r语言导入csv文件后找不到变量
setwd("~/Zoom")
> read.csv("Mall_Customers.csv")
CustomerID Gender Age Annual.Income..k.. Spending.Score..1.100.
1 1 Male 19 15 39
2 2 Male 21 15 81
3 3 Female 20 16 6
4 4 Female 23 16 77
5 5 Female 31 17 40
6 6 Female 22 17 76
7 7 Female 35 18 6
8 8 Female 23 18 94
9 9 Male 64 19 3
10 10 Female 30 19 72
11 11 Male 67 19 14
12 12 Female 35 19 99
13 13 Female 58 20 15
14 14 Female 24 20 77
15 15 Male 37 20 13
16 16 Male 22 20 79
17 17 Female 35 21 35
18 18 Male 20 21 66
19 19 Male 52 23 29
20 20 Female 35 23 98
21 21 Male 35 24 35
22 22 Male 25 24 73
23 23 Female 46 25 5
24 24 Male 31 25 73
25 25 Female 54 28 14
26 26 Male 29 28 82
27 27 Female 45 28 32
28 28 Male 35 28 61
29 29 Female 40 29 31
30 30 Female 23 29 87
31 31 Male 60 30 4
32 32 Female 21 30 73
33 33 Male 53 33 4
34 34 Male 18 33 92
35 35 Female 49 33 14
36 36 Female 21 33 81
37 37 Female 42 34 17
38 38 Female 30 34 73
39 39 Female 36 37 26
40 40 Female 20 37 75
41 41 Female 65 38 35
42 42 Male 24 38 92
43 43 Male 48 39 36
44 44 Female 31 39 61
45 45 Female 49 39 28
46 46 Female 24 39 65
47 47 Female 50 40 55
48 48 Female 27 40 47
49 49 Female 29 40 42
50 50 Female 31 40 42
51 51 Female 49 42 52
52 52 Male 33 42 60
53 53 Female 31 43 54
54 54 Male 59 43 60
55 55 Female 50 43 45
56 56 Male 47 43 41
57 57 Female 51 44 50
58 58 Male 69 44 46
59 59 Female 27 46 51
60 60 Male 53 46 46
61 61 Male 70 46 56
62 62 Male 19 46 55
63 63 Female 67 47 52
64 64 Female 54 47 59
65 65 Male 63 48 51
66 66 Male 18 48 59
67 67 Female 43 48 50
68 68 Female 68 48 48
69 69 Male 19 48 59
70 70 Female 32 48 47
71 71 Male 70 49 55
72 72 Female 47 49 42
73 73 Female 60 50 49
74 74 Female 60 50 56
75 75 Male 59 54 47
76 76 Male 26 54 54
77 77 Female 45 54 53
78 78 Male 40 54 48
79 79 Female 23 54 52
80 80 Female 49 54 42
81 81 Male 57 54 51
82 82 Male 38 54 55
83 83 Male 67 54 41
84 84 Female 46 54 44
85 85 Female 21 54 57
86 86 Male 48 54 46
87 87 Female 55 57 58
88 88 Female 22 57 55
89 89 Female 34 58 60
90 90 Female 50 58 46
91 91 Female 68 59 55
92 92 Male 18 59 41
93 93 Male 48 60 49
94 94 Female 40 60 40
95 95 Female 32 60 42
96 96 Male 24 60 52
97 97 Female 47 60 47
98 98 Female 27 60 50
99 99 Male 48 61 42
100 100 Male 20 61 49
101 101 Female 23 62 41
102 102 Female 49 62 48
103 103 Male 67 62 59
104 104 Male 26 62 55
105 105 Male 49 62 56
106 106 Female 21 62 42
107 107 Female 66 63 50
108 108 Male 54 63 46
109 109 Male 68 63 43
110 110 Male 66 63 48
111 111 Male 65 63 52
112 112 Female 19 63 54
113 113 Female 38 64 42
114 114 Male 19 64 46
115 115 Female 18 65 48
116 116 Female 19 65 50
117 117 Female 63 65 43
118 118 Female 49 65 59
119 119 Female 51 67 43
120 120 Female 50 67 57
121 121 Male 27 67 56
122 122 Female 38 67 40
123 123 Female 40 69 58
124 124 Male 39 69 91
125 125 Female 23 70 29
126 126 Female 31 70 77
127 127 Male 43 71 35
128 128 Male 40 71 95
129 129 Male 59 71 11
130 130 Male 38 71 75
131 131 Male 47 71 9
132 132 Male 39 71 75
133 133 Female 25 72 34
134 134 Female 31 72 71
135 135 Male 20 73 5
136 136 Female 29 73 88
137 137 Female 44 73 7
138 138 Male 32 73 73
139 139 Male 19 74 10
140 140 Female 35 74 72
