小编Ste*_*ole的帖子

我的数据看起来像这样：

我希望能够了解每个卖家在每个国家/地区选择的频率。我已经像这样漫长而缓慢地做到了：

competitor_by_country <- df %>% 
  group_by(country) %>% 
  summarise(
    Test_count = sum(!is.na(Test)),
    Test2_count = sum(!is.na(Test2)),
    Shopify_count = sum(!is.na(Shopify_)),
    Aliexpress_count = sum(!is.na(Aliexpress)),
    JD_count = sum(!is.na(JD)),
    Flipkart_count = sum(!is.na(Flipkart_)),
    Rakuten_count = sum(!is.na(Rakuten_)),
    `John Lewis_count` = sum(!is.na(`John Lewis_`)),
    Otto_count = sum(!is.na(Otto_)),
    Noon_count = sum(!is.na(Noon_)),
    `Walmart (3rd Party)_count` = sum(!is.na(`Walmart (3rd Party)`)),
    `Amazon Vendor Central_count` = sum(!is.na(`Amazon Vendor Central_`)),
    `Walmart (Supplier_count` = sum(!is.na(`Walmart (Supplier`)),
    Zalando_count = sum(!is.na(Zalando_)),
    Tmall_count = sum(!is.na(Tmall)),
    
  )

但这非常乏味，而且我还有其他 50-100 列的数据。有人可以建议我一种缩短此时间的方法，例如循环吗？

这是当前代码的输出：