Week 5!
1.)
library(tidyverse)
## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag(): dplyr, stats
wide <- readRDS("data5/bomber_wide.rds")
as_tibble(wide)
## # A tibble: 3 x 21
## Type MD 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
## * <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 Bomber B-1 26914 25219 24205 23306 25013 25059 26581 21491 28118 21859
## 2 Bomber B-2 2364 2776 2166 3672 4543 4754 5969 6801 5677 5228
## 3 Bomber B-52 28511 26034 25639 24500 24387 24813 36687 30230 25493 27838
## # ... with 9 more variables: 2006 <int>, 2007 <int>, 2008 <int>,
## # 2009 <int>, 2010 <int>, 2011 <int>, 2012 <int>, 2013 <int>, 2014 <int>
wide %>%
gather(`1996`:`2014`, key = "year", value = "FH")
## Type MD year FH
## 1 Bomber B-1 1996 26914
## 2 Bomber B-2 1996 2364
## 3 Bomber B-52 1996 28511
## 4 Bomber B-1 1997 25219
## 5 Bomber B-2 1997 2776
## 6 Bomber B-52 1997 26034
## 7 Bomber B-1 1998 24205
## 8 Bomber B-2 1998 2166
## 9 Bomber B-52 1998 25639
## 10 Bomber B-1 1999 23306
## 11 Bomber B-2 1999 3672
## 12 Bomber B-52 1999 24500
## 13 Bomber B-1 2000 25013
## 14 Bomber B-2 2000 4543
## 15 Bomber B-52 2000 24387
## 16 Bomber B-1 2001 25059
## 17 Bomber B-2 2001 4754
## 18 Bomber B-52 2001 24813
## 19 Bomber B-1 2002 26581
## 20 Bomber B-2 2002 5969
## 21 Bomber B-52 2002 36687
## 22 Bomber B-1 2003 21491
## 23 Bomber B-2 2003 6801
## 24 Bomber B-52 2003 30230
## 25 Bomber B-1 2004 28118
## 26 Bomber B-2 2004 5677
## 27 Bomber B-52 2004 25493
## 28 Bomber B-1 2005 21859
## 29 Bomber B-2 2005 5228
## 30 Bomber B-52 2005 27838
## 31 Bomber B-1 2006 20163
## 32 Bomber B-2 2006 6100
## 33 Bomber B-52 2006 28206
## 34 Bomber B-1 2007 24629
## 35 Bomber B-2 2007 6266
## 36 Bomber B-52 2007 22484
## 37 Bomber B-1 2008 23024
## 38 Bomber B-2 2008 4074
## 39 Bomber B-52 2008 20284
## 40 Bomber B-1 2009 23065
## 41 Bomber B-2 2009 4567
## 42 Bomber B-52 2009 18765
## 43 Bomber B-1 2010 23398
## 44 Bomber B-2 2010 4638
## 45 Bomber B-52 2010 18250
## 46 Bomber B-1 2011 24166
## 47 Bomber B-2 2011 4873
## 48 Bomber B-52 2011 21727
## 49 Bomber B-1 2012 29292
## 50 Bomber B-2 2012 4955
## 51 Bomber B-52 2012 21748
## 52 Bomber B-1 2013 25253
## 53 Bomber B-2 2013 4076
## 54 Bomber B-52 2013 19309
## 55 Bomber B-1 2014 22204
## 56 Bomber B-2 2014 5070
## 57 Bomber B-52 2014 21297
2.)
