creating a variable from multiple string variables

[victoralos]

I am working with NHANES examination data and would like to create a variable that includes the sum from many other variables in the same data set. All 30 are string variables. Example of two below.

. describe ohx02ctc ohx03ctc

storage display value
variable name type format label variable label
----------------------------------------------------------------------
ohx02ctc str1 %9s Coronal Caries: Tooth Count #2
ohx03ctc str1 %9s Coronal Caries: Tooth Count #3



Using tab (below) we can see that the code or value for this variable (ohx03ctc) is a letter, for example the letter Z represents a diseased permanent tooth surface on tooth number 02. Next to the Z is a frequency of 127, meaning that from the whole number of persons examined, 127 had a “Z” or diseased permanent tooth surface02. Using tab on the next tooth 03 will also give me the same series of letters and same meaning (see below).

I would like to create a variable that adds the numerical values of both letters and will provide me with the result, for example, “Z” from tooth 02 is 127, and “Z” from tooth 03 is 125, combined “Z” should add to 127 + 125 = 152

Your help is appreciated,
Victor

. tab ohx02ctc

Coronal |
Caries: |
Tooth Count |
#2 | Freq. Percent Cum.
------------+-----------------------------------
E | 758 9.36 9.36
F | 1,747 21.57 30.93
J | 77 0.95 31.88
M | 23 0.28 32.16
P | 677 8.36 40.52
Q | 30 0.37 40.89
R | 9 0.11 41.00
S | 2,701 33.35 74.35
U | 1,945 24.01 98.36
Y | 6 0.07 98.43
Z | 127 1.57 100.00
------------+-----------------------------------
Total | 8,100 100.00


. tab ohx03ctc

Coronal |
Caries: |
Tooth Count |
#3 | Freq. Percent Cum.
------------+-----------------------------------
D | 1 0.01 0.01
E | 645 7.96 7.98
F | 2,283 28.19 36.16
J | 84 1.04 37.20
M | 16 0.20 37.40
P | 733 9.05 46.44
Q | 31 0.38 46.83
R | 78 0.96 47.79
S | 3,144 38.81 86.60
U | 955 11.79 98.40
X | 2 0.02 98.42
Y | 3 0.04 98.46
Z | 125 1.54 100.00
------------+-----------------------------------
Total | 8,100 100.00


. list ohx02ctc ohx03ctc

+---------------------+
| ohx02ctc ohx03ctc |
|---------------------|
1. | U U |
2. | U U |
3. | F E |
4. | S F |
5. | U S |
|---------------------|
6. | S S |
7. | P P |
8. | S S |
9. | S M |
10. | E E |
|---------------------|
11. | E S |
12. | P P |
13. | F F |
14. | F F |
15. | F F |
|---------------------|
16. | S S |
17. | U U |
--Break--
r(1);

[Clyde Schechter]

I have reproduced part of your example data to develop and test this code:

* Example generated by -dataex-. To install: ssc install dataex
clear*
input str1(ohx02ctc ohx03ctc)
"U" "U"
"U" "U"
"F" "E"
"S" "F"
"U" "S"
"S" "S"
"P" "P"
"S" "S"
"S" "M"
"E" "E"
"E" "S"
"P" "P"
"F" "F"
"F" "F"
"F" "F"
"S" "S"
"U" "U"
end

gen long id = _n
frame put id ohx*ctc, into(aux)
frame change aux
reshape long ohx@ctc, i(id) j(_j) string
tab ohxctc

1. The -list- and -describe- outputs you show give a picture of the data, but do not provide an actual data set that can be use to run code. So in the future please show example data by using the -dataex- command, as I have done here. If you are running version 16 or a fully updated version 15.1 or 14.2, -dataex- is already part of your official Stata installation. If not, run -ssc install dataex- to get it. Either way, run -help dataex- to read the simple instructions for using it. -dataex- will save you time; it is easier and quicker than typing out tables. It includes complete information about aspects of the data that are often critical to answering your question but cannot be seen from tabular displays or screenshots. It also makes it possible for those who want to help you to create a faithful representation of your example to try out their code, which in turn makes it more likely that their answer will actually work in your data.

