Week-5
Anitha Sreedhar Babu
2016-11-12
Synopsis
This is my report for week 5 assignment on managing dataframes, creating tibbles, tidying data and working with multiple dataframes
Packages
This packages contains multiple packages within it with can be used for data representation and manipulation
library(tidyverse)
library(tibble)
library(ggplot2)Source Code
The following datasets are used: bombers_wide.rds bombers_long.rds bombers_wide.rds bombers_combined.rds ws_programmatics.rds ws_categorizations.rds
Assignment problems
- 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:
setwd("C:/Users/Anitha/Documents/Data Wrangling with R (BANA 8090)/data/")
bomber_wide <- readRDS("bomber_wide.rds")
bomber_wide<-as_tibble(bomber_wide)
bomber_gather <-bomber_wide %>% gather(3:21,key="year", value="FH")
head(bomber_gather)## # A tibble: 6 x 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- 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:
setwd("C:/Users/Anitha/Documents/Data Wrangling with R (BANA 8090)/data/")
bomber_long <- readRDS("bomber_long.rds")
bomber_long<-as_tibble(bomber_long)
bomber_spread <- bomber_long %>% spread(4, key="Output", value="Value")
head(bomber_spread)## # A tibble: 6 x 6
## Type MD FY Cost FH Gallons
## <chr> <chr> <int> <dbl> <dbl> <dbl>
## 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- 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:
setwd("C:/Users/Anitha/Documents/Data Wrangling with R (BANA 8090)/data/")
bomber_combined <- readRDS("bomber_combined.rds")
bomber_combined <-as_tibble(bomber_combined)
bomber_separate <- bomber_combined %>% separate(AC, into = c("Type", "MD"), sep=6)
head(bomber_separate)## # A tibble: 6 x 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- 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
setwd("C:/Users/Anitha/Documents/Data Wrangling with R (BANA 8090)/data/")
bomber_prefix <- readRDS("bomber_prefix.rds")
bomber_prefix<-as_tibble(bomber_prefix)
bomber_unite <- bomber_prefix %>% unite(MD,prefix,number, sep="-")
head(bomber_unite)## # A tibble: 6 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- Import the bomber_mess.rds file so that it is a tibble. Clean this data up by making it contain the following variables:
setwd("C:/Users/Anitha/Documents/Data Wrangling with R (BANA 8090)/data/")
bomber_mess <- readRDS("bomber_mess.rds")
bomber_mess <-as_tibble(bomber_mess)
bomber_mess_multiple <- bomber_mess %>% unite(MD,prefix,number,sep="-") %>% separate(Metric, into = c("Year","Output"),sep="_") %>%spread(key = Output, value = Value)
bomber_mess_multiple %>%
gather(`Cost`,`FH`,`Gallons`,key = "Metrics", value = "Value") %>%
transform(Year = as.numeric(Year)) %>%
ggplot(.) + geom_line(mapping = aes(x = Year, y = Value,group = MD,colour = MD))+
facet_wrap(~ Metrics, nrow = 3, scales = "free") +
scale_x_continuous(name="Year", breaks=seq(1996,2014,2))
head(bomber_mess_multiple)## # A tibble: 6 x 6
## Type MD Year 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- 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 (%>%).
setwd("C:/Users/Anitha/Documents/Data Wrangling with R (BANA 8090)/data/")
ws_programmatics <-readRDS("ws_programmatics.rds")
ws_programmatics <- as_tibble(ws_programmatics)
ws_categorization <-readRDS("ws_categorizations.rds")
ws_categorization<-as_tibble(ws_categorization)
ws_categorization %>%
inner_join(ws_programmatics, by = c("Base","MD")) %>%
filter(Base == "MINOT AFB (ND)",FY == 2014) %>%
filter(System %in% c("AIRCRAFT","MISSILES")) %>%
group_by(System) %>%
summarize(Sum_Total_OS = sum(Total_O.S,na.rm=TRUE),
End_Strength = sum(End_Strength,na.rm=TRUE))## # A tibble: 2 x 3
## System Sum_Total_OS End_Strength
## <chr> <dbl> <dbl>
## 1 AIRCRAFT 297398235 2023
## 2 MISSILES 112224909 1951- Once again, join the ws_programmatics.rds & ws_categorization.rds data; however, this time identify which Base had the largest cost per flying hour (defined as which requires you to create a new variable) in 2014. Using a bar chart in ggplot2, plot these values for the top 10 bases with the largest cost per flying hour.
ws_question7<-
ws_categorization %>%
inner_join(ws_programmatics, by = c("Base","MD")) %>%
select(Base,Total_O.S,FH)%>%
na.omit() %>%
group_by(Base) %>%
summarize(Sum_Total_OS = sum(Total_O.S,na.rm=TRUE),
SUm_FH = sum(FH,na.rm=TRUE)) %>%
mutate(CPFH = Sum_Total_OS / SUm_FH) %>%
select(Base,CPFH) %>%
arrange(desc(CPFH)) %>%
head(10)
ws_question7## # A tibble: 10 x 2
## Base CPFH
## <chr> <dbl>
## 1 TINKER AFB (OK) 61780.13
## 2 ELLSWORTH AFB (SD) 39916.41
## 3 LANGLEY AFB (VA) 34467.86
## 4 MINOT AFB (ND) 31164.68
## 5 BARKSDALE AFB (LA) 30798.51
## 6 MOUNTAIN HOME AFB (ID) 25321.45
## 7 DYESS AFB (TX) 25216.68
## 8 HICKAM AFB (HI) 25090.59
## 9 LACKLAND AFB (TX) 23187.37
## 10 TYNDALL AFB (FL) 22426.92ggplot(data = ws_question7) +
geom_bar(mapping = aes(x = Base, y = CPFH),stat = 'identity') +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
scale_x_discrete(name ="Base",
limits=ws_question7$Base)
- 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).
ws_programmatics %>%
select(End_Strength,Total_O.S,FY) %>%
na.omit() %>%
ggplot(.) +
geom_point(mapping = aes(x = End_Strength, y = Total_O.S))+
facet_wrap(~ FY, nrow = 5)