141 141 Female 57 75 5
142 142 Male 32 75 93
143 143 Female 28 76 40
144 144 Female 32 76 87
145 145 Male 25 77 12
146 146 Male 28 77 97
147 147 Male 48 77 36
148 148 Female 32 77 74
149 149 Female 34 78 22
150 150 Male 34 78 90
151 151 Male 43 78 17
152 152 Male 39 78 88
153 153 Female 44 78 20
154 154 Female 38 78 76
155 155 Female 47 78 16
156 156 Female 27 78 89
157 157 Male 37 78 1
158 158 Female 30 78 78
159 159 Male 34 78 1
160 160 Female 30 78 73
161 161 Female 56 79 35
162 162 Female 29 79 83
163 163 Male 19 81 5
164 164 Female 31 81 93
165 165 Male 50 85 26
166 166 Female 36 85 75
167 167 Male 42 86 20
168 168 Female 33 86 95
169 169 Female 36 87 27
170 170 Male 32 87 63
171 171 Male 40 87 13
172 172 Male 28 87 75
173 173 Male 36 87 10
174 174 Male 36 87 92
175 175 Female 52 88 13
176 176 Female 30 88 86
177 177 Male 58 88 15
178 178 Male 27 88 69
179 179 Male 59 93 14
180 180 Male 35 93 90
181 181 Female 37 97 32
182 182 Female 32 97 86
183 183 Male 46 98 15
184 184 Female 29 98 88
185 185 Female 41 99 39
186 186 Male 30 99 97
187 187 Female 54 101 24
188 188 Male 28 101 68
189 189 Female 41 103 17
190 190 Female 36 103 85
191 191 Female 34 103 23
192 192 Female 32 103 69
193 193 Male 33 113 8
194 194 Female 38 113 91
195 195 Female 47 120 16
196 196 Female 35 120 79
197 197 Female 45 126 28
198 198 Male 32 126 74
199 199 Male 32 137 18
200 200 Male 30 137 83
> plot(CustomerID,Age)
Error: object 'CustomerID' not found
RStudio导入结果如上但是找不到对象
不知道你这个问题是否已经解决, 如果还没有解决的话:- 建议你看下这篇博客👉 ：Rstudio读取csv文件
- 除此之外, 这篇博客: R语言 读取csv文件 解决分割符不能正确识别导致的错位现象中的 R语言 读取csv文件 解决分割符不能正确识别导致的错位现象 部分也许能够解决你的问题, 你可以仔细阅读以下内容或跳转源博客中阅读:
看到不少童鞋都遇到过类似问题。
使用python爬取了一些微博数据，存储在csv文件中：
需要导入R中进行数据清洗和绘图，使用read.csv()函数读取（下面是有问题的，最常用读取csv文件的代码）：path = "D:/资料/eclipse/work-pace/weibo-crawler-master/weibo" setwd(path) #获得csv文件列表 df = read.csv(file_list[1],header=F,sep = ',',comment.char = '', na.strings = '',fill = T,quote = '\"',encoding = 'UTF-8') # file_list是文件全目录，file_list[1]是其中一个文件的全目录读取后在Rstudio查看：
前几列是正常的，但是从第10列开始出问题了，有一些分隔符被识别为了文本，出现错列。导致最后一列情感得分(grade)也出现问题(图片未展示最后一列)解决方案一
把csv、txt文件用别的工具（如notepad++）转成 ansi 编码的，然后再用read.csv命令去读。
缺陷：要手动去做
解决方案二
有博主指出是由于文件中文本双引号导致的问题，替换文件中的双引号为单引号即可。我没有试过，不知是否可行。
解决方案三
最简单的办法。直接使用fread（）函数library(data.table) df = fread(file = file_list[1],encoding = 'UTF-8') #file_list[1]是文件全目录读取后最后几列效果:
最后几列非常完美，列名也自动读出。
附上函数的介绍：Fast and friendly file finagler Description： Similar to read.table but faster and more convenient. All controls such as sep,
colClasses and nrows are automatically detected. bit64::integer64
types are also detected and read directly without needing to read as
character before converting.Dates are read as character currently. They can be converted
afterwards using the excellent fasttime package or standard base
functions.‘fread’ is for regular delimited files; i.e., where every row has the
same number of columns. In future, secondary separator (sep2) may be
specified within each column. Such columns will be read as type list
where each cell is itself a vector.
如果你已经解决了该问题, 非常希望你能够分享一下解决方案, 写成博客, 将相关链接放在评论区, 以帮助更多的人 ^-^
你这个只是展示文件，导入要给他设定个变量，像data
data <- read.csv("Mall_Customers.csv")