ls() in R: list objects/variables in your environment

R Function

rstats

Often while working in R, you might want to check your environments for all the objects you have created and available in your environment. ls() function in …

Published

July 9, 2021

list objects in environment with ls() in R

Let us assume that we have started a new R session and create a new variable x

x <- "Hello"

By using ls() function we can see that our current environment has a single object x

ls()

## [1] "x"

By creating couple of more variables and then using ls() function, we can see that we have additional objects available in our current working enviornment.

y <- "Welcome to RStats101.com"
z <- "Learn Basics of R"

Note that the results of using ls() function is a vector containing the names of the variables or objects in the working enviornment.

ls()
## [1] "x" "y" "z"

list hidden objects in environment with ls() in R

By default ls() function ignores listing objects or variables whose name begin with a dot “.”. Let us create a new variable that begins with a “.”

.dotted <- hidden()

Note that using ls() function, we are not seeing the object name in the resulting vector.

ls()
## [1] "x" "y" "z"

Typically, variable names that begin with a dot are considered hidden. By using the “all.names=TRUE” argument within ls() function we can see the variables that has dot at the start.

ls(all.names = TRUE)
## [1] ".dotted" "x"       "y"       "z"

list variables in environment with obejcts() in R

Another related function available in R is objects(). objects() function behave very similarly. In this example below we can see that, using objects() function gives us the same results as ls() function.

objects()
## [1] "x" "y" "z"

list variables in environment and structure with ls.str() in R

ls.str() function is another useful function that lists the variables in your current environment with additional details. ls.str() function kind of combines the functionality of str() function in R that helps to look at the structure of an object in R with ls() function.

For example, if apply ls.str() function to the current environment, we can see the object names and its values.

ls.str()
## x :  chr "Hello"
## y :  chr "Welcome to RStats101.com"
## z :  chr "Learn Basics of R"

Let us add a new variable to the environment. This time we add a dataframe, instead of a simple object.

df <- mtcars

As we saw before, ls() function simply returns the names of variables in the environment as a vector.

ls()
## [1] "df" "x"  "y"  "z"

However, ls.str() function gives us more details by providing structure of each object. In the case of a dataframe, it gives us variable names in the dataframe, their types and their first few values.

ls.str()
## df : '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 ...
## x :  chr "Hello"
## y :  chr "Welcome to RStats101.com"
## z :  chr "Learn Basics of R"

Note that ls.str() also ignored variable names that begins with a dot. We need to use all.names=TRUE argument to see the hidden variables.

ls.str(all.names=TRUE)
## .dotted :  chr "hidden"
## df : '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 ...
## x :  chr "Hello"
## y :  chr "Welcome to RStats101.com"
## z :  chr "Learn Basics of R"

To summarise, in this tutorial we saw how to identify the objects/variables in your current working environment in R using ls() function. One of the common next steps after identifying object/variables is to remove them. Tune in for another tutorial to remove one or multiple objects in your environment.

--- title: "ls() in R: list objects/variables in your environment" description: "Often while working in R, you might want to check your environments for all the objects you have created and available in your environment. ls() function in ..." date: 2021-07-09 categories: ['R Function', 'rstats'] format: html: code-fold: false code-tools: true --- ### list objects in environment with ls() in R Let us assume that we have started a new R session and create a new variable x ```r x <- "Hello" ``` By using ls() function we can see that our current environment has a single object x ```r ls() ## [1] "x" ``` By creating couple of more variables and then using ls() function, we can see that we have additional objects available in our current working enviornment. ```r y <- "Welcome to RStats101.com" z <- "Learn Basics of R" ``` Note that the results of using ls() function is a vector containing the names of the variables or objects in the working enviornment. ```r ls() ## [1] "x" "y" "z" ``` ### list hidden objects in environment with ls() in R By default ls() function ignores listing objects or variables whose name begin with a dot ".". Let us create a new variable that begins with a "." ```r .dotted <- hidden() ``` Note that using ls() function, we are not seeing the object name in the resulting vector. ```r ls() ## [1] "x" "y" "z" ``` Typically, variable names that begin with a dot are considered hidden. By using the "all.names=TRUE" argument within ls() function we can see the variables that has dot at the start. ```r ls(all.names = TRUE) ## [1] ".dotted" "x" "y" "z" ``` ### list variables in environment with obejcts() in R Another related function available in R is objects(). objects() function behave very similarly. In this example below we can see that, using objects() function gives us the same results as ls() function. ```r objects() ## [1] "x" "y" "z" ``` ### list variables in environment and structure with ls.str() in R ls.str() function is another useful function that lists the variables in your current environment with additional details. ls.str() function kind of combines the functionality of str() function in R that helps to look at the structure of an object in R with ls() function. For example, if apply ls.str() function to the current environment, we can see the object names and its values. ```r ls.str() ## x : chr "Hello" ## y : chr "Welcome to RStats101.com" ## z : chr "Learn Basics of R" ``` Let us add a new variable to the environment. This time we add a dataframe, instead of a simple object. ```r df <- mtcars ``` As we saw before, ls() function simply returns the names of variables in the environment as a vector. ```r ls() ## [1] "df" "x" "y" "z" ``` However, ls.str() function gives us more details by providing structure of each object. In the case of a dataframe, it gives us variable names in the dataframe, their types and their first few values. ```r ls.str() ## df : '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 ... ## x : chr "Hello" ## y : chr "Welcome to RStats101.com" ## z : chr "Learn Basics of R" ``` Note that ls.str() also ignored variable names that begins with a dot. We need to use all.names=TRUE argument to see the hidden variables. ```r ls.str(all.names=TRUE) ## .dotted : chr "hidden" ## df : '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 ... ## x : chr "Hello" ## y : chr "Welcome to RStats101.com" ## z : chr "Learn Basics of R" ``` To summarise, in this tutorial we saw how to identify the objects/variables in your current working environment in R using ls() function. One of the common next steps after identifying object/variables is to remove them. Tune in for another tutorial to remove one or multiple objects in your environment. ## Related Tutorials - [How to use dollar $ operator in R](dollar-operator-in-r.html) - [How to Find difference between two dates as days, weeks, months and years](find-difference-between-two-dates.html) - [How to compute annualized return of a stock with tidyverse](compute-annualized-return-of-a-stock.html) - [How to Compute row means](compute-rowmeans-with-dplyr.html) - [tidyverse all_of(): select columns from a vector](tidyverse-all_of-select-columns-from-a-vector.html)