我想删除名为“的列”;行名称。”; 结构(列表年龄=c(33L,21L,37L,29L,45L),工人阶级=结构(c(4L, 7L,4L,4L,4L),.Label=c(“联邦政府”,“地方政府”,“从未工作过”, “私人”、“自我emp公司”、“自我emp非公司”、“州政府”, “无报酬”,class=“factor”),fnlwgt=c(319854L,41183L, 103323L,176027L,264526L),教育=结构(c(10L,16L, 12L、10L、8L),标签=c(“第十”、“第十一”、“第十二”、“第一至第四”, “第5-6”、“第7-8”、“第9”、“Assoc acdm”、“Assoc voc”, “学士”、“博士”、“硕士”、“学前教育”, “Prof school”、“Some college”)、class=“factor”)、education.num=c(13L, 10升,9升,13升,12升),婚姻状况=结构(c(6升,5升,5升, 5L,1L),.Label=c(“离婚”、“已婚AF配偶”、“已婚公民配偶”, “已婚配偶缺席”、“从未结婚”、“分居”、“丧偶” ),class=“factor”),职业=结构(c(10L,10L,3L, 10L,7L),.Label=c(“行政文书”、“武装部队”、“工艺维修”, “高级管理人员”、“农耕渔业”、“搬运工人和清洁工”, “机器操作检验”、“其他服务”、“私人住宅服务”、“教授专业”, “保护性服务”、“销售”、“技术支持”、“运输移动” ),class=“factor”),关系=结构(c(2L,4L,2L, 2L,4L),.Label=c(“丈夫”、“不在家”、“其他亲属”, “自己的孩子”、“未婚”、“妻子”、class=“factor”、种族=结构(c(5L、, 5L,5L,5L,5L),.Label=c(“美国印第安爱斯基摩人”,“亚洲太平洋岛民”, “黑色”、“其他”、“白色”)、class=”因子“、性别=结构(c(2L、, 1L、2L、1L、2L),.Label=c(“女性”、“男性”),class=“系数”), 资本收益=c(4650L,0L,0L,0L,0L),资本损失=c(0L, 0L,0L,0L,0L),每周小时数=c(35L,20L,40L,40L,40L ),native.country=结构(c(39L,39L,39L,39L,39L),.Label=c(“柬埔寨”), “加拿大”、“中国”、“哥伦比亚”、“古巴”、“多米尼加共和国”, “厄瓜多尔”、“萨尔瓦多”、“英国”、“法国”、“德国”, “希腊”、“危地马拉”、“海地”、“荷兰荷兰”, “洪都拉斯”、“香港”、“匈牙利”、“印度”、“伊朗”、“爱尔兰”, “意大利”、“牙买加”、“日本”、“老挝”、“墨西哥”、“尼加拉瓜”, “美国边远地区(关岛、美属维尔京群岛等)”、“秘鲁”、“菲律宾”、“波兰”, “葡萄牙”、“波多黎各”、“苏格兰”、“南方”、“台湾”, “泰国”、“特立尼达和多巴哥”、“美国”、“越南”, “南斯拉夫”,class=“factor”),结果=结构(c(1L, 1L,1L,1L,1L),.Label=c(“50K”),class=“factor”),.Names=c(“年龄”, “工人阶级”、“fnlwgt”、“教育程度”、“教育人数”、“婚姻状况”, “职业”、“关系”、“种族”、“性别”、“资本收益”, “资本损失”、“每周小时数”、“本国”、“结果”),row.names=c(25231L, 17952L、24945L、25524L、11025L),class=“data.frame”)

