new generic function updated.

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+ "contents" : "hyfo 1.3.0\n==========\nDate: 2015.11.2\n\n- new generic function biasCorrect, extractPeriod, resample added, \n No need to designate inputtype any more, R will detect automatically.\n- new user manual added.\n\n\n\nhyfo 1.2.9\n==========\nDate: 2015.10.30\n\n- new biasFactor S4 class added, to avoid set the input type every time.\n- operational bias correction has been changed to generic function.\n- news file added.\n\n\n\nhyfo 1.2.8\n==========\nDate: 2015.10.10\n\n- operational bias correction added, in normal function.",
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+ "contents" : "#' plot time series, with marks on missing value.\n#' \n#' @param ... input time series.\n#' @param type A string representing the type of the time series, e.g. 'line' or 'bar'.\n#' @param output A string showing which type of output you want. Default is \"data\", if \"ggplot\", the \n#' data that can be directly plotted by ggplot2 will be returned, which is easier for you to make series\n#' plots afterwards. \n#' @param name If \\code{output = 'ggplot'}, name has to be assigned to your output, in order to differentiate\n#' different outputs in the later multiplot using \\code{plotTS_comb}.\n#' @param plot representing the plot type, there are two types, \"norm\" and \"cum\", \"norm\" gives an normal\n#' plot, and \"cum\" gives a cumulative plot. Default is \"norm\".\n#' @param x label for x axis.\n#' @param y label for y axis.\n#' @param title plot title.\n#' @param list If your input is a list of time series, then use \\code{list = your time sereis list}\n#' @return A plot of the input time series.\n#' @details \n#' If your input has more than one time series, the program will only plot the common period of \n#' different time series.\n#' @examples\n#' plotTS(testdl[[1]])\n#' plotTS(testdl[[1]], x = 'xxx', y = 'yyy', title = 'aaa')\n#' \n#' # If input is a datalist\n#' plotTS(list = testdl)\n#' \n#' # Or if you want to input time series one by one\n#' # If plot = 'cum' then cumulative curve will be plotted.\n#' plotTS(testdl[[1]], testdl[[2]], plot = 'cum')\n#' \n#' # You can also directly plot multicolumn dataframe\n#' dataframe <- list2Dataframe(extractPeriod(testdl, commonPeriod = TRUE))\n#' plotTS(dataframe, plot = 'cum')\n#' \n#' # Sometimes you may want to process the dataframe and compare with the original one\n#' dataframe1 <- dataframe\n#' dataframe1[, 2:4] <- dataframe1[, 2:4] + 3\n#' plotTS(dataframe, dataframe1, plot = 'cum')\n#' # But note, if your input is a multi column dataframe, it's better to plot one using plotTS,\n#' # and compare them using plotTS_comb. If all data are in one plot, there might be too messy.\n#' \n#' \n#' # More examples can be found in the user manual on http:https://yuanchao-xu.github.io/hyfo/\n#' \n#' @references \n#' \\itemize{\n#' \\item H. Wickham. ggplot2: elegant graphics for data analysis. Springer New York, 2009.\n#' }\n#' \n#' @import ggplot2\n#' @importFrom reshape2 melt\n#' @export\nplotTS <- function(..., type = 'line', output = 'data', plot = 'norm', name = NULL, x = NULL, \n y = NULL, title = NULL, list = NULL) {\n ## arrange input TS or TS list.\n if (is.null(list)) {\n list <- list(...)\n if (!class(list[[1]]) == 'data.frame') {\n warning('Your input is probably a list, but you forget to add \"list = \" before it.\n Try again, or check help for more information.')\n }\n# Following part is for plot different time series with different date, but too complicated\n# using ggplot. and normal use doesn't need such process. So save it as backup.\n# listNames <- names(list)\n# # in order to be used later to differentiate lists, there should be a name for each element.\n# # Then assign the name column to each list element.