Among Us VGA

Clear

rm(list = ls())

Process VGA Files

Reading MIF Line Files

map_w_furniture = readOGR("Maps_Analysis/1_With_Furniture.mif", verbose = FALSE)
map_wo_furniture = readOGR("Maps_Analysis/2_Without_Furniture.mif", verbose = FALSE)

## Warning in readOGR("Maps_Analysis/2_Without_Furniture.mif", verbose = FALSE):
## Dropping null geometries: 4, 5, 6, 7, 8, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
## 48, 49, 50, 51, 170, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,
## 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 200, 201, 202

#map_ac = readOGR("Maps_Analysis/3_ac.dxf", verbose = FALSE)
#plot(map_w_furniture)
#plot(map_wo_furniture)
#plot(map_ac)

Make Grid on Depthmap and Conduct Basic VGA Processes

#Map1
rdepthmap::importLines(map_w_furniture, "Maps_Analysis/1_With_Furniture.graph")
rdepthmap::createGrid("Maps_Analysis/1_With_Furniture.graph", gridSize = 700)
rdepthmap::fillGrid("Maps_Analysis/1_With_Furniture.graph", fillX = -39506, fillY = -9412)
rdepthmap::makeVGAGraph("Maps_Analysis/1_With_Furniture.graph")
gallery_w_furniture = rdepthmap::getPointmapData("Maps_Analysis/1_With_Furniture.graph")$map
#names(gallery_w_furniture)

#Map2
rdepthmap::importLines(map_wo_furniture, "Maps_Analysis/2_Without_Furniture.graph")
rdepthmap::createGrid("Maps_Analysis/2_Without_Furniture.graph", gridSize = 700)
rdepthmap::fillGrid("Maps_Analysis/2_Without_Furniture.graph", fillX = -39506, fillY = -9412)
rdepthmap::makeVGAGraph("Maps_Analysis/2_Without_Furniture.graph")
gallery_wo_furniture = rdepthmap::getPointmapData("Maps_Analysis/2_Without_Furniture.graph")$map
#names(gallery_wo_furniture)

#Map3
#rdepthmap::importLines(map_ac, "Maps_Analysis/3_Accessibility.graph")
#rdepthmap::createGrid("Maps_Analysis/3_Accessibility.graph", gridSize = 700)
#rdepthmap::fillGrid("Maps_Analysis/3_Accessibility.graph", fillX = -53601, fillY = -9458)
#rdepthmap::makeVGAGraph("Maps_Analysis/3_Accessibility.graph")
gallery_ac = rdepthmap::getPointmapData("Maps_Analysis/3_Accessibility.graph")$map
#names(gallery_ac)

Test Plot

plot(gallery_w_furniture[, "Point.First.Moment"])

plot(gallery_wo_furniture[, "Point.First.Moment"])

plot(gallery_ac[, "Point.First.Moment"])

1) Process VGA Analysis : map with furniture

rdepthmap::VGA("Maps_Analysis/1_With_Furniture.graph", vgaMode = "visibility-local", radii = c("2,3,4,5,6,7"))
rdepthmap::VGA("Maps_Analysis/1_With_Furniture.graph", vgaMode = "visibility-global", radii = c("2"))
rdepthmap::VGA("Maps_Analysis/1_With_Furniture.graph", vgaMode = "visibility-global", radii = c("3"))
rdepthmap::VGA("Maps_Analysis/1_With_Furniture.graph", vgaMode = "visibility-global", radii = c("4"))
rdepthmap::VGA("Maps_Analysis/1_With_Furniture.graph", vgaMode = "visibility-global", radii = c("5"))
rdepthmap::VGA("Maps_Analysis/1_With_Furniture.graph", vgaMode = "visibility-global", radii = c("6"))
rdepthmap::VGA("Maps_Analysis/1_With_Furniture.graph", vgaMode = "visibility-global", radii = c("7"))
gallery_w_furniture = rdepthmap::getPointmapData("Maps_Analysis/1_With_Furniture.graph")$map
#names(gallery_w_furniture)

