I have a Stata dataset organized as follows:
payment class molecule State
10 1 1 1
8 2 1 1
25 3 2 1
7 4 2 1
12 1 1 2
5 2 1 2
24 3 2 2
7 4 2 2
How do I create a variable that is the difference of the payment variable between classes within the same molecule?
Expected output:
payment class molecule State payment_difference
10 1 1 1 2
8 2 1 1 2
25 3 2 1 18
7 4 2 1 18
12 1 1 2 7
5 2 1 2 7
24 3 2 2 17
7 4 2 2 17
Using your toy example:
clear
input payment class molecule state
10 1 1 1
8 2 1 1
25 3 2 1
7 4 2 1
12 1 1 2
5 2 1 2
24 3 2 2
7 4 2 2
end
The following works for me:
bysort state molecule (class) : generate diff = payment[1] - payment[2]
list, separator(0)
+-------------------------------------------+
| payment class molecule state diff |
|-------------------------------------------|
1. | 10 1 1 1 2 |
2. | 8 2 1 1 2 |
3. | 25 3 2 1 18 |
4. | 7 4 2 1 18 |
5. | 12 1 1 2 7 |
6. | 5 2 1 2 7 |
7. | 24 3 2 2 17 |
8. | 7 4 2 2 17 |
+-------------------------------------------+
For details, read Speaking Stata: How to move step by: step
on Stata Journal.
Related
I have a pandas data frame with three variables. The first is a grouping variable, the second a within group "scenario" and the third an outcome. I would like to calculate the within group difference between the null condition, scenario zero, and the other scenarios within the group. The number of scenarios varies between the different groups. My data looks like:
ipdb> aDf
FieldId Scenario TN_load
0 0 0 134.922952
1 0 1 111.787326
2 0 2 104.805951
3 1 0 17.743467
4 1 1 13.411849
5 1 2 13.944552
6 1 3 17.499152
7 1 4 17.640090
8 1 5 14.220673
9 1 6 14.912306
10 1 7 17.233862
11 1 8 13.313953
12 1 9 17.967438
13 1 10 14.051882
14 1 11 16.307317
15 1 12 12.506358
16 1 13 16.266233
17 1 14 12.913150
18 1 15 18.149811
19 1 16 12.337736
20 1 17 12.008868
21 1 18 13.434605
22 2 0 454.857959
23 2 1 414.372215
24 2 2 478.371387
25 2 3 385.973388
26 2 4 487.293966
27 2 5 481.280175
28 2 6 403.285123
29 3 0 30.718375
... ... ...
29173 4997 3 53.193992
29174 4997 4 45.800968
I will also have to write functions to get percentage differences etc. but this has me stumped. Any help greatly appreciated.
You can get the difference with the scenario 0 within groups using groupby and transform like:
df['TN_load_0'] = df['TN_load'].groupby(df['FieldId']).transform(lambda x: x - x.iloc[0])
df
FieldId Scenario TN_load TN_load_0
0 0 0 134.922952 0.000000
1 0 1 111.787326 -23.135626
2 0 2 104.805951 -30.117001
3 1 0 17.743467 0.000000
4 1 1 13.411849 -4.331618
5 1 2 13.944552 -3.798915
6 1 3 17.499152 -0.244315
I have the following data in Stata:
clear
* Input data
input grade id exit time
1 1 . 10
2 1 . 20
3 1 2 30
4 1 0 40
5 1 . 50
1 2 0 10
2 2 0 20
3 2 0 30
4 2 0 40
5 2 0 50
1 3 1 10
2 3 1 20
3 3 0 30
4 3 . 40
5 3 . 50
1 4 . 10
2 4 . 20
3 4 . 30
4 4 . 40
5 4 . 50
1 5 1 10
2 5 2 20
3 5 1 30
4 5 1 40
5 5 1 50
end
The objective is to take the first row foreach id when a event occurs and if no event occur then take the last report foreach id. Here is a example for the data I hope to attain
* Input data
input grade id exit time
3 1 2 30
5 2 0 50
1 3 1 10
5 4 . 50
1 5 1 10
end
The definition of an event appears to be that exit is not zero or missing. If so, then all you need to do is tweak the code in my previous answer:
bysort id (time): egen when_first_e = min(cond(exit > 0 & exit < ., time, .))
by id: gen tokeep = cond(when_first_e == ., time == time[_N], time == when_first_e)
Previous thread was here.
