Data Visualization
Honey, Vuthy, James, Sonya, Mohamed
March 24, 2015
Data Visualization for the IS607 Project No. 2 | CUNY MSDA
Load data
load("Awards.Data.RData")
#View(data.awards)library(tidyr)
library(dplyr)
library(ggplot2)
library(knitr)
#library(grid)
library(gridExtra)Table 1. Number of times a Best Picture winner was nominated or won another category.
# Create BP, a dataframe showing only Best Picture Winners
BP <- filter(data.awards, category_name == "BEST PICTURE", won == 1)
# Create BP.AlsoNom, a dataframe showing what other categories the Best Picture Winners were NOMINATED for
BP.AlsoNom <- left_join(BP, data.awards, by = "movie_name")
BP.AlsoNom <- filter(BP.AlsoNom, category_name.y != "BEST PICTURE")
BP.AlsoNom <- select(BP.AlsoNom, category_name.y)
BP.AlsoNom <- count(BP.AlsoNom, category_name.y)
BP.AlsoNom <- arrange(BP.AlsoNom, desc(n))
# Create BP.AlsoWon, a dataframe showing what other categories the Best Picture winners WON
data.awards.won <- filter(data.awards, won == 1)
BP.AlsoWon <- left_join(BP, data.awards.won, by = "movie_name")
BP.AlsoWon <- filter(BP.AlsoWon, category_name.y != "BEST PICTURE")
BP.AlsoWon <- select(BP.AlsoWon, category_name.y)
BP.AlsoWon <- count(BP.AlsoWon, category_name.y)
BP.AlsoWon <- arrange(BP.AlsoWon, desc(n))
kable(cbind(BP.AlsoNom, BP.AlsoWon),
col.names = c("Category Nominations", "Count", "Category Winners", "Count"))| Category Nominations | Count | Category Winners | Count |
|---|---|---|---|
| WRITING | 80 | DIRECTING | 61 |
| DIRECTING | 78 | WRITING | 56 |
| FILM EDITING | 69 | FILM EDITING | 33 |
| ACTOR – LEADING ROLE | 59 | ART DIRECTION | 27 |
| ACTOR – SUPPORTING ROLE | 55 | ACTOR – LEADING ROLE | 26 |
| CINEMATOGRAPHY | 54 | CINEMATOGRAPHY | 25 |
| MUSIC (SCORING) | 50 | MUSIC (SCORING) | 24 |
| ART DIRECTION | 48 | SOUND | 23 |
| SOUND | 45 | COSTUME DESIGN | 19 |
| ACTRESS – SUPPORTING ROLE | 36 | ACTOR – SUPPORTING ROLE | 15 |
| COSTUME DESIGN | 34 | ACTRESS – SUPPORTING ROLE | 12 |
| ACTRESS – LEADING ROLE | 28 | ACTRESS – LEADING ROLE | 11 |
| MAKEUP | 10 | MUSIC (SONG) | 5 |
| MUSIC (SONG) | 10 | MAKEUP | 4 |
| SOUND EDITING | 7 | VISUAL EFFECTS | 4 |
| VISUAL EFFECTS | 5 | SOUND EDITING | 3 |
Figure 1. Barchart showing the number of times a Best Picture winner was nominated or won another category.
