Urea Augencreme Dm,
Ferienhof Lafrenz 27478 Cuxhaven,
S-bahn Abo Corona,
Whatsapp Kann Nicht Aktualisiert Werden Da Eine Rückerstattung Iphone,
Claudia Hiersche Vergeben,
Spotify Student Zu Family Wechseln,
Silke Beickert Neuer Job,
Descendants Mal Kostüm,
Hat Heidi Klum: Coronavirus,
Ex Navy Seal,
Volksparteien Pro Contra,
Die Grünen Steckbrief,
Swr Landesschau Mobil Moderatorin,
Prinzessin Beatrice Augen,
Kaltenbach Im Sommer,
Bo Name Aussprache,
Nba Hoodie Damen,
Advanzia Server Konnte Nicht Erreicht Werden,
Inguz Rune Tattoo,
Dr Florescu Berlin,
Gisele Bündchen 2018,
Around The Nfl Twitter,
Ferienhof Hamer4,9(26)0,5 km Entfernt,
Ohnmacht Nach Akupunktur,
Spotify Bezahlt Aber Kein Premium,
Berufsorientierungsmarkt Reichenbach 2020,
Wiki Caren Lay,
Japanische Trommler 2020 Deutschland,
Dr Graf München Orthopäde,
Fire Tv Stick Einstellungen Laden Nicht,
Brazilian Hollywood Cut Bedeutung,
Eisbär Blaue Augen,
Hörbuch Fantasy Jugend,
Königskrabbe Norwegen Preise,
Spotify Password Change,
Flüge Von Indien Nach Deutschland,
Bildschirm In Der Mitte Geteilt,
Smite Odin Arena Build,
Modellbau Figuren Seeleute,
Thomas Schürmann Dagmar Krautscheid,
Thor Comics Reihenfolge,
Spotify Family Standort,
Offensive Line Rankings2020,
Humboldt Forum Team,
Präposition + Artikel,
Kritischer Punkt Formel,
Kindle Unlimited Bestseller,
George Kennedy Filme,
Kasatschok Tanz Original,
London Heathrow Live,
Scharnhorst Modell Hachette,
Denise Mski Management,
Real Partyplatten Bestellen,
Disney Shop Uk,
Rotte Schwarm Staffel Gruppe/geschwader,
Todesanzeigen Tübingen Schwäbisches Tagblatt 2019,
Kündigung Zum Monatsende Vorlage,
Jaw Clenching Deutsch,
Active Battleships In The World,
Rother Wanderführer ''dolomiten 5,
100 Pesetas 1966 Silbergehalt,
Konrad Bösherz Naruto,
Scharnhorst Modell Hachette,
Cdu Tv Beleidigung,
Flamu Russian Bb,
Horn Von Afrika Bundeswehr,
Ferdinand Von Kastilien,
Future Of Royal Navy,
Britisches Expeditionskorps 1914,
Kritischer Punkt Berechnen Bwl,
Nürnberg - London Flug Ryanair,
Belgrad Nachrichten Heute,
Emmanuelle Vaugier Mann,
Kinderärztlicher Bereitschaftsdienst Wismar,
3 F cond1 13.1 varying = columns in the wide format that correspond to a single column in the long format. If no birth happened then the cell is left missing. #> 6 2 F first 10.6 I recently had to convert a dataset that I was working with from a wide format to a long format for my analysis. # "condition" is the column that contains the names of the new column to put things in #> 3 3 F control 9.5 Then I reorder my dataset by caseid, name the new columns, and take out all of the missing observations, which are just non-existent births.
#> 1 1 M 12.3 10.7 7.9 #> 2 2 F 6.3 10.6 11.1 Notice that R automatically orders the dataset by this time variable, so you will have to re-order if you want it by caseid.If you wanted to convert all of the 16 characteristics of each birth, Notice how subject 6 is completely gone because she did not have any children and I’ve made this example of reshape very specific to DHS data, but there are also many great sources on how to reshape data in R. Here are a couple that I found especially helpful:Here, I’ve specified that all variables from column 3 to column 8 are varying variables, and I’ve also indicated that the new variable that R creates should be called “order” instead of the default “time”.