long <- readRDS("data5/bomber_long.rds")
as_tibble(long)
## # A tibble: 171 x 5
## Type MD FY Output Value
## <chr> <chr> <int> <chr> <int>
## 1 Bomber B-1 1996 FH 26914
## 2 Bomber B-1 1997 FH 25219
## 3 Bomber B-1 1998 FH 24205
## 4 Bomber B-1 1999 FH 23306
## 5 Bomber B-1 2000 FH 25013
## 6 Bomber B-1 2001 FH 25059
## 7 Bomber B-1 2002 FH 26581
## 8 Bomber B-1 2003 FH 21491
## 9 Bomber B-1 2004 FH 28118
## 10 Bomber B-1 2005 FH 21859
## # ... with 161 more rows
long %>%
spread(key = "Output", value = "Value")
## Type MD FY Cost FH Gallons
## 1 Bomber B-1 1996 72753781 26914 88594449
## 2 Bomber B-1 1997 71297263 25219 85484074
## 3 Bomber B-1 1998 84026805 24205 85259038
## 4 Bomber B-1 1999 71848336 23306 79323816
## 5 Bomber B-1 2000 58439777 25013 86230284
## 6 Bomber B-1 2001 94946077 25059 86892432
## 7 Bomber B-1 2002 96458536 26581 89198262
## 8 Bomber B-1 2003 68650070 21491 74485788
## 9 Bomber B-1 2004 101895634 28118 101397707
## 10 Bomber B-1 2005 124816690 21859 78410415
## 11 Bomber B-1 2006 174627869 20163 69984142
## 12 Bomber B-1 2007 204486404 24629 85112485
## 13 Bomber B-1 2008 266109848 23024 78084791
## 14 Bomber B-1 2009 185902082 23065 81030579
## 15 Bomber B-1 2010 237413270 23398 81253214
## 16 Bomber B-1 2011 299215343 24166 82471246
## 17 Bomber B-1 2012 374028795 29292 101238385
## 18 Bomber B-1 2013 346054359 25253 85970844
## 19 Bomber B-1 2014 289357667 22204 73145048
## 20 Bomber B-2 1996 5863355 2364 3098498
## 21 Bomber B-2 1997 6866121 2776 4323992
## 22 Bomber B-2 1998 7891851 2166 4352750
## 23 Bomber B-2 1999 9801821 3672 7325932
## 24 Bomber B-2 2000 9618219 4543 10192466
## 25 Bomber B-2 2001 13949654 4754 9693145
## 26 Bomber B-2 2002 15938278 5969 11753559
## 27 Bomber B-2 2003 15401463 6801 13877997
## 28 Bomber B-2 2004 14158041 5677 11237771
## 29 Bomber B-2 2005 21637940 5228 10770041
## 30 Bomber B-2 2006 36500436 6100 12843899
## 31 Bomber B-2 2007 35775133 6266 12683783
## 32 Bomber B-2 2008 36623261 4074 8472438
## 33 Bomber B-2 2009 27095024 4567 9299978
## 34 Bomber B-2 2010 33674977 4638 9269549
## 35 Bomber B-2 2011 43326111 4873 9553217
## 36 Bomber B-2 2012 43322482 4955 9370581
## 37 Bomber B-2 2013 41433404 4076 7925422
## 38 Bomber B-2 2014 46326775 5070 9638853
## 39 Bomber B-52 1996 71051283 28511 99014843
## 40 Bomber B-52 1997 63767578 26034 88483438
## 41 Bomber B-52 1998 72563957 25639 85983307
## 42 Bomber B-52 1999 63267336 24500 82023136
## 43 Bomber B-52 2000 46983727 24387 80046278
## 44 Bomber B-52 2001 74897262 24813 81077269
## 45 Bomber B-52 2002 112111826 36687 117879809
## 46 Bomber B-52 2003 86410724 30230 108126138
## 47 Bomber B-52 2004 72806150 25493 86288229
## 48 Bomber B-52 2005 139059663 27838 99083069
## 49 Bomber B-52 2006 201736741 28206 100297373
## 50 Bomber B-52 2007 161218235 22484 80940424
## 51 Bomber B-52 2008 196858812 20284 71592012
## 52 Bomber B-52 2009 123246431 18765 66665854
## 53 Bomber B-52 2010 152292780 18250 65486545
## 54 Bomber B-52 2011 221528055 21727 74623498
## 55 Bomber B-52 2012 222391448 21748 73356234
## 56 Bomber B-52 2013 209017819 19309 64933573
## 57 Bomber B-52 2014 224248841 21297 70891306
3.)
