使用 R 控制台和 R 代码的结果将对象的名称生成到 R 脚本中
Using results from the R console and R code to generate the name of an object into R script
我对使用 R 控制台和 R 代码的结果将对象的名称生成到 R 脚本中很感兴趣。
我用 1 个 IV 创建了一个线性回归，其中 DV 是 mpg，感兴趣的 2 个 IV 是 cyl 和 disp。我调用通用模型代码对象 lm_DVmpg。我使用代码 paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "") 为控制台中包含 lm() 的对象生成了一个唯一名称。然后我将结果粘贴到脚本中并将该文本用作 IV 特定回归对象的名称。
例如使用cyl的IV的对象名称为lm_DVmpg_IVcyl，disp的对象名称为lm_DVmpg_IVdisp.
以下是来自控制台的一些示例代码：
> ## first lm
> # ---- NOTE: Iv is cyl
> # ---- NOTE: creates lm() object
> lm_DVmpg <- lm(mpg~cyl, data = mtcars)
> # ---- NOTE: creates unique name for lm() object
> paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "")
[1] "lm_DVmpg_IVcyl"
> # ---- NOTE: moves lm_DVmpg to object unique to IV used
> lm_DVmpg_IVcyl <- lm_DVmpg
有没有办法使用 R 脚本文件中的 R 代码将 lm_DVmpg <- lm(mpg~cyl, data = mtcars) 行中 lm_DVmpg 代码的名称更改为 lm_DVmpg_IVcyl，派生自 paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "") 命令，使用 R 代码而无需 copy/paste 文本 lm_DVmpg_IVcyl 从控制台到适当的 R 脚本行？
或者，是否可以使用 R 代码将文本 lm_DVmpg_IVcyl 放入行 lm_DVmpg_IVcyl <- lm_DVmpg 中，而无需 copy/paste在 运行 代码 paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "")?
之后从控制台
如有任何帮助，我们将不胜感激。
仅供参考，我使用的是 2013 Macbook Pro，配备 2.4 GHz 双核英特尔芯片、8 GB 内存、macOS big sur 11.2.2、RStudio 版本 1.4.1106 和 R 基础包 4.04。
谢谢。
我使用的R脚本如下：
# Naming objects from returns on console
# ---- NOTE: will do several linear regressions using the mtcars dataset, with IV and the DV being "mpg"
# ---- NOTE: IVs - cyl, disp
## gives info on dataset
head(mtcars)
str(mtcars)
colnames(mtcars)
## first lm
# ---- NOTE: Iv is cyl
# ---- NOTE: creates lm() object
lm_DVmpg <- lm(mpg~cyl, data = mtcars)
# ---- NOTE: creates unique name for lm() object
paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "")
# ---- NOTE: moves lm_DVmpg to object unique to IV used
lm_DVmpg_IVcyl <- lm_DVmpg
## second lm
# ---- NOTE: Iv is disp
# ---- NOTE: creates lm() object
lm_DVmpg <- lm(mpg~disp, data = mtcars)
# ---- NOTE: creates unique name for lm() object
paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "")
# ---- NOTE: moves lm_DVmpg to object unique to IV used
lm_DVmpg_IVdisp <- lm_DVmpg
## displays summaries of regression objects
# ---- NOTE: latest regression object summary
summary(lm_DVmpg)
# ---- NOTE: unique regression objects
lm_DVmpg_IVmpg
lm_DVmpg_IVdisp
# ---- NOTE: lm_DVmpg = lm_DVmpg_IVdisp, since the lm_DVmpg_IVmpg object when IV == disp was the latest lm() to be run
这是我在 运行 R 脚本时从控制台得到的结果。
> # Naming objects from returns on console
> # ---- NOTE: will do several linear regressions using the mtcars dataset, with IV and the DV being "mpg"
> # ---- NOTE: IVs - cyl, disp
> # Naming objects from returns on console
> # ---- NOTE: will do several linear regressions using the mtcars dataset, with IV and the DV being "mpg"
> # ---- NOTE: IVs - cyl, disp
>
> ## gives info on dataset
> head(mtcars)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
> str(mtcars)
'data.frame': 32 obs. of 11 variables:
$ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
$ disp: num 160 160 108 258 360 ...
$ hp : num 110 110 93 110 175 105 245 62 95 123 ...
$ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt : num 2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num 16.5 17 18.6 19.4 17 ...
$ vs : num 0 0 1 1 0 1 0 1 1 1 ...
$ am : num 1 1 1 0 0 0 0 0 0 0 ...
$ gear: num 4 4 4 3 3 3 3 4 4 4 ...
$ carb: num 4 4 1 1 2 1 4 2 2 4 ...
