我正在处理内布拉斯加州城市的统一犯罪报告数据（慷慨的分类），并计算了从1995年到2010年的主要分类的犯罪率，以5年为增量。
我想绘制多年来犯罪率。 但是，由于UCR工作报告的方式，并非所有城市都报告了所有年份的价值。
我对R很新，但是一位同事建议我尝试创建一个for循环，给出每个城市名称的唯一值的计数。然后我可以使用这些计数来丢弃数据或将数据子集化，这样我至少有三个观察值可用于绘图。这是我已经得到的，有什么不起作用。不幸的是，我需要关注本周余下时间的一些更紧迫的问题，所以我想我会把它扔给社区以获得一些见解。
代码和名称数据如下。感谢。
drop = NULL
city.names <- unique(cnames)
for (i in 1:length(city.names)){
x = sum(cnames==i)
if (x < 3) {c(drop,i)}
}
共有191个独特名称的观察结果。 数据为csv并导入为
data <- read.csv("cities.csv", header=TRUE, sep=",")
"","year","cnames"
"1",1995,"Beatrice"
"2",1995,"Bellevue"
"3",1995,"Columbus"
"4",1995,"Fremont"
"5",1995,"Grand Island"
"6",1995,"Hastings"
"7",1995,"Kearney"
"8",1995,"La Vista"
"9",1995,"Lincoln"
"10",1995,"Norfolk"
"11",1995,"North Platte"
"12",1995,"Omaha"
"13",1995,"Papillion"
"14",1995,"Scottsbluff"
"15",1995,"South Sioux City"
"16",2000,"Bellevue"
"17",2000,"Columbus"
"18",2000,"Fremont"
"19",2000,"Grand Island"
"20",2000,"Hastings"
"21",2000,"Kearney"
"22",2000,"La Vista"
"23",2000,"Lincoln"
"24",2000,"Norfolk"
"25",2000,"Omaha"
"26",2000,"Papillion"
"27",2000,"Scottsbluff"
"28",2000,"South Sioux City"
"29",2005,"Alliance"
"30",2005,"Ashland"
"31",2005,"Auburn"
"32",2005,"Bayard"
"33",2005,"Beatrice"
"34",2005,"Bellevue"
"35",2005,"Blair"
"36",2005,"Bridgeport"
"37",2005,"Broken Bow"
"38",2005,"Central City"
"39",2005,"Chadron"
"40",2005,"Columbus"
"41",2005,"Cozad"
"42",2005,"Crete"
"43",2005,"David City"
"44",2005,"Elkhorn"
"45",2005,"Falls City"
"46",2005,"Fremont"
"47",2005,"Gering"
"48",2005,"Gothenburg"
"49",2005,"Grand Island"
"50",2005,"Hastings"
"51",2005,"Holdrege"
"52",2005,"Imperial"
"53",2005,"Kearney"
"54",2005,"La Vista"
"55",2005,"Lexington"
"56",2005,"Lincoln"
"57",2005,"Lyons"
"58",2005,"Madison"
"59",2005,"McCook"
"60",2005,"Milford"
"61",2005,"Minden"
"62",2005,"Mitchell"
"63",2005,"Nebraska City"
"64",2005,"Norfolk"
"65",2005,"North Platte"
"66",2005,"Ogallala"
"67",2005,"Omaha"
"68",2005,"O'Neill"
"69",2005,"Ord"
"70",2005,"Papillion"
"71",2005,"Plainview"
"72",2005,"Plattsmouth"
"73",2005,"Ralston"
"74",2005,"Schuyler"
"75",2005,"Scottsbluff"
"76",2005,"Seward"
"77",2005,"Sidney"
"78",2005,"South Sioux City"
"79",2005,"St. Paul"
"80",2005,"Superior"
"81",2005,"Valley"
"82",2005,"Wahoo"
"83",2005,"West Point"
"84",2005,"Wymore"
"85",2005,"York"
"86",2010,"Alliance"
"87",2010,"Ashland"
"88",2010,"Auburn"
"89",2010,"Aurora"
"90",2010,"Bayard"
"91",2010,"Beatrice"
"92",2010,"Bellevue"
"93",2010,"Bennington"
"94",2010,"Blair"
"95",2010,"Bridgeport"
"96",2010,"Broken Bow"
"97",2010,"Central City"
"98",2010,"Chadron"
"99",2010,"Columbus"
"100",2010,"Cozad"
"101",2010,"Crete"
