#' Calculate similarity of each pair of groups.
#' @param df data frame in tidy format
#' @param subject A column you want to calculate the correlations for.
#' @param key A column you want to use as a dimension to calculate the correlations.
#' @param value A column for the values you want to use to calculate the correlations.
#' @param distinct The returned pair should be duplicated in swapped order or not.
#' TRUE makes it easy to filter group names.
#' @param diag If similarity between itself should be returned or not.
#' @param method Type of calculation. https://cran.r-project.org/web/packages/proxy/vignettes/overview.pdf
#' @param fun.aggregate Set an aggregate function when there are multiple entries for the key column per each category.
#' @export
do_cosine_sim.kv <- function(df, subject, key, value = NULL, distinct=FALSE, diag=FALSE, fun.aggregate=mean){
validate_empty_data(df)
loadNamespace("qlcMatrix")
loadNamespace("tidytext")
loadNamespace("Matrix")
loadNamespace("stringr")
subject_col <- col_name(substitute(subject))
key_col <- col_name(substitute(key))
value_col <- if(is.null(substitute(value))) NULL else col_name(substitute(value))
grouped_column <- grouped_by(df)
if(subject_col %in% grouped_column){
stop(paste0(subject_col, " is a grouping column. ungroup() may be necessary before this operation."))
}
# column names are "{subject}.x", "{subject}.y", "value"
cnames <- avoid_conflict(grouped_column,
c(paste0(subject_col, c(".x", ".y")), # We use paste0 since str_c garbles multibyte column names here for some reason.
"value")
)
# this is executed on each group
calc_doc_sim_each <- function(df){
mat <- sparse_cast(df, key_col, subject_col, val = value_col, fun.aggregate = fun.aggregate, count = TRUE)
sim <- qlcMatrix::cosSparse(mat)
# Most likely, because of Matrix upgraded to 1.5-3, output from qlcMatrix::cosSparse loses rownames/colnames.
# Set them back.
colnames(sim) <- colnames(mat)
rownames(sim) <- colnames(mat)
if(distinct){
if(!diag){
diag <- NULL
}
df <- upper_gather(sim, rownames(mat), diag=diag, cnames=cnames, na.rm = FALSE, zero.rm = FALSE)
}else{
loadNamespace("reshape2")
loadNamespace("dplyr")
df <- sim %>% as.matrix() %>% mat_to_df(cnames, na.rm = FALSE, zero.rm = FALSE)
if(!diag){
df <- df[df[,1] != df[,2],]
}
}
df
}
# Calculation is executed in each group.
# Storing the result in this tmp_col and
# unnesting the result.
# If the original data frame is grouped by "tmp",
# overwriting it should be avoided,
# so avoid_conflict is used here.
tmp_col <- avoid_conflict(grouped_column, "tmp")
df %>%
dplyr::do_(.dots=setNames(list(~calc_doc_sim_each(.)), tmp_col)) %>%
dplyr::ungroup() %>%
unnest_with_drop(!!rlang::sym(tmp_col))
}
#' integrated do_dist
#' @export
do_dist <- function(df, ..., skv = NULL, fun.aggregate=mean, fill=0){
validate_empty_data(df)
if (!is.null(skv)) {
#.kv pattern
if (!length(skv) %in% c(2, 3)) {
stop("length of skv has to be 2 or 3")
}
value <- if(length(skv) == 2) NULL else skv[[3]]
do_dist.kv_(df, skv[[1]], skv[[2]], value, fun.aggregate = fun.aggregate, fill = fill, ...)
} else {
#.cols pattern
do_dist.cols(df, ...)
}
}
#' Non Standard Evaluation version of do_dist
#' Calculate distance of each pair of groups
#' @export
do_dist.kv <- function(df, subject, key, value = NULL, ...){
subject_col <- col_name(substitute(subject))
key_col <- col_name(substitute(key))
if(!is.null(substitute(value))){
value_col <- col_name(substitute(value))
} else {
value_col <- NULL
}
do_dist.kv_(df, subject_col, key_col, value_col, ...)
}
#' Calculate distance of each pair of groups
#' @param df data frame in tidy format
#' @param group A column you want to calculate the correlations for.
#' @param dimension A column you want to use as a dimension to calculate the correlations.
#' @param value A column for the values you want to use to calculate the correlations.
#' @param distinct The returned pair should be duplicated in swapped order or not.
#' TRUE makes it easy to filter group names.
#' @param diag If similarity between itself should be returned or not.