Process VGA Analysis : map without furniture

rdepthmap::VGA("Maps_Analysis/2_Without_Furniture.graph", vgaMode = "visibility-local", radii = c("2,3,4,5,6,7"))
rdepthmap::VGA("Maps_Analysis/2_Without_Furniture.graph", vgaMode = "visibility-global", radii = c("2"))
rdepthmap::VGA("Maps_Analysis/2_Without_Furniture.graph", vgaMode = "visibility-global", radii = c("3"))
rdepthmap::VGA("Maps_Analysis/2_Without_Furniture.graph", vgaMode = "visibility-global", radii = c("4"))
rdepthmap::VGA("Maps_Analysis/2_Without_Furniture.graph", vgaMode = "visibility-global", radii = c("5"))
rdepthmap::VGA("Maps_Analysis/2_Without_Furniture.graph", vgaMode = "visibility-global", radii = c("6"))
rdepthmap::VGA("Maps_Analysis/2_Without_Furniture.graph", vgaMode = "visibility-global", radii = c("7"))
gallery_wo_furniture = rdepthmap::getPointmapData("Maps_Analysis/2_Without_Furniture.graph")$map
#names(gallery_wo_furniture)

Process VGA Analysis : map for accessibility

rdepthmap::VGA("Maps_Analysis/3_Accessibility.graph", vgaMode = "visibility-local", radii = c("2,3,4,5,6,7"))
rdepthmap::VGA("Maps_Analysis/3_Accessibility.graph", vgaMode = "visibility-global", radii = c("2"))
rdepthmap::VGA("Maps_Analysis/3_Accessibility.graph", vgaMode = "visibility-global", radii = c("3"))
rdepthmap::VGA("Maps_Analysis/3_Accessibility.graph", vgaMode = "visibility-global", radii = c("4"))
rdepthmap::VGA("Maps_Analysis/3_Accessibility.graph", vgaMode = "visibility-global", radii = c("5"))
rdepthmap::VGA("Maps_Analysis/3_Accessibility.graph", vgaMode = "visibility-global", radii = c("6"))
rdepthmap::VGA("Maps_Analysis/3_Accessibility.graph", vgaMode = "visibility-global", radii = c("7"))
gallery_ac = rdepthmap::getPointmapData("Maps_Analysis/3_Accessibility.graph")$map
#names(gallery_wo_furniture)

List of metrics

names(gallery_w_furniture)

##  [1] "Ref"                             "Connectivity"                   
##  [3] "Point.First.Moment"              "Point.Second.Moment"            
##  [5] "Visual.Entropy.R2"               "Visual.Entropy.R3"              
##  [7] "Visual.Entropy.R4"               "Visual.Entropy.R5"              
##  [9] "Visual.Entropy.R6"               "Visual.Entropy.R7"              
## [11] "Visual.Integration..HH..R2"      "Visual.Integration..HH..R3"     
## [13] "Visual.Integration..HH..R4"      "Visual.Integration..HH..R5"     
## [15] "Visual.Integration..HH..R6"      "Visual.Integration..HH..R7"     
## [17] "Visual.Integration..P.value..R2" "Visual.Integration..P.value..R3"
## [19] "Visual.Integration..P.value..R4" "Visual.Integration..P.value..R5"
## [21] "Visual.Integration..P.value..R6" "Visual.Integration..P.value..R7"
## [23] "Visual.Integration..Tekl..R2"    "Visual.Integration..Tekl..R3"   
## [25] "Visual.Integration..Tekl..R4"    "Visual.Integration..Tekl..R5"   
## [27] "Visual.Integration..Tekl..R6"    "Visual.Integration..Tekl..R7"   
## [29] "Visual.Mean.Depth.R2"            "Visual.Mean.Depth.R3"           
## [31] "Visual.Mean.Depth.R4"            "Visual.Mean.Depth.R5"           
## [33] "Visual.Mean.Depth.R6"            "Visual.Mean.Depth.R7"           
## [35] "Visual.Node.Count.R2"            "Visual.Node.Count.R3"           
## [37] "Visual.Node.Count.R4"            "Visual.Node.Count.R5"           
## [39] "Visual.Node.Count.R6"            "Visual.Node.Count.R7"           
## [41] "Visual.Relativised.Entropy.R2"   "Visual.Relativised.Entropy.R3"  
## [43] "Visual.Relativised.Entropy.R4"   "Visual.Relativised.Entropy.R5"  
## [45] "Visual.Relativised.Entropy.R6"   "Visual.Relativised.Entropy.R7"  
## [47] "i"                               "j"