2. You don't say exactly how you want your results, so I've simply created a tabulation.

3. If you already have a variable, or set of variables that uniquely identify observations in your data set (NHANES data does have a person id variable) you don't need the command to create variable id. Just use the variable(s) from the dataset in its place.

4. You don't have to go to a new frame to do this--it can be done in the default frame. But the -reshape- is really going to be slow if your data set has a large number of variables that are extraneous to this problem (which an NHANES data set probably will). So this will run more quickly in a separate frame. If you don't want to do this (or can't because you are using a version of Stata before 16 and don't have frames) then you can just -preserve- the data, -keep- the id and ohx*ctc variables, and then do the -reshape- and -tab- commands, and then -restore- your original data.



When asking for help with code, always show example data. When showing example data, always use -dataex-.

[Mike Lacy]

(superseded by Clyde's response, which crossed with mine.)

[Marcos Almeida]

I’m not sure if I understood correctly what you wish.

That said, I suggest: to - destring - the count variables; to use - egen - so as to get the total() by each category.

[Marcos Almeida]

Crossed with Clyde’s insightful reply.

[victoralos]

My sincerest thank you for helping with my question. I installed dataex and it is a great tool (see original NHANES data below). I am learning STATA on my own and you may know this approach usually takes more time. I will try your suggestions. In my to quest to find a quick answer, I used Excel's function =COUNTIF(C2:C8367,"Z") and then created a new file for STATA. With this new file I was then able to calculate the overall prevalence of diseased permanent dentition among 8,367 persons that had an examination by the NHANES team in 2017-2018. I feel confident about my estimated prevalence (tab carious below) because the sum of all "Z" in each variable and my estimated prevalence from all 28 variables agrees with the NHANES documentation and published reports on prevalence using NHANES data sets.

. tab carious

Tooth with |
caries | Freq. Percent Cum.
------------+-----------------------------------
No Caries | 7,171 77.49 77.49
Yes Caries | 2,083 22.51 100.00
------------+-----------------------------------
Total | 9,254 100.00



Again, thank you for your input and help,
Warmest Regards,
Victor

My original NHAHES data set using dataex command below. Had to do two runs due to this message "input statement exceeds linesize limit. Try specifying fewer variables
r(1000);"

. dataex ohx02ctc ohx03ctc ohx04ctc ohx05ctc ohx06ctc ohx07ctc ohx08ctc ohx09ctc ohx10ctc ohx11ctc
> ohx12ctc ohx13ctc ohx14ctc ohx15ctc ohx18ctc

