A long dataframe can be reduced by mergeing certain rows into a
single one. These new variables are constructed as a SimpleList
containing all the original values. Invariant columns, i.e columns
that have the same value along all the rows that need to be
merged, can be shrunk into a new variables containing that
invariant value (rather than in list columns). The grouping of
rows, i.e. the rows that need to be shrunk together as one, is
defined by a vector.
The opposite operation is expand. But note that for a
DataFrame to be expanded back, it must not to be simplified.
Usage
reduceDataFrame(x, k, count = FALSE, simplify = TRUE, drop = FALSE)
expandDataFrame(x, k = NULL)Arguments
- x
The
DataFrameto be reduced or expanded.- k
A ‘vector’ of length
nrow(x)defining the grouping based on which theDataFramewill be shrunk.- count
logical(1)specifying of an additional column (called by default.n) with the tally of rows shrunk into on new row should be added. Note that if already existing,.nwill be silently overwritten.- simplify
A
logical(1)defining if invariant columns should be converted to simple lists. Default isTRUE.- drop
A
logical(1)specifying whether the non-invariant columns should be dropped altogether. Default isFALSE.
Missing values
Missing values do have an important effect on reduce. Unless all
values to be reduces are missing, they will result in an
non-invariant column, and will be dropped with drop = TRUE. See
the example below.
The presence of missing values can have side effects in higher
level functions that rely on reduction of DataFrame objects.
Examples
library("IRanges")
k <- sample(100, 1e3, replace = TRUE)
df <- DataFrame(k = k,
x = round(rnorm(length(k)), 2),
y = seq_len(length(k)),
z = sample(LETTERS, length(k), replace = TRUE),
ir = IRanges(seq_along(k), width = 10),
r = Rle(sample(5, length(k), replace = TRUE)),
invar = k + 1)
df
#> DataFrame with 1000 rows and 7 columns
#> k x y z ir r invar
#> <integer> <numeric> <integer> <character> <IRanges> <Rle> <numeric>
#> 1 37 -0.31 1 M 1-10 5 38
#> 2 64 0.87 2 O 2-11 1 65
#> 3 18 -0.51 3 K 3-12 5 19
#> 4 3 -0.77 4 V 4-13 5 4
#> 5 99 -1.24 5 P 5-14 2 100
#> ... ... ... ... ... ... ... ...
#> 996 30 -0.20 996 S 996-1005 5 31
#> 997 75 -0.66 997 X 997-1006 3 76
#> 998 1 0.45 998 P 998-1007 4 2
#> 999 13 -0.76 999 Q 999-1008 2 14
#> 1000 23 -0.14 1000 T 1000-1009 3 24
## Shinks the DataFrame
df2 <- reduceDataFrame(df, df$k)
df2
#> DataFrame with 100 rows and 7 columns
#> k x y z
#> <integer> <NumericList> <IntegerList> <CharacterList>
#> 1 1 -0.69,-0.15,-1.90,... 30,165,295,... P,I,M,...
#> 2 2 0.33, 1.16,-0.88,... 90,267,411,... P,S,W,...
#> 3 3 -0.77,-0.14, 0.65,... 4,112,449,... V,M,T,...
#> 4 4 0.73,0.52,1.67,... 166,291,293,... E,U,H,...
#> 5 5 -0.47,-0.38,-0.10,... 35,93,106,... K,C,P,...
#> ... ... ... ... ...
#> 96 96 -0.32,-0.63, 1.09,... 233,382,385,... U,U,B,...
#> 97 97 0.79, 0.43,-0.66,... 9,14,76,... I,T,Z,...
#> 98 98 0.83,-0.53, 0.25,... 116,135,193,... A,C,S,...
#> 99 99 -1.24,-1.10, 0.48,... 5,54,131,... P,Y,X,...
#> 100 100 2.13,0.19,1.29,... 15,38,461,... L,B,E,...
