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### ** Examples # Packing ============================================================= # It's not currently clear why you would ever want to pack columns # since few functions work with this sort of data. df <- tibble(x1 = 1:3, x2 = 4:6, x3 = 7:9, y = 1:3) df
# A tibble: 3 × 4
x1 x2 x3 y
<int> <int> <int> <int>
1 1 4 7 1
2 2 5 8 2
3 3 6 9 3
df %>% pack(x = starts_with("x"))
# A tibble: 3 × 2
y x$x1 $x2 $x3
<int> <int> <int> <int>
1 1 1 4 7
2 2 2 5 8
3 3 3 6 9
df %>% pack(x = c(x1, x2, x3), y = y)
# A tibble: 3 × 2 x$x1 $x2 $x3 y$y <int> <int> <int> <int> 1 1 4 7 1 2 2 5 8 2 3 3 6 9 3
# .names_sep allows you to strip off common prefixes; this # acts as a natural inverse to name_sep in unpack() iris %>% as_tibble() %>% pack( Sepal = starts_with("Sepal"), Petal = starts_with("Petal"), .names_sep = "." )
# A tibble: 150 × 3 Species Sepal$Length $Width Petal$Length $Width <fct> <dbl> <dbl> <dbl> <dbl> 1 setosa 5.1 3.5 1.4 0.2 2 setosa 4.9 3 1.4 0.2 3 setosa 4.7 3.2 1.3 0.2 4 setosa 4.6 3.1 1.5 0.2 5 setosa 5 3.6 1.4 0.2 6 setosa 5.4 3.9 1.7 0.4 7 setosa 4.6 3.4 1.4 0.3 8 setosa 5 3.4 1.5 0.2 9 setosa 4.4 2.9 1.4 0.2 10 setosa 4.9 3.1 1.5 0.1 # … with 140 more rows
# Unpacking =========================================================== df <- tibble( x = 1:3, y = tibble(a = 1:3, b = 3:1), z = tibble(X = c("a", "b", "c"), Y = runif(3), Z = c(TRUE, FALSE, NA)) ) df
# A tibble: 3 × 3
x y$a $b z$X $Y $Z
<int> <int> <int> <chr> <dbl> <lgl>
1 1 1 3 a 0.677 TRUE
2 2 2 2 b 0.631 FALSE
3 3 3 1 c 0.903 NA
df %>% unpack(y)
# A tibble: 3 × 4
x a b z$X $Y $Z
<int> <int> <int> <chr> <dbl> <lgl>
1 1 1 3 a 0.677 TRUE
2 2 2 2 b 0.631 FALSE
3 3 3 1 c 0.903 NA
df %>% unpack(c(y, z))
# A tibble: 3 × 6
x a b X Y Z
<int> <int> <int> <chr> <dbl> <lgl>
1 1 1 3 a 0.677 TRUE
2 2 2 2 b 0.631 FALSE
3 3 3 1 c 0.903 NA
df %>% unpack(c(y, z), names_sep = "_")
# A tibble: 3 × 6
x y_a y_b z_X z_Y z_Z
<int> <int> <int> <chr> <dbl> <lgl>
1 1 1 3 a 0.677 TRUE
2 2 2 2 b 0.631 FALSE
3 3 3 1 c 0.903 NA