Recoding data

Problem

You want to recode data or calculate new data columns from existing ones.

Solution

The examples below will use this data:

  1. data <- read.table(header=T, text='
  2.  subject sex control cond1 cond2
  3.        1   M     7.9  12.3  10.7
  4.        2   F     6.3  10.6  11.1
  5.        3   F     9.5  13.1  13.8
  6.        4   M    11.5  13.4  12.9
  7. ')

Recoding a categorical variable

The easiest way is to use revalue() or mapvalues() from the plyr package.This will code M as 1 and F as 2, and put it in a new column.Note that these functions preserves the type: if the input is a factor, the output will be a factor; and if the input is a character vector, the output will be a character vector.

  1. library(plyr)
  3. # The following two lines are equivalent:
  4. data$scode <- revalue(data$sex, c("M"="1", "F"="2"))
  5. data$scode <- mapvalues(data$sex, from = c("M", "F"), to = c("1", "2"))
  6. data
  7. #>   subject sex control cond1 cond2 scode
  8. #> 1       1   M     7.9  12.3  10.7     1
  9. #> 2       2   F     6.3  10.6  11.1     2
  10. #> 3       3   F     9.5  13.1  13.8     2
  11. #> 4       4   M    11.5  13.4  12.9     1
  13. # data$sex is a factor, so data$scode is also a factor

See ../Mapping vector values and ../Renaming levels of a factor for more information about these functions.

If you don’t want to rely on plyr, you can do the following with R’s built-in functions:

  1. data$scode[data$sex=="M"] <- "1"
  2. data$scode[data$sex=="F"] <- "2"
  4. # Convert the column to a factor
  5. data$scode <- factor(data$scode)
  6. data
  7. #>   subject sex control cond1 cond2 scode
  8. #> 1       1   M     7.9  12.3  10.7     1
  9. #> 2       2   F     6.3  10.6  11.1     2
  10. #> 3       3   F     9.5  13.1  13.8     2
  11. #> 4       4   M    11.5  13.4  12.9     1

Another way to do it is to use the match function:

  1. oldvalues <- c("M", "F")
  2. newvalues <- factor(c("g1","g2"))  # Make this a factor
  4. data$scode <- newvalues[ match(data$sex, oldvalues) ]
  5. data
  6. #>   subject sex control cond1 cond2 scode
  7. #> 1       1   M     7.9  12.3  10.7    g1
  8. #> 2       2   F     6.3  10.6  11.1    g2
  9. #> 3       3   F     9.5  13.1  13.8    g2
  10. #> 4       4   M    11.5  13.4  12.9    g1

Recoding a continuous variable into categorical variable

Mark those whose control measurement is <7 as “low”, and those with >=7 as “high”:

  1. data$category[data$control< 7] <- "low"
  2. data$category[data$control>=7] <- "high"
  4. # Convert the column to a factor
  5. data$category <- factor(data$category)
  6. data
  7. #>   subject sex control cond1 cond2 scode category
  8. #> 1       1   M     7.9  12.3  10.7    g1     high
  9. #> 2       2   F     6.3  10.6  11.1    g2      low
  10. #> 3       3   F     9.5  13.1  13.8    g2     high
  11. #> 4       4   M    11.5  13.4  12.9    g1     high

With the cut function, you specify boundaries and the resulting values:

  1. data$category <- cut(data$control,
  2.                      breaks=c(-Inf, 7, 9, Inf),
  3.                      labels=c("low","medium","high"))
  4. data
  5. #>   subject sex control cond1 cond2 scode category
  6. #> 1       1   M     7.9  12.3  10.7    g1   medium
  7. #> 2       2   F     6.3  10.6  11.1    g2      low
  8. #> 3       3   F     9.5  13.1  13.8    g2     high
  9. #> 4       4   M    11.5  13.4  12.9    g1     high

By default, the ranges are open on the left, and closed on the right, as in (7,9]. To set it so that ranges are closed on the left and open on the right, like [7,9), use right=FALSE.

Calculating a new continuous variable

Suppose you want to add a new column with the sum of the three measurements.

  1. data$total <- data$control + data$cond1 + data$cond2
  2. data
  3. #>   subject sex control cond1 cond2 scode category total
  4. #> 1       1   M     7.9  12.3  10.7    g1   medium  30.9
  5. #> 2       2   F     6.3  10.6  11.1    g2      low  28.0
  6. #> 3       3   F     9.5  13.1  13.8    g2     high  36.4
  7. #> 4       4   M    11.5  13.4  12.9    g1     high  37.8