Influence of the human field of view on visual and nonvisual quantities

Supplemental Material: Analysis of spectral measurements

Author

Johannes Zauner, Kai Broszio, Karin Bieske

Setup

Setup of all the packages and functions.

Code

library(tidyverse)
library(cowplot)
library(gt)
library(ggridges)
library(patchwork)
library(ggtext)
library(ggimage)
library(ggdist)
library(magick)
library(readxl)

#function for creating a void plot. The measurement without the FOV-occluder is
#the maximum, with the occluder the minimum. The deep spectral color shows the
#reduced part of the plot
Red_Plot <- function(Data, Bezeichnung_Wellenlaenge, Bezeichnung_Voll, 
                     Bezeichnung_Blende) {
  #palette
  regenbogen <- c(rev(rainbow(150, start = 0.4, end = 0.77)),
                  rev(rainbow(31, start = 0.20, end = 0.4)),
                  rev(rainbow(145, start = 0, end = 0.20)),
                  rev(rainbow(75, start = 0.96, end = 1)))

  ggplot(Data) +
  geom_ridgeline_gradient(
    aes(x={{ Bezeichnung_Wellenlaenge }}, 
        y=0, 
        height={{ Bezeichnung_Voll }}, 
        fill = {{ Bezeichnung_Wellenlaenge }}), 
    col = NA) +
  geom_ridgeline(
    aes(x={{ Bezeichnung_Wellenlaenge }}, 
        y=0, 
        height= {{ Bezeichnung_Blende }}), 
    fill = "white", 
    alpha=0.75) +
  geom_hline(aes(yintercept=0), col = "white")+
  scale_fill_gradientn(colors = regenbogen, guide = "none")+
  theme_void()
}

#function to create a void plot. The relative reduction depending on the
#wavelength is shown in the spectral color of each wavelength.
#A horizontal dashed line shows the median (value is also shown).
Red_pct_Plot <- function(Data, Bezeichnung_Wellenlaenge, Bezeichnung_Voll, 
                         Bezeichnung_Blende) {
  #palette
  regenbogen <- c(rev(rainbow(150, start = 0.4, end = 0.77)),
                  rev(rainbow(31, start = 0.20, end = 0.4)),
                  rev(rainbow(145, start = 0, end = 0.20)),
                  rev(rainbow(75, start = 0.96, end = 1)))

  Data_int <- Data %>% 
    mutate(PCT = {{ Bezeichnung_Blende }} / {{ Bezeichnung_Voll }},
           PCT = ifelse(PCT > 1, NA, PCT))

  ggplot(Data_int) +
    geom_line(aes(y = PCT, 
                  x = {{ Bezeichnung_Wellenlaenge }}, 
                  col = {{ Bezeichnung_Wellenlaenge }}),
              linewidth = 1,
              na.rm = TRUE) +
    coord_cartesian(ylim = c(0, 1), xlim = c(380, 780), clip = "off") +
    geom_hline(aes(yintercept = median((PCT), na.rm = TRUE)),
               col = "black",
               lty = 2) +
    geom_label(aes(label = scales::percent(median((PCT), na.rm = TRUE)), 
                   x = 580, y = median((PCT), na.rm = TRUE)), 
               fill = "white", label.size = NA, 
               label.padding = unit(0.05, "lines"), 
               hjust = 0.5)+
    scale_color_gradientn(colors = regenbogen, guide = "none") +
    scale_y_continuous(breaks = c(0, 1),
                       labels = scales::percent)+
    theme_void()+
    theme(axis.text.y = element_text(),
          axis.line.y = element_line())
}

#Function that brings both plots together
Combined_Plot <- function(..., Aufteilung = c(0.75, 0.25), 
                          Ordner = "", Bezeichnung) {
  Plot <- Red_Plot(...) / Red_pct_Plot(...) +
    plot_layout(heights = Aufteilung)

  Plot
  ggsave(paste0(Ordner, Bezeichnung, ".png"), Plot, width = 2.5, height = 2)
  
}

#takes a filename and a path and fits it together to a full pathname
image_file_path <- function(Bildname, Pfad) {
  if_else(Bildname == "", "&mdash;", paste0(Pfad,"/",Bildname))
}

#creates a table
Table <- function(Daten, Titel) {
  Table <- Daten %>% 
  mutate(
    MDER_Voll = MEDI_Voll/E_Voll,
    MDER_Blende = MEDI_Blende/E_Blende,
    MDER_pct = scales::percent(-1+round(MDER_Blende/MDER_Voll, 2))
  ) %>% 
  gt() %>% 
  tab_header(title = Titel) %>%
  fmt_number(columns = starts_with(c("E_", "MEDI_")) & is.numeric, 
             decimals=0) %>%
  fmt_number(columns = starts_with(c("MDER_")) & is.numeric, 
             decimals=3) %>%
  cols_move(Plot, Bild) %>%
  cols_merge(c(MEDI_Voll, MEDI_Blende, MEDI_pct),
             pattern = "<i>2π:</i> {1} lx<i style = 'color:white'>a</i><br><i>FOV:</i> {2} lx<i style = 'color:white'>a</i><br><strong>{3}</strong><i style = 'color:white'>a</i>") %>%
  cols_merge(c(E_Voll, E_Blende, E_pct),
             pattern = "<i>2π:</i> {1} lx<i style = 'color:white'>a</i><br><i>FOV:</i> {2} lx<i style = 'color:white'>a</i><br><strong>{3}</strong><i style = 'color:white'>a</i>") %>%
  cols_merge(c(MDER_Voll, MDER_Blende, MDER_pct),
  pattern = "<i>2π: </i> {1}<i style = 'color:white'>a</i><br><i>FOV:</i> {2}<i style = 'color:white'>a</i><br><strong>{3}</strong><i style = 'color:white'>a</i>") %>%
  cols_label(
    Szene = "Name",
    Plot = "rSPD",
    E_Voll = "Illuminance",
    MEDI_Voll = "MEDI",
    Bild = "",
    MDER_Voll = "MDER"
  ) %>%
  cols_align(align = "center") %>%
  text_transform(
    locations = cells_body(columns = Bild, rows = Bild != "&mdash;"),
    fn = function(x) {
      local_image(filename = x, height = 100)
      }
    ) %>%
  text_transform(
    locations = cells_body(columns = Plot),
    fn = function(x) {
      local_image(filename = x, height = 100)
      }
  ) %>%
  fmt_markdown(columns = Bild, rows = Bild == "&mdash;")

  Table
}

#add names of columns with FOV-occluders
Blendennamen <- function(Bezeichnung) {
  str_c("Ev_SK_", str_sub(Bezeichnung, start = 4))
}

#New Function to identify the Scene with the FOV-occluder
Blendennamen2 <- function(Bezeichnung) {
  str_c(Bezeichnung, "_SK")
}

#create an empty tibble to fit all the measurements into
Meta <- tibble()

Homeoffice

Code

#Setting up the Information and Paths
Bezeichnungen <- c("Ev_Morgen", "Ev_Tag", "Ev_Abend", "Ev_Nacht")
Wellenlängenspalte <- "Name"
Pfad_Bild <- "Pictures/Homeoffice"
Pfad_Spec <- "Results/Homeoffice"
file_homeoffice <- "Data/Homeoffice/Heimarbeitsplatz.csv"
file_homeoffice_params <- "Data/Homeoffice/Homeoffice_Parameter.csv"

#Import the spectral Data  
Homeoffice <- 
  read.csv(file_homeoffice, sep = ";", dec = ",", skip = 3) %>% 
  slice(-(1:126)) %>% 
  slice(1:401) %>% 
  mutate(across(.fns=function(x) str_replace(x, ",", ".")),
         across(.fns=as.numeric),
         Name = as.integer(Name))

#Import the visual and nonvisual quantities
Homeoffice_params <- read.csv(file_homeoffice_params, sep = ";") %>% 
  mutate(Bild = image_file_path(Bild, Pfad_Bild),
         Plot = image_file_path(str_c(Plot, ".png"), Pfad_Spec),
         E_pct = scales::percent(-1+round(E_Blende/E_Voll, 2)),
         MEDI_pct = scales::percent(-1+round(MEDI_Blende/MEDI_Voll, 2))
  )
Homeoffice_params <- Homeoffice_params %>% mutate(MEDI_pct = MEDI_pct %>% 
                                                    str_replace("\\.0%", "\\%"))

