I want to get the input parameter of a function to create an output with the same prefix in the global env
fun_mtcars <- function(name_ref,...){
df <- name_ref %>%
select(mpg,...)
.GlobalEnv$selec_name_ref <- df
}
fun_mtcars(mtcars,disp)
In global env a new data frame was created with the name "selec_name_ref " but I want a name "selec_mtcars"
I can do selec_mtcars <- fun_mtcars(mtcars,disp)
but I have a lot of function to execute
We may extract the object name as a string with deparse/substitute and use that in paste to create new object and assign to .GlobalEnv with [[ instead of $
fun_mtcars <- function(name_ref,...){
name_ref_str <- deparse(substitute(name_ref))
df <- name_ref %>%
select(mpg,...)
.GlobalEnv[[paste0("select_", name_ref_str)]] <- df
}
-checking
fun_mtcars(mtcars,disp)
> head(select_mtcars)
mpg disp
Mazda RX4 21.0 160
Mazda RX4 Wag 21.0 160
Datsun 710 22.8 108
Hornet 4 Drive 21.4 258
Hornet Sportabout 18.7 360
Valiant 18.1 225
Related
I've spent hours trying to make glue on the RHS of a formula work and out of clues. Here is a simple reprex.
meta <- function(x, var, suffix){
x<- x %>% mutate("{{var}}_{suffix}":= 5)
x<- x %>% mutate("{{var}}_{suffix}_new":= {{var}} - "{{var}}_{suffix}")
}
x<- meta(mtcars, mpg, suf)
#Should be equivalent to
x<- mtcars %>% mutate(mpg_suf:= 5)
x<- x%>% mutate(mpg_suf_new:= mpg - mpg_suf)
#N: Tried https://stackoverflow.com/questions/70427403/how-to-correctly-glue-together-prefix-suffix-in-a-function-call-rhs but none of the methods in it worked, unfortunately
Meta function gives me "Error in local_error_context(dots = dots, .index = i, mask = mask) :
promise already under evaluation: recursive default argument reference or earlier problems? "
Went over all hits for the searchwords for it on SO but nothing worked at the moment.
Would really appreciate any insights. Thank you!
Here is a working version:
meta <- function(x, var, suffix){
new_name <- rlang::englue("{{ var }}_{{ suffix }}")
x %>%
mutate("{new_name}" := 5) %>%
mutate("{new_name}_new" := {{ var }} - .data[[new_name]])
}
names(meta(mtcars, mpg, suf))
#> [1] "mpg" "cyl" "disp" "hp"
#> [5] "drat" "wt" "qsec" "vs"
#> [9] "am" "gear" "carb" "mpg_suf"
#> [13] "mpg_suf_new"
To understand what is going on:
Learn about the difference between "{{ var }}" and "{var}" in tidyeval glue strings: https://rlang.r-lib.org/reference/glue-operators.html
Learn about englue() to create glue strings outside of the LHS of :=: https://rlang.r-lib.org/reference/englue.html. This part is not necessary but I thought it was nicer to create and reuse a variable.
Tricky part, you create a new column with a constructed name and then want to use the new column that this name refers to. You'll have to subset it with .data, see: https://rlang.r-lib.org/reference/dot-data.html
See also the general topic: https://rlang.r-lib.org/reference/topic-data-mask-programming.html
I think it's best if we define the pieces we need first, then we can use them as needed on the LHS or the RHS of the calculation. I will add that it doesn't make much sense to me to pass the suffix argument as a bare name. I think it would be a clearer choice to make it string only.
library(dplyr)
meta <- function(x, var, suffix) {
var <- rlang::as_name(enquo(var))
suffix <- rlang::as_name(enquo(suffix)) # Remove this to make "suffix" string only.
new_var <- glue::glue("{var}_{suffix}")
x %>%
mutate("{new_var}" := 5,
"{new_var}_new" := !!sym(var) - !!sym(new_var))
}
mtcars %>%
head() %>%
meta(mpg, suf)
mpg cyl disp hp drat wt qsec vs am gear carb mpg_suf mpg_suf_new
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 5 16.0
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 5 16.0
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 5 17.8
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 5 16.4
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 5 13.7
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 5 13.1
I am looking for an easy way to have my function work with input the comes from Shiny (i.e. string input) or with typical interactive use that is Tidyverse functions enable with NSE. Without the need to duplicate my code to handle each case separately.
An example of usage:
library(dplyr)
flexible_input <- function(var){
mtcars %>%
select(var)
}
# This works for NSE
nse_input <- function(var){
mtcars %>%
select({{ var }})
}
# This works for shiny but now I am duplicated my code essentially
shiny_input <- function(var){
mtcars %>%
select(.data[[var]])
}
flexible_input(mpg)
flexible_input('mpg')
If we need flexible_input to take string and unquoted input, convert to symbol and evaluate (!!)
flexible_input <- function(var){
mtcars %>%
dplyr::select(!! rlang::ensym(var))
}
-testing
> flexible_input(mpg) %>% head
mpg
Mazda RX4 21.0
Mazda RX4 Wag 21.0
Datsun 710 22.8
Hornet 4 Drive 21.4
Hornet Sportabout 18.7
Valiant 18.1
> flexible_input("mpg") %>% head
mpg
Mazda RX4 21.0
Mazda RX4 Wag 21.0
Datsun 710 22.8
Hornet 4 Drive 21.4
Hornet Sportabout 18.7
Valiant 18.1
I want to know to use a short script to eliminate all but one duplicate column variables based on the prefix of the colname without inputting the variables I want to remove by hand.
