What's the difference between using select + unlist from dplyr package and using the dollar sign?

I've been taking an online course in which the instructor always does the following to obtain, say, the column Col1 from a data.frame object Dat:

library(dplyr)

unlist(select(Dat, Col1))

Why not simply run Dat$Col1? I notice a difference in the "presentation" of both results, but is there any other significant divergence between the two forms? Any operation will result in the same product for both?

edited 2 days ago

Ben Bolker

132k11222309

asked 2 days ago

G. Monteiro

513

New contributor

8

Your instructor is probably just a fan of the tidyverse. Dat$Col1 is my preferred method for getting a column.
– Rich Scriven
2 days ago

9

...but not well-versed enough to know of pull.
– Henrik
2 days ago

4

Touche. Maybe OP can take the instructor to school.
– Rich Scriven
2 days ago

3

Wow, he loads an entire new library for that?
– cory
2 days ago

5

The rumours about the SO incident spread, and the instructor decided to gather every student. today he had started to select cigarettes without filter, and took a long, deep pull to compose himself for a minute. It was a mare’s nest. “Well, I have a crow to pluck with you, who did this?”, the teacher started. "between you and me: this is no funs. You better not cross me, or I will reduce your grades, every grade. Whichever way you slice it - period!" The students looked at each other. A complete farce. Happy new year every one - tally-ho!
– Henrik
2 days ago

|
show 1 more comment

I've been taking an online course in which the instructor always does the following to obtain, say, the column Col1 from a data.frame object Dat:

library(dplyr)

unlist(select(Dat, Col1))

edited 2 days ago

Ben Bolker

132k11222309

asked 2 days ago

G. Monteiro

513

New contributor

8

Your instructor is probably just a fan of the tidyverse. Dat$Col1 is my preferred method for getting a column.
– Rich Scriven
2 days ago

9

...but not well-versed enough to know of pull.
– Henrik
2 days ago

4

Touche. Maybe OP can take the instructor to school.
– Rich Scriven
2 days ago

3

Wow, he loads an entire new library for that?
– cory
2 days ago

5

The rumours about the SO incident spread, and the instructor decided to gather every student. today he had started to select cigarettes without filter, and took a long, deep pull to compose himself for a minute. It was a mare’s nest. “Well, I have a crow to pluck with you, who did this?”, the teacher started. "between you and me: this is no funs. You better not cross me, or I will reduce your grades, every grade. Whichever way you slice it - period!" The students looked at each other. A complete farce. Happy new year every one - tally-ho!
– Henrik
2 days ago

|
show 1 more comment

I've been taking an online course in which the instructor always does the following to obtain, say, the column Col1 from a data.frame object Dat:

library(dplyr)

unlist(select(Dat, Col1))

edited 2 days ago

Ben Bolker

132k11222309

asked 2 days ago

G. Monteiro

513

New contributor

I've been taking an online course in which the instructor always does the following to obtain, say, the column Col1 from a data.frame object Dat:

library(dplyr)

unlist(select(Dat, Col1))

r dplyr

edited 2 days ago

Ben Bolker

132k11222309

asked 2 days ago

G. Monteiro

513

New contributor

edited 2 days ago

Ben Bolker

132k11222309

asked 2 days ago

G. Monteiro

513

New contributor

edited 2 days ago

Ben Bolker

132k11222309

edited 2 days ago

Ben Bolker

132k11222309

edited 2 days ago

Ben Bolker

132k11222309

asked 2 days ago

G. Monteiro

513

New contributor

asked 2 days ago

G. Monteiro

513

asked 2 days ago

G. Monteiro

513

New contributor

G. Monteiro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

8

Your instructor is probably just a fan of the tidyverse. Dat$Col1 is my preferred method for getting a column.
– Rich Scriven
2 days ago

