Mixing
Extracting and using information from other rows in a dataframe?
So I have a data frame like the following:
id age friend1 friend2
01 15 02 05
02 23 01 05
03 51 04
04 41 03
05 33 01 02
How can I calculate the average age of the friends and create a new column that stores this information? So ideally it looks something like this:
id age friend1 friend2 AvgAgeF
01 15 02 05 28
02 23 01 05 24
03 51 04 41
04 41 03 51
05 33 01 02 19
Currently I can run the following code to do this:
inx <- grep("friend", names(dat))
tmp <- sapply(inx, function(i) dat$age[dat[[i]]])
dat$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
However, when the "id" for the people aren't in order or aren't numbers at all such as in the following example:
dat <- read.table(text = "
id age friend1 friend2
Bob 15 Jack Sam
Jack 23 Sam Bob
Sam 51 Bob Jack
Sara 41 Henry NA
Henry 33 Sara NA
", header = TRUE)
The code appears to process incorrectly resulting in the following table (All but Sam's AvgAgeF is incorrect in this case):
id age friend1 friend2 AvgAgeF
1 Bob 15 Jack Sam 51
2 Jack 23 Sam Bob 28
3 Sam 51 Bob Jack 19
4 Sara 41 Henry <NA> 23
5 Henry 33 Sara <NA> 33
How can I solve for this without changing the Names into numerical ids.
r
asked Nov 21 '18 at 23:56
JackJack
164
add a comment |
So I have a data frame like the following:
id age friend1 friend2
01 15 02 05
02 23 01 05
03 51 04
04 41 03
05 33 01 02
How can I calculate the average age of the friends and create a new column that stores this information? So ideally it looks something like this:
id age friend1 friend2 AvgAgeF
01 15 02 05 28
02 23 01 05 24
03 51 04 41
04 41 03 51
05 33 01 02 19
Currently I can run the following code to do this:
inx <- grep("friend", names(dat))
tmp <- sapply(inx, function(i) dat$age[dat[[i]]])
dat$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
However, when the "id" for the people aren't in order or aren't numbers at all such as in the following example:
dat <- read.table(text = "
id age friend1 friend2
Bob 15 Jack Sam
Jack 23 Sam Bob
Sam 51 Bob Jack
Sara 41 Henry NA
Henry 33 Sara NA
", header = TRUE)
The code appears to process incorrectly resulting in the following table (All but Sam's AvgAgeF is incorrect in this case):
id age friend1 friend2 AvgAgeF
1 Bob 15 Jack Sam 51
2 Jack 23 Sam Bob 28
3 Sam 51 Bob Jack 19
4 Sara 41 Henry <NA> 23
5 Henry 33 Sara <NA> 33
How can I solve for this without changing the Names into numerical ids.
r
asked Nov 21 '18 at 23:56
JackJack
164
add a comment |
So I have a data frame like the following:
id age friend1 friend2
01 15 02 05
02 23 01 05
03 51 04
04 41 03
05 33 01 02
How can I calculate the average age of the friends and create a new column that stores this information? So ideally it looks something like this:
id age friend1 friend2 AvgAgeF
01 15 02 05 28
02 23 01 05 24
03 51 04 41
04 41 03 51
05 33 01 02 19
Currently I can run the following code to do this:
inx <- grep("friend", names(dat))
tmp <- sapply(inx, function(i) dat$age[dat[[i]]])
dat$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
However, when the "id" for the people aren't in order or aren't numbers at all such as in the following example:
dat <- read.table(text = "
id age friend1 friend2
Bob 15 Jack Sam
Jack 23 Sam Bob
Sam 51 Bob Jack
Sara 41 Henry NA
Henry 33 Sara NA
", header = TRUE)
The code appears to process incorrectly resulting in the following table (All but Sam's AvgAgeF is incorrect in this case):
id age friend1 friend2 AvgAgeF
1 Bob 15 Jack Sam 51
2 Jack 23 Sam Bob 28
3 Sam 51 Bob Jack 19
4 Sara 41 Henry <NA> 23
5 Henry 33 Sara <NA> 33
How can I solve for this without changing the Names into numerical ids.
