{"id":4053,"date":"2023-07-13T22:03:29","date_gmt":"2023-07-13T22:03:29","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%89%e1%85%b5%e1%84%80%e1%85%a1%e1%86%ab%e1%84%83%e1%85%a1%e1%86%bc-r-%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae%e1%86%b8\/"},"modified":"2023-07-13T22:03:29","modified_gmt":"2023-07-13T22:03:29","slug":"%e1%84%89%e1%85%b5%e1%84%80%e1%85%a1%e1%86%ab%e1%84%83%e1%85%a1%e1%86%bc-r-%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae%e1%86%b8","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%89%e1%85%b5%e1%84%80%e1%85%a1%e1%86%ab%e1%84%83%e1%85%a1%e1%86%bc-r-%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae%e1%86%b8\/","title":{"rendered":"R\uc5d0\uc11c \uc2dc\uac04\ubcc4\ub85c \ub370\uc774\ud130\ub97c \uadf8\ub8f9\ud654\ud558\ub294 \ubc29\ubc95(\uc608\uc81c \ud3ec\ud568)"},"content":{"rendered":"

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\ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \uc2dc\uac04\ubcc4\ub85c \uadf8\ub8f9\ud654\ud558\uace0 R\uc5d0\uc11c \uc9d1\uacc4\ub97c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 library<\/span> (dplyr)\nlibrary<\/span> (lubridate)\n\n#group by hours in time column and calculate sum of sales\n<\/span>df %>%\n  group_by(time=floor_date(time, ' 1 hour<\/span> ')) %>%\n  summarize(sum_sales=sum(sales))\n<\/strong><\/pre>\n
 \uc774 \ud2b9\uc815 \uc608\uc5d0\uc11c\ub294 \uc2dc\uac04\ubcc4 \uac12\uc744 Hour<\/strong> \ub77c\ub294 \uc5f4\ub85c \uadf8\ub8f9\ud654\ud55c \ub2e4\uc74c \uac01 \uc2dc\uac04\uc5d0 \ub300\ud55c Sales<\/strong> \uc5f4\uc758 \uac12 \ud569\uacc4\ub97c \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc2e4\uc81c\ub85c \uc774 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608: R\uc5d0\uc11c \uc2dc\uac04\ubcc4\ub85c \ub370\uc774\ud130 \uadf8\ub8f9\ud654<\/strong><\/span><\/h2>\n
 \ub9e4\uc7a5\uc758 \ud558\ub8e8 \uc911 \ub2e4\uc591\ud55c \uc2dc\uac04\ub300\uc5d0 \ubc1c\uc0dd\ud55c \ud310\ub9e4 \uac74\uc218\ub97c \ud45c\uc2dc\ud558\ub294 \ub2e4\uc74c \ub370\uc774\ud130 \ud504\ub808\uc784\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #create data frame\n<\/span>df <- data. frame<\/span> (time=as. POSIXct<\/span> (c('2022-01-01 01:14:00', '2022-01-01 01:24:15',\n                                 '2022-01-01 02:52:19', '2022-01-01 02:54:00',\n                                 '2022-01-01 04:05:10', '2022-01-01 05:35:09')),\n                 sales=c(18, 20, 15, 14, 10, 9))\n\n#view data frame\n<\/span>df\n\n                 time sales\n1 2022-01-01 01:14:00 18\n2 2022-01-01 01:24:15 20\n3 2022-01-01 02:52:19 15\n4 2022-01-01 02:54:00 14\n5 2022-01-01 04:05:10 10\n6 2022-01-01 05:35:09 9\n<\/span><\/span><\/strong><\/pre>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc2dc\uac04<\/strong> \uc5f4\uc744 \uc2dc\uac04\ubcc4\ub85c \uadf8\ub8f9\ud654\ud558\uace0 \uac01 \uc2dc\uac04\uc758 \ub9e4\ucd9c<\/strong> \ud569\uacc4\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 library<\/span> (dplyr)\nlibrary<\/span> (lubridate)\n\n#group by hours in time column and calculate sum of sales\n<\/span>df %>%\n  group_by(time=floor_date(time, ' 1 hour<\/span> ')) %>%\n  summarize(sum_sales=sum(sales))\n\n`summarise()` ungrouping output (override with `.groups` argument)\n# A tibble: 4 x 2\n  time sum_sales\n                    \n1 2022-01-01 01:00:00 38\n2 2022-01-01 02:00:00 29\n3 2022-01-01 04:00:00 10\n4 2022-01-01 05:00:00 9\n<\/strong><\/pre>\n
 \uacb0\uacfc\uc5d0\uc11c \uc6b0\ub9ac\ub294 \ub2e4\uc74c\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/span><\/p>\n