My goal is to convert a subject-triannual data set to one with subject-month observations, and specify the month at which one string variable (named "strvar" below) should change value, according to the var called "exact_time".
I have a data set with four records per subject (subject-year observations, aka multiple-record-per-subject data set), information was recorded every three years for each subject as follows:
Table with tri-annual-subject obs. & exact_time var
"strvar" changes its value every three years. The variable "exact_time" records the exact (month.day.year) moment at which each the variable "strvar" changes its value. Once "strvar" varies, it keeps the same value for the following months, until the moment indicated by the next value of "exact_time"
I want Stata to change the value of "strvar" according to the variable "exact_time". For instance, subject 1 changed value of "strvar" in April 1, 1992, hence, I want Stata to assign the new value of "strvar" in April 1992. The value of "strvar" for subject 1 should remain the same until "exact_time" changes value (November.30.1995), hence, starting in November 1995, subject one should adopt the new value of "strvar". In 1998, "strvar" of subject one changed value once again, this time at the beginning of next year (January.1.1999), hence, "strvar" will adopt a new value starting in January.1999, until subject one's last observation (December 2002). As follows:
table with monthly-subject obs, example
I believe this can be achieved in in two steps, the second of which I need your support with:
Expand each tri-annual observation 36 times, so as to have monthly-subject observations, i.e., generate var "new_time". I guess this can be achieved through:
expandcl 36, generate(new_time) cluster(subject)
Instruct Stata to change the value "strvar" according to the date specified by "exact_time", which I have no idea how to do, and for which I would appreciate your support.
Thank you in advance!
For future questions, please provide your failed attemps in form of code. They prove that you have done your part trying to solve the problem.
Also, please provide example data that can easily be copied/pasted by other users. Linking images is not the best option, for several reasons.
Find example code below.
clear
set more off
*----- example data -----
input ///
id str1 strvar str22 xtime
1 z "april 1, 1992"
1 u "november 30, 1995"
1 a "january 1, 1999"
2 b "january 15, 1989"
2 z "june 15, 1992"
2 c "august 30, 1995"
end
gen xtime2 = date(xtime, "MDY")
format %td xtime2
list, sepby(id)
*----- what you want -----
xtset id xtime2
tsfill
gen strvar2 = strvar
replace strvar2 = strvar2[_n-1] if missing(strvar2)
browse
tsfill facilitates the job. Se also help xtset, help subscripting and help datetime.
Think about whether you actually need this. You are not adding any new information to the dataset, so what's the point of having a blown-up version of the original?
(The output doesn't exactly match the one in your image; but this really is meant to be an example.)
Related
I have a string variable for time that has the timestamp. For example, one entry would look like: "4/25/2022 17:26". I have over 1,000 observations. I need to categorize the dates (like time period A, time period B...). I want one category per month so I would have 25 categories (because I have data from over 2 years ago). I thought I would first make the string variable a continuous/ numeric variable so that I can do an if...then statement, creating a new, categorical variable, where I can efficiently say if a date is within a certain range it would go to the new categorical variable for the time period.
I also know I might be planning this all wrong, any suggestions?
If your string variable holding the timestamp is called date_string, you can use clock() like this to create a new variable (in this example d)
gen double d = clock(date_string, "MDYhm")
format d %tc
That code would convert this variable:
date_string
1. 4/25/2022 17:26
2. 4/26/2022 19:52
3. 5/17/2023 7:16
into a new variable d:
date_string d
1. 4/25/2022 17:26 25apr2022 17:26:00
2. 4/26/2022 19:52 26apr2022 19:52:00
3. 5/17/2023 7:16 17may2023 07:16:00
I have a data step where I have a few columns that need tied to one other column.
I have tried using multiple "from" statements and " to" statements and a couple other permutations of that, but nothing seems to do the trick. The code looks something like this:
data analyze;
set css_email_analysis;
from = bill_account_number;
to = customer_number;
output;
from = bill_account_number;
to = email_addr;
output;
from = bill_account_number;
to = e_customer_nm;
output;
run;
I would like to see two columns showing bill accounts in the "from" column, and the other values in the "to", but instead I get a bill account and its customer number, with some "..."'s for the other values.
Issue
This is most likely because SAS has two datatypes and the first time the to variable is set up, it has the value of customer_number. At your second to statement you attempt to set to to have the value of email_addr. Assuming email_addr is a character variable, two things can happen here:
Customer_number is a number - to has already been set up as a number, so SAS cannot force to to become a character, an error like this may appear:
NOTE: Invalid numeric data, 'me#mywebsite.com' , at line 15 column 8. to=.