#Generating the Arguments for the spectral plots
Homeoffice_args <-
  list(
    Data = list(Homeoffice),
    Bezeichnung_Wellenlaenge = syms(Wellenlängenspalte),
    Bezeichnung_Voll = syms(Bezeichnungen),
    Bezeichnung_Blende = syms(Blendennamen(Bezeichnungen)),
    Ordner = list("Results/Homeoffice/"),
    Bezeichnung = as.list(Bezeichnungen)
  )

#Generating the spectral plots
pwalk(Homeoffice_args,Combined_Plot)

#Changing Names to english
Homeoffice_params <- Homeoffice_params %>% 
  mutate(
    Szene = case_match(Szene,
      "Morgen" ~ "Morning",
      "Tag" ~ "Daytime",
      "Abend" ~ "Evening",
      "Nacht" ~ "Night"
    )
  )

#Generating the table, displaying and saving it
Table_Homeoffice <- Table(Homeoffice_params, 
                          NULL) 

Table_Homeoffice

Name	Illuminance	MEDI	MDER
Morning	2π: 450 lxa FOV: 184 lxa -59%a	2π: 426 lxa FOV: 171 lxa -60%a	2π: 0.947a FOV: 0.927a -2%a
Daytime	2π: 390 lxa FOV: 161 lxa -59%a	2π: 324 lxa FOV: 131 lxa -60%a	2π: 0.831a FOV: 0.812a -2%a
Evening	2π: 199 lxa FOV: 83 lxa -58%a	2π: 100 lxa FOV: 42 lxa -58%a	2π: 0.502a FOV: 0.501a 0%a
Night	2π: 139 lxa FOV: 58 lxa -58%a	2π: 55 lxa FOV: 23 lxa -59%a	2π: 0.397a FOV: 0.396a 0%a

Code

gtsave(Table_Homeoffice, "Results/Table_Homeoffice.png", zoom = 4)

#Putting data to the Meta-Frame
Meta <- rbind(Meta, 
              cbind(Projekt = "Homeoffice", Homeoffice_params))

Learning Room

Link to the Project Publication

Code

#Setting up the Information and Paths
Pfad_Bild <- "Pictures/Lernraum"
Pfad_Spec <- "Results/Lernraum"
Pfad <- "Data/Lernraum"
Wellenlängenspalte <- "Wavelength"
Bezeichnungen <- c("Ev_Abend", "Ev_Tag", "Ev_Morgen")
file_lernraum_params <- "Data/Lernraum/Lernraum_Parameter.csv"
file_lernraum <- list.files(Pfad, pattern = "[.]csv$")
filepath_lernraum <-
  list.files(Pfad, pattern = "Ev_.+[.]csv$", full.names = TRUE) %>% 
  set_names(c(Bezeichnungen, Blendennamen(Bezeichnungen)))

#Import the spectral Data
Lernraum <-  map_dfr(filepath_lernraum, read.csv, skip = 11, .id = "Quelle") %>% 
    pivot_wider(names_from = Quelle, 
                values_from = Spectral.Irradiance..W..sqm.nm..)

#Import the visual and nonvisual quantities
Lernraum_params <- read.csv(file_lernraum_params, sep = ";") %>% 
  mutate(Bild = image_file_path(Bild, Pfad_Bild),
         Plot = image_file_path(str_c(Plot, ".png"), Pfad_Spec),
         E_pct = scales::percent(-1+round(E_Blende/E_Voll, 2)),
         MEDI_pct = scales::percent(-1+round(MEDI_Blende/MEDI_Voll, 2))
  )

#Generating the Arguments for the spectral plots
Lernraum_args <-
  list(
    Data = list(Lernraum),
    Bezeichnung_Wellenlaenge = syms(Wellenlängenspalte),
    Bezeichnung_Voll = syms(Bezeichnungen),
    Bezeichnung_Blende = syms(Blendennamen(Bezeichnungen)),
    Ordner = list(paste0(Pfad_Spec, "/")),
    Bezeichnung = as.list(Bezeichnungen)
  )

#Generating the spectral plots
pwalk(Lernraum_args,Combined_Plot)

#Changing Names to english
Lernraum_params <- Lernraum_params %>% 
  mutate(
    Szene = case_match(Szene,
      "Morgen" ~ "Morning",
      "Tag" ~ "Daytime",
      "Abend" ~ "Evening",
      "Nacht" ~ "Night"
    )
  )

#Generating the table, displaying and saving it
Table_Lernraum <- Table(Lernraum_params, NULL)

Table_Lernraum

Name	Illuminance	MEDI	MDER
Morning	2π: 261 lxa FOV: 232 lxa -11%a	2π: 242 lxa FOV: 215 lxa -11%a	2π: 0.927a FOV: 0.927a 0%a
Daytime	2π: 209 lxa FOV: 164 lxa -22%a	2π: 128 lxa FOV: 100 lxa -22%a	2π: 0.612a FOV: 0.610a 0%a
Evening	2π: 147 lxa FOV: 87 lxa -41%a	2π: 54 lxa FOV: 31 lxa -43%a	2π: 0.367a FOV: 0.356a -3%a

Code

gtsave(Table_Lernraum, "Results/Table_Lernraum.png", zoom = 4)

#Putting data to the Meta-Frame
Meta <- rbind(Meta, 
              cbind(Projekt = "Lernraum", Lernraum_params))

Industry Workplace (Machine)

Link to the Project Publication

Code

#Setting up the Information and Paths
Pfad_Bild <- "Pictures/RHI/Maschinenarbeitsplatz"
Pfad_Spec <- "Results/RHI/Maschinenarbeitsplatz"
Wellenlängenspalte <- "Wavelength"
Bezeichnungen <- c("Ev_Morgen", "Ev_Tag", "Ev_Nacht", "Ev_Nachtende")
file_RHI_params <- "Data/RHI/Maschinenarbeitsplatz/RHI_Parameter.csv"
file_RHI <- "Data/RHI/Maschinenarbeitsplatz/AP1-3.csv"

#Import the spectral Data
RHI <- read.csv(file_RHI, sep = ";", dec = ",", skip = 16, header = FALSE) %>% 
  select(V1, V6, V9, V13, V14, V7, V8, V11, V15) %>% 
  set_names(c(Wellenlängenspalte, Bezeichnungen, Blendennamen(Bezeichnungen)))

#Import the visual and nonvisual quantities
RHI_params <- read.csv(file_RHI_params, sep = ";") %>%
  rowwise() %>% 
  mutate(Bild = image_file_path(Bild, Pfad_Bild),
         Plot = image_file_path(str_c(Plot, ".png"), Pfad_Spec),
         E_pct = scales::percent(-1+round(E_Blende/E_Voll, 2)),
         MEDI_pct = scales::percent(-1+round(MEDI_Blende/MEDI_Voll, 2))
  )

#Generating the Arguments for the spectral plots
RHI_args <-
  list(
    Data = list(RHI),
    Bezeichnung_Wellenlaenge = syms(Wellenlängenspalte),
    Bezeichnung_Voll = syms(Bezeichnungen),
    Bezeichnung_Blende = syms(Blendennamen(Bezeichnungen)),
    Ordner = list(paste0(Pfad_Spec, "/")),
    Bezeichnung = as.list(Bezeichnungen)
  )

#Changing Names to english
RHI_params <- RHI_params %>% 
  mutate(
    Szene = case_match(Szene,
      "Morgen" ~ "Morning",
      "Tag" ~ "Daytime",
      "Abend" ~ "Evening",
      "Nacht" ~ "Night",
      "Nachtende" ~ "Night's End"
    )
  )

#Generating the spectral plots
pwalk(RHI_args,Combined_Plot)

#Generating the table, displaying and saving it
Table_RHI_Maschine <- Table(RHI_params, NULL)

Table_RHI_Maschine

Name	Illuminance	MEDI	MDER
Morning	2π: 449 lxa FOV: 433 lxa -3%a	2π: 412 lxa FOV: 399 lxa -3%a	2π: 0.918a FOV: 0.920a 0%a
Daytime	2π: 344 lxa FOV: 331 lxa -4%a	2π: 278 lxa FOV: 268 lxa -4%a	2π: 0.809a FOV: 0.809a 0%a
Night	2π: 156 lxa FOV: 150 lxa -4%a	2π: 72 lxa FOV: 70 lxa -3%a	2π: 0.463a FOV: 0.466a 1%a
Night's End	2π: 218 lxa FOV: 211 lxa -3%a	2π: 100 lxa FOV: 96 lxa -3%a	2π: 0.457a FOV: 0.457a 0%a

