Group unique values in r

2022-05-06 13:12 Teena imported from Stackoverflow

I have a following data:

I want o summarise the data in three columns by Subject type and other two columns sates number of schools and number of students by each subject type. I have used group_by function by it keeps giving the number of types subjects appeared rather then by the number of schools or students

TIA

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Can't group employees list without years
my issue is that when I want to group a set of employees according to the month of their birth date, Odoo separates the months by years, which I want to avoid because i wanna generate a list of people who have their birthday in the month.

This is code:
```
<filter
    name="birthday_groupby"
    string="Birthday month"
    domain="[]"
    context="{'group_by': 'birthday:week'}"
/>
```
And this is result:

List screenshot

As you can see, it separates by months, but also by years. I think solution may be more easier than than I think, but i can't figure it out.

Fetching values from one column based on other column keys in long-formatted dataset

I have a long format dataset of 100,000+ individuals, capturing clinic visits at 5 different time points (not chronological). I've included an example dataset below that replicates the formatting of my data:

ID: participant ID visit_number: order of the clinic visits in the original dataset
age_visit: age at the time of the visit
clinic_number: the identifier for the specific clinic location
age_sorted: For each ID, age sorted in ascending order across the 5 clinic visits
age_sorted_index: For each ID, the visit number corresponding to the sorted age

I would like to create a new column (clinic_number_extracted) that fetches the clinic identifier (clinic_number) corresponding to each sorted age (age_sorted value) for each participant. I was thinking that it might be possible to use the age_sorted_index and visit_number variables to do so (generating key-value pairs?), but am not quite sure how to do this outside of data.table. A tidyverse solution would be preferred.

I've looked on R community and stack exchange for clues, but haven't been able to find exactly what I'm looking for (likely not using the correct search terms). I tried to play around with group_by(across()) and with_order(order_by()) functions without much success. I can potentially create a new variable with a few case_when() conditions but might run into issues if there are repeated age_assessment_sorted values.

set.seed(42)

# Beginning dataset
das <- data.frame(id = rep(letters[1:3], each = 5), 
                  visit_number = rep(1:5, times = 3),
                  age_visit = c(50, rep(NA_real_, times = 7), 34, 40, 72, rep(NA_real_, times = 3), 87), 
                  clinic_number = sample(30:50, 15, replace=TRUE), 
                  age_sorted = c(50, rep(NA_real_, times = 4), 34, 40,rep(NA_real_, times = 3), 72, 87, rep(NA_real_, times = 3)), 
                  age_sorted_index = c(rep(1:5), 4, 5, rep(1:3), 1, 5, 2, 3, 4)) 

# Print out dataset
das
#>    id visit_number age_visit clinic_number age_sorted age_sorted_index
#> 1   a            1        50            46         50                1
#> 2   a            2        NA            34         NA                2
#> 3   a            3        NA            30         NA                3
#> 4   a            4        NA            39         NA                4
#> 5   a            5        NA            33         NA                5
#> 6   b            1        NA            47         34                4
#> 7   b            2        NA            46         40                5
#> 8   b            3        NA            44         NA                1
#> 9   b            4        34            36         NA                2
#> 10  b            5        40            33         NA                3
#> 11  c            1        72            34         72                1
#> 12  c            2        NA            43         87                5
#> 13  c            3        NA            49         NA                2
#> 14  c            4        NA            47         NA                3
#> 15  c            5        87            44         NA                4

Desired data:

das_final <- cbind(das, 
                  clinic_number_extracted = c(46, rep(NA_real_, times = 4), 36, 33, rep(NA_real_, times = 3), 34, 44, rep(NA_real_, times = 3)))

# Print out final dataset
das_final 
#>    id visit_number age_visit clinic_number age_sorted age_sorted_index
#> 1   a            1        50            46         50                1
#> 2   a            2        NA            34         NA                2
#> 3   a            3        NA            30         NA                3
#> 4   a            4        NA            39         NA                4
#> 5   a            5        NA            33         NA                5
#> 6   b            1        NA            47         34                4
#> 7   b            2        NA            46         40                5
#> 8   b            3        NA            44         NA                1
#> 9   b            4        34            36         NA                2
#> 10  b            5        40            33         NA                3
#> 11  c            1        72            34         72                1
#> 12  c            2        NA            43         87                5
#> 13  c            3        NA            49         NA                2
#> 14  c            4        NA            47         NA                3
#> 15  c            5        87            44         NA                4
#>    clinic_number_extracted
#> 1                       46
#> 2                       NA
#> 3                       NA
#> 4                       NA
#> 5                       NA
#> 6                       36
#> 7                       33
#> 8                       NA
#> 9                       NA
#> 10                      NA
#> 11                      34
#> 12                      44
#> 13                      NA
#> 14                      NA
#> 15                      NA

^{Created on 2022-05-06 by the reprex package (v2.0.1)}

How does java.util.stream.Stream.distinct() method work? Can I override the equals() method of the stream of the objects?