Load Danger Maps Data

Visualize Ethnography (Danger Map)

eth.grid = as(eth,"SpatialPixelsDataFrame")
plot(eth.grid[, "Verified8k"])

plot(eth.grid[, "Verified9k"])

Process Reddit Post with 8k Likes

v_8k_df = data.frame(values)
v_8k_df$risk = eth$Verified8k
#write_csv(v_8k_df,"ouputdata/Verification8k.csv")

rsq_8k_df = purrr::map(v_8k_df[,-1],  ~lm(risk ~ .x, data = v_8k_df)) %>%
  purrr::map(broom::glance) %>%
  dplyr::bind_rows(.id = "variable") %>%
  dplyr::select(variable, r.squared)
#rsq_8k_df

f_rsq_8k_df  = rsq_8k_df %>% 
  .[-c(46, 47, 48, 49,50), ] %>% 
  arrange(desc(r.squared))

Process Reddit Post with 9k Likes

v_9k_df = data.frame(values)
v_9k_df$risk = eth$Verified9k
#write_csv(v_9k_df,"ouputdata/Verification9k.csv")

rsq_9k_df = purrr::map(v_9k_df[,-1],  ~lm(risk ~ .x, data = v_9k_df)) %>%
  purrr::map(broom::glance) %>%
  dplyr::bind_rows(.id = "variable") %>%
  dplyr::select(variable, r.squared)
#rsq_9k_df

f_rsq_9k_df  = rsq_9k_df %>% 
  .[-c(46, 47, 48, 49,50), ] %>% 
  arrange(desc(r.squared))

Compare R.Squared Values

p_8k <- ggplot(data=f_rsq_8k_df, aes(x=variable, y=r.squared)) +
  geom_bar(stat="identity")
p_8k + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

p_9k <- ggplot(data=f_rsq_9k_df, aes(x=variable, y=r.squared)) +
  geom_bar(stat="identity")
p_9k + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

## Scatter different Visual Metrics

#ggplot(v_8k_df, aes(x=v_8k_df$risk, y=v_8k_df$Visual.Node.Count.R3)) + geom_point()
ggplot(v_8k_df, aes(x=v_8k_df$risk, y=v_8k_df$Visual.Relativised.Entropy.R6)) + geom_point(shape=1,size=5)

## Visualize Actual on Map

#plot(gallery_w_furniture[, "Visual.Node.Count.R3"])
plot(gallery_w_furniture[, "Visual.Relativised.Entropy.R6"])

plot(gallery_w_furniture[, "Visual.Integration..P.value..R7"])

eth.grid = as(eth,"SpatialPixelsDataFrame")
plot(eth.grid[, "Verified8k"])

plot(eth.grid[, "Verified9k"])

Load Kill Map Data

Find Point Deaths

#Visualize Points
pd = readOGR("Maps_Ethnography/Deaths.mif")

## OGR data source with driver: MapInfo File 
## Source: "D:\Google Drive 0\GitHub\AmongUsVGA\Maps_Ethnography\Deaths.mif", layer: "Deaths"
## with 102 features
## It has 1 fields

plot(gallery_w_furniture[, "Visual.Integration..Tekl..R7"])
plot(pd, add = TRUE, col = "red")

Kernel Density Graph at 2500 RADIUS

ma = as(eth.grid[, "Verified8k"],"owin")
pp = as(pd,"ppp")
marks(pp) = NULL
Window(pp) = ma
K <- density(pp,sigma = 3000)  #more is more spread
plot(K, main=NULL, las=1)
contour(K, add=TRUE)

Convert Kernel Density Graph into SPDF to Visualize

d = eth
d1 = cbind(d,K)
d1$x <- NULL
d1$y <- NULL
d1$optional <- NULL
d.grid = as(d1,"SpatialPixelsDataFrame")
plot(d.grid[, "value"])

pd = readOGR("Maps_Ethnography/Deaths.mif")

## OGR data source with driver: MapInfo File 
## Source: "D:\Google Drive 0\GitHub\AmongUsVGA\Maps_Ethnography\Deaths.mif", layer: "Deaths"
## with 102 features
## It has 1 fields

plot(pd, add = TRUE, col = "red")

