marrow | A boilerplate for backbone.js | Frontend Framework library

by kud JavaScript Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | marrow Summary

marrow is a JavaScript library typically used in User Interface, Frontend Framework, Boilerplate applications. marrow has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Marrow is a boilerplate for backbone.js.

Support

Quality

Security

License

Reuse

Support

marrow has a low active ecosystem.

It has 29 star(s) with 11 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of marrow is current.

Quality

marrow has 0 bugs and 0 code smells.

Security

marrow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

marrow code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

marrow does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

marrow releases are not available. You will need to build from source code and install.

Installation instructions are not available. Examples and code snippets are available.

marrow saves you 7 person hours of effort in developing the same functionality from scratch.

It has 21 lines of code, 0 functions and 30 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of marrow

Get all kandi verified functions for this library.

marrow Key Features

No Key Features are available at this moment for marrow.

marrow Examples and Code Snippets

No Code Snippets are available at this moment for marrow.

Community Discussions

Trending Discussions on marrow

Plotting continuous distribution in horizontal bar plot

Multiple variable distribution plot using ggplot2

How to split a dataset with multiple variables into train and test while both having the same composition using python?

MySQL query with regular expression

My Activity comes out as a Blank even though it shouldn't?

How to add text in specific location beyond the plot in ggplot2

How to adjust the each bar's width after using faceting function

Can I adjust the fill(color) of different label regions when using ggh4x package

MySQL query to get common or duplicate values from different columns

Need to replace multiple independent if statements

QUESTION

Plotting continuous distribution in horizontal bar plot

Asked 2022-Mar-14 at 18:59

This was my earlier question where it was solved using multiple distribution.

I want to plot the continuous variable like age or tumor mutation burden as shown in first figure with a range like a window such 20-30 age group or some mutational burden range

The frequencies are calculated for all the variables of the metadata, but when plotting the age is not mapped to the final plot as show in the second plot.

Does the age need to be converted into other class before plotting?

...

ANSWER

Answered 2022-Mar-14 at 09:14

Rename Diagnosis-Age and use cut to convert to a factor. Add labels as required for appearance of age groups in legend.

Note I have swapped name and perc in the call to aes to avoid the call to coord_flip.

Source https://stackoverflow.com/questions/71463663

QUESTION

Multiple variable distribution plot using ggplot2

Asked 2022-Mar-02 at 12:15

I have different categorical variable which I would like to show in terms of distribution.

So in my data-frame I have like 147 patients and their traits such as age,gender,disease subtypes etc etc.

This is my dataframe subset

...

ANSWER

Answered 2022-Mar-02 at 12:15

library(tidyverse)
library(ggnewscale)

