Generate a dummy-variable

Question

I have trouble generating the following dummy-variables in R     I m analyzing yearly time series data  time period 1948-2009   I have two questions    How do I generate a dummy variable for observation  10  i e  for year 1957  value   1 at 1957 and zero otherwise   How do I generate a dummy variable which is zero before 1957 and takes the value 1 from 1957 and onwards to 2009

User · Answer

Hi i wrote this general function to generate a dummy variable which essentially replicates the replace function in Stata.

If x is the data frame is x and i want a dummy variable called a which will take value 1 when x$b takes value c

introducedummy<-function(x,a,b,c){
   g<-c(a,b,c)
  n<-nrow(x)
  newcol<-g[1]
  p<-colnames(x)
  p2<-c(p,newcol)
  new1<-numeric(n)
  state<-x[,g[2]]
  interest<-g[3]
  for(i in 1:n){
    if(state[i]==interest){
      new1[i]=1
    }
    else{
      new1[i]=0
    }
  }
    x$added<-new1
    colnames(x)<-p2
    x
  }

User · Answer

another way you can do it is use   ifelse year  lt  1965   1  0

User · Answer

I read this on the kaggle forum     Generate example dataframe with character column example  lt - as data frame c  A    A    B    F    C    G    C    D    E    F    names example   lt -  strcol    For every unique value in the string column  create a new 1 0 column  This is what Factors do  under-the-hood  automatically when passed to function requiring numeric data for level in unique example strcol      example paste  dummy   level  sep          lt - ifelse example strcol    level  1  0

User · Answer

For the usecase as presented in the question  you can also just multiply the logical condition with 1  or maybe even better  with 1L      example data df1  lt - data frame yr   1951 1960     create the dummies df1 is 1957  lt - 1L    df1 yr    1957  df1 after 1957  lt - 1L    df1 yr  gt   1957    which gives     gt  df1      yr is 1957 after 1957 1  1951       0          0 2  1952       0          0 3  1953       0          0 4  1954       0          0 5  1955       0          0 6  1956       0          0 7  1957       1          1 8  1958       0          1 9  1959       0          1 10 1960       0          1      For the usecases as presented in for example the answers of  zx8754 and  Sotos  there are still some other options which haven t been covered yet imo   1  Make your own make dummies-function    example data df2  lt - data frame id   1 5  year   c 1991 1994 1992      create a function make dummies  lt - function v  prefix           s  lt - sort unique v     d  lt - outer v  s  function v  s  1L    v    s     colnames d   lt - paste0 prefix  s    d      bind the dummies to the original dataframe cbind df2  make dummies df2 year  prefix    y      which gives      id year y1991 y1992 y1993 y1994 1  1 1991     1     0     0     0 2  2 1992     0     1     0     0 3  3 1993     0     0     1     0 4  4 1994     0     0     0     1 5  5 1992     0     1     0     0    2  use the dcast-function from either data table or reshape2   dcast df2  id   year   year  fun aggregate   length    which gives      id year 1991 1992 1993 1994 1  1 1991    1    0    0    0 2  2 1992    0    1    0    0 3  3 1993    0    0    1    0 4  4 1994    0    0    0    1 5  5 1992    0    1    0    0    However  this will not work when there are duplicate values in the column for which the dummies have to be created  In the case a specific aggregation function is needed for dcast and the result of of dcast need to be merged back to the original     example data df3  lt - data frame var   c  B    C    A    B    C       aggregation function to get dummy values f  lt - function x  as integer length x   gt  0     reshape to wide with the cumstom aggregation function and merge back to the original merge df3  dcast df3  var   var  fun aggregate   f   by    var   all x   TRUE    which gives  note that the result is ordered according to the by column       var A B C 1   A 1 0 0 2   B 0 1 0 3   B 0 1 0 4   C 0 0 1 5   C 0 0 1    3  use the spread-function from tidyr  with mutate from dplyr   library dplyr  library tidyr   df2   gt      mutate v   1  yr   year    gt      spread yr  v  fill   0    which gives      id year 1991 1992 1993 1994 1  1 1991    1    0    0    0 2  2 1992    0    1    0    0 3  3 1993    0    0    1    0 4  4 1994    0    0    0    1 5  5 1992    0    1    0    0

