Truncating a dataframe according to count of vector elements in R
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0
down vote
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I have a dataframe df, containing three vectors:
subject condition value
01 A 12
01 A 6
01 B 10
01 B 2
02 A 5
02 A 11
02 B 3
02 B 5
02 B 9
...
There are four observations (and hence four rows) for subject 01, with two observations corresponding to condition A and two corresponding to condition B. Let's say that due to a technical error, there are three condition B observations for subject 02.
My question is this: how can I truncate df to ensure that each condition only has two observations for each individual subject (hence removing the erroneous third row where condition==B for subject 02)?
Thanks in advance for any assistance!
r dataframe vector
asked Nov 10 at 23:56
Lyam
287
add a comment |
up vote
0
down vote
favorite
I have a dataframe df, containing three vectors:
subject condition value
01 A 12
01 A 6
01 B 10
01 B 2
02 A 5
02 A 11
02 B 3
02 B 5
02 B 9
...
There are four observations (and hence four rows) for subject 01, with two observations corresponding to condition A and two corresponding to condition B. Let's say that due to a technical error, there are three condition B observations for subject 02.
My question is this: how can I truncate df to ensure that each condition only has two observations for each individual subject (hence removing the erroneous third row where condition==B for subject 02)?
Thanks in advance for any assistance!
r dataframe vector
asked Nov 10 at 23:56
Lyam
287
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I have a dataframe df, containing three vectors:
subject condition value
01 A 12
01 A 6
01 B 10
01 B 2
02 A 5
02 A 11
02 B 3
02 B 5
02 B 9
...
There are four observations (and hence four rows) for subject 01, with two observations corresponding to condition A and two corresponding to condition B. Let's say that due to a technical error, there are three condition B observations for subject 02.
My question is this: how can I truncate df to ensure that each condition only has two observations for each individual subject (hence removing the erroneous third row where condition==B for subject 02)?
Thanks in advance for any assistance!
r dataframe vector
asked Nov 10 at 23:56
Lyam
287
I have a dataframe df, containing three vectors:
subject condition value
01 A 12
01 A 6
01 B 10
01 B 2
02 A 5
02 A 11
02 B 3
02 B 5
02 B 9
...
There are four observations (and hence four rows) for subject 01, with two observations corresponding to condition A and two corresponding to condition B. Let's say that due to a technical error, there are three condition B observations for subject 02.
My question is this: how can I truncate df to ensure that each condition only has two observations for each individual subject (hence removing the erroneous third row where condition==B for subject 02)?
Thanks in advance for any assistance!
r dataframe vector
r dataframe vector
asked Nov 10 at 23:56
Lyam
287
asked Nov 10 at 23:56
Lyam
287
edited Nov 11 at 0:01
asked Nov 10 at 23:56
Lyam
287
asked Nov 10 at 23:56
Lyam
287
asked Nov 10 at 23:56
Lyam
287
287
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
up vote
1
down vote
accepted
Here's a dplyr solution -
df %>%
group_by(subject, condition) %>%
filter(row_number() < 3) %>%
ungroup()
# A tibble: 8 x 3
subject condition value
<chr> <chr> <dbl>
1 01 A 12
2 01 A 6
3 01 B 10
4 01 B 2
5 02 A 5
6 02 A 11
7 02 B 3
8 02 B 5
answered Nov 11 at 0:06
Shree
2,708321
perfect, thank you! Just as a side note, the solution provided by G. Grothendieck also works, but has to be modified if the dataframe contains other vectors that are not defined in seq = ave()
– Lyam
Nov 11 at 1:44
add a comment |
up vote
0
down vote
For each subject/condition pair create a sequence number seq for its rows and then only keep those rows whose sequence number is less than 3.