nom <- ggplot(data = BP.AlsoNom, aes(x = reorder(category_name.y, n), y=n)) + geom_bar(stat="identity") + coord_flip() + labs(x = "Category", y = "Nominations")
win <- ggplot(data = BP.AlsoWon, aes(x = reorder(category_name.y, n), y=n)) + geom_bar(stat="identity") + coord_flip() + labs(x = "Category", y = "Wins") + ylim(0, 80)
grid.arrange(nom, win, ncol=1)
Correlation Matrices
Use Sekhar’s method to create award and category matrices
library(dplyr)
library(tidyr)
awards_df <- read.csv("Awards_File.csv",stringsAsFactors=F)
awards_modified <- awards_df[,c(1,3,4,6)]
head(awards_modified)## movie_id year category_id won
## 1 1 2010 1 0
## 2 2 2010 1 0
## 3 2 2010 4 0
## 4 2 2010 6 0
## 5 2 2010 7 0
## 6 2 2010 8 0awards_modified <- awards_modified %>%
group_by(movie_id,year,category_id) %>%
summarise(won=max(won))
awards_re_modified <- spread((awards_modified),category_id,won)
head(awards_re_modified)## Source: local data frame [6 x 25]
##
## movie_id year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
## 1 1 2010 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 2 2010 0 NA NA 0 NA 0 0 0 0 NA NA NA NA NA NA 0 NA NA 0
## 3 3 2010 0 NA NA NA NA NA 0 NA 0 NA NA 1 NA 1 NA 0 NA NA 0
## 4 4 2010 1 0 NA 0 NA 0 0 0 1 NA NA 0 NA 0 NA 1 NA NA 0
## 5 5 2010 0 NA NA NA NA NA NA NA NA NA NA 0 NA 0 0 0 NA NA NA
## 6 6 2010 NA 1 NA 1 NA NA NA NA 0 NA NA 0 NA NA NA 0 NA NA NA
## Variables not shown: 20 (int), 21 (int), 22 (int), 23 (int)award_categories <- unique(data.frame(category_id=awards_df$category_id,category_name=awards_df$category_name))
rownames(award_categories) <- NULL
names(awards_re_modified) <- c("movie_id", "year", "c1", "c2", "c3", "c4", "c5", "c6", "c7", "c8", "c9", "c10", "c11", "c12", "c13", "c14", "c15","c16", "c17", "c18", "c19", "c20", "c21", "c22", "c23")Modify awards matrix to set NA = 0
####
names(awards_re_modified) <- c("movie_id", "year","ACTOR -- LEADING ROLE","ACTOR -- SUPPORTING ROLE","ACTRESS
LEADING ROLE","ACTRESS SUPPORTING ROLE","ANIMATED FEATURE FILM","ART DIRECTION","CINEMATOGRAPHY","COSTUME
DESIGN","DIRECTING","DOCUMENTARY FEATURE","DOCUMENTARY SHORT SUBJECT","FILM EDITING","MAKEUP","MUSIC
SCORING","MUSIC SONG","BEST PICTURE","SHORT FILM ANIMATED","SHORT FILM LIVE ACTION","SOUND","SOUND
EDITING","VISUAL EFFECTS","WRITING","DOCUMENTARY OTHER")
awards_mod <- awards_re_modified #create new var to preserve original
awards_mod[is.na(awards_mod)] <- 0 #set NA = 0Figure 2. Correlation matrix, display circles
library(corrplot)## Warning: package 'corrplot' was built under R version 3.1.3W <- cor(awards_mod[3:25]) #correlate c1:c23
corrplot(W, method = "circle", tl.cex = 0.5)
Figure 3. Correlation matrix, display ellipses
corrplot(W, method = "ellipse", tl.cex = 0.5)
Figure 4. Correlation matrix, display squares
corrplot(W, method = "square", tl.cex = 0.5)
Figure 5. Correlation matrix, using ggplot2
library(ggplot2)
library(reshape2)## Warning: package 'reshape2' was built under R version 3.1.3cm <- qplot(x=Var1, y=Var2, data=melt(cor(awards_mod[3:25])), fill=value, geom="tile")
cm + theme(axis.text=element_text(size=8))
Table 2. Correlation Matrix, numerical values
x <- awards_mod[3:25]
y <- awards_mod[3:25]
cor(x, y)## ACTOR -- LEADING ROLE ACTOR -- SUPPORTING ROLE