For example, I created repeats of the mtcars$am variables, called am1, am2, am3, and am4 in a data frame called mtcars_example_2. I removed the original am variable in the mtcars_example_2 data frame.
I can use the script below to eliminate all variables with the prefix "am" but the am1 variable into a new variable called mtcars_example_3 using the code below, which inputs all variables to remove by hand:
## long way of removing all variable with am prefix that were not am1
mtcars_example_3 <-
mtcars_example_2 %>%
select(
-c(
"am2", "am3", "am4"
)
)
But this seems like the long way of doing this. Is there a faster way that does not require me to individual type in the names of each of the variables that I want to remove from the data.
Is this possible? If so, how can this be done?
Thanks ahead of time.
Here is the code for the example:
# example data
## loads packages
library(tidyverse)
## creates mtcars_example data
mtcars_example_1 <- data.frame(mtcars)
mtcars_example_2 <- data.frame(mtcars_example_1)
## creates duplicate variables, based on am variable
mtcars_example_2$am1 <- mtcars_example_1$am
mtcars_example_2$am2 <- mtcars_example_1$am
mtcars_example_2$am3 <- mtcars_example_1$am
mtcars_example_2$am4 <- mtcars_example_1$am
## removes original variable
mtcars_example_2 <-
mtcars_example_2 %>%
select(
-c(
"am"
)
)
## long way of removing all variable with am prefix that were not am1
mtcars_example_3 <-
mtcars_example_2 %>%
select(
-c(
"am2", "am3", "am4"
)
)
You can remove all the variables that start with am but keep am1 :
library(dplyr)
mtcars_example_2 %>% select(-starts_with('am'), am1) %>% head
# mpg cyl disp hp drat wt qsec vs gear carb am1
#Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 4 4 1
#Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 4 4 1
#Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 4 1 1
#Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 3 1 0
#Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 3 2 0
#Valiant 18.1 6 225 105 2.76 3.460 20.22 1 3 1 0
Depending on your actual scenario you can also use regex to remove columns.
mtcars_example_2 %>% select(-matches('am[2-4]')) %>% head
We could also do
library(dplyr)
mtcars_example_2 %>%
select(-contains('am'), am1)
I'm wondering if there is a way to select a group of columns by the name of the first column in the group and then all the next columns either a) to the end of the data frame, or b) to another column, also using its name.
a) As an example for the first question, in the mtcars dataset, is there a way to select the columns from drat to the end of the data frame? (Something like mtcars[,'drat':ncol(mtcars)])
b) For the second question, is there a way to select the columns starting at cyl and ending at wt? (Something like mtcars[,'cyl':'wt'])
Many elegant solutions already provided but one can even use base-R to get the desired result using which as:
Ans a:
mtcars[,which(names(mtcars) == "drat"):ncol(mtcars)]
Ans b:
mtcars[,which(names(mtcars) == "cyl"):which(names(mtcars) == "wt")]
# cyl disp hp drat wt
#Mazda RX4 6 160.0 110 3.90 2.620
#Mazda RX4 Wag 6 160.0 110 3.90 2.875
#Datsun 710 4 108.0 93 3.85 2.320
#Hornet 4 Drive 6 258.0 110 3.08 3.215
#Hornet Sportabout 8 360.0 175 3.15 3.440
#......so on
We can do with this with select from dplyr
Answer a)
mtcars %>% select(drat:get(last(names(.))))
Answer b)
mtcars %>% select(cyl:wt)
In dplyr, the select function does exactly this (no quotes needed):
mtcards %>%
select(cyl:wt)
If we need to use a quoted string, convert it to sym (symbol) and then do the evaluation (!!
mtcars %>%
select(!! (rlang::sym("cyl")): !!(rlang::sym("wt")))
It would be when these are stored in an object
a <- "cyl"
b <- "wt"
mtcars %>%
select(!! (rlang::sym(a)): !!(rlang::sym(b)))
Or another option is
mtcars %>%
select(!! rlang::parse_expr(glue::glue("{a}:{b}")))
I'm trying to write my function and need to pass argument inside.
Use mtcars dataset as an example:
get.param <- function(data, var){
data %>% select(eval(var)) %>%
head()
}
get.param(mtcars, 'hp')
In the above function, replacing eval() with get() gave me error.
I'm little bit confused which one should I use. I use get() i some other functions and work. What is the difference between these two?
You can get it to work via
get.param <- function(data, var){
var <- enquo(var)
data %>% select(!!var) %>%
head()
}
get.param(mtcars, hp)
hp
Mazda RX4 110
Mazda RX4 Wag 110
Datsun 710 93
Hornet 4 Drive 110
Hornet Sportabout 175
Valiant 105
Normally one does not use get or eval with dplyr. See the vignette in the rlang package for how it is done with that package; however, in this particular case one can just pass var directly to select adding parentheses around it so that it does not confuse it with a column called "var" should it exist. If you are not worried about that edge case you could omit the parentheses.
get.param <- function(data, var) {
data %>% select((var)) %>% head
}
get.param(mtcars, 'hp')
giving:
hp
Mazda RX4 110
Mazda RX4 Wag 110
Datsun 710 93
Hornet 4 Drive 110
Hornet Sportabout 175
Valiant 105
Another possibility is to use ... like this and giving the same answer. In this variation we don't need to add the parentheses to eliminate an edge case. It also allows multiple columns to be specified.
get.param <- function(data, ...) {
data %>% select(...) %>% head
}
get.param(mtcars, 'hp')