plot_meta <- structure(list(
  patient = structure(c(
    36L, 33L, 122L, 95L, 66L,
    49L
  ), .Label = c(
    "TCGA-AB-2805", "TCGA-AB-2806", "TCGA-AB-2808",
    "TCGA-AB-2810", "TCGA-AB-2811", "TCGA-AB-2812", "TCGA-AB-2813",
    "TCGA-AB-2814", "TCGA-AB-2815", "TCGA-AB-2817", "TCGA-AB-2818",
    "TCGA-AB-2819", "TCGA-AB-2820", "TCGA-AB-2821", "TCGA-AB-2822",
    "TCGA-AB-2823", "TCGA-AB-2825", "TCGA-AB-2826", "TCGA-AB-2828",
    "TCGA-AB-2830", "TCGA-AB-2834", "TCGA-AB-2835", "TCGA-AB-2836",
    "TCGA-AB-2839", "TCGA-AB-2840", "TCGA-AB-2841", "TCGA-AB-2842",
    "TCGA-AB-2843", "TCGA-AB-2844", "TCGA-AB-2845", "TCGA-AB-2846",
    "TCGA-AB-2847", "TCGA-AB-2849", "TCGA-AB-2851", "TCGA-AB-2853",
    "TCGA-AB-2856", "TCGA-AB-2857", "TCGA-AB-2858", "TCGA-AB-2859",
    "TCGA-AB-2861", "TCGA-AB-2862", "TCGA-AB-2863", "TCGA-AB-2865",
    "TCGA-AB-2866", "TCGA-AB-2867", "TCGA-AB-2869", "TCGA-AB-2870",
    "TCGA-AB-2871", "TCGA-AB-2872", "TCGA-AB-2873", "TCGA-AB-2874",
    "TCGA-AB-2875", "TCGA-AB-2876", "TCGA-AB-2877", "TCGA-AB-2878",
    "TCGA-AB-2880", "TCGA-AB-2881", "TCGA-AB-2882", "TCGA-AB-2883",
    "TCGA-AB-2884", "TCGA-AB-2885", "TCGA-AB-2886", "TCGA-AB-2888",
    "TCGA-AB-2889", "TCGA-AB-2890", "TCGA-AB-2891", "TCGA-AB-2892",
    "TCGA-AB-2893", "TCGA-AB-2894", "TCGA-AB-2895", "TCGA-AB-2896",
    "TCGA-AB-2897", "TCGA-AB-2898", "TCGA-AB-2899", "TCGA-AB-2900",
    "TCGA-AB-2901", "TCGA-AB-2908", "TCGA-AB-2910", "TCGA-AB-2911",
    "TCGA-AB-2912", "TCGA-AB-2913", "TCGA-AB-2914", "TCGA-AB-2915",
    "TCGA-AB-2916", "TCGA-AB-2917", "TCGA-AB-2918", "TCGA-AB-2919",
    "TCGA-AB-2920", "TCGA-AB-2921", "TCGA-AB-2924", "TCGA-AB-2925",
    "TCGA-AB-2927", "TCGA-AB-2928", "TCGA-AB-2929", "TCGA-AB-2930",
    "TCGA-AB-2931", "TCGA-AB-2932", "TCGA-AB-2933", "TCGA-AB-2934",
    "TCGA-AB-2935", "TCGA-AB-2936", "TCGA-AB-2937", "TCGA-AB-2938",
    "TCGA-AB-2939", "TCGA-AB-2940", "TCGA-AB-2941", "TCGA-AB-2942",
    "TCGA-AB-2943", "TCGA-AB-2944", "TCGA-AB-2946", "TCGA-AB-2948",
    "TCGA-AB-2949", "TCGA-AB-2950", "TCGA-AB-2952", "TCGA-AB-2955",
    "TCGA-AB-2956", "TCGA-AB-2959", "TCGA-AB-2963", "TCGA-AB-2965",
    "TCGA-AB-2966", "TCGA-AB-2970", "TCGA-AB-2971", "TCGA-AB-2973",
    "TCGA-AB-2975", "TCGA-AB-2976", "TCGA-AB-2977", "TCGA-AB-2979",
    "TCGA-AB-2980", "TCGA-AB-2981", "TCGA-AB-2982", "TCGA-AB-2983",
    "TCGA-AB-2984", "TCGA-AB-2986", "TCGA-AB-2987", "TCGA-AB-2988",
    "TCGA-AB-2990", "TCGA-AB-2991", "TCGA-AB-2992", "TCGA-AB-2994",
    "TCGA-AB-2995", "TCGA-AB-2996", "TCGA-AB-2998", "TCGA-AB-2999",
    "TCGA-AB-3000", "TCGA-AB-3001", "TCGA-AB-3002", "TCGA-AB-3007",
    "TCGA-AB-3008", "TCGA-AB-3009", "TCGA-AB-3011", "TCGA-AB-3012"
  ), class = "factor"), Sex = structure(c(2L, 2L, 1L, 1L, 2L, 2L), .Label = c("Female", "Male"), class = "factor"), FAB = structure(c(
    5L,
    1L, 5L, 3L, 2L, 4L
  ), .Label = c(
    "M0", "M1", "M2", "M3", "M4",
    "M5", "M6", "M7", "nc"
  ), class = "factor"), `Diagnosis-Age` = c(
    63L,
    39L, 76L, 62L, 42L, 42L
  ), `Bone-Marrow-Blast-Percentage` = c(
    82L,
    83L, 91L, 72L, 68L, 88L
  ), Cytogenetics = structure(c(
    75L, 93L,
    51L, 27L, 21L, 57L
  ), .Label = c(
    "37~49,XY,+Y,der(1)add(1)(p13)del(1)(q21q25),-5,der(7)inv(7)(p15q11.2)?inv(7)(q22q32),+17,add(17)(p13),+21,+mar[cp20]",
    "39~47,XX,del(5)(q13q33),-7,der(8)t(8;?8;8)(p23;?p11.2p23;q11.2),der(14)t(1;14)(p12;p11.2)der(1)t(7;16)(p15;q22),+2mar[cp19]",
    "41~44,X,?i(X)(p10),-7,der(12)t(8;12)(q11.2;p11.2),-8 [cp11]/46,XX[8[",
    "42,XY,-5,-7,add(12)(p13),t(14;15)(q10;q10),der(17)t(5;17)(p13;p11.2),-18[6]/40,idem,-11,-add(12)(p13),der(12)t(?;12)(?;p13),-19[6]/41,idem,-der(17)[3]/41,idem,-der(17),+mar1,+mar[3]/41,idem,der(1)der(1)(p12)add(1)(p12),+der(1)(q21)add(1)(q21),-3,-8[2]",
    "43,XY-3,del(5)(q12q33),-7,der(10)t(10;11)(q26;q13),-12,-18,+2mar[20]",
    "44-45,X,-Y,-5,add(16)(q22),-17,-18,iso(21),+mars[cp5]/82-84,XX,-Y,-3,-4,-11,-12,-19,-21,+21[cp5}",
    "44~46,XX,del(11)(q23),der(19)?t(11;19)(q23;p13.1)[cp11]/44~45,XX,-19[cp4]/46,XX [5]",
    "44~47,XX,t(1;15)(q32;q26)[14],del(5)(q13q33)[19],-7[20],+8[7],del(12)(p11.2p11.2)[15],del(17)(q21)[8],der(22)t(1;22)(p13;p11.2)[20],+mar[13][cp20]",
    "44~47,XY,del(5)(q22q35)[20],-7[14],-8[6],der(12)t(10;12)(p11.2q21)[2],add(14)(p12)[11],-17[13],der(17)t(10;17)(q11.2;p13)[14],-18[7],add(18)(p11.2)[7],-21[10],i(21)(q10)[4],-22[4],+mar[10],+mar1x2[6][cp20]",
    "45,X,-X,t(8;21)(q22;q22)[20]", "45,X,-Y, t(8;7;21)(q22;p15;q22[22]/46,XY[3]",
    "45,X,-Y,t(8;21)(q22;q22)[13]/45,idem,del(9)(q22;q32)[7]", "45,X,-Y,t(8;21)(q22;q22)[19]/46,XY[1]",
    "45,X,-Y[3]/46,XY [17]", "45,XX-7[5]-only 5 metaphases", "45,XX,-7,t(9;11)(p22;q23)[19]/46,XX[1]",