----------------------- copy starting from the next line -----------------------

* Example generated by -dataex-. To install: ssc install dataex
clear
input str1(ohx02ctc ohx03ctc ohx04ctc ohx05ctc ohx06ctc ohx07ctc ohx08ctc ohx09ctc ohx10ctc ohx11ctc ohx12ctc ohx13ctc ohx14ctc ohx15ctc ohx18ctc)
"U" "U" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "U" "U" "U"
"U" "U" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "U" "U" "U"
"F" "E" "F" "F" "S" "S" "S" "Y" "S" "S" "F" "F" "F" "F" "F"
"S" "F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"U" "S" "D" "D" "D" "S" "S" "S" "S" "D" "D" "D" "S" "U" "U"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "E" "S" "S" "F" "S" "S"
"S" "M" "F" "F" "S" "Z" "S" "Z" "Z" "S" "F" "F" "F" "F" "F"
"E" "E" "E" "E" "R" "R" "E" "R" "R" "E" "E" "E" "E" "E" "E"
"E" "S" "S" "J" "S" "S" "S" "S" "S" "S" "S" "S" "J" "S" "E"
"P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P"
"F" "F" "F" "F" "S" "S" "S" "F" "S" "S" "F" "F" "E" "F" "F"
"F" "F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F"
"F" "F" "F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"U" "U" "U" "D" "D" "D" "D" "D" "D" "D" "D" "U" "U" "U" "U"
"S" "F" "F" "F" "S" "F" "F" "F" "F" "S" "F" "F" "F" "F" "F"
"E" "F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "F" "P"
"F" "P" "P" "F" "S" "F" "F" "S" "S" "S" "P" "P" "P" "F" "P"
"U" "U" "A" "D" "D" "A" "U" "U" "A" "D" "A" "A" "U" "U" "U"
"U" "S" "D" "D" "D" "S" "S" "S" "S" "D" "D" "D" "S" "U" "U"
"F" "F" "R" "F" "F" "S" "S" "S" "M" "S" "S" "S" "F" "F" "F"
"P" "P" "E" "S" "S" "S" "S" "S" "S" "S" "F" "F" "F" "F" "P"
"E" "F" "F" "F" "Y" "Y" "Y" "Y" "Y" "Y" "F" "F" "E" "E" "F"
"E" "E" "E" "Z" "S" "S" "S" "S" "S" "S" "E" "E" "E" "E" "E"
"P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "E"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"S" "S" "E" "F" "S" "S" "S" "F" "F" "S" "F" "E" "F" "E" "F"
"U" "S" "D" "S" "D" "S" "S" "S" "S" "D" "S" "D" "S" "U" "U"
"S" "E" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "E" "F"
"U" "S" "K" "S" "D" "S" "S" "S" "S" "D" "A" "A" "S" "U" "U"
""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  "" 
"E" "F" "F" "F" "S" "S" "S" "S" "S" "S" "F" "F" "F" "S" "E"
"U" "S" "A" "A" "D" "A" "S" "A" "U" "A" "A" "A" "S" "U" "U"
"E" "E" "S" "S" "S" "E" "S" "F" "F" "S" "S" "S" "S" "E" "E"
"U" "S" "A" "A" "D" "S" "S" "S" "S" "D" "A" "A" "S" "U" "U"
"F" "P" "F" "S" "S" "F" "F" "F" "F" "F" "S" "F" "P" "F" "P"
"Y" "Y" "E" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "E" "E"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "E" "S"
"U" "U" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "U" "U" "U"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "S"
"U" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "U" "S"
"U" "U" "U" "D" "U" "D" "D" "D" "D" "U" "D" "U" "U" "U" "U"
"U" "S" "D" "A" "D" "S" "S" "S" "S" "D" "D" "D" "S" "U" "U"
""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  "" 