> colnames(mtcars)
[1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear" "carb"
>
> ## first lm
> # ---- NOTE: Iv is cyl
> # ---- NOTE: creates lm() object
> lm_DVmpg <- lm(mpg~cyl, data = mtcars)
> # ---- NOTE: creates unique name for lm() object
> paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "")
[1] "lm_DVmpg_IVcyl"
> # ---- NOTE: moves lm_DVmpg to object unique to IV used
> lm_DVmpg_IVcyl <- lm_DVmpg
>
> ## second lm
> # ---- NOTE: Iv is disp
> # ---- NOTE: creates lm() object
> lm_DVmpg <- lm(mpg~disp, data = mtcars)
> # ---- NOTE: creates unique name for lm() object
> paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "")
[1] "lm_DVmpg_IVdisp"
> # ---- NOTE: moves lm_DVmpg to object unique to IV used
> lm_DVmpg_IVdisp <- lm_DVmpg
>
> ## displays summaries of regression objects
> # ---- NOTE: latest regression object summary
> summary(lm_DVmpg)
Call:
lm(formula = mpg ~ disp, data = mtcars)
Residuals:
Min 1Q Median 3Q Max
-4.8922 -2.2022 -0.9631 1.6272 7.2305
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 29.599855 1.229720 24.070 < 2e-16 ***
disp -0.041215 0.004712 -8.747 9.38e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.251 on 30 degrees of freedom
Multiple R-squared: 0.7183, Adjusted R-squared: 0.709
F-statistic: 76.51 on 1 and 30 DF, p-value: 9.38e-10
> # ---- NOTE: unique regression objects
> lm_DVmpg_IVmpg
Call:
lm(formula = mpg ~ cyl, data = mtcars)
Coefficients:
(Intercept) cyl
37.885 -2.876
> lm_DVmpg_IVdisp
Call:
lm(formula = mpg ~ disp, data = mtcars)
Coefficients:
(Intercept) disp
29.59985 -0.04122
> # ---- NOTE: lm_DVmpg = lm_DVmpg_IVdisp, since the lm_DVmpg_IVmpg object when IV == disp was the latest lm() to be run
我不确定你为什么需要这个，但我认为 assign + get 是你要找的。
lm_DVmpg <- lm(mpg~cyl, data = mtcars)
new_name <- paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "")
assign(new_name, lm(mpg~disp, data = mtcars))
get(new_name)
#Call:
#lm(formula = mpg ~ disp, data = mtcars)
#Coefficients:
#(Intercept) disp
# 29.59985 -0.04122
我对使用 R 控制台和 R 代码的结果将对象的名称生成到 R 脚本中很感兴趣。
我用 1 个 IV 创建了一个线性回归，其中 DV 是 mpg，感兴趣的 2 个 IV 是 cyl 和 disp。我调用通用模型代码对象 lm_DVmpg。我使用代码 paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "") 为控制台中包含 lm() 的对象生成了一个唯一名称。然后我将结果粘贴到脚本中并将该文本用作 IV 特定回归对象的名称。
例如使用cyl的IV的对象名称为lm_DVmpg_IVcyl，disp的对象名称为lm_DVmpg_IVdisp.
以下是来自控制台的一些示例代码：
> ## first lm
> # ---- NOTE: Iv is cyl
> # ---- NOTE: creates lm() object
> lm_DVmpg <- lm(mpg~cyl, data = mtcars)
> # ---- NOTE: creates unique name for lm() object
> paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "")
[1] "lm_DVmpg_IVcyl"
> # ---- NOTE: moves lm_DVmpg to object unique to IV used
> lm_DVmpg_IVcyl <- lm_DVmpg
有没有办法使用 R 脚本文件中的 R 代码将 lm_DVmpg <- lm(mpg~cyl, data = mtcars) 行中 lm_DVmpg 代码的名称更改为 lm_DVmpg_IVcyl，派生自 paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "") 命令，使用 R 代码而无需 copy/paste 文本 lm_DVmpg_IVcyl 从控制台到适当的 R 脚本行？
或者，是否可以使用 R 代码将文本 lm_DVmpg_IVcyl 放入行 lm_DVmpg_IVcyl <- lm_DVmpg 中，而无需 copy/paste在 运行 代码 paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "")?