    "45,XX,-7[12]/46,XX[8]", "45,XX,-7[20]", "45,XY,-7, t(9;22)(q34;q11.20) [19]/46,XY[1]",
    "45,XY,-7[20]", "45,XY,der(7)(t:7;12)(p11.1;p11.2),-12,-13,+mar[19]/46,XY[1]",
    "45~46,XY,add(X)(q22)[7],Y[4],der(5)t(5;17)(q13;21)[18],-7[18],+8[17],del(12)(q23)[16],-17[18],add(18)(p11.2)[14][cp18]",
    "46, XX[14]", "46, XX[15]", "46, XX[16]", "46, XX[19]", "46, XX[20]",
    "46, XY[15]", "46, XY[20]", "46,XX,1~50dmin[12]/46,idem,der(6)t(6;?)(q22;?)[2]/46,XX[6]",
    "46,XX,9qh+[20]", "46,XX,del(3)(q23q26.2),der(7)t(1:7)(q32;q32),del(10)(q22q25),t(13;16)(q34;p11.2)dup(21)(q22)[cp20]",
    "46,XX,del(5)(q11.2q33)[1]/48~52,idem,+1,+?del(5)(q15q33),+11,+11,?t(12;22)(p13;q12),-13,-17,+i(22)(q10),+i(22)(q10),+mar[cp19]",
    "46,XX,del(5)(q22q33)[4]/46,XX[16]", "46,XX,i(17)(q10)[1]/45,sl-7[2]/48,sl,+13,+19[3]/46,XX[15]",
    "46,XX,inv(16)(p13q22)[15]/46,XX[2]", "46,XX,inv(16)(p13q22)[19]/46,XX[1]",
    "46,XX,inv(16)(p13q22)[20]", "46,XX,inv(16)(p13q22)[5]/46,idem,t(3;3)(p13;q?28)[5]/46,XX[6]",
    "46,XX,t(15;17)(q22;q21.1)[19]/47,idem,+8 [1]", "46,XX,t(15;17)(q22;q21),t(16;19)(p13.3;p13.1)[17]/46,XX[3]",
    "46,XX,t(15;17)(q22;q21)[11]/46,XX[9]", "46,XX,t(15;17)(q22;q21)[12]/46,XX[8]",
    "46,XX,t(15;17)(q22;q21)[20]", "46,XX,t(8;21)(q22;q22)[17]/46,XX[3]",
    "46,XX,t(8;21)(q22;q22)[20]", "46,XX,t(8;21)[15]/46,idem,del(9)(q12q22)[5]",
    "46,XX[15]", "46,XX[18]", "46,XX[19]/46,XX,add(7)(p?22)[1]",
    "46,XX[20]", "46,XX+13,21[cp17]/46,XX[3]", "46,XY,9qh+[19]",
    "46,XY,del(11)(p12)[2]/46,XY[18]", "46,XY,del(20)(q11.2)[23]/92,XXYY,del(20)(q11.2)x2[2]/46,XY[3]",
    "46,XY,del(7)(q21q36)[18]/46,XY[2]", "46,XY,del(9)(q13:q22),t(11:21)(p13;q22),t(15;17)(q22;q210[20]",
    "46,XY,i(17)(q10)[15]/47,XY,idem+13[3]/46,XY[2]", "46,XY,inv(16)(p13;q22)[20]",
    "46,XY,inv(16)(p13q22)[17]/46,XY[3]", "46,XY,inv(16)(p13q22)[9]/46,XY[10]",
    "46,XY,t(11;19)(q23;p13)[17]/46,XY,t(11;19)(q23;p13),inv(12)(p12p13)[3]",
    "46,XY,t(11;19)(q23;p13)[20]", "46,XY,t(15;17)(q22;q21)[19]/46,XY[1]",
    "46,XY,t(15;17)(q22;q21)[20]", "46,XY,t(15;17)(q22:q21)[11]/46,XY[9]",
    "46,XY,t(2;4)(q34;q21)inv(16)(p13q22) [20]", "46,XY,t(6;11)(q27;q23)[15]",
    "46,XY,t(9;11)(p22;q23)[7]/47,XY,t(9;11)(p22;q23)[7]/46,XY[4]",
    "46,XY,t(9;22)(q34;q11.2)[13]/34~37,idem,-3,del(4),-4,-5,-7,-9,-10,t?(11;12),-12,-14,-14,-16,-17,-22[cp6]/46,XY[1]",
    "46,XY,t(9;22)(q34;q11.2[4]/50,idem,+8,+10,+21,+der(22)(t(9;22)(q34;q11.2)[16]",
    "46,XY[13]", "46,XY[15]", "46,XY[19]", "46,XY[20]", "46,XY[30]",
    "46~49,XY,del(3)(p14),del(5)(p11.2q33),del(17)(q21q21),add(21)(p11.2),+22,mar[cp20]",
    "47,XX,+der(5)t(2;5)(p11.2;q11.2)?,t(8;16)(p11.2;p13.3)[19]",
    "47,XX,i(11)(q10)[18]/46,XX [2]", "47,XX,t(15;17)(q22:q21)+mar[20]",
    "47,XX+11 [20]", "47,XX+8 [20]", "47,XXY [17]", "47,XY,+13[5]/46,XY[15]",
    "47,XY,+21 [6]/46,XY[13]", "47,XY,+21[11]/48,XY,+3,+21[8]", "47,XY,+22[10]/47,XY,+8[7]/45,XY,del(3)(p21),del(4)(p12p15),-7,?dup(7)(q11.2q36)[3]",
    "47,XY,+8 [10]/46,XY [10]", "47,XY,+8 [19]", "47,XY,+8 [20]",
    "47,XY,+8[15]/46,+8,-17[3]", "47,XY,+9[10]/46,XY[10]", "47,XY,del(5)(q22q33),t(10;11)(p13~p15;q22~23),i(17)(q10)[3]/46,XY[17]",
    "47,XY,del(7)(q22),+8,t(15;17)(q22;q21)[18]/46,XY,del(7)(q22),t(15;17)(q22;q21)[2]",
    "47,XY+8 [15]/48,XY+8+8[4]/46,XY[1]", "48,XY,+8,+8[16]/46,XY[4]",
    "52~54,XY,+2,+4,+6,+8,del(11)(q23),+19,+19,+21[17]/46,XY[3]",
    "53~56,XY,+1,del(2)(q33q34),+8,+10,+11x2,+13x1-2,+14,del(17)(p11.2),+19,add(21)(q22),+22[cp20]",
    "incomplete-46,XY,del(12)(p11.20[2]/46,XY[3]", "N.D.", "ND",
    "Outside hospital with inv(16)"
  ), class = "factor"), `Cytogenetic-Code--Other-` = structure(c(
    8L,
    3L, 8L, 8L, 3L, 9L
  ), .Label = c(
    "BCR-ABL1", "CBFB-MYH11", "Complex Cytogenetics",
    "Intermediate Risk Cytogenetic Abnormality", "MLL translocation, poor risk",
    "MLL translocation, t(9;11)", "N.D.", "Normal Karyotype", "PML-RARA",
    "Poor Risk Cytogenetic Abnormality", "RUNX1-RUNX1T1"
  ), class = "factor"),
  Induction = structure(c(11L, 4L, 1L, 8L, 4L, 9L), .Label = c(
    "7+3",
    "7+3, dauna", "7+3, IT", "7+3+3", "7+3+3, gleevec", "7+3+3, then 5+2+2",
    "7+3+3+PSC", "7+3+AMD", "7+3+ATRA", "7+3+dauno", "7+3+Genasense",
    "7+3+study drug", "7+4+ATRA", "Azacitidine", "CLAM", "Cytarabine only",
    "Decitabine", "Decitabine then 7+3", "Hydrea & Idarubicin",
    "Hydrea, ATRA started", "hydrea, didn't get add'l chemo",
    "LBH/Decitabine", "low dose Ara C", "no treatment", "Revlimid",
    "Revlmd then Decitbne,7+3,5+2"
  ), class = "factor")
), row.names = c(
  NA,
  6L
), class = "data.frame")

