DATA607 - Project 3 - Meeting 2 Findings
Synopsis
Summary
Loaded the data directly from data.world website.
Only chose one csv file THOR_WWII_DATA_CLEAN.csv because it contained the most information.
Removed unnecessary fields because they contained either no information or useless information for the scope of this project.
Separate data frames (FOR NOW) to just ETO and PTO operations.
Summarized data for all team members to see.
Findings and Opportunities
I removed a large number of columns because I felt that they were not necessary OR that they yield too many blank and NA data. Examples are: “TGT_COUNTRY_CODE”,“TGT_ID”,“TGT_INDUSTRY_CODE”,“SOURCE_LATITUDE”,“SOURCE_LONGITUDE”,“MDS”,“TAKEOFF_LATITUDE”,“TAKEOFF_LONGITUDE”,“TARGET_COMMENT”,“MISSION_COMMENTS”,“SOURCE”,“DATABASE_EDIT_COMMENTS”, “BDA”, “CALLSIGN”, “ROUNDS_AMMO”, “SPARES_RETURN_AC”,“WX_FAIL_AC”, “MECH_FAIL_AC”, “MISC_FAIL_AC”, “TIME_OVER_TARGET”,“SIGHTING_METHOD_CODE”,“SIGHTING_EXPLANATION”.
I reduced the data frame by only counting ETO and PTO data. I also reduced the number of columns to 38.
Number of operations were 59,359 and 23,837 for ETO and PTO respectively.
Altitude of bombing missions were higher in ETO than PTO. Perhaps the German AA were more accurate than the Japanese AA.
In ETO, Great Britain and USA share almost identical number of bombing missions at 15081 and 14605 respectively. In PTO, The USA overwhelming has more bombing missions at 23,406.
Recommendations
Proceed with this data given the time constraints we have.
Combine both data frames into one big uber data frame containing only ETO and PTO bombing operations.
Eliminate the following fields from the tables because they may not seem important OR that data is too sparse: WWII_ID, MASTER_INDEX_NUMBER, AC_LOST, AC_DAMAGED, TGT_INDUSTRY, TGT_PRIORITY_EXPLANATION,TAKEOFF_BASE, TAKEOFF_COUNTRY. This will reduce the data to 30 fields.
We could further reduce the size of the table to focus on bombing operations for a specific date range or year.
Normalize the table by exporting bombers and weaponry to their own separate tables.
Since the remaining data is in a database of your own choosing, we can conduct simple and advanced SQL queries to answer some questions we may have on the data. We could agree on what queries we want to conduct and do our own analyses and combine them together.
Tasks
I. Load Appropriate Libraries
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionII. Load Data
Loading of THOR_WWII_DATA_CLEAN.csv directly from data.world website.
# ref: [HOW]
urlfile <- "https://query.data.world/s/7tdvewopqdr5mu4zwlqeuy4c7nyeco"
tableWW2 <- read.table(file = urlfile, header = TRUE, fill = TRUE, sep = ",")## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec =
## dec, : EOF within quoted stringIII. Clean Data
Removing unnecessary fields.