如有任何帮助，我们将不胜感激。
仅供参考，我使用的是 2013 Macbook Pro，配备 2.4 GHz 双核英特尔芯片、8 GB 内存、macOS big sur 11.2.2、RStudio 版本 1.4.1106 和 R 基础包 4.04。
谢谢。
我使用的R脚本如下：
# Naming objects from returns on console
# ---- NOTE: will do several linear regressions using the mtcars dataset, with IV and the DV being "mpg"
# ---- NOTE: IVs - cyl, disp
## gives info on dataset
head(mtcars)
str(mtcars)
colnames(mtcars)
## first lm
# ---- NOTE: Iv is cyl
# ---- NOTE: creates lm() object
lm_DVmpg <- lm(mpg~cyl, data = mtcars)
# ---- NOTE: creates unique name for lm() object
paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "")
# ---- NOTE: moves lm_DVmpg to object unique to IV used
lm_DVmpg_IVcyl <- lm_DVmpg
## second lm
# ---- NOTE: Iv is disp
# ---- NOTE: creates lm() object
lm_DVmpg <- lm(mpg~disp, data = mtcars)
# ---- NOTE: creates unique name for lm() object
paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "")
# ---- NOTE: moves lm_DVmpg to object unique to IV used
lm_DVmpg_IVdisp <- lm_DVmpg
## displays summaries of regression objects
# ---- NOTE: latest regression object summary
summary(lm_DVmpg)
# ---- NOTE: unique regression objects
lm_DVmpg_IVmpg
lm_DVmpg_IVdisp
# ---- NOTE: lm_DVmpg = lm_DVmpg_IVdisp, since the lm_DVmpg_IVmpg object when IV == disp was the latest lm() to be run
这是我在 运行 R 脚本时从控制台得到的结果。
> # Naming objects from returns on console
> # ---- NOTE: will do several linear regressions using the mtcars dataset, with IV and the DV being "mpg"
> # ---- NOTE: IVs - cyl, disp
> # Naming objects from returns on console
> # ---- NOTE: will do several linear regressions using the mtcars dataset, with IV and the DV being "mpg"
> # ---- NOTE: IVs - cyl, disp
>
> ## gives info on dataset
> head(mtcars)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
> str(mtcars)
'data.frame': 32 obs. of 11 variables:
$ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
$ disp: num 160 160 108 258 360 ...
$ hp : num 110 110 93 110 175 105 245 62 95 123 ...
$ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt : num 2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num 16.5 17 18.6 19.4 17 ...
$ vs : num 0 0 1 1 0 1 0 1 1 1 ...
$ am : num 1 1 1 0 0 0 0 0 0 0 ...
$ gear: num 4 4 4 3 3 3 3 4 4 4 ...
$ carb: num 4 4 1 1 2 1 4 2 2 4 ...
> colnames(mtcars)
[1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear" "carb"
>
> ## first lm
> # ---- NOTE: Iv is cyl
> # ---- NOTE: creates lm() object
> lm_DVmpg <- lm(mpg~cyl, data = mtcars)
> # ---- NOTE: creates unique name for lm() object
> paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "")
[1] "lm_DVmpg_IVcyl"
> # ---- NOTE: moves lm_DVmpg to object unique to IV used
> lm_DVmpg_IVcyl <- lm_DVmpg
>
> ## second lm
> # ---- NOTE: Iv is disp
> # ---- NOTE: creates lm() object
> lm_DVmpg <- lm(mpg~disp, data = mtcars)
> # ---- NOTE: creates unique name for lm() object
> paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "")
[1] "lm_DVmpg_IVdisp"
> # ---- NOTE: moves lm_DVmpg to object unique to IV used
> lm_DVmpg_IVdisp <- lm_DVmpg
>
> ## displays summaries of regression objects
> # ---- NOTE: latest regression object summary
> summary(lm_DVmpg)
Call:
lm(formula = mpg ~ disp, data = mtcars)
Residuals:
Min 1Q Median 3Q Max
-4.8922 -2.2022 -0.9631 1.6272 7.2305
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 29.599855 1.229720 24.070 < 2e-16 ***
disp -0.041215 0.004712 -8.747 9.38e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.251 on 30 degrees of freedom
Multiple R-squared: 0.7183, Adjusted R-squared: 0.709
F-statistic: 76.51 on 1 and 30 DF, p-value: 9.38e-10
> # ---- NOTE: unique regression objects
> lm_DVmpg_IVmpg
Call:
lm(formula = mpg ~ cyl, data = mtcars)
Coefficients:
(Intercept) cyl
37.885 -2.876
> lm_DVmpg_IVdisp
Call:
lm(formula = mpg ~ disp, data = mtcars)
Coefficients:
(Intercept) disp
29.59985 -0.04122
> # ---- NOTE: lm_DVmpg = lm_DVmpg_IVdisp, since the lm_DVmpg_IVmpg object when IV == disp was the latest lm() to be run
我不确定你为什么需要这个，但我认为 assign + get 是你要找的。
lm_DVmpg <- lm(mpg~cyl, data = mtcars)
new_name <- paste("lm_DVmpg","_IV", as.character(lm_DVmpg$call$formula[[3]]), sep = "")
assign(new_name, lm(mpg~disp, data = mtcars))
get(new_name)
#Call:
#lm(formula = mpg ~ disp, data = mtcars)
#Coefficients:
#(Intercept) disp
# 29.59985 -0.04122