9

...but not well-versed enough to know of pull.
– Henrik
2 days ago

4

Touche. Maybe OP can take the instructor to school.
– Rich Scriven
2 days ago

3

Wow, he loads an entire new library for that?
– cory
2 days ago

5

The rumours about the SO incident spread, and the instructor decided to gather every student. today he had started to select cigarettes without filter, and took a long, deep pull to compose himself for a minute. It was a mare’s nest. “Well, I have a crow to pluck with you, who did this?”, the teacher started. "between you and me: this is no funs. You better not cross me, or I will reduce your grades, every grade. Whichever way you slice it - period!" The students looked at each other. A complete farce. Happy new year every one - tally-ho!
– Henrik
2 days ago

|
show 1 more comment

8

Your instructor is probably just a fan of the tidyverse. Dat$Col1 is my preferred method for getting a column.
– Rich Scriven
2 days ago

9

...but not well-versed enough to know of pull.
– Henrik
2 days ago

4

Touche. Maybe OP can take the instructor to school.
– Rich Scriven
2 days ago

3

Wow, he loads an entire new library for that?
– cory
2 days ago

5

The rumours about the SO incident spread, and the instructor decided to gather every student. today he had started to select cigarettes without filter, and took a long, deep pull to compose himself for a minute. It was a mare’s nest. “Well, I have a crow to pluck with you, who did this?”, the teacher started. "between you and me: this is no funs. You better not cross me, or I will reduce your grades, every grade. Whichever way you slice it - period!" The students looked at each other. A complete farce. Happy new year every one - tally-ho!
– Henrik
2 days ago

Your instructor is probably just a fan of the tidyverse. Dat$Col1 is my preferred method for getting a column.
– Rich Scriven
2 days ago

...but not well-versed enough to know of pull.
– Henrik
2 days ago

Touche. Maybe OP can take the instructor to school.
– Rich Scriven
2 days ago

Wow, he loads an entire new library for that?
– cory
2 days ago

The rumours about the SO incident spread, and the instructor decided to gather every student. today he had started to select cigarettes without filter, and took a long, deep pull to compose himself for a minute. It was a mare’s nest. “Well, I have a crow to pluck with you, who did this?”, the teacher started. "between you and me: this is no funs. You better not cross me, or I will reduce your grades, every grade. Whichever way you slice it - period!" The students looked at each other. A complete farce. Happy new year every one - tally-ho!
– Henrik
2 days ago

|
show 1 more comment

1 Answer
1

active

oldest

votes

(Posting comments as community wiki.)

These are not quite equivalent - unlist(select(.)) keeps (probably unwanted) names.

dd <- data.frame(Col1=c("abc","def"))

str(unlist(select(dd,Col1)))

##  Factor w/ 2 levels "abc","def": 1 2

##  - attr(*, "names")= chr [1:2] "Col11" "Col12"

str(dd$Col1)

##  Factor w/ 2 levels "abc","def": 1 2

Your instructor is probably just a fan of the tidyverse (@RichScriven); pull(Dat, Col1) or (for extreme "tidiness") Dat %>% pull(Col1) would be more idiomatic (@Henrik). Dat$Col1 or Dat[["Col1"]] would be the base-R equivalents (the former is more convenient for interactive use, the latter is marginally safer for programming purposes since it won't do name-completion).

It hardly matters, but the tidyverse approaches are much slower.

microbenchmark(dd$Col1,dd[["Col1"]],pull(dd,Col1),unlist(select(dd,Col1)))

Unit: microseconds

                     expr     min        lq       mean    median       uq

                  dd$Col1   5.296   10.9630   14.86871   13.4040   17.160

             dd[["Col1"]]   7.870    9.6535   15.18874   11.8270   16.635

           pull(dd, Col1)  44.160  108.7625  128.89342  117.8415  136.890

 unlist(select(dd, Col1)) 601.480 1132.8240 1436.44178 1214.4420 1378.141

      max neval cld

   31.036   100  a 

   88.842   100  a 

  422.462   100  a 

 8796.964   100   b

edited 2 days ago

community wiki

2 revs
Ben Bolker

2

Since you mentioned it, pull(dd, Col1) is twice as fast as dd %>% pull(Col1). Tested with a much larger dd <- data.frame(Col1 = sample(c("abc", "def"), 1e6, TRUE)).
– Rui Barradas
2 days ago