## ACTOR -- LEADING ROLE 1.000000000 0.031442183
## ACTOR -- SUPPORTING ROLE 0.031442183 1.000000000
## ACTRESS\nLEADING ROLE 0.063755763 0.044702504
## ACTRESS SUPPORTING ROLE 0.069755123 0.090204750
## ANIMATED FEATURE FILM -0.007796584 -0.007314613
## ART DIRECTION 0.072506810 0.080054937
## CINEMATOGRAPHY 0.058936153 0.087843420
## COSTUME\nDESIGN 0.078686034 0.033580145
## DIRECTING 0.267328311 0.197464921
## DOCUMENTARY FEATURE -0.018964075 -0.017791749
## DOCUMENTARY SHORT SUBJECT -0.018825820 -0.017662041
## FILM EDITING 0.131378762 0.101967154
## MAKEUP 0.007197662 0.009224239
## MUSIC\nSCORING 0.074022300 0.039563592
## MUSIC SONG 0.030521724 0.021538257
## BEST PICTURE 0.295265477 0.174406734
## SHORT FILM ANIMATED -0.020168626 -0.018921837
## SHORT FILM LIVE ACTION -0.023553485 -0.022097450
## SOUND 0.091877626 0.113451180
## SOUND\nEDITING -0.013917970 0.005933184
## VISUAL EFFECTS 0.016160545 0.002224411
## WRITING 0.272537746 0.141419198
## DOCUMENTARY OTHER -0.004497010 -0.004219012
## ACTRESS\nLEADING ROLE ACTRESS SUPPORTING ROLE
## ACTOR -- LEADING ROLE 0.0637557625 0.069755123
## ACTOR -- SUPPORTING ROLE 0.0447025037 0.090204750
## ACTRESS\nLEADING ROLE 1.0000000000 0.108928458
## ACTRESS SUPPORTING ROLE 0.1089284579 1.000000000
## ANIMATED FEATURE FILM -0.0077496318 -0.007314613
## ART DIRECTION 0.0839561720 0.125503007
## CINEMATOGRAPHY 0.0595665577 0.132112232
## COSTUME\nDESIGN 0.0420486161 0.073047923
## DIRECTING 0.0879173749 0.120839042
## DOCUMENTARY FEATURE -0.0188498701 -0.017791749
## DOCUMENTARY SHORT SUBJECT -0.0187124480 -0.017662041
## FILM EDITING 0.0182542530 0.101967154
## MAKEUP 0.0272607454 -0.011783712
## MUSIC\nSCORING 0.0347082404 0.028980108
## MUSIC SONG 0.0055738644 -0.018676191
## BEST PICTURE 0.1138887590 0.135644487
## SHORT FILM ANIMATED -0.0200471669 -0.018921837
## SHORT FILM LIVE ACTION -0.0234116423 -0.022097450
## SOUND -0.0075252400 0.060501973
## SOUND\nEDITING -0.0138341537 -0.013057585
## VISUAL EFFECTS 0.0004362834 -0.014693358
## WRITING 0.1728635223 0.132467112
## DOCUMENTARY OTHER -0.0044699280 -0.004219012
## ANIMATED FEATURE FILM ART DIRECTION
## ACTOR -- LEADING ROLE -0.007796584 0.072506810
## ACTOR -- SUPPORTING ROLE -0.007314613 0.080054937
## ACTRESS\nLEADING ROLE -0.007749632 0.083956172
## ACTRESS SUPPORTING ROLE -0.007314613 0.125503007
## ANIMATED FEATURE FILM 1.000000000 -0.008813338
## ART DIRECTION -0.008813338 1.000000000
## CINEMATOGRAPHY -0.009061580 0.370686282
## COSTUME\nDESIGN -0.007559144 0.417374104
## DIRECTING -0.007796584 0.222109306
## DOCUMENTARY FEATURE -0.007062251 -0.021437183
## DOCUMENTARY SHORT SUBJECT -0.007010764 -0.021280899
## FILM EDITING -0.007413321 0.257903256
## MAKEUP -0.004677423 0.073692036
## MUSIC\nSCORING 0.016754610 0.236824382
## MUSIC SONG 0.025842324 0.022362133
## BEST PICTURE -0.007702417 0.268522271
## SHORT FILM ANIMATED -0.007510827 -0.022798820
## SHORT FILM LIVE ACTION -0.008771354 -0.026625099
## SOUND -0.007510827 0.209798842
## SOUND\nEDITING 0.041930610 0.016047368
## VISUAL EFFECTS -0.005832377 0.095541216
## WRITING -0.011380431 0.152717654
## DOCUMENTARY OTHER -0.001674693 -0.005083466
## CINEMATOGRAPHY COSTUME\nDESIGN DIRECTING
## ACTOR -- LEADING ROLE 0.05893615 0.078686034 0.267328311
## ACTOR -- SUPPORTING ROLE 0.08784342 0.033580145 0.197464921
## ACTRESS\nLEADING ROLE 0.05956656 0.042048616 0.087917375
## ACTRESS SUPPORTING ROLE 0.13211223 