r
asked Nov 21 '18 at 23:56
JackJack
164
So I have a data frame like the following:
id age friend1 friend2
01 15 02 05
02 23 01 05
03 51 04
04 41 03
05 33 01 02
How can I calculate the average age of the friends and create a new column that stores this information? So ideally it looks something like this:
id age friend1 friend2 AvgAgeF
01 15 02 05 28
02 23 01 05 24
03 51 04 41
04 41 03 51
05 33 01 02 19
Currently I can run the following code to do this:
inx <- grep("friend", names(dat))
tmp <- sapply(inx, function(i) dat$age[dat[[i]]])
dat$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
However, when the "id" for the people aren't in order or aren't numbers at all such as in the following example:
dat <- read.table(text = "
id age friend1 friend2
Bob 15 Jack Sam
Jack 23 Sam Bob
Sam 51 Bob Jack
Sara 41 Henry NA
Henry 33 Sara NA
", header = TRUE)
The code appears to process incorrectly resulting in the following table (All but Sam's AvgAgeF is incorrect in this case):
id age friend1 friend2 AvgAgeF
1 Bob 15 Jack Sam 51
2 Jack 23 Sam Bob 28
3 Sam 51 Bob Jack 19
4 Sara 41 Henry <NA> 23
5 Henry 33 Sara <NA> 33
How can I solve for this without changing the Names into numerical ids.
r
r
asked Nov 21 '18 at 23:56
JackJack
164
asked Nov 21 '18 at 23:56
JackJack
164
edited Nov 23 '18 at 22:26
Jack
asked Nov 21 '18 at 23:56
JackJack
164
asked Nov 21 '18 at 23:56
JackJack
164
asked Nov 21 '18 at 23:56
JackJack
164
164
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Subset column age with friend1 and friend2 as indices, then cbind the resulting vectors. It's now a simple call to rowMeans.
tmp <- with(dat, cbind(age[friend1], age[friend2]))
dat$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
rm(tmp)
dat
# id age friend1 friend2 AvgAgeF
#1 1 15 2 5 28
#2 2 23 1 5 24
#3 3 51 4 NA 41
#4 4 41 3 NA 51
#5 5 33 1 2 19
Edit.
If there are an indeterminate number of friend* columns, another strategy must be followed.
First a dataset, starting with the question data.
set.seed(1234)
tmp <- matrix(sample(c(NA, 1:5), 20, TRUE), nrow = 5)
colnames(tmp) <- paste0("friend", 3:6)
dat <- cbind(dat, tmp)
Now, use grep to get the friend columns.
inx <- grep("friend", names(dat))
tmp <- sapply(inx, function(i) dat$age[dat[[i]]])
dat$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
Final cleanup.
rm(tmp)
Edit 2 - id are character strings.
If the columns id and friend* are character strings instead of numeric, index column age with match.
inx <- grep("friend", names(dat2))
tmp <- sapply(inx, function(i) {
x <- as.character(dat2[[i]])
y <- as.character(dat2$id)
dat2$age[match(x, y)]
})
dat2$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
rm(tmp)
dat2
# id age friend1 friend2 AvgAgeF
#1 Bob 15 Jack Sam 37
#2 Jack 23 Sam Bob 33
#3 Sam 51 Bob Jack 19
#4 Sara 41 Henry <NA> 33
#5 Henry 33 Sara <NA> 41
Data.
dat <- read.table(text = "
id age friend1 friend2
01 15 02 05
02 23 01 05
03 51 04 NA
04 41 03 NA
05 33 01 02
", header = TRUE)
dat2 <- read.table(text = "
id age friend1 friend2
Bob 15 Jack Sam
Jack 23 Sam Bob
Sam 51 Bob Jack
Sara 41 Henry NA
Henry 33 Sara NA
", header = TRUE)
answered Nov 22 '18 at 0:26
Rui BarradasRui Barradas
17k51730
Is there an easier way to do this if I had, let's say like 20 "friend" columns and I didn't want to do it manually?
