides a set of instruments for calling Python from inside R, in addition to “translating” between R and Python objects similar to Pandas information frames and R information frames. CRAN.
    See How to run Python in R or the reticulate package deal web site.
    JJ Allaire
    beepr
    misc
    This is just about pure enjoyable. Yes, getting an audible notification when code finishes working or encounters an error might be helpful; however right here, the accessible sounds embody choices like a fanfare flourish, a Mario Brothers tune, and even a scream. CRAN.
    beep(“wilhelm”)
    Rasmus Bååth

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