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helper functions for nivce labels and FreesearchR color palette
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4 changed files with 84 additions and 2 deletions
15
R/theme.R
15
R/theme.R
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@ -46,7 +46,7 @@ FreesearchR_colors <- function(choose = NULL) {
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secondary = "#FF6F61",
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success = "#00C896",
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warning = "#FFB100",
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danger = "#FF3A2F",
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danger = "#CC2E25",
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extra = "#8A4FFF",
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info = "#11A0EC",
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bg = "#FFFFFF",
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@ -60,7 +60,18 @@ FreesearchR_colors <- function(choose = NULL) {
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}
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}
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#' Use the FreesearchR colors
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#'
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#' @param n number of colors
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#'
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#' @returns character vector
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#' @export
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#'
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#' @examples
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#' FreesearchR_palette(n=7)
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FreesearchR_palette <- function(n){
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rep_len(FreesearchR_colors(),n)
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}
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20
man/FreesearchR_palette.Rd
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20
man/FreesearchR_palette.Rd
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@ -0,0 +1,20 @@
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/theme.R
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\name{FreesearchR_palette}
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\alias{FreesearchR_palette}
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\title{Use the FreesearchR colors}
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\usage{
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FreesearchR_palette(n)
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}
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\arguments{
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\item{n}{number of colors}
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}
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\value{
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character vector
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}
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\description{
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Use the FreesearchR colors
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}
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\examples{
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FreesearchR_palette(n=7)
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}
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23
man/unique_short.Rd
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23
man/unique_short.Rd
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@ -0,0 +1,23 @@
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/visual_summary.R
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\name{unique_short}
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\alias{unique_short}
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\title{Create unique short names of character vector items based on index}
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\usage{
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unique_short(data, max = 15)
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}
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\arguments{
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\item{data}{character vector}
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\item{max}{maximum final name length}
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}
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\value{
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character vector
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}
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\description{
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The function will prefer original names, and only append index to long
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strings.
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}
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\examples{
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c("kahdleidnsallskdj", "hej") |> unique_short()
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}
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28
man/visual_summary.Rd
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28
man/visual_summary.Rd
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@ -0,0 +1,28 @@
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/visual_summary.R
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\name{visual_summary}
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\alias{visual_summary}
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\title{Ggplot2 data summary visualisation based on visdat::vis_dat.}
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\usage{
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visual_summary(data, legend.title = "Data class", ...)
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}
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\arguments{
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\item{data}{data}
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\item{...}{optional arguments passed to data_summary_gather()}
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}
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\value{
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ggplot2 object
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}
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\description{
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Ggplot2 data summary visualisation based on visdat::vis_dat.
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}
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\examples{
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data_demo <- mtcars
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data_demo[sample(1:32, 10), "cyl"] <- NA
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data_demo[sample(1:32, 8), "vs"] <- NA
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visual_summary(data_demo)
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visual_summary(data_demo, palette.fun = scales::hue_pal())
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visual_summary(dplyr::storms)
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visual_summary(dplyr::storms, summary.fun = data_type)
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}
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