– Jack
Nov 22 '18 at 19:04
@Jack Done, see the edit.
– Rui Barradas
Nov 22 '18 at 20:33
I was using this code and I realized that if the id is numerically " out of place " per say, that it ceases to work properly. The error I got was when I replaced the id 05 with 06 and all instances of it with the id 06. When this happens, the code treats the id as if it is a NA. Is there a way to go around this problem? I realize that I can just convert all the ids so that there isn't this problem but in the data I am handling I cannot do this since the id contains other information.
– Jack
Nov 23 '18 at 20:19
@Jack I don't understand, the code only processes the other columns, why is theidrelevant? Can you edit the question with an example?
– Rui Barradas
Nov 23 '18 at 22:10
I edited the post. So in this case the id's/identifiers are no longer numerical and when this happens the code doesn't run correctly.
– Jack
Nov 23 '18 at 22:30
|
show 1 more comment
Your Answer
StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53422093%2fextracting-and-using-information-from-other-rows-in-a-dataframe%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Subset column age with friend1 and friend2 as indices, then cbind the resulting vectors. It's now a simple call to rowMeans.
tmp <- with(dat, cbind(age[friend1], age[friend2]))
dat$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
rm(tmp)
dat
# id age friend1 friend2 AvgAgeF
#1 1 15 2 5 28
#2 2 23 1 5 24
#3 3 51 4 NA 41
#4 4 41 3 NA 51
#5 5 33 1 2 19
Edit.
If there are an indeterminate number of friend* columns, another strategy must be followed.
First a dataset, starting with the question data.
set.seed(1234)
tmp <- matrix(sample(c(NA, 1:5), 20, TRUE), nrow = 5)
colnames(tmp) <- paste0("friend", 3:6)
dat <- cbind(dat, tmp)
Now, use grep to get the friend columns.
inx <- grep("friend", names(dat))
tmp <- sapply(inx, function(i) dat$age[dat[[i]]])
dat$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
Final cleanup.
rm(tmp)
Edit 2 - id are character strings.
If the columns id and friend* are character strings instead of numeric, index column age with match.
inx <- grep("friend", names(dat2))
tmp <- sapply(inx, function(i) {
x <- as.character(dat2[[i]])
y <- as.character(dat2$id)
dat2$age[match(x, y)]
})
dat2$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
rm(tmp)
dat2
# id age friend1 friend2 AvgAgeF
#1 Bob 15 Jack Sam 37
#2 Jack 23 Sam Bob 33
#3 Sam 51 Bob Jack 19
#4 Sara 41 Henry <NA> 33
#5 Henry 33 Sara <NA> 41
Data.
dat <- read.table(text = "
id age friend1 friend2
01 15 02 05
02 23 01 05
03 51 04 NA
04 41 03 NA
05 33 01 02
", header = TRUE)
dat2 <- read.table(text = "
id age friend1 friend2
Bob 15 Jack Sam
Jack 23 Sam Bob
Sam 51 Bob Jack
Sara 41 Henry NA
Henry 33 Sara NA
", header = TRUE)
answered Nov 22 '18 at 0:26
Rui BarradasRui Barradas
17k51730
Is there an easier way to do this if I had, let's say like 20 "friend" columns and I didn't want to do it manually?
– Jack
Nov 22 '18 at 19:04
@Jack Done, see the edit.
– Rui Barradas
Nov 22 '18 at 20:33
I was using this code and I realized that if the id is numerically " out of place " per say, that it ceases to work properly. The error I got was when I replaced the id 05 with 06 and all instances of it with the id 06. When this happens, the code treats the id as if it is a NA. Is there a way to go around this problem? I realize that I can just convert all the ids so that there isn't this problem but in the data I am handling I cannot do this since the id contains other information.
– Jack
Nov 23 '18 at 20:19
@Jack I don't understand, the code only processes the other columns, why is theidrelevant? Can you edit the question with an example?