Code

gtsave(Table_RHI_Maschine, "Results/Table_RHI_Maschine.png", zoom = 4)

#Putting data to the Meta-Frame
Meta <- rbind(Meta, 
              cbind(Projekt = "Industriehalle", RHI_params))

Industry Workplace (Packaging)

Link to the Project Publication

Code

#Setting up the Information and Paths
Pfad_Bild <- "Pictures/RHI/Verpackung"
Pfad_Spec <- "Results/RHI/Verpackung"
Wellenlängenspalte <- "Wavelength"
Bezeichnungen <- c("Ev_Morgen", "Ev_Tag", "Ev_Nacht", "Ev_Nachtende")
file_RHI2_params <- "Data/RHI/Verpackung/RHI_Parameter.csv"
file_RHI2 <- "Data/RHI/Verpackung/AP2.csv"

#Import the spectral Data
RHI2 <- read.csv(file_RHI2, sep = ";", dec = ",", skip = 16, header = FALSE) %>% 
  select(V1, V9, V4, V7, V10, V3, V5, V6, V11) %>% 
  set_names(c(Wellenlängenspalte, Bezeichnungen, Blendennamen(Bezeichnungen)))

#Import the visual and nonvisual quantities
RHI2_params <- read.csv(file_RHI2_params, sep = ";") %>%
  rowwise() %>% 
  mutate(Bild = image_file_path(Bild, Pfad_Bild),
         Plot = image_file_path(str_c(Plot, ".png"), Pfad_Spec),
         E_pct = scales::percent(-1+round(E_Blende/E_Voll, 2)),
         MEDI_pct = scales::percent(-1+round(MEDI_Blende/MEDI_Voll, 2))
  )

#Generating the Arguments for the spectral plots
RHI2_args <-
  list(
    Data = list(RHI2),
    Bezeichnung_Wellenlaenge = syms(Wellenlängenspalte),
    Bezeichnung_Voll = syms(Bezeichnungen),
    Bezeichnung_Blende = syms(Blendennamen(Bezeichnungen)),
    Ordner = list(paste0(Pfad_Spec, "/")),
    Bezeichnung = as.list(Bezeichnungen)
  )

#Changing Names to english
RHI2_params <- RHI2_params %>% 
  mutate(
    Szene = case_match(Szene,
      "Morgen" ~ "Morning",
      "Tag" ~ "Daytime",
      "Abend" ~ "Evening",
      "Nacht" ~ "Night",
      "Nachtende" ~ "Night's End"
    )
  )

#Generating the spectral plots
pwalk(RHI2_args,Combined_Plot)

#Generating the table, displaying and saving it
Table_RHI_Verpackung <- Table(RHI2_params, NULL)

Table_RHI_Verpackung

Name	Illuminance	MEDI	MDER
Morning	2π: 460 lxa FOV: 206 lxa -55%a	2π: 322 lxa FOV: 151 lxa -53%a	2π: 0.702a FOV: 0.736a 5%a
Daytime	2π: 225 lxa FOV: 154 lxa -31%a	2π: 158 lxa FOV: 110 lxa -31%a	2π: 0.704a FOV: 0.712a 1%a
Night	2π: 153 lxa FOV: 120 lxa -22%a	2π: 97 lxa FOV: 79 lxa -19%a	2π: 0.633a FOV: 0.654a 3%a
Night's End	2π: 199 lxa FOV: 128 lxa -36%a	2π: 122 lxa FOV: 81 lxa -33%a	2π: 0.614a FOV: 0.637a 4%a

Code

gtsave(Table_RHI_Verpackung, "Results/Table_RHI_Verpackung.png", zoom = 4)

#Putting data to the Meta-Frame
Meta <- rbind(Meta,
              cbind(Projekt = "Industriehalle2", RHI2_params))

BAuA Lab-Study (tilted downwards)

Code

#Setting up the Information and Paths
Pfad <- "Data/BAuA/geneigt"
Pfad_Results <- "Results/BAuA/geneigt"
Wellenlängenspalte <- "Name"
file_params <- paste0(Pfad, "/Parameter.csv")
file_Spektrum <- paste0(Pfad, "/BAuA-Studie_Messungen.csv")

#Import the spectral Data
Spektraldaten <- read.csv(file_Spektrum, sep = ";", dec = ",") %>% 
  select(1:10)

#Import the visual and nonvisual quantities
Params <- read.csv(file_params, sep = ";", dec = ",") %>%
  select(1:4) %>% filter(!str_detect(Name, "Mun")) %>% 
  separate(Name, c("Name", "SK"), sep = "_") %>% 
  mutate(SK = recode(SK, "SK" = "Blende",
                     .missing = "Voll")) %>% 
  pivot_wider(values_from = c(E,MEDI, CCT), names_from = SK) %>% 
  mutate(Bild = image_file_path("", Pfad),
         Plot = 
           image_file_path(str_c(Name, ".png") %>% 
                             str_replace(" ", "."), Pfad_Results),
         E_pct = scales::percent(-1+round(E_Blende/E_Voll, 2)),
         MEDI_pct = scales::percent(-1+round(MEDI_Blende/MEDI_Voll, 2))
  ) %>% 
  select(-CCT_Voll, -CCT_Blende) %>% 
  relocate(Bild, Plot) %>% 
  rename("Szene" = "Name")

#Adjust Scene-Names
Bezeichnungen <- Params$Szene %>% str_replace(" ", ".")

#Generating the Arguments for the spectral plots
Argumente <-
  list(
    Data = list(Spektraldaten),
    Bezeichnung_Wellenlaenge = syms(Wellenlängenspalte),
    Bezeichnung_Voll = syms(Bezeichnungen),
    Bezeichnung_Blende = syms(Blendennamen2(Bezeichnungen)),
    Ordner = list(paste0(Pfad_Results, "/")),
    Bezeichnung = as.list(Bezeichnungen)
  )

#Generating the spectral plots
pwalk(Argumente,Combined_Plot)

#Generating the table, displaying and saving it
Tabellendaten <- 
Table(Params, NULL) %>% cols_hide(Bild)

Tabellendaten

Name	Illuminance	MEDI	MDER
warm low	2π: 208 lxa FOV: 168 lxa -19%a	2π: 99 lxa FOV: 80 lxa -20%a	2π: 0.477a FOV: 0.475a 0%a
cold low	2π: 209 lxa FOV: 168 lxa -19%a	2π: 209 lxa FOV: 168 lxa -20%a	2π: 1.000a FOV: 0.995a 0%a
cold bright	2π: 579 lxa FOV: 466 lxa -20%a	2π: 581 lxa FOV: 465 lxa -20%a	2π: 1.002a FOV: 0.998a 0%a

Code

gtsave(Tabellendaten, "Results/Table_BAuA-Studie_geneigt.png", zoom = 4)

#Putting data to the Meta-Frame
Meta <- rbind(Meta,
              cbind(Projekt = "BAuA Studie", Params))

BAuA Lab-Study (vertical)

Code

#Setting up the Information and Paths
Pfad <- "Data/BAuA/vertikal"
Pfad_Bild <- "Pictures/BAuA/vertikal"
Pfad_Results <- "Results/BAuA/vertikal"
Wellenlängenspalte <- "Name"
file_params <- paste0(Pfad, "/Parameter.csv")
file_Spektrum <- paste0(Pfad, "/Messdaten.csv")

#Import the spectral Data
Spektraldaten <- read.csv(file_Spektrum, sep = ";", dec = ",") %>% 
  select(1:10)

#Import the visual and nonvisual quantities
Params <- read.csv(file_params, sep = ";", dec = ",") %>%
  select(1:5) %>% filter(!str_detect(Name, "Mun")) %>% 
  separate(Name, c("Name", "SK"), sep = "_") %>% 
  mutate(SK = recode(SK, "SK" = "Blende",
                     .missing = "Voll")) %>% 
  pivot_wider(values_from = c(E,MEDI, CCT), names_from = SK) %>% 
  rowwise() %>% 
  mutate(Bild = image_file_path(Bild, Pfad_Bild),
         Plot = 
           image_file_path(str_c(Name, ".png") %>% 
                             str_replace(" ", "."), Pfad_Results),
         E_pct = scales::percent(-1+round(E_Blende/E_Voll, 2)),
         MEDI_pct = scales::percent(-1+round(MEDI_Blende/MEDI_Voll, 2))
  ) %>% 
  select(-CCT_Voll, -CCT_Blende) %>% 
  relocate(Bild, Plot) %>% 
  rename("Szene" = "Name")%>% 
  arrange(Szene)

#Adjust Scene-Names
Bezeichnungen <- Params$Szene %>% str_replace(" ", ".")