I have this data
ID A1 A2 B1 B2 C
1 0 1 2 3 4
2 5 6 7 8 9
Here, A1 means A at year 1, A2 means A at year 2. Same goes for B.
I want to make a data where each row is ID-year pair, not just ID.
Like this:
ID year A B C
1 1 0 2 4
1 2 1 3 4
2 1 5 7 9
2 2 6 8 9
Luckily, there are same number of years of A and B.
Honestly I am stuck and all I could come up was just create the desired data structure first and manually copy and paste things. But the data is too big to do it manually.
How should I go about it?
EDIT:
The names of the variables should be more like below:
ID A00 A01 B00 B01 C
1 0 1 2 3 4
2 5 6 7 8 9
See help for the reshape command. It's a reshape long problem.
clear
input ID A1 A2 B1 B2 C
1 0 1 2 3 4
2 5 6 7 8 9
end
reshape long A B , i(ID) j(Year)
list, sepby(ID)
+-----------------------+
| ID Year A B C |
|-----------------------|
1. | 1 1 0 2 4 |
2. | 1 2 1 3 4 |
|-----------------------|
3. | 2 1 5 7 9 |
4. | 2 2 6 8 9 |
+-----------------------+
I have data which is as follows:
data have;
length
group 8
replicate $ 1
day 8
observation 8
;
input (_all_) (:);
datalines;
1 A 1 0
1 A 1 5
1 A 1 3
1 A 1 3
1 A 2 7
1 A 2 2
1 A 2 4
1 A 2 2
1 B 1 1
1 B 1 3
1 B 1 8
1 B 1 0
1 B 2 3
1 B 2 8
1 B 2 1
1 B 2 3
1 C 1 1
1 C 1 5
1 C 1 2
1 C 1 7
1 C 2 2
1 C 2 1
1 C 2 4
1 C 2 1
2 A 1 7
2 A 1 5
2 A 1 3
2 A 1 1
2 A 2 0
2 A 2 5
2 A 2 3
2 A 2 0
2 B 1 0
2 B 1 3
2 B 1 4
2 B 1 8
2 B 2 1
2 B 2 3
2 B 2 4
2 B 2 0
2 C 1 0
2 C 1 4
2 C 1 3
2 C 1 1
2 C 2 2
2 C 2 3
2 C 2 0
2 C 2 1
3 A 1 4
3 A 1 5
3 A 1 6
3 A 1 7
3 A 2 3
3 A 2 1
3 A 2 5
3 A 2 2
3 B 1 2
3 B 1 0
3 B 1 2
3 B 1 3
3 B 2 0
3 B 2 6
3 B 2 3
3 B 2 7
3 C 1 7
3 C 1 5
3 C 1 3
3 C 1 1
3 C 2 0
3 C 2 3
3 C 2 2
3 C 2 1
;
run;
I want to split observation into two columns based on day.
observation_ observation_
Obs group replicate day_1 day_2
1 1 A 0 7
2 1 A 5 2
3 1 A 3 4
4 1 A 3 2
5 1 B 1 3
6 1 B 3 8
7 1 B 8 1
8 1 B 0 3
9 1 C 1 2
10 1 C 5 1
11 1 C 2 4
12 1 C 7 1
13 2 A 7 0
14 2 A 5 5
15 2 A 3 3
16 2 A 1 0
17 2 B 0 1
18 2 B 3 3
19 2 B 4 4
20 2 B 8 0
21 2 C 0 2
22 2 C 4 3
23 2 C 3 0
24 2 C 1 1
25 3 A 4 3
26 3 A 5 1
27 3 A 6 5
28 3 A 7 2
29 3 B 2 0
30 3 B 0 6
31 3 B 2 3
32 3 B 3 7
33 3 C 7 0
34 3 C 5 3
35 3 C 3 2
36 3 C 1 1
The observant SO reader will notice that I have asked essentially the same question previously. However, because of SAS's obsession with "levels" and "by groups", since the variable being used to split the variable of interest isn't binary, that solution doesn't generalize.
