r中

Question

写多个变量，在很短的路我已经从2005年的年data.frames名单 - 2016年它们都写同样的方式，除了年的数字：

m =list(X2016_kvish_1_10t = X2016_kvish_1_10t, X2015_kvish_1_10t = X2015_kvish_1_10t, X2014_kvish_1_10t = X2014_kvish_1_10t, 
    X2013_kvish_1_10t = X2013_kvish_1_10t, X2012_kvish_1_10t = X2012_kvish_1_10t, X2011_kvish_1_10t = X2011_kvish_1_10t, 
    X2010_kvish_1_10t = X2010_kvish_1_10t, X2009_kvish_1_10t = X2009_kvish_1_10t, X2008_kvish_1_10t = X2008_kvish_1_10t, 
    X2007_kvish_1_10t = X2007_kvish_1_10t, X2006_kvish_1_10t = X2006_kvish_1_10t, X2005_kvish_1_10t = X2005_kvish_1_10t) 

有编写它的更短的方式，而不需要单独写所有它们？

Answer 1

尝试mget：

df_names = paste0("X", 2005:2016, "_kvish_1_10t") 
m = mget(df_names) 

编辑

由于@Db指出的那样，你甚至不需要创建df_names

m = mget(ls(pattern="_kvish_1_10t$")) 

Answer 2

可以使用mget函数提供工作区中对象名称的字符向量。

我做了一个可复制的例子，目的是说明如何做到这一点。

df_name <- paste0("x", 2005:2016, "_kvish_1_10t") 
df_name 
#> [1] "x2005_kvish_1_10t" "x2006_kvish_1_10t" "x2007_kvish_1_10t" 
#> [4] "x2008_kvish_1_10t" "x2009_kvish_1_10t" "x2010_kvish_1_10t" 
#> [7] "x2011_kvish_1_10t" "x2012_kvish_1_10t" "x2013_kvish_1_10t" 
#> [10] "x2014_kvish_1_10t" "x2015_kvish_1_10t" "x2016_kvish_1_10t" 
# juste create some dummy table for example 
l <- lapply(df_name, assign, value = mtcars[1:2], envir= .GlobalEnv) 
# Use mget to get a list of all the object 
m <- mget(df_name, envir = .GlobalEnv) 
str(m) 
#> List of 12 
#> $ x2005_kvish_1_10t:'data.frame': 32 obs. of 2 variables: 
#> ..$ mpg: num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... 
#> ..$ cyl: num [1:32] 6 6 4 6 8 6 8 4 4 6 ... 
#> $ x2006_kvish_1_10t:'data.frame': 32 obs. of 2 variables: 
#> ..$ mpg: num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... 
#> ..$ cyl: num [1:32] 6 6 4 6 8 6 8 4 4 6 ... 
#> $ x2007_kvish_1_10t:'data.frame': 32 obs. of 2 variables: 
#> ..$ mpg: num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... 
#> ..$ cyl: num [1:32] 6 6 4 6 8 6 8 4 4 6 ... 
#> $ x2008_kvish_1_10t:'data.frame': 32 obs. of 2 variables: 
#> ..$ mpg: num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... 
#> ..$ cyl: num [1:32] 6 6 4 6 8 6 8 4 4 6 ... 
#> $ x2009_kvish_1_10t:'data.frame': 32 obs. of 2 variables: 
#> ..$ mpg: num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... 
#> ..$ cyl: num [1:32] 6 6 4 6 8 6 8 4 4 6 ... 
#> $ x2010_kvish_1_10t:'data.frame': 32 obs. of 2 variables: 
#> ..$ mpg: num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... 
#> ..$ cyl: num [1:32] 6 6 4 6 8 6 8 4 4 6 ... 
#> $ x2011_kvish_1_10t:'data.frame': 32 obs. of 2 variables: 
#> ..$ mpg: num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... 
#> ..$ cyl: num [1:32] 6 6 4 6 8 6 8 4 4 6 ... 
#> $ x2012_kvish_1_10t:'data.frame': 32 obs. of 2 variables: 
#> ..$ mpg: num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... 
#> ..$ cyl: num [1:32] 6 6 4 6 8 6 8 4 4 6 ... 
#> $ x2013_kvish_1_10t:'data.frame': 32 obs. of 2 variables: 
#> ..$ mpg: num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... 
#> ..$ cyl: num [1:32] 6 6 4 6 8 6 8 4 4 6 ... 
#> $ x2014_kvish_1_10t:'data.frame': 32 obs. of 2 variables: 
#> ..$ mpg: num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... 
#> ..$ cyl: num [1:32] 6 6 4 6 8 6 8 4 4 6 ... 
#> $ x2015_kvish_1_10t:'data.frame': 32 obs. of 2 variables: 
#> ..$ mpg: num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... 
#> ..$ cyl: num [1:32] 6 6 4 6 8 6 8 4 4 6 ... 
#> $ x2016_kvish_1_10t:'data.frame': 32 obs. of 2 variables: 
#> ..$ mpg: num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... 
#> ..$ cyl: num [1:32] 6 6 4 6 8 6 8 4 4 6 ...