根据条件替换值

Posted 2023-02-14

【中文标题】根据条件替换值

【英文标题】：Replacing values based on conditions

【发布时间】：2022-01-19 14:42:36

【问题描述】：

我有一个数据框，其中一个列是id，并且在记录数据期间有些值被弄乱了。

这是数据类型的示例

dput(df)
structure(list(Id = c("'110171786'", "'1103fbfd5'", "'0700edf6dc'", 
"'1103fad09'", "'01103fc9bb'", "''", "''", "0000fba2b'", "'01103fb169'", 
"'01103fd723'", "'01103f9c34'", "''", "''", "''", "'01103fc088'", 
"'01103fa6d8'", "'01103fb374'", "'01103fce8c'", "'01103f955d'", 
"'011016e633'", "'01103fa0da'", "''", "''", "''", "'01103fa4bd'", 
"'01103fb5c4'", "'01103fd0d7'", "'01103f9e2e'", "'01103fc657'", 
"'01103fd4d1'", "'011016e78e'", "'01103fbda2'", "'01103fbae7'", 
"'011016ee23'", "'01103fc847'", "'01103fbfbb'", "''", "'01103fb8bb'", 
"'01103fc853'", "''", "'01103fbcd5'", "'011016e690'", "'01103fb253'", 
"'01103fcb19'", "'01103fb446'", "'01103fa4fa'", "'011016cfbd'", 
"'01103fd250'", "'01103fac7d'", "'011016a86e'"), Weight = c(11.5, 
11.3, 11.3, 10.6, 10.6, 8.9, 18.7, 10.9, 11.3, 18.9, 18.9, 8.6, 
8.8, 8.4, 11, 10.4, 10.4, 10.8, 11.2, 11, 10.3, 9.5, 8.1, 9.3, 
10.2, 10.5, 11.2, 21.9, 18, 17.8, 11.3, 11.5, 10.8, 10.5, 12.8, 
10.9, 8.9, 10.3, 10.8, 8.9, 10.9, 9.9, 19, 11.6, 11.3, 11.7, 
10.9, 12.1, 11.3, 10.6)), class = "data.frame", row.names = c(NA, 
-50L))
> 

我想做的是搜索id列并替换以下错误

有些值的前面缺少一个零，现在所有这些都以 1 开头，这样可以很容易地找到它们。所以基本上任何字符长度为 9 并以 1 开头的东西都需要 0 作为第一个字符。 有些值的长度小于 10 个字符，需要删除。 有些有多个前导 0，需要删除。

【问题讨论】：

为什么你的ID被双引号例如"'110171786'"而不是"110171786"？只是好奇

我认为最初只是为了阻止 excel 将它们视为数字并删除零（这不起作用），并且一些 ID 在中间有一个“E”，而 excel 将其变成科学计数法。旧数据库系统的遗物

【参考方案1】：

df$Id <- gsub("^('?)(1.8')$", "\\10\\2", df$Id)
df[ !grepl("^'?(00|'$)", df$Id),]
#              Id Weight
# 1  '0110171786'   11.5
# 2  '01103fbfd5'   11.3
# 3  '0700edf6dc'   11.3
# 4  '01103fad09'   10.6
# 5  '01103fc9bb'   10.6
# 9  '01103fb169'   11.3
# 10 '01103fd723'   18.9
# 11 '01103f9c34'   18.9
# 15 '01103fc088'   11.0
# 16 '01103fa6d8'   10.4
# 17 '01103fb374'   10.4
# 18 '01103fce8c'   10.8
# 19 '01103f955d'   11.2
# 20 '011016e633'   11.0
# 21 '01103fa0da'   10.3
# 25 '01103fa4bd'   10.2
# 26 '01103fb5c4'   10.5
# 27 '01103fd0d7'   11.2
# 28 '01103f9e2e'   21.9
# 29 '01103fc657'   18.0
# 30 '01103fd4d1'   17.8
# 31 '011016e78e'   11.3
# 32 '01103fbda2'   11.5
# 33 '01103fbae7'   10.8
# 34 '011016ee23'   10.5
# 35 '01103fc847'   12.8
# 36 '01103fbfbb'   10.9
# 38 '01103fb8bb'   10.3
# 39 '01103fc853'   10.8
# 41 '01103fbcd5'   10.9
# 42 '011016e690'    9.9
# 43 '01103fb253'   19.0
# 44 '01103fcb19'   11.6
# 45 '01103fb446'   11.3
# 46 '01103fa4fa'   11.7
# 47 '011016cfbd'   10.9
# 48 '01103fd250'   12.1
# 49 '01103fac7d'   11.3
# 50 '011016a86e'   10.6