This function is a wrapper around RowGroups() for the specific case where the input
contains two columns. It calls RowGroups() with returnGroups = TRUE, and extends
the resulting data frame of unique code combinations with additional information
about common groups, difference groups, and sum groups.
Usage
diff_groups(
x,
...,
hiddenNA = TRUE,
sep_common = "_=_",
sep_diff = "_-_",
sep_sum = c("_=_", "_+_"),
outputNA = "NA"
)Arguments
- x
A data frame with exactly two columns.
- ...
Additional arguments passed to
RowGroups().Logical. When
TRUE(default), missing codes (NA) are treated as hidden categories — they are not available for computing difference and sum groups. See Note for details on how this differs from theNAomitparameter inRowGroups().- sep_common
A character string used in the
commoncolumn to separate codes that are identical across the two input columns.- sep_diff
A character string used in the
diff_1_2anddiff_2_1columns to indicate difference groups. The first column contains the parent code, and one or more child codes from the other column are subtracted.- sep_sum
A character vector of one or two elements used in the
sum_1_2andsum_2_1columns to describe relationships where a code in one column represents the sum of several codes in the other. The first element (sep_sum[1]) acts as an equality sign, and the second element (sep_sum[2]) acts as a plus sign. Ifsep_sumhas length 1, the same value is used for both positions.- outputNA
Character string used to represent
NAvalues within the newly constructed text strings in the additional output columns. Only relevant whenhiddenNA = FALSE.
Value
A list (as returned by RowGroups()), where the groups data frame is
extended with additional descriptive columns indicating common, difference, and sum
relationships between the two code columns.
Details
The returned list contains the same elements as from RowGroups(), but with an
extended groups data frame. The columns describe relationships between the two
input columns as follows:
is_common —
TRUEwhen the two codes on the row are identical.is_child_1, is_child_2 —
TRUEwhen the code in the column is a subset or subgroup of a code in the other column.common — identical code pairs, formatted using
sep_common.diff_1_2, diff_2_1 — difference groups. The first element is the parent from the source column, followed by one or more child codes from the opposite column, joined using
sep_diff.sum_1_2, sum_2_1 — sum groups where a parent code in one column equals the sum of several codes in the other.
Note
The parameter NAomit from RowGroups() can still be set via ..., but using it
will remove rows containing NA before processing. The relationships found will then
reflect the reduced data, which is usually not the intended behaviour when identifying
relationships between code sets.
See also
