在 R 中将多列从整数转换为数字类型

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在 R 中将多列从整数转换为数字类型

作者：迹忆客最近更新：2023/03/21 浏览次数：

R 具有矢量化函数，可通过一行代码将多列从整数类型转换为数值类型，而无需使用循环。本文探讨了完成此任务的两种方法。

在这两种情况下，每一列的实际转换都是由 as.numeric() 函数完成的。

在 R 中将多列从整数转换为数字类型

首先，我们将创建一些示例数据。

示例代码：

# Create vectors.
n = letters[1:5]
p = as.integer(c(11:15))
q = as.integer(c(51:55))

# Create a data frame.
df = data.frame(Names = n, Col1 = p, Col2 = q)
df

# See the structure of the data frame.
# Note that two columns are of integer type.
str(df)

输出：

> df
  Names Col1 Col2
1     a   11   51
2     b   12   52
3     c   13   53
4     d   14   54
5     e   15   55
>
> # See the structure of the data frame.
> # Note that two columns are of integer type.
> str(df)
'data.frame':    5 obs. of  3 variables:
 $ Names: chr  "a" "b" "c" "d" ...
 $ Col1 : int  11 12 13 14 15
 $ Col2 : int  51 52 53 54 55

使用 `lapply()` 函数将多列从整数转换为 R 中的数字类型

Base R 的 lapply() 函数允许我们将函数应用于列表的元素。我们将应用 as.numeric() 函数。

lapply() 函数的文档建议对我们在其中指定的函数名称使用包装函数。

示例代码：

# First, we will create a copy of our data frame.
df1 = df

# Columns 2 and 3 are integer type.
# We will convert these to numeric.
# We will use a wrapper function as recommended.
df1[2:3] = lapply(df1[2:3], FUN = function(y){as.numeric(y)})

# Check that the columns are converted to numeric.
str(df1)

输出：

> df1[2:3] = lapply(df1[2:3], FUN = function(y){as.numeric(y)})
>
> # Check that the columns are converted to numeric.
> str(df1)
'data.frame':    5 obs. of  3 variables:
 $ Names: chr  "a" "b" "c" "d" ...
 $ Col1 : num  11 12 13 14 15
 $ Col2 : num  51 52 53 54 55

使用 `dplyr` 包函数将多列从整数转换为 R 中的数字类型

我们可以使用 dplyr 的 mutate() 和 across() 函数将整数列转换为数字。这样做的好处是整个系列的 tidyselect 函数都可用于选择列。

我们将在示例代码中使用标准列表语法和 tidyselect 函数 where() 选择列。

示例代码：

# Load the dplyr package.
library(dplyr)

# USING STANDARD LIST SYNTAX.
# Convert the columns.
df2 = df %>% mutate(across(.cols=2:3, .fns=as.numeric))

# Check that the columns are converted.
str(df2)

# USING TIDYSELECT WHERE FUNCTION.
# Convert ALL integer columns to numeric.
df3 = df %>% mutate(across(.cols=where(is.integer), .fns=as.numeric))

# Check that the columns are converted.
str(df3)

输出：

# USING STANDARD LIST SYNTAX.
# Convert the columns.
df2 = df %>% mutate(across(.cols=2:3, .fns=as.numeric))

# Check that the columns are converted.
str(df2)

# USING TIDYSELECT WHERE FUNCTION.
# Convert ALL integer columns to numeric.
df3 = df %>% mutate(across(.cols=where(is.integer), .fns=as.numeric))

