plotting.R

# plotting functions for naniar


#' @importFrom visdat vis_miss
#' @export
visdat::vis_miss

#' Plot the number of missings per case (row)
#'
#' This function draws a ggplot of the number of missings in each row.
#' The plot is a ggplot object with a basic customisation.
#' You can customise it the way you wish, just like classic ggplot.
#'
#' @param x a dataframe
#'
#' @return a ggplot object depicting the number of missings in a given case.
#' @export
#'
#' @examples
#'
#' gg_miss_case(airquality)
#' library(ggplot2)
#' gg_miss_case(airquality) + labs(x = "Number of Cases")
#' gg_miss_case(airquality) + theme_linedraw()
#'
gg_miss_case <- function(x){

  ggobject <- ggplot(data = miss_case_summary(x),
         aes(y = n_missing,
             x = case)) +
    geom_bar(stat="identity", position="dodge", width = 0, colour="grey") +
    geom_point() +
    coord_flip() +
    labs(y = "# Missing",
         x = "Cases") +
    theme_minimal()
  return(ggobject)
}

#' Plot the number of missings for each variable
#'
#' This function draws a ggplot plot of the number of missings in each column, rearranged to show which variables have the most missing data.
#' The plot is a ggplot object with a basic customisation.
#' You can customise it the way you wish, just like classic ggplot.
#'
#' @param x a dataframe
#'
#' @return a ggplot object depicting the number of missings in a given column
#' @export
#'
#' @examples
#'
#' gg_miss_var(airquality)
#' library(ggplot2)
#' gg_miss_var(airquality) + theme_bw()
#' gg_miss_var(airquality) + labs(y = "Look at all the missing ones")
#'
gg_miss_var <- function(x){

  # get a tidy data frame of the number of missings in each column
  ggobject <- x %>%
    miss_var_summary() %>%
    ggplot(data = .,
           aes(x = stats::reorder(variable, n_missing),
               y = n_missing,
               colour = variable)) +
    geom_bar(stat="identity", position="dodge", width = 0) +
    geom_point() +
    coord_flip() +
    scale_color_discrete(guide = FALSE) +
    labs(y = "# Missing",
         x = "Variables") +
    theme_minimal()
  return(ggobject)
}

#' Plot which variables contain a missing value
#'
#' `gg_miss_which` (need a better name!) produces a set of rectangles that indicate whether there is a missing element in a column or not
#' The plot is a ggplot object with a basic customisation.
#' You can customise it the way you wish, just like classic ggplot.
#'
#' @param x a dataframe
#'
#' @return a ggplot object
#'
#' @export
#'
#' @examples
#'
#' gg_miss_which(airquality)
#' library(ggplot2)
#' gg_miss_which(airquality) + theme(panel.background = element_rect(fill = "grey"))
#'

gg_miss_which <- function(x){

  # tell us which columns have missing data
  # airquality %>%
  ggobject <- x %>%
    purrr::map_df(anyNA) %>%
    purrr::map_df(function(x) ifelse(x == 0, "complete", "missing")) %>%
    tidyr::gather(key = "variable",
                  value = "value") %>%
    dplyr::mutate(nheight = 1) %>%
    ggplot(data = .,
           aes(x = variable,
               y = nheight,
               fill = factor(value))) +
    geom_tile(colour = "white") +
    theme_minimal() +
    scale_fill_grey(name = "") +
    scale_x_discrete(limits = names(x)) +
    theme(legend.position = "none") +
    scale_y_discrete(breaks=c(""),
                     labels=c("")) +
    labs(y = " ",
         x = " ")
}

#' Plot the number of missings for each variable, broken down by a factor
#'
#' This function draws a ggplot plot of the number of missings in each column,
#' broken down by a categorical variable from the dataset.
#' The plot is a ggplot object with a basic customisation.
#' You can customise it the way you wish, just like classic ggplot.
#'
#' @param x dataframe
#' @param fct column containing the factor variable
#'
#' @return ggplot object depicting the number of missings
#' @export
#'
#' @examples
#'
#' gg_miss_fct(x = riskfactors, fct = marital)
#' library(ggplot2)
#' gg_miss_fct(x = riskfactors, fct = marital) + theme_bw()
#' gg_miss_fct(x = riskfactors, fct = marital) + labs(title = "NA in Risk Factors and Marital status")
#'

gg_miss_fct <- function(x, fct){

  enquo_fct <- rlang::enquo(fct)

  ggobject <- x %>%
    dplyr::group_by(!!enquo_fct) %>%
    dplyr::do(miss_var_summary(.)) %>%
    ggplot(aes_string(x = quo_name(enquo_fct),
                      y = "variable",
                      fill = "percent")) +
    geom_tile() +
    viridis::scale_fill_viridis()

  return(ggobject)
}

#' Plot the proportion of missings in a given repeating span
#'
#' `gg_miss_span` is a replacement function to
#' `imputeTS::plotNA.distributionBar(tsNH4, breaksize = 100)``, which shows the
#' number of missings in a given span, or breaksize. The produced plot is a
#' ggplot object which you can customise the way you  wish, just like classic
#' ggplot.
#'
#' @param data data.frame
#' @param var a bare unquoted variable name from the data.frame
#' @param span_every integer describing the length of the span to be explored
#'
#' @return ggplot2 object
#' @export
#'
#' @examples
#'
#' miss_var_span(pedestrian, hourly_counts, span_every = 3000)
#' library(ggplot2)
#' gg_miss_span(pedestrian, hourly_counts, span_every = 3000)
#' # works with the rest of ggplot
#' gg_miss_span(pedestrian, hourly_counts, span_every = 3000) + labs(x = "custom")
#' gg_miss_span(pedestrian, hourly_counts, span_every = 3000) + theme_dark()

gg_miss_span <- function(data,
                         var,
                         span_every){

  var_enquo <- rlang::enquo(var)

  ggobject <-  miss_var_span(data = data,
                             var = !!var_enquo,
                             span_every = span_every) %>%
    # miss_var_span(pedestrian, hourly_counts, span_every = 3000) %>%
    tidyr::gather(key = variable,
                  value = value,
                  prop_miss:prop_complete) %>%
    ggplot2::ggplot(ggplot2::aes(x = span_counter,
                                 y = value,
                                 fill = variable)) +
    ggplot2::geom_col(colour = "white") +
    ggplot2::scale_fill_manual(name = "",
                               values = c("grey80",
                                          "grey20"),
                               label = c("Present",
                                         "Missing")) +
    ggplot2::theme_minimal() +
    ggplot2::labs(title = "Proportion of missing values",
                  subtitle = sprintf("Over a repeating span of %s", span_every),
                  x = "Span",
                  y = "Proportion Missing")
  return(ggobject)

}

# possible alternative plot for missings over a span, using loess to control
# the smoothing of the missingness
#
# tsNH4_NA %>%
#   ggplot(aes(x = date_time,
#              y = x_NA)) +
#   geom_point()
#
# loess_NA <- loess(as.numeric(x_NA) ~ as.numeric(date_time),
#                   data = tsNH4_NA,
#                   degree = 0,
#                   span = 0.75)
#
# modelr::add_predictions(data = tsNH4_NA,
#                         model = loess_NA) %>%
#   mutate(pred = pred - 1) %>%
#   ggplot(aes(x = date_time,
#              y = pred)) +
#   geom_line() +
#   ylim(0,1)