1

Just to note, there's always the use.names argument to the base R function, unlist which will drop any unwanted names: unlist(select(.), use.names=FALSE).
– lmo
2 days ago

add a comment |

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1 Answer
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active

oldest

votes

1 Answer
1

active

oldest

votes

(Posting comments as community wiki.)

These are not quite equivalent - unlist(select(.)) keeps (probably unwanted) names.

dd <- data.frame(Col1=c("abc","def"))

str(unlist(select(dd,Col1)))

##  Factor w/ 2 levels "abc","def": 1 2

##  - attr(*, "names")= chr [1:2] "Col11" "Col12"

str(dd$Col1)

##  Factor w/ 2 levels "abc","def": 1 2

It hardly matters, but the tidyverse approaches are much slower.

microbenchmark(dd$Col1,dd[["Col1"]],pull(dd,Col1),unlist(select(dd,Col1)))

Unit: microseconds

                     expr     min        lq       mean    median       uq

                  dd$Col1   5.296   10.9630   14.86871   13.4040   17.160

             dd[["Col1"]]   7.870    9.6535   15.18874   11.8270   16.635

           pull(dd, Col1)  44.160  108.7625  128.89342  117.8415  136.890

 unlist(select(dd, Col1)) 601.480 1132.8240 1436.44178 1214.4420 1378.141

      max neval cld

   31.036   100  a 

   88.842   100  a 

  422.462   100  a 

 8796.964   100   b

edited 2 days ago

community wiki

2 revs
Ben Bolker

2

Since you mentioned it, pull(dd, Col1) is twice as fast as dd %>% pull(Col1). Tested with a much larger dd <- data.frame(Col1 = sample(c("abc", "def"), 1e6, TRUE)).
– Rui Barradas
2 days ago

1

Just to note, there's always the use.names argument to the base R function, unlist which will drop any unwanted names: unlist(select(.), use.names=FALSE).
– lmo
2 days ago

add a comment |

(Posting comments as community wiki.)

These are not quite equivalent - unlist(select(.)) keeps (probably unwanted) names.

dd <- data.frame(Col1=c("abc","def"))

str(unlist(select(dd,Col1)))

##  Factor w/ 2 levels "abc","def": 1 2

##  - attr(*, "names")= chr [1:2] "Col11" "Col12"

str(dd$Col1)

##  Factor w/ 2 levels "abc","def": 1 2

It hardly matters, but the tidyverse approaches are much slower.

microbenchmark(dd$Col1,dd[["Col1"]],pull(dd,Col1),unlist(select(dd,Col1)))

Unit: microseconds

                     expr     min        lq       mean    median       uq

                  dd$Col1   5.296   10.9630   14.86871   13.4040   17.160

             dd[["Col1"]]   7.870    9.6535   15.18874   11.8270   16.635

           pull(dd, Col1)  44.160  108.7625  128.89342  117.8415  136.890

 unlist(select(dd, Col1)) 601.480 1132.8240 1436.44178 1214.4420 1378.141

      max neval cld

   31.036   100  a 

   88.842   100  a 

  422.462   100  a 

 8796.964   100   b

edited 2 days ago

community wiki

2 revs
Ben Bolker

2

Since you mentioned it, pull(dd, Col1) is twice as fast as dd %>% pull(Col1). Tested with a much larger dd <- data.frame(Col1 = sample(c("abc", "def"), 1e6, TRUE)).
– Rui Barradas
2 days ago

1

Just to note, there's always the use.names argument to the base R function, unlist which will drop any unwanted names: unlist(select(.), use.names=FALSE).
– lmo
2 days ago

add a comment |

(Posting comments as community wiki.)