subset(transform(DF, seq = ave(value, subject, condition, FUN = seq_along)), seq < 3)
giving:
subject condition value seq
1 01 A 12 1
2 01 A 6 2
3 01 B 10 1
4 01 B 2 2
5 02 A 5 1
6 02 A 11 2
7 02 B 3 1
8 02 B 5 2
Note
The input in reprodudible form is assumed to be:
Lines <- "subject condition value
01 A 12
01 A 6
01 B 10
01 B 2
02 A 5
02 A 11
02 B 3
02 B 5
02 B 9"
DF <- read.table(text = Lines, header = TRUE, strip.white = TRUE,
colClasses = c("character", "character", "numeric"))
answered Nov 11 at 0:02
G. Grothendieck
142k9123227
add a comment |
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
accepted
Here's a dplyr solution -
df %>%
group_by(subject, condition) %>%
filter(row_number() < 3) %>%
ungroup()
# A tibble: 8 x 3
subject condition value
<chr> <chr> <dbl>
1 01 A 12
2 01 A 6
3 01 B 10
4 01 B 2
5 02 A 5
6 02 A 11
7 02 B 3
8 02 B 5
answered Nov 11 at 0:06
Shree
2,708321
perfect, thank you! Just as a side note, the solution provided by G. Grothendieck also works, but has to be modified if the dataframe contains other vectors that are not defined in seq = ave()
– Lyam
Nov 11 at 1:44
add a comment |
up vote
1
down vote
accepted
Here's a dplyr solution -
df %>%
group_by(subject, condition) %>%
filter(row_number() < 3) %>%
ungroup()
# A tibble: 8 x 3
subject condition value
<chr> <chr> <dbl>
1 01 A 12
2 01 A 6
3 01 B 10
4 01 B 2
5 02 A 5
6 02 A 11
7 02 B 3
8 02 B 5
answered Nov 11 at 0:06
Shree
2,708321
perfect, thank you! Just as a side note, the solution provided by G. Grothendieck also works, but has to be modified if the dataframe contains other vectors that are not defined in seq = ave()
– Lyam
Nov 11 at 1:44
add a comment |
up vote
1
down vote
accepted
up vote
1
down vote
accepted
Here's a dplyr solution -
df %>%
group_by(subject, condition) %>%
filter(row_number() < 3) %>%
ungroup()
# A tibble: 8 x 3
subject condition value
<chr> <chr> <dbl>
1 01 A 12
2 01 A 6
3 01 B 10
4 01 B 2
5 02 A 5
6 02 A 11
7 02 B 3
8 02 B 5
answered Nov 11 at 0:06
Shree
2,708321
Here's a dplyr solution -
df %>%
group_by(subject, condition) %>%
filter(row_number() < 3) %>%
ungroup()
# A tibble: 8 x 3
subject condition value
<chr> <chr> <dbl>
1 01 A 12
2 01 A 6
3 01 B 10
4 01 B 2
5 02 A 5
6 02 A 11
7 02 B 3
8 02 B 5
answered Nov 11 at 0:06
Shree
2,708321
answered Nov 11 at 0:06
Shree
2,708321
answered Nov 11 at 0:06
Shree
2,708321
answered Nov 11 at 0:06
Shree
2,708321
2,708321
perfect, thank you! Just as a side note, the solution provided by G. Grothendieck also works, but has to be modified if the dataframe contains other vectors that are not defined in seq = ave()
– Lyam
Nov 11 at 1:44
add a comment |
perfect, thank you! Just as a side note, the solution provided by G. Grothendieck also works, but has to be modified if the dataframe contains other vectors that are not defined in seq = ave()
– Lyam
Nov 11 at 1:44
perfect, thank you! Just as a side note, the solution provided by G. Grothendieck also works, but has to be modified if the dataframe contains other vectors that are not defined in seq = ave()
– Lyam
Nov 11 at 1:44
perfect, thank you! Just as a side note, the solution provided by G. Grothendieck also works, but has to be modified if the dataframe contains other vectors that are not defined in seq = ave()
– Lyam
Nov 11 at 1:44
add a comment |
up vote
0
down vote
For each subject/condition pair create a sequence number seq for its rows and then only keep those rows whose sequence number is less than 3.