– Rui Barradas
Nov 23 '18 at 22:10
I edited the post. So in this case the id's/identifiers are no longer numerical and when this happens the code doesn't run correctly.
– Jack
Nov 23 '18 at 22:30
|
show 1 more comment
Subset column age with friend1 and friend2 as indices, then cbind the resulting vectors. It's now a simple call to rowMeans.
tmp <- with(dat, cbind(age[friend1], age[friend2]))
dat$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
rm(tmp)
dat
# id age friend1 friend2 AvgAgeF
#1 1 15 2 5 28
#2 2 23 1 5 24
#3 3 51 4 NA 41
#4 4 41 3 NA 51
#5 5 33 1 2 19
Edit.
If there are an indeterminate number of friend* columns, another strategy must be followed.
First a dataset, starting with the question data.
set.seed(1234)
tmp <- matrix(sample(c(NA, 1:5), 20, TRUE), nrow = 5)
colnames(tmp) <- paste0("friend", 3:6)
dat <- cbind(dat, tmp)
Now, use grep to get the friend columns.
inx <- grep("friend", names(dat))
tmp <- sapply(inx, function(i) dat$age[dat[[i]]])
dat$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
Final cleanup.
rm(tmp)
Edit 2 - id are character strings.
If the columns id and friend* are character strings instead of numeric, index column age with match.
inx <- grep("friend", names(dat2))
tmp <- sapply(inx, function(i) {
x <- as.character(dat2[[i]])
y <- as.character(dat2$id)
dat2$age[match(x, y)]
})
dat2$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
rm(tmp)
dat2
# id age friend1 friend2 AvgAgeF
#1 Bob 15 Jack Sam 37
#2 Jack 23 Sam Bob 33
#3 Sam 51 Bob Jack 19
#4 Sara 41 Henry <NA> 33
#5 Henry 33 Sara <NA> 41
Data.
dat <- read.table(text = "
id age friend1 friend2
01 15 02 05
02 23 01 05
03 51 04 NA
04 41 03 NA
05 33 01 02
", header = TRUE)
dat2 <- read.table(text = "
id age friend1 friend2
Bob 15 Jack Sam
Jack 23 Sam Bob
Sam 51 Bob Jack
Sara 41 Henry NA
Henry 33 Sara NA
", header = TRUE)
answered Nov 22 '18 at 0:26
Rui BarradasRui Barradas
17k51730
Is there an easier way to do this if I had, let's say like 20 "friend" columns and I didn't want to do it manually?
– Jack
Nov 22 '18 at 19:04
@Jack Done, see the edit.
– Rui Barradas
Nov 22 '18 at 20:33
I was using this code and I realized that if the id is numerically " out of place " per say, that it ceases to work properly. The error I got was when I replaced the id 05 with 06 and all instances of it with the id 06. When this happens, the code treats the id as if it is a NA. Is there a way to go around this problem? I realize that I can just convert all the ids so that there isn't this problem but in the data I am handling I cannot do this since the id contains other information.
– Jack
Nov 23 '18 at 20:19
@Jack I don't understand, the code only processes the other columns, why is theidrelevant? Can you edit the question with an example?
– Rui Barradas
Nov 23 '18 at 22:10
I edited the post. So in this case the id's/identifiers are no longer numerical and when this happens the code doesn't run correctly.
– Jack
Nov 23 '18 at 22:30
|
show 1 more comment
Subset column age with friend1 and friend2 as indices, then cbind the resulting vectors. It's now a simple call to rowMeans.
tmp <- with(dat, cbind(age[friend1], age[friend2]))
dat$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
rm(tmp)
dat
# id age friend1 friend2 AvgAgeF
#1 1 15 2 5 28
#2 2 23 1 5 24
#3 3 51 4 NA 41
#4 4 41 3 NA 51
#5 5 33 1 2 19
Edit.
If there are an indeterminate number of friend* columns, another strategy must be followed.