df <-
  plot_meta %>%
  mutate(across(everything(), as.character)) %>%
  pivot_longer(everything()) %>%
  count(name, value) %>%
  group_by(name) %>%
  mutate(perc = n / sum(n) * 100)
df
#> # A tibble: 38 × 4
#> # Groups:   name [8]
#>    name                         value                                    n  perc
#>                                                             
#>  1 Bone-Marrow-Blast-Percentage 68                                       1  16.7
#>  2 Bone-Marrow-Blast-Percentage 72                                       1  16.7
#>  3 Bone-Marrow-Blast-Percentage 82                                       1  16.7
#>  4 Bone-Marrow-Blast-Percentage 83                                       1  16.7
#>  5 Bone-Marrow-Blast-Percentage 88                                       1  16.7
#>  6 Bone-Marrow-Blast-Percentage 91                                       1  16.7
#>  7 Cytogenetic-Code--Other-     Complex Cytogenetics                     2  33.3
#>  8 Cytogenetic-Code--Other-     Normal Karyotype                         3  50  
#>  9 Cytogenetic-Code--Other-     PML-RARA                                 1  16.7
#> 10 Cytogenetics                 45,XY,der(7)(t:7;12)(p11.1;p11.2),-…     1  16.7
#> # … with 28 more rows

