Modern Dive 1

library(nycflights13)

library(ggplot2)

library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Learning Check 1.3

D- Data on multiple flights

Learning Check 1.4

Some other variables are when the flight takes off, or the minutes its in the air. They are different because they can judge variables other than numbers.

Learning Check 1.5

They describe characteristics that are not numbers.

Learning Check 1.6

lat,name,alt

Learning Check 1.7

lat,lon- Location of airport. alt- Altitude, in feet. tz- Timezone offset from GMT. dst- Daylight savings time zone. A = Standard US DST: starts on the second Sunday of March, ends on the first Sunday of November. U = unknown. N = no dst. tzone- IANA time zone, as determined by GeoNames webservice.

Learning Check 2.1

There are less entries.

Learning Check 2.2

They are the x and y values.

Learning Check 2.3

The heat because it would effect the rest of the weather.

Learning Check 2.4

Flights that did not leave the ground.

Learning Check 2.5

All the dots are on the chart.

Learning Check 2.6

ggplot(data = flights, mapping = aes(x = hour, y = distance)) + geom_point()

Learning Check 2.7

A alpha plot gives you transparancy of points,

Learning Check 2.8

ggplot(data = flights, mapping = aes(x = dep_delay, y = arr_delay)) + geom_point(alpha = 0.5)

Warning: Removed 9430 rows containing missing values (geom_point).

Learning Check 2.9

The differ because of the different seasons.

Learning Check 2.10

hour judges how long the plane was in the air.

Learning Check 2.11

Because it will get confusing.

Learning Check 2.12

Because it shows pregresion

Learning Check 2.13

early_january_weather <- weather %>%
  filter(origin == "EWR" & month ==1 & day <= 15)

ggplot(data = early_january_weather,mapping = aes(x = time_hour, y = pressure)) + geom_line()

Learning Check 2.14

They are close together.

Learning Check 2.15

symmetric

Learning Check 2.16

60, it would be close to the median

Learning Check 2.17

It is close to the center because of median temperatures,

Learning Check 2.18

It gives you many different things to look at, at the same time.

Learning Check 2.19

Months,temp

Learning Check 2.20

An important characteristic would be what time of year it is.

Learning Check 2.21

No, the only variabilty is the time of year.