{"id":400,"date":"2023-03-23T07:29:49","date_gmt":"2023-03-23T07:29:49","guid":{"rendered":"https:\/\/yunshangtulv.com.cn\/?p=400"},"modified":"2023-03-23T07:29:49","modified_gmt":"2023-03-23T07:29:49","slug":"%e6%95%b0%e6%8d%ae%e5%b8%a7%ef%bc%88%e8%a1%a5%e5%85%85%ef%bc%89","status":"publish","type":"post","link":"https:\/\/yunshangtulv.com.cn\/?p=400","title":{"rendered":"\u6570\u636e\u5e27\uff08\u8865\u5145\uff09"},"content":{"rendered":"
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\u6570\u636e\u5e27\uff08data frame\uff09\u662fR\u8bed\u8a00\u4e2d\u975e\u5e38\u91cd\u8981\u4e14\u5e38\u7528\u7684\u6570\u636e\u7ed3\u6784\u3002\u6570\u636e\u5e27\u7c7b\u4f3c\u4e8e\u4e00\u4e2a\u4e8c\u7ef4\u8868\u683c\uff0c\u5176\u4e2d\u6bcf\u5217\u53ef\u4ee5\u5305\u542b\u4e0d\u540c\u7c7b\u578b\u7684\u6570\u636e\uff08\u5982\u6570\u503c\u3001\u5b57\u7b26\u6216\u56e0\u5b50\uff09\uff0c\u6bcf\u884c\u4ee3\u8868\u4e00\u4e2a\u89c2\u6d4b\u503c\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u89e3\u91caR\u8bed\u8a00\u7684\u6570\u636e\u5e27\u64cd\u4f5c\uff0c\u5e76\u63d0\u4f9b\u4e00\u4e9b\u5e94\u7528\u5b9e\u4f8b\u3002<\/p>\n

    \n
  1. \u521b\u5efa\u6570\u636e\u5e27<\/li>\n<\/ol>\n

    \u53ef\u4ee5\u4f7f\u7528data.frame<\/code>\u51fd\u6570\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u5e27\u3002\u4f8b\u5982\uff0c\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u5458\u5de5\u4fe1\u606f\u7684\u6570\u636e\u5e27\uff1a<\/p>\n

    \n
    <\/div>\n
    # \u521b\u5efa\u6570\u636e\u5e27<\/span>
    \nemployees <-<\/span> data.frame(<\/span>
    \n name =<\/span> c<\/span>(<\/span>\"John Doe\"<\/span>,<\/span> \"Jane Smith\"<\/span>,<\/span> \"Mike Brown\"<\/span>)<\/span>,<\/span>
    \n age =<\/span> c<\/span>(<\/span>30<\/span>,<\/span> 28<\/span>,<\/span> 35<\/span>)<\/span>,<\/span>
    \n position =<\/span> c<\/span>(<\/span>\"Software Engineer\"<\/span>,<\/span> \"Data Analyst\"<\/span>,<\/span> \"Project Manager\"<\/span>)<\/span>,<\/span>
    \n stringsAsFactors =<\/span> FALSE<\/span>
    \n)<\/span><\/p>\n

    # \u6253\u5370\u6570\u636e\u5e27<\/span>
    \nprint(<\/span>employees)<\/span>
    \n<\/code><\/div>\n<\/div>\n

    \u6ce8\u610f\uff1a\u5728\u521b\u5efa\u6570\u636e\u5e27\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u5c06stringsAsFactors<\/code>\u53c2\u6570\u8bbe\u7f6e\u4e3aFALSE<\/code>\u6765\u907f\u514d\u81ea\u52a8\u5c06\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u56e0\u5b50\u3002<\/p>\n

      \n
    1. \u8bbf\u95ee\u6570\u636e\u5e27\u4e2d\u7684\u6570\u636e<\/li>\n<\/ol>\n

      \u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u6cd5\u8bbf\u95ee\u6570\u636e\u5e27\u4e2d\u7684\u6570\u636e\uff1a<\/p>\n