#> ir r invar
#> <IRangesList> <RleList> <numeric>
#> 1 30-39,165-174,295-304,... 1,1,5,... 2
#> 2 90-99,267-276,411-420,... 5,2,3,... 3
#> 3 4-13,112-121,449-458,... 5,4,4,... 4
#> 4 166-175,291-300,293-302,... 3,1,1,... 5
#> 5 35-44,93-102,106-115,... 5,1,2,... 6
#> ... ... ... ...
#> 96 233-242,382-391,385-394,... 4,5,1,... 97
#> 97 9-18,14-23,76-85,... 3,4,5,... 98
#> 98 116-125,135-144,193-202,... 4,1,4,... 99
#> 99 5-14,54-63,131-140,... 2,5,2,... 100
#> 100 15-24,38-47,461-470,... 5,2,2,... 101
## With a tally of the number of members in each group
reduceDataFrame(df, df$k, count = TRUE)
#> DataFrame with 100 rows and 8 columns
#> k x y z
#> <integer> <NumericList> <IntegerList> <CharacterList>
#> 1 1 -0.69,-0.15,-1.90,... 30,165,295,... P,I,M,...
#> 2 2 0.33, 1.16,-0.88,... 90,267,411,... P,S,W,...
#> 3 3 -0.77,-0.14, 0.65,... 4,112,449,... V,M,T,...
#> 4 4 0.73,0.52,1.67,... 166,291,293,... E,U,H,...
#> 5 5 -0.47,-0.38,-0.10,... 35,93,106,... K,C,P,...
#> ... ... ... ... ...
#> 96 96 -0.32,-0.63, 1.09,... 233,382,385,... U,U,B,...
#> 97 97 0.79, 0.43,-0.66,... 9,14,76,... I,T,Z,...
#> 98 98 0.83,-0.53, 0.25,... 116,135,193,... A,C,S,...
#> 99 99 -1.24,-1.10, 0.48,... 5,54,131,... P,Y,X,...
#> 100 100 2.13,0.19,1.29,... 15,38,461,... L,B,E,...
#> ir r invar .n
#> <IRangesList> <RleList> <numeric> <integer>
#> 1 30-39,165-174,295-304,... 1,1,5,... 2 13
#> 2 90-99,267-276,411-420,... 5,2,3,... 3 9
#> 3 4-13,112-121,449-458,... 5,4,4,... 4 9
#> 4 166-175,291-300,293-302,... 3,1,1,... 5 11
#> 5 35-44,93-102,106-115,... 5,1,2,... 6 8
#> ... ... ... ... ...
#> 96 233-242,382-391,385-394,... 4,5,1,... 97 6
#> 97 9-18,14-23,76-85,... 3,4,5,... 98 12
#> 98 116-125,135-144,193-202,... 4,1,4,... 99 10
#> 99 5-14,54-63,131-140,... 2,5,2,... 100 16
#> 100 15-24,38-47,461-470,... 5,2,2,... 101 11
## Much faster, but more crowded result
df3 <- reduceDataFrame(df, df$k, simplify = FALSE)
df3
#> DataFrame with 100 rows and 7 columns
#> k x y z
#> <IntegerList> <NumericList> <IntegerList> <CharacterList>
#> 1 1,1,1,... -0.69,-0.15,-1.90,... 30,165,295,... P,I,M,...
#> 2 2,2,2,... 0.33, 1.16,-0.88,... 90,267,411,... P,S,W,...
#> 3 3,3,3,... -0.77,-0.14, 0.65,... 4,112,449,... V,M,T,...
#> 4 4,4,4,... 0.73,0.52,1.67,... 166,291,293,... E,U,H,...
#> 5 5,5,5,... -0.47,-0.38,-0.10,... 35,93,106,... K,C,P,...
#> ... ... ... ... ...
#> 96 96,96,96,... -0.32,-0.63, 1.09,... 233,382,385,... U,U,B,...
#> 97 97,97,97,... 0.79, 0.43,-0.66,... 9,14,76,... I,T,Z,...