Separating data frame into two theaters: European and Pacific
IV-A. Summmary for ETO Data Frame
## WWII_ID MASTER_INDEX_NUMBER MSNDATE
## Min. : 12 Min. : 92928 8/15/1944: 448
## 1st Qu.: 30419 1st Qu.:161362 7/25/1944: 442
## Median :102131 Median :186021 2/22/1945: 434
## Mean : 94921 Mean :195026 8/25/1944: 434
## 3rd Qu.:146929 3rd Qu.:223321 8/14/1944: 420
## Max. :178505 Max. :699555 7/20/1944: 419
## NA's :3 (Other) :56762
## THEATER NAF COUNTRY_FLYING_MISSION
## ETO :59359 :29673 :29673
## : 0 RAF :15081 205 GP : 0
## 42.92 : 0 8 AF : 6454 AUSTRALIA : 0
## CBI : 0 9 AF : 3761 GREAT BRITAIN:15081
## EAST AFRICA: 0 15 AF : 3369 NEW ZEALAND : 0
## MADAGASCAR : 0 12 AF : 999 SOUTH AFRICA : 0
## (Other) : 0 (Other): 22 USA :14605
## TGT_COUNTRY TGT_LOCATION
## GERMANY :36875 BERLIN : 1353
## FRANCE :15646 HAMBURG : 1168
## AUSTRIA : 3065 COLOGNE : 1124
## BELGIUM : 1381 BREMEN : 979
## HOLLAND OR NETHERLANDS: 1381 UNIDENTIFIED: 948
## CZECHOSLOVAKIA : 528 KIEL : 861
## (Other) : 483 (Other) :52926
## TGT_TYPE
## :15146
## CITY AREA :11671
## AIRDROME : 4797
## MARSHALLING YARD : 2405
## UNIENTIFIED TARGET : 1766
## UNIDENTIFIED TARGET: 1310
## (Other) :22264
## TGT_INDUSTRY
## CITIES TOWNS AND URBAN AREAS :13397
## "RR INSTALLATIONS, TRACKS, MARSHALLING YARDS, AND STATIONS": 8128
## AIR FIELDS AND AIRDROMES : 7052
## UNIDENTIFIED TARGETS : 4502
## BRIDGES : 2499
## TACTICAL TARGETS: (UNIDENTIFIED OR NOT LISTED BELOW) : 2481
## (Other) :21300
## LATITUDE LONGITUDE UNIT_ID AIRCRAFT_NAME
## Min. : 29.92 Min. : -4.500 :59336 B17 :24310
## 1st Qu.: 48.90 1st Qu.: 4.850 205 GP : 9 B24 :11953
## Median : 50.53 Median : 7.780 211 SQDN: 5 B26 : 5690
## Mean : 50.36 Mean : 8.657 37 SQDN : 2 LANCASTER : 4154
## 3rd Qu.: 51.72 3rd Qu.: 10.500 84 SQDN : 2 WELLINGTON: 2995
## Max. :100.65 Max. :1000.650 86 FBG : 2 LIGHT : 2249
## NA's :2604 NA's :2604 (Other) : 3 (Other) : 8008
## MSN_TYPE TGT_PRIORITY TGT_PRIORITY_EXPLANATION
## :59359 1 :25414 :21533
## 1 : 0 :21176 4 : 0
## 10 : 0 3 : 5683 PRIMARY TARGET :25414
## 11 : 0 2 : 5286 SECONDARY TARGET : 5286
## 12 : 0 4 : 1443 TARGET OF LAST RESORT: 1443
## 13 : 0 0 : 301 TARGET OF OPPORTUNITY: 5683
## (Other): 0 (Other): 56
## AC_ATTACKING ALTITUDE ALTITUDE_FEET NUMBER_OF_HE
## Min. : 0.00 Min. : 0.0 Min. : 0 :59356
## 1st Qu.: 2.00 1st Qu.:140.0 1st Qu.:12500 0 : 1
## Median : 11.00 Median :220.0 Median :21000 12 : 1
## Mean : 12.39 Mean :198.6 