combined <- readRDS("data5/bomber_combined.rds")
as_tibble(combined)
## # A tibble: 57 x 5
## AC FY Cost FH Gallons
## * <chr> <int> <int> <int> <int>
## 1 Bomber B-1 1996 72753781 26914 88594449
## 2 Bomber B-1 1997 71297263 25219 85484074
## 3 Bomber B-1 1998 84026805 24205 85259038
## 4 Bomber B-1 1999 71848336 23306 79323816
## 5 Bomber B-1 2000 58439777 25013 86230284
## 6 Bomber B-1 2001 94946077 25059 86892432
## 7 Bomber B-1 2002 96458536 26581 89198262
## 8 Bomber B-1 2003 68650070 21491 74485788
## 9 Bomber B-1 2004 101895634 28118 101397707
## 10 Bomber B-1 2005 124816690 21859 78410415
## # ... with 47 more rows
combined %>%
separate(AC, into = c("Type", "MD"), sep = " ")
## Type MD FY Cost FH Gallons
## 1 Bomber B-1 1996 72753781 26914 88594449
## 2 Bomber B-1 1997 71297263 25219 85484074
## 3 Bomber B-1 1998 84026805 24205 85259038
## 4 Bomber B-1 1999 71848336 23306 79323816
## 5 Bomber B-1 2000 58439777 25013 86230284
## 6 Bomber B-1 2001 94946077 25059 86892432
## 7 Bomber B-1 2002 96458536 26581 89198262
## 8 Bomber B-1 2003 68650070 21491 74485788
## 9 Bomber B-1 2004 101895634 28118 101397707
## 10 Bomber B-1 2005 124816690 21859 78410415
## 11 Bomber B-1 2006 174627869 20163 69984142
## 12 Bomber B-1 2007 204486404 24629 85112485
## 13 Bomber B-1 2008 266109848 23024 78084791
## 14 Bomber B-1 2009 185902082 23065 81030579
## 15 Bomber B-1 2010 237413270 23398 81253214
## 16 Bomber B-1 2011 299215343 24166 82471246
## 17 Bomber B-1 2012 374028795 29292 101238385
## 18 Bomber B-1 2013 346054359 25253 85970844
## 19 Bomber B-1 2014 289357667 22204 73145048
## 20 Bomber B-2 1996 5863355 2364 3098498
## 21 Bomber B-2 1997 6866121 2776 4323992
## 22 Bomber B-2 1998 7891851 2166 4352750
## 23 Bomber B-2 1999 9801821 3672 7325932
## 24 Bomber B-2 2000 9618219 4543 10192466
## 25 Bomber B-2 2001 13949654 4754 9693145
## 26 Bomber B-2 2002 15938278 5969 11753559
## 27 Bomber B-2 2003 15401463 6801 13877997
## 28 Bomber B-2 2004 14158041 5677 11237771
## 29 Bomber B-2 2005 21637940 5228 10770041
## 30 Bomber B-2 2006 36500436 6100 12843899
## 31 Bomber B-2 2007 35775133 6266 12683783
## 32 Bomber B-2 2008 36623261 4074 8472438
## 33 Bomber B-2 2009 27095024 4567 9299978
## 34 Bomber B-2 2010 33674977 4638 9269549
## 35 Bomber B-2 2011 43326111 4873 9553217
## 36 Bomber B-2 2012 43322482 4955 9370581
## 37 Bomber B-2 2013 41433404 4076 7925422
## 38 Bomber B-2 2014 46326775 5070 9638853
## 39 Bomber B-52 1996 71051283 28511 99014843
## 40 Bomber B-52 1997 63767578 26034 88483438
## 41 Bomber B-52 1998 72563957 25639 85983307
## 42 Bomber B-52 1999 63267336 24500 82023136
## 43 Bomber B-52 2000 46983727 24387 80046278
## 44 Bomber B-52 2001 74897262 24813 81077269
## 45 Bomber B-52 2002 112111826 36687 117879809
## 46 Bomber B-52 2003 86410724 30230 108126138
## 47 Bomber B-52 2004 72806150 25493 86288229
## 48 Bomber B-52 2005 139059663 27838 99083069
## 49 Bomber B-52 2006 201736741 28206 100297373
## 50 Bomber B-52 2007 161218235 22484 80940424
## 51 Bomber B-52 2008 196858812 20284 71592012
## 52 Bomber B-52 2009 123246431 18765 66665854
## 53 Bomber B-52 2010 152292780 18250 65486545
## 54 Bomber B-52 2011 221528055 21727 74623498
## 55 Bomber B-52 2012 222391448 21748 73356234
## 56 Bomber B-52 2013 209017819 19309 64933573
## 57 Bomber B-52 2014 224248841 21297 70891306
4.)