0.073047923 0.120839042
## ANIMATED FEATURE FILM -0.00906158 -0.007559144 -0.007796584
## ART DIRECTION 0.37068628 0.417374104 0.222109306
## CINEMATOGRAPHY 1.00000000 0.223022531 0.256699968
## COSTUME\nDESIGN 0.22302253 1.000000000 0.152924338
## DIRECTING 0.25669997 0.152924338 1.000000000
## DOCUMENTARY FEATURE -0.02204100 -0.018386537 -0.018964075
## DOCUMENTARY SHORT SUBJECT -0.02188031 -0.018252492 -0.018825820
## FILM EDITING 0.28276926 0.162519375 0.408735615
## MAKEUP 0.03676774 0.069235822 0.046713306
## MUSIC\nSCORING 0.19461560 0.200844175 0.213373931
## MUSIC SONG 0.04241460 0.032648003 0.043128854
## BEST PICTURE 0.23922825 0.217791704 0.720580863
## SHORT FILM ANIMATED -0.02344099 -0.019554404 -0.020168626
## SHORT FILM LIVE ACTION -0.02737504 -0.022836180 -0.023553485
## SOUND 0.18154401 0.147168546 0.253722211
## SOUND\nEDITING 0.07669114 0.023303969 0.057524736
## VISUAL EFFECTS 0.05073911 0.066768589 0.032071581
## WRITING 0.13958953 0.126487780 0.407247664
## DOCUMENTARY OTHER -0.00522665 -0.004360056 -0.004497010
## DOCUMENTARY FEATURE DOCUMENTARY SHORT SUBJECT
## ACTOR -- LEADING ROLE -0.018964075 -0.018825820
## ACTOR -- SUPPORTING ROLE -0.017791749 -0.017662041
## ACTRESS\nLEADING ROLE -0.018849870 -0.018712448
## ACTRESS SUPPORTING ROLE -0.017791749 -0.017662041
## ANIMATED FEATURE FILM -0.007062251 -0.007010764
## ART DIRECTION -0.021437183 -0.021280899
## CINEMATOGRAPHY -0.022040997 -0.021880311
## COSTUME\nDESIGN -0.018386537 -0.018252492
## DIRECTING -0.018964075 -0.018825820
## DOCUMENTARY FEATURE 1.000000000 -0.017052681
## DOCUMENTARY SHORT SUBJECT -0.017052681 1.000000000
## FILM EDITING -0.018031842 -0.017900384
## MAKEUP -0.011377161 -0.011294218
## MUSIC\nSCORING -0.023111450 -0.022942959
## MUSIC SONG -0.004165055 -0.017900384
## BEST PICTURE -0.018735026 -0.018598441
## SHORT FILM ANIMATED -0.018269012 -0.018135825
## SHORT FILM LIVE ACTION -0.021335064 -0.009288812
## SOUND -0.018269012 -0.018135825
## SOUND\nEDITING -0.012607084 -0.012515174
## VISUAL EFFECTS -0.014186421 -0.014082997
## WRITING -0.027681270 -0.027479464
## DOCUMENTARY OTHER -0.004073452 -0.004043755
## FILM EDITING MAKEUP MUSIC\nSCORING
## ACTOR -- LEADING ROLE 0.131378762 0.007197662 0.07402230
## ACTOR -- SUPPORTING ROLE 0.101967154 0.009224239 0.03956359
## ACTRESS\nLEADING ROLE 0.018254253 0.027260745 0.03470824
## ACTRESS SUPPORTING ROLE 0.101967154 -0.011783712 0.02898011
## ANIMATED FEATURE FILM -0.007413321 -0.004677423 0.01675461
## ART DIRECTION 0.257903256 0.073692036 0.23682438
## CINEMATOGRAPHY 0.282769264 0.036767744 0.19461560
## COSTUME\nDESIGN 0.162519375 0.069235822 0.20084417
## DIRECTING 0.408735615 0.046713306 0.21337393
## DOCUMENTARY FEATURE -0.018031842 -0.011377161 -0.02311145
## DOCUMENTARY SHORT SUBJECT -0.017900384 -0.011294218 -0.02294296
## FILM EDITING 1.000000000 0.008795693 0.25782754
## MAKEUP 0.008795693 1.000000000 0.03381370
## MUSIC\nSCORING 0.257827544 0.033813704 1.00000000
## MUSIC SONG 0.073701618 0.029534115 0.17424595
## BEST PICTURE 0.401247896 0.067549655 0.21648362
## SHORT FILM ANIMATED -0.019177180 -0.012099810 -0.02457943
## SHORT FILM LIVE ACTION -0.022395647 -0.014130497 -0.02870455
## SOUND 0.372846880 0.049337977 0.23334904
## SOUND\nEDITING 0.155490284 0.079791297 0.02744237
## VISUAL EFFECTS 0.118714091 0.095297214 