First a dataset, starting with the question data.
set.seed(1234)
tmp <- matrix(sample(c(NA, 1:5), 20, TRUE), nrow = 5)
colnames(tmp) <- paste0("friend", 3:6)
dat <- cbind(dat, tmp)
Now, use grep to get the friend columns.
inx <- grep("friend", names(dat))
tmp <- sapply(inx, function(i) dat$age[dat[[i]]])
dat$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
Final cleanup.
rm(tmp)
Edit 2 - id are character strings.
If the columns id and friend* are character strings instead of numeric, index column age with match.
inx <- grep("friend", names(dat2))
tmp <- sapply(inx, function(i) {
x <- as.character(dat2[[i]])
y <- as.character(dat2$id)
dat2$age[match(x, y)]
})
dat2$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
rm(tmp)
dat2
# id age friend1 friend2 AvgAgeF
#1 Bob 15 Jack Sam 37
#2 Jack 23 Sam Bob 33
#3 Sam 51 Bob Jack 19
#4 Sara 41 Henry <NA> 33
#5 Henry 33 Sara <NA> 41
Data.
dat <- read.table(text = "
id age friend1 friend2
01 15 02 05
02 23 01 05
03 51 04 NA
04 41 03 NA
05 33 01 02
", header = TRUE)
dat2 <- read.table(text = "
id age friend1 friend2
Bob 15 Jack Sam
Jack 23 Sam Bob
Sam 51 Bob Jack
Sara 41 Henry NA
Henry 33 Sara NA
", header = TRUE)
answered Nov 22 '18 at 0:26
Rui BarradasRui Barradas
17k51730
Subset column age with friend1 and friend2 as indices, then cbind the resulting vectors. It's now a simple call to rowMeans.
tmp <- with(dat, cbind(age[friend1], age[friend2]))
dat$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
rm(tmp)
dat
# id age friend1 friend2 AvgAgeF
#1 1 15 2 5 28
#2 2 23 1 5 24
#3 3 51 4 NA 41
#4 4 41 3 NA 51
#5 5 33 1 2 19
Edit.
If there are an indeterminate number of friend* columns, another strategy must be followed.
First a dataset, starting with the question data.
set.seed(1234)
tmp <- matrix(sample(c(NA, 1:5), 20, TRUE), nrow = 5)
colnames(tmp) <- paste0("friend", 3:6)
dat <- cbind(dat, tmp)
Now, use grep to get the friend columns.
inx <- grep("friend", names(dat))
tmp <- sapply(inx, function(i) dat$age[dat[[i]]])
dat$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
Final cleanup.
rm(tmp)
Edit 2 - id are character strings.
If the columns id and friend* are character strings instead of numeric, index column age with match.
inx <- grep("friend", names(dat2))
tmp <- sapply(inx, function(i) {
x <- as.character(dat2[[i]])
y <- as.character(dat2$id)
dat2$age[match(x, y)]
})
dat2$AvgAgeF <- rowMeans(tmp, na.rm = TRUE)
rm(tmp)
dat2
# id age friend1 friend2 AvgAgeF
#1 Bob 15 Jack Sam 37
#2 Jack 23 Sam Bob 33
#3 Sam 51 Bob Jack 19
#4 Sara 41 Henry <NA> 33
#5 Henry 33 Sara <NA> 41
Data.
dat <- read.table(text = "
id age friend1 friend2
01 15 02 05
02 23 01 05
03 51 04 NA
04 41 03 NA
05 33 01 02
", header = TRUE)
dat2 <- read.table(text = "
id age friend1 friend2
Bob 15 Jack Sam
Jack 23 Sam Bob
Sam 51 Bob Jack
Sara 41 Henry NA
Henry 33 Sara NA
", header = TRUE)
answered Nov 22 '18 at 0:26
Rui BarradasRui Barradas
17k51730
edited Nov 24 '18 at 11:40
answered Nov 22 '18 at 0:26
Rui BarradasRui Barradas
17k51730
answered Nov 22 '18 at 0:26
Rui BarradasRui Barradas
17k51730
answered Nov 22 '18 at 0:26
Rui BarradasRui Barradas
17k51730
17k51730
Is there an easier way to do this if I had, let's say like 20 "friend" columns and I didn't want to do it manually?
