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4
我有一个 df，我想按链接对其进行分组，并按时间对其进行排序。然后，每次 type == 'vehicle Leaves Traffic' 时，count 列中的单元格应在前一行中的单元格值的基础上添加 +1。如果type == '车辆进入交通'，则应从前一行中扣除 1。
需要澄清的是，不应更改前一行的值，而是应基于前一行的值更改该行的值。
这是我的方法，但我得到的只是 0、1 和 2。我预计某些链接会有更高的值。
parking_min <- cars %>%
group_by(link)%>%
dplyr::mutate(count = if_else(type == 'vehicle leaves traffic', lag(count, n=1,order_by=time)+1,lag(count))) %>%
dplyr::mutate(count = if_else(type == 'vehicle enters traffic',lag(count, n=1, order_by=time)-1,lag(count)))
这是我的数据:
structure(list(time = c("23707.0", "31209.0", "31210.0", "36230.0",
"36231.0", "38925.0", "39583.0", "40198.0", "40818.0", "41974.0",
"42895.0", "43099.0", "43683.0", "44645.0", "45730.0", "46785.0",
"48846.0", "48905.0", "52790.0", "53829.0", "55021.0", "58240.0",
"58635.0", "59682.0", "59683.0", "63740.0", "63776.0", "68607.0",
"24607.0", "25218.0", "26442.0", "28004.0", "29884.0", "37750.0",
"42623.0", "43965.0", "49426.0", "54925.0", "56688.0", "56689.0",
"57738.0", "61900.0", "64221.0", "67065.0", "69404.0", "77454.0",
"83588.0", "89601.0", "27452.0", "27743.0", "33598.0", "36297.0",
"60604.0", "62940.0", "63184.0", "63250.0", "20911.0", "27013.0",
"29815.0", "49550.0", "53991.0", "55620.0", "61200.0", "67672.0",
"78558.0", "79245.0", "27006.0", "29085.0", "31411.0", "36747.0",
"36877.0", "38434.0", "38807.0", "44607.0", "58068.0", "58800.0",
"65236.0", "65431.0", "69013.0", "69072.0", "25609.0", "29520.0",
"46559.0", "66916.0", "74904.0", "78407.0", "20445.0", "23938.0",
"24017.0", "24281.0", "25283.0", "25873.0", "27137.0", "27342.0",
"28790.0", "28910.0", "29241.0", "29345.0", "29465.0", "29525.0",
"29909.0", "30719.0", "31092.0", "31356.0", "31786.0", "32829.0",
"33966.0", "34175.0", "34545.0", "34888.0", "34977.0", "35775.0",
"35950.0", "38409.0", "38636.0", "39259.0", "39527.0", "40256.0",
"40385.0", "40564.0", "40691.0", "40774.0", "42271.0", "42469.0",
"42895.0", "43103.0", "43223.0", "43599.0", "44476.0", "44903.0",
"44904.0", "45774.0", "45834.0", "45915.0", "46230.0", "46287.0",
"46626.0", "47215.0", "48784.0", "50266.0", "50361.0", "50763.0",
"52685.0", "52793.0", "54688.0", "55105.0", "56310.0", "57885.0",
"58093.0", "58153.0", "59223.0", "60460.0", "60597.0", "60676.0",
"61307.0", "61457.0", "61974.0", "62141.0", "62165.0", "62347.0",
"62591.0", "64175.0", "64280.0", "65555.0", "65808.0", "66038.0",
"66391.0", "66723.0", "66735.0", "66736.0", "66933.0", "67502.0",
"67989.0", "68322.0", "68785.0", "69318.0", "70450.0", "70634.0",
"71069.0", "71741.0", "72121.0", "72292.0", "73236.0", "73775.0",
"74280.0", "80298.0", "80458.0", "80976.0", "81035.0", "84189.0",
"84302.0", "85602.0", "23296.0", "34106.0", "34107.0", "55975.0",
"55976.0", "57434.0", "60561.0", "70091.0", "26085.0", "26163.0",
"26654.0", "27473.0", "28303.0", "29212.0", "29380.0", "29581.0",
"29707.0", "30802.0", "31052.0", "33174.0", "34020.0", "36031.0",