User · Answer

We can also use cSplit e from splitstackshape  Using  zx8754 s data  df1  lt - data frame id   1 4  year   1991 1994  splitstackshape  cSplit e df1   year   fill   0      id year year 1 year 2 year 3 year 4  1  1 1991      1      0      0      0  2  2 1992      0      1      0      0  3  3 1993      0      0      1      0  4  4 1994      0      0      0      1   To make it work for data other than numeric we need to specify type as  character  explicitly  df1  lt - data frame id   1 4  let   LETTERS 1 4   splitstackshape  cSplit e df1   let   fill   0  type    character       id let let A let B let C let D  1  1   A     1     0     0     0  2  2   B     0     1     0     0  3  3   C     0     0     1     0  4  4   D     0     0     0     1

User · Answer

I use such a function  for data table       Ta funkcja dla obiektu data table i zmiennej var name typu factor tworzy dummy variables o nazwach  var name   level1   factorToDummy  lt - function dtable  var name     stopifnot is data table dtable     stopifnot var name  in  names dtable     stopifnot is factor dtable   get var name        dtable   paste0 var name      levels get var name     - gt  new names   dtable    new names     transpose lapply get var name   FUN   function x  x    levels get var name           cat paste   nDodano zmienne dummy     paste0 new names  collapse               Usage   data  lt - data table data  data   x   droplevels x   factorToDummy data   x

User · Answer

What I normally do to work with this kind of dummy variables is    1  how do I generate a dummy variable for observation  10  i e  for year 1957  value   1 at 1957 and zero otherwise   data factor year 1  lt - factor   with   data  ifelse     year    1957    1   0          2  how do I generate a dummy-variable which is zero before 1957 and takes the value 1 from 1957 and onwards to 2009   data factor year 2  lt - factor   with   data  ifelse     year  lt  1957    0   1         Then  I can introduce this factor as a dummy variable in my models  For example  to see whether there is a long-term trend in a varible y     summary   lm   y   t   data   data       Hope this helps

User · Answer

Convert your data to a data table and use set by reference and row filtering  library data table   dt  lt - as data table your dataframe or whatever  dt   is 1957    0  dt year    1957  is 1957    1    Proof-of-concept toy example   library data table   dt  lt - as data table cbind c 1  1  1   c 2  2  3    dt   is 3    0  dt V2    3  is 3    1

User · Answer

If you want to get K dummy variables  instead of K-1  try     dummies   table 1 length year  as factor year       Best

User · Answer

Another way is to use mtabulate from qdapTools package  i e   df  lt - data frame var   sample c  A    B    C    5  replace   TRUE     var  1   C  2   A  3   C  4   B  5   B  library qdapTools  mtabulate df var    which gives      A B C 1 0 0 1 2 1 0 0 3 0 0 1 4 0 1 0 5 0 1 0

User · Answer

The ifelse function is best for simple logic like this    gt  x  lt - seq 1950  1960  1       ifelse x    1957  1  0      ifelse x  lt   1957  1  0    gt    1  0 0 0 0 0 0 0 1 0 0 0  gt    1  1 1 1 1 1 1 1 1 0 0 0   Also  if you want it to return character data then you can do so    gt  x  lt - seq 1950  1960  1       ifelse x    1957   foo    bar       ifelse x  lt   1957   foo    bar     gt    1   bar   bar   bar   bar   bar   bar   bar   foo   bar   bar   bar   gt    1   foo   foo   foo   foo   foo   foo   foo   foo   bar   bar   bar    Categorical variables with nesting      gt  x  lt - seq 1950  1960  1       ifelse x    1957   foo   ifelse x    1958   bar   baz      gt    1   baz   baz   baz   baz   baz   baz   baz   foo   bar   baz   baz    This is the most straightforward option

User · Answer

Another option that can work better if you have many variables is factor and model matrix    gt  year f   factor year   gt  dummies   model matrix  year f    This will include an intercept column  all ones  and one column for each of the years in your data set except one  which will be the  default  or intercept value   You can change how the  default  is chosen by messing with contrasts arg in model matrix   Also  if you want to omit the intercept  you can just drop the first column or add  0 to the end of the formula   Hope this is useful

