To do so, specify the collapse attribute in paste() function:
paste(..., collapse = "")
Collapsing elements can be quite useful, not only for text processing and cleaning purposes, but also filtering a dataset to narrow it down to observations that are of interest. Of course, there are a ton of tools out there to achieve this (e.g. dplyr::filter) but to me it’s more natural sometimes to just write out what I want to look at in a string vector first. I’ll explain with an example.
Consider the classic mtcars dataset:
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
There are many models here, and in fact I only need to look at Mazda RX4 and Valiant.
# Code "Mazda RX4" and "Valiant" in a string vector
string_model <- c("^Mazda RX4$", "Valiant")
# Collapse the two elements into one
string_model_collapse <- paste(string_model, collapse = "|")
string_model_collapse
## [1] "^Mazda RX4$|Valiant"
Note that ^ and $ were used to define boundary at the start and the end, so that only “Mazda RX4” will be grabbed and not “Mazda RX4 Wag”. See this blog for details.
Now I have a single-element string vector, Next is to plug the new vector into the grep() function:
id_model_of_interest <- grep(string_model_collapse, row.names(mtcars))
id_model_of_interest
## [1] 1 6
Note that the model names are not stored in any column, which needs to be retrived using row.names() function.
mtcars[id_model_of_interest,]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.62 16.46 0 1 4 4
## Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1