These are not quite equivalent - unlist(select(.)) keeps (probably unwanted) names.

dd <- data.frame(Col1=c("abc","def"))

str(unlist(select(dd,Col1)))

##  Factor w/ 2 levels "abc","def": 1 2

##  - attr(*, "names")= chr [1:2] "Col11" "Col12"

str(dd$Col1)

##  Factor w/ 2 levels "abc","def": 1 2

It hardly matters, but the tidyverse approaches are much slower.

microbenchmark(dd$Col1,dd[["Col1"]],pull(dd,Col1),unlist(select(dd,Col1)))

Unit: microseconds

                     expr     min        lq       mean    median       uq

                  dd$Col1   5.296   10.9630   14.86871   13.4040   17.160

             dd[["Col1"]]   7.870    9.6535   15.18874   11.8270   16.635

           pull(dd, Col1)  44.160  108.7625  128.89342  117.8415  136.890

 unlist(select(dd, Col1)) 601.480 1132.8240 1436.44178 1214.4420 1378.141

      max neval cld

   31.036   100  a 

   88.842   100  a 

  422.462   100  a 

 8796.964   100   b

edited 2 days ago

community wiki

2 revs
Ben Bolker

(Posting comments as community wiki.)

These are not quite equivalent - unlist(select(.)) keeps (probably unwanted) names.

dd <- data.frame(Col1=c("abc","def"))

str(unlist(select(dd,Col1)))

##  Factor w/ 2 levels "abc","def": 1 2

##  - attr(*, "names")= chr [1:2] "Col11" "Col12"

str(dd$Col1)

##  Factor w/ 2 levels "abc","def": 1 2

It hardly matters, but the tidyverse approaches are much slower.

microbenchmark(dd$Col1,dd[["Col1"]],pull(dd,Col1),unlist(select(dd,Col1)))

Unit: microseconds

                     expr     min        lq       mean    median       uq

                  dd$Col1   5.296   10.9630   14.86871   13.4040   17.160

             dd[["Col1"]]   7.870    9.6535   15.18874   11.8270   16.635

           pull(dd, Col1)  44.160  108.7625  128.89342  117.8415  136.890

 unlist(select(dd, Col1)) 601.480 1132.8240 1436.44178 1214.4420 1378.141

      max neval cld

   31.036   100  a 

   88.842   100  a 

  422.462   100  a 

 8796.964   100   b

edited 2 days ago

community wiki

2 revs
Ben Bolker

edited 2 days ago

community wiki

2 revs
Ben Bolker

community wiki

2 revs
Ben Bolker

community wiki

2 revs
Ben Bolker

2

Since you mentioned it, pull(dd, Col1) is twice as fast as dd %>% pull(Col1). Tested with a much larger dd <- data.frame(Col1 = sample(c("abc", "def"), 1e6, TRUE)).
– Rui Barradas
2 days ago

1

Just to note, there's always the use.names argument to the base R function, unlist which will drop any unwanted names: unlist(select(.), use.names=FALSE).
– lmo
2 days ago

add a comment |

2

Since you mentioned it, pull(dd, Col1) is twice as fast as dd %>% pull(Col1). Tested with a much larger dd <- data.frame(Col1 = sample(c("abc", "def"), 1e6, TRUE)).
– Rui Barradas
2 days ago

1

Just to note, there's always the use.names argument to the base R function, unlist which will drop any unwanted names: unlist(select(.), use.names=FALSE).
– lmo
2 days ago

Since you mentioned it, pull(dd, Col1) is twice as fast as dd %>% pull(Col1). Tested with a much larger dd <- data.frame(Col1 = sample(c("abc", "def"), 1e6, TRUE)).
– Rui Barradas
2 days ago

Just to note, there's always the use.names argument to the base R function, unlist which will drop any unwanted names: unlist(select(.), use.names=FALSE).
– lmo
2 days ago

add a comment |

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