Testing R Correlation

v_0k_df = data.frame(values)
v_0k_df$risk = d1$value

rsq_0k_df = purrr::map(v_0k_df[,-1],  ~lm(risk ~ .x, data = v_0k_df)) %>%
  purrr::map(broom::glance) %>%
  dplyr::bind_rows(.id = "variable") %>%
  dplyr::select(variable, r.squared)

## Warning in summary.lm(x): essentially perfect fit: summary may be unreliable

## Warning in summary.lm(x): essentially perfect fit: summary may be unreliable

#rsq_0k_df

f_rsq_0k_df  = rsq_0k_df %>% 
  .[-c(46, 47, 48, 49,50), ] %>% 
  arrange(desc(r.squared))


p_0k <- ggplot(data=f_rsq_0k_df, aes(x=variable, y=r.squared)) +
  geom_bar(stat="identity")
p_0k + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

Using QGIS manipulated map

#Visualize Heatmap Diagram
death = readOGR("Maps_Ethnography/Ethnography_2.mif")

## OGR data source with driver: MapInfo File 
## Source: "D:\Google Drive 0\GitHub\AmongUsVGA\Maps_Ethnography\Ethnography_2.mif", layer: "Ethnography_2"
## with 2998 features
## It has 7 fields

death.grid = as(death,"SpatialPixelsDataFrame")
plot(death.grid[, "one"])

plot(death.grid[, "onehalf"])

plot(death.grid[, "two"])

plot(death.grid[, "twohalf"])

Check Distribution of Values (TOTAL 3000 points)

Correlation Test

Correlation Based on Heatmap

p_1k <- ggplot(data=f_rsq_1k_df, aes(x=variable, y=r.squared)) +
  geom_bar(stat="identity")
p_1k + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

p_1_5k <- ggplot(data=f_rsq_1_5k_df, aes(x=variable, y=r.squared)) +
  geom_bar(stat="identity")
p_1_5k + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

p_2k <- ggplot(data=f_rsq_2k_df, aes(x=variable, y=r.squared)) +
  geom_bar(stat="identity")
p_2k + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

p_2_5k <- ggplot(data=f_rsq_2_5k_df, aes(x=variable, y=r.squared)) +
  geom_bar(stat="identity")
p_2_5k + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

Distribution of Data Points based on Visual Metrics

overlapsPD = over(pd,gallery_w_furniture)
overlapsPD.a <- scale(overlapsPD[,2:46],center=T,scale=T)
overlapsPD.b = as.data.frame(overlapsPD.a)
overlapsPD.b$Ref =overlapsPD$Ref
DAT.melt = melt(overlapsPD.b,id=c("Ref"))
ggplot(DAT.melt, aes(x=variable,y=value) ) + geom_point(shape=1,size=4)  + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

## Warning: Removed 102 rows containing missing values (geom_point).

General Map Visibility

Compare visibility radius 2-7

1. map with furniture

plot(gallery_w_furniture[, "Visual.Integration..HH..R2"])

plot(gallery_w_furniture[, "Visual.Integration..HH..R3"])

plot(gallery_w_furniture[, "Visual.Integration..HH..R7"])

### 2. map without furniture

plot(gallery_wo_furniture[, "Visual.Integration..HH..R2"])

plot(gallery_wo_furniture[, "Visual.Integration..HH..R3"])

plot(gallery_wo_furniture[, "Visual.Integration..HH..R7"])

### 3. map accessibility

plot(gallery_ac[, "Visual.Integration..HH..R2"])

plot(gallery_ac[, "Visual.Integration..HH..R3"])

plot(gallery_ac[, "Visual.Integration..HH..R7"])

Compare with and without furniture

Visual Integration R7

plot(gallery_w_furniture[, "Visual.Integration..HH..R7"])

plot(gallery_wo_furniture[, "Visual.Integration..HH..R7"])

#### Visual Entropy, R6

plot(gallery_w_furniture[, "Visual.Relativised.Entropy.R6"])

plot(gallery_wo_furniture[, "Visual.Relativised.Entropy.R6"])