#Generating the Arguments for the spectral plots
Argumente <-
  list(
    Data = list(Spektraldaten),
    Bezeichnung_Wellenlaenge = syms(Wellenlängenspalte),
    Bezeichnung_Voll = syms(Bezeichnungen),
    Bezeichnung_Blende = syms(Blendennamen2(Bezeichnungen)),
    Ordner = list(paste0(Pfad_Results, "/")),
    Bezeichnung = as.list(Bezeichnungen)
  )

#Generating the spectral plots
pwalk(Argumente,Combined_Plot)

#Generating the table, displaying and saving it
Tabellendaten <- 
Table(Params %>% arrange(rev(Szene)), NULL)

Tabellendaten

Name	Illuminance	MEDI	MDER
warm low	2π: 296 lxa FOV: 212 lxa -28%a	2π: 141 lxa FOV: 101 lxa -28%a	2π: 0.476a FOV: 0.476a 0%a
cold low	2π: 298 lxa FOV: 213 lxa -29%a	2π: 300 lxa FOV: 214 lxa -29%a	2π: 1.007a FOV: 1.005a 0%a
cold bright	2π: 830 lxa FOV: 592 lxa -29%a	2π: 838 lxa FOV: 595 lxa -29%a	2π: 1.010a FOV: 1.005a 0%a

Code

gtsave(Tabellendaten, "Results/Table_BAuA-Studie_vertikal.png", zoom = 4)

#Putting data to the Meta-Frame
Meta <- rbind(Meta, 
              cbind(Projekt = "BAuA Studie", Params))

TUB-HK Lab-Study

Code

#Setting up the Information and Paths
Pfad <- "Data/TUB-HK"
Pfad_Bild <- "Pictures/TUB-HK"
Pfad_Spec <- "Results/TUB-HK"
Wellenlängenspalte <- "Name"
file_params <- paste0(Pfad, "/Parameter.csv")
file_Spektrum <- paste0(Pfad, "/Messdaten.csv")

#Import the spectral Data
Spektraldaten <- read.csv(file_Spektrum, sep = ";", dec = ",") %>% 
  select(1:4)

#Import the visual and nonvisual quantities
Params <- read.csv(file_params, sep = ";", dec = ",") %>%
  select(1:5) %>% filter(!str_detect(Name, "Mun")) %>% 
  separate(Name, c("Name", "SK"), sep = "_") %>% 
  mutate(SK = recode(SK, "SK" = "Blende",
                     .missing = "Voll")) %>% 
  pivot_wider(values_from = c(E,MEDI, CCT), names_from = SK) %>% 
  rowwise() %>% 
  mutate(Bild = image_file_path(Bild, Pfad_Bild),
         Plot = 
           image_file_path(str_c(Name, ".png") %>% 
                             str_replace(" ", "."), Pfad_Spec),
         E_pct = scales::percent(-1+round(E_Blende/E_Voll, 2)),
         MEDI_pct = scales::percent(-1+round(MEDI_Blende/MEDI_Voll, 2))
  ) %>% 
  select(-CCT_Voll, -CCT_Blende) %>% 
  relocate(Bild, Plot) %>% 
  rename("Szene" = "Name")

#Changing Names to english
Params <- Params %>% 
  mutate(
    Szene = case_match(Szene,
      "Halbkugel" ~ "Halfdome"
    )
  )

#Adjust Scene-Names
Bezeichnungen <- Params$Szene %>% str_replace(" ", ".")

#Generating the Arguments for the spectral plots
Argumente <-
  list(
    Data = list(Spektraldaten),
    Bezeichnung_Wellenlaenge = syms(Wellenlängenspalte),
    Bezeichnung_Voll = syms("Ulbrichtkugel"),
    Bezeichnung_Blende = syms(Blendennamen2("Ulbrichtkugel")),
    Ordner = list(paste0(Pfad_Spec, "/")),
    Bezeichnung = as.list(Bezeichnungen)
  )

#Generating the spectral plots
pwalk(Argumente,Combined_Plot)

#Generating the table, displaying and saving it
Tabellendaten <- 
Table(Params,NULL)

Tabellendaten

	rSPD	Name	Illuminance	MEDI	MDER
		Halfdome	2π: 1,982 lxa FOV: 1,829 lxa -8%a	2π: 2,323 lxa FOV: 2,147 lxa -8%a	2π: 1.172a FOV: 1.174a 0%a

Code

gtsave(Tabellendaten, "Results/Table_TUB_HK_Studie.png", zoom = 4)

#Putting data to the Meta-Frame
Meta <- rbind(Meta,
              cbind(Projekt = "TUB-Halbk. Studie", Params))

TUB-BAuA Lab-Study (tilted downwards)

Code

#Setting up the Information and Paths
Pfad <- "Data/TUB-BAuA/geneigt"
Pfad_Spec <- "Results/TUB-BAuA/geneigt"
Wellenlängenspalte <- "Name"
file_params <- paste0(Pfad, "/Parameter.csv")
file_Spektrum <- paste0(Pfad, "/Messdaten.csv")

#Import the spectral Data
Spektraldaten <- read.csv(file_Spektrum, sep = ";", dec = ",") %>% 
  select(1:13)

#Import the visual and nonvisual quantities
Params <- read.csv(file_params, sep = ";", dec = ",") %>%
  select(1:4) %>% filter(!str_detect(Name, "Mun")) %>% 
  separate(Name, c("Name", "SK"), sep = "_") %>% 
  mutate(SK = recode(SK, "SK" = "Blende",
                     .missing = "Voll")) %>% 
  pivot_wider(values_from = c(E,MEDI, CCT), names_from = SK) %>% 
  mutate(Bild = image_file_path("", Pfad),
         Plot = 
           image_file_path(str_c(Name, ".png") %>% 
                             str_replace(" ", "."), Pfad_Spec),
         E_pct = scales::percent(-1+round(E_Blende/E_Voll, 2)),
         MEDI_pct = scales::percent(-1+round(MEDI_Blende/MEDI_Voll, 2))
  ) %>% 
  select(-CCT_Voll, -CCT_Blende) %>% 
  relocate(Bild, Plot) %>% 
  rename("Szene" = "Name")

#Adjust Scene-Names
Bezeichnungen <- Params$Szene %>% str_replace(" ", ".")

#Generating the Arguments for the spectral plots
Argumente <-
  list(
    Data = list(Spektraldaten),
    Bezeichnung_Wellenlaenge = syms(Wellenlängenspalte),
    Bezeichnung_Voll = syms(Bezeichnungen),
    Bezeichnung_Blende = syms(Blendennamen2(Bezeichnungen)),
    Ordner = list(paste0(Pfad_Spec, "/")),
    Bezeichnung = as.list(Bezeichnungen)
  )

#Generating the spectral plots
pwalk(Argumente,Combined_Plot)

#Generating the table, displaying and saving it
Tabellendaten <- 
Table(Params, NULL) %>% cols_hide(Bild)

Tabellendaten

Name	Illuminance	MEDI	MDER
LS1	2π: 236 lxa FOV: 214 lxa -10%a	2π: 106 lxa FOV: 97 lxa -9%a	2π: 0.450a FOV: 0.454a 1%a
LS2	2π: 146 lxa FOV: 133 lxa -9%a	2π: 222 lxa FOV: 199 lxa -11%a	2π: 1.515a FOV: 1.495a -1%a
LS3	2π: 138 lxa FOV: 126 lxa -9%a	2π: 196 lxa FOV: 181 lxa -7%a	2π: 1.419a FOV: 1.444a 2%a
DIM	2π: 8 lxa FOV: 8 lxa -2%a	2π: 3 lxa FOV: 3 lxa -2%a	2π: 0.422a FOV: 0.422a 0%a

Code

gtsave(Tabellendaten, "Results/Table_TUB_BAuA_Studie_geneigt.png", zoom = 4)

