#install.packages("devtools")
#devtools::install_github("hellodata-science/hellodatascience")HDS 2.1-2.2
Loading R Packages
To begin, copy these two lines (without the number sign) to the console and run them, one at a time:
This will install the hellodatascience package (be patient). You will also need to install the tidyverse and nycflights23 packages the ordinary way (again, in the console, without the #):
#install.packages("tidyverse")
#install.packages("nycflights23")Now you can load the packages you just installed. Insert the code for loading them here:
library(tidyverse)
library(nycflights23)
library(devtools)
library(hellodatascience)Data Frames
Read Section 2.1 of Hello Data Science. In that section, you found that the planets data has 8 rows and 7 columns. Note that I didn’t type the number of rows or columns; R calculated them and inserted them into my text. This is important because if the data changes (someone adds a row for Pluto, for example), I don’t have to change my text.
The flights data in the nycflights23 package gives on-time data for all flights scheduled to depart from one of the three New York City airports in 2023. Type ?flights in the console to see the help page. Write a sentence (like the one in the previous paragraph) that describes the number of rows and columns in the flights dataset by mixing R code into your sentence:
There are 435352 rows and 19 columns in the nycflights23 data file.
Getting to Know Data
Read Section 2.2 of Hello Data Science. Print the first few rows of the flights data (you certainly don’t want to print them all!):
head(flights)# A tibble: 6 × 19
year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
<int> <int> <int> <int> <int> <dbl> <int> <int>
1 2023 1 1 1 2038 203 328 3
2 2023 1 1 18 2300 78 228 135
3 2023 1 1 31 2344 47 500 426
4 2023 1 1 33 2140 173 238 2352
5 2023 1 1 36 2048 228 223 2252
6 2023 1 1 503 500 3 808 815
# ℹ 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>Use the glimpse function to get to know the variables in the flights data:
glimpse(flights)Rows: 435,352
Columns: 19
$ year <int> 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2…
$ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ day <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ dep_time <int> 1, 18, 31, 33, 36, 503, 520, 524, 537, 547, 549, 551, 5…
$ sched_dep_time <int> 2038, 2300, 2344, 2140, 2048, 500, 510, 530, 520, 545, …
$ dep_delay <dbl> 203, 78, 47, 173, 228, 3, 10, -6, 17, 2, -10, -9, -7, -…
$ arr_time <int> 328, 228, 500, 238, 223, 808, 948, 645, 926, 845, 905, …
$ sched_arr_time <int> 3, 135, 426, 2352, 2252, 815, 949, 710, 818, 852, 901, …
$ arr_delay <dbl> 205, 53, 34, 166, 211, -7, -1, -25, 68, -7, 4, -13, -14…
$ carrier <chr> "UA", "DL", "B6", "B6", "UA", "AA", "B6", "AA", "UA", "…
$ flight <int> 628, 393, 371, 1053, 219, 499, 996, 981, 206, 225, 800,…
$ tailnum <chr> "N25201", "N830DN", "N807JB", "N265JB", "N17730", "N925…
$ origin <chr> "EWR", "JFK", "JFK", "JFK", "EWR", "EWR", "JFK", "EWR",…
$ dest <chr> "SMF", "ATL", "BQN", "CHS", "DTW", "MIA", "BQN", "ORD",…
$ air_time <dbl> 367, 108, 190, 108, 80, 154, 192, 119, 258, 157, 164, 1…
$ distance <dbl> 2500, 760, 1576, 636, 488, 1085, 1576, 719, 1400, 1065,…
$ hour <dbl> 20, 23, 23, 21, 20, 5, 5, 5, 5, 5, 5, 6, 5, 6, 6, 6, 6,…
$ minute <dbl> 38, 0, 44, 40, 48, 0, 10, 30, 20, 45, 59, 0, 59, 0, 0, …
$ time_hour <dttm> 2023-01-01 20:00:00, 2023-01-01 23:00:00, 2023-01-01 2…Describe how these two functions are similar and how they differ:
While the head function shows the first six rows, giving a better idea of the values. The glimpse function gives a better idea of the columns and structures. Both show all the columns available.