Install packages and load libraries

#install.packages("psych")
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.4     v dplyr   1.0.7
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   2.0.1     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
library(dbplyr)
## 
## Attaching package: 'dbplyr'
## The following objects are masked from 'package:dplyr':
## 
##     ident, sql
library("scales")
## 
## Attaching package: 'scales'
## The following objects are masked from 'package:psych':
## 
##     alpha, rescale
## The following object is masked from 'package:purrr':
## 
##     discard
## The following object is masked from 'package:readr':
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##     col_factor
#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

Create a new object to visualize dataset

flights <- flights

Create new object - Group by carrier - Summarize Mean of dep_delay

df1 <- group_by(flights, carrier)%>%
  summarize(Mean=mean(dep_delay, na.rm=TRUE))
  
df1
## # A tibble: 16 x 2
##    carrier  Mean
##    <chr>   <dbl>
##  1 9E      16.7 
##  2 AA       8.59
##  3 AS       5.80
##  4 B6      13.0 
##  5 DL       9.26
##  6 EV      20.0 
##  7 F9      20.2 
##  8 FL      18.7 
##  9 HA       4.90
## 10 MQ      10.6 
## 11 OO      12.6 
## 12 UA      12.1 
## 13 US       3.78
## 14 VX      12.9 
## 15 WN      17.7 
## 16 YV      19.0

Create bar chart - Mean Departure delays for all airlines

#ggplot(flights, aes(x=carrier, y=dep_delay, fill=carrier))+
  ggplot(df1, aes(x=reorder(carrier, Mean), y=Mean, fill=carrier))+
  geom_bar(stat="summary")+
  coord_flip()+
  theme_bw()+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
  #scale_y_continuous(NULL)+
  labs(x="Major Carriers", y="Departure Delays in minutes", title= "Average Departure Delays - 2013")
## No summary function supplied, defaulting to `mean_se()`

About this Bar Chart

The above visualization depicts the mean flight departure delays (in minutes) for 16 major US airlines in 2013. As the legends show, each bar represents a particular airline, and the bars are sorted by mean of delays. For example, Frontier airlines (9E) had more departure delays in 2013 while US airways (US) had the lowest mean of delays in the group.