#Putting data to the Meta-Frame
Meta <- rbind(Meta, 
              cbind(Projekt = "TUB-BAuA Studie", Params))

TUB-BAuA Lab-Study (vertical)

Code

#Setting up the Information and Paths
Pfad <- "Data/TUB-BAuA/vertikal"
Pfad_Bild <- "Pictures/TUB-BAuA/vertikal"
Pfad_Spec <- "Results/TUB-BAuA/vertikal"
Wellenlängenspalte <- "Name"
file_params <- paste0(Pfad, "/Parameter.csv")
file_Spektrum <- paste0(Pfad, "/Messdaten.csv")

#Import the spectral Data
Spektraldaten <- read.csv(file_Spektrum, sep = ";", dec = ",") %>% 
  select(1:13)

#Import the visual and nonvisual quantities
Params <- read.csv(file_params, sep = ";", dec = ",") %>%
  select(1:5) %>% filter(!str_detect(Name, "Mun")) %>% 
  separate(Name, c("Name", "SK"), sep = "_") %>% 
  mutate(SK = recode(SK, "SK" = "Blende",
                     .missing = "Voll")) %>% 
  pivot_wider(values_from = c(E,MEDI, CCT), names_from = SK) %>% 
  rowwise() %>% 
  mutate(Bild = image_file_path(Bild, Pfad_Bild),
         Plot = 
           image_file_path(str_c(Name, ".png") %>% 
                             str_replace(" ", "."), Pfad_Spec),
         E_pct = scales::percent(-1+round(E_Blende/E_Voll, 2)),
         MEDI_pct = scales::percent(-1+round(MEDI_Blende/MEDI_Voll, 2))
  ) %>% 
  select(-CCT_Voll, -CCT_Blende) %>% 
  relocate(Bild, Plot) %>% 
  rename("Szene" = "Name")%>% 
  arrange(Szene)

#Adjust Scene-Names
Bezeichnungen <- Params$Szene %>% str_replace(" ", ".")

#Generating the Arguments for the spectral plots
Argumente <-
  list(
    Data = list(Spektraldaten),
    Bezeichnung_Wellenlaenge = syms(Wellenlängenspalte),
    Bezeichnung_Voll = syms(Bezeichnungen),
    Bezeichnung_Blende = syms(Blendennamen2(Bezeichnungen)),
    Ordner = list(paste0(Pfad_Spec, "/")),
    Bezeichnung = as.list(Bezeichnungen)
  )

#Generating the spectral plots
pwalk(Argumente,Combined_Plot)

#Generating the table, displaying and saving it
Tabellendaten <- 
Table(Params, NULL)

Tabellendaten

Name	Illuminance	MEDI	MDER
DIM	2π: 6 lxa FOV: 6 lxa -6%a	2π: 3 lxa FOV: 3 lxa -5%a	2π: 0.428a FOV: 0.431a 1%a
LS1	2π: 276 lxa FOV: 254 lxa -8%a	2π: 164 lxa FOV: 90 lxa -45%a	2π: 0.594a FOV: 0.353a -41%a
LS2	2π: 178 lxa FOV: 126 lxa -29%a	2π: 256 lxa FOV: 238 lxa -7%a	2π: 1.438a FOV: 1.889a 31%a
LS3	2π: 181 lxa FOV: 117 lxa -35%a	2π: 385 lxa FOV: 111 lxa -71%a	2π: 2.127a FOV: 0.949a -55%a

Code

gtsave(Tabellendaten, "Results/Table_TUB_BAuA_Studie_vertikal.png", zoom = 4)

#Putting data to the Meta-Frame
Meta <- rbind(Meta, 
              cbind(Projekt = "TUB-BAuA Studie", Params))

TU-Ilmenau Field-Study

Code

#Setting up the Information and Paths
Pfad <- "Data/TUI"
Pfad_Bild <- "Pictures/TUI"
Pfad_Spec <- "Results/TUI"
Wellenlängenspalte <- "Wellenlänge"
file_params <- paste0(Pfad, "/Parameter.csv")
file_Spektrum <- paste0(Pfad, "/Messungen.csv")

#Import the spectral Data
Spektraldaten <- read.csv(file_Spektrum, sep = ";", dec = ",")

#Import the visual and nonvisual quantities
Params <- read.csv(file_params, sep = ";", dec = ",") %>%
  select(1:5) %>% separate(
  Szene, c("X", "Y", "APL", "SK"), sep = "_", remove = FALSE) %>% 
  mutate(SK = recode(SK, "SK" = "Blende",
                     .missing = "Voll"),
         APL = recode(APL, "oAPL" = "nein",
                      "APL" = "ja"),
         Szene = str_remove(Szene, "_SK"),
         X = str_replace(X, "m", "-"),
         Y = str_replace(Y, "m", "-")
         ) %>% 
  pivot_wider(values_from = c(E,MEDI, CCT), names_from = SK) %>% 
      rowwise() %>% 
  mutate(Bild = image_file_path(Bild, Pfad_Bild),
         Plot = image_file_path(str_c(Szene, ".png"), Pfad_Spec),
         E_pct = scales::percent(-1+round(E_Blende/E_Voll, 2)),
         MEDI_pct = scales::percent(-1+round(MEDI_Blende/MEDI_Voll, 2))
  ) %>% 
  select(-CCT_Voll, -CCT_Blende) %>% 
  relocate(Y, Bild, Plot)

#Adjust Scene-Names
Bezeichnungen <- Params$Szene

#Generating the Arguments for the spectral plots
Argumente <-
  list(
    Data = list(Spektraldaten),
    Bezeichnung_Wellenlaenge = syms(Wellenlängenspalte),
    Bezeichnung_Voll = syms(Bezeichnungen),
    Bezeichnung_Blende = syms(Blendennamen2(Bezeichnungen)),
    Ordner = list(paste0(Pfad_Spec, "/")),
    Bezeichnung = as.list(Bezeichnungen)
  )

#Generating the spectral plots
pwalk(Argumente,Combined_Plot)

#Generating the tables, displaying and saving it
Tabellendaten <- Table(Params %>% filter(APL == "nein") %>% 
                         mutate(
                           Y = str_remove(Y, "Y") %>% str_c("°"),
                           X = str_remove(X, "X") %>% str_c("°")
                           ), NULL) %>% 
  cols_hide(c(Szene, APL)) %>% 
  cols_label(Y="Gaze Y",
             X="Gaze X") %>% 
  cols_move(Y, X)

Tabellendaten

	Gaze X	Gaze Y	Illuminance	MEDI	MDER
—	0°	45°	2π: 532 lxa FOV: 480 lxa -10%a	2π: 378 lxa FOV: 339 lxa -10%a	2π: 0.709a FOV: 0.707a 0%a
—	0°	30°	2π: 437 lxa FOV: 261 lxa -40%a	2π: 310 lxa FOV: 184 lxa -41%a	2π: 0.709a FOV: 0.705a -1%a
—	0°	15°	2π: 323 lxa FOV: 98 lxa -70%a	2π: 229 lxa FOV: 68 lxa -70%a	2π: 0.707a FOV: 0.695a -2%a
	0°	0°	2π: 195 lxa FOV: 79 lxa -59%a	2π: 137 lxa FOV: 55 lxa -60%a	2π: 0.703a FOV: 0.690a -2%a
—	0°	-15°	2π: 112 lxa FOV: 75 lxa -33%a	2π: 78 lxa FOV: 52 lxa -34%a	2π: 0.698a FOV: 0.691a -1%a
—	0°	-30°	2π: 92 lxa FOV: 66 lxa -29%a	2π: 63 lxa FOV: 45 lxa -29%a	2π: 0.689a FOV: 0.687a 0%a
—	0°	-45°	2π: 82 lxa FOV: 55 lxa -33%a	2π: 56 lxa FOV: 37 lxa -34%a	2π: 0.687a FOV: 0.680a -1%a
—	-30°	0°	2π: 134 lxa FOV: 87 lxa -35%a	2π: 94 lxa FOV: 61 lxa -35%a	2π: 0.702a FOV: 0.699a 0%a
—	-15°	0°	2π: 154 lxa FOV: 82 lxa -47%a	2π: 108 lxa FOV: 57 lxa -48%a	2π: 0.703a FOV: 0.694a -1%a
—	15°	0°	2π: 232 lxa FOV: 84 lxa -64%a	2π: 164 lxa FOV: 58 lxa -65%a	2π: 0.706a FOV: 0.693a -2%a
—	30°	0°	2π: 280 lxa FOV: 107 lxa -62%a	2π: 198 lxa FOV: 75 lxa -62%a	2π: 0.707a FOV: 0.700a -1%a
—	-30°	-15°	2π: 105 lxa FOV: 73 lxa -30%a	2π: 73 lxa FOV: 51 lxa -30%a	2π: 0.696a FOV: 0.695a 0%a
—	-15°	-15°	2π: 105 lxa FOV: 70 lxa -33%a	2π: 73 lxa FOV: 49 lxa -33%a	2π: 0.693a FOV: 0.692a 0%a
—	15°	-15°	2π: 141 lxa FOV: 74 lxa -48%a	2π: 98 lxa FOV: 51 lxa -48%a	2π: 0.699a FOV: 0.692a -1%a
—	30°	-15°	2π: 160 lxa FOV: 70 lxa -56%a	2π: 112 lxa FOV: 48 lxa -57%a	2π: 0.701a FOV: 0.691a -1%a