Feature Visibility

Visualizing Feature Spaces

#names(gallery_w_furniture)

feature_layer = readOGR("Maps_Analysis/Feature_Polygon2.mif", verbose = FALSE)
plot(gallery_w_furniture[, "Visual.Integration..HH..R7"])
plot(feature_layer, add = TRUE, col = "red")

## Complete Feature List

overlapsDF = over(gallery_w_furniture[, "Visual.Integration..HH..R7"], feature_layer)
visi_df = aggregate(gallery_w_furniture$Visual.Integration..HH..R7, by = list(overlapsDF$Layer,overlapsDF$Text,overlapsDF$LengthTask,overlapsDF$VisualTask), mean, areaWeighted = TRUE)
vif = visi_df %>% arrange(Group.4)
write.csv(vif,"ouputdata/visualintegration_features.csv")

Comparisons by Averaging

@Types_of_Task

visi_df %>%
            group_by(Group.1) %>%
            summarise(V_r7 = mean(x))

## # A tibble: 5 x 2
##   Group.1       V_r7
##   <chr>        <dbl>
## 1 emergency     8.52
## 2 sabotage      3.14
## 3 surveillance  3.43
## 4 tasks         3.87
## 5 vents         3.79

@Length_of_Task

visi_df %>%
          filter(Group.1 == "tasks" | Group.1 == "hotpink" | Group.1 == "lightpink") %>%
            group_by(Group.3) %>%
            summarise(V_r7 = mean(x))

## # A tibble: 3 x 2
##   Group.3  V_r7
##   <chr>   <dbl>
## 1 Common   4.00
## 2 Long     4.34
## 3 Short    3.61

@Visual_Type_of_Task

visi_df %>%
          filter(Group.1 == "tasks" | Group.1 == "hotpink" | Group.1 == "lightpink") %>% 
            group_by(Group.4) %>%
            summarise(V_r7 = mean(x))

## # A tibble: 2 x 2
##   Group.4   V_r7
##   <chr>    <dbl>
## 1 ""        3.66
## 2 "Visual"  4.96

Plot of Visibility for Each Tasks

task_all_df = visi_df %>% filter(Group.1 == "tasks") %>% .[c(3,5)] 
task_time_df = visi_df %>% filter(Group.1 == "tasks") %>% .[c(3,5)]
task_visual_df = visi_df %>% filter(Group.1 == "tasks") %>% .[c(4,5)]

task_all_df$Group.3 = "All"
taskall = melt(task_all_df,id=c("Group.3"))
tasklength = melt(task_time_df,id=c("Group.3"))
taskvisual = melt(task_visual_df,id=c("Group.4")) %>% filter(Group.4 == 'Visual')
names(taskvisual)[1] <- "Group.3"

task_df = rbind(taskall,tasklength,taskvisual)
ggplot(task_df, aes(x=Group.3,y=value, color = Group.3) ) + geom_boxplot(shape=2,size=1)  + theme(axis.text.x = element_text(angle = 0, vjust = 0.5, hjust=0.5)) + labs(x="")

Feature Tables

@Types_of_Task

visi_df %>% filter(Group.1 == "tasks")