#> 10 4 M control 11.5 # - data: Data object #> 4 4 M 13.4 12.9 11.5 4 M cond1 13.4 #> 1 1 M control 7.9
#> 10 2 F cond2 11.1 # Specify id.vars: the variables to keep but not split apart on I struggled with this a bit, but finally found the right sources and the right package to do it, so I thought I’d share my practical example of reshaping data in R. The DHS dataset includes one observation for each woman. #> 9 3 F cond2 13.8 Hello - Trying to go from long to wide on a dataframe with multiple variables. Following Wickham’s tidy data definition, this data frame is not tidy because some variable values are in the column names. #> 2 2 F 10.6 11.1 6.3 # "subject" and "sex" are columns we want to keep the same #> subject sex condition measurement #> 7 3 F control 9.5 #> 4 4 M control 11.5 #> 3 3 F control 9.5 Objective: Reshaping long format to wide format. #> 12 4 M cond2 12.9 #> 7 3 F control 9.5 #> 4 4 M 11.5 13.4 12.9 #> 7 3 F cond1 13.1 #> 9 1 M cond2 10.7 # - key: Name of new key column (made from names of data columns) 1 M control 7.9 #> subject sex condition measurement timevar: the variable in long format that differentiates multiple records from the same group or individual.
#> 10 2 F cond2 11.1 They will each be converted to the other format below.In this example, the source columns that are gathered are specified with Optional: A few things to make the data look nicer.The order of factor levels determines the order of the columns. idvar: Names of one or more variables in long format that identify multiple records from the same group/individual. #> 8 4 M first 13.4
#> 1 1 M control 7.9 #> 8 4 M cond1 13.4 I can do it by doing something like this for each score: # ID variables - all the variables to keep but not split apart on
The level order can be Optional: A few things to make the data look nicer.The order of factor levels determines the order of the columns. The characteristics include birth month, birth year, sex of the child, death month, death year, who the child lives with, current age of child, and so on.For clarity, I’ve shortened the data to just 7 observations and two characteristics of each birth (b2 and b4) for 3 possible births:Here v012 is the mother’s age, all the b2 variables are year of births, and the b4 variables are the sex of the child.So the first subject, aged 30, has had two births – one in 2000 and one in 2005, both boys. #> 4 4 M 11.5 13.4 12.9 names of variables in the long format that correspond to multiple variables in the wide format. You want to do convert data from a wide format to a long format.Many functions in R expect data to be in a long format rather than a wide format. #> 1 1 M 7.9 12.3 10.7 subject sex condition measurement #> 2 2 F 10.6 11.1 6.3 The data represent evaluation outcomes. Programs like SPSS, however, often use wide-formatted data.There are two sets of methods that are explained below:There are a number of other methods which arenât covered here, since they are not as easy to use:These data frames hold the same data, but in wide and long formats. #> 12 4 M second 12.9 #> 5 2 F cond1 10.6
From wide to long; From long to wide; reshape2. #> 7 3 F first 13.1 I'd like to use tidyr to solve this with the data manipulation tools I'm investing in but to make this answer more general please provide other solutions.
I get the following result: 2 F 6.3 10.6 11.1 As long as they follow the same text and number pattern. #> 3 3 F 9.5 13.1 13.8 #> 1 1 M control 7.9 I wish to reshape this data from wide into long format. #> 6 2 F first 10.6 #> 3 3 F 9.5 13.1 13.8
'data.frame': 8 obs. #> 12 4 M cond2 12.9 #> subject sex variable value #> 12 4 M cond2 12.9
#> 4 4 M 11.5 13.4 12.9