Package used

library(tidyverse)
library(ggplot2)
library(dplyr)

1. Import the bomber_wide.rds file, which lists the flying hours for each aircraft by year. Convert this data to a tibble and tidy it by changing it from a wide format to a long format so that you have the following columns: Type, MD, Year, & FH. The final data should look like:

bombw<-readRDS("bomber_wide.rds")

t1<-as_tibble(bombw)
t2<-gather(
  t1,
  Year,
  FH,
  `1996`:`2014`,
  factor_key = TRUE
)

2. Import the bomber_long.rds data, which provides the value for three different outputs for each aircraft by year. The output measures include cost, flying hours, and gallons of gas consumed but these variables are “stacked” in the Output variable. Change this data to a tibble and convert to a wider format so that you have the following columns: Type, MD, FY, Cost, FH, & Gallons. Your data should look like:

bomln<-readRDS("bomber_long.rds")
t3<-as_tibble(bomln)
t4<-spread(t3,Output,Value)
t4
## # 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

3.Import the bomber_combined.rds file. Note that the first variable in this data (AC) combines the aircraft type (Bomber) and aircraft designator (i.e. B-1). This variable should be split into two. Take this data and convert it to a tibble and separate the AC variable into “Type” and “MD” so that your data looks like:

bomcom<-readRDS("bomber_combined.rds")
t5<-as_tibble(bomcom)
t6<-separate(t5,AC,c("TYPE","MD"),sep=" ")
t6
## # 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

4. Import the bomber_prefix.rds data. Take this data and convert it to a tibble and unite the prefix and number variables into an “MD” variable so that the data matches the tidy data sets you produced in problems #2 and #3.

bompre<-readRDS("bomber_prefix.rds")
t7<-as_tibble(bompre)
t8<-unite(t7,MD,c(prefix,number),sep = "-")
t8
## # 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

5.Import the bomber_mess.rds file so that it is a tibble. Clean this data up by making it contain the following variables:

Type MD which combines the prefix and number variable (i.e. “B-1”) FY which is the left part of the Metric variable Cost which is captured in the right part of the Metric variable FH which is captured in the right part of the Metric variable Gallons which is captured in the right part of the Metric variable

bommess<-readRDS("bomber_mess.rds")
t9<-as_tibble(bommess)
t10<-t9 %>% unite(.,MD,c(prefix,number),sep="-") %>% separate(.,Metric,c("FY","Cost"),sep="_") %>% spread(Cost,Value)

t10
## # A tibble: 57 × 6
##      Type    MD    FY      Cost    FH   Gallons
## *   <chr> <chr> <chr>     <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

Once you’ve created the above tidy data, plot the historical trends of this data in ggplot2 with a line chart such that the plot is facetted by the Cost, FH, and Gallons variables and each facet compares the different MDs (“B-1”, “B-2”, “B-52”).

ggplot(data=t10,aes(y=Cost,x=FH,colour=MD,group=MD))+
  geom_line()+
  facet_wrap(~MD)

ggplot(data=t10,aes(y=Cost,x=Gallons,colour=MD,group=MD))+
  geom_line()+
  facet_wrap(~MD)

6.Import the ws_programmatics.rds & ws_categorization.rds data so that they are tibbles and perform the following steps in sequence using the pipe operator (%>%).

Join the ws_categorization data to the ws_programmatics data Filter for only FY 2014 data at the following Base: Minot AFB (ND) Filter for only Systems classified as “AIRCRAFT” or “MISSILES” Group the data by System level Calculate the total sum of the Total_O.S and End_Strength variables

pro1<-as_tibble(readRDS("ws_programmatics.rds"))
cat1<-as_tibble(readRDS("ws_categorizations.rds"))
merge(pro1,cat1) %>% filter(FY==2014,Base=="MINOT AFB (ND)")  %>%
filter(System %in% c("AIRCRAFT","MISSILES")) %>% group_by(System) %>% summarise(Total_OS=sum(Total_O.S,na.rm=TRUE),End_Strength_sum=sum(End_Strength,na.rm=TRUE))
## # A tibble: 2 × 3
##     System  Total_OS End_Strength_sum
##      <chr>     <dbl>            <dbl>
## 1 AIRCRAFT 297398235             2023
## 2 MISSILES 112224909             1951

7. Once again, join the ws_programmatics.rds & ws_categorization.rds data; however, this time identify which Base had the largest cost per flying hour in 2014. Using a bar chart in ggplot2, plot these values for the top 10 bases with the largest cost per flying hour.

CPFH14<-merge(pro1,cat1) %>% filter(FY==2014) %>% group_by(Base) %>%
summarise(CPFH=(sum(Total_O.S,na.rm=TRUE)/sum(FH,na.rm=TRUE))) %>% filter(CPFH != 'Inf') %>% arrange(desc(CPFH)) 
  

CPFH14[CPFH14$CPFH == max(CPFH14$CPFH,na.rm = TRUE),]
## # A tibble: 2 × 2
##               Base   CPFH
##              <chr>  <dbl>
## 1 PATRICK AFB (FL) 178960
## 2             <NA>     NA
ggplot(data=head(CPFH14,10),aes(x=reorder(Base,CPFH),y=CPFH)) + 
  geom_bar(stat="identity") +
  coord_flip()

8. Using scatter plots in ggplot2, assess the relationship between the end strength (End_Strength) variable and total costs (Total_O.S). Provide three scatter plots that visually assesses this replationship from different angles (by FY, System, etc).

newpc<-as_tibble(merge(pro1,cat1))
ggplot(newpc, aes(x = Total_O.S, y = End_Strength)) +
        geom_line() +
        geom_point()
## Warning: Removed 6 rows containing missing values (geom_path).
## Warning: Removed 15487 rows containing missing values (geom_point).

ggplot(newpc, aes(x = Total_O.S, y =End_Strength ))+
  geom_point()+
  facet_wrap(~System)
## Warning: Removed 15487 rows containing missing values (geom_point).

ggplot(newpc, aes(x = Total_O.S, y =End_Strength ))+
  geom_point()+
  facet_wrap(~FY,nrow = 4)
## Warning: Removed 15487 rows containing missing values (geom_point).