"102",2010,"Falls City"
"103",2010,"Fremont"
"104",2010,"Gering"
"105",2010,"Gothenburg"
"106",2010,"Grand Island"
"107",2010,"Hastings"
"108",2010,"Holdrege"
"109",2010,"Imperial"
"110",2010,"Kearney"
"111",2010,"La Vista"
"112",2010,"Lexington"
"113",2010,"Lincoln"
"114",2010,"Lyons"
"115",2010,"Madison"
"116",2010,"McCook"
"117",2010,"Milford"
"118",2010,"Minden"
"119",2010,"Nebraska City"
"120",2010,"Norfolk"
"121",2010,"North Platte"
"122",2010,"Ogallala"
"123",2010,"Omaha"
"124",2010,"O'Neill"
"125",2010,"Papillion"
"126",2010,"Plainview"
"127",2010,"Plattsmouth"
"128",2010,"Ralston"
"129",2010,"Scottsbluff"
"130",2010,"Seward"
"131",2010,"Sidney"
"132",2010,"South Sioux City"
"133",2010,"Superior"
"134",2010,"Valentine"
"135",2010,"Valley"
"136",2010,"Wahoo"
"137",2010,"Wayne"
"138",2010,"West Point"
"139",2010,"Wilber"
"140",2010,"York"
"141",2013,"Alliance"
"142",2013,"Ashland"
"143",2013,"Aurora"
"144",2013,"Beatrice"
"145",2013,"Bellevue"
"146",2013,"Bennington"
"147",2013,"Blair"
"148",2013,"Bridgeport"
"149",2013,"Broken Bow"
"150",2013,"Central City"
"151",2013,"Chadron"
"152",2013,"Columbus"
"153",2013,"Cozad"
"154",2013,"Crete"
"155",2013,"Falls City"
"156",2013,"Fremont"
"157",2013,"Gering"
"158",2013,"Gordon"
"159",2013,"Gothenburg"
"160",2013,"Grand Island"
"161",2013,"Hastings"
"162",2013,"Holdrege"
"163",2013,"Imperial"
"164",2013,"Kearney"
"165",2013,"Kimball"
"166",2013,"La Vista"
"167",2013,"Lexington"
"168",2013,"Lincoln"
"169",2013,"Madison"
"170",2013,"McCook"
"171",2013,"Milford"
"172",2013,"Minden"
"173",2013,"Mitchell"
"174",2013,"Nebraska City"
"175",2013,"Norfolk"
"176",2013,"Ogallala"
"177",2013,"Omaha"
"178",2013,"O'Neill"
"179",2013,"Papillion"
"180",2013,"Plattsmouth"
"181",2013,"Ralston"
"182",2013,"Scottsbluff"
"183",2013,"Seward"
"184",2013,"South Sioux City"
"185",2013,"Superior"
"186",2013,"Valentine"
"187",2013,"Valley"
"188",2013,"Wahoo"
"189",2013,"West Point"
"190",2013,"Wilber"
"191",2013,"York"
答案 0 :(得分：2)
对于按列的“频率”进行子集化，base R和其他包中有许多选项。一种选择是在“cnames”列上使用table函数并获取频率。输出将是vector，其中“键/值”对应于每个唯一“cnames”的names/frequency。检查值是否小于3（tbl <3），其逻辑索引为“TRUE / FALSE”。使用该索引子集“tbl”的名称，并使用它来使用%in%索引“cnames”列。我展示了两种方法，一种是否定（!）和使用<，另一种使用>=
tbl <- table(data$cnames)
data[!data$cnames %in% names(tbl)[tbl <3],]
或者
data[data$cnames %in% names(tbl)[tbl >=3],]
或使用ave获取每个唯一“cnames”的length，并通过>=运算符获取逻辑索引。 ave以与原始数据集中相同的顺序返回输出。这可以用于子集化。
data[with(data, ave(seq_along(cnames), cnames, FUN=length)>=3),]
如果您使用data.table，代码将更紧凑，并且对于大数据集更快。使用setDT将“data.frame”转换为“data.table”，为每个唯一的“cnames”分配计数（n:=.N），最后使用>=对数据集进行子集化
library(data.table)
setDT(data)[,n:=.N, cnames][n>=3]