#> 98 98,98,98,... 0.83,-0.53, 0.25,... 116,135,193,... A,C,S,...
#> 99 99,99,99,... -1.24,-1.10, 0.48,... 5,54,131,... P,Y,X,...
#> 100 100,100,100,... 2.13,0.19,1.29,... 15,38,461,... L,B,E,...
#> ir r invar
#> <IRangesList> <RleList> <NumericList>
#> 1 30-39,165-174,295-304,... 1,1,5,... 2,2,2,...
#> 2 90-99,267-276,411-420,... 5,2,3,... 3,3,3,...
#> 3 4-13,112-121,449-458,... 5,4,4,... 4,4,4,...
#> 4 166-175,291-300,293-302,... 3,1,1,... 5,5,5,...
#> 5 35-44,93-102,106-115,... 5,1,2,... 6,6,6,...
#> ... ... ... ...
#> 96 233-242,382-391,385-394,... 4,5,1,... 97,97,97,...
#> 97 9-18,14-23,76-85,... 3,4,5,... 98,98,98,...
#> 98 116-125,135-144,193-202,... 4,1,4,... 99,99,99,...
#> 99 5-14,54-63,131-140,... 2,5,2,... 100,100,100,...
#> 100 15-24,38-47,461-470,... 5,2,2,... 101,101,101,...
## Drop all non-invariant columns
reduceDataFrame(df, df$k, drop = TRUE)
#> DataFrame with 100 rows and 2 columns
#> k invar
#> <integer> <numeric>
#> 1 1 2
#> 2 2 3
#> 3 3 4
#> 4 4 5
#> 5 5 6
#> ... ... ...
#> 96 96 97
#> 97 97 98
#> 98 98 99
#> 99 99 100
#> 100 100 101
## Missing values
d <- DataFrame(k = rep(1:3, each = 3),
x = letters[1:9],
y = rep(letters[1:3], each = 3),
y2 = rep(letters[1:3], each = 3))
d
#> DataFrame with 9 rows and 4 columns
#> k x y y2
#> <integer> <character> <character> <character>
#> 1 1 a a a
#> 2 1 b a a
#> 3 1 c a a
#> 4 2 d b b
#> 5 2 e b b
#> 6 2 f b b
#> 7 3 g c c
#> 8 3 h c c
#> 9 3 i c c
## y is invariant and can be simplified
reduceDataFrame(d, d$k)
#> DataFrame with 3 rows and 4 columns
#> k x y y2
#> <integer> <CharacterList> <character> <character>
#> 1 1 a,b,c a a
#> 2 2 d,e,f b b
#> 3 3 g,h,i c c
## y isn't not dropped
reduceDataFrame(d, d$k, drop = TRUE)
#> DataFrame with 3 rows and 3 columns
#> k y y2
#> <integer> <character> <character>
#> 1 1 a a
#> 2 2 b b
#> 3 3 c c
## BUT with a missing value
d[1, "y"] <- NA
d
#> DataFrame with 9 rows and 4 columns
#> k x y y2
#> <integer> <character> <character> <character>
#> 1 1 a NA a
#> 2 1 b a a
#> 3 1 c a a
#> 4 2 d b b
#> 5 2 e b b
#> 6 2 f b b
#> 7 3 g c c
#> 8 3 h c c
#> 9 3 i c c
## y isn't invariant/simplified anymore
reduceDataFrame(d, d$k)
#> DataFrame with 3 rows and 4 columns
#> k x y y2
#> <integer> <CharacterList> <CharacterList> <character>
#> 1 1 a,b,c NA,a,a a
#> 2 2 d,e,f b,b,b b
#> 3 3 g,h,i c,c,c c
## y now gets dropped
reduceDataFrame(d, d$k, drop = TRUE)
#> DataFrame with 3 rows and 2 columns
#> k y2
#> <integer> <character>
#> 1 1 a
#> 2 2 b
#> 3 3 c