Mean :18533 8 : 1
## 3rd Qu.: 16.00 3rd Qu.:248.0 3rd Qu.:24400 1 : 0
## Max. :313.00 Max. :430.0 Max. :42400 1.95 : 0
## NA's :21834 NA's :21871 NA's :51308 (Other): 0
## TYPE_OF_HE LBS_HE
## :59356 :59359
## 500 LB GP (GP-M43/M64) : 3 "TORTORELLA, FOGGIA": 0
## 0 : 0 0 : 0
## 100 LB GP (GP-M30) : 0 1000 : 0
## 1000 LB AP (AP-MK 33) : 0 10000 : 0
## 1000 LB GP (GP-M44/M65): 0 10400 : 0
## (Other) : 0 (Other) : 0
## TONS_OF_HE NUMBER_OF_IC TYPE_OF_IC
## : 6487 Min. : NA :59359
## 1 : 6044 1st Qu.: NA 10 LB INCENDIARY : 0
## 2 : 4493 Median : NA 100 LB INCENDIARY : 0
## 3 : 3972 Mean :NaN 100 LB WP (WHITE PHOSPHROUS) : 0
## 30 : 1888 3rd Qu.: NA 1000 LB AUX FUEL TANK INCENDIARY: 0
## 36 : 1345 Max. : NA 110 LB INCENDIARY : 0
## (Other):35130 NA's :59359 (Other) : 0
## LBS_IC TONS_OF_IC NUMBER_OF_FRAG
## Min. : NA Min. : 0.0 Min. : NA
## 1st Qu.: NA 1st Qu.: 3.0 1st Qu.: NA
## Median : NA Median : 11.0 Median : NA
## Mean :NaN Mean : 27.1 Mean :NaN
## 3rd Qu.: NA 3rd Qu.: 25.0 3rd Qu.: NA
## Max. : NA Max. :999.0 Max. : NA
## NA's :59359 NA's :49803 NA's :59359
## TYPE_OF_FRAG LBS_FRAG TONS_OF_FRAG
## :59359 Min. : NA Min. : 0.00
## 1 : 0 1st Qu.: NA 1st Qu.: 11.00
## 120 LB FRAG (6X20 CLUSTERS): 0 Median : NA Median : 30.00
## 20 LB FRAG : 0 Mean :NaN Mean : 33.99
## 23 LB FRAG : 0 3rd Qu.: NA 3rd Qu.: 45.00
## 23 LB PARAFRAG : 0 Max. : NA Max. :360.00
## (Other) : 0 NA's :59359 NA's :57660
## TOTAL_LBS TOTAL_TONS TAKEOFF_BASE
## Min. : NA 1 : 5901 :59347
## 1st Qu.: NA 3 : 4466 "TORTORELLA, FOGGIA" : 9
## Median : NA 2 : 4376 PORETTA AIRFIELD : 2
## Mean :NaN : 2241 SERRAGIA AIRFIELD : 1
## 3rd Qu.: NA 30 : 2118 "MINGALADON AIRFIELD, RANGOON": 0
## Max. : NA 36 : 1699 "PIVA AIRSTRIP, BOUGAINVILLE" : 0
## NA's :59359 (Other):38558 (Other) : 0
## TAKEOFF_COUNTRY AC_LOST AC_DAMAGED AC_AIRBORNE
## :59347 Min. : NA Min. : NA Min. : 0.0
## ITALY : 11 1st Qu.: NA 1st Qu.: NA 1st Qu.: 2.0
## CORSICA : 1 Median : NA Median : NA Median : 11.0
## AUSTRALIA: 0 Mean :NaN Mean :NaN Mean : 12.4
## BORNEO : 0 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: 16.0
## BURMA : 0 Max. : NA Max. : NA Max. :313.0
## (Other) : 0 NA's :59359 NA's :59359 NA's :21842
## AC_DROPPING
## Min. : 0.00
## 1st Qu.: 2.00
## Median : 11.00
## Mean : 12.39
## 3rd Qu.: 16.00
## Max. :313.00
## NA's :21833IV-B. Summmary for PTO Data Frame
## WWII_ID MASTER_INDEX_NUMBER MSNDATE
## Min. : 2 Min. : 3 6/21/1945: 129
## 