My use case is that I am trying to use distinct method of Stream to remove Students with same roll number from list of objects of class StudentCourseMapping. Pojo details are below

public class StudentCourseMapping implements Serializable{
    private String name;
    private String dept;
    private Integer roll;
    private String course;

Below is the equals method

    @Override
    public boolean equals(Object obj) {
        StudentCourseMapping other = (StudentCourseMapping) obj;
        if (roll == null) {
            if (other.roll != null)
                return false;
        } else if (!roll.equals(other.roll))
            return false;
        return true;
    }

Below is the implementation

public class RemoveDuplicateUsingStream {
    public static void main(String[] args) {
        List<StudentCourseMapping> studentCourceList = JacksonJSONReaderObjectMapper.jsonReader();
        
        studentCourceList.stream().distinct().forEach(System.out::println);
        StudentCourseMapping s0 = studentCourceList.get(0);
        StudentCourseMapping s1 = studentCourceList.get(1);
        System.out.println(s0.equals(s1));

        Set<Integer> st = new HashSet();
        List<StudentCourseMapping>studentCourceList2 = studentCourceList.stream().filter(s -> st.add(s.getRoll()))
                .collect(Collectors.toCollection(ArrayList::new));
        System.out.println(studentCourceList2.size());
    }
}

And the output is

StudentCourseMapping [name=Alu, dept=Physics, roll=12, course=Quantum Theory]
StudentCourseMapping [name=Alu, dept=Physics, roll=12, course=English]
StudentCourseMapping [name=Sam, dept=Commerce, roll=16, course=English]
StudentCourseMapping [name=Sam, dept=Commerce, roll=16, course=Accounts]
StudentCourseMapping [name=Joe, dept=Arts, roll=19, course=English]
StudentCourseMapping [name=Joe, dept=Arts, roll=19, course=Hindi]
true
3

JacksonJSONReaderObjectMapper.jsonReader(); is a custom method which reads below JSON. I am able to achieve the same by using filter and adding to HashSet but I really want to know what is wrong with my distinct implementation.

{
    "studentCourseMapping": [
        {
            "name": "Alu",
            "dept": "Physics",
            "roll": 12,
            "course": "Quantum Theory"
        },
        {
            "name": "Alu",
            "dept": "Physics",
            "roll": 12,
            "course": "English"
        },
        {
            "name": "Sam",
            "dept": "Commerce",
            "roll": 16,
            "course": "English"
        },
        {
            "name": "Sam",
            "dept": "Commerce",
            "roll": 16,
            "course": "Accounts"
        },
        {
            "name": "Joe",
            "dept": "Arts",
            "roll": 19,
            "course": "English"
        },
        {
            "name": "Joe",
            "dept": "Arts",
            "roll": 19,
            "course": "Hindi"
        }
    ]
}

When I try to test the equals method directly it was working properly and returning true since both the s0 and s1 has roll as 12.

        StudentCourseMapping s0 = studentCourceList.get(0);
        StudentCourseMapping s1 = studentCourceList.get(1);
        System.out.println(s0.equals(s1));

But when I am using distinct all the Objects are getting printed and also while trying to debug in eclipse the distinct method I wrote is not getting called. but the documentation says it should be called. Btw this from Oracle docs 8 but I am using JDK 11

Stream distinct() Returns a stream consisting of the distinct elements (according to Object.equals(Object)) of this stream.

Django multiple distinct values queryset for SQLDB

I'm trying to get a list of results that have distinct x and y values, but I also want to return color along with them.

Since I'm using an SQLDB I'm not allowed to use the fieldnames arg of distinct. Is there a good work around for this? All the examples I'm been looking up haven't been working out.

The query that I'm not allowed to run: queryset = Tile.objects.values('x', 'y', 'color').distinct('x', 'y')

Current Work Around: For now I'm just flagging old rows as retired and pulling everything else as not retired. Probably the right way of doing things, but I didn't want to add additional columns if not needed.

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Group unique values in r

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