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Warmup 1: Basic Manipulation of Data Tables

Stat 133

Spring 2022

Introduction

The main goal of this warmup is to give you practice on working with data frames (and tibbles), by using a couple of tidyverse packages:

• manipulation with "dplyr"

This assignment is heavily based on chapters

• https://www.gastonsanchez.com/intro2cwd/eda-dplyr.html

General Instructions

• Write your narrative and code in an Rmd (R markdown) ﬁle.

• Please do not use code chunk options such as: echo = FALSE, eval = FALSE, results = 'hide '. All chunks must be visible and evaluated.

1) Data "storms"

You will be working with the data set storms from the R package "dplyr". This data is a subset of the NOAA Atlantic hurricane database best track data, http://www.nhc.noaa. gov/data/#hurdat. The data includes the positions and attributes of 198 tropical storms,

documentation for storms.

Recall that you need to load the tidyverse packages ("dplyr" and "ggplot2” among them) in a code chunk; use the command:

2) Storms in 2015

Use "dplyr" functions to answer the following parts.

a) Write a "dplyr" command to create a table (i.e. tibble) storms2015 containing the storms that took place in the year 2015.

2015. In other words, the output should display only the unique names and nothing else.

c) With storms2015, write a command that returns a table with the name of each unique storm and the number of times it appears. In other words, this is a table with two columns: 1) name of storm, and 2) the number of counts of each storm.

d) With storms2015, write a command that gives you the name, month and day of the ﬁrst storm recorded in 2015.

e) With storms2015, write a command that gives you the number of hurricanes that occurred in 2015. Hint: How does a storm acquire hurricane status?

f) With storms2015, write a command that gives you the names of the hurricanes that occurred during that year. Hint: How does a storm acquire hurricane status?

3) More manipulation

a) Use "dplyr" functions/commands to create a table (e.g. tibble) storm_names_2010s containing columns name and year of storms recorded from 2010 to 2015. To clarify, this table should contain only one occurrence of each storm. Use head() and tail() to display its ﬁrst 5 rows, and also its last 5 rows.

b) With the entire storms data, use "dplyr" functions/commands to create a table (or tibble) storm_counts_per_year containing the number of unique storms in each year (i.e. counts of storms in each year). This table should contain two columns: year values in the ﬁrst column, and the number of unique storms in the second column. Display its last 15 rows.

c) With the entire storms data, use "dplyr" functions/commands to create a table (e.g. tibble) max_wind_per_storm containing three columns: 1) year of storm, 2) name of storm, and 3) max_wind maximum wind speed record (for that storm). Display its ﬁrst 10 rows, and also its last 10 rows.

4) Some basic plots with "ggplot2"

a) Make a barchart for the number of (unique) storms in each year during the period 2010 to 2015. Make sure that the axis-label of each bar indicates the associated year. Also, add a meaningful title to the plot.

b) Using the entire storms table, make a density graph for the variable wind, adding color to the border line, as well as the ﬁlling color of the density curve. Also, add a meaningful title to the plot, and choose the “Black-White” theme for the background of the graph.

c) Make boxplots for the variable pressure of storms in each year during the period 2000 to 2011. Use facets for year (i.e. one facet per year). Also, add a meaningful title to the plot, and choose the “Minimal” theme for the background of the graph.

d) Using the entire storms table, graph a timeline of the median wind speed by year. That is: years in the x-axis, median wind-speed in the y-axis, timeline connecting the dots for median wind speed in each year. Also, add a meaningful title to the plot.

5) Wind Speed and Pressure

a) With the entire storms table, use "ggplot2" functions to make a scatterplot of wind (x-axis) and pressure (y-axis). Because of the large number of dots, add an alpha value in order to make the dots somewhat transparent. Likewise, see how to add a “smoother” with the function stat_smooth(). Also, add a meaningful title to the plot.

b) With the entire storms table, use "ggplot2" functions to make the previous scatterplot of wind (x-axis) and pressure (y-axis). This time don’t include a smoother; instead use the variable category to color-code the dots in the scatterplot. Also, add a meaningful title to the plot.

6) Storm Categories

a) Use "dplyr" functions/commands to display, in ascending order, the diﬀerent (unique) types of storm categories (using the entire storms table).

b) With the entire storms tabl, use "dplyr" functions/commands to display a table showing the category, avg_pressure (average pressure), and avg_wind (average wind speed), for each type of storm category. This table should contain three columns: 1) category, 2) avg_pressure, and 3) avg_wind.

c) With the entire storms table, make a chart to visualize pressure in terms of the diﬀerent category values. For example, you can use either histograms, or density curves, or boxplot, or violin plots. Add a meaningful title to the plot.