1st Qu.: 73274 1st Qu.: 7951 3/13/1945: 116
## Median :115336 Median : 18400 4/25/1945: 115
## Mean :105123 Mean : 23131 2/14/1945: 111
## 3rd Qu.:149141 3rd Qu.: 40311 4/26/1945: 104
## Max. :178722 Max. :402344 4/15/1945: 103
## NA's :625 (Other) :23159
## THEATER NAF COUNTRY_FLYING_MISSION
## PTO :23837 5 AF :12085 : 0
## : 0 7 AF : 4430 205 GP : 0
## 42.92 : 0 13 AF : 4378 AUSTRALIA : 49
## CBI : 0 20 AF : 1682 GREAT BRITAIN: 0
## EAST AFRICA: 0 14 AF : 443 NEW ZEALAND : 382
## ETO : 0 RNZAF : 382 SOUTH AFRICA : 0
## (Other) : 0 (Other): 437 USA :23406
## TGT_COUNTRY TGT_LOCATION
## PHILIPPINE ISLANDS :5335 WEWAK : 502
## NEW GUINEA :4845 IWO JIMA : 361
## BISMARK ARCHIPELAGO:2273 BALIK PAPAN : 258
## JAPAN :2103 CORREGIDOR : 250
## CELEBES ISLANDS :1721 RABAUL : 245
## SOLOMON ISLANDS :1259 CAPE GLOUCESTER: 240
## (Other) :6301 (Other) :21981
## TGT_TYPE
## UNIDENTIFIED TARGET: 6455
## AIRDROME : 3412
## URBAN AREA : 869
## TOWN : 863
## AREA : 754
## AIRFIELD : 748
## (Other) :10736
## TGT_INDUSTRY
## :23837
## "IRON AND STEEL PRODUCTION FACILITIES, BLAST FURNACES, BOILER SHOPS, FORGES, FOUNDRIES, STEEL WORKS, ROLLING-MILLS ": 0
## "PUBLIC UTILITIES - ELECTRIC LIGHT AND POWER COMPANIES, GAS COMPANIES, TELEPHONE COMPANIES, WATER COMPANIES. " : 0
## "RR INSTALLATIONS, TRACKS, MARSHALLING YARDS, AND STATIONS" : 0
## "TUGS, BARGES, AND SAMPANS " : 0
## 32.5 : 0
## (Other) : 0
## LATITUDE LONGITUDE UNIT_ID AIRCRAFT_NAME
## Min. :-22.267 Min. : -4.527 42 BG : 490 B24 :8691
## 1st Qu.: -3.583 1st Qu.:121.617 64 BS : 434 B25 :4312
## Median : 6.083 Median :134.167 63 BS : 428 A20 :2581
## Mean : 8.364 Mean :134.996 321 BS : 421 P47 :1685
## 3rd Qu.: 16.750 3rd Qu.:145.833 868 BS : 421 B29 :1682
## Max. :152.561 Max. :179.733 320 BS : 404 P38 :1536
## NA's :2 NA's :10 (Other):21239 (Other):3350
## MSN_TYPE TGT_PRIORITY TGT_PRIORITY_EXPLANATION
## 1 :14180 1 :19410 :23836
## 10 : 6513 9 : 1493 4 : 1
## 12 : 1291 2 : 1207 PRIMARY TARGET : 0
## 6 : 760 3 : 876 SECONDARY TARGET : 0
## : 307 : 640 TARGET OF LAST RESORT: 0
## 99 : 298 4 : 202 TARGET OF OPPORTUNITY: 0
## (Other): 488 (Other): 9
## AC_ATTACKING ALTITUDE ALTITUDE_FEET NUMBER_OF_HE
## Min. : 0.000 Min. : 0.00 Min. : 0 :9129
## 1st Qu.: 3.000 1st Qu.: 1.00 1st Qu.: 88 8 :2348
## Median : 6.000 Median : 64.00 Median : 3000 4 :1420
## Mean : 6.584 Mean : 67.65 Mean : 5216 16 :1148
## 3rd Qu.: 9.000 3rd Qu.: 105.00 3rd Qu.: 9500 20 : 