        \n
      • \u4f7f\u7528\u5217\u540d\u8bbf\u95ee\u6570\u636e\uff1aemployees$name<\/code><\/li>\n
      • \u4f7f\u7528\u884c\u7d22\u5f15\u548c\u5217\u7d22\u5f15\u8bbf\u95ee\u6570\u636e\uff1aemployees[1, 2]<\/code>\uff08\u8bbf\u95ee\u7b2c1\u884c\uff0c\u7b2c2\u5217\u7684\u6570\u636e\uff09<\/li>\n
      • \u4f7f\u7528\u884c\u7d22\u5f15\u548c\u5217\u540d\u8bbf\u95ee\u6570\u636e\uff1aemployees[1, \"age\"]<\/code><\/li>\n
      • \u4f7f\u7528\u6761\u4ef6\u8bbf\u95ee\u6570\u636e\uff1aemployees[employees$age > 30, ]<\/code>\uff08\u8bbf\u95ee\u5e74\u9f84\u5927\u4e8e30\u7684\u5458\u5de5\u4fe1\u606f\uff09<\/li>\n<\/ul>\n
          \n
        1. \u5411\u6570\u636e\u5e27\u4e2d\u6dfb\u52a0\u6216\u5220\u9664\u6570\u636e<\/li>\n<\/ol>\n

          \u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u5411\u6570\u636e\u5e27\u4e2d\u6dfb\u52a0\u6216\u5220\u9664\u6570\u636e\uff1a<\/p>\n

            \n
          • \u6dfb\u52a0\u65b0\u5217\uff1aemployees$salary <- c(90000, 80000, 100000)<\/code><\/li>\n
          • \u6dfb\u52a0\u65b0\u884c\uff1aemployees <- rbind(employees, data.frame(name = \"Alice Johnson\", age = 32, position = \"HR Specialist\", stringsAsFactors = FALSE))<\/code><\/li>\n
          • \u5220\u9664\u5217\uff1aemployees$salary <- NULL<\/code><\/li>\n
          • \u5220\u9664\u884c\uff1aemployees <- employees[-4, ]<\/code>\uff08\u5220\u9664\u7b2c4\u884c\uff09<\/li>\n<\/ul>\n
              \n
            1. \u6570\u636e\u5e27\u7684\u6392\u5e8f\u548c\u7b5b\u9009<\/li>\n<\/ol>\n

              \u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u5bf9\u6570\u636e\u5e27\u8fdb\u884c\u6392\u5e8f\u548c\u7b5b\u9009\uff1a<\/p>\n

                \n
              • \u5bf9\u6570\u636e\u5e27\u6309\u5e74\u9f84\u6392\u5e8f\uff08\u5347\u5e8f\uff09\uff1aemployees <- employees[order(employees$age), ]<\/code><\/li>\n
              • \u5bf9\u6570\u636e\u5e27\u6309\u5e74\u9f84\u6392\u5e8f\uff08\u964d\u5e8f\uff09\uff1aemployees <- employees[order(-employees$age), ]<\/code><\/li>\n
              • \u7b5b\u9009\u5e74\u9f84\u5927\u4e8e30\u7684\u5458\u5de5\uff1aolder_employees <- employees[employees$age > 30, ]<\/code><\/li>\n<\/ul>\n
                  \n
                1. \u6570\u636e\u5e27\u7684\u7edf\u8ba1\u64cd\u4f5c<\/li>\n<\/ol>\n

                  \u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u5bf9\u6570\u636e\u5e27\u8fdb\u884c\u7edf\u8ba1\u64cd\u4f5c\uff1a<\/p>\n

                    \n
                  • \u8ba1\u7b97\u5e74\u9f84\u7684\u5e73\u5747\u503c\uff1amean(employees$age)<\/code><\/li>\n
                  • \u8ba1\u7b97\u5e74\u9f84\u7684\u6700\u5c0f\u503c\uff1amin(employees$age)<\/code><\/li>\n
                  • \u8ba1\u7b97\u5e74\u9f84\u7684\u6700\u5927\u503c\uff1amax(employees$age)<\/code><\/li>\n
                  • \u8ba1\u7b97\u5e74\u9f84\u7684\u4e2d\u4f4d\u6570\uff1amedian(employees$age)<\/code><\/li>\n<\/ul>\n

