Week5

Problem 1-

Importing Bomber_wide data

library(dplyr)
library(tidyr)
library(tidyverse)
library(prettydoc)
BomberWide<-readRDS("R/data/bomber_wide.rds")%>%
  as_tibble() %>%
  gather(Year, FH, `1996`:`2014`)
BomberWide

## # A tibble: 57 × 4
##      Type    MD  Year    FH
##     <chr> <chr> <chr> <int>
## 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
## # ... with 47 more rows

Problem 2-

Importing Bomber_long data

BomberLong<-readRDS("R/data/bomber_long.rds")%>%
  as_tibble() %>%
  spread(key = "Output", value = "Value")
BomberLong

## # A tibble: 57 × 6
##      Type    MD    FY      Cost    FH   Gallons
## *   <chr> <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

Problem 3-

Importing Bomber_combined data

BomberCombined<-readRDS("R/data/bomber_combined.rds")%>% as_tibble() %>%
  separate(AC, into = c("Type", "MD"), sep = " ")
BomberCombined

## # A tibble: 57 × 6
##      Type    MD    FY      Cost    FH   Gallons
## *   <chr> <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

Problem 4

Importing bomber_prefix data

BomberPrefix<-readRDS("R/data/bomber_prefix.rds") %>% as_tibble() %>%
  unite('MD', prefix, number, sep = '-')
BomberPrefix

## # A tibble: 171 × 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

Problem 5

Importing bomber_mess data

Bomber_mess<-readRDS("R/data/bomber_mess.rds")
UpdatedBomberMess<-as_tibble(Bomber_mess)
New<-UpdatedBomberMess %>% unite(MD,prefix,number,sep="-") %>% separate(Metric,into =c("FY","Output"),sep="_") %>% spread(key=Output,value=Value)
New %>% gather(`Cost`,`FH`,`Gallons`,key="Output",value="Value")%>% ggplot(aes(x=FY,y=Value,group=MD))+geom_line()+facet_wrap(~Output,scales="free",nrow=3)

Problem 6

Importing WSProgrammatics and wecategorization data

WSProgrammatics <- readRDS("R/data/ws_programmatics.rds")
WSCategorization <- readRDS("R/data/ws_categorizations.rds")
Prob7<-WSProgrammatics %>% left_join(WSCategorization,by=c("Base","MD"))%>%filter(FY==2014)%>%
  group_by(Base)%>%
  mutate(CPFH=Total_O.S/FH) %>%
  aggregate(CPFH ~Base,data=.,mean)%>%
  arrange(desc(CPFH))%>%top_n(10)

Problem 7

Bar graph and other manipulations on the combined dataset.

WSProgrammatics %>%
  full_join(WSCategorization, by = c("Base","MD")) %>%
  filter(FY=="2014")%>%
  select(Base, Total_O.S, FH, FY, MD) %>%
  na.omit() %>%
  group_by(Base) %>%
  summarize(Total_O.S = sum(Total_O.S, na.rm = T), FH = sum(FH)) %>%
  mutate(CPFH = Total_O.S/FH) %>%
  top_n(10, CPFH) %>%
  ggplot(mapping = aes(x=Base,y=CPFH))+
  geom_bar(stat = "identity",fill="pink")

Problem 8

Visualizations on the data

library(ggplot2)
WSProgrammatics%>%left_join(WSCategorization,by=c("Base","MD"))%>%
  ggplot(aes(End_Strength,Total_O.S))+geom_point(color="magenta")+facet_wrap(~FY,scales="free",nrow=3)

WSProgrammatics%>%left_join(WSCategorization,by=c("Base","MD"))%>%
  ggplot(aes(End_Strength,Total_O.S))+geom_point(color="Blue")+facet_wrap(~System,scales="free",nrow=3)