960
## Max. :152.000 Max. :22000.00 Max. :70000 24 : 876
## NA's :1907 NA's :3018 NA's :2149 (Other):7956
## TYPE_OF_HE LBS_HE TONS_OF_HE
## 500 LB GP (GP-M43/M64) :8036 :23263 :4564
## 250 LB GP (GP-M57) :4292 4000 : 47 1 :2910
## 1000 LB GP (GP-M44/M65):3745 6000 : 47 2 :2857
## :3551 12000 : 41 3 :1874
## 100 LB GP (GP-M30) :3259 2000 : 33 4 :1662
## 2000 LB GP (GP-M34/M66): 316 8000 : 33 6 :1483
## (Other) : 638 (Other): 373 (Other):8487
## NUMBER_OF_IC TYPE_OF_IC
## Min. : 0.0 :21062
## 1st Qu.: 8.0 100 LB INCENDIARY : 581
## Median : 15.0 1000 LB AUX FUEL TANK INCENDIARY: 518
## Mean : 161.5 500 LB INCENDIARY : 393
## 3rd Qu.: 32.0 400 LB INCENDIARY : 192
## Max. :4875.0 20 LB INCENDIARY : 158
## NA's :22298 (Other) : 933
## LBS_IC TONS_OF_IC NUMBER_OF_FRAG
## Min. : 0 Min. : 0.00 Min. : 0.00
## 1st Qu.: 500 1st Qu.: 1.00 1st Qu.: 23.08
## Median : 8000 Median : 3.00 Median : 86.96
## Mean : 30005 Mean : 28.92 Mean : 132.79
## 3rd Qu.: 56000 3rd Qu.: 8.00 3rd Qu.: 183.33
## Max. :160000 Max. :999.00 Max. :1700.00
## NA's :23676 NA's :21266 NA's :21314
## TYPE_OF_FRAG LBS_FRAG TONS_OF_FRAG
## :21314 Min. : 80 Min. : 0.000
## 120 LB FRAG (6X20 CLUSTERS) : 1019 1st Qu.: 760 1st Qu.: 1.000
## 260 LB FRAG : 476 Median :1000 Median : 4.000
## 400 LB FRAG (20X20 CLUSTERS): 252 Mean :3305 Mean : 6.967
## 23 LB FRAG : 251 3rd Qu.:6012 3rd Qu.: 9.000
## 20 LB FRAG : 241 Max. :9936 Max. :132.000
## (Other) : 284 NA's :23826 NA's :21193
## TOTAL_LBS TOTAL_TONS TAKEOFF_BASE
## Min. : 0 2 : 3259 :23621
## 1st Qu.: 2600 1 : 3177 BOUGAINVILLE ISLAND: 42
## Median : 5500 3 : 2270 USS HORNET : 31
## Mean : 7732 4 : 1934 MALANG : 26
## 3rd Qu.: 10000 6 : 1731 CAIRNS : 11
## Max. :112000 5 : 1419 PORT MORESBY : 11
## NA's :23220 (Other):10047 (Other) : 95
## TAKEOFF_COUNTRY AC_LOST AC_DAMAGED AC_AIRBORNE
## :23628 Min. : NA Min. : NA Min. : 1.000
## INDONESIA : 50 1st Qu.: NA 1st Qu.: NA 1st Qu.: 1.000
## SOLOMON ISLANDS: 43 Median : NA Median : NA Median : 4.000
## AUSTRALIA : 42 Mean :NaN Mean :NaN Mean : 4.694
## USA : 31 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: 7.000
## PHILLIPINES : 14 Max. : NA Max. : NA Max. :23.000
## (Other) : 29 NA's :23837 NA's :23837 NA's :23572
## AC_DROPPING
## Min. : 1.000
## 1st Qu.: 1.000
## Median : 2.000
## Mean : 3.457
## 3rd Qu.: 5.250
## Max. :22.000
## NA's :23653V. Questions about Data
V-A. What are the allocation of bombers used in ETO and PTO?
