NYC Flights Homework

Load the libraries and view the “flights” dataset

library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.0.6     v dplyr   1.0.4
## v tidyr   1.1.2     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(nycflights13)
library(psych)
## 
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha
view(flights)
describe(flights)
## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
##                vars      n    mean      sd median trimmed     mad  min  max
## year              1 336776 2013.00    0.00   2013 2013.00    0.00 2013 2013
## month             2 336776    6.55    3.41      7    6.56    4.45    1   12
## day               3 336776   15.71    8.77     16   15.70   11.86    1   31
## dep_time          4 328521 1349.11  488.28   1401 1346.82  634.55    1 2400
## sched_dep_time    5 336776 1344.25  467.34   1359 1341.60  613.80  106 2359
## dep_delay         6 328521   12.64   40.21     -2    3.32    5.93  -43 1301
## arr_time          7 328063 1502.05  533.26   1535 1526.42  619.73    1 2400
## sched_arr_time    8 336776 1536.38  497.46   1556 1550.67  618.24    1 2359
## arr_delay         9 327346    6.90   44.63     -5   -1.03   20.76  -86 1272
## carrier*         10 336776    7.14    4.14      6    7.00    5.93    1   16
## flight           11 336776 1971.92 1632.47   1496 1830.51 1608.62    1 8500
## tailnum*         12 334264 1814.32 1199.75   1798 1778.21 1587.86    1 4043
## origin*          13 336776    1.95    0.82      2    1.94    1.48    1    3
## dest*            14 336776   50.03   28.12     50   49.56   32.62    1  105
## air_time         15 327346  150.69   93.69    129  140.03   75.61   20  695
## distance         16 336776 1039.91  733.23    872  955.27  569.32   17 4983
## hour             17 336776   13.18    4.66     13   13.15    5.93    1   23
## minute           18 336776   26.23   19.30     29   25.64   23.72    0   59
## time_hour        19 336776     NaN      NA     NA     NaN      NA  Inf -Inf
##                range  skew kurtosis   se
## year               0   NaN      NaN 0.00
## month             11 -0.01    -1.19 0.01
## day               30  0.01    -1.19 0.02
## dep_time        2399 -0.02    -1.09 0.85
## sched_dep_time  2253 -0.01    -1.20 0.81
## dep_delay       1344  4.80    43.95 0.07
## arr_time        2399 -0.47    -0.19 0.93
## sched_arr_time  2358 -0.35    -0.38 0.86
## arr_delay       1358  3.72    29.23 0.08
## carrier*          15  0.36    -1.21 0.01
## flight          8499  0.66    -0.85 2.81
## tailnum*        4042  0.17    -1.24 2.08
## origin*            2  0.09    -1.50 0.00
## dest*            104  0.13    -1.08 0.05
## air_time         675  1.07     0.86 0.16
## distance        4966  1.13     1.19 1.26
## hour              22  0.00    -1.21 0.01
## minute            59  0.09    -1.24 0.03
## time_hour       -Inf    NA       NA   NA

Now create one data visualization with this dataset

Your assignment is to create one plot to visualize one aspect of this dataset. The plot may be any type we have covered so far in this class (bargraphs, scatterplots, boxplots, histograms, treemaps, heatmaps, streamgraphs, or alluvials)

flights_nona <- na.omit(flights) #clear NA data
flights_nona %>%
  group_by(carrier) %>%
  summarize(count = n()) %>%
  arrange(desc(count)) %>%
  head(5) 
## # A tibble: 5 x 2
##   carrier count
##   <chr>   <int>
## 1 UA      57782
## 2 B6      54049
## 3 EV      51108
## 4 DL      47658
## 5 AA      31947

Sort top 5 most popular airline carrier.

flights_nona %>%
  filter(carrier == "UA" | carrier == "B6" | carrier == "EV" | carrier == "DL" | carrier == "AA") %>%
  group_by(month, carrier) %>%
  summarize(avg_dep_delay = mean(dep_delay)) %>%
  ggplot(mapping = aes(x = month, y = avg_dep_delay, color = carrier)) +
    geom_line(size = 1) +
    scale_x_continuous(breaks = 1:12) +
    xlab("Month") +
    ylab("Average Departure Delay Time in Minutes") +
      ggtitle("Flight Departure Delay Time in NYC in 2013")
## `summarise()` has grouped output by 'month'. You can override using the `.groups` argument.

This visualization shows the top 5 most popular airline carriers and their average departure delay time throughout the year 2013. We can see from the plot that summer time and winter holiday season is the peak time for flight delays. It could due to thunderstorm in summertime ,heavy snow in winter time, or other weather conditions. In addition, in summer time and holiday season, the demand of travel increases and that could take extra or unexpected time to handle high demand.

Requirements for the plot:

  1. Include at least one dplyr command (filter, sort, summarize, group_by, select, mutate, ….)
  2. Include labels for the x- and y-axes
  3. Include a title
  4. Your plot must incorporate at least 2 colors
  5. Include a legend that indicates what the colors represent
  6. Write a brief paragraph that describes the visualization you have created and at least one aspect of the plot that you would like to highlight.

Start early so that if you do have trouble, you can email me with questions