Right now you have two age values for Female and three for Male, and no other variables keeping them from being collapsed into a single row, as spread tries to do with values with similar/no index values:
library(tidyverse)
df <- data_frame(x = c('a', 'b'), y = 1:2)
df # 2 rows...
#> # A tibble: 2 x 2
#> x y
#> <chr> <int>
#> 1 a 1
#> 2 b 2
df %>% spread(x, y) # ...become one if there's only one value for each.
#> # A tibble: 1 x 2
#> a b
#> * <int> <int>
#> 1 1 2
spread doesn’t apply a function to combine multiple values (à la dcast), so rows must be indexed so there’s one or zero values for a location, e.g.
df <- data_frame(i = c(1, 1, 2, 2, 3, 3),
x = c('a', 'b', 'a', 'b', 'a', 'b'),
y = 1:6)
df # the two rows with each `i` value here...
#> # A tibble: 6 x 3
#> i x y
#> <dbl> <chr> <int>
#> 1 1 a 1
#> 2 1 b 2
#> 3 2 a 3
#> 4 2 b 4
#> 5 3 a 5
#> 6 3 b 6
df %>% spread(x, y) # ...become one row here.
#> # A tibble: 3 x 3
#> i a b
#> * <dbl> <int> <int>
#> 1 1 1 2
#> 2 2 3 4
#> 3 3 5 6
If you your values aren’t indexed naturally by the other columns you can add a unique index column (e.g. by adding the row numbers as a column) which will stop spread from trying to collapse the rows:
df <- structure(list(age = c("21", "17", "32", "29", "15"),
gender = structure(c(2L, 1L, 1L, 2L, 2L),
.Label = c("Female", "Male"), class = "factor")),
row.names = c(NA, -5L),
class = c("tbl_df", "tbl", "data.frame"),
.Names = c("age", "gender"))
df %>% mutate(i = row_number()) %>% spread(gender, age)
#> # A tibble: 5 x 3
#> i Female Male
#> * <int> <chr> <chr>
#> 1 1 <NA> 21
#> 2 2 17 <NA>
#> 3 3 32 <NA>
#> 4 4 <NA> 29
#> 5 5 <NA> 15
If you want to remove it afterwards, add on select(-i). This doesn’t produce a terribly useful data.frame in this case, but can be very useful in the midst of more complicated reshaping.