These functions allow you to select variables based operations with prefixes and suffixes and length of names.
difference_var(): Select variables that start with a prefix AND NOT end wiht a suffix.intersect_var(): Select variables that start with a prefix AND end wiht a suffix.union_var(): Select variables that start with a prefix OR end wiht a suffix.width_of(): Select variables with width ofn.width_greater_than(): Select variables with width greater thann.width_less_than(): Select variables with width less thann.lower_case_only(): Select variables that contains lower case only (e.g., "env").upper_case_only(): Select variables that contains upper case only (e.g., "ENV").title_case_only(): Select variables that contains upper case in the first character only (e.g., "Env").
Usage
difference_var(prefix, suffix)
intersect_var(prefix, suffix)
union_var(prefix, suffix)
width_of(n, vars = peek_vars(fn = "width_of"))
width_greater_than(n, vars = peek_vars(fn = "width_greater_than"))
width_less_than(n, vars = peek_vars(fn = "width_less_than"))
lower_case_only(vars = peek_vars(fn = "lower_case_only"))
upper_case_only(vars = peek_vars(fn = "upper_case_only"))
title_case_only(vars = peek_vars(fn = "title_case_only"))Arguments
- prefix
A prefix that start the variable name.
- suffix
A suffix that end the variable name.
- n
The length of variable names to select. For
width_of()the selected variables containsncharacters. Forwidth_greater_than()andwidth_less_than()the selected variables contains greater and less characteres thann, respectively.- vars
A character vector of variable names. When called from inside selecting functions like
select_cols()these are automatically set to the names of the table.
Examples
# \donttest{
library(metan)
# Select variables that start with "C" and not end with "D".
data_ge2 %>%
select_cols(difference_var("C", "D"))
#> # A tibble: 156 × 2
#> CL CW
#> <dbl> <dbl>
#> 1 28.1 25.1
#> 2 27.6 21.4
#> 3 28.4 24.0
#> 4 31.7 26.2
#> 5 32.0 20.7
#> 6 30.4 26.8
#> 7 30.6 26.2
#> 8 28.7 24.1
#> 9 27.6 20.5
#> 10 28.2 20.1
#> # … with 146 more rows
# Select variables that start with "C" and end with "D".
data_ge2 %>%
select_cols(intersect_var("C", "D"))
#> # A tibble: 156 × 2
#> CD CDED
#> <dbl> <dbl>
#> 1 16.3 0.538
#> 2 14.5 0.551
#> 3 16.4 0.561
#> 4 17.4 0.586
#> 5 15.5 0.607
#> 6 17.5 0.577
#> 7 18.0 0.594
#> 8 17.2 0.608
#> 9 16.4 0.576
#> 10 15.5 0.597
#> # … with 146 more rows
# Select variables that start with "C" or end with "D".
data_ge2 %>%
select_cols(union_var("C", "D"))
#> # A tibble: 156 × 5
#> CL CD CW CDED ED
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 28.1 16.3 25.1 0.538 52.2
#> 2 27.6 14.5 21.4 0.551 50.3
#> 3 28.4 16.4 24.0 0.561 50.7
#> 4 31.7 17.4 26.2 0.586 54.1
#> 5 32.0 15.5 20.7 0.607 52.7
#> 6 30.4 17.5 26.8 0.577 52.7
#> 7 30.6 18.0 26.2 0.594 51.7
#> 8 28.7 17.2 24.1 0.608 47.2
#> 9 27.6 16.4 20.5 0.576 47.9
#> 10 28.2 15.5 20.1 0.597 47.5
#> # … with 146 more rows
# Select variables with width name of 4
data_ge2 %>%
select_cols(width_of(4))
#> # A tibble: 156 × 2
#> CDED PERK
#> <dbl> <dbl>
#> 1 0.538 89.6
#> 2 0.551 89.5
#> 3 0.561 89.7
#> 4 0.586 87.9
#> 5 0.607 89.7
#> 6 0.577 88.5
#> 7 0.594 89.1
#> 8 0.608 88.3
#> 9 0.576 89.0
#> 10 0.597 88.7
#> # … with 146 more rows
# Select variables with width name greater than 2
data_ge2 %>%
select_cols(width_greater_than(2))
#> # A tibble: 156 × 8
#> ENV GEN REP NKR CDED PERK TKW NKE
#> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 A1 H1 1 36.6 0.538 89.6 418. 521.
#> 2 A1 H1 2 31.4 0.551 89.5 361. 494.
#> 3 A1 H1 3 31.8 0.561 89.7 367. 565.
#> 4 A1 H10 1 32.8 0.586 87.9 374. 519.
#> 5 A1 H10 2 28 0.607 89.7 347. 502.
#> 6 A1 H10 3 32.8 0.577 88.5 394. 525.
#> 7 A1 H11 1 34.6 0.594 89.1 377. 575
#> 8 A1 H11 2 34.4 0.608 88.3 361. 501.
#> 9 A1 H11 3 34.8 0.576 89.0 322. 513.
#> 10 A1 H12 1 31.6 0.597 88.7 345. 480.
#> # … with 146 more rows
# Select variables with width name less than 3
data_ge2 %>%
select_cols(width_less_than(3))
#> # A tibble: 156 × 10
#> PH EH EP EL ED CL CD CW KW NR
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2.61 1.71 0.658 16.1 52.2 28.1 16.3 25.1 217. 15.6
#> 2 2.87 1.76 0.628 14.2 50.3 27.6 14.5 21.4 184. 16
#> 3 2.68 1.58 0.591 16.0 50.7 28.4 16.4 24.0 208. 17.2
#> 4 2.83 1.64 0.581 16.7 54.1 31.7 17.4 26.2 194. 15.6
#> 5 2.79 1.71 0.616 14.9 52.7 32.0 15.5 20.7 176. 17.6
#> 6 2.72 1.51 0.554 16.7 52.7 30.4 17.5 26.8 207. 16.8
#> 7 2.75 1.51 0.549 17.4 51.7 30.6 18.0 26.2 217. 16.8
#> 8 2.72 1.56 0.573 16.7 47.2 28.7 17.2 24.1 181. 13.6
#> 9 2.77 1.67 0.600 15.8 47.9 27.6 16.4 20.5 166. 15.2
#> 10 2.73 1.54 0.563 14.9 47.5 28.2 15.5 20.1 161. 14.8
#> # … with 146 more rows
# Creating data with messy column names
df <- head(data_ge, 3)
colnames(df) <- c("Env", "gen", "Rep", "GY", "hm")
select_cols(df, lower_case_only())
#> # A tibble: 3 × 2
#> gen hm
#> <fct> <dbl>
#> 1 G1 44.9
#> 2 G1 46.9
#> 3 G1 47.8
select_cols(df, upper_case_only())
#> # A tibble: 3 × 1
#> GY
#> <dbl>
#> 1 2.17
#> 2 2.50
#> 3 2.43
select_cols(df, title_case_only())
#> # A tibble: 3 × 2
#> Env Rep
#> <fct> <fct>
#> 1 E1 1
#> 2 E1 2
#> 3 E1 3
# }