ETOAircrafttype <- as.data.frame(table(tableWW2ETO$AIRCRAFT_NAME))
names(ETOAircrafttype) <- c("Aircraft", "Count")
ETOAircrafttype <- ETOAircrafttype[-which(ETOAircrafttype$Count == 0 ), ] # removing rows with 0 fields
ggplot(ETOAircrafttype, aes(x = reorder(Aircraft, -Count), y = Count)) +
geom_bar(stat = "identity", position = "dodge", fill = "green") +
geom_text(aes(label=Count), vjust=1.6, color="black", position = position_dodge(0.9), size=3.5) +
scale_fill_brewer(palette="Paired") +
theme(axis.text.x=element_text(angle = 90, vjust = 0.5)) +
ggtitle("ETO Aircraft Types") +
labs(x = "Aircraft Type")
PTOAircrafttype <- as.data.frame(table(tableWW2PTO$AIRCRAFT_NAME))
names(PTOAircrafttype) <- c("Aircraft", "Count")
PTOAircrafttype <- PTOAircrafttype[-which(PTOAircrafttype$Count == 0 ), ] # removing rows with 0 fields
ggplot(PTOAircrafttype, aes(x = reorder(Aircraft, -Count), y = Count)) +
geom_bar(stat = "identity", position = "dodge", fill = "red") +
geom_text(aes(label=Count), vjust=1.6, color="black", position = position_dodge(0.9), size=3.5) +
scale_fill_brewer(palette="Paired") +
theme(axis.text.x=element_text(angle = 90, vjust = 0.5)) +
ggtitle("PTO Aircraft Types") +
labs(x = "Aircraft Type")
V-B.What are the allocation of tons of explosives of target countries in ETO and PTO?
ETOExplosives <- tableWW2ETO[, which(names(tableWW2ETO) %in% c("TGT_COUNTRY","TOTAL_TONS"))]
ETOExplosives <- ETOExplosives[!(is.na(ETOExplosives$TOTAL_TONS) | ETOExplosives$TOTAL_TONS==""), ]
head(ETOExplosives, n=100)## TGT_COUNTRY TOTAL_TONS
## 12 GERMANY 1
## 13 GERMANY 4
## 14 HOLLAND OR NETHERLANDS 39
## 15 HOLLAND OR NETHERLANDS 39
## 16 HOLLAND OR NETHERLANDS 39
## 17 FRANCE 5
## 18 FRANCE 5
## 19 FRANCE 5
## 20 FRANCE 20
## 21 FRANCE 20
## 22 FRANCE 20
## 23 HOLLAND OR NETHERLANDS 36
## 24 HOLLAND OR NETHERLANDS 36
## 25 HOLLAND OR NETHERLANDS 36
## 29 HOLLAND OR NETHERLANDS 32
## 30 HOLLAND OR NETHERLANDS 32
## 31 FRANCE 48
## 32 FRANCE 48
## 33 FRANCE 48
## 34 FRANCE 25
## 35 FRANCE 25
## 36 FRANCE 25
## 37 FRANCE 25
## 38 FRANCE 25
## 39 FRANCE 25
## 40 FRANCE 25
## 41 FRANCE 25
## 42 FRANCE 25
## 43 FRANCE 25
## 44 FRANCE 25
## 45 FRANCE 25
## 46 FRANCE 25
## 47 FRANCE 25
## 48 FRANCE 25
## 49 FRANCE 19
## 50 FRANCE 19
## 51 FRANCE 19
## 52 HOLLAND OR NETHERLANDS 37
## 53 HOLLAND OR NETHERLANDS 37
## 54 HOLLAND OR NETHERLANDS 37
## 55 HOLLAND OR NETHERLANDS 36
## 56 HOLLAND OR NETHERLANDS 36
## 57 HOLLAND OR NETHERLANDS 36
## 58 GERMANY 87
## 59 FRANCE 29
## 60 FRANCE 29
## 61 FRANCE 33
## 62 FRANCE 33
## 63 FRANCE 2
## 64 FRANCE 2
## 65 FRANCE 2
## 129 FRANCE 30
## 130 FRANCE 56
## 131 FRANCE 36
## 132 FRANCE 36
## 135 FRANCE 36
## 136 FRANCE 32
## 138 FRANCE 50
## 139 FRANCE 30
## 141 FRANCE 32
## 142 FRANCE 41
## 146 