R/GtoP_trans.R

In TSplotly: Create Interactive Plots on Time Series Dataset

#' @title 'GtoP_trans'
#' @description a dataset transformation method, change \code{ggplot2} dataset into dataset that can be used by \code{plot_ly}
#'
#' @param dataframe a data frame data that can be used on \code{ggplot2}
#' @param NAME column that will be used on creating different lines on \code{plot_ly}
#' @param X column that will serve as the x axis on \code{plot_ly}
#' @param Y column that will serve as the value(y) on \code{plot_ly}
#'
#' @details
#' The function \code{GtoP_trans} is used to conduct a data transformation. Usually dataset working on ggplot2 is a kind of
#' dataset that has long rows and short columns. And we may wish to create plot based on different levels for a particular column.
#' However, this dataset won't work for \code{plot_ly} as it requires each level as a column. So this method will separate
#' different levels in a column to create a new dataset with multiple columns representing the original levels.
#'
#' @author SOCR team <\url{http://socr.umich.edu/people/}>
#'
#' @return a data frame with each column serve as a line in \code{plot_ly}
#' @export
#'
#' @import plotly zoo
#' @importFrom stats time
#' @importFrom ggplot2 ggplot geom_line geom_point geom_smooth coord_trans xlab ylab scale_x_date theme
#'
#' @examples
#' require(ggplot2)
#' require(plotly)
#' require(zoo)
#' # Dataset in this example. We wish to generate plot with different lines based on variable "series".
#' # However, this dataset now can only be used on ggplot2
#' head(MCSI_Data_monthAvg_melt)
#'
#' # A ggplot2 example here:
#' ggplot(MCSI_Data_monthAvg_melt[MCSI_Data_monthAvg_melt$series!="INCOME", ],
#'        aes(YYYYMM, value)) +
#'   geom_line(aes(linetype=series, colour = series), size=2) +
#'   geom_point(aes(shape=series, colour = series), size=0.3) +
#'   geom_smooth(aes(colour = series), se = TRUE) +
#'   coord_trans(y="log10") +
#'   xlab("Time (monthly)") + ylab("Index Values (log-scale)") +
#'   scale_x_date(date_breaks = "12 month", date_labels =  "%m-%Y")  +
#'   theme(axis.text.x = element_text(angle = 45, hjust = 1),
#'         text = element_text(size=20))+ theme(legend.position="top")
#'
#' # Change the dataset with our function
#' PYdf<-GtoP_trans(MCSI_Data_monthAvg_melt[MCSI_Data_monthAvg_melt$series!="INCOME", ],
#' NAME="series",X="YYYYMM",Y="value")
#' PYdf$INCOME<-NULL
#' head(PYdf)
#'
#' #Log transformation
#' PYdf<-log10(PYdf)
#'
#' # Generate a plot_ly result
#' ## Note that we've done a log transformation on y-axis
#' plot_ly(type="scatter",mode="lines")%>%
#'   add_lines(x=as.yearmon(rownames(PYdf)),text=rownames(PYdf),
#'   y=PYdf$ICS,name="ICS",line=list(color="powderblue"))%>%
#'   add_lines(x=as.yearmon(rownames(PYdf)),text=rownames(PYdf),
#'   y=PYdf$ICC,name="ICC",line=list(color="red"))%>%
#'   add_lines(x=as.yearmon(rownames(PYdf)),text=rownames(PYdf),
#'   y=PYdf$GOVT,name="GOVT",line=list(color="green"))%>%
#'   add_lines(x=as.yearmon(rownames(PYdf)),text=rownames(PYdf),
#'   y=PYdf$DUR,name="DUR",line=list(color="orange"))%>%
#'   add_lines(x=as.yearmon(rownames(PYdf)),text=rownames(PYdf),
#'   y=PYdf$HOM,name="HOM",line=list(color="purple"))%>%
#'   add_lines(x=as.yearmon(rownames(PYdf)),text=rownames(PYdf),
#'   y=PYdf$CAR,name="CAR",line=list(color="pink"))%>%
#'   add_lines(x=as.yearmon(rownames(PYdf)),text=rownames(PYdf),
#'   y=PYdf$AGE,name="AGE",line=list(color="brown"))%>%
#'   add_lines(x=as.yearmon(rownames(PYdf)),text=rownames(PYdf),
#'   y=PYdf$EDUC,name="EDUC",line=list(color="black"))%>%
#'   layout(title= list(text="Time series for 8 indexes",
#'   font=list(family = "Times New Roman",size = 16,color = "black" )),
#'          paper_bgcolor='rgb(255,255,255)', plot_bgcolor='rgb(229,229,229)',
#'          xaxis = list(title ="Time (monthly)",
#'                       gridcolor = 'rgb(255,255,255)',
#'                       showgrid = TRUE,
#'                       showline = FALSE,
#'                       showticklabels = TRUE,
#'                       tickcolor = 'rgb(127,127,127)',
#'                       ticks = 'outside',
#'                       zeroline = FALSE),
#'          yaxis = list(title = "Index Values (log-scale)",
#'                       gridcolor = 'rgb(255,255,255)',
#'                       showgrid = TRUE,
#'                       showline = FALSE,
#'                       showticklabels = TRUE,
#'                       tickcolor = 'rgb(127,127,127)',
#'                       ticks = 'outside',
#'                       zeroline = FALSE))

GtoP_trans <- function(dataframe,NAME=NULL,X=NULL,Y=NULL) {
  levelnames<-levels(dataframe[,which(colnames(dataframe)==NAME)])
  NAmatrix<-matrix(NA,nrow =nrow(dataframe[dataframe[,which(colnames(dataframe)==NAME)]==levelnames[1],]) ,ncol = length(levelnames))
  NAmatrix<-as.data.frame(NAmatrix)
  colnames(NAmatrix)<-levelnames
  rownames(NAmatrix)<-dataframe[dataframe[,which(colnames(dataframe)==NAME)]==levelnames[1],][,which(colnames(dataframe)==X)]
  for (i in 1:length(levelnames)) {
    NAmatrix[,i]<-dataframe[dataframe[,which(colnames(dataframe)==NAME)]==levelnames[i],][,which(colnames(dataframe)==Y)]
  }
  return(NAmatrix)
}