Problem while using Group_by/mutate function in R
0
I am trying to calculate the variance between preceding rows using group_by and lag on the below dataframe
ID DATE Value
555 1/9/2018 10
555 2/9/2018 20
555 3/9/2018 50
555 4/9/2018 70
000 1/9/2018 0
000 2/9/2018 5
000 3/9/2018 15
111 1/9/2018 0
111 2/9/2018 15
111 3/9/2018 20
111 4/9/2018 25
The difference is supposed to show as follow:
ID DATE Value Diff
555 1/9/2018 10 0
555 2/9/2018 20 10
555 3/9/2018 50 30
555 4/9/2018 70 20
000 1/9/2018 0 0
000 2/9/2018 5 5
000 3/9/2018 15 10
111 1/9/2018 0 0
111 2/9/2018 15 15
111 3/9/2018 20 5
111 4/9/2018 25 5
By using this line of code
data %>%
group_by(ID) %>%
arrange(DATE) %>%
mutate(Diff= Value - lag(Value, default = first(Value)))
It skips the grouping condition by ID and calculate the difference between all the rows like this:
ID DATE Value Diff
555 1/9/2018 10 0
555 2/9/2018 20 10
555 3/9/2018 50 30
555 4/9/2018 70 20
000 1/9/2018 0 -70
000 2/9/2018 5 5
000 3/9/2018 15 10
111 1/9/2018 0 -15
111 2/9/2018 15 15
111 3/9/2018 20 5
111 4/9/2018 25 5
r group-by dplyr rstudio mutate
asked Nov 24 '18 at 23:44
Zidane AhmedZidane Ahmed
112
add a comment |
0
I am trying to calculate the variance between preceding rows using group_by and lag on the below dataframe
ID DATE Value
555 1/9/2018 10
555 2/9/2018 20
555 3/9/2018 50
555 4/9/2018 70
000 1/9/2018 0
000 2/9/2018 5
000 3/9/2018 15
111 1/9/2018 0
111 2/9/2018 15
111 3/9/2018 20
111 4/9/2018 25
The difference is supposed to show as follow:
ID DATE Value Diff
555 1/9/2018 10 0
555 2/9/2018 20 10
555 3/9/2018 50 30
555 4/9/2018 70 20
000 1/9/2018 0 0
000 2/9/2018 5 5
000 3/9/2018 15 10
111 1/9/2018 0 0
111 2/9/2018 15 15
111 3/9/2018 20 5
111 4/9/2018 25 5
By using this line of code
data %>%
group_by(ID) %>%
arrange(DATE) %>%
mutate(Diff= Value - lag(Value, default = first(Value)))
It skips the grouping condition by ID and calculate the difference between all the rows like this:
ID DATE Value Diff
555 1/9/2018 10 0
555 2/9/2018 20 10
555 3/9/2018 50 30
555 4/9/2018 70 20
000 1/9/2018 0 -70
000 2/9/2018 5 5
000 3/9/2018 15 10
111 1/9/2018 0 -15
111 2/9/2018 15 15
111 3/9/2018 20 5
111 4/9/2018 25 5
r group-by dplyr rstudio mutate
asked Nov 24 '18 at 23:44
Zidane AhmedZidane Ahmed
112
1
To me your code works fine, just the order or rows is different.
– Julius Vainora
Nov 24 '18 at 23:52
Actually, your code works fine for me as well.
– arg0naut
Nov 24 '18 at 23:57
@JuliusVainora can concatenation affects the group_by? because the ID is originally concatenated from 2 columns
– Zidane Ahmed
Nov 25 '18 at 0:06
@ZidaneAhmed, if I understand correctly what you mean, concatenation alone shouldn't be a problem, but perhaps the way it was done (the order of different actions, e.g.) has an effect. I guess you should revise your example.
– Julius Vainora
Nov 25 '18 at 0:16
Your code works fine. Diff = c(0, diff(Value)) is another option where you dont need to use lag...
