string 将字符串转换为数字
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Converting string to numeric
提问by eliavs
I've imported a test file and tried to make a histogram
我导入了一个测试文件并尝试制作直方图
pichman <- read.csv(file="picman.txt", header=TRUE, sep="/t")
hist <- as.numeric(pichman$WS)
However, I get different numbers from values in my dataset. Originally I thought that this because I had text, so I deleted the text:
但是，我从数据集中的值中得到了不同的数字。本来我以为这是因为我有文字，所以我删除了文字：
table(pichman$WS)
ws <- pichman$WS[pichman$WS!="Down" & pichman$WS!="NoData"]
However, I am still getting very high numbers does anyone have an idea?
但是，我的数字仍然很高，有人知道吗？
回答by csgillespie
I suspect you are having a problem with factors. For example,
我怀疑你的因素有问题。例如，
> x = factor(4:8)
> x
[1] 4 5 6 7 8
Levels: 4 5 6 7 8
> as.numeric(x)
[1] 1 2 3 4 5
> as.numeric(as.character(x))
[1] 4 5 6 7 8
Some comments:
一些评论：
- You mention that your vector contains the characters "Down" and "NoData". What do expect/want
as.numericto do with these values? - In
read.csv, try using the argumentstringsAsFactors=FALSE - Are you sure it's
sep="/tand notsep="\t" - Use the command
head(pitchman)to check the first fews rows of your data - Also, it's very tricky to guess what your problem is when you don't provide data. A minimal working example is always preferable. For example, I can't run the command
pichman <- read.csv(file="picman.txt", header=TRUE, sep="/t")since I don't have access to the data set.
- 您提到您的向量包含字符“Down”和“NoData”。期望/想要
as.numeric用这些值做什么？ - 在 中
read.csv，尝试使用参数stringsAsFactors=FALSE - 你确定是
sep="/t不是sep="\t" - 使用命令
head(pitchman)检查数据的前几行 - 此外，当您不提供数据时，很难猜测您的问题是什么。最小的工作示例总是可取的。例如，我无法运行命令，
pichman <- read.csv(file="picman.txt", header=TRUE, sep="/t")因为我无权访问数据集。
回答by Joris Meys
As csgillespie said. stringsAsFactors is default on TRUE, which converts any text to a factor. So even after deleting the text, you still have a factor in your dataframe.
正如 csgillespie 所说。stringsAsFactors 默认为 TRUE，它将任何文本转换为因子。因此，即使删除文本后，您的数据框中仍然有一个因素。
Now regarding the conversion, there's a more optimal way to do so. So I put it here as a reference :
现在关于转换，有一种更优化的方法来做到这一点。所以我把它放在这里作为参考：
> x <- factor(sample(4:8,10,replace=T))
> x
[1] 6 4 8 6 7 6 8 5 8 4
Levels: 4 5 6 7 8
> as.numeric(levels(x))[x]
[1] 6 4 8 6 7 6 8 5 8 4
To show it works.
为了证明它有效。
The timings :
时间：
> x <- factor(sample(4:8,500000,replace=T))
> system.time(as.numeric(as.character(x)))
user system elapsed
0.11 0.00 0.11
> system.time(as.numeric(levels(x))[x])
user system elapsed
0 0 0
It's a big improvement, but not always a bottleneck. It gets important however if you have a big dataframe and a lot of columns to convert.
这是一个很大的改进，但并不总是一个瓶颈。但是，如果您有一个大数据框和许多要转换的列，这将变得很重要。