"U" "S" "D" "D" "D" "S" "S" "S" "S" "D" "D" "D" "S" "U" "U"
"R" "R" "F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "F" "E"
"F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"P" "P" "P" "P" "S" "P" "P" "P" "P" "P" "P" "S" "P" "P" "P"
"F" "F" "P" "P" "S" "P" "P" "R" "S" "S" "F" "F" "P" "P" "F"
"E" "E" "E" "E" "S" "E" "E" "Z" "E" "E" "E" "E" "E" "E" "E"
"E" "F" "R" "F" "S" "Z" "S" "F" "S" "F" "R" "F" "F" "S" "E"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P"
"F" "E" "S" "Z" "S" "S" "S" "S" "S" "S" "F" "E" "E" "F" "F"
"U" "U" "U" "U" "U" "D" "D" "D" "D" "U" "U" "U" "U" "U" "U"
"F" "F" "F" "F" "F" "S" "F" "S" "F" "F" "Z" "F" "F" "F" "F"
"F" "F" "S" "S" "S" "S" "F" "F" "S" "S" "S" "S" "F" "F" "F"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P"
"U" "S" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "S" "U" "U"
"F" "E" "S" "E" "S" "S" "S" "S" "S" "S" "E" "E" "S" "F" "E"
"U" "U" "K" "K" "D" "K" "K" "K" "K" "D" "K" "K" "U" "U" "U"
"P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P"
"F" "F" "S" "M" "S" "F" "F" "F" "F" "F" "F" "F" "E" "F" "E"
"S" "F" "R" "F" "S" "S" "F" "F" "F" "F" "E" "F" "F" "S" "F"
"F" "F" "S" "M" "S" "S" "S" "S" "S" "S" "M" "S" "F" "F" "F"
"E" "E" "S" "E" "S" "S" "S" "S" "S" "S" "Z" "S" "E" "E" "E"
"P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "S" "S"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"E" "E" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "Z" "E" "E"
"S" "S" "S" "S" "S" "F" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"S" "S" "Z" "S" "S" "S" "S" "F" "F" "S" "S" "S" "S" "S" "F"
"U" "U" "U" "D" "D" "D" "D" "D" "D" "D" "D" "U" "U" "U" "U"
"F" "F" "F" "F" "X" "Y" "S" "Y" "R" "Y" "M" "F" "F" "F" "F"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"F" "F" "S" "S" "S" "S" "S" "S" "F" "S" "S" "F" "F" "R" "E"
"F" "F" "S" "M" "S" "S" "S" "S" "S" "S" "M" "S" "F" "F" "S"
"U" "U" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "U" "U" "U"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "F" "S"
"E" "F" "E" "E" "S" "S" "S" "S" "M" "S" "F" "F" "E" "F" "E"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"F" "F" "S" "S" "S" "F" "Y" "Y" "F" "S" "F" "F" "R" "F" "F"
"E" "E" "E" "F" "E" "E" "E" "E" "E" "E" "E" "E" "E" "E" "E"
"E" "E" "S" "E" "J" "E" "E" "E" "E" "E" "S" "S" "E" "E" "E"
"F" "F" "S" "F" "S" "S" "S" "S" "F" "S" "F" "F" "F" "F" "F"
"E" "E" "E" "S" "S" "S" "F" "F" "F" "S" "F" "E" "E" "F" "E"
"S" "S" "S" "S" "D" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"F" "F" "F" "F" "F" "F" "S" "S" "F" "S" "F" "F" "F" "F" "F"
"E" "E" "F" "E" "S" "S" "F" "R" "R" "R" "F" "S" "F" "E" "E"
"U" "U" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "U" "U" "U"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"U" "U" "D" "D" "D" "D" "D" "D" "D" "D" "D" "U" "U" "U" "U"
"F" "F" "S" "S" "S" "S" "Y" "Y" "S" "S" "S" "S" "F" "F" "F"
"E" "F" "S" "S" "S" "S" "F" "F" "S" "S" "J" "S" "F" "S" "E"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
end