我想删除名为“的列”;行名称。”; 结构(列表年龄=c(33L,21L,37L,29L,45L),工人阶级=结构(c(4L, 7L,4L,4L,4L),.Label=c(“联邦政府”,“地方政府”,“从未工作过”, “私人”、“自我emp公司”、“自我emp非公司”、“州政府”, “无报酬”,class=“factor”),fnlwgt=c(319854L,41183L, 103323L,176027L,264526L),教育=结构(c(10L,16L, 12L、10L、8L),标签=c(“第十”、“第十一”、“第十二”、“第一至第四”, “第5-6”、“第7-8”、“第9”、“Assoc acdm”、“Assoc voc”, “学士”、“博士”、“硕士”、“学前教育”, “Prof school”、“Some college”)、class=“factor”)、education.num=c(13L, 10升,9升,13升,12升),婚姻状况=结构(c(6升,5升,5升, 5L,1L),.Label=c(“离婚”、“已婚AF配偶”、“已婚公民配偶”, “已婚配偶缺席”、“从未结婚”、“分居”、“丧偶” ),class=“factor”),职业=结构(c(10L,10L,3L, 10L,7L),.Label=c(“行政文书”、“武装部队”、“工艺维修”, “高级管理人员”、“农耕渔业”、“搬运工人和清洁工”, “机器操作检验”、“其他服务”、“私人住宅服务”、“教授专业”, “保护性服务”、“销售”、“技术支持”、“运输移动” ),class=“factor”),关系=结构(c(2L,4L,2L, 2L,4L),.Label=c(“丈夫”、“不在家”、“其他亲属”, “自己的孩子”、“未婚”、“妻子”、class=“factor”、种族=结构(c(5L、, 5L,5L,5L,5L),.Label=c(“美国印第安爱斯基摩人”,“亚洲太平洋岛民”, “黑色”、“其他”、“白色”)、class=”因子“、性别=结构(c(2L、, 1L、2L、1L、2L),.Label=c(“女性”、“男性”),class=“系数”), 资本收益=c(4650L,0L,0L,0L,0L),资本损失=c(0L, 0L,0L,0L,0L),每周小时数=c(35L,20L,40L,40L,40L ),native.country=结构(c(39L,39L,39L,39L,39L),.Label=c(“柬埔寨”), “加拿大”、“中国”、“哥伦比亚”、“古巴”、“多米尼加共和国”, “厄瓜多尔”、“萨尔瓦多”、“英国”、“法国”、“德国”, “希腊”、“危地马拉”、“海地”、“荷兰荷兰”, “洪都拉斯”、“香港”、“匈牙利”、“印度”、“伊朗”、“爱尔兰”, “意大利”、“牙买加”、“日本”、“老挝”、“墨西哥”、“尼加拉瓜”, “美国边远地区(关岛、美属维尔京群岛等)”、“秘鲁”、“菲律宾”、“波兰”, “葡萄牙”、“波多黎各”、“苏格兰”、“南方”、“台湾”, “泰国”、“特立尼达和多巴哥”、“美国”、“越南”, “南斯拉夫”,class=“factor”),结果=结构(c(1L, 1L,1L,1L,1L),.Label=c(“50K”),class=“factor”),.Names=c(“年龄”, “工人阶级”、“fnlwgt”、“教育程度”、“教育人数”、“婚姻状况”, “职业”、“关系”、“种族”、“性别”、“资本收益”, “资本损失”、“每周小时数”、“本国”、“结果”),row.names=c(25231L, 17952L、24945L、25524L、11025L),class=“data.frame”),r,R,这是从原始数据“成人”中采集的数据 样本(1:nrow(成人),nrow(成人)*0.4,替换=假) 我做了上面的样品。但问题是我有row.name。有时使用行名称真的不太好,尤其是当我需要使用回归时 您能帮助我吗?假设您的数据帧的名称是test 数据集中没有列名row.names: structure(list(age = c(33L, 21L, 37L, 29L, 45L), workclass = structure(c(4L, 7L, 4L, 4L, 4L), .Label = c("

这是从原始数据“成人”中采集的数据 样本(1:nrow(成人),nrow(成人)*0.4,替换=假)

我做了上面的样品。但问题是我有row.name。有时使用行名称真的不太好,尤其是当我需要使用回归时


您能帮助我吗?