ERROR=1 N=1
Customer_number is a character - to has been set up as a character, but without explicitly defining its length, if it happens to be shorter than the value of email_addr then the email address will be truncated. SAS will not show an error if this happens:
Code:
data _NULL_;
to = 'hiya';
to = 'me#mydomain.com';
put to=;
run;
short=me#m
to is set with a length of 4, and SAS does not expand it to fit the new data.
Detail
The thing to bear in mind here is how SAS works behind the scenes.
The data statement sets up an output location
The set statement adds the variables from first observation of the dataset specified to a space in memory called the PDV, inheriting lengths and data types.
PDV:
bill_account_number|customer_number|email_addr|e_customer_nm
===================================================================
010101 | 758|me#my.com |John Smith
The to statement adds another variable inheriting the characteristics of customer_number
PDV:
bill_account_number|customer_number|email_addr|e_customer_nm|to
===================================================================
010101 | 758|me#my.com |John Smith |758
(to is either char length 3 or a numeric)
Subsequent to statements will not alter the characteristics of the variable and SAS will continue processing
PDV (if customer_number is character = TRUNCATION):
bill_account_number|customer_number|email_addr|e_customer_nm|to
===================================================================
010101 | 758|me#my.com |John Smith |me#
PDV (if customer_number is numeric = DATA ERROR, to set to missing):
bill_account_number|customer_number|email_addr|e_customer_nm|to
===================================================================
010101 | 758|me#my.com |John Smith |.
Resolution
To resolve this issue it's probably easiest to set the length and type of to before your first to statement:
data analyze;
set css_email_analysis;
from = bill_account_number;
length to $200;
to = customer_number;
output;
...
You may get messages like this, where SAS has converted data on your behalf:
NOTE: Numeric values have been converted to character
values at the places given by: (Line):(Column).
27:8
N.B. it's not necessary to explicitly define the length and type of from, because as far as I can see, you only ever get the values for this variable from one variable in the source dataset. You could also achieve this with a rename if you don't need to keep the bill_account_number variable:
rename bill_account_number = from;
I'm now working on a household survey data set and I'd like to give certain members extra IDs according to their relationship to the household head. More specifically, I need to identify the adult children of household head and his/her spouse, if married, and assign them "sub-household IDs".
The variables are: hhid - household ID; pid -individual ID; relhead - relationship with head.
Regarding relhead, a 1 represents the head, a 6 represents a child, and a 7 represents a child-in-law. Below some example data, including in the last column the desired outcome. I assume that whenever a 6 is followed by a 7, they constitute a couple and belong to the same sub-household.
hhid pid relhead sub_hhid(desired)
50 1 1 1
50 2 3 1
50 3 6 2
50 4 6 3
50 5 7 3
-----------------------------------------------
67 1 1 1
67 3 6 2
67 4 7 2
Here are some thoughts:
There may be married and unmarried adult children within one household, the family structure is a little bit complicated, so I want to write some loop across the members in a household.
The basic idea is in the outer loop we identify the children staying-at-home and then check if there's a spouse presented, if there is, then we give the couple an indicator, if not, we continue and give the single stay_chil other indicator. After walking through all the possible members within a household, we get a series of within-household IDs. To facilitate further analysis , I need some kind of external ID variable to separate the sub-families.
* Define N as the total number of household, n as number of individual household size
* sty_chil is indicator for adult child who living with parents(head)
* sty_chil_sp is adult child's spouse
* "hid" and "ind_id" are local macros
forvalue hid=1/N {
forvalue ind_id= 1/n {
if sty_chil[`ind_id']==1 {
check if sty_chil_sp[`ind_id+1']==1 {
if yes then assign sub_hhid to this couples *a 6-7 pairs,identifid as couple
}
else { * single 6 identifid as single child
assign sub_hhid to this child
}
else { *Other relationships rather than 6, move forward
++ind_id the members within a household
}
++hid *move forward across households
}
The built-in stata by,sort: is pretty powerful but here I want to treat part of family members who fall into certain criterion and leave other untouched, so a if-else type loop is more natural for me (even by: may achieve my goal,it's always too tactful when situation become not so simpleļ¼and we cannot exhaust all the possible pattern of household pattern).
An immediate problem is that I don't know how to write loop across house IDs and individual IDs, because I used to acquire the household size (increment of outer loop) using by command (I'm not sure in this case it's 1 or the numerber of family members), and I'm not sure if mix up the by and if loops is a good programming practice, I favor write a "full loop" in this case. Please give me some clues how to achieve my goal and provide (illustrate)pseudo code for me.