"36392.0", "38037.0", "40717.0", "42099.0", "43154.0", "44413.0",
"44414.0", "44730.0", "44770.0", "46863.0", "46876.0", "48318.0",
"48435.0", "48493.0", "48700.0", "51736.0", "51747.0", "51748.0",
"52221.0", "52302.0", "52599.0", "52921.0", "53104.0", "53230.0",
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"29531.0", "29532.0", "32320.0", "32735.0", "47644.0", "50879.0",
"50971.0", "51427.0", "55554.0", "57334.0", "57971.0", "59064.0",
"66852.0", "68689.0", "69206.0", "72502.0", "84592.0", "84593.0",
"90207.0", "90208.0", "34426.0", "74433.0", "32354.0", "64161.0",
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47741L, 53329L, 54310L, 55545L, 56255L, 62460L, 63930L, 75298L,
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159131L, 160368L, 177328L, 177392L, 177394L, 179845L, 180260L,
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249842L, 250280L, 254468L, 262163L, 262164L, 263107L, 263117L,
272463L, 274866L, 279549L, 282270L, 286442L, 286606L, 287265L,
292441L, 293100L, 298081L, 308952L, 312468L, 312471L, 314309L,
317028L, 334227L, 336208L, 17419L, 19144L, 55246L, 55251L, 70799L,
73085L, 154041L, 172793L, 173296L, 175721L, 196355L, 206048L,
209794L, 216742L, 274263L, 285199L, 287908L, 302276L, 330230L,
330233L, 335274L, 335275L, 81402L, 308426L, 71000L, 255248L,
280759L, 10158L), class = "data.frame")
可能的输出:
time type vehicle_id link count
18798 23707.0 vehicle enters traffic 1267069 90 0 #start point
64777 31209.0 vehicle leaves traffic 810534 90 1 #+1
64783 31210.0 vehicle enters traffic 810534 90 0 #-1
90025 36230.0 vehicle leaves traffic 51825 90 1
90030 36231.0 vehicle enters traffic 51825 90 0
102868 38925.0 vehicle leaves traffic 1326473 90 1
105834 39583.0 vehicle leaves traffic 1199672 90 2 #here as well 1+1 =2
108690 40198.0 vehicle leaves traffic 1111105 90 3 #2+1 =3
111727 40818.0 vehicle enters traffic 1111105 90 2 #3-1 =2
118283 41974.0 vehicle leaves traffic 532654 90 3
124349 42895.0 vehicle enters traffic 532654 90 2
125700 43099.0 vehicle leaves traffic 1267069 90 3
129642 43683.0 vehicle enters traffic 1199672 90 2
135888 44645.0 vehicle leaves traffic 1398907 90 3
142577 45730.0 vehicle enters traffic 1398907 90 2
148772 46785.0 vehicle leaves traffic 1239391 90 3
161264 48846.0 vehicle enters traffic 1239391 90 2
161590 48905.0 vehicle enters traffic 1326473 90 1
182778 52790.0 vehicle leaves traffic 46491 90 2
最终我想找到每个链接的最大计数。但这可以在另一个步骤中完成，并且不需要成为解决方案的一部分，也许它有助于澄清问题。
最佳答案
我想这就是你想要的:
df %>%
arrange(link, time) %>%
group_by(link) %>%
mutate(vehicles_entered_traffic = cumsum(type == "vehicle enters traffic")
, vehicles_left_traffic = cumsum(type == "vehicle leaves traffic")
, count = count[1] + vehicles_left_traffic - vehicles_entered_traffic)
关于r - 如何根据条件和上面的行来操作行的值？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/62678318/
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