##    Group.1                          Group.2 Group.3 Group.4        x
## 1    tasks                   Fix Wiring [1]  Common         3.714819
## 2    tasks                   Fix Wiring [2]  Common         5.800073
## 3    tasks                   Fix Wiring [3]  Common         4.576949
## 4    tasks                   Fix Wiring [4]  Common         3.415989
## 5    tasks                   Fix Wiring [5]  Common         2.404976
## 6    tasks                       Swipe Card  Common         4.111414
## 7    tasks        Fuel Engines Part 1 and 3    Long         3.863964
## 8    tasks              Fuel Engines Part 2    Long         3.126319
## 9    tasks              Fuel Engines Part 4    Long         2.767753
## 10   tasks                   Inspect Sample    Long         3.915823
## 11   tasks                    Start Reactor    Long         3.232568
## 12   tasks Accept Diverted Power Part 2 [1]   Short         2.656431
## 13   tasks Accept Diverted Power Part 2 [2]   Short         3.231415
## 14   tasks Accept Diverted Power Part 2 [3]   Short         4.173330
## 15   tasks Accept Diverted Power Part 2 [4]   Short         3.218441
## 16   tasks Accept Diverted Power Part 2 [5]   Short         3.028439
## 17   tasks Accept Diverted Power Part 2 [6]   Short         4.205980
## 18   tasks Accept Diverted Power Part 2 [7]   Short         6.372041
## 19   tasks Accept Diverted Power Part 2 [8]   Short         3.641347
## 20   tasks    Align Engine Output (1/2) [1]   Short         3.068366
## 21   tasks    Align Engine Output (1/2) [2]   Short         2.770892
## 22   tasks           Callibrate Distributor   Short         3.236655
## 23   tasks                     Chart Course   Short         3.568187
## 24   tasks                  Clean O2 Filter   Short         3.640360
## 25   tasks              Divert Power Part 1   Short         2.401324
## 26   tasks             Download Data Part 2   Short         4.328570
## 27   tasks               Stabilize Steering   Short         3.676948
## 28   tasks                 Unlock Manifolds   Short         3.198719
## 29   tasks           Upload Data Part 1 [1]   Short         2.925612
## 30   tasks           Upload Data Part 1 [2]   Short         3.121275
## 31   tasks           Upload Data Part 1 [3]   Short         6.170896
## 32   tasks                  Clear Asteroids    Long  Visual 6.468803
## 33   tasks               Empty Chute Part 1    Long  Visual 3.638143
## 34   tasks             Empty Garbage Part 1    Long  Visual 6.372089
## 35   tasks       Empty Garbage/Chute Part 2    Long  Visual 6.069834
## 36   tasks                      Submit Scan    Long  Visual 3.934792
## 37   tasks                    Prime Shields   Short  Visual 3.251733

visi_df %>% filter(Group.1 == "sabotage")

##    Group.1                 Group.2 Group.3 Group.4        x
## 1 sabotage Communications Disabled                 3.076162
## 2 sabotage              Fix Lights                 3.219213
## 3 sabotage    Oxygen Depletion [1]                 3.646781
## 4 sabotage    Oxygen Depletion [2]                 4.098094
## 5 sabotage    Reactor Meltdown [1]                 2.411017
## 6 sabotage    Reactor Meltdown [2]                 2.411449

visi_df %>% filter(Group.1 == "surveillance")

##        Group.1         Group.2 Group.3 Group.4        x
## 1 surveillance       Admin Map                 4.142141
## 2 surveillance Security Camera                 2.707994

visi_df %>% filter(Group.1 == "vents")

##    Group.1              Group.2 Group.3 Group.4        x
## 1    vents           vent@admin                 3.088639
## 2    vents       vent@cafeteria                 6.507001
## 3    vents      vent@electrical                 2.399609
## 4    vents         vent@hallway                 3.873818
## 5    vents     vent@lowerengine                 3.365943
## 6    vents vent@navigations [1]                 2.661428
## 7    vents vent@navigations [2]                 2.658795
## 8    vents     vent@reactor [1]                 3.206355
## 9    vents     vent@reactor [2]                 3.195865
## 10   vents    vent@security [1]                 3.969348
## 11   vents    vent@security [2]                 3.155351
## 12   vents         vent@shields                 3.274905
## 13   vents     vent@upperengine                 4.285781
## 14   vents     vent@weapons [1]                 5.120081
## 15   vents     vent@weapons [2]                 6.062708

@Length_of_Task

visi_df %>% filter(Group.3 == "Long") %>% select(-one_of(c("Group.1","Group.2")))

##    Group.3 Group.4        x
## 1     Long         3.863964
## 2     Long         3.126319
## 3     Long         2.767753
## 4     Long         3.915823
## 5     Long         3.232568
## 6     Long  Visual 6.468803
## 7     Long  Visual 3.638143
## 8     Long  Visual 6.372089
## 9     Long  Visual 6.069834
## 10    Long  Visual 3.934792

visi_df %>% filter(Group.3 == "Common") %>% select(-one_of(c("Group.1","Group.2")))

##   Group.3 Group.4        x
## 1  Common         3.714819
## 2  Common         5.800073
## 3  Common         4.576949
## 4  Common         3.415989
## 5  Common         2.404976
## 6  Common         4.111414

visi_df %>% filter(Group.3 == "Short") %>% select(-one_of(c("Group.1","Group.2")))