假设您的数据帧的名称是
test

数据集中没有列名row.names:

structure(list(age = c(33L, 21L, 37L, 29L, 45L), workclass = structure(c(4L, 
7L, 4L, 4L, 4L), .Label = c(" Federal-gov", " Local-gov", " Never-worked", 
" Private", " Self-emp-inc", " Self-emp-not-inc", " State-gov", 
" Without-pay"), class = "factor"), fnlwgt = c(319854L, 41183L, 
103323L, 176027L, 264526L), education = structure(c(10L, 16L, 
12L, 10L, 8L), .Label = c(" 10th", " 11th", " 12th", " 1st-4th", 
" 5th-6th", " 7th-8th", " 9th", " Assoc-acdm", " Assoc-voc", 
" Bachelors", " Doctorate", " HS-grad", " Masters", " Preschool", 
" Prof-school", " Some-college"), class = "factor"), education.num = c(13L, 
10L, 9L, 13L, 12L), marital.status = structure(c(6L, 5L, 5L, 
5L, 1L), .Label = c(" Divorced", " Married-AF-spouse", " Married-civ-spouse", 
" Married-spouse-absent", " Never-married", " Separated", " Widowed"
), class = "factor"), occupation = structure(c(10L, 10L, 3L, 
10L, 7L), .Label = c(" Adm-clerical", " Armed-Forces", " Craft-repair", 
" Exec-managerial", " Farming-fishing", " Handlers-cleaners", 
" Machine-op-inspct", " Other-service", " Priv-house-serv", " Prof-specialty", 
" Protective-serv", " Sales", " Tech-support", " Transport-moving"
), class = "factor"), relationship = structure(c(2L, 4L, 2L, 
2L, 4L), .Label = c(" Husband", " Not-in-family", " Other-relative", 
" Own-child", " Unmarried", " Wife"), class = "factor"), race = structure(c(5L, 
5L, 5L, 5L, 5L), .Label = c(" Amer-Indian-Eskimo", " Asian-Pac-Islander", 
" Black", " Other", " White"), class = "factor"), sex = structure(c(2L, 
1L, 2L, 1L, 2L), .Label = c(" Female", " Male"), class = "factor"), 
capital.gain = c(4650L, 0L, 0L, 0L, 0L), capital.loss = c(0L, 
0L, 0L, 0L, 0L), hours.per.week = c(35L, 20L, 40L, 40L, 40L
), native.country = structure(c(39L, 39L, 39L, 39L, 39L), .Label = c(" Cambodia", 
" Canada", " China", " Columbia", " Cuba", " Dominican-Republic", 
" Ecuador", " El-Salvador", " England", " France", " Germany", 
" Greece", " Guatemala", " Haiti", " Holand-Netherlands", 
" Honduras", " Hong", " Hungary", " India", " Iran", " Ireland", 
" Italy", " Jamaica", " Japan", " Laos", " Mexico", " Nicaragua", 
" Outlying-US(Guam-USVI-etc)", " Peru", " Philippines", " Poland", 
" Portugal", " Puerto-Rico", " Scotland", " South", " Taiwan", 
" Thailand", " Trinadad&Tobago", " United-States", " Vietnam", 
" Yugoslavia"), class = "factor"), RESULT = structure(c(1L, 
1L, 1L, 1L, 1L), .Label = c(" <=50K", " >50K"), class = "factor")), .Names = c("age", 
"workclass", "fnlwgt", "education", "education.num", "marital.status", 
"occupation", "relationship", "race", "sex", "capital.gain", 
"capital.loss", "hours.per.week", "native.country", "RESULT"), row.names = c(25231L, 
17952L, 24945L, 25524L, 11025L), class = "data.frame")
row.names是您的行的名称:

> names(test)
[1] "age"            "workclass"      "fnlwgt"         "education"      "education.num"  "marital.status" "occupation"     "relationship"   "race"           "sex"           [11] "capital.gain"   "capital.loss"   "hours.per.week" "native.country" "RESULT"        
以下命令起作用:

> row.names(test)
[1] "25231" "17952" "24945" "25524" "11025"

row.names(test)
row.names(test)@DavidArenburg check out
dput(test)
空赋值前后;行名称“显示”,但表示为
c(NA,nrow(测试))
。在最初的问题中不清楚为什么行名是个问题(通常它们是朋友;它们是唯一的标识符,提供每行的出处),并且它们不断返回
dput(test[2:1,])
!当然,但我认为这是问题所在。
row.names
不是一个列,您不能像fa一样将其删除,因为我可以告诉您,行名如何影响回归?命令
“attr@SvenHohenstein那么它就不再是数据帧了more@hrbrmstr对!不能同时具有这两个属性(data.frame和缺少行名称)。
row.names(test)  <- NULL