0.06002750
## WRITING 0.218385155 0.023215303 0.13718831
## DOCUMENTARY OTHER -0.004275947 -0.002697901 -0.00548049
## MUSIC SONG BEST PICTURE SHORT FILM ANIMATED
## ACTOR -- LEADING ROLE 0.030521724 0.295265477 -0.020168626
## ACTOR -- SUPPORTING ROLE 0.021538257 0.174406734 -0.018921837
## ACTRESS\nLEADING ROLE 0.005573864 0.113888759 -0.020047167
## ACTRESS SUPPORTING ROLE -0.018676191 0.135644487 -0.018921837
## ANIMATED FEATURE FILM 0.025842324 -0.007702417 -0.007510827
## ART DIRECTION 0.022362133 0.268522271 -0.022798820
## CINEMATOGRAPHY 0.042414599 0.239228246 -0.023440987
## COSTUME\nDESIGN 0.032648003 0.217791704 -0.019554404
## DIRECTING 0.043128854 0.720580863 -0.020168626
## DOCUMENTARY FEATURE -0.004165055 -0.018735026 -0.018269012
## DOCUMENTARY SHORT SUBJECT -0.017900384 -0.018598441 -0.018135825
## FILM EDITING 0.073701618 0.401247896 -0.019177180
## MAKEUP 0.029534115 0.067549655 -0.012099810
## MUSIC\nSCORING 0.174245949 0.216483617 -0.024579432
## MUSIC SONG 1.000000000 0.044108527 -0.019177180
## BEST PICTURE 0.044108527 1.000000000 -0.019925028
## SHORT FILM ANIMATED -0.019177180 -0.019925028 1.000000000
## SHORT FILM LIVE ACTION -0.022395647 -0.023269005 -0.022690214
## SOUND 0.020025226 0.269773396 -0.019429415
## SOUND\nEDITING 0.005513327 0.040460535 -0.013407855
## VISUAL EFFECTS 0.068611942 0.048918152 -0.015087507
## WRITING 0.015128829 0.429787279 -0.029439516
## DOCUMENTARY OTHER -0.004275947 -0.004442695 -0.004332187
## SHORT FILM LIVE ACTION SOUND
## ACTOR -- LEADING ROLE -0.023553485 0.091877626
## ACTOR -- SUPPORTING ROLE -0.022097450 0.113451180
## ACTRESS\nLEADING ROLE -0.023411642 -0.007525240
## ACTRESS SUPPORTING ROLE -0.022097450 0.060501973
## ANIMATED FEATURE FILM -0.008771354 -0.007510827
## ART DIRECTION -0.026625099 0.209798842
## CINEMATOGRAPHY -0.027375040 0.181544012
## COSTUME\nDESIGN -0.022836180 0.147168546
## DIRECTING -0.023553485 0.253722211
## DOCUMENTARY FEATURE -0.021335064 -0.018269012
## DOCUMENTARY SHORT SUBJECT -0.009288812 -0.018135825
## FILM EDITING -0.022395647 0.372846880
## MAKEUP -0.014130497 0.049337977
## MUSIC\nSCORING -0.028704548 0.233349036
## MUSIC SONG -0.022395647 0.020025226
## BEST PICTURE -0.023269005 0.269773396
## SHORT FILM ANIMATED -0.022690214 -0.019429415
## SHORT FILM LIVE ACTION 1.000000000 -0.022690214
## SOUND -0.022690214 1.000000000
## SOUND\nEDITING -0.015658068 0.245771967
## VISUAL EFFECTS -0.017619613 0.199308678
## WRITING -0.034380290 0.110189225
## DOCUMENTARY OTHER -0.005059250 -0.004332187
## SOUND\nEDITING VISUAL EFFECTS WRITING
## ACTOR -- LEADING ROLE -0.013917970 0.0161605455 0.2725377457
## ACTOR -- SUPPORTING ROLE 0.005933184 0.0022244112 0.1414191978
## ACTRESS\nLEADING ROLE -0.013834154 0.0004362834 0.1728635223
## ACTRESS SUPPORTING ROLE -0.013057585 -0.0146933581 0.1324671121
## ANIMATED FEATURE FILM 0.041930610 -0.0058323766 -0.0113804313
## ART DIRECTION 0.016047368 0.0955412164 0.1527176545
## CINEMATOGRAPHY 0.076691137 0.0507391100 0.1395895318
## COSTUME\nDESIGN 0.023303969 0.0667685886 0.1264877804
## DIRECTING 0.057524736 0.0320715813 0.4072476641
## DOCUMENTARY FEATURE -0.012607084 -0.0141864212 -0.0276812702
## DOCUMENTARY SHORT SUBJECT -0.012515174 -0.0140829972 -0.0274794641
## FILM EDITING 0.155490284 0.1187140907 0.2183851548
## MAKEUP 0.079791297 0.0952972142 