NYCFlights13
Lulua Shahmalak
9/24/2020
NYC Flights Homework
Load the libraries and view the “flights” dataset
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.0 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(nycflights13)
library(psych)##
## Attaching package: 'psych'
##
## The following objects are masked from 'package:ggplot2':
##
## %+%, alphaview(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 NANow 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)
Requirements for the plot:
- Include at least one dplyr command (filter, sort, summarize, group_by, select, mutate, ….)
- Include labels for the x- and y-axes
- Include a title
- Your plot must incorporate at least 2 colors
- Include a legend that indicates what the colors represent
- 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
library(treemap)
flights_nona <- flights %>%
filter(!is.na(flight) & !is.na(dep_delay))flights_nona <- flights %>%
filter(dep_delay > 300)
flights$month[flights$month == 1]<- "Jan"
flights$month[flights$month == 2]<- "Feb"
flights$month[flights$month == 3]<- "March"
flights$month[flights$month == 4]<- "April"
flights$month[flights$month == 5]<- "May"
flights$month[flights$month == 6]<- "June"
flights$month[flights$month == 7]<- "July"
flights$month[flights$month == 8]<- "Aug"
flights$month[flights$month == 9]<- "Sept"
flights$month[flights$month == 10]<- "Oct"
flights$month[flights$month == 11]<- "Nov"
flights$month[flights$month == 12]<- "Dec"
treemap(flights, index="month", vSize="dep_delay", vColor="flight", type="manual",
palette="RdPu", title = "Total Flights Travelled in Each Month Compared to Departure Delays Greater Than \n 5 Hours", title.legend = "Number of flights")
I have created a treemap to visualize the total flights traveled in each month of 2013 with the size of the boxes differing according to the amount of departure delays greater than 5 hours. I chose the red and purple color palette where purple is for highest flights and lighter shades of red indicating less flights of that month.
Something that I noticed, which is quite understandable is that the darker the color of the month the greater the size too. This seems quite accurate as the more the flights, the more the chance of having departure delays as well. Another aspect we could assume is that the most of the months with a dark color are the months of school vacations such as June, July, and August during the summer, or March during Spring. These factors could influence flight rates and delays. Another factor could be the weather. During stormy or snowy weathers, there are higher chances of delays in flights which can be seen in the month of December. While the flight rates in that month are not as high, the departure rates are comparatively more than other months such as October. One month that really stands out is February. It holds a very light shade of red indicating the least flights flown in the year of 2013. Even the size is moderate compared to the other months indicating many delays.