\n# if (is.null(listNames)) listNames <- 1:length(list)\n# \n# giveName <- function(x, y) {\n# colnames(x) <- NULL\n# x$TSname <- rep(listNames[y], nrow(x))\n# return(x)\n# }\n# list1 <- mapply(FUN = giveName, x = list, y = 1:length(list), SIMPLIFY = FALSE)\n# \n# checkBind(list1, 'rbind')\n# \n# TS <- do.call('rbind', list1)\n }\n \n list_common <- extractPeriod(list, commonPeriod = TRUE)\n TS <- list2Dataframe(list_common)\n \n if (!is.null(names(list)) & (ncol(TS) - 1) == length(list)) colnames(TS)[2:(length(list) + 1)] <- names(list)\n \n # Check input, only check the first column and first row.\n if (!grepl('-|/', TS[1, 1])) {\n stop('First column is not date or Wrong Date formate, check the format in ?as.Date{base} \n and use as.Date to convert.')\n }\n \n TS[, 1] <- as.Date(TS[, 1])\n colnames(TS)[1] <- 'Date'\n \n # first column's name may not be Date, so change its name to Date\n \n data_plot <- melt(TS, id.var = 'Date')\n NAIndex <- which(is.na(data_plot$value))\n \n # assign 0 to NA values\n if (plot == 'norm') {\n data_plot$value[NAIndex] <- 0\n lineSize <- 0.7\n } else if (plot == 'cum') {\n TS[is.na(TS)] <- 0\n cum <- cbind(data.frame(Date = TS[, 1]), cumsum(TS[2:ncol(TS)]))\n \n data_plot <- melt(cum, id.var = 'Date')\n lineSize <- 1\n }\n \n \n # Assigning x, y and title\n if (is.null(x)) x <- colnames(TS)[1]\n # y aixs cannot decide if it's a multi column dataframe\n #if (is.null(y)) y <- names[2]\n \n theme_set(theme_bw())\n mainLayer <- with(data_plot, {\n ggplot(data = data_plot) +\n # It's always better to use colname to refer to\n aes(x = Date, y = value, color = variable) +\n theme(plot.title = element_text(size = rel(1.8), face = 'bold'),\n axis.text.x = element_text(size = rel(1.8)),\n axis.text.y = element_text(size = rel(1.8)),\n axis.title.x = element_text(size = rel(1.8)),\n axis.title.y = element_text(size = rel(1.8))) +\n labs(x = x, y = y, title = title)\n })\n \n \n# color <- 'dodgerblue4'\n if (type == 'bar') {\n secondLayer <- with(data_plot, {\n geom_bar(stat = 'identity')\n })\n } else if (type == 'line') {\n secondLayer <- with(data_plot, {\n geom_line(size = lineSize)\n })\n } else {\n stop(\"No such plot type.\")\n }\n \n \n missingVLayer <- with(TS, {\n geom_point(data = data_plot[NAIndex, ], group = 1, size = 3, shape = 4, color = 'black')\n })\n \n plotLayer <- mainLayer + secondLayer + missingVLayer\n \n print(plotLayer) \n \n if (output == 'ggplot') {\n if (is.null(name)) stop('\"name\" argument not found, \n If you choose \"ggplot\" as output, please assign a name.')\n \n data_plot$name <- rep(name, nrow(data_plot)) \n data_plot$nav <- rep(0, nrow(data_plot))\n data_plot$nav[NAIndex] <- 1\n return(data_plot)\n }\n}\n\n\n\n\n#' Combine time seires plot together\n#' @param ... different time series plots generated by \\code{plotTS(, output = 'ggplot')}, refer to details.\n#' @details\n#' ..., representing different ouput file generated by \\code{plotTS(, output = 'ggplot'), name = yourname}, \n#' different names must be assigned when generating different output.\n#' \n#' e.g.\n#' a1, a2, a3 are different files generated by \\code{plotTS(, output = 'ggplot'), name = yourname}, you can\n#' set \\code{plotTS(a1,a2,a3)} or \\code{plotTS(list = list(a1,a2,a3))}\n#' \n#' @param nrow A number showing the number of rows.\n#' @param type A string showing 'line' or 'bar'.\n#' @param list If input is a list containing different ggplot data, use l\\code{list = inputlist}.\n#' @param x A string of x axis name.\n#' @param y A string of y axis name.\n#' @param title A string of the title.\n#' @param output A boolean, if chosen TRUE, the output will be given.\n#' NOTE: yOU HAVE TO PUT A \\code{list = }, before your list.\n#' @return A combined time series plot.\n#' @examples\n#' a1 <- plotTS(testdl[[1]], output = 'ggplot', name = 1)\n#' a2 <- plotTS(testdl[[2]], output = 'ggplot', name = 2)\n#' \n#' plotTS_comb(a1, a2)\n#' plotTS_comb(list = list(a1, a2), y = 'y axis', nrow = 2)\n#' \n#' # More examples can be found in the user manual on http:https://yuanchao-xu.github.io/hyfo/\n#' \n#' @references \n#' \\itemize{\n#' \\item H. Wickham. ggplot2: elegant graphics for data analysis. Springer New York, 2009.