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您将如何构建C＃3.0中的桌面应用程序？(How would you architect a desktop application in C# 3.0)

我在C＃3.0中创建了一个简单的桌面应用程序来学习一些C＃，wpf和.Net 3.5。 我的应用程序基本上从csv文件读取数据并将其存储在SQL服务器CE数据库中。 我使用sqlmetal为数据库生成ORM代码。 我的这个应用程序的第一次迭代是丑陋的，我正在重构它。
这使我想到了我的问题。 你将如何在C＃中构建桌面数据库应用程序？ 什么是最佳实践？
你是否创建了一个使用sqlmetal生成的代码的数据库抽象层（DAL）？ 或者是生成的代码足够抽象？
如果您使用DAL模式，您是否将它设为单身或静态成员？ 您是否在DAL模式中使用View-Model-ModelView模式？
如果这似乎是一个长期的开放式问题，我很抱歉，但我最近一直在给这个想法。 我看到很多关于如何在C＃中构建企业n层应用程序的例子，但在构建独立桌面应用程序时却没有那么多例子。

I've created a simple desktop application in C# 3.0 to learn some C#, wpf and .Net 3.5. My application essentially reads data from a csv file and stores it in a SQL server CE database. I use sqlmetal to generate the ORM code for the database. My first iteration of this app is ugly as hell and I'm in the process of refactoring it.
Which brings me to my question. How would you architect a desktop database app in C#? What are the best practices?
Do you create a Database Abstraction Layer (DAL) which uses the sqlmetal generated code? Or is the generated code enough of an abstraction?
If you use DAL pattern, do you make it a singleton or a static member? Do you use the View-Model-ModelView pattern with the DAL pattern?
Apologies if this seems like a long open ended question, but I have been giving this a lot of thought recently. I see a lot of examples on how to architect an enterprise n-tier app in C# but not that many on architecting standalone desktop apps.

原文：https://stackoverflow.com/questions/45705

更新时间：2023-10-28 09:10

最满意答案

 您可以使用dplyr中的group_by()函数：  
library(dplyr)

d <- d %>%
  group_by(group) %>%
  mutate(value.TA = ifelse(value < quantile(value, 0.99),
                           value, NA)) %>%
  ungroup()

> d    
# A tibble: 6 x 4
     id  group value value.TA
  <int> <fctr> <dbl>    <dbl>
1     1      A     1        1
2     2      A     2        2
3     3      A    10       NA
4     4      B   100      100
5     5      B   200      200
6     6      B  1000       NA
 
 数据：  
d <- data.frame(
  id = seq(1, 6),
  group = rep(c("A", "B"), each = 3),
  value = c(1,2,10,100,200,1000)
)

You can use the group_by() function from dplyr: 
library(dplyr)

d <- d %>%
  group_by(group) %>%
  mutate(value.TA = ifelse(value < quantile(value, 0.99),
                           value, NA)) %>%
  ungroup()

> d    
# A tibble: 6 x 4
     id  group value value.TA
  <int> <fctr> <dbl>    <dbl>
1     1      A     1        1
2     2      A     2        2
3     3      A    10       NA
4     4      B   100      100
5     5      B   200      200
6     6      B  1000       NA
 
Data: 
d <- data.frame(
  id = seq(1, 6),
  group = rep(c("A", "B"), each = 3),
  value = c(1,2,10,100,200,1000)
)

您将如何构建C＃3.0中的桌面应用程序？(How would you architect a desktop application in C# 3.0)

最满意答案

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