– Ika8
Nov 27 '18 at 15:07
add a comment |
0
0
0
I am trying to calculate the variance between preceding rows using group_by and lag on the below dataframe
ID DATE Value
555 1/9/2018 10
555 2/9/2018 20
555 3/9/2018 50
555 4/9/2018 70
000 1/9/2018 0
000 2/9/2018 5
000 3/9/2018 15
111 1/9/2018 0
111 2/9/2018 15
111 3/9/2018 20
111 4/9/2018 25
The difference is supposed to show as follow:
ID DATE Value Diff
555 1/9/2018 10 0
555 2/9/2018 20 10
555 3/9/2018 50 30
555 4/9/2018 70 20
000 1/9/2018 0 0
000 2/9/2018 5 5
000 3/9/2018 15 10
111 1/9/2018 0 0
111 2/9/2018 15 15
111 3/9/2018 20 5
111 4/9/2018 25 5
By using this line of code
data %>%
group_by(ID) %>%
arrange(DATE) %>%
mutate(Diff= Value - lag(Value, default = first(Value)))
It skips the grouping condition by ID and calculate the difference between all the rows like this:
ID DATE Value Diff
555 1/9/2018 10 0
555 2/9/2018 20 10
555 3/9/2018 50 30
555 4/9/2018 70 20
000 1/9/2018 0 -70
000 2/9/2018 5 5
000 3/9/2018 15 10
111 1/9/2018 0 -15
111 2/9/2018 15 15
111 3/9/2018 20 5
111 4/9/2018 25 5
r group-by dplyr rstudio mutate
asked Nov 24 '18 at 23:44
Zidane AhmedZidane Ahmed
112
I am trying to calculate the variance between preceding rows using group_by and lag on the below dataframe
ID DATE Value
555 1/9/2018 10
555 2/9/2018 20
555 3/9/2018 50
555 4/9/2018 70
000 1/9/2018 0
000 2/9/2018 5
000 3/9/2018 15
111 1/9/2018 0
111 2/9/2018 15
111 3/9/2018 20
111 4/9/2018 25
The difference is supposed to show as follow:
ID DATE Value Diff
555 1/9/2018 10 0
555 2/9/2018 20 10
555 3/9/2018 50 30
555 4/9/2018 70 20
000 1/9/2018 0 0
000 2/9/2018 5 5
000 3/9/2018 15 10
111 1/9/2018 0 0
111 2/9/2018 15 15
111 3/9/2018 20 5
111 4/9/2018 25 5
By using this line of code
data %>%
group_by(ID) %>%
arrange(DATE) %>%
mutate(Diff= Value - lag(Value, default = first(Value)))
It skips the grouping condition by ID and calculate the difference between all the rows like this:
ID DATE Value Diff
555 1/9/2018 10 0
555 2/9/2018 20 10
555 3/9/2018 50 30
555 4/9/2018 70 20
000 1/9/2018 0 -70
000 2/9/2018 5 5
000 3/9/2018 15 10
111 1/9/2018 0 -15
111 2/9/2018 15 15
111 3/9/2018 20 5
111 4/9/2018 25 5
r group-by dplyr rstudio mutate
r group-by dplyr rstudio mutate
asked Nov 24 '18 at 23:44
Zidane AhmedZidane Ahmed
112
asked Nov 24 '18 at 23:44
Zidane AhmedZidane Ahmed
112
asked Nov 24 '18 at 23:44
Zidane AhmedZidane Ahmed
112
asked Nov 24 '18 at 23:44
Zidane AhmedZidane Ahmed
112
asked Nov 24 '18 at 23:44
Zidane AhmedZidane Ahmed
112
112
1
To me your code works fine, just the order or rows is different.
– Julius Vainora
Nov 24 '18 at 23:52
Actually, your code works fine for me as well.
– arg0naut
Nov 24 '18 at 23:57
@JuliusVainora can concatenation affects the group_by? because the ID is originally concatenated from 2 columns
– Zidane Ahmed
Nov 25 '18 at 0:06
@ZidaneAhmed, if I understand correctly what you mean, concatenation alone shouldn't be a problem, but perhaps the way it was done (the order of different actions, e.g.) has an effect. I guess you should revise your example.
– Julius Vainora
Nov 25 '18 at 0:16
Your code works fine. Diff = c(0, diff(Value)) is another option where you dont need to use lag...
– Ika8
Nov 27 '18 at 15:07
add a comment |
1
To me your code works fine, just the order or rows is different.
– Julius Vainora
Nov 24 '18 at 23:52
Actually, your code works fine for me as well.
– arg0naut
Nov 24 '18 at 23:57
@JuliusVainora can concatenation affects the group_by? because the ID is originally concatenated from 2 columns
– Zidane Ahmed
Nov 25 '18 at 0:06
@ZidaneAhmed, if I understand correctly what you mean, concatenation alone shouldn't be a problem, but perhaps the way it was done (the order of different actions, e.g.) has an effect. I guess you should revise your example.
– Julius Vainora
Nov 25 '18 at 0:16
Your code works fine. Diff = c(0, diff(Value)) is another option where you dont need to use lag...