                    \u901a\u8fc7\u4ee5\u4e0a\u793a\u4f8b\uff0c\u6211\u4eec\u4e86\u89e3\u4e86\u5982\u4f55\u5728R\u8bed\u8a00\u4e2d\u64cd\u4f5c\u6570\u636e\u5e27\u3002\u5b9e\u9645\u5e94\u7528\u4e2d\u53ef\u80fd\u4f1a\u9047\u5230\u66f4\u590d\u6742\u7684\u64cd\u4f5c\uff0c\u4f46\u57fa\u672c\u64cd\u4f5c\u6570\u636e\u5e27\u7684\u5176\u4ed6\u65b9\u6cd5\uff1a<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

                    \n
                    \n
                    \n
                    \n
                    \n
                    \n
                      \n
                    1. \u6570\u636e\u5e27\u7684\u5408\u5e76\u4e0e\u62c6\u5206<\/li>\n<\/ol>\n

                      \u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u5408\u5e76\u6216\u62c6\u5206\u6570\u636e\u5e27\uff1a<\/p>\n

                        \n
                      • \u6309\u5217\u5408\u5e76\u4e24\u4e2a\u6570\u636e\u5e27\uff08\u5177\u6709\u76f8\u540c\u884c\u6570\uff09\uff1amerged_data <- cbind(data_frame1, data_frame2)<\/code><\/li>\n
                      • \u6309\u884c\u5408\u5e76\u4e24\u4e2a\u6570\u636e\u5e27\uff08\u5177\u6709\u76f8\u540c\u5217\u540d\uff09\uff1amerged_data <- rbind(data_frame1, data_frame2)<\/code><\/li>\n
                      • \u62c6\u5206\u6570\u636e\u5e27\u4e3a\u5217\u8868\uff1asplit_data <- split(employees, employees$position)<\/code><\/li>\n<\/ul>\n
                          \n
                        1. \u5e94\u7528\u51fd\u6570\u5230\u6570\u636e\u5e27\u7684\u5217\u6216\u884c<\/li>\n<\/ol>\n

                          \u53ef\u4ee5\u4f7f\u7528apply<\/code>\u3001lapply<\/code>\u548csapply<\/code>\u7b49\u51fd\u6570\u5c06\u51fd\u6570\u5e94\u7528\u5230\u6570\u636e\u5e27\u7684\u5217\u6216\u884c\uff1a<\/p>\n

                            \n
                          • \u5c06\u51fd\u6570\u5e94\u7528\u5230\u6bcf\u5217\uff1acol_results <- sapply(employees, function(x) mean(as.numeric(x)))<\/code><\/li>\n
                          • \u5c06\u51fd\u6570\u5e94\u7528\u5230\u6bcf\u884c\uff1arow_results <- apply(employees, 1, function(x) paste(x[1], x[2], x[3], sep = \", \"))<\/code><\/li>\n<\/ul>\n

                            \u6ce8\u610f\uff1a\u5728\u4f7f\u7528\u8fd9\u4e9b\u51fd\u6570\u65f6\uff0c\u9700\u8981\u786e\u4fdd\u6570\u636e\u5e27\u4e2d\u7684\u6570\u636e\u7c7b\u578b\u4e0e\u5e94\u7528\u7684\u51fd\u6570\u517c\u5bb9\u3002<\/p>\n

                              \n
                            1. \u6570\u636e\u5e27\u7684\u91cd\u5851\u4e0e\u900f\u89c6<\/li>\n<\/ol>\n

                              \u4f7f\u7528tidyverse<\/code>\u4e2d\u7684dplyr<\/code>\u548ctidyr<\/code>\u5305\uff0c\u53ef\u4ee5\u8fdb\u884c\u66f4\u9ad8\u7ea7\u7684\u6570\u636e\u5e27\u64cd\u4f5c\uff0c\u5982\u6570\u636e\u900f\u89c6\u548c\u91cd\u5851\uff1a<\/p>\n

