Week_5_data607

Import the Necessary Libraries and Import the Data

library(tidyverse)

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## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.3     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(dplyr)
library(tidyr)
library(readr)
df <- read_csv("https://raw.githubusercontent.com/sphill12/DATA607/main/Week_5_data_607.csv")

## New names:
## Rows: 5 Columns: 7
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (2): ...1, ...2 dbl (5): Los Angeles, Phoenix, San Diego, San Francisco,
## Seattle
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
## • `` -> `...2`

head(df)

## # A tibble: 5 × 7
##   ...1    ...2    `Los Angeles` Phoenix `San Diego` `San Francisco` Seattle
##   <chr>   <chr>           <dbl>   <dbl>       <dbl>           <dbl>   <dbl>
## 1 ALASKA  on time           497     221         212             503    1841
## 2 <NA>    delayed            62      12          20             102     305
## 3 <NA>    <NA>               NA      NA          NA              NA      NA
## 4 AM WEST on time           694    4840         383             320     201
## 5 <NA>    delayed           117     415          65             129      61

First I added column names that were missing from the data. Because I wanted to shift the data to be long rather than wide, I had to input values to make the data more descriptive

colnames(df)[1:2] <- c("airline", "delay")
df$airline[2] <- "ALASKA"
df$airline[5] <- "AM WEST"
df <- df %>% drop_na()

Rather than have a column for each location, I made all locations into one column, and added a “total” column to display the values that were in each column.

longer_df <- pivot_longer(df, cols = 3:7, names_to = "Location", values_to = "total")
head(longer_df)

## # A tibble: 6 × 4
##   airline delay   Location      total
##   <chr>   <chr>   <chr>         <dbl>
## 1 ALASKA  on time Los Angeles     497
## 2 ALASKA  on time Phoenix         221
## 3 ALASKA  on time San Diego       212
## 4 ALASKA  on time San Francisco   503
## 5 ALASKA  on time Seattle        1841
## 6 ALASKA  delayed Los Angeles      62

For visualizations, I first displayed the difference in total delays and on times between the two airlines

airline_group <- data.frame(longer_df %>% group_by(airline, delay) %>% summarize(total = sum(total)))

## `summarise()` has grouped output by 'airline'. You can override using the
## `.groups` argument.

airline_group

##   airline   delay total
## 1  ALASKA delayed   501
## 2  ALASKA on time  3274
## 3 AM WEST delayed   787
## 4 AM WEST on time  6438

ggplot(data = airline_group, aes(x = airline, y = total, fill = delay)) + 
  geom_bar(stat = "identity", position = position_dodge()) +
  geom_text(aes(label= airline_group$total), position=position_dodge(width=0.9), vjust=-0.25)

## Warning: Use of `airline_group$total` is discouraged.
## ℹ Use `total` instead.

airline_number <- airline_group[airline_group$total == "delayed"]

I wanted to show the ratio of delays to on time counts. Alaska has a higher proportion of delays.

airline_group %>%
  group_by(airline) %>%
  summarise(delayed_to_ontime_ratio = sum(total[delay == "delayed"]) / sum(total[delay == "on time"]))

## # A tibble: 2 × 2
##   airline delayed_to_ontime_ratio
##   <chr>                     <dbl>
## 1 ALASKA                    0.153
## 2 AM WEST                   0.122

airline_number <- airline_group$total[airline_group$delay == "delayed"] / airline_group$total[airline_group$delay == "on time"]
airline_number

## [1] 0.1530238 0.1222429

Here I showed the delays and on times for each location. We can see that Pheonix contains much more traffic than other locations. Overall San Fransisco appears to be the worst location to have flights, as the delays are quite high compared to on time flights.

location_group <- data.frame(longer_df %>% group_by(Location, delay) %>% summarise(total = sum(total)))

