HDS 2.1-2.2

Author

Yenran Yiran

Loading R Packages

To begin, copy these two lines (without the number sign) to the console and run them, one at a time:

#install.packages("devtools")
#devtools::install_github("hellodata-science/hellodatascience")

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(hellodatascience)
library(tidyverse)
library(nycflights23)

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: The flights data in the nycflights23 package has 435352 rows and 19 coloumns.

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>

tail(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    12    31     2207           2200         7       58            115
2  2023    12    31     2218           2224        -6      304            325
3  2023    12    31     2243           2150        53      143             56
4  2023    12    31     2248           2259       -11      338            350
5  2023    12    31     2326           2325         1      412            405
6  2023    12    31     2345           2255        50      425            347
# 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:

dplyr::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:

The glimpse function appears to be the transpose of the head and tail functions. The latter relay the flights data in a tibble, with the data listed within columns of their corresponding category, while the former relays the flights data across rows. The glimpse function provides a clearer view of the variables represented within the data set and their data type. However, the head and tail functions format the data to be easily readible.

In the end, they both reflect a portion of the flights data, with the glimpse function displaying all the variable names compared to only the first or last 6 rows visible with the head or tail functions.