                              \n
                              # \u5b89\u88c5 tidyverse \u5305<\/span>
                              \ninstall.packages(<\/span>\"tidyverse\"<\/span>)<\/span><\/p>\n

                              # \u52a0\u8f7d\u6240\u9700\u7684\u5305<\/span>
                              \nlibrary(<\/span>dplyr)<\/span>
                              \nlibrary(<\/span>tidyr)<\/span><\/p>\n

                              # \u793a\u4f8b\u6570\u636e\u5e27<\/span>
                              \ndata <-<\/span> data.frame(<\/span>
                              \n city =<\/span> c<\/span>(<\/span>\"New York\"<\/span>,<\/span> \"Los Angeles\"<\/span>,<\/span> \"Chicago\"<\/span>)<\/span>,<\/span>
                              \n temperature =<\/span> c<\/span>(<\/span>75<\/span>,<\/span> 77<\/span>,<\/span> 68<\/span>)<\/span>,<\/span>
                              \n humidity =<\/span> c<\/span>(<\/span>60<\/span>,<\/span> 45<\/span>,<\/span> 70<\/span>)<\/span>,<\/span>
                              \n stringsAsFactors =<\/span> FALSE<\/span>
                              \n)<\/span><\/p>\n

                              # \u4ece\u5bbd\u683c\u5f0f\u8f6c\u4e3a\u957f\u683c\u5f0f<\/span>
                              \nlong_data <-<\/span> data %>%<\/span>
                              \n gather(<\/span>key =<\/span> \"variable\"<\/span>,<\/span> value =<\/span> \"value\"<\/span>,<\/span> -<\/span>city)<\/span><\/p>\n

                              # \u4ece\u957f\u683c\u5f0f\u8f6c\u4e3a\u5bbd\u683c\u5f0f<\/span>
                              \nwide_data <-<\/span> long_data %>%<\/span>
                              \n spread(<\/span>key =<\/span> \"variable\"<\/span>,<\/span> value =<\/span> \"value\"<\/span>)<\/span>
                              \n<\/code><\/div>\n<\/div>\n

                              \u901a\u8fc7\u4ee5\u4e0a\u8865\u5145\u793a\u4f8b\uff0c\u60a8\u53ef\u4ee5\u4e86\u89e3R\u8bed\u8a00\u4e2d\u66f4\u591a\u5173\u4e8e\u6570\u636e\u5e27\u64cd\u4f5c\u7684\u65b9\u6cd5\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u60a8\u53ef\u80fd\u4f1a\u9047\u5230\u66f4\u590d\u6742\u7684\u573a\u666f\uff0c\u4f46\u8fd9\u4e9b\u57fa\u672c\u6982\u5ff5\u548c\u65b9\u6cd5\u5c06\u4e3a\u60a8\u5904\u7406\u5404\u79cd\u6570\u636e\u5e27\u95ee\u9898\u63d0\u4f9b\u575a\u5b9e\u7684\u57fa\u7840\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

                              \u6570\u636e\u5e27\uff08data frame\uff09\u662fR\u8bed\u8a00\u4e2d\u975e\u5e38\u91cd\u8981\u4e14\u5e38\u7528\u7684\u6570\u636e\u7ed3\u6784\u3002\u6570\u636e\u5e27\u7c7b\u4f3c\u4e8e\u4e00\u4e2a\u4e8c\u7ef4\u8868\u683c\uff0c\u5176\u4e2d\u6bcf\u5217\u53ef\u4ee5\u5305\u542b\u4e0d […]<\/p>\n","protected":false},"author":111,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[],"class_list":["post-400","post","type-post","status-publish","format-standard","hentry","category-r"],"_links":{"self":[{"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=\/wp\/v2\/posts\/400","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=\/wp\/v2\/users\/111"}],"replies":[{"embeddable":true,"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=400"}],"version-history":[{"count":0,"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=\/wp\/v2\/posts\/400\/revisions"}],"wp:attachment":[{"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=400"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=400"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=400"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}