## `summarise()` has grouped output by 'Location'. You can override using the
## `.groups` argument.

ggplot(data = location_group, aes(x = Location, y = total, fill = delay)) + 
  geom_bar(stat = "identity", position = position_dodge()) +
  geom_text(aes(label=total), position=position_dodge(width=0.9), vjust=-0.25)

location_group

##         Location   delay total
## 1    Los Angeles delayed   179
## 2    Los Angeles on time  1191
## 3        Phoenix delayed   427
## 4        Phoenix on time  5061
## 5      San Diego delayed    85
## 6      San Diego on time   595
## 7  San Francisco delayed   231
## 8  San Francisco on time   823
## 9        Seattle delayed   366
## 10       Seattle on time  2042

I separated the data into two plots for locations. One that shows the delays, and one that shows the on time for each airline. We can see that Pheonix is a hot area for AM west, while Seattle is a hot area for Alaska.

delayed = longer_df %>% filter(delay == "delayed")
ggplot(data = delayed, aes(x = Location, y = total, fill = airline)) + 
  geom_bar(stat = "identity", position = position_dodge()) +
  geom_text(aes(label=total), position=position_dodge(width=0.9), vjust=-0.25)

on_time = longer_df %>% filter(delay == "on time")
ggplot(data = on_time, aes(x = Location, y = total, fill = airline)) + 
  geom_bar(stat = "identity", position = position_dodge()) +
  geom_text(aes(label=total), position=position_dodge(width=0.9), vjust=-0.25)

 location_ratios <- data.frame(longer_df %>%
  group_by(Location, airline) %>%
  summarise(delayed_to_ontime_ratio = (sum(total[delay == "delayed"]) / sum(total[delay == "on time"]))))

## `summarise()` has grouped output by 'Location'. You can override using the
## `.groups` argument.

I felt that showing the ratio of delays to on times was more representative of airline quality. Here are the delay ratios for each place. We can see that Alaska performs better in each place. Overall, it is better to fly Alaska.

location_ratios

##         Location airline delayed_to_ontime_ratio
## 1    Los Angeles  ALASKA              0.12474849
## 2    Los Angeles AM WEST              0.16858790
## 3        Phoenix  ALASKA              0.05429864
## 4        Phoenix AM WEST              0.08574380
## 5      San Diego  ALASKA              0.09433962
## 6      San Diego AM WEST              0.16971279
## 7  San Francisco  ALASKA              0.20278330
## 8  San Francisco AM WEST              0.40312500
## 9        Seattle  ALASKA              0.16567083
## 10       Seattle AM WEST              0.30348259

location_ratios

##         Location airline delayed_to_ontime_ratio
## 1    Los Angeles  ALASKA              0.12474849
## 2    Los Angeles AM WEST              0.16858790
## 3        Phoenix  ALASKA              0.05429864
## 4        Phoenix AM WEST              0.08574380
## 5      San Diego  ALASKA              0.09433962
## 6      San Diego AM WEST              0.16971279
## 7  San Francisco  ALASKA              0.20278330
## 8  San Francisco AM WEST              0.40312500
## 9        Seattle  ALASKA              0.16567083
## 10       Seattle AM WEST              0.30348259

ggplot(location_ratios, aes(x = Location, y = delayed_to_ontime_ratio, fill = airline)) + geom_bar(stat = "identity", position = position_dodge())

Week_5_data607

Steve Phillips

2023-09-25

Import the Necessary Libraries and Import the Data

First I added column names that were missing from the data. Because I wanted to shift the data to be long rather than wide, I had to input values to make the data more descriptive

Rather than have a column for each location, I made all locations into one column, and added a “total” column to display the values that were in each column.

For visualizations, I first displayed the difference in total delays and on times between the two airlines

I wanted to show the ratio of delays to on time counts. Alaska has a higher proportion of delays.

Here I showed the delays and on times for each location. We can see that Pheonix contains much more traffic than other locations. Overall San Fransisco appears to be the worst location to have flights, as the delays are quite high compared to on time flights.

I separated the data into two plots for locations. One that shows the delays, and one that shows the on time for each airline. We can see that Pheonix is a hot area for AM west, while Seattle is a hot area for Alaska.

I felt that showing the ratio of delays to on times was more representative of airline quality. Here are the delay ratios for each place. We can see that Alaska performs better in each place. Overall, it is better to fly Alaska.