Trying it directly, the following occurs:
proc sort data = have out = sorted;
by
group
replicate
;
run;
proc transpose data = sorted out = test;
by
group
replicate
;
var observation;
id day;
run;
ERROR: The ID value "_1" occurs twice in the same BY group.
I can use a LET statement to repress the errors, but in addition to cluttering up the log, SAS retains only the last observation of each BY group.
proc sort data = have out = sorted;
by
group
replicate
;
run;
proc transpose data = sorted out = test let;
by
group
replicate
;
var observation;
id day;
run;
Obs group replicate _NAME_ _1 _2
1 1 A observation 3 2
2 1 B observation 0 3
3 1 C observation 7 1
4 2 A observation 1 0
5 2 B observation 8 0
6 2 C observation 1 1
7 3 A observation 7 2
8 3 B observation 3 7
9 3 C observation 1 1
I don't doubt there's some kludgy way it could be done, such as splitting each group into a separate data set and then re-merging them. It seems like it should be doable with PROC TRANSPOSE, although how escapes me. Any ideas?
Not sure what you're talking about with "SAS's obsession...", but the issue here is fairly straightforward; you need to tell SAS about the four rows (or whatever) being separate, distinct rows. by tells SAS what the row-level ID is, but you're lying to it when you say by group replicate, since there are still multiple rows under that. So you need to have a unique key. (This would be true in any database-like language, nothing unique to SAS here. )
I would do this - make a day_row field, then sort by that.
data have_id;
set have;
by group replicate day;
if first.day then day_row = 0;
day_row+1;
run;
proc sort data=have_id;
by group replicate day_row;
run;
proc transpose data=have_id out=want(drop=_name_) prefix=observation_day_;
by group replicate day_row;
var observation;
id day;
run;
Your output looks like you don't want to transpose the data but instead just want split it into DAY1 and DAY2 sets and merge them back together. This will just pair the multiple readings per BY group in the same order that they appear, which is what it looks like you did in your example.
data want ;
merge
have(where=(day=1) rename=(observation=day_1))
have(where=(day=2) rename=(observation=day_2))
;
by group replicate;
drop day ;
run;
You can read the source data as many times as you need for the number of values of DAY.
If you think that you might not have the same number of observations per BY group for each DAY then you should add these statements at the end of the data step.
output;
call missing(of day_:);
I have dataframe look like this:
a b c d e
0 0 1 2 1 0
1 3 0 0 4 3
2 3 4 0 4 2
3 4 1 0 4 3
4 2 1 3 4 3
5 3 2 0 3 3
6 2 1 1 1 0
7 1 1 0 3 3
8 3 3 3 3 4
9 2 3 4 2 2
I do following command:
df.groupby('A').sum()
And i get:
b c d e
a
0 1 2 1 0
1 1 0 3 3
2 5 8 7 5
3 9 3 14 12
4 1 0 4 3
And after that I want to access
labels = df['A']
But I have an error that there are no such column.
So does pandas have some syntax to get something like this?
a b c d e
0 0 1 2 1 0
1 1 1 0 3 3
2 2 5 8 7 5
3 3 9 3 14 12
4 4 1 0 4 3
I need to sum all values of columns b, c, d, e to column a with the relevant index
You can just access the index with df.index, and add it back into your dataframe as another column.
grouped_df = df.groupby('A').sum()
grouped_df['A'] = grouped_df.index
grouped_df.sum(axis=1)
Alternatively, groupby has 'as_index' option to keep the column 'A'
groupby('A', as_index=False)
or, after groupby, you can use reset_index to put the column 'A' back.