#' @param method Type of calculation. https://cran.r-project.org/web/packages/proxy/vignettes/overview.pdf
#' @param p P parameter for "minkowski" method.
#' @param normalize Whether to normalize values for each key.
#' @param cmdscale_k Number of dimention to map the result.
#' @param time_unit Unit of time to aggregate key_col if key_col is Date or POSIXct#' @param time_unit Unit of time to aggregate key_col if key_col is Date or POSIXct. NULL doesn't aggregate.
#' @export
do_dist.kv_ <- function(df,
subject_col,
key_col,
value_col = NULL,
fill=0,
fun.aggregate=mean,
distinct=FALSE,
diag=FALSE,
method="euclidean",
p=2,
normalize=FALSE,
cmdscale_k = NULL,
time_unit = NULL){
validate_empty_data(df)
loadNamespace("dplyr")
loadNamespace("tidyr")
loadNamespace("reshape2")
loadNamespace("stats")
grouped_column <- grouped_by(df)
if(subject_col %in% grouped_column){
stop(paste0(subject_col, " is a grouping column. ungroup() may be necessary before this operation."))
}
# column names are "{subject}.x", "{subject}.y", "value"
cnames <- avoid_conflict(grouped_column,
c(paste0(subject_col, c(".x", ".y")), # We use paste0 since str_c garbles multibyte column names here for some reason.
"value")
)
# this is executed on each group
calc_dist_each <- function(df){
# t is used because dist calculates distances of rows
# but simple_cast is designed for subject to columns,
# so the matrix is transposed
mat <- df %>%
simple_cast(
key_col,
subject_col,
value_col,
fill=fill,
fun.aggregate=fun.aggregate,
time_unit = time_unit,
na.rm = TRUE
)
mat <- t(mat)
if (normalize) {
# normalize each key.
# where normalization should take place is debatable.
# we may want to do it outside of group_by to have uniform definition of distance across groups.
# on the other hand, a good definition of distance for a group might not work well for another group,
# in which case normalization per group might be better...
mat <- scale(mat)
}
# Dist is actually an atomic vector of upper half so upper and diag arguments don't matter
dist <- stats::dist(mat, method=method, diag=FALSE, p=p)
if(distinct){
if(diag){
diag <- 0
}else{
diag <- NULL
}
ret <- upper_gather(
as.vector(dist),
rownames(mat),
diag=diag,
cnames=cnames,
na.rm = FALSE,
zero.rm = FALSE
)
}else{
ret <- dist %>% as.matrix() %>% mat_to_df(cnames, na.rm = FALSE, zero.rm = FALSE)
if(!diag){
ret<- ret[ret[,1] != ret[,2],]
}
}
rownames(ret) <- NULL
if (!is.null(cmdscale_k)) {
ret <- do_cmdscale_(ret, cnames[[1]], cnames[[2]], cnames[[3]], k = cmdscale_k)
# the label for each point should be the subject
# so the column name should be the same
colnames(ret)[[1]] <- subject_col
}
ret
}
# Calculation is executed in each group.
# Storing the result in this tmp_col and
# unnesting the result.
# If the original data frame is grouped by "tmp",
# overwriting it should be avoided,
# so avoid_conflict is used here.
tmp_col <- avoid_conflict(grouped_column, "tmp")
df %>%
dplyr::do_(.dots=setNames(list(~calc_dist_each(.)), tmp_col)) %>%
dplyr::ungroup() %>%
unnest_with_drop(!!rlang::sym(tmp_col))
}
#' A symmetric version of KL-divergence
#' This is often used with topic model to calculate distances between topics
#' Ref: https://github.com/cpsievert/LDAvis/blob/master/R/createJSON.R
#' @export
do_kl_dist.kv_ <- function(df,
subject_col,
key_col,
value_col = NULL,
fill=0,
fun.aggregate=mean,
distinct=FALSE,
diag=FALSE,
method="euclidean",
p=2,
cmdscale_k = NULL){
validate_empty_data(df)
loadNamespace("dplyr")
loadNamespace("tidyr")
loadNamespace("reshape2")
loadNamespace("stats")
loadNamespace("proxy")
grouped_column <- grouped_by(df)
if(subject_col %in% grouped_column){
stop(paste0(subject_col, " is a grouping column. ungroup() may be necessary before this operation."))
}
# column names are "{subject}.x", "{subject}.y", "value"
cnames <- avoid_conflict(grouped_column,
c(paste0(subject_col, c(".x", ".y")), # We use paste0 since str_c garbles multibyte column names here for some reason.