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This one liner in base R  model matrix    iris Species - 1    gives      iris Speciessetosa iris Speciesversicolor iris Speciesvirginica 1                    1                      0                     0 2                    1                      0                     0 3                    1                      0                     0 4                    1                      0                     0 5                    1                      0                     0 6                    1                      0                     0 7                    1                      0                     0 8                    1                      0                     0 9                    1                      0                     0 10                   1                      0                     0 11                   1                      0                     0 12                   1                      0                     0 13                   1                      0                     0 14                   1                      0                     0 15                   1                      0                     0 16                   1                      0                     0 17                   1                      0                     0 18                   1                      0                     0 19                   1                      0                     0 20                   1                      0                     0 21                   1                      0                     0 22                   1                      0                     0 23                   1                      0                     0 24                   1                      0                     0 25                   1                      0                     0 26                   1                      0                     0 27                   1                      0                     0 28                   1                      0                     0 29                   1                      0                     0 30                   1                      0                     0 31                   1                      0                     0 32                   1                      0                     0 33                   1                      0                     0 34                   1                      0                     0 35                   1                      0                     0 36                   1                      0                     0 37                   1                      0                     0 38                   1                      0                     0 39                   1                      0                     0 40                   1                      0                     0 41                   1                      0                     0 42                   1                      0                     0 43                   1                      0                     0 44                   1                      0                     0 45                   1                      0                     0 46                   1                      0                     0 47                   1                      0                     0 48                   1                      0                     0 49                   1                      0                     0 50                   1                      0                     0 51                   0                      1                     0 52                   0                      1                     0 53                   0                      1                     0 54                   0                      1                     0 55                   0                      1                     0 56                   0                      1                     0 57                   0                      1                     0 58                   0                      1                     0 59                   0                      1                     0 60                   0                      1                     0 61                   0                      1                     0 62                   0                      1                     0 63                   0                      1                     0 64                   0                      1                     0 65                   0                      1                     0 66                   0                      1                     0 67                   0                      1                     0 68                   0                      1                     0 69                   0                      1                     0 70                   0                      1                     0 71                   0                      1                     0 72                   0                      1                     0 73                   0                      1                     0 74                   0                      1                     0 75                   0                      1                     0 76                   0                      1                     0 77                   0                      1                     0 78                   0                      1                     0 79                   0                      1                     0 80                   0                      1                     0 81                   0                      1                     0 82                   0                      1                     0 83                   0                      1                     0 84                   0                      1                     0 85                   0                      1                     0 86                   0                      1                     0 87                   0                      1                     0 88                   0                      1                     0 89                   0                      1                     0 90                   0                      1                     0 91                   0                      1                     0 92                   0                      1                     0 93                   0                      1                     0 94                   0                      1                     0 95                   0                      1                     0 96                   0                      1                     0 97                   0                      1                     0 98                   0                      1                     0 99                   0                      1                     0 100                  0                      1                     0 101                  0                      0                     1 102                  0                      0                     1 103                  0                      0                     1 104                  0                      0                     1 105                  0                      0                     1 106                  0                      0                     1 107                  0                      0                     1 108                  0                      0                     1 109                  0                      0                     1 110                  0                      0                     1 111                  0                      0                     1 112                  0                      0                     1 113                  0                      0                     1 114                  0                      0                     1 115                  0                      0                     1 116                  0                      0                     1 117                  0                      0                     1 118                  0                      0                     1 119                  0                      0                     1 120                  0                      0                     1 121                  0                      0                     1 122                  0                      0                     1 123                  0                      0                     1 124                  0                      0                     1 125                  0                      0                     1 126                  0                      0                     1 127                  0                      0                     1 128                  0                      0                     1 129                  0                      0                     1 130                  0                      0                     1 131                  0                      0                     1 132                  0                      0                     1 133                  0                      0                     1 134                  0                      0                     1 135                  0                      0                     1 136                  0                      0                     1 137                  0                      0                     1 138                  0                      0                     1 139                  0                      0                     1 140                  0                      0                     1 141                  0                      0                     1 142                  0                      0                     1 143                  0                      0                     1 144                  0                      0                     1 145                  0                      0                     1 146                  0                      0                     1 147                  0                      0                     1 148                  0                      0                     1 149                  0                      0                     1 150                  0                      0                     1