subset(transform(DF, seq = ave(value, subject, condition, FUN = seq_along)), seq < 3)
giving:
subject condition value seq
1 01 A 12 1
2 01 A 6 2
3 01 B 10 1
4 01 B 2 2
5 02 A 5 1
6 02 A 11 2
7 02 B 3 1
8 02 B 5 2
Note
The input in reprodudible form is assumed to be:
Lines <- "subject condition value
01 A 12
01 A 6
01 B 10
01 B 2
02 A 5
02 A 11
02 B 3
02 B 5
02 B 9"
DF <- read.table(text = Lines, header = TRUE, strip.white = TRUE,
colClasses = c("character", "character", "numeric"))
answered Nov 11 at 0:02
G. Grothendieck
142k9123227
add a comment |
up vote
0
down vote
For each subject/condition pair create a sequence number seq for its rows and then only keep those rows whose sequence number is less than 3.
subset(transform(DF, seq = ave(value, subject, condition, FUN = seq_along)), seq < 3)
giving:
subject condition value seq
1 01 A 12 1
2 01 A 6 2
3 01 B 10 1
4 01 B 2 2
5 02 A 5 1
6 02 A 11 2
7 02 B 3 1
8 02 B 5 2
Note
The input in reprodudible form is assumed to be:
Lines <- "subject condition value
01 A 12
01 A 6
01 B 10
01 B 2
02 A 5
02 A 11
02 B 3
02 B 5
02 B 9"
DF <- read.table(text = Lines, header = TRUE, strip.white = TRUE,
colClasses = c("character", "character", "numeric"))
answered Nov 11 at 0:02
G. Grothendieck
142k9123227
add a comment |
up vote
0
down vote
up vote
0
down vote
For each subject/condition pair create a sequence number seq for its rows and then only keep those rows whose sequence number is less than 3.
subset(transform(DF, seq = ave(value, subject, condition, FUN = seq_along)), seq < 3)
giving:
subject condition value seq
1 01 A 12 1
2 01 A 6 2
3 01 B 10 1
4 01 B 2 2
5 02 A 5 1
6 02 A 11 2
7 02 B 3 1
8 02 B 5 2
Note
The input in reprodudible form is assumed to be:
Lines <- "subject condition value
01 A 12
01 A 6
01 B 10
01 B 2
02 A 5
02 A 11
02 B 3
02 B 5
02 B 9"
DF <- read.table(text = Lines, header = TRUE, strip.white = TRUE,
colClasses = c("character", "character", "numeric"))
answered Nov 11 at 0:02
G. Grothendieck
142k9123227
For each subject/condition pair create a sequence number seq for its rows and then only keep those rows whose sequence number is less than 3.
subset(transform(DF, seq = ave(value, subject, condition, FUN = seq_along)), seq < 3)
giving:
subject condition value seq
1 01 A 12 1
2 01 A 6 2
3 01 B 10 1
4 01 B 2 2
5 02 A 5 1
6 02 A 11 2
7 02 B 3 1
8 02 B 5 2
Note
The input in reprodudible form is assumed to be:
Lines <- "subject condition value
01 A 12
01 A 6
01 B 10
01 B 2
02 A 5
02 A 11
02 B 3
02 B 5
02 B 9"
DF <- read.table(text = Lines, header = TRUE, strip.white = TRUE,
colClasses = c("character", "character", "numeric"))
answered Nov 11 at 0:02
G. Grothendieck
142k9123227
answered Nov 11 at 0:02
G. Grothendieck
142k9123227
answered Nov 11 at 0:02
G. Grothendieck
142k9123227
answered Nov 11 at 0:02
G. Grothendieck
142k9123227
142k9123227
add a comment |
add a comment |
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