– Ika8
Nov 27 '18 at 15:07
1
1
To me your code works fine, just the order or rows is different.
– Julius Vainora
Nov 24 '18 at 23:52
To me your code works fine, just the order or rows is different.
– Julius Vainora
Nov 24 '18 at 23:52
Actually, your code works fine for me as well.
– arg0naut
Nov 24 '18 at 23:57
Actually, your code works fine for me as well.
– arg0naut
Nov 24 '18 at 23:57
@JuliusVainora can concatenation affects the group_by? because the ID is originally concatenated from 2 columns
– Zidane Ahmed
Nov 25 '18 at 0:06
@JuliusVainora can concatenation affects the group_by? because the ID is originally concatenated from 2 columns
– Zidane Ahmed
Nov 25 '18 at 0:06
@ZidaneAhmed, if I understand correctly what you mean, concatenation alone shouldn't be a problem, but perhaps the way it was done (the order of different actions, e.g.) has an effect. I guess you should revise your example.
– Julius Vainora
Nov 25 '18 at 0:16
@ZidaneAhmed, if I understand correctly what you mean, concatenation alone shouldn't be a problem, but perhaps the way it was done (the order of different actions, e.g.) has an effect. I guess you should revise your example.
– Julius Vainora
Nov 25 '18 at 0:16
Your code works fine. Diff = c(0, diff(Value)) is another option where you dont need to use lag...
– Ika8
Nov 27 '18 at 15:07
Your code works fine. Diff = c(0, diff(Value)) is another option where you dont need to use lag...
– Ika8
Nov 27 '18 at 15:07
add a comment |
1 Answer
1
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Your code works for me (with a little tweak).
> data_new
# A tibble: 11 x 4
# Groups: ID [3]
ID DATE Value Diff
<chr> <fct> <int> <int>
1 555 1/9/2018 10 0
2 555 2/9/2018 20 10
3 555 3/9/2018 50 30
4 555 4/9/2018 70 20
5 000 1/9/2018 0 0
6 000 2/9/2018 5 5
7 000 3/9/2018 15 10
8 111 1/9/2018 0 0
9 111 2/9/2018 15 15
10 111 3/9/2018 20 5
11 111 4/9/2018 25 5
Code
data_new <- data %>%
group_by(ID) %>%
mutate(Diff = Value - lag(Value, default = first(Value)))
Data
data <- structure(list(ID = c("555", "555", "555", "555", "000", "000",
"000", "111", "111", "111", "111"), DATE = structure(c(1L, 2L,
3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 4L), .Label = c("1/9/2018", "2/9/2018",
"3/9/2018", "4/9/2018"), class = "factor"), Value = c(10L, 20L,
50L, 70L, 0L, 5L, 15L, 0L, 15L, 20L, 25L)), row.names = c(NA,
-11L), class = "data.frame")
answered Nov 24 '18 at 23:56
RomanRoman
2,1341532
add a comment |
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0
Your code works for me (with a little tweak).
> data_new
# A tibble: 11 x 4
# Groups: ID [3]
ID DATE Value Diff
<chr> <fct> <int> <int>
1 555 1/9/2018 10 0
2 555 2/9/2018 20 10
3 555 3/9/2018 50 30
4 555 4/9/2018 70 20
5 000 1/9/2018 0 0
6 000 2/9/2018 5 5
7 000 3/9/2018 15 10
8 111 1/9/2018 0 0
9 111 2/9/2018 15 15
10 111 3/9/2018 20 5
11 111 4/9/2018 25 5
Code
data_new <- data %>%
group_by(ID) %>%
mutate(Diff = Value - lag(Value, default = first(Value)))
Data
data <- structure(list(ID = c("555", "555", "555", "555", "000", "000",
"000", "111", "111", "111", "111"), DATE = structure(c(1L, 2L,
3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 4L), .Label = c("1/9/2018", "2/9/2018",
"3/9/2018", "4/9/2018"), class = "factor"), Value = c(10L, 20L,
50L, 70L, 0L, 5L, 15L, 0L, 15L, 20L, 25L)), row.names = c(NA,
-11L), class = "data.frame")
answered Nov 24 '18 at 23:56
RomanRoman
2,1341532
add a comment |
0
Your code works for me (with a little tweak).
