This analysis aims to explore the relationship between flight arrival times and scheduled arrival times. We will perform exploratory data analysis and regression analysis on the flights dataset. Understanding this relationship is crucial for optimizing airline schedules and improving overall travel experiences.
2023-10-31
Introduction
Data Loading
str(flights)
## tibble [336,776 × 19] (S3: tbl_df/tbl/data.frame) ## $ year : int [1:336776] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ... ## $ month : int [1:336776] 1 1 1 1 1 1 1 1 1 1 ... ## $ day : int [1:336776] 1 1 1 1 1 1 1 1 1 1 ... ## $ dep_time : int [1:336776] 517 533 542 544 554 554 555 557 557 558 ... ## $ sched_dep_time: int [1:336776] 515 529 540 545 600 558 600 600 600 600 ... ## $ dep_delay : num [1:336776] 2 4 2 -1 -6 -4 -5 -3 -3 -2 ... ## $ arr_time : int [1:336776] 830 850 923 1004 812 740 913 709 838 753 ... ## $ sched_arr_time: int [1:336776] 819 830 850 1022 837 728 854 723 846 745 ... ## $ arr_delay : num [1:336776] 11 20 33 -18 -25 12 19 -14 -8 8 ... ## $ carrier : chr [1:336776] "UA" "UA" "AA" "B6" ... ## $ flight : int [1:336776] 1545 1714 1141 725 461 1696 507 5708 79 301 ... ## $ tailnum : chr [1:336776] "N14228" "N24211" "N619AA" "N804JB" ... ## $ origin : chr [1:336776] "EWR" "LGA" "JFK" "JFK" ... ## $ dest : chr [1:336776] "IAH" "IAH" "MIA" "BQN" ... ## $ air_time : num [1:336776] 227 227 160 183 116 150 158 53 140 138 ... ## $ distance : num [1:336776] 1400 1416 1089 1576 762 ... ## $ hour : num [1:336776] 5 5 5 5 6 5 6 6 6 6 ... ## $ minute : num [1:336776] 15 29 40 45 0 58 0 0 0 0 ... ## $ time_hour : POSIXct[1:336776], format: "2013-01-01 05:00:00" "2013-01-01 05:00:00" ...
Data Wrangling
# Select relevant columns
flights_sml <- select(flights,
year:day,
ends_with("delay"),
distance,
air_time)
flights_sml
## # A tibble: 336,776 × 7 ## year month day dep_delay arr_delay distance air_time ## <int> <int> <int> <dbl> <dbl> <dbl> <dbl> ## 1 2013 1 1 2 11 1400 227 ## 2 2013 1 1 4 20 1416 227 ## 3 2013 1 1 2 33 1089 160 ## 4 2013 1 1 -1 -18 1576 183 ## 5 2013 1 1 -6 -25 762 116 ## 6 2013 1 1 -4 12 719 150 ## 7 2013 1 1 -5 19 1065 158 ## 8 2013 1 1 -3 -14 229 53 ## 9 2013 1 1 -3 -8 944 140 ## 10 2013 1 1 -2 8 733 138 ## # ℹ 336,766 more rows
Data Wrangling
mutate(flights_sml,
gain = arr_delay - dep_delay,
speed = distance/air_time * 60)
## # A tibble: 336,776 × 9 ## year month day dep_delay arr_delay distance air_time gain speed ## <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 2013 1 1 2 11 1400 227 9 370. ## 2 2013 1 1 4 20 1416 227 16 374. ## 3 2013 1 1 2 33 1089 160 31 408. ## 4 2013 1 1 -1 -18 1576 183 -17 517. ## 5 2013 1 1 -6 -25 762 116 -19 394. ## 6 2013 1 1 -4 12 719 150 16 288. ## 7 2013 1 1 -5 19 1065 158 24 404. ## 8 2013 1 1 -3 -14 229 53 -11 259. ## 9 2013 1 1 -3 -8 944 140 -5 405. ## 10 2013 1 1 -2 8 733 138 10 319. ## # ℹ 336,766 more rows
Exploratory Data Analysis
summary(flights)