df %>%
  ggplot(aes(name, perc)) +
  geom_col(data = ~ filter(.x, name == "FAB") %>% rename(FAB = value), mapping = aes(fill = FAB)) +
  new_scale_fill() +
  geom_col(data = ~ filter(.x, name == "Sex") %>% rename(Sex = value), mapping = aes(fill = Sex)) +
  new_scale_fill() +
  geom_col(data = ~ filter(.x, name == "Induction") %>% rename(Induction = value), mapping = aes(fill = Induction)) +
  coord_flip()

Source https://stackoverflow.com/questions/71318781

QUESTION

How to split a dataset with multiple variables into train and test while both having the same composition using python?

Asked 2021-Sep-12 at 23:05

I have a list of brain metastasis MRIs that I want to use for training and testing purposes. These images are all similar but the original tumor sites differs. See the following example:

From Lungs:

"Image01.1"
"Image01.2"
"Image01.3"
"Image01.4"

From Breasts:

"Image02.1"
"Image02.2"
"Image02.3"
"Image02.4"
"Image02.5"

From Skin:

"Image03.1"
"Image03.2"

From Lung Tissue:

"Image04.1"
"Image04.2"
"Image04.3"

From Bone Marrow:

"Image05.1"
"Image05.2"

I want the testing and validation set to contain the same amount of images without losing a similar composition (both lists containing the same amount of each subtype).

For this purpose can I create lists for each subtype and then randomly split those 50/50. Followed by adding all these lists together?

...

ANSWER

Answered 2021-Sep-12 at 22:53

If you want to get specific rows from a pandas DataFrame that meet certain criteria, you can filter. In your case, something like:

Source https://stackoverflow.com/questions/69149736

QUESTION

MySQL query with regular expression

Asked 2021-Aug-19 at 00:08

I have a MySQL table with some addresses on it. I would like to find the addresses that contain double letters between the 5th and 11th position. There is a test table with some data for reference.