##    Group.3 Group.4        x
## 1    Short         2.656431
## 2    Short         3.231415
## 3    Short         4.173330
## 4    Short         3.218441
## 5    Short         3.028439
## 6    Short         4.205980
## 7    Short         6.372041
## 8    Short         3.641347
## 9    Short         3.068366
## 10   Short         2.770892
## 11   Short         3.236655
## 12   Short         3.568187
## 13   Short         3.640360
## 14   Short         2.401324
## 15   Short         4.328570
## 16   Short         3.676948
## 17   Short         3.198719
## 18   Short         2.925612
## 19   Short         3.121275
## 20   Short         6.170896
## 21   Short  Visual 3.251733

@Visual_Type_of_Task

visi_df %>% filter(Group.4 == "Visual") %>% select(-one_of(c("Group.1")))

##                      Group.2 Group.3 Group.4        x
## 1            Clear Asteroids    Long  Visual 6.468803
## 2         Empty Chute Part 1    Long  Visual 3.638143
## 3       Empty Garbage Part 1    Long  Visual 6.372089
## 4 Empty Garbage/Chute Part 2    Long  Visual 6.069834
## 5                Submit Scan    Long  Visual 3.934792
## 6              Prime Shields   Short  Visual 3.251733

visi_df %>% filter(Group.1 == "tasks" | Group.1 == "hotpink" | Group.1 == "lightpink") %>% filter(Group.4 != "Visual") %>% select(-one_of(c("Group.1")))

##                             Group.2 Group.3 Group.4        x
## 1                    Fix Wiring [1]  Common         3.714819
## 2                    Fix Wiring [2]  Common         5.800073
## 3                    Fix Wiring [3]  Common         4.576949
## 4                    Fix Wiring [4]  Common         3.415989
## 5                    Fix Wiring [5]  Common         2.404976
## 6                        Swipe Card  Common         4.111414
## 7         Fuel Engines Part 1 and 3    Long         3.863964
## 8               Fuel Engines Part 2    Long         3.126319
## 9               Fuel Engines Part 4    Long         2.767753
## 10                   Inspect Sample    Long         3.915823
## 11                    Start Reactor    Long         3.232568
## 12 Accept Diverted Power Part 2 [1]   Short         2.656431
## 13 Accept Diverted Power Part 2 [2]   Short         3.231415
## 14 Accept Diverted Power Part 2 [3]   Short         4.173330
## 15 Accept Diverted Power Part 2 [4]   Short         3.218441
## 16 Accept Diverted Power Part 2 [5]   Short         3.028439
## 17 Accept Diverted Power Part 2 [6]   Short         4.205980
## 18 Accept Diverted Power Part 2 [7]   Short         6.372041
## 19 Accept Diverted Power Part 2 [8]   Short         3.641347
## 20    Align Engine Output (1/2) [1]   Short         3.068366
## 21    Align Engine Output (1/2) [2]   Short         2.770892
## 22           Callibrate Distributor   Short         3.236655
## 23                     Chart Course   Short         3.568187
## 24                  Clean O2 Filter   Short         3.640360
## 25              Divert Power Part 1   Short         2.401324
## 26             Download Data Part 2   Short         4.328570
## 27               Stabilize Steering   Short         3.676948
## 28                 Unlock Manifolds   Short         3.198719
## 29           Upload Data Part 1 [1]   Short         2.925612
## 30           Upload Data Part 1 [2]   Short         3.121275
## 31           Upload Data Part 1 [3]   Short         6.170896

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Among-Us-VGA.md

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Among Us VGA

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Process Reddit Post with 9k Likes

Compare R.Squared Values

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Among Us VGA

Process VGA Files

Load Danger Maps Data

Visualize Ethnography (Danger Map)

Process Reddit Post with 8k Likes

Process Reddit Post with 9k Likes

Compare R.Squared Values

Load Kill Map Data

Find Point Deaths

Using QGIS manipulated map

Correlation Based on Heatmap

Distribution of Data Points based on Visual Metrics

General Map Visibility

Compare visibility radius 2-7

1. map with furniture

Compare with and without furniture

Visual Integration R7

Feature Visibility

Visualizing Feature Spaces

Comparisons by Averaging

Feature Tables