Problem Set # 1

Your Name

date()

## [1] "Mon Sep 30 22:39:38 2024"

require(spgwr)

## Loading required package: spgwr

## Loading required package: sp

## Loading required package: spData

## To access larger datasets in this package, install the spDataLarge
## package with: `install.packages('spDataLarge',
## repos='https://nowosad.github.io/drat/', type='source')`

## NOTE: This package does not constitute approval of GWR
## as a method of spatial analysis; see example(gwr)

Due Date: September 18, 2022

Total Points: 42

1 The following values are the annual number hurricanes that have hit the United States since 1990. Answer the questions by typing R commands.

0 1 1 1 0 2 2 1 3 3 0 0 1 2 6 6 0 1 3 0 1

1. Enter the data into R. (2)

canes = c(0, 1, 1, 1, 0, 2, 2, 1, 3, 3, 0, 0, 1, 2, 6, 6, 0, 1, 3, 0, 1)

2. How many years are there? (2)

length(canes)

## [1] 21

3. What is the total number of hurricanes over all years? (2)

sum(canes)

## [1] 34

2 Answer the following questions by typing R commands.

1. Create a vector of numbers starting with 0 and ending with 25. (2)

quarter = c(0:25)
quarter

##  [1]  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
## [26] 25

2. What is the length of this vector? (2)

length(quarter)

## [1] 26

3. Create a new vector from the original vector by subtracting the mean value over all numbers in the vector. (2)

mean(quarter)

## [1] 12.5

l = quarter[]-mean(quarter)
l

##  [1] -12.5 -11.5 -10.5  -9.5  -8.5  -7.5  -6.5  -5.5  -4.5  -3.5  -2.5  -1.5
## [13]  -0.5   0.5   1.5   2.5   3.5   4.5   5.5   6.5   7.5   8.5   9.5  10.5
## [25]  11.5  12.5

3 Suppose you keep track of your mileage each time you fill your car’s gas tank. At your last 8 fill-ups the mileage was

65311 65624 65908 66219 66499 66821 67145 67447

1. Enter these numbers into a vector called miles. (2)

miles = c(65311, 65624, 65908, 66219, 66499, 66821, 67145, 67447)

2. Use the function diff() to determine the number of miles between fill-ups. (2)

y = diff(miles)
y

## [1] 313 284 311 280 322 324 302

3. What is the maximum, minimum, and mean number of miles between fill-ups? (3)

max(y)

## [1] 324

min(y)

## [1] 280

mean(y)

## [1] 305.1429

4 Create the following sequences using the seq() and rep() functions as appropriate.

1. “a”, “a”, “a”, “a” (2)

rep('a',4)

## [1] "a" "a" "a" "a"

2. The odd numbers in the interval from 1 to 100 (2)

seq(1,100, by = 2)

##  [1]  1  3  5  7  9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
## [26] 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99

3. 1, 1, 1, 2, 2, 2, 3, 3, 3 (2)

rep(c(1,2,3),c(3,3,3))

## [1] 1 1 1 2 2 2 3 3 3

4. 1, 1, 1, 2, 2, 3 (2)

rep(c(1,2,3),c(3,2,1))

## [1] 1 1 1 2 2 3

5. 1, 2, 3, 4, 5, 4, 3, 2, 1 (3) Hint: Use the c() function.

5 Read the monthly precipitation dataset from my website (https://moraviansoundscapes.music.fsu.edu/sites/g/files/upcbnu1806/files/Media/Sciuchetti/ALMonthlyP.txt).

loc = "https://moraviansoundscapes.music.fsu.edu/sites/g/files/upcbnu1806/files/Media/Sciuchetti/ALMonthlyP.txt"
G = read.table(file = loc, header = TRUE)

head(G)

##   Year  Jan  Feb   Mar   Apr  May   Jun  Jul  Aug  Sep  Oct  Nov  Dec
## 1 1895 7.37 1.41  7.17  2.72 3.06  4.04 4.58 4.00 3.41 2.28 1.83 5.83
## 2 1896 2.47 7.46  6.23  4.34 2.92  4.50 3.78 1.94 2.67 1.59 6.20 1.32
## 3 1897 3.85 3.74 14.40  4.99 2.87  2.12 3.93 3.66 0.03 1.74 2.13 8.54
## 4 1898 7.07 1.34  4.43  4.29 1.86  2.61 5.52 3.67 2.83 3.72 3.55 2.43
## 5 1899 5.79 6.39  9.93  2.99 1.50  2.22 6.04 3.44 0.57 1.85 3.93 7.28
## 6 1900 3.64 4.92  4.17 10.56 3.86 12.40 4.64 2.26 2.76 6.40 3.44 3.25

1. What are the wettest and driest values during the month of January? (2)

max(G$Jan)

## [1] 13.09

min(G$Jan)

## [1] 0.8

2. Sort the February rainfall values from wettest to driest. (2)

sort(G$Feb,decreasing = TRUE)

##   [1] 13.35 12.16 11.42 10.69 10.18 10.13  9.85  9.58  9.26  9.23  9.08  8.76
##  [13]  8.60  8.57  8.46  8.24  8.13  7.89  7.80  7.56  7.50  7.46  7.14  7.05
##  [25]  7.02  6.70  6.64  6.57  6.56  6.56  6.39  6.12  6.10  6.09  6.04  6.02
##  [37]  5.90  5.80  5.63  5.59  5.56  5.49  5.29  5.17  5.13  5.08  5.04  5.04
##  [49]  5.02  4.98  4.94  4.92  4.89  4.84  4.80  4.75  4.74  4.69  4.53  4.52
##  [61]  4.47  4.43  4.41  4.39  4.38  4.34  4.32  4.31  4.27  4.26  4.23  4.11
##  [73]  4.05  3.94  3.87  3.82  3.81  3.74  3.73  3.72  3.68  3.60  3.60  3.60
##  [85]  3.56  3.56  3.53  3.46  3.43  3.40  3.37  3.35  3.25  3.24  3.24  3.22
##  [97]  3.21  3.16  3.13  3.00  2.92  2.91  2.82  2.73  2.71  2.69  2.60  2.52
## [109]  2.48  2.45  2.39  2.37  2.29  2.23  2.14  2.09  1.86  1.45  1.41  1.39
## [121]  1.34  1.32  1.29  1.25  0.76

3. Compute the variance of the March rainfall values. (2)

var(G$Mar)

## [1] 7.41769

4. What is the 95th percentile value of April rainfall? (2)

quantile(G$Apr, probs = .95)

##   95% 
## 10.21

5. Create a time series graph of April rainfall. (4)

library(ggplot2)
ggplot(G, aes(x = Year, y = Apr)) +
  geom_line()+
  ylab("April Rainfall (in)")