prefix <- readRDS("data5/bomber_prefix.rds")
prefix %>%
unite(MD, prefix, number, sep = "-") %>%
spread(key = "Output", value = "Value")
## Type MD FY Cost FH Gallons
## 1 Bomber B-1 1996 72753781 26914 88594449
## 2 Bomber B-1 1997 71297263 25219 85484074
## 3 Bomber B-1 1998 84026805 24205 85259038
## 4 Bomber B-1 1999 71848336 23306 79323816
## 5 Bomber B-1 2000 58439777 25013 86230284
## 6 Bomber B-1 2001 94946077 25059 86892432
## 7 Bomber B-1 2002 96458536 26581 89198262
## 8 Bomber B-1 2003 68650070 21491 74485788
## 9 Bomber B-1 2004 101895634 28118 101397707
## 10 Bomber B-1 2005 124816690 21859 78410415
## 11 Bomber B-1 2006 174627869 20163 69984142
## 12 Bomber B-1 2007 204486404 24629 85112485
## 13 Bomber B-1 2008 266109848 23024 78084791
## 14 Bomber B-1 2009 185902082 23065 81030579
## 15 Bomber B-1 2010 237413270 23398 81253214
## 16 Bomber B-1 2011 299215343 24166 82471246
## 17 Bomber B-1 2012 374028795 29292 101238385
## 18 Bomber B-1 2013 346054359 25253 85970844
## 19 Bomber B-1 2014 289357667 22204 73145048
## 20 Bomber B-2 1996 5863355 2364 3098498
## 21 Bomber B-2 1997 6866121 2776 4323992
## 22 Bomber B-2 1998 7891851 2166 4352750
## 23 Bomber B-2 1999 9801821 3672 7325932
## 24 Bomber B-2 2000 9618219 4543 10192466
## 25 Bomber B-2 2001 13949654 4754 9693145
## 26 Bomber B-2 2002 15938278 5969 11753559
## 27 Bomber B-2 2003 15401463 6801 13877997
## 28 Bomber B-2 2004 14158041 5677 11237771
## 29 Bomber B-2 2005 21637940 5228 10770041
## 30 Bomber B-2 2006 36500436 6100 12843899
## 31 Bomber B-2 2007 35775133 6266 12683783
## 32 Bomber B-2 2008 36623261 4074 8472438
## 33 Bomber B-2 2009 27095024 4567 9299978
## 34 Bomber B-2 2010 33674977 4638 9269549
## 35 Bomber B-2 2011 43326111 4873 9553217
## 36 Bomber B-2 2012 43322482 4955 9370581
## 37 Bomber B-2 2013 41433404 4076 7925422
## 38 Bomber B-2 2014 46326775 5070 9638853
## 39 Bomber B-52 1996 71051283 28511 99014843
## 40 Bomber B-52 1997 63767578 26034 88483438
## 41 Bomber B-52 1998 72563957 25639 85983307
## 42 Bomber B-52 1999 63267336 24500 82023136
## 43 Bomber B-52 2000 46983727 24387 80046278
## 44 Bomber B-52 2001 74897262 24813 81077269
## 45 Bomber B-52 2002 112111826 36687 117879809
## 46 Bomber B-52 2003 86410724 30230 108126138
## 47 Bomber B-52 2004 72806150 25493 86288229
## 48 Bomber B-52 2005 139059663 27838 99083069
## 49 Bomber B-52 2006 201736741 28206 100297373
## 50 Bomber B-52 2007 161218235 22484 80940424
## 51 Bomber B-52 2008 196858812 20284 71592012
## 52 Bomber B-52 2009 123246431 18765 66665854
## 53 Bomber B-52 2010 152292780 18250 65486545
## 54 Bomber B-52 2011 221528055 21727 74623498
## 55 Bomber B-52 2012 222391448 21748 73356234
## 56 Bomber B-52 2013 209017819 19309 64933573
## 57 Bomber B-52 2014 224248841 21297 70891306
5.)
mess <- readRDS("data5/bomber_mess.rds")
unmess <- mess %>%
unite(MD, prefix, number, sep = "-") %>%
separate(Metric, into = c("FY", "temp")) %>%
spread(key = "temp", value = "Value")
ggplot(data = unmess) +
geom_smooth(mapping = aes(x = FY, y = Cost , group = MD, color = MD))
ggplot(data = unmess) +
geom_smooth(mapping = aes(x = FY, y = FH , group = MD, color = MD))
ggplot(data = unmess)+
geom_smooth(mapping = aes(x = FY, y = Gallons , group = MD, color = MD))
6.)