...

ANSWER

Answered 2021-Aug-18 at 19:39

Use SUBSTR() to get the substring between 5th and 11th position.

Source https://stackoverflow.com/questions/68838292

QUESTION

My Activity comes out as a Blank even though it shouldn't?

Asked 2021-Jul-09 at 18:33

I'm a complete beginner with coding and decided to try out android studios for fun without having formal training or lessons for the basics so I'm sorry if this seems like a dumb problem but whenever I try to run my code it just comes off as blank? Any idea how to solve this?

Here's a screenshot of what comes up

...

ANSWER

Answered 2021-Jul-09 at 18:33

Did you clean and rebuild your project or Invalid caches/restart from IDE?You will find it in under File menu.

It seems like your code is fine , so I decided to run in IDE its compiling and running perfectly .

Firebase console-

So you can do some checks in your side - 1.

Source https://stackoverflow.com/questions/68319263

QUESTION

How to add text in specific location beyond the plot in ggplot2

Asked 2021-Apr-11 at 12:38

Here is my sample data and code. I wanna add two wards in specific location just as the picture shows below.

...

ANSWER

Answered 2021-Apr-11 at 12:38

I found a better way to add text outside plot region.

just like this:

Source https://stackoverflow.com/questions/67042161

QUESTION

How to adjust the each bar's width after using faceting function

Asked 2021-Apr-10 at 11:09

Here is my code and sample data:

I wanna all of the bars have the same width. Now it's not so.

...

ANSWER

Answered 2021-Apr-10 at 11:09

Here is now the solution: @teunbrand: Sorry for this but I really got nervous after you commented my first answer. I just messed up something. After checking facet_nested I come to this solution:

Just add space = "free_x" to facet_nested

Source https://stackoverflow.com/questions/67030433

QUESTION

Can I adjust the fill(color) of different label regions when using ggh4x package

Asked 2021-Apr-10 at 10:00

Here is my sample data and code:

Now I wanna adjust the blank region to different colors. And I used the ggh4x package () and its vital function: facet_nested(.~Organ+Disease, scales="free",switch = "x",nest_line = TRUE)

...

ANSWER

Answered 2021-Apr-10 at 10:00

There is no nice function to do this, but you can manually change the colours of the strips if you want to get your hands dirty with some gtable

Source https://stackoverflow.com/questions/67031477

QUESTION

MySQL query to get common or duplicate values from different columns

Asked 2021-Apr-03 at 08:49

I have a table with two columns, one column (AffiliationCountry) shows the countries and the other column (ArtSubareaKeyword) shows the subject areas in related countries with comma-separated values.

I want to extract the subject area which is repeating for a country the same country one or more times and save it in a new column with the name "MostPopularSubjectArea".

Table with values:

As you can see in the table that a country is repeating and its values are also repeating.

AffiliationCountry ArtSubareaKeyword1 ArtSubareaKeyword1 ArtSubareaKeyword1 Spain Cell membranes Cell staining Coimmunoprecipitation Kazakhstan Factor analysis Human performance Immunofluorescence Japan Bone marrow Diagnostic medicine Genetic loci Kazakhstan Drug research Factor analysis Human performance

Results that are required:

I want a SQL query that can store for that country a new column that stores the common subjects area which is occurring more.

AffiliationCountry MostPopularSubjectArea Kazakhstan Human performance ...

ANSWER

Answered 2021-Apr-03 at 08:49

As per the table, you can select the pair of columns, union them and find the count using group by:

Source https://stackoverflow.com/questions/66840594

QUESTION

Need to replace multiple independent if statements

Asked 2021-Mar-17 at 21:46

I am a beginner to Java and I have a health insurance program, which returns a total quote based on if the customer has any health conditions already present or not. Each health condition increases the total amount by a different %, and there can be more than one health condition present, in which case the total will be increased according to the order of the if statements. For example, the customer may have "Bone marrow", in which case the total is multiplied by 20%, or they may have "Bone marrow" and "Cancer" in which case the total is increased by 20% and then 25% in that order.

I have this written in multiple independent if statements because unlike with an if else statement, there can be more than one health condition present. Is there a way I can write this in a way that's more elegant than just a long list of if statements?

...

ANSWER

Answered 2021-Mar-17 at 21:17

It seems that switch statement is more appropriate in this case:

Source https://stackoverflow.com/questions/66680861

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install marrow

You can download it from GitHub.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: