Select random rows that match string
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1
I want to randomly select rows from df2 that matches Birth_Sex. The number of rows selected should be equal to the number of rows in df1 (2 in this example).
df1
Birth_Sex
1999_1
1969_1
df2
ID Birth_Sex
123113 1999_1
123123 1992_0
123233 1959_0
124513 1969_1
124993 1969_1
124545 1969_1
Example output
ID Birth_Sex
124545 1969_1
123113 1999_1
r
asked Jan 4 at 9:09
user2300940user2300940
729515
add a comment |
1
I want to randomly select rows from df2 that matches Birth_Sex. The number of rows selected should be equal to the number of rows in df1 (2 in this example).
df1
Birth_Sex
1999_1
1969_1
df2
ID Birth_Sex
123113 1999_1
123123 1992_0
123233 1959_0
124513 1969_1
124993 1969_1
124545 1969_1
Example output
ID Birth_Sex
124545 1969_1
123113 1999_1
r
asked Jan 4 at 9:09
user2300940user2300940
729515
add a comment |
1
1
1
I want to randomly select rows from df2 that matches Birth_Sex. The number of rows selected should be equal to the number of rows in df1 (2 in this example).
df1
Birth_Sex
1999_1
1969_1
df2
ID Birth_Sex
123113 1999_1
123123 1992_0
123233 1959_0
124513 1969_1
124993 1969_1
124545 1969_1
Example output
ID Birth_Sex
124545 1969_1
123113 1999_1
r
asked Jan 4 at 9:09
user2300940user2300940
729515
I want to randomly select rows from df2 that matches Birth_Sex. The number of rows selected should be equal to the number of rows in df1 (2 in this example).
df1
Birth_Sex
1999_1
1969_1
df2
ID Birth_Sex
123113 1999_1
123123 1992_0
123233 1959_0
124513 1969_1
124993 1969_1
124545 1969_1
Example output
ID Birth_Sex
124545 1969_1
123113 1999_1
r
r
asked Jan 4 at 9:09
user2300940user2300940
729515
asked Jan 4 at 9:09
user2300940user2300940
729515
asked Jan 4 at 9:09
user2300940user2300940
729515
asked Jan 4 at 9:09
user2300940user2300940
729515
asked Jan 4 at 9:09
user2300940user2300940
729515
729515
add a comment |
add a comment |
3 Answers
3
active
oldest
votes
2
For each entry of Birth_Sex in df1 we first find the row numbers which have that entry in df2 and select index of any one row to subset df2.
df2[sapply(df1$Birth_Sex, function(x) {
inds = which(df2$Birth_Sex %in% x)
if(length(inds) > 1) sample(inds, 1) else inds
}), ]
# ID Birth_Sex
#1 123113 1999_1
#4 124513 1969_1
answered Jan 4 at 9:11
Ronak ShahRonak Shah
47.1k104269
add a comment |
3
With dplyr
library(dplyr)
df2 %>%
inner_join(df1) %>%
group_by(Birth_Sex) %>%
sample_n(1)
Joining, by = "Birth_Sex"
# A tibble: 2 x 2
# Groups: Birth_Sex [2]
ID Birth_Sex
<int> <chr>
1 124993 1969_1
2 123113 1999_1
answered Jan 4 at 9:12
jyjekjyjek
1,502314
add a comment |
2
Here is the data.table solution as well
library(data.table)
setDT(df1)[setDT(df2), nomatch = 0L,on = 'Birth_Sex'][, .SD[sample(.N, 1)], by = Birth_Sex]
# Birth_Sex ID
#1: 1969_1 124513
#2: 1999_1 123113
answered Jan 4 at 9:24
SotosSotos
31.5k51741
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
2
For each entry of Birth_Sex in df1 we first find the row numbers which have that entry in df2 and select index of any one row to subset df2.
df2[sapply(df1$Birth_Sex, function(x) {
inds = which(df2$Birth_Sex %in% x)
if(length(inds) > 1) sample(inds, 1) else inds
}), ]
# ID Birth_Sex
#1 123113 1999_1
#4 124513 1969_1
answered Jan 4 at 9:11
Ronak ShahRonak Shah
47.1k104269
add a comment |
2
For each entry of Birth_Sex in df1 we first find the row numbers which have that entry in df2 and select index of any one row to subset df2.
df2[sapply(df1$Birth_Sex, function(x) {
inds = which(df2$Birth_Sex %in% x)
if(length(inds) > 1) sample(inds, 1) else inds
}), ]
# ID Birth_Sex
#1 123113 1999_1
#4 124513 1969_1
answered Jan 4 at 9:11
Ronak ShahRonak Shah
47.1k104269
add a comment |
2
2
2
For each entry of Birth_Sex in df1 we first find the row numbers which have that entry in df2 and select index of any one row to subset df2.
