我想创建一个dplyr::bind_rows
的替代版本,当我们尝试合并的dfs中存在因子列时,它会避免出现Unequal factor levels: coercing to character
警告(它也可能具有非因子列)。这是一个例子:
df1 <- dplyr::data_frame(age = 1:3, gender = factor(c("male", "female", "female")), district = factor(c("north", "south", "west")))
df2 <- dplyr::data_frame(age = 4:6, gender = factor(c("male", "neutral", "neutral")), district = factor(c("central", "north", "east")))
然后
bind_rows_with_factor_columns(df1, df2)
返回(无警告):dplyr::data_frame(
age = 1:6,
gender = factor(c("male", "female", "female", "male", "neutral", "neutral")),
district = factor(c("north", "south", "west", "central", "north", "east"))
)
这是我到目前为止的内容:
bind_rows_with_factor_columns <- function(...) {
factor_columns <- purrr::map(..., function(df) {
colnames(dplyr::select_if(df, is.factor))
})
if (length(unique(factor_columns)) > 1) {
stop("All factor columns in dfs must have the same column names")
}
df_list <- purrr::map(..., function (df) {
purrr::map_if(df, is.factor, as.character) %>% dplyr::as_data_frame()
})
dplyr::bind_rows(df_list) %>%
purrr::map_at(factor_columns[[1]], as.factor) %>%
dplyr::as_data_frame()
}
我想知道是否有人对如何合并
forcats
包有任何想法,从而有可能避免不得不将字符强制转换为字符,或者有人是否总体上有任何建议来提高其性能,同时保持相同的功能(我想d坚持使用tidyverse
语法)。谢谢! 最佳答案
根据朋友的出色解决方案回答我自己的问题:
bind_rows_with_factor_columns <- function(...) {
purrr::pmap_df(list(...), function(...) {
cols_to_bind <- list(...)
if (all(purrr::map_lgl(cols_to_bind, is.factor))) {
forcats::fct_c(cols_to_bind)
} else {
unlist(cols_to_bind)
}
})
}