\n#' }\n#' @export\n#' @import ggplot2\nplotTS_comb <- function(..., nrow = 1, type = 'line', list = NULL, x = 'Date', y = '', title = '', \n output = FALSE){\n # In ploting the time series, since the data comes from outside of hyfo, \n # It's more complicated, since they don't always have the same\n # column name, if not, there is not possible to do rbind.\n # So we need to first save the name, and rbind, and put back the name.\n \n if (!is.null(list)) {\n checkBind(list, 'rbind')\n data_ggplot <- do.call('rbind', list)\n } else {\n \n bars <- list(...)\n checkBind(bars, 'rbind')\n data_ggplot <- do.call('rbind', bars)\n }\n \n if (!class(data_ggplot) == 'data.frame') {\n warning('Your input is probably a list, but you forget to add \"list = \" before it.\n Try again, or check help for more information.')\n } else if (is.null(data_ggplot$name)) {\n stop('No \"name\" column in the input data, check the arguments in getPreciBar(), if \n output = \"ggplot\" is assigned, more info please check ?getPreciBar.')\n }\n\n \n theme_set(theme_bw())\n mainLayer <- with(data_ggplot, {\n ggplot(data = data_ggplot) +\n # It's always better to use colname to refer to\n aes(x = Date, y = value, color = variable) +\n theme(plot.title = element_text(size = rel(1.8), face = 'bold'),\n axis.text.x = element_text(angle = 90, hjust = 1, size = rel(1.8)),\n axis.text.y = element_text(size = rel(1.8)),\n axis.title.x = element_text(size = rel(1.8)),\n axis.title.y = element_text(size = rel(1.8))) +\n geom_point(data = data_ggplot[data_ggplot$nav == 1, ], size = 2, shape = 4, color = 'red') +\n facet_wrap( ~ name, nrow = nrow) +\n labs(x = x, y = y, title = title)\n \n })\n \n \n if (type == 'bar') {\n secondLayer <- with(data_ggplot, {\n geom_bar(stat = 'identity', size = 1)\n })\n } else if (type == 'line') {\n secondLayer <- with(data_ggplot, {\n geom_line(size = 1)\n })\n } else {\n stop(\"No such plot type.\")\n }\n \n print(mainLayer + secondLayer)\n \n if (output == TRUE) return(data_ggplot)\n}\n\n\n\n\n#' get L moment analysis of the input distribution\n#' \n#' @param dis A distribution, for hydrology usually a time series with only data column without time.\n#' @return The mean, L-variation, L-skewness and L-kurtosis of the input distribution\n#' @examples\n#' dis <- seq(1, 100)\n#' getLMom(dis)\n#' \n#' # More examples can be found in the user manual on http:https://yuanchao-xu.github.io/hyfo/\n#' \n#' @export\n#' @references \n#' \n#' \\itemize{\n#' \\item J. R. M. Hosking (2015). L-moments. R package, version 2.5. URL:\n#' http:https://CRAN.R-project.org/package=lmom.\n#' }\n#' \n#' \n#' @importFrom lmom samlmu\n#' \ngetLMom <- function(dis){\n \n LMom <- samlmu(dis, nmom = 4, ratios = TRUE)\n \n mean <- LMom[1]\n LCV <- LMom[2]/LMom[1]\n Lskew <- LMom[3]\n Lkur <- LMom[4]\n \n output <- data.frame(mean = mean, Lcv = LCV, Lskew = Lskew, Lkur = Lkur)\n return(output)\n}\n\n#' get moment analysis of the input distribution\n#' \n#' @param dis A distribution, for hydrology usually a time series with only data column without time.\n#' @return The mean, variation, skewness and kurtosis of the input distribution\n#' @examples\n#' dis <- seq(1, 100)\n#' getMoment(dis)\n#' \n#' # More examples can be found in the user manual on http:https://yuanchao-xu.github.io/hyfo/\n#' \n#' @export\n#' @references \n#' \n#' \\itemize{\n#' \\item Lukasz Komsta and Frederick Novomestky (2015). moments: Moments, cumulants, skewness, kurtosis and\n#' related tests. R package version 0.14. http:https://CRAN.R-project.org/package=moments\n#' \n#' \\item R Core Team (2015). R: A language and environment for statistical computing. R Foundation for\n#' Statistical Computing, Vienna, Austria. URL http:https://www.R-project.org/.\n#' }\n#' \n#' @importFrom moments skewness kurtosis\n#' @importFrom stats var\ngetMoment <- function(dis) {\n mean <- mean(dis, na.rm = TRUE)\n variance <- var(dis, na.rm = TRUE)\n skewness <- skewness(dis, na.rm = TRUE)\n kurtosis <- kurtosis(dis, na.rm = TRUE)\n \n output <- data.frame(mean=mean, Variance = variance, Skewness = skewness, Kurtosis = kurtosis)\n \n return(output)\n}\n",
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