Closed njtierney closed 4 years ago
vis_value works for continuous values, but for cases where there are only binary values, this might be useful.
vis_value
library(visdat) library(tidyverse) assign_na <- function(x, size){ x[sample(length(x), size = size)] <- NA x } dat <- tibble(x = sample(x = c(0L, 1L), size = 100, replace = TRUE), y = sample(x = c(0L, 1L), size = 100, replace = TRUE), z = sample(x = c(0L, 1L), size = 100, replace = TRUE)) %>% mutate_all(assign_na, 10) vis_dat(dat)
vis_miss(dat)
vis_value(dat)
vis_value(dat, viridis_option = "E", na_colour = "grey")
# or scale_01 <- function(x) { (x - min(x, na.rm = TRUE)) / diff(range(x, na.rm = TRUE)) } vis_binary <- function(data) { purrr::map_dfr(data, scale_01) %>% visdat:::vis_gather_() %>% dplyr::mutate(value = visdat:::vis_extract_value_(data)) %>% mutate(valueType = as.factor(valueType), value = as.factor(value)) %>% visdat:::vis_create_() + # change the limits etc. ggplot2::guides(fill = ggplot2::guide_legend(title = "Value")) + # add info about the axes ggplot2::scale_x_discrete(position = "top") + ggplot2::theme(axis.text.x = ggplot2::element_text(hjust = 0)) + ggplot2::scale_fill_manual(values = c("salmon", "steelblue2")) } vis_miss(dat)
vis_binary(dat) #> Warning: Removed 30 rows containing missing values (geom_raster).
dat %>% arrange(x) %>% vis_binary() #> Warning: Removed 30 rows containing missing values (geom_raster).
dat %>% arrange(x,y,z) %>% vis_binary() #> Warning: Removed 30 rows containing missing values (geom_raster).
Created on 2019-12-06 by the reprex package (v0.3.0)
in the future I might consider vis_integer as well.
vis_integer
vis_value
works for continuous values, but for cases where there are only binary values, this might be useful.Created on 2019-12-06 by the reprex package (v0.3.0)
in the future I might consider
vis_integer
as well.