progr <- readRDS("data5/ws_programmatics.rds")
categ <- readRDS("data5/ws_categorizations.rds")
as_tibble(progr)
## # A tibble: 36,328 x 18
## Base MD FY Manpower_Ops Manpower_Mx
## <chr> <chr> <int> <dbl> <dbl>
## 1 ALTUS AFB (OK) A-10 2007 NA NA
## 2 ALTUS AFB (OK) A-10 2008 NA NA
## 3 ALTUS AFB (OK) AT-38 1997 NA NA
## 4 ALTUS AFB (OK) AT-38 1998 NA NA
## 5 ALTUS AFB (OK) C-130 1998 NA NA
## 6 ALTUS AFB (OK) C-130 1999 NA NA
## 7 ALTUS AFB (OK) C-130 2000 NA NA
## 8 ALTUS AFB (OK) C-130 2002 NA NA
## 9 ALTUS AFB (OK) C-130 2003 NA NA
## 10 ALTUS AFB (OK) C-130 2004 NA 19912
## # ... with 36,318 more rows, and 13 more variables:
## # Manpower_Support_Staff <dbl>, Operating_Material <dbl>,
## # Mx_Consumables <dbl>, Mx_DLR <dbl>, Mx_Depot_AC <dbl>,
## # Mx_Depot_Missile <dbl>, Mx_Depot_Engine <dbl>, CLS <dbl>,
## # Total_O.S <dbl>, Avg_Inv <dbl>, TAI <dbl>, End_Strength <dbl>,
## # FH <dbl>
as_tibble(categ)
## # A tibble: 4,336 x 3
## Base System MD
## <chr> <chr> <chr>
## 1 ALTUS AFB (OK) AIRCRAFT A-10
## 2 ALTUS AFB (OK) AIRCRAFT AT-38
## 3 ALTUS AFB (OK) AIRCRAFT C-130
## 4 ALTUS AFB (OK) AIRCRAFT C-135
## 5 ALTUS AFB (OK) AIRCRAFT C-141
## 6 ALTUS AFB (OK) AIRCRAFT C-17
## 7 ALTUS AFB (OK) AIRCRAFT C-5
## 8 ALTUS AFB (OK) AIRCRAFT E-3
## 9 ALTUS AFB (OK) AIRCRAFT EC-130
## 10 ALTUS AFB (OK) AIRCRAFT F-15
## # ... with 4,326 more rows
left_join(progr, categ, by = c("Base", "MD")) %>%
filter(FY == "2014") %>%
filter(Base == "MINOT AFB (ND)") %>%
filter(System == "AIRCRAFT" | System == "MISSILES") %>%
group_by(System) %>%
mutate(sum(.$Total_O.S), sum(.$End_Strength, na.rm = TRUE))
## Source: local data frame [8 x 21]
## Groups: System [2]
##
## Base MD FY Manpower_Ops Manpower_Mx
## <chr> <chr> <int> <dbl> <dbl>
## 1 MINOT AFB (ND) B-52 2014 30526714 96851312
## 2 MINOT AFB (ND) E-4 2014 NA 92794
## 3 MINOT AFB (ND) GB-52 2014 NA NA
## 4 MINOT AFB (ND) OC-135 2014 NA NA
## 5 MINOT AFB (ND) T-38 2014 NA NA
## 6 MINOT AFB (ND) UH-1 2014 3984555 277855
## 7 MINOT AFB (ND) AGM-86 2014 NA 19789965
## 8 MINOT AFB (ND) LGM-30 2014 31565144 31425933
## # ... with 16 more variables: Manpower_Support_Staff <dbl>,
## # Operating_Material <dbl>, Mx_Consumables <dbl>, Mx_DLR <dbl>,
## # Mx_Depot_AC <dbl>, Mx_Depot_Missile <dbl>, Mx_Depot_Engine <dbl>,
## # CLS <dbl>, Total_O.S <dbl>, Avg_Inv <dbl>, TAI <dbl>,
## # End_Strength <dbl>, FH <dbl>, System <chr>, sum(.$Total_O.S) <dbl>,
## # sum(.$End_Strength, na.rm = TRUE) <dbl>
7.)
procat2 <- left_join(progr, categ, by = c("Base", "MD")) %>%
subset(!is.na(Total_O.S)) %>%
subset(!is.na(FH)) %>%
mutate(CPFH = Total_O.S / FH)
top10cpfh <- top_n(procat2, 10, wt = CPFH) %>%
select(Base,CPFH) %>%
arrange(CPFH)
ggplot(top10cpfh, aes(Base, CPFH)) +
geom_bar(stat = "identity")
8.)
ggplot(data = procat2) +
geom_point(mapping = aes(x = End_Strength, y = Total_O.S), na.rm = TRUE, color = "blue")