Code

gtsave(Tabellendaten, "Results/Table_TU Ilmenau_ohne_APL.png", zoom = 4)

Tabellendaten <- Table(Params %>% filter(APL == "ja") %>% 
                         mutate(
                           Y = str_remove(Y, "Y") %>% str_c("°"),
                           X = str_remove(X, "X") %>% str_c("°")
                           ), NULL) %>% 
  cols_hide(c(Szene, APL)) %>% 
  cols_label(Y="Gaze Y",
             X="Gaze X") %>% 
  cols_move(Y, X)

Tabellendaten

	Gaze X	Gaze Y	Illuminance	MEDI	MDER
—	0°	45°	2π: 596 lxa FOV: 564 lxa -5%a	2π: 415 lxa FOV: 391 lxa -6%a	2π: 0.696a FOV: 0.693a 0%a
—	0°	30°	2π: 506 lxa FOV: 337 lxa -33%a	2π: 350 lxa FOV: 230 lxa -34%a	2π: 0.692a FOV: 0.681a -2%a
—	0°	15°	2π: 391 lxa FOV: 177 lxa -55%a	2π: 267 lxa FOV: 116 lxa -57%a	2π: 0.684a FOV: 0.655a -4%a
	0°	0°	2π: 270 lxa FOV: 160 lxa -41%a	2π: 182 lxa FOV: 104 lxa -43%a	2π: 0.673a FOV: 0.647a -4%a
—	0°	-15°	2π: 192 lxa FOV: 150 lxa -22%a	2π: 126 lxa FOV: 98 lxa -22%a	2π: 0.656a FOV: 0.653a -1%a
—	0°	-30°	2π: 168 lxa FOV: 135 lxa -20%a	2π: 110 lxa FOV: 88 lxa -20%a	2π: 0.652a FOV: 0.651a 0%a
—	0°	-45°	2π: 147 lxa FOV: 105 lxa -28%a	2π: 96 lxa FOV: 68 lxa -29%a	2π: 0.654a FOV: 0.650a -1%a
—	-30°	0°	2π: 203 lxa FOV: 159 lxa -21%a	2π: 135 lxa FOV: 105 lxa -22%a	2π: 0.665a FOV: 0.657a -1%a
—	-15°	0°	2π: 236 lxa FOV: 162 lxa -31%a	2π: 157 lxa FOV: 105 lxa -33%a	2π: 0.666a FOV: 0.650a -2%a
—	15°	0°	2π: 313 lxa FOV: 163 lxa -48%a	2π: 212 lxa FOV: 106 lxa -50%a	2π: 0.678a FOV: 0.651a -4%a
—	30°	0°	2π: 335 lxa FOV: 178 lxa -47%a	2π: 229 lxa FOV: 118 lxa -48%a	2π: 0.684a FOV: 0.663a -3%a
—	-30°	-15°	2π: 178 lxa FOV: 138 lxa -22%a	2π: 117 lxa FOV: 91 lxa -22%a	2π: 0.657a FOV: 0.655a 0%a
—	-15°	-15°	2π: 183 lxa FOV: 144 lxa -22%a	2π: 120 lxa FOV: 94 lxa -22%a	2π: 0.654a FOV: 0.652a 0%a
—	15°	-15°	2π: 213 lxa FOV: 147 lxa -31%a	2π: 142 lxa FOV: 96 lxa -32%a	2π: 0.665a FOV: 0.652a -2%a
—	30°	-15°	2π: 222 lxa FOV: 136 lxa -39%a	2π: 149 lxa FOV: 88 lxa -41%a	2π: 0.672a FOV: 0.653a -3%a

Code

gtsave(Tabellendaten, "Results/Table_TU Ilmenau_mit_APL.png", zoom = 4)

Tabellendaten <- Table(Params %>% 
                         filter(Y == "Y0", X == "X0") %>% 
                         mutate(APL = case_match(APL,
                           "ja" ~ "yes",
                           "nein" ~ "no"
                         )) %>% 
                         arrange(desc(APL)),
      NULL) %>% 
  cols_hide(c(Szene, Y, X)) %>% 
  cols_label(APL = "Task Light")

Tabellendaten

	rSPD	Task Light	Illuminance	MEDI	MDER
		yes	2π: 270 lxa FOV: 160 lxa -41%a	2π: 182 lxa FOV: 104 lxa -43%a	2π: 0.673a FOV: 0.647a -4%a
		no	2π: 195 lxa FOV: 79 lxa -59%a	2π: 137 lxa FOV: 55 lxa -60%a	2π: 0.703a FOV: 0.690a -2%a

Code

gtsave(Tabellendaten, "Results/Table_TU Ilmenau_00.png", zoom = 4)

Meta <- rbind(Meta,
              cbind(Projekt = "Montage- arbeitsplatz", Params %>% select(-c(X,Y,APL))))

Gaze Direction (TU-Ilmenau)

Code

# Preparing the TU-Ilmenau Parameters for Gaze Direction Analysis
Params_numeric <- Params %>% mutate(
  across(.cols=c(X,Y), ~ str_remove(.x, "X|Y") %>% as.numeric())
)

# We need a Tibble of Gaze Directions in 15° steps
Points <- tibble(Y=seq(-45,45,15)) %>% crossing(X= seq(-30,30,15))

# Plot creation
Params_numeric %>% filter(APL == "ja") %>% 
  ggplot(aes(x=X, y = Y)) +
  geom_point(data=Points, col = "grey") +
  geom_label(aes(label=str_c(MEDI_Blende, " lx\n",MEDI_pct), 
                 fill = MEDI_Blende, size = -1+MEDI_Blende/MEDI_Voll)) +
  theme_cowplot(font_size = 8)+
  scale_fill_viridis_c(name = "MEDI (lx)", alpha = 0.5)+
  scale_y_continuous(breaks=seq(-45,45,15), 
                     label = scales::label_number(suffix = "°") ) +
  scale_x_continuous(breaks=seq(-45,45,15), 
                     label = scales::label_number(suffix = "°") ) +
  scale_size_continuous(name = "Impact", range = c(4.5,2.5),
                        label = scales::label_percent())+
  coord_cartesian(xlim = c(-32,32), ylim = c(-47,47), clip = "off")+
  # labs(title = "Gaze-Direction in the vertical (y) and horizontal (x) direction greatly influence FOV (label size) and nonvisual stimulus (binned color)",
       # subtitle = "") +
  theme(plot.title = element_textbox_simple(margin = margin(b=2)))

Code

ggsave("Results/Gaze-Direction.png", height = 4, width = 5, units = "in")

Summary Analysis

Code

#Preserve the complete set of Measurements
Meta %>% write.csv(file = "Results/Meta.csv", row.names = FALSE)

#Backup, should this chunk be used without the chunks above
# Meta <- read.csv("Results/Meta.csv")

#If Project-Names are used (which they are not), line-breaks are needed
Meta <- Meta %>% mutate(Projekt= str_replace(Projekt, " ", "\\\n"))

#We want a certain ordering of the projects
Projekte <- c("BAuA\nStudie",
              "Homeoffice", 
              "Industriehalle",
              "Montage-\narbeitsplatz",
              "Lernraum",
              "Industriehalle2",
              "TUB-Halbk.\nStudie",
              "TUB-BAuA\nStudie")

Meta <- Meta %>% mutate(Projekt = factor(Projekt, levels = (Projekte)))