– Jack
Nov 22 '18 at 19:04
@Jack Done, see the edit.
– Rui Barradas
Nov 22 '18 at 20:33
I was using this code and I realized that if the id is numerically " out of place " per say, that it ceases to work properly. The error I got was when I replaced the id 05 with 06 and all instances of it with the id 06. When this happens, the code treats the id as if it is a NA. Is there a way to go around this problem? I realize that I can just convert all the ids so that there isn't this problem but in the data I am handling I cannot do this since the id contains other information.
– Jack
Nov 23 '18 at 20:19
@Jack I don't understand, the code only processes the other columns, why is theidrelevant? Can you edit the question with an example?
– Rui Barradas
Nov 23 '18 at 22:10
I edited the post. So in this case the id's/identifiers are no longer numerical and when this happens the code doesn't run correctly.
– Jack
Nov 23 '18 at 22:30
|
show 1 more comment
Is there an easier way to do this if I had, let's say like 20 "friend" columns and I didn't want to do it manually?
– Jack
Nov 22 '18 at 19:04
@Jack Done, see the edit.
– Rui Barradas
Nov 22 '18 at 20:33
I was using this code and I realized that if the id is numerically " out of place " per say, that it ceases to work properly. The error I got was when I replaced the id 05 with 06 and all instances of it with the id 06. When this happens, the code treats the id as if it is a NA. Is there a way to go around this problem? I realize that I can just convert all the ids so that there isn't this problem but in the data I am handling I cannot do this since the id contains other information.
– Jack
Nov 23 '18 at 20:19
@Jack I don't understand, the code only processes the other columns, why is theidrelevant? Can you edit the question with an example?
– Rui Barradas
Nov 23 '18 at 22:10
I edited the post. So in this case the id's/identifiers are no longer numerical and when this happens the code doesn't run correctly.
– Jack
Nov 23 '18 at 22:30
Is there an easier way to do this if I had, let's say like 20 "friend" columns and I didn't want to do it manually?
– Jack
Nov 22 '18 at 19:04
Is there an easier way to do this if I had, let's say like 20 "friend" columns and I didn't want to do it manually?
– Jack
Nov 22 '18 at 19:04
@Jack Done, see the edit.
– Rui Barradas
Nov 22 '18 at 20:33
@Jack Done, see the edit.
– Rui Barradas
Nov 22 '18 at 20:33
I was using this code and I realized that if the id is numerically " out of place " per say, that it ceases to work properly. The error I got was when I replaced the id 05 with 06 and all instances of it with the id 06. When this happens, the code treats the id as if it is a NA. Is there a way to go around this problem? I realize that I can just convert all the ids so that there isn't this problem but in the data I am handling I cannot do this since the id contains other information.
– Jack
Nov 23 '18 at 20:19
I was using this code and I realized that if the id is numerically " out of place " per say, that it ceases to work properly. The error I got was when I replaced the id 05 with 06 and all instances of it with the id 06. When this happens, the code treats the id as if it is a NA. Is there a way to go around this problem? I realize that I can just convert all the ids so that there isn't this problem but in the data I am handling I cannot do this since the id contains other information.
– Jack
Nov 23 '18 at 20:19
@Jack I don't understand, the code only processes the other columns, why is the
id relevant? Can you edit the question with an example?– Rui Barradas
Nov 23 '18 at 22:10
@Jack I don't understand, the code only processes the other columns, why is the
id relevant? Can you edit the question with an example?– Rui Barradas
Nov 23 '18 at 22:10
I edited the post. So in this case the id's/identifiers are no longer numerical and when this happens the code doesn't run correctly.
– Jack
Nov 23 '18 at 22:30
I edited the post. So in this case the id's/identifiers are no longer numerical and when this happens the code doesn't run correctly.
– Jack
Nov 23 '18 at 22:30
|
show 1 more comment
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53422093%2fextracting-and-using-information-from-other-rows-in-a-dataframe%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