User · Answer

The other answers here offer direct routes to accomplish this task   one that many models  e g  lm  will do for you internally anyway  Nonetheless  here are ways to make dummy variables with Max Kuhn s popular caret and recipes packages  While somewhat more verbose  they both scale easily to more complicated situations  and fit neatly into their respective frameworks     caret  dummyVars  With caret  the relevant function is dummyVars  which has a predict method to apply it on a data frame     df  lt - data frame letter   rep c  a    b    c    each   2                    y   1 6   library caret   dummy  lt - dummyVars      data   df  fullRank   TRUE   dummy   gt  Dummy Variable Object   gt     gt  Formula       gt  2 variables  1 factors   gt  Variables and levels will be separated by       gt  A full rank encoding is used  predict dummy  df    gt    letter b letter c y   gt  1        0        0 1   gt  2        0        0 2   gt  3        1        0 3   gt  4        1        0 4   gt  5        0        1 5   gt  6        0        1 6     recipes  step dummy  With recipes  the relevant function is step dummy   library recipes   dummy recipe  lt - recipe y   letter  df    gt        step dummy letter   dummy recipe   gt  Data Recipe   gt     gt  Inputs    gt     gt        role  variables   gt     outcome          1   gt   predictor          1   gt     gt  Steps    gt     gt  Dummy variables from letter   Depending on context  extract the data with prep and either bake or juice     Prep and bake on new data    dummy recipe   gt        prep     gt        bake df    gt    A tibble  6 x 3   gt        y letter b letter c   gt     lt int gt      lt dbl gt      lt dbl gt    gt  1     1        0        0   gt  2     2        0        0   gt  3     3        1        0   gt  4     4        1        0   gt  5     5        0        1   gt  6     6        0        1       or use  retain   TRUE  and  juice  to extract training data dummy recipe   gt        prep retain   TRUE    gt        juice     gt    A tibble  6 x 3   gt        y letter b letter c   gt     lt int gt      lt dbl gt      lt dbl gt    gt  1     1        0        0   gt  2     2        0        0   gt  3     3        1        0   gt  4     4        1        0   gt  5     5        0        1   gt  6     6        0        1

User · Answer

Package mlr includes createDummyFeatures for this purpose   library mlr  df  lt - data frame var   sample c  A    B    C    10  replace   TRUE   df       var   1    B   2    A   3    C   4    B   5    C   6    A   7    C   8    A   9    B   10   C  createDummyFeatures df  cols    var         var A var B var C   1      0     1     0   2      1     0     0   3      0     0     1   4      0     1     0   5      0     0     1   6      1     0     0   7      0     0     1   8      1     0     0   9      0     1     0   10     0     0     1   createDummyFeatures drops original variable     https   www rdocumentation org packages mlr versions 2 9 topics createDummyFeatures

User · Answer

The simplest way to produce these dummy variables is something like the following    gt  print year   1  1956 1957 1957 1958 1958 1959  gt  dummy  lt - as numeric year    1957   gt  print dummy   1  0 1 1 0 0 0  gt  dummy2  lt - as numeric year  gt   1957   gt  print dummy2   1  0 1 1 1 1 1   More generally  you can use ifelse to choose between two values depending on a condition  So if instead of a 0-1 dummy variable  for some reason you wanted to use  say  4 and 7  you could use ifelse year    1957  4  7

User · Answer

Using dummies  dummy     library dummies     example data df1  lt - data frame id   1 4  year   1991 1994   df1  lt - cbind df1  dummy df1 year  sep          df1     id year df1 1991 df1 1992 df1 1993 df1 1994   1  1 1991        1        0        0        0   2  2 1992        0        1        0        0   3  3 1993        0        0        1        0   4  4 1994        0        0        0        1

[r] Generate a dummy-variable

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