# Check that the columns are converted.
str(df3)

在 R 中将多列从因子转换为数值类型

有时，因子水平用数字编码，主要是整数。我们不想转换这些列。

但是，在其他时候，具有整数的列可能会表示为 R 中的因子。将这些列转换为数字会带来挑战。

示例代码显示了将因子列转换为数值时会发生什么。

示例代码：

# Create a factor vector.
x = factor(c(15,15,20,25,30,30,30))

# See that these are 4 levels of factors.
# They are not numbers.
str(x)

# Convert the factor vector to numeric.
as.numeric(x) # This is not the result we want.

输出：

> # Create a factor vector.
> x = factor(c(15,15,20,25,30,30,30))
>
> # See that these are 4 levels of factors.
> # They are not numbers.
> str(x)
 Factor w/ 4 levels "15","20","25",..: 1 1 2 3 4 4 4
>
> # Convert the factor vector to numeric.
> as.numeric(x) # This is not the result we want.
[1] 1 1 2 3 4 4 4

当整数列碰巧被错误地表示为因子时，我们需要添加一个预备步骤以将其正确转换为数字。

我们必须先将因子转换为字符类型，然后再将字符转换为数值类型。

示例代码：

# First, convert the factor vector to a character type.
# Then convert the character type to numeric.
# Both the above can be done in a single step, as follows.
y = as.numeric(as.character(x))
y

# Check that y is numeric.
str(y)

输出：

> y = as.numeric(as.character(x))
> y
[1] 15 15 20 25 30 30 30
>
> # Check that y is numeric.
> str(y)
 num [1:7] 15 15 20 25 30 30 30

让我们看一个带有数据框的示例。我们将使用 dplyr 方法。

示例代码：

# Create a factor vector.
f = factor(c(20,20,30,30,30))

# Create a data frame.
df4 = data.frame(Name=n, Col1=p, Col2=q, Fac=f)
df4

# Check the structure.
str(df4)

# We will use the dplyr approach.

# First only convert integer type columns.
df5 = df4 %>% mutate(across(.cols=where(is.integer), .fns=as.numeric))
# Factor column did not get converted.
str(df5)

# Now, we will START AGAIN, and convert the factor column as well.
# To modify an existing column by name, we will give it the SAME name.
df6 = df4 %>% mutate(across(.cols=where(is.integer), .fns=as.numeric), Fac=as.numeric(as.character(Fac)))
df6
# Check that the factor column has also got converted.
str(df6)

输出：

> # Create a factor vector.
> f = factor(c(20,20,30,30,30))
>
> # Create a data frame.
> df4 = data.frame(Name=n, Col1=p, Col2=q, Fac=f)
> df4
  Name Col1 Col2 Fac
1    a   11   51  20
2    b   12   52  20
3    c   13   53  30
4    d   14   54  30
5    e   15   55  30
>
> # Check the structure.
> str(df4)
'data.frame':    5 obs. of  4 variables:
 $ Name: chr  "a" "b" "c" "d" ...
 $ Col1: int  11 12 13 14 15
 $ Col2: int  51 52 53 54 55
 $ Fac : Factor w/ 2 levels "20","30": 1 1 2 2 2
>
> # We will use the dplyr approach.
>
> # First only convert integer type columns.
> df5 = df4 %>% mutate(across(.cols=where(is.integer), .fns=as.numeric))
> # Factor column did not get converted.
> str(df5)
'data.frame':    5 obs. of  4 variables:
 $ Name: chr  "a" "b" "c" "d" ...
 $ Col1: num  11 12 13 14 15
 $ Col2: num  51 52 53 54 55
 $ Fac : Factor w/ 2 levels "20","30": 1 1 2 2 2
>
> # Now, we will START AGAIN, and convert the factor column as well.
> # To modify an existing column by name, we will give it the SAME name.
> df6 = df4 %>% mutate(across(.cols=where(is.integer), .fns=as.numeric), Fac=as.numeric(as.character(Fac)))
> df6
  Name Col1 Col2 Fac
1    a   11   51  20
2    b   12   52  20
3    c   13   53  30
4    d   14   54  30
5    e   15   55  30
> # Check that the factor column has also got converted.
> str(df6)
'data.frame':    5 obs. of  4 variables:
 $ Name: chr  "a" "b" "c" "d" ...
 $ Col1: num  11 12 13 14 15
 $ Col2: num  51 52 53 54 55
 $ Fac : num  20 20 30 30 30