#We need a summary dataframe for all the projects
Meta2 <- Meta %>%
  group_by(Projekt) %>%
  summarize(Median = median(1-MEDI_Blende/MEDI_Voll)) %>%
  mutate(Projektbild = 
           str_c("Pictures/Zusammenfassung/", Projekt %>% 
                   str_replace("\\\n", ""), ".png"))

#Create a Summary-Plot with project images on the x-Axis
Zusammenfassung <- 
  Meta %>% 
  ggplot(aes(x = Projekt, y = 1-MEDI_Blende/MEDI_Voll)) +
  stat_pointinterval(col = "red",
    point_interval = "median_qi",
    slab_fill = "black",
    slab_color = "black",
    slab_size = 2,
    .width = c(1,0.5),
    interval_size_range = c(0.75, 2),
  )+
  stat_dots(position = position_nudge(x= 0.11), col = "red",
    point_interval = "median_qi",
    slab_fill = "black",
    slab_color = "black",
    binwidth = 0.02
  )+
  geom_text(data = Meta2, aes(x=Projekt, y=Median, 
                              label = scales::percent(Median, accuracy = 1)), 
            hjust = 1, nudge_x = -0.1, col = "red")+
  theme_cowplot()+
  scale_x_discrete(labels = 
                     str_c("<img src='", Meta2$Projektbild, "' width='50' /><br>",
                           LETTERS[1:length(levels(Meta2$Projekt))]))+
  scale_y_continuous(labels = scales::label_percent(prefix = "-")) +
  labs(x = NULL, y = "Impact of FOV occlusion on MEDI")+
  coord_cartesian(clip="off", ylim = c(-0.02, NA))+
  theme(axis.text.x = element_markdown(),
        axis.title.y = element_text(hjust = 1))
  # labs(title = "Projektübersicht als Boxplot mit Einzel-Ergebnissen")

Zusammenfassung

Warning in png::readPNG(get_file(path), native = TRUE): Image uses 16-bit
channels but R native format only supports 8-bit, truncating LSB.

Code

#Export the Plot
ggsave(
  "Results/Summary.png", Zusammenfassung, height = 3.2, width = 8, units = "in")

Warning in png::readPNG(get_file(path), native = TRUE): Image uses 16-bit
channels but R native format only supports 8-bit, truncating LSB.

Summary table

Code

#Backup, should this chunk be used without the chunks above
Meta <- read.csv("Results/Meta.csv")

#We only wan´t certain scenarios in the summary table
Meta3 <- Meta %>% 
  slice(c(21:19, 1:2, 4, 8, 10, 34, 49, 5:7, 12:14, 22, 28:30)) %>% 
  mutate(Szene = 
           Szene %>% case_match(
             "X0_Y0_oAPL" ~ "without\nTask\nLighting",
             "X0_Y0_APL" ~ "with Task\nLighting",
             .default = Szene
  ))

#repeated footnotes in the summary tables
Footnotes <-   function(Data, rows) {
  Data %>% 
  tab_footnote(
    footnote = "This viewing position equals the spectral measurement position",
    locations = cells_body(columns = Bild, rows = rows),
    placement = "left"
  ) %>% 
  tab_footnote(
    footnote = "Relative Spectral Power Distribution (rSPD), Hemispheric (2π) Measurement in stark color, FOV in faded color",
    locations = cells_column_labels(Plot)) %>% 
  tab_footnote(
    footnote = "Melanopic Equivalent Daylight (D65) Illuminance (MEDI)",
    locations = cells_column_labels(MEDI_Voll)) %>% 
  tab_footnote(
    footnote = "Melanopic Daylight (D65) Efficacy Ratio (MDER)",
    locations = cells_column_labels(MDER_Voll)) %>% 
  tab_footnote(
    footnote = "2π = Hemispheric Measurements, FOV = Measurements witht the Field of View (FOV) occlusion") %>% 
  tab_options(
    footnotes.padding = 0
  )
}

#summary table 1: create, show, save
Postertabelle1 <- 
Table(Meta3 %>% slice_head(n=10) %>% rownames_to_column(),NULL) %>%
  tab_row_group(label = "D. Industry Workplace",
                rows = 9:10) %>% 
  tab_row_group(label = "C. Industry Field Study (Machine Workplace)",
                rows = 7:8) %>% 
    tab_row_group(label = "B. Home Office Workplace",
                rows = 4:6) %>% 
    tab_row_group(label = "A. Realistic Office Lab Study",
                rows = 1:3) %>% 
  cols_hide("Projekt") %>% 
  tab_style(style = cell_text(size = "large"),
            locations = list(cells_row_groups(),cells_column_labels(),
                             cells_body(), cells_footnotes(), cells_stub())) %>%
  tab_style(style = cell_text(weight = "bold"), 
            locations = cells_row_groups()) %>% 
  cols_width(ends_with("_Voll") ~px(175),
             Szene ~ px(130),
             stub() ~ px(31)) %>% 
  Footnotes(rows = 7:10) %>% 
  cols_align(align = "right", columns = ends_with("_Voll") | Bild)

Postertabelle1

		Name	Illuminance	MEDI²	MDER³
A. Realistic Office Lab Study
1		warm low	2π: 296 lxa FOV: 212 lxa -28%a	2π: 141 lxa FOV: 101 lxa -28%a	2π: 0.476a FOV: 0.476a 0%a
2		cold low	2π: 298 lxa FOV: 213 lxa -29%a	2π: 300 lxa FOV: 214 lxa -29%a	2π: 1.007a FOV: 1.005a 0%a
3		cold bright	2π: 830 lxa FOV: 592 lxa -29%a	2π: 838 lxa FOV: 595 lxa -29%a	2π: 1.010a FOV: 1.005a 0%a
B. Home Office Workplace
4		Morning	2π: 450 lxa FOV: 184 lxa -59%a	2π: 426 lxa FOV: 171 lxa -60%a	2π: 0.947a FOV: 0.927a -2%a
5		Daytime	2π: 390 lxa FOV: 161 lxa -59%a	2π: 324 lxa FOV: 131 lxa -60%a	2π: 0.831a FOV: 0.812a -2%a
6		Night	2π: 139 lxa FOV: 58 lxa -58%a	2π: 55 lxa FOV: 23 lxa -59%a	2π: 0.397a FOV: 0.396a 0%a
C. Industry Field Study (Machine Workplace)
7	⁴	Morning	2π: 449 lxa FOV: 433 lxa -3%a	2π: 412 lxa FOV: 399 lxa -3%a	2π: 0.918a FOV: 0.920a 0%a
8	⁴	Night	2π: 156 lxa FOV: 150 lxa -4%a	2π: 72 lxa FOV: 70 lxa -3%a	2π: 0.463a FOV: 0.466a 1%a
D. Industry Workplace
9	⁴	without Task Lighting	2π: 195 lxa FOV: 79 lxa -59%a	2π: 137 lxa FOV: 55 lxa -60%a	2π: 0.703a FOV: 0.690a -2%a
10	⁴	with Task Lighting	2π: 270 lxa FOV: 160 lxa -41%a	2π: 182 lxa FOV: 104 lxa -43%a	2π: 0.673a FOV: 0.647a -4%a
2π = Hemispheric Measurements, FOV = Measurements witht the Field of View (FOV) occlusion
¹ Relative Spectral Power Distribution (rSPD), Hemispheric (2π) Measurement in stark color, FOV in faded color
² Melanopic Equivalent Daylight (D65) Illuminance (MEDI)
³ Melanopic Daylight (D65) Efficacy Ratio (MDER)
⁴ This viewing position equals the spectral measurement position

Code

gtsave(Postertabelle1, "Results/Table_Part1.png", expand = c(5, 20, 5, 0))

 #summary table 2: create, show, save
Postertabelle2 <- 
Table(Meta3 %>% rownames_to_column() %>% slice_tail(n=10),NULL) %>%
  tab_row_group(label = "H. Artificial Office Lab Study ",
                rows = 8:10) %>%   
    tab_row_group(label = "G. Halfdome Ganzfeld Lab Setup",
                rows = 7) %>% 
    tab_row_group(label = "F. Industry Field Study (Packaging Workplace)",
                rows = 4:6) %>% 
    tab_row_group(label = "E. Learning Space",
                rows = 1:3) %>% 
  cols_hide("Projekt") %>% 
    tab_style(style = cell_text(size = "large"),
            locations = list(cells_row_groups(),cells_column_labels(),
                             cells_body(), cells_footnotes(), cells_stub())) %>%
  tab_style(style = cell_text(weight = "bold"), 
            locations = cells_row_groups()) %>% 
  cols_width(ends_with("_Voll") ~px(175),
             Szene ~ px(130)) %>%
  Footnotes(rows = 1:3) %>% 
  cols_align(align = "right", columns = ends_with("_Voll") | Bild)