An extra question is I cannot find the ado file which contains the content of by command, does it exist?
I will abstract from the issue of whether the assumption used to create matches is a sensible one or not. Rather, let this be an example of reaching the desired results without using explicit loops. Some logic and the use of subscripting (see help subscripting) can get you far.
clear
set more off
*----- example data -----
input ///
hhid pid relhead sub_hhid
50 1 1 1
50 3 6 2
50 4 6 3
50 5 7 3
67 1 1 1
67 3 6 2
67 4 7 2
67 5 6 3
end
list, sepby(hhid)
*----- what you want -----
bysort hhid (pid): gen hhid2 = sum( !(relhead == 7 & relhead[_n-1] == 6) )
list, sepby(hhid)
As you can see, one line of code gets you there. The reasoning is the following:
sum() gives the running sum. The arguments to sum(), being conditions, can either be True or False. The ! denotes the logical not (see help operators).
If it is not the case that the relationship is daughter/son-in-law AND the previous relationship is daughter/son, the condition evaluates to True and takes on the value of 1, increasing the running sum by 1. If it evaluates to False, meaning that the relationship is daughter/son-in-law AND the previous relationship is daughter/son, then it takes on the value of 0 and the running sum will not increase. This gives the result you seek.
You do this using the by: prefix, since you want to check each original household independently, so to speak.
For the the first observation of each original household, the condition always evaluates to True. This is because there exist no "previous" observation (relationship), and Stata considers relhead to be missing (., a very large number) and therefore, not equal to 6. This takes the running sum from 0 to 1 for the first observation of each sub-group, and so on.
Bottom line: learn how to use by: and take advantage of the features offered by Stata. Do not swim against the current; not here.
Edit
Please note that instead of progressively changing your example data set, you should provide a representative example from the beginning. Not doing so can render answers that are initially OK, completely inadequate.
For your modified example, add:
replace hhid2 = 1 if !inlist(relhead,6,7)
That will simply assign anyone not 6 or 7 to the same household as the head. The head is assumed to always have hhid2 == 1. If the head can have hhid2 != 1, then
bysort hhid (relhead): replace hhid2 = hhid2[1] if !inlist(relhead,6,7)
should work.
You can follow with:
bysort hhid (pid): replace hhid2 = hhid2[_n-1] + 1 if hhid2 != hhid2[_n-1] & _n > 1
but because they are IDs, it's not really necessary.
Finally, use:
gen hhid3 = string(hhid) + "_" + string(hhid2)
to create IDs with the form 50_1, 50_2, 50_3, etc.
Like I said before, if your data presents more complications, you should present a relevant example.
I have an unbalanced panel data set (countries and years). For simplicity let's say I have one variable, x, that I am measuring. The panel data sorted first by country (a 3-digit numeric country-code) and then by year. I would like to write a .do file that generates a new variable, z_x, containing the standardized values of the variable x. The variables should be standardized by subtracting the mean from the preceding (exclusive) m time periods, and then dividing by the standard deviation from those same time periods. If this is not possible, return a missing value.
Currently, the code I am using to accomplish this is the following (edited now for clarity)
xtset weocountrycode year
sort weocountrycode year
local win_len = 5 // Defining rolling window length.
quietly: rolling sd_x=r(sd) mean_x=r(mean), window(`win_len') saving(stats_x, replace): sum x
use stats_x, clear
rename end year
save, replace
use all_data_PROCESSED_FINAL.dta, clear
quietly: merge 1:1 (weocountrycode year) using stats_x
replace sd_x = . if `x'[_n-`win_len'+1] == . | weocountrycode[_n-`win_len'+1] != weocountrycode[_n] // This and next line are for deleting values that rolling calculates when I actually want missing values.
replace mean_`x' = . if `x'[_n-`win_len'+1] == . | weocountrycode[_n-`win_len'+1] != weocountrycode[_n]
gen z_`x' = (`x' - mean_`x'[_n-1])/sd_`x'[_n-1] // calculate z-score
UPDATE:
My struggle with rolling is that when rolling is set up to use a window length 5 rolling mean, it automatically does window length 1,2,3,4 means for the first, second, third and fourth entries (when there are not 5 preceding entries available to average out). In fact, it does this in general - if the first non-missing value is on entry 5, it will do a length 1 rolling average on entry 5, length 2 rolling average on entry 6, ..... and then finally start doing length 5 moving averages on entry 9. My issue is that I do not want this, so I would like to avoid performing these calculations. Until now, I have only been able to figure out how to delete them after they are done, which is both inefficient and bothersome.
