NYC Flights Homework

Author

Kepm

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.2     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.2     ✔ 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(nycflights13)
library(treemap)

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" ...

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

dim(flights)

[1] 336776     19

head(flights)

# A tibble: 6 × 19
   year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
  <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
1  2013     1     1      517            515         2      830            819
2  2013     1     1      533            529         4      850            830
3  2013     1     1      542            540         2      923            850
4  2013     1     1      544            545        -1     1004           1022
5  2013     1     1      554            600        -6      812            837
6  2013     1     1      554            558        -4      740            728
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

tail(flights)

# A tibble: 6 × 19
   year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
  <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
1  2013     9    30       NA           1842        NA       NA           2019
2  2013     9    30       NA           1455        NA       NA           1634
3  2013     9    30       NA           2200        NA       NA           2312
4  2013     9    30       NA           1210        NA       NA           1330
5  2013     9    30       NA           1159        NA       NA           1344
6  2013     9    30       NA            840        NA       NA           1020
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

nrow(flights)

[1] 336776

ncol(flights)

[1] 19

glimpse(flights)

Rows: 336,776
Columns: 19
$ year           <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2…
$ month          <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ day            <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ dep_time       <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 558, …
$ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 600, …
$ dep_delay      <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2, -1…
$ arr_time       <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 849,…
$ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 851,…
$ arr_delay      <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7, -1…
$ carrier        <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6", "…
$ flight         <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301, 4…
$ tailnum        <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N394…
$ origin         <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LGA",…
$ dest           <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IAD",…
$ air_time       <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149, 1…
$ distance       <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 733, …
$ hour           <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6…
$ minute         <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, 0…
$ time_hour      <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 0…

filter(flights, month == 12)

# A tibble: 28,135 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
 1  2013    12     1       13           2359        14      446            445
 2  2013    12     1       17           2359        18      443            437
 3  2013    12     1      453            500        -7      636            651
 4  2013    12     1      520            515         5      749            808
 5  2013    12     1      536            540        -4      845            850
 6  2013    12     1      540            550       -10     1005           1027
 7  2013    12     1      541            545        -4      734            755
 8  2013    12     1      546            545         1      826            835
 9  2013    12     1      549            600       -11      648            659
10  2013    12     1      550            600       -10      825            854
# ℹ 28,125 more rows
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

arrange(flights, desc(dep_time))

# A tibble: 336,776 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
 1  2013    10    30     2400           2359         1      327            337
 2  2013    11    27     2400           2359         1      515            445
 3  2013    12     5     2400           2359         1      427            440
 4  2013    12     9     2400           2359         1      432            440
 5  2013    12     9     2400           2250        70       59           2356
 6  2013    12    13     2400           2359         1      432            440
 7  2013    12    19     2400           2359         1      434            440
 8  2013    12    29     2400           1700       420      302           2025
 9  2013     2     7     2400           2359         1      432            436
10  2013     2     7     2400           2359         1      443            444
# ℹ 336,766 more rows
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

view(flights)

flights_at <- flights %>%
  filter(!is.na(dep_time) & !is.na(air_time))

flights_at

# A tibble: 327,346 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
 1  2013     1     1      517            515         2      830            819
 2  2013     1     1      533            529         4      850            830
 3  2013     1     1      542            540         2      923            850
 4  2013     1     1      544            545        -1     1004           1022
 5  2013     1     1      554            600        -6      812            837
 6  2013     1     1      554            558        -4      740            728
 7  2013     1     1      555            600        -5      913            854
 8  2013     1     1      557            600        -3      709            723
 9  2013     1     1      557            600        -3      838            846
10  2013     1     1      558            600        -2      753            745
# ℹ 327,336 more rows
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

flights_at <- flights %>%
  group_by (air_time) %>% 
  summarize (airt = mean (distance), 
             dep_time = mean (arr_delay),
             airtime = mean(air_time/60))

flights_at

# A tibble: 510 × 4
   air_time  airt dep_time airtime
      <dbl> <dbl>    <dbl>   <dbl>
 1       20  116     12.5    0.333
 2       21  114.    26.8    0.35 
 3       22  114.    -5.12   0.367
 4       23  113.    -4.79   0.383
 5       24  110.     6      0.4  
 6       25  105.     1.10   0.417
 7       26  103.     3.70   0.433
 8       27  101.     6.72   0.45 
 9       28  112.    10.2    0.467
10       29  121.     4.13   0.483
# ℹ 500 more rows

head(flights_at)

# A tibble: 6 × 4
  air_time  airt dep_time airtime
     <dbl> <dbl>    <dbl>   <dbl>
1       20  116     12.5    0.333
2       21  114.    26.8    0.35 
3       22  114.    -5.12   0.367
4       23  113.    -4.79   0.383
5       24  110.     6      0.4  
6       25  105.     1.10   0.417

tail(flights_at)

# A tibble: 6 × 4
  air_time  airt dep_time airtime
     <dbl> <dbl>    <dbl>   <dbl>
1      679 4983        15    11.3
2      683 4983        14    11.4
3      686 4983        29    11.4
4      691 4983         2    11.5
5      695 4963        61    11.6
6       NA  746.       NA    NA

treemap(flights_at, index="airtime", vSize="air_time", 
        vColor="airtime", type="manual", 
        title = "Flights Air Time expressed in hours", fontsize.title = 16,
        palette="Set2", mapping=c(0.30,12), range=c(0.30,12))

I’ve created a visualization based on “NYCflights13” dataset. My first step was to install packages in the library, for example, tidyverse, treemap and nycflights data set. To get to know widely the data set I’ve checked for structure, dimension, columns and rows number, summary, and glimpse so I can know what type of variables I have. I applied filter function using the pipe filter, so I got a subset regarding only the flights during December. I wanted to know how crowded were the airports during Holidays. Also, I applied the arrange function for the variable departure time in descendant order, so I was able to observe the flights that departed starting at midnight in descending order and finishing at 00:01 am. Then, I’ve applied the filter looking for the missing values using the pipe command. Finally, I’ve reduced the air time variable to convert it into statistical variable in which I was able to see the time flying for each flight but I wanted to see analyze all the data expressed in hours, since the set have expressed this variable in minutes. I would like to highlight that I created a treemap in which is observed in hierarchical order the flights that last from 12 hours to less than one hour. I used the brewer pallet “set2” which display a beautiful plot.