"value")
)
# this is executed on each group
calc_dist_each <- function(df){
mat <- df %>% simple_cast(
subject_col,
key_col,
value_col,
fill=fill,
fun.aggregate=fun.aggregate,
na.rm = TRUE
)
# Dist is actually an atomic vector of upper half so upper and diag arguments don't matter
jensenShannon <- function(x, y) {
m <- 0.5*(x + y)
0.5*sum(x*log(x/m)) + 0.5*sum(y*log(y/m))
}
dist <- proxy::dist(x = mat, method = jensenShannon)
if(distinct){
if(diag){
diag <- 0
}else{
diag <- NULL
}
ret <- upper_gather(
as.vector(dist),
rownames(mat),
diag=diag,
cnames=cnames,
na.rm = FALSE,
zero.rm = FALSE
)
}else{
ret <- dist %>% as.matrix() %>% mat_to_df(cnames)
if(!diag){
ret<- ret[ret[,1] != ret[,2],]
}
}
rownames(ret) <- NULL
if (!is.null(cmdscale_k)) {
ret <- do_cmdscale_(ret, cnames[[1]], cnames[[2]], cnames[[3]], k = cmdscale_k)
}
ret
}
df %>%
dplyr::do_(.dots=setNames(list(~calc_dist_each(.)), cnames[[1]])) %>%
dplyr::ungroup() %>%
unnest_with_drop(!!rlang::sym(cnames[[1]]))
}
#' Calculate distance of each pair of groups.
#' @param df data frame in tidy format
#' @param group A column you want to calculate the correlations for.
#' @param dimension A column you want to use as a dimension to calculate the correlations.
#' @param value A column for the values you want to use to calculate the correlations.
#' @param distinct The returned pair should be duplicated in swapped order or not.
#' TRUE makes it easy to filter group names.
#' @param diag If similarity between itself should be returned or not.
#' @param method Type of calculation. https://cran.r-project.org/web/packages/proxy/vignettes/overview.pdf
#' @param p The power of the Minkowski distance.
#' @export
do_dist.cols <- function(df,
...,
label=NULL,
fill=0,
fun.aggregate=mean,
distinct=FALSE,
diag=FALSE,
method="euclidean",
p=2,
cmdscale_k = NULL){
validate_empty_data(df)
loadNamespace("dplyr")
loadNamespace("tidyr")
loadNamespace("reshape2")
loadNamespace("stats")
loadNamespace("lazyeval")
grouped_column <- grouped_by(df)
label_col <- col_name(substitute(label))
# using the new way of NSE column selection evaluation
# ref: https://github.com/tidyverse/tidyr/blob/3b0f946d507f53afb86ea625149bbee3a00c83f6/R/spread.R
select_dots <- tidyselect::vars_select(names(df), !!! rlang::quos(...))
cnames <- avoid_conflict(grouped_column, c("pair.name.x", "pair.name.y", "value"))
# this is executed on each group
calc_dist_each <- function(df){
mat <- df %>% dplyr::select(!!!select_dots) %>% as.matrix()
# sort the column name so that the output of pair.name.1 and pair.name.2 will be sorted
# it's better to be sorted so that heatmap in exploratory can be triangle if distinct is TRUE
# We use stringr::str_sort() as opposed to base sort() so that the result is consistent on Windows too.
sortedNames <- stringr::str_sort(colnames(mat))
mat <- t(mat)
mat <- mat[sortedNames, ]
# Dist is actually an atomic vector of upper half so upper and diag arguments don't matter
dist <- stats::dist(mat, method=method, diag=FALSE, p=p)
if(distinct){
if(diag){
diag <- 0
}else{
diag <- NULL
}
ret <- upper_gather(
as.vector(dist),
rownames(mat),
diag=diag,
cnames=cnames,
na.rm = FALSE,
zero.rm = FALSE
)
}else{
ret <- dist %>% as.matrix() %>% mat_to_df(
cnames,
na.rm = FALSE,
zero.rm = FALSE
)
if(!diag){
ret <- ret[ret[,1] != ret[,2],]
}
}
rownames(ret) <- NULL
if (!is.null(cmdscale_k)) {
ret <- do_cmdscale_(ret, cnames[[1]], cnames[[2]], cnames[[3]], k = cmdscale_k)
}
ret
}
df %>%
dplyr::do_(.dots=setNames(list(~calc_dist_each(.)), cnames[[1]])) %>%
dplyr::ungroup() %>%
unnest_with_drop(!!rlang::sym(cnames[[1]]))
}
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