FRANCE 39
## 149 FRANCE 36
## 150 FRANCE 50
## 151 FRANCE 32
## 152 FRANCE 31
## 153 FRANCE 33
## 154 FRANCE 33
## 155 FRANCE 36
## 158 FRANCE 34
## 159 FRANCE 36
## 160 FRANCE 56
## 161 FRANCE 38
## 162 FRANCE 48
## 163 FRANCE 36
## 164 FRANCE 36
## 165 FRANCE 41
## 166 FRANCE 36
## 169 FRANCE 36
## 170 FRANCE 32
## 171 FRANCE 32
## 174 FRANCE 36
## 175 FRANCE 36
## 176 FRANCE 41
## 177 FRANCE 36
## 182 FRANCE 45
## 184 GERMANY 42
## 185 GERMANY 20
## 186 GERMANY 2
## 187 GERMANY 42
## 188 GERMANY 3
## 189 GERMANY 3
## 190 GERMANY 3
## 191 GERMANY 5
## 192 GERMANY 3
## 193 GERMANY 3
## 194 GERMANY 1
## 195 GERMANY 1
## 196 GERMANY 1
## 197 GERMANY 2V-C. What is the dispersion of target countries for either ETO and PTO?
ETOTCountry <- as.data.frame(table(tableWW2ETO$TGT_COUNTRY))
names(ETOTCountry) <- c("Country", "Missions")
ETOTCountry <- ETOTCountry[-which(ETOTCountry$Missions == 0 ), ] # removing rows with 0 fields
ggplot(ETOTCountry, aes(x = reorder(Country, -Missions), y = Missions)) +
geom_bar(stat = "identity", position = "dodge", fill = "green") +
geom_text(aes(label=Missions), vjust=1.6, color="black", position = position_dodge(0.9), size=3.5) +
scale_fill_brewer(palette="Paired") +
theme(axis.text.x=element_text(angle = 90, vjust = 0.5)) +
ggtitle("ETO Missions by Target Country") +
labs(x = "Target Countries")
PTOTCountry <- as.data.frame(table(tableWW2PTO$TGT_COUNTRY))
names(PTOTCountry) <- c("Country", "Missions")
PTOTCountry <- PTOTCountry[-which(PTOTCountry$Missions == 0 ), ] # removing rows with 0 fields
ggplot(PTOTCountry, aes(x = reorder(Country, -Missions), y = Missions)) +
geom_bar(stat = "identity", position = "dodge", fill = "green") +
geom_text(aes(label=Missions), vjust=1.6, color="black", position = position_dodge(0.9), size=3.5) +
scale_fill_brewer(palette="Paired") +
theme(axis.text.x=element_text(angle = 90, vjust = 0.5)) +
ggtitle("PTO Missions by Target Country") +
labs(x = "Target Countries")
Mass Bomb dates (bombings greater than 400) for ETO are 8/15, 7/25, 2/22, 8/25, 8/14, and 7/20 1944. Why? Notice that Pre-Day bombardment dates are not Mass Bomb dates.
Mass Bomb dates (bombings greater than 100) for PTO are 6/21, 3/13, 4/25, 2/14, 4/26, and 4/15 1945. Notice that the number of bombing missions for these dates are 1/4 the number than that of the ETO. Also, all mass bomb dates occurred in 1945. Why?
As expected in ETO operations, Germany was the target country with the largest number of bombing missions. However, according to the PTO data, the Philippines was the target country with the largest number of bombing missions. Japan, an Axis opponent, had been the recipient of less than half of that of the Philippines. Why?
Does bombing tonnage increase for a specific period and what theater?