Understanding tidyr::gather key/value arguments - r

Question

Simple question,
I've provided two different data frames below with code/output, why does one work and the other doesn't? Having trouble understanding the Key/Value inputs (when they need to be explicitly defined/and what it means to just have them as strings in the input).
library(tidyverse)
dat <- data.frame(one = c("x", "x", "x"), two = c("x", "", "x"),
three = c("", "", ""), type = c("chocolate", "vanilla", "strawberry"))
dat %>%
na_if("") %>%
gather("Key", "Val", -type,na.rm=TRUE) %>%
rowid_to_column %>%
spread(Key, Val,fill = "") %>%
select(-1) # works well
dat %>%
na_if("") %>%
gather("Key", "Val", -type,na.rm=TRUE)
Error: Strings must match column names. Unknown columns: Val
Extra Credit: if someone could explain the effect of rowit_to_column & spread(), that'd be helpful.

Answer 1

Perhaps I'm missing something, but I can't reproduce your error.
dat %>%
na_if("") %>% # Replace "" with NA
gather("Key", "Val", -type, na.rm = TRUE) %>% # wide -> long
rowid_to_column() %>% # Sequentially number rows
spread(Key, Val, fill = "") %>% # long -> wide
select(-1) # works well # remove row number
# type one two
#1 chocolate x
#2 vanilla x
#3 strawberry x
#4 chocolate x
#5 strawberry x
dat %>%
na_if("") %>% # Replace "" with NA
gather("Key", "Val", -type, na.rm = TRUE) # wide -> long
# type Key Val
#1 chocolate one x
#2 vanilla one x
#3 strawberry one x
#4 chocolate two x
#6 strawberry two x
Explanation:
na_if("") replaces "" entries with NA.
gather("Key", "Val", -type, na.rm = TRUE) turns a wide table into a long "key-value" table, by storing entries in all columns except type in two columns Key (i.e. the column name) and Val (i.e. the entry). na.rm = TRUE removes rows with NA values.
rowid_to_column sequentially numbers the rows.
spread(Key, Val, fill = "") turns a long "key-value" table into a wide table, with as many columns as there are unique keys in Key. Entries are taken from column Val, if an entry is missing it's filled with "".
select(-1) removes the first column.