------------------ copy up to and including the previous line ------------------

Listed 100 out of 8366 observations
Use the count() option to list more

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. dataex ohx19ctc ohx20ctc ohx21ctc ohx22ctc ohx23ctc ohx24ctc ohx25ctc ohx26ctc ohx27ctc ohx28ctc
> ohx29ctc ohx30ctc ohx31ctc

----------------------- copy starting from the next line -----------------------

* Example generated by -dataex-. To install: ssc install dataex
clear
input str1(ohx19ctc ohx20ctc ohx21ctc ohx22ctc ohx23ctc ohx24ctc ohx25ctc ohx26ctc ohx27ctc ohx28ctc ohx29ctc ohx30ctc ohx31ctc)
"U" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "U" "U"
"U" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "U" "U"
"R" "F" "F" "S" "S" "S" "S" "S" "S" "F" "F" "R" "F"
"F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"S" "D" "S" "S" "S" "S" "S" "S" "S" "D" "S" "S" "S"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P"
"J" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"S" "S" "F" "S" "S" "S" "S" "S" "S" "F" "S" "F" "S"
"E" "E" "J" "S" "S" "S" "S" "S" "S" "Z" "F" "F" "F"
"E" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "E" "S"
"P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P"
"F" "E" "S" "S" "S" "S" "S" "S" "S" "S" "F" "E" "E"
"F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "F"
"E" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "F" "S"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"U" "U" "D" "U" "D" "D" "D" "D" "U" "D" "U" "U" "U"
"F" "F" "F" "S" "S" "S" "S" "S" "S" "S" "S" "F" "F"
"P" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "E" "F"
"P" "F" "S" "S" "S" "S" "S" "S" "S" "F" "F" "F" "E"
"U" "A" "D" "D" "D" "D" "D" "D" "D" "D" "D" "U" "U"
"S" "D" "D" "D" "S" "S" "S" "S" "D" "D" "D" "S" "S"
"R" "F" "F" "S" "S" "S" "S" "S" "S" "F" "F" "R" "F"
"P" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "P"
"F" "F" "S" "S" "S" "S" "S" "S" "S" "S" "F" "F" "F"
"E" "Z" "S" "S" "S" "S" "S" "S" "S" "J" "S" "Z" "E"
"E" "E" "E" "F" "Z" "F" "S" "F" "Z" "E" "E" "E" "F"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"F" "F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "E" "Z"
"S" "D" "A" "U" "S" "S" "S" "S" "U" "S" "D" "S" "U"
"F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "S"
"S" "D" "S" "D" "S" "S" "S" "S" "D" "D" "D" "S" "U"
""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  "" 
"J" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "F" "F"
"S" "A" "A" "D" "A" "S" "S" "A" "A" "A" "D" "S" "U"
"E" "S" "S" "S" "E" "S" "S" "S" "S" "E" "E" "E" "E"
"S" "A" "D" "D" "S" "S" "S" "S" "D" "D" "A" "F" "U"
"P" "F" "F" "S" "S" "S" "S" "S" "S" "P" "P" "P" "F"
"T" "T" "T" "Z" "Z" "S" "S" "Z" "Z" "Z" "J" "J" "E"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"U" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "U" "U"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "Z" "S"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"U" "U" "U" "U" "D" "D" "D" "U" "U" "U" "U" "U" "U"
"S" "D" "A" "D" "S" "S" "S" "S" "D" "A" "D" "S" "U"
""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  ""  "" 
"S" "D" "D" "D" "S" "S" "S" "S" "D" "D" "D" "S" "U"
"R" "F" "F" "S" "S" "S" "S" "S" "S" "S" "S" "F" "F"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F"
"P" "P" "P" "S" "P" "P" "P" "S" "S" "P" "P" "P" "P"
"F" "S" "S" "S" "R" "R" "R" "R" "S" "S" "S" "F" "E"
"E" "J" "J" "S" "E" "E" "E" "E" "S" "E" "E" "E" "E"
"E" "S" "S" "S" "S" "S" "S" "S" "S" "F" "F" "J" "E"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P"
"E" "S" "S" "S" "S" "S" "S" "S" "S" "S" "E" "F" "F"
"U" "U" "U" "U" "U" "D" "D" "D" "U" "U" "U" "U" "U"
"F" "S" "S" "F" "S" "S" "S" "S" "S" "S" "F" "E" "F"
"F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "F"
"F" "F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P"
"S" "D" "D" "D" "D" "S" "S" "D" "D" "D" "D" "U" "U"
"E" "E" "F" "S" "S" "S" "S" "S" "S" "S" "E" "E" "E"
"U" "K" "K" "D" "D" "D" "D" "D" "D" "K" "K" "U" "U"
"P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P" "P"
"E" "E" "F" "S" "S" "S" "S" "S" "S" "S" "R" "E" "E"
"E" "S" "E" "S" "S" "S" "S" "S" "S" "S" "S" "E" "F"
"F" "S" "M" "S" "Y" "S" "S" "S" "S" "M" "F" "E" "F"
"E" "S" "S" "S" "S" "E" "S" "S" "S" "Z" "S" "Z" "E"
"P" "P" "P" "F" "F" "F" "F" "F" "F" "P" "P" "P" "P"
"F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "F"
"F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "S"
"J" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "F"
"F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "S"
"U" "U" "D" "D" "D" "D" "D" "D" "D" "D" "U" "U" "U"
"F" "F" "F" "S" "S" "S" "S" "S" "S" "S" "F" "R" "F"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"F" "F" "S" "S" "S" "S" "S" "S" "S" "S" "F" "E" "F"
"F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "F" "F"
"U" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "U" "U"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "F"
"E" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "E" "F"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"R" "R" "F" "S" "S" "S" "S" "S" "S" "F" "R" "R" "F"
"S" "E" "F" "E" "E" "E" "E" "E" "E" "E" "E" "E" "E"
"E" "E" "S" "S" "E" "S" "E" "E" "S" "S" "Z" "E" "E"
"F" "F" "F" "S" "S" "S" "S" "S" "S" "S" "F" "F" "F"
"E" "F" "F" "S" "S" "S" "S" "S" "S" "F" "F" "E" "E"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "E"
"F" "F" "F" "S" "S" "S" "S" "S" "S" "F" "F" "F" "F"
"E" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "E" "E"
"U" "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" "U" "U"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
"U" "U" "D" "U" "D" "D" "D" "D" "U" "D" "U" "U" "U"
"F" "S" "S" "S" "S" "S" "S" "S" "S" "S" "F" "F" "S"
"E" "S" "S" "S" "S" "S" "S" "S" "S" "S" "E" "F" "E"
"S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S" "S"
end

------------------ copy up to and including the previous line ------------------

Listed 100 out of 8366 observations
Use the count() option to list more

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