> data_new
# A tibble: 11 x 4
# Groups: ID [3]
ID DATE Value Diff
<chr> <fct> <int> <int>
1 555 1/9/2018 10 0
2 555 2/9/2018 20 10
3 555 3/9/2018 50 30
4 555 4/9/2018 70 20
5 000 1/9/2018 0 0
6 000 2/9/2018 5 5
7 000 3/9/2018 15 10
8 111 1/9/2018 0 0
9 111 2/9/2018 15 15
10 111 3/9/2018 20 5
11 111 4/9/2018 25 5
Code
data_new <- data %>%
group_by(ID) %>%
mutate(Diff = Value - lag(Value, default = first(Value)))
Data
data <- structure(list(ID = c("555", "555", "555", "555", "000", "000",
"000", "111", "111", "111", "111"), DATE = structure(c(1L, 2L,
3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 4L), .Label = c("1/9/2018", "2/9/2018",
"3/9/2018", "4/9/2018"), class = "factor"), Value = c(10L, 20L,
50L, 70L, 0L, 5L, 15L, 0L, 15L, 20L, 25L)), row.names = c(NA,
-11L), class = "data.frame")
answered Nov 24 '18 at 23:56
RomanRoman
2,1341532
add a comment |
0
0
0
Your code works for me (with a little tweak).
> data_new
# A tibble: 11 x 4
# Groups: ID [3]
ID DATE Value Diff
<chr> <fct> <int> <int>
1 555 1/9/2018 10 0
2 555 2/9/2018 20 10
3 555 3/9/2018 50 30
4 555 4/9/2018 70 20
5 000 1/9/2018 0 0
6 000 2/9/2018 5 5
7 000 3/9/2018 15 10
8 111 1/9/2018 0 0
9 111 2/9/2018 15 15
10 111 3/9/2018 20 5
11 111 4/9/2018 25 5
Code
data_new <- data %>%
group_by(ID) %>%
mutate(Diff = Value - lag(Value, default = first(Value)))
Data
data <- structure(list(ID = c("555", "555", "555", "555", "000", "000",
"000", "111", "111", "111", "111"), DATE = structure(c(1L, 2L,
3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 4L), .Label = c("1/9/2018", "2/9/2018",
"3/9/2018", "4/9/2018"), class = "factor"), Value = c(10L, 20L,
50L, 70L, 0L, 5L, 15L, 0L, 15L, 20L, 25L)), row.names = c(NA,
-11L), class = "data.frame")
answered Nov 24 '18 at 23:56
RomanRoman
2,1341532
Your code works for me (with a little tweak).
> data_new
# A tibble: 11 x 4
# Groups: ID [3]
ID DATE Value Diff
<chr> <fct> <int> <int>
1 555 1/9/2018 10 0
2 555 2/9/2018 20 10
3 555 3/9/2018 50 30
4 555 4/9/2018 70 20
5 000 1/9/2018 0 0
6 000 2/9/2018 5 5
7 000 3/9/2018 15 10
8 111 1/9/2018 0 0
9 111 2/9/2018 15 15
10 111 3/9/2018 20 5
11 111 4/9/2018 25 5
Code
data_new <- data %>%
group_by(ID) %>%
mutate(Diff = Value - lag(Value, default = first(Value)))
Data
data <- structure(list(ID = c("555", "555", "555", "555", "000", "000",
"000", "111", "111", "111", "111"), DATE = structure(c(1L, 2L,
3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 4L), .Label = c("1/9/2018", "2/9/2018",
"3/9/2018", "4/9/2018"), class = "factor"), Value = c(10L, 20L,
50L, 70L, 0L, 5L, 15L, 0L, 15L, 20L, 25L)), row.names = c(NA,
-11L), class = "data.frame")
answered Nov 24 '18 at 23:56
RomanRoman
2,1341532
answered Nov 24 '18 at 23:56
RomanRoman
2,1341532
answered Nov 24 '18 at 23:56
RomanRoman
2,1341532
answered Nov 24 '18 at 23:56
RomanRoman
2,1341532
2,1341532
add a comment |
add a comment |
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1
To me your code works fine, just the order or rows is different.
– Julius Vainora
Nov 24 '18 at 23:52
Actually, your code works fine for me as well.
– arg0naut
Nov 24 '18 at 23:57
@JuliusVainora can concatenation affects the group_by? because the ID is originally concatenated from 2 columns
– Zidane Ahmed
Nov 25 '18 at 0:06
@ZidaneAhmed, if I understand correctly what you mean, concatenation alone shouldn't be a problem, but perhaps the way it was done (the order of different actions, e.g.) has an effect. I guess you should revise your example.
– Julius Vainora
Nov 25 '18 at 0:16
Your code works fine. Diff = c(0, diff(Value)) is another option where you dont need to use lag...
– Ika8
Nov 27 '18 at 15:07