for (a in b) { a <- 123 }

[pathos]

I would like to assign a variable name programmatically (metaprogramming?). Here is an example.

for (a in b) {
{{ a }} = 123
}

Is there a way to make this work? Or something along the lines of

for (a in b) {
temp_name = paste0('value_', a)
{{ temp_name  }} = 123

vector = c(0)

vector <- c(vector, {{ temp_name  }})
}

[GreyMerchant]

Hi there,

It would have helped if you provided more detail about what you're expecting as input and output. Always best to make a reprex. Below I have made 2 examples for you. One is with assign which is often used in these cases while creating the objects in your global environment.

The other is maybe more a general approach to rename an existing dataframe based on the number of columns etc. See which works for you.

# Example 1 ---------------------------------------------------------------



for(i in 1:6) { #-- Create objects  'r.1', 'r.2', ... 'r.6' --
  nam <- paste("r", i, sep = ".")
  assign(nam, 1:i)
}

ls(pattern = "^r..$")
#> [1] "r.1" "r.2" "r.3" "r.4" "r.5" "r.6"

r.1
#> [1] 1
r.2
#> [1] 1 2
r.3
#> [1] 1 2 3




# Example 2 ---------------------------------------------------------------

#assigning dummy data
df <- mtcars

#creating empty list
new_names <- list()

#creating new set of labels for the data based on the number of columns
for(i in 1:length(mtcars)){
  new_names[i] <- paste0("car",i)
}

#unlisting the list into a vector
new_names_to_use <- unlist(new_names)

#assigning new names to our original df
names(df) <- new_names_to_use
 
df
#>                     car1 car2  car3 car4 car5  car6  car7 car8 car9 car10 car11
#> 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
#> Merc 280C           17.8    6 167.6  123 3.92 3.440 18.90    1    0     4     4
#> Merc 450SE          16.4    8 275.8  180 3.07 4.070 17.40    0    0     3     3
#> Merc 450SL          17.3    8 275.8  180 3.07 3.730 17.60    0    0     3     3
#> Merc 450SLC         15.2    8 275.8  180 3.07 3.780 18.00    0    0     3     3
#> Cadillac Fleetwood  10.4    8 472.0  205 2.93 5.250 17.98    0    0     3     4
#> Lincoln Continental 10.4    8 460.0  215 3.00 5.424 17.82    0    0     3     4
#> Chrysler Imperial   14.7    8 440.0  230 3.23 5.345 17.42    0    0     3     4
#> Fiat 128            32.4    4  78.7   66 4.08 2.200 19.47    1    1     4     1
#> Honda Civic         30.4    4  75.7   52 4.93 1.615 18.52    1    1     4     2
#> Toyota Corolla      33.9    4  71.1   65 4.22 1.835 19.90    1    1     4     1
#> Toyota Corona       21.5    4 120.1   97 3.70 2.465 20.01    1    0     3     1
#> Dodge Challenger    15.5    8 318.0  150 2.76 3.520 16.87    0    0     3     2
#> AMC Javelin         15.2    8 304.0  150 3.15 3.435 17.30    0    0     3     2
#> Camaro Z28          13.3    8 350.0  245 3.73 3.840 15.41    0    0     3     4
#> Pontiac Firebird    19.2    8 400.0  175 3.08 3.845 17.05    0    0     3     2
#> Fiat X1-9           27.3    4  79.0   66 4.08 1.935 18.90    1    1     4     1
#> Porsche 914-2       26.0    4 120.3   91 4.43 2.140 16.70    0    1     5     2
#> Lotus Europa        30.4    4  95.1  113 3.77 1.513 16.90    1    1     5     2
#> Ford Pantera L      15.8    8 351.0  264 4.22 3.170 14.50    0    1     5     4
#> Ferrari Dino        19.7    6 145.0  175 3.62 2.770 15.50    0    1     5     6
#> Maserati Bora       15.0    8 301.0  335 3.54 3.570 14.60    0    1     5     8
#> Volvo 142E          21.4    4 121.0  109 4.11 2.780 18.60    1    1     4     2

^{Created on 2022-01-10 by the reprex package (v2.0.0)}