NYCFlights13

NYC Flights Homework

Load the libraries and view the “flights” dataset

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.7
## v tidyr   1.1.4     v stringr 1.4.0
## v readr   2.1.1     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)

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

library(tidyverse)

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  
##  1st Qu.: 8.00   1st Qu.:2013-04-04 13:00:00  
##  Median :29.00   Median :2013-07-03 10:00:00  
##  Mean   :26.23   Mean   :2013-07-03 05:22:54  
##  3rd Qu.:44.00   3rd Qu.:2013-10-01 07:00:00  
##  Max.   :59.00   Max.   :2013-12-31 23:00:00  
## 

unique(flights$origin)

## [1] "EWR" "LGA" "JFK"

view(flights)

flights$month[flights$month == 1]<-"January"
flights$month[flights$month == 2]<-"February"
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]<- "August"
flights$month[flights$month == 9]<- "September"
flights$month[flights$month == 10]<-"October"
flights$month[flights$month == 11]<-"November"
flights$month[flights$month == 12]<-"December"

LGA <- flights[flights$origin == "LGA", ]

EWR <- flights[flights$origin == "EWR", ]

JFK <- flights[flights$origin == "JFK", ]

LGA %>% filter(carrier == "AA")

## # A tibble: 15,459 x 19
##     year month     day dep_time sched_dep_time dep_delay arr_time sched_arr_time
##    <int> <chr>   <int>    <int>          <int>     <dbl>    <int>          <int>
##  1  2013 January     1      558            600        -2      753            745
##  2  2013 January     1      559            600        -1      941            910
##  3  2013 January     1      623            610        13      920            915
##  4  2013 January     1      629            630        -1      824            810
##  5  2013 January     1      635            635         0     1028            940
##  6  2013 January     1      656            700        -4      854            850
##  7  2013 January     1      659            700        -1     1008           1015
##  8  2013 January     1      724            730        -6     1111           1040
##  9  2013 January     1      739            745        -6      918            930
## 10  2013 January     1      753            755        -2     1056           1110
## # ... with 15,449 more rows, and 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>

LGA %>% 
 ggplot (data = LGA, mapping = aes(x = factor(month), y = air_time)) +
  geom_boxplot(color = "blue", fill = "pink") +
   labs(x = "Month", y = "Air Time Minutes)",
         title = "Air Time LGA")  +
   scale_x_discrete(labels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun",
                               "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))

## Warning: Removed 3522 rows containing non-finite values (stat_boxplot).

Blue: Range, Median, Q1, Q3

Pink: Interquartile Range

EWR <- flights[flights$origin == "EWR", ]

EWR %>% filter(carrier == "AA") %>%
 ggplot (data = EWR, mapping = aes(x = factor(month), y = arr_time)) +
  geom_boxplot(color = "black", fill = "gray") +
   labs(x = "Month", y = "Air Time Minutes)",
         title = "Air Times for EWR")  +
   scale_x_discrete(labels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun",
                               "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))

## Warning: Removed 3390 rows containing non-finite values (stat_boxplot).

# Black: Range, Median, Q1, Q3 # Grey: Interquartile Range

JFK <- flights[flights$origin == "JFK", ]

JFK %>% filter(carrier == "AA")

## # A tibble: 13,783 x 19
##     year month     day dep_time sched_dep_time dep_delay arr_time sched_arr_time
##    <int> <chr>   <int>    <int>          <int>     <dbl>    <int>          <int>
##  1  2013 January     1      542            540         2      923            850
##  2  2013 January     1      628            630        -2     1137           1140
##  3  2013 January     1      656            659        -3      949            959
##  4  2013 January     1      712            715        -3     1023           1035
##  5  2013 January     1      743            730        13     1107           1100
##  6  2013 January     1      745            745         0     1135           1125
##  7  2013 January     1      803            810        -7      903            925
##  8  2013 January     1      826            715        71     1136           1045
##  9  2013 January     1      840            845        -5     1311           1350
## 10  2013 January     1      856            900        -4     1226           1220
## # ... with 13,773 more rows, and 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>

JFK %>% filter(carrier == "AA") %>%
 ggplot (data = JFK, mapping = aes(x = factor(month), y = air_time)) +
  geom_boxplot(color = "blue", fill = "red") +
   labs(x = "Month", y = "Air Time Minutes)",
         title = "Air Times for JFK")  +
   scale_x_discrete(labels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun",
                               "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))

## Warning: Removed 2200 rows containing non-finite values (stat_boxplot).

Blue: Range, Median, Q1, Q3

Red: Interquartile Range

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

I did three different visuals for my RMD; for each origin(JFK,EWR,LGA) and for every month, I calculated points of air time. The visuals are box plots, and the reason for the visual is that they give readers a good idea of all air times these origins have and what times they generally do. I think that there are many aspects of each of these graphs to highlight, for example the the interquartile ranges are varied from graph to graph. For JFK, the interquartile range is approximately between 100 and 300 minutes. For LGA, the interquartile range is approximately between 80 and 150 minutes.For EWR, the interquartile range is approximately between 1000 and 2000 minutes, which has the highest valued air time. The airtimes give so much information because not only does it give readers an idea of how long the planes are in the air, but will give readesr an idea of distance and destination. The reason for this is because the higher the air time, the further away the destination would have to be and the farther the distance is. The last thing I would highlight would be the interquartile ranges for all three graphs; you can see that LGA and JFk interquartile ranges are on the lower end of the plots and the interquartile ranges are on the higher end of the plots for EWR.