#' @title Time series plot of document frequency
#' @description Produces a time series plot of the frequency of documents within a
#' corpus by publication date.
#' @details This function first identifies dates listed in the date column. It
#' then sums the number of documents associated with each identified calendar
#' date and generates a line plot for visual representation of these findings.
#' This output is useful in highlighting increases in document production,
#' which may identify blocks of time in which interesting events may have
#' occurred. Using the 'iso.date' function, these observed increases can be
#' converted into subset corpuses used for further analysis.
#' @param data.td A tidy dataset
#' @return Time series line plot of documents in \code{data.td} by day
#' @export
#' @import dplyr ggplot2 scales rlang
#################Corp Plot###########################
corp.plot <- function(data.td){
`%>%` <- dplyr::`%>%`
#Error checking performs check of data class
if(as.logical(sum(class(data.td) %in% c("tbl_df","tbl","data.frame")==0))) stop('Data is not in the correct form Data must be in a tibble or data frame')
daterng <-as.Date(c(min(data.td$date),max(data.td$date)))
data.td %>%
mutate(date=as.Date(date)) %>%
group_by(date) %>%
summarise(n = n()) %>%
ggplot(aes(x=date, y=n))+
geom_line()+
geom_point()+
scale_x_date(breaks= date_breaks(width= "1 day"),
labels = date_format("%b-%d-%y"),
limits = c(daterng[1], daterng[2]))+
theme(axis.text.x=element_text(size=6,angle=90))+
ggtitle(paste0("Frequency of Documents per Day"))+
labs(x = "dates", y = "frequency")
}
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