df2[sapply(df1$Birth_Sex, function(x) {
inds = which(df2$Birth_Sex %in% x)
if(length(inds) > 1) sample(inds, 1) else inds
}), ]
# ID Birth_Sex
#1 123113 1999_1
#4 124513 1969_1
answered Jan 4 at 9:11
Ronak ShahRonak Shah
47.1k104269
For each entry of Birth_Sex in df1 we first find the row numbers which have that entry in df2 and select index of any one row to subset df2.
df2[sapply(df1$Birth_Sex, function(x) {
inds = which(df2$Birth_Sex %in% x)
if(length(inds) > 1) sample(inds, 1) else inds
}), ]
# ID Birth_Sex
#1 123113 1999_1
#4 124513 1969_1
answered Jan 4 at 9:11
Ronak ShahRonak Shah
47.1k104269
edited Jan 4 at 9:25
answered Jan 4 at 9:11
Ronak ShahRonak Shah
47.1k104269
answered Jan 4 at 9:11
Ronak ShahRonak Shah
47.1k104269
answered Jan 4 at 9:11
Ronak ShahRonak Shah
47.1k104269
47.1k104269
add a comment |
add a comment |
3
With dplyr
library(dplyr)
df2 %>%
inner_join(df1) %>%
group_by(Birth_Sex) %>%
sample_n(1)
Joining, by = "Birth_Sex"
# A tibble: 2 x 2
# Groups: Birth_Sex [2]
ID Birth_Sex
<int> <chr>
1 124993 1969_1
2 123113 1999_1
answered Jan 4 at 9:12
jyjekjyjek
1,502314
add a comment |
3
With dplyr
library(dplyr)
df2 %>%
inner_join(df1) %>%
group_by(Birth_Sex) %>%
sample_n(1)
Joining, by = "Birth_Sex"
# A tibble: 2 x 2
# Groups: Birth_Sex [2]
ID Birth_Sex
<int> <chr>
1 124993 1969_1
2 123113 1999_1
answered Jan 4 at 9:12
jyjekjyjek
1,502314
add a comment |
3
3
3
With dplyr
library(dplyr)
df2 %>%
inner_join(df1) %>%
group_by(Birth_Sex) %>%
sample_n(1)
Joining, by = "Birth_Sex"
# A tibble: 2 x 2
# Groups: Birth_Sex [2]
ID Birth_Sex
<int> <chr>
1 124993 1969_1
2 123113 1999_1
answered Jan 4 at 9:12
jyjekjyjek
1,502314
With dplyr
library(dplyr)
df2 %>%
inner_join(df1) %>%
group_by(Birth_Sex) %>%
sample_n(1)
Joining, by = "Birth_Sex"
# A tibble: 2 x 2
# Groups: Birth_Sex [2]
ID Birth_Sex
<int> <chr>
1 124993 1969_1
2 123113 1999_1
answered Jan 4 at 9:12
jyjekjyjek
1,502314
answered Jan 4 at 9:12
jyjekjyjek
1,502314
answered Jan 4 at 9:12
jyjekjyjek
1,502314
answered Jan 4 at 9:12
jyjekjyjek
1,502314
1,502314
add a comment |
add a comment |
2
Here is the data.table solution as well
library(data.table)
setDT(df1)[setDT(df2), nomatch = 0L,on = 'Birth_Sex'][, .SD[sample(.N, 1)], by = Birth_Sex]
# Birth_Sex ID
#1: 1969_1 124513
#2: 1999_1 123113
answered Jan 4 at 9:24
SotosSotos
31.5k51741
add a comment |
2
Here is the data.table solution as well
library(data.table)
setDT(df1)[setDT(df2), nomatch = 0L,on = 'Birth_Sex'][, .SD[sample(.N, 1)], by = Birth_Sex]
# Birth_Sex ID
#1: 1969_1 124513
#2: 1999_1 123113
answered Jan 4 at 9:24
SotosSotos
31.5k51741
add a comment |
2
2
2
Here is the data.table solution as well
library(data.table)
setDT(df1)[setDT(df2), nomatch = 0L,on = 'Birth_Sex'][, .SD[sample(.N, 1)], by = Birth_Sex]
# Birth_Sex ID
#1: 1969_1 124513
#2: 1999_1 123113
answered Jan 4 at 9:24
SotosSotos
31.5k51741
Here is the data.table solution as well
library(data.table)
setDT(df1)[setDT(df2), nomatch = 0L,on = 'Birth_Sex'][, .SD[sample(.N, 1)], by = Birth_Sex]
# Birth_Sex ID
#1: 1969_1 124513
#2: 1999_1 123113
answered Jan 4 at 9:24
SotosSotos
31.5k51741
answered Jan 4 at 9:24
SotosSotos
31.5k51741
answered Jan 4 at 9:24
SotosSotos
31.5k51741
answered Jan 4 at 9:24
SotosSotos
31.5k51741
31.5k51741
add a comment |
add a comment |
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