data_diff_groups() for adding the results back as new columns in the data frame.
Examples
df <- SSBtoolsData("code_pairs")
df
#> code_1 code_2
#> 1 d O
#> 2 a N
#> 3 f S
#> 4 b S
#> 5 d N
#> 6 j V
#> 7 g U
#> 8 h V
#> 9 d O
#> 10 d N
#> 11 g T
#> 12 f R
#> 13 a S
#> 14 c N
#> 15 e P
#> 16 a N
#> 17 e P
#> 18 d N
#> 19 f S
#> 20 f Q
#> 21 i V
#> 22 i V
#> 23 c N
#> 24 a S
#> 25 a N
#> 26 a S
diff_groups(df)
#> $idx
#> [1] 6 1 10 3 5 15 12 13 6 5 11 9 2 4 7 1 7 5 10 8 14 14 4 2 1
#> [26] 2
#>
#> $groups
#> code_1 code_2 is_common is_child_1 is_child_2 common diff_1_2 diff_2_1
#> 1 a N FALSE FALSE FALSE <NA> <NA> N_-_c
#> 2 a S FALSE FALSE FALSE <NA> <NA> S_-_b
#> 3 b S FALSE TRUE FALSE <NA> <NA> <NA>
#> 4 c N FALSE TRUE FALSE <NA> <NA> <NA>
#> 5 d N FALSE FALSE FALSE <NA> d_-_O N_-_c
#> 6 d O FALSE FALSE TRUE <NA> <NA> <NA>
#> 7 e P TRUE FALSE FALSE e_=_P <NA> <NA>
#> 8 f Q FALSE FALSE TRUE <NA> <NA> <NA>
#> 9 f R FALSE FALSE TRUE <NA> <NA> <NA>
#> 10 f S FALSE FALSE FALSE <NA> f_-_Q_-_R S_-_b
#> 11 g T FALSE FALSE TRUE <NA> <NA> <NA>
#> 12 g U FALSE FALSE TRUE <NA> <NA> <NA>
#> 13 h V FALSE TRUE FALSE <NA> <NA> <NA>
#> 14 i V FALSE TRUE FALSE <NA> <NA> <NA>
#> 15 j V FALSE TRUE FALSE <NA> <NA> <NA>
#> sum_1_2 sum_2_1
#> 1 <NA> <NA>
#> 2 <NA> <NA>
#> 3 <NA> <NA>
#> 4 <NA> <NA>
#> 5 <NA> <NA>
#> 6 <NA> <NA>
#> 7 <NA> <NA>
#> 8 <NA> <NA>
#> 9 <NA> <NA>
#> 10 <NA> <NA>
#> 11 g_=_T_+_U <NA>
#> 12 g_=_T_+_U <NA>
#> 13 <NA> V_=_h_+_i_+_j
#> 14 <NA> V_=_h_+_i_+_j
#> 15 <NA> V_=_h_+_i_+_j
#>
d2 <- SSBtoolsData("d2")
diff_groups(d2[1:2])$groups
#> region county is_common is_child_1 is_child_2 common diff_1_2 diff_2_1
#> 1 A 1 FALSE TRUE FALSE <NA> <NA> NA
#> 2 B 4 TRUE FALSE FALSE B_=_4 <NA> NA
#> 3 C 5 FALSE TRUE FALSE <NA> <NA> NA
#> 4 D 5 FALSE TRUE FALSE <NA> <NA> NA
#> 5 E 6 FALSE TRUE FALSE <NA> <NA> NA
#> 6 F 6 FALSE TRUE FALSE <NA> <NA> NA
#> 7 G 8 FALSE TRUE FALSE <NA> <NA> NA
#> 8 H 8 FALSE TRUE FALSE <NA> <NA> NA
#> 9 I 1 FALSE TRUE FALSE <NA> <NA> NA
#> 10 J 10 FALSE TRUE FALSE <NA> <NA> NA
#> 11 K 10 FALSE TRUE FALSE <NA> <NA> NA
#> sum_1_2 sum_2_1
#> 1 <NA> 1_=_A_+_I
#> 2 <NA> <NA>
#> 3 <NA> 5_=_C_+_D
#> 4 <NA> 5_=_C_+_D
#> 5 <NA> 6_=_E_+_F
#> 6 <NA> 6_=_E_+_F
#> 7 <NA> 8_=_G_+_H
#> 8 <NA> 8_=_G_+_H
#> 9 <NA> 1_=_A_+_I
#> 10 <NA> 10_=_J_+_K
#> 11 <NA> 10_=_J_+_K
diff_groups(d2[2:3])$groups
#> county k_group is_common is_child_1 is_child_2 common diff_1_2
#> 1 1 300 FALSE FALSE FALSE <NA> NA
#> 2 1 400 FALSE FALSE FALSE <NA> NA
#> 3 4 300 FALSE TRUE FALSE <NA> NA
#> 4 5 300 FALSE TRUE FALSE <NA> NA
#> 5 6 300 FALSE TRUE FALSE <NA> NA
#> 6 8 300 FALSE TRUE FALSE <NA> NA
#> 7 10 400 FALSE TRUE FALSE <NA> NA
#> diff_2_1 sum_1_2 sum_2_1
#> 1 300_-_4_-_5_-_6_-_8 <NA> <NA>
#> 2 400_-_10 <NA> <NA>
#> 3 <NA> <NA> <NA>
#> 4 <NA> <NA> <NA>
#> 5 <NA> <NA> <NA>
#> 6 <NA> <NA> <NA>
#> 7 <NA> <NA> <NA>