Combine data frames with columns of class `labelled`
rbind_labelled(..., labels = NULL, warn = TRUE)data frames to bind together, potentially with columns of class "labelled". The first argument can be a list of data frames, similar to `plyr::rbind.fill`.
A named list providing vectors of value labels or describing how to handle columns of class `labelled`. See details for usage.
Logical indicating to warn if combining variables with different value labels. Defaults to TRUE.
A data frame.
The argument `labels` provides options for how to handle binding of columns of class `labelled`. Typical use is to provide a named list with elements for each labelled column. Elements of the list are either a vector of labels that should be applied to the column or the character string "concatenated", which indicates that labels should be concatenated such that all unique labels are distinct values in the combined vector. This is accomplished by converting to character strings, binding, and then casting back to labelled. For labelled columns for which labels are not provided in the `label` argument, the default behaviour is that the labels from the first data frame with labels for that column are inherited by the combined data.
See examples.
df1 <- data.frame(
area = haven::labelled(c(1L, 2L, 3L), c("reg 1"=1,"reg 2"=2,"reg 3"=3)),
climate = haven::labelled(c(0L, 1L, 1L), c("cold"=0,"hot"=1))
)
df2 <- data.frame(
area = haven::labelled(c(1L, 2L), c("reg A"=1, "reg B"=2)),
climate = haven::labelled(c(1L, 0L), c("cold"=0, "warm"=1))
)
# Default: all data frames inherit labels from first df. Incorrect if
# "reg 1" and "reg A" are from different countries, for example.
dfA <- rbind_labelled(df1, df2)
#> Warning: Some variables have non-matching value labels: area, climate.
#> Inheriting labels from first data frame with labels.
haven::as_factor(dfA)
#> area climate
#> 1 reg 1 cold
#> 2 reg 2 hot
#> 3 reg 3 hot
#> 4 reg 1 hot
#> 5 reg 2 cold
# Concatenate value labels for "area". Regions are coded separately,
# and original integer values are lost (by necessity of more levels now).
# For "climate", codes "1 = hot" and "1 = warm", are coded as the same
# outcome, inheriting "1 = hot" from df1 by default.
dfB <- rbind_labelled(df1, df2, labels=list(area = "concatenate"))
#> Warning: Some variables have non-matching value labels: climate.
#> Inheriting labels from first data frame with labels.
dfB
#> area climate
#> 1 1 0
#> 2 2 1
#> 3 3 1
#> 4 4 1
#> 5 5 0
haven::as_factor(dfB)
#> area climate
#> 1 reg 1 cold
#> 2 reg 2 hot
#> 3 reg 3 hot
#> 4 reg A hot
#> 5 reg B cold
# We can specify to code as "1=warm/hot" rather than inheriting "hot".
dfC <- rbind_labelled(df1, df2,
labels=list(area = "concatenate", climate = c("cold"=0, "warm/hot"=1)))
dfC$climate
#> <labelled<integer>[5]>
#> [1] 0 1 1 1 0
#>
#> Labels:
#> value label
#> 0 cold
#> 1 warm/hot
haven::as_factor(dfC)
#> area climate
#> 1 reg 1 cold
#> 2 reg 2 warm/hot
#> 3 reg 3 warm/hot
#> 4 reg A warm/hot
#> 5 reg B cold
# Or use `climate="concatenate"` to code "warm" and "hot" as different.
dfD <- rbind_labelled(df1, df2,
labels=list(area = "concatenate", climate="concatenate"))
dfD
#> area climate
#> 1 1 1
#> 2 2 2
#> 3 3 2
#> 4 4 3
#> 5 5 1
haven::as_factor(dfD)
#> area climate
#> 1 reg 1 cold
#> 2 reg 2 hot
#> 3 reg 3 hot
#> 4 reg A warm
#> 5 reg B cold