## year month day dep_time sched_dep_time ## Min. :2013 Min. : 1.000 Min. : 1.00 Min. : 1 Min. : 106 ## 1st Qu.:2013 1st Qu.: 4.000 1st Qu.: 8.00 1st Qu.: 907 1st Qu.: 906 ## Median :2013 Median : 7.000 Median :16.00 Median :1401 Median :1359 ## Mean :2013 Mean : 6.549 Mean :15.71 Mean :1349 Mean :1344 ## 3rd Qu.:2013 3rd Qu.:10.000 3rd Qu.:23.00 3rd Qu.:1744 3rd Qu.:1729 ## Max. :2013 Max. :12.000 Max. :31.00 Max. :2400 Max. :2359 ## NA's :8255 ## dep_delay arr_time sched_arr_time arr_delay ## Min. : -43.00 Min. : 1 Min. : 1 Min. : -86.000 ## 1st Qu.: -5.00 1st Qu.:1104 1st Qu.:1124 1st Qu.: -17.000 ## Median : -2.00 Median :1535 Median :1556 Median : -5.000 ## Mean : 12.64 Mean :1502 Mean :1536 Mean : 6.895 ## 3rd Qu.: 11.00 3rd Qu.:1940 3rd Qu.:1945 3rd Qu.: 14.000 ## Max. :1301.00 Max. :2400 Max. :2359 Max. :1272.000 ## NA's :8255 NA's :8713 NA's :9430 ## carrier flight tailnum origin ## Length:336776 Min. : 1 Length:336776 Length:336776 ## Class :character 1st Qu.: 553 Class :character Class :character ## Mode :character Median :1496 Mode :character Mode :character ## Mean :1972 ## 3rd Qu.:3465 ## Max. :8500 ## ## dest air_time distance hour ## Length:336776 Min. : 20.0 Min. : 17 Min. : 1.00 ## Class :character 1st Qu.: 82.0 1st Qu.: 502 1st Qu.: 9.00 ## Mode :character Median :129.0 Median : 872 Median :13.00 ## Mean :150.7 Mean :1040 Mean :13.18 ## 3rd Qu.:192.0 3rd Qu.:1389 3rd Qu.:17.00 ## Max. :695.0 Max. :4983 Max. :23.00 ## NA's :9430 ## minute time_hour ## Min. : 0.00 Min. :2013-01-01 05:00:00.00 ## 1st Qu.: 8.00 1st Qu.:2013-04-04 13:00:00.00 ## Median :29.00 Median :2013-07-03 10:00:00.00 ## Mean :26.23 Mean :2013-07-03 05:22:54.64 ## 3rd Qu.:44.00 3rd Qu.:2013-10-01 07:00:00.00 ## Max. :59.00 Max. :2013-12-31 23:00:00.00 ##
cor(flights$arr_time, flights$sched_arr_time)
## [1] NA
Data Visualization
ggplot(flights_sml, aes(x = flights$arr_time , y = flights$sched_arr_time)) +
geom_point() +
geom_smooth(method = "lm", col = "red") +
labs(title = "Relationship between arr_time and sched_arr_time",
x = "arr_time",
y = "sched_arr_time")
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 8713 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 8713 rows containing missing values (`geom_point()`).
Regression Analysis
analysis <- lm(flights$sched_arr_time ~ flights$arr_time, flights) summary(analysis)
## ## Call: ## lm(formula = flights$sched_arr_time ~ flights$arr_time, data = flights) ## ## Residuals: ## Min 1Q Median 3Q Max ## -2193.42 -130.23 -27.61 80.49 1931.90 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 4.264e+02 1.596e+00 267.1 <2e-16 *** ## flights$arr_time 7.367e-01 1.002e-03 735.5 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 305.9 on 328061 degrees of freedom ## (8713 observations deleted due to missingness) ## Multiple R-squared: 0.6225, Adjusted R-squared: 0.6225 ## F-statistic: 5.41e+05 on 1 and 328061 DF, p-value: < 2.2e-16
Assumption Checks
plot(analysis, which = 1)
Assumption Checks
plot(analysis, which = 2)
Conclusion
In this analysis, we explored the relationship between arrival time and scheduled arrival time for flights. The regression analysis indicated a significant correlation between the two variables. Additionally, the assumption checks provided insights into the model’s validity. Overall, this analysis provides valuable insights into the factors influencing flight schedules.