0.0232153029
## MUSIC\nSCORING 0.027442375 0.0600275014 0.1371883115
## MUSIC SONG 0.005513327 0.0686119417 0.0151288288
## BEST PICTURE 0.040460535 0.0489181524 0.4297872785
## SHORT FILM ANIMATED -0.013407855 -0.0150875074 -0.0294395156
## SHORT FILM LIVE ACTION -0.015658068 -0.0176196131 -0.0343802898
## SOUND 0.245771967 0.1993086779 0.1101892247
## SOUND\nEDITING 1.000000000 0.2735097458 -0.0077958126
## VISUAL EFFECTS 0.273509746 1.0000000000 -0.0005542952
## WRITING -0.007795813 -0.0005542952 1.0000000000
## DOCUMENTARY OTHER -0.002989557 -0.0033640699 -0.0065641452
## DOCUMENTARY OTHER
## ACTOR -- LEADING ROLE -0.004497010
## ACTOR -- SUPPORTING ROLE -0.004219012
## ACTRESS\nLEADING ROLE -0.004469928
## ACTRESS SUPPORTING ROLE -0.004219012
## ANIMATED FEATURE FILM -0.001674693
## ART DIRECTION -0.005083466
## CINEMATOGRAPHY -0.005226650
## COSTUME\nDESIGN -0.004360056
## DIRECTING -0.004497010
## DOCUMENTARY FEATURE -0.004073452
## DOCUMENTARY SHORT SUBJECT -0.004043755
## FILM EDITING -0.004275947
## MAKEUP -0.002697901
## MUSIC\nSCORING -0.005480490
## MUSIC SONG -0.004275947
## BEST PICTURE -0.004442695
## SHORT FILM ANIMATED -0.004332187
## SHORT FILM LIVE ACTION -0.005059250
## SOUND -0.004332187
## SOUND\nEDITING -0.002989557
## VISUAL EFFECTS -0.003364070
## WRITING -0.006564145
## DOCUMENTARY OTHER 1.000000000An analysis of the various correlation matrix visualizations and table of numerical values shows the primary determining factor is DIRECTING, followed by WRITING and then FILM EDITING.
Note: There was an issue with NA values; we could not get them to be ignored, so instead had to set them equal to 0, which may have resulted in some skewing of the results.
require(ggplot2)
library(ggplot2)
library(plot3D)## Warning: package 'plot3D' was built under R version 3.1.3library(scatterplot3d)## Warning: package 'scatterplot3d' was built under R version 3.1.3I took the summary table that Honey, Vuthy, James, and Sonya had created and added few graphics as well
Please note that i only took the summary with the below following columns: Category.Nominations, Count, Category.Winners, Count
Also, I loaded and shared the table as csv using dropconvas website as per below
data1<- read.table(file= "http://dropcanvas.com/dcm0h/1", header = TRUE, sep=",")
str(data1)## 'data.frame': 16 obs. of 4 variables:
## $ Category.Nominations: Factor w/ 16 levels "ACTOR – LEADING ROLE",..: 16 8 9 1 2 6 11 5 13 4 ...
## $ Count : int 80 78 69 59 55 54 50 48 45 36 ...
## $ Category.Winners : Factor w/ 16 levels "ACTOR – LEADING ROLE",..: 8 16 9 5 1 6 11 13 7 2 ...
## $ Count.1 : int 61 56 33 27 26 25 24 23 19 15 ...head(data1)## Category.Nominations Count Category.Winners Count.1
## 1 WRITING 80 DIRECTING 61
## 2 DIRECTING 78 WRITING 56
## 3 FILM EDITING 69 FILM EDITING 33
## 4 ACTOR – LEADING ROLE 59 ART DIRECTION 27
## 5 ACTOR – SUPPORTING ROLE 55 ACTOR – LEADING ROLE 26
## 6 CINEMATOGRAPHY 54 CINEMATOGRAPHY 25Distribution Nominations By Category
Distribution of Winning By Category
Distribution of Wins over Winning by Nominations Category
attach(data1)
scatterplot3d(Category.Nominations, Category.Winners, Count.1, pch=16, highlight.3d=TRUE,
type="h", main="Distribution of Wins over Winning by Nominations Category",
xlab = "Category.Nominations", ylab = "Winning Category", zlab = " Winners")