I tried adding an if clause to the -rolling- statement:
quietly: rolling sd_x=r(sd) mean_x=r(mean) if x[_n-`win_len'+1] != . & weocountrycode[_n-`win_len'+1] != weocountrycode[_n], window(`win_len') saving(stats_x, replace): sum x
But it did not fix the problem and the output is "weird" in the sense that
1) If `win_len' is equal to, say, 10, there are 15 missing values in the resulting z_x variable, instead of 9.
2) Even though there are "extra" missing values in z_x, the observations still start out as window length 1 means, then window length 2 means, etc. which makes no sense to me.
Which leads me to believe I fundamentally don't understand 1) what -rolling- is doing and 2) how an if clause works in the context of -rolling-.
Does this help?
Thanks!
I'm not sure I understand completely but I'll try to answer based on what I think your problem is, and based on a comment by #NickCox.
You say:
... when rolling is set up to use a window length 5 rolling mean...
if the first non-missing value is
on entry 5, it will do a length 1 rolling average on entry 5, length 2
rolling average on entry 6, ...
This is expected. help rolling states:
The window size refers to calendar periods, not the number of
observations. If there
are missing data (for example, because of weekends), the actual number of observations used by command may be less than
window(#).
It's not actually doing a "length 1 rolling average", but I get to that later.
Below some examples to see what rolling does:
clear all
set more off
*-------------------------- example data -----------------------------
set obs 92
gen dat = _n - 1
format dat %tq
egen seq = fill(1 1 1 1 2 2 2 2)
tsset dat
tempfile main
save "`main'"
list in 1/12, separator(4)
*------------------- Example 1. None missing ------------------------
rolling mean=r(mean), window(4) stepsize(4) clear: summarize seq, detail
list in 1/12, separator(0)
*------- Example 2. All but one value, missing in first window ------
use "`main'", clear
replace seq = . in 1/3
list in 1/8
rolling mean=r(mean), window(4) stepsize(4) clear: summarize seq, detail
list in 1/12, separator(0)
*------------- Example 3. All missing in first window --------------
use "`main'", clear
replace seq = . in 1/4
list in 1/8
rolling mean=r(mean), window(4) stepsize(4) clear: summarize seq, detail
list in 1/12, separator(0)
Note I use the stepsize option to make things much easier to follow. Because the date variable is in quarters, I set windowsize(4) and stepsize(4) so rolling is just computing averages by year. I hope that's easy to see.
Example 1 does as expected. No problem here.
Example 2 on the other hand, should be more interesting for you. We've said that what matters are calendar periods, so the mean is computed for the whole year (four quarters), even though it contains missings. There are three missings and one non-missing. summarize is computing the mean over the whole year, but summarize ignores missings, so it just outputs the mean of non-missings, which in this case is just one value.
Example 3 has missings for all four quarters of the year. Therefore, summarize outputs . (missing).
Your problem, as I understand it, is that when you face a situation like Example 2, you'd like the output to be missing. This is where I think Nick Cox's advice comes in. You could try something like:
rolling mean=r(mean) N=r(N), window(4) stepsize(4) clear: summarize seq, detail
replace mean = . if N != 4
list in 1/12, separator(0)
This says: if the number of non-missings for the window (r(N), also computed by summarize), is not the same as the window size, then replace it with missing.
I have files at a particular path on my computer that look like "Report Name May 13.xls" and I'd like to be able to reference filenames dynamically. For example, say I wanted to return the latest file that fit the pattern "Report Name <>.xls" where <> is any particular text string. By "latest" I imply the most recent modified date.
You can extract the date piece from each file name easily enough, as in this Immediate window session.
strFile = "Report Name May 13.xls"
? Mid(strFile, 13)
May 13.xls
? Split(Mid(strFile, 13), ".")(0)
May 13
Assuming those dates are all from the current year, you can construct a full date string.
? Split(Mid(strFile, 13), ".")(0) & " " & Year(Date())
May 13 2013
Finally you can use CDate to convert that string to an actual Date/Time value.
? CDate(Split(Mid(strFile, 13), ".")(0) & " " & Year(Date()))
5/13/2013
So you could have a variable to store the maximum date, walk your list of file names, determine the date associated with each, and store the max date value as needed.
That should not be too difficult. A greater concern for me is the file names do not include the year. So that leaves me wondering what will happen on Jan 1 2014. Will you discard all the .xls files and start over?