Postertabelle2

		Name	Illuminance	MEDI²	MDER³
E. Learning Space
11	⁴	Morning	2π: 261 lxa FOV: 232 lxa -11%a	2π: 242 lxa FOV: 215 lxa -11%a	2π: 0.927a FOV: 0.927a 0%a
12	⁴	Daytime	2π: 209 lxa FOV: 164 lxa -22%a	2π: 128 lxa FOV: 100 lxa -22%a	2π: 0.612a FOV: 0.610a 0%a
13	⁴	Evening	2π: 147 lxa FOV: 87 lxa -41%a	2π: 54 lxa FOV: 31 lxa -43%a	2π: 0.367a FOV: 0.356a -3%a
F. Industry Field Study (Packaging Workplace)
14		Morning	2π: 460 lxa FOV: 206 lxa -55%a	2π: 322 lxa FOV: 151 lxa -53%a	2π: 0.702a FOV: 0.736a 5%a
15		Daytime	2π: 225 lxa FOV: 154 lxa -31%a	2π: 158 lxa FOV: 110 lxa -31%a	2π: 0.704a FOV: 0.712a 1%a
16		Night	2π: 153 lxa FOV: 120 lxa -22%a	2π: 97 lxa FOV: 79 lxa -19%a	2π: 0.633a FOV: 0.654a 3%a
G. Halfdome Ganzfeld Lab Setup
17		Halfdome	2π: 1,982 lxa FOV: 1,829 lxa -8%a	2π: 2,323 lxa FOV: 2,147 lxa -8%a	2π: 1.172a FOV: 1.174a 0%a
H. Artificial Office Lab Study
18		LS1	2π: 276 lxa FOV: 254 lxa -8%a	2π: 164 lxa FOV: 90 lxa -45%a	2π: 0.594a FOV: 0.353a -41%a
19		LS2	2π: 178 lxa FOV: 126 lxa -29%a	2π: 256 lxa FOV: 238 lxa -7%a	2π: 1.438a FOV: 1.889a 31%a
20		LS3	2π: 181 lxa FOV: 117 lxa -35%a	2π: 385 lxa FOV: 111 lxa -71%a	2π: 2.127a FOV: 0.949a -55%a
2π = Hemispheric Measurements, FOV = Measurements witht the Field of View (FOV) occlusion
¹ Relative Spectral Power Distribution (rSPD), Hemispheric (2π) Measurement in stark color, FOV in faded color
² Melanopic Equivalent Daylight (D65) Illuminance (MEDI)
³ Melanopic Daylight (D65) Efficacy Ratio (MDER)
⁴ This viewing position equals the spectral measurement position

Code

gtsave(Postertabelle2, "Results/Table_Part2.png", expand = c(5, 20, 5, 0))

#put the summary tables together
Part1 <- image_read(path = "Results/Table_Part1.png")
Part2 <- image_read(path = "Results/Table_Part2.png")

Summary <- image_append(c(Part1, Part2))

image_write(Summary, path = "Results/Table_Summary.png", format = "png")

FOV

Code

#Import the datasat
Polar_data <- read_csv("Data/wpd_datasets.csv")

Rows: 57 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (4): r.Binocular, A.Binocular, r.CIE, A.CIE

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Code

#Tidy the dataat
Polar_data <- Polar_data %>% pivot_longer(cols = everything(),
                            names_to = c(".value", "set"),
                            names_sep = "[.]")

Polar_data <- Polar_data %>% mutate(r = ifelse(r > 90, 90, r),
                                    set = factor(set, levels = 
                                                   c("Binocular", "CIE")),
                                    set = fct_recode(set,
                                                     "CIE S026" = "CIE")) %>% 
  drop_na()

#create a dataset for binocular vision
Binokular2 <- Polar_data %>% filter(set == "Binocular") %>% mutate(
  A = case_when(
    A <= 180 ~ 90-(A-90),
    TRUE ~ 270-(A-270)
),
set = factor(set, levels = c("Binocular", "CIE"))) %>% 
  add_row(set = "Binocular", r = 90, A =0) %>% 
  add_row(set = "Binocular", r = 90, A =360)

#create and save the plot for the FOV
Polar_data %>% 
  ggplot(aes(x=A, y=r))+
  geom_hline(yintercept = seq(10, 90, by= 20), col="grey90", linewidth = 0.2)+
  geom_area(
    data = Binokular2, outline.type = "full", alpha = 0.5, fill = "blue")+
  geom_area(outline.type = "full", alpha = 0.55, fill = "red")+
  scale_y_continuous(limits = c(0,90),
                     breaks = seq(10, 90, by=20), expand = expansion(),
                     labels = scales::label_number(suffix = "°"))+
  scale_x_continuous(limits = c(0,360), breaks = seq(0, 330, by = 30),
                     labels = scales::label_number(suffix = "°"))+
  coord_polar(start = -1/2*pi, direction = -1)+
  facet_grid(~set)+
  theme_minimal()+
  theme(axis.line.x = element_blank(),
        axis.line.y = element_blank(),
        panel.grid.minor  = element_blank(),
        panel.grid.major.y  = element_blank(),
        text = element_text(size = 15)
        )+
  labs(x = NULL, y = NULL)

Code

ggsave("Results/view.png", height = 3.5)

Saving 7 x 3.5 in image

Verification of FOV occlusion cutoff angles

Code

filepath <- "Data/Validation/rel_Illuminance.xlsx"

Validation_data <- read_xlsx(filepath)

Validation_data <- Validation_data %>% mutate(Illuminance_rel = Illuminance/max(Illuminance),
                           .by = Type)

shaded_areas <- tibble(
  xmin = 0,
  ymin = c(-90, 50),
  xmax = 1,
  ymax = c(-70, 90)
)

Initial_plot <- function(Filter, Variable) {
  Validation_data %>% filter({{ Filter }}==0) %>%  
  ggplot(aes(x={{ Variable }}, y=Illuminance_rel, color=Type)) +
    {
      if(substitute(Filter) == sym("X")){
            geom_rect(data = shaded_areas, aes(
    ymin = xmin, xmin = ymin, ymax = xmax, xmax = ymax), fill = "black", 
    alpha =0.5, color = NA, inherit.aes = FALSE)
      }
      }+
  Commons
}

Commons <- list(
  geom_point(size = 2),
  geom_path(aes(group=Run), lwd = 1),
  theme_cowplot(),
  scale_color_viridis_d(),
  geom_function(fun = \(x) cos(x/90*(pi/2)), inherit.aes = FALSE,
              lwd = 1, lty = 2),
  scale_x_continuous(breaks=c(seq(-90,90,by=20), 0), 
                   label = scales::label_number(suffix = "°") ),
  labs(y="Relative Illuminance"),
  scale_y_continuous(labels = scales::label_percent())
)

Caption <- "Validation of FOV occlusion cutoff angles in the vertical (A) and horizontal (B) direction, depending on Type of occlusion. 'Jeti' specifies the standard measurement, 'Preliminary' the preliminary FOV occlusion, and 'FOV occlusion' is eponymous. A dashed curve shows the idealized behavior of a cosine-adapted sensor. (A) Both FOV occlusion types have a similar cutoff angle. The non-zero beamwidth of the illuminant leads to some variance at the exact cutoff angles. (B) The FOV occlusion types behave almost identical in the horizontal direction and with minimal difference to the 2π measurement without any occlusion."

Plot1 <- Initial_plot(X, Y) +
  guides(color = "none", lwd = "none")+
  geom_vline(aes(xintercept = 0), lty=3)+
  coord_flip()

Plot2 <- Initial_plot(Y, X)

(Plot1 | (Plot2 / guide_area() + 
            plot_layout(guides = "collect", heights = c(3,1)))) +
  plot_annotation(caption = Caption,
                  tag_levels = "A",
                  theme = theme(
                    plot.caption = element_textbox_simple()
                  ))

Code

ggsave("Results/S3.png", height = 4.75)

Saving 7 x 4.75 in image