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spineScaler.R
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min <- 14.9
max <- 74.8
mean <- 37.9
polarity <- 1:2
## Sets polarity
set_best <- function(polarity = 1, max = 74.8, min = 14.9){
if(polarity == 1){
best = max
worst = min
} else {
best = min
worst = max
}
return(c(best, worst))
}
set_best(polarity = 2)
#################
### Sets scale values
spine_scale <- function(min = 14.9, max = 74.8, mean = 37.9, polarity = 1, q1 = 27.9, q3 = 47.2, colour = 1, value = 53.5){
## scale minimum
if(mean - min > max - mean){
scale_min = min
} else {
scale_min = mean - (max - mean)
}
## scale maximum
if(min) {
scale_max = max
} else {
scale_max = mean + (mean - min)
}
#return(c(scale_min, scale_max ))
if(polarity == 1){
best = scale_max
worst = scale_min
} else {
best = scale_min
worst = scale_max
}
## scale values
scale_val <- (value - worst)/(best - worst)
scale_average = 0.5
## scale worst
if(polarity == 1){
scale_worst = (min - worst)/(best - worst)
} else {
scale_worst = (max - worst)/ (best - worst)
}
## scale best
if(polarity == 1){
scale_best = (max - worst)/(best - worst)
} else {
scale_best = (min - worst)/(best - worst)
}
## scale q1
if(polarity == 1){
scale_q1 = (q1 - worst)/(best - worst)
} else {
scale_q1 = (q3 - worst)/(best - worst)
}
## q3
if(polarity == 1){
scale_q3 = (q3 - worst) / (best - worst)
} else {
scale_q3 = (q1 - worst) / (best - worst)
}
## scalepad
if(polarity == 1){
scale_pad = 0
} else {
scale_pad = 1
}
if(polarity == 1){
chart_bottom = scale_worst - scale_pad
} else {
chart_bottom = scale_pad - scale_best
}
if(polarity == 1){
chart_q1 = scale_q1 - scale_worst
} else {
chart_q1 = scale_best - scale_q3
}
if(polarity == 1){
chart_iqr = scale_q3 - scale_q1
} else {
chart_iqr = scale_q3 - scale_q1
}
if(polarity == 1){
chart_top = scale_best - scale_q3
} else {
chart_top = scale_q1 - scale_worst
}
return(data.frame(cbind(scale_val, scale_average,chart_bottom, chart_iqr, chart_q1, chart_top)))
}
dat1 <- spine_scale(value = c(50), polarity = 1)
dat2 <- spine_scale(value = c(50), polarity = 2)
dat3 <- rep(dat1, 4)
dat <- bind_rows(dat1, dat2)
dat <- bind_rows(dat, dat)
dat %>%
mutate(rows = row_number()) %>%
gather(metric, value, chart_bottom:chart_top) %>%
ggplot(aes(rows, value, fill = metric)) +
geom_col(aes(position = "fill"))
exp2 <- dat %>%
mutate(rows = row_number()) %>%
gather(metric, value, scale_worst:scale_pad)
require(tidyverse)
cols <- c("scale_pad" = "white", "scale_q1" = "darkgrey", "scale_q3" = "darkgrey",
"scale_worst" = "lightgrey", "scale_best" = "lightgrey")
exp2 %>%
filter(rows == 2) %>%
ggplot() +
geom_col(aes(row, scale_best))
geom_point(aes(rows, scale_val)) +
geom_hline(yintercept = exp1$scale_average) +
coord_flip() +
scale_fill_manual(values = cols)