Problem Set # 1
Joshua Osula
date()## [1] "Sat Sep 12 16:28:35 2020"1 The following values are the annual number hurricanes that have hit the United States since 1990 0 1 1 1 0 2 2 1 3 3 0 0 1 2 6 6 0 1 3 0 1 a. Enter the data into R. (2)
Hurry = c(0, 1, 1, 1, 0, 2, 2, 1, 3, 3, 0, 0, 1, 2, 6, 6, 0, 1, 3, 0, 1)
Hurry## [1] 0 1 1 1 0 2 2 1 3 3 0 0 1 2 6 6 0 1 3 0 1- How many years are there? (2)
Year = 1990:2010
Hurry = data.frame(Year, Hurry)
Hurry## Year Hurry
## 1 1990 0
## 2 1991 1
## 3 1992 1
## 4 1993 1
## 5 1994 0
## 6 1995 2
## 7 1996 2
## 8 1997 1
## 9 1998 3
## 10 1999 3
## 11 2000 0
## 12 2001 0
## 13 2002 1
## 14 2003 2
## 15 2004 6
## 16 2005 6
## 17 2006 0
## 18 2007 1
## 19 2008 3
## 20 2009 0
## 21 2010 1length(Hurry$Year)## [1] 21- What is the total number of hurricanes over all years? (2)
sum(Hurry)## [1] 42034NB I don’t know why the sum showed 42034 when i was knitting it to html. When i ran it the first time it showed 34
2 a. Create a vector of numbers starting with 0 and ending with 25. (2)
Numb = c(0:25) - What is the length of this vector? (2)
length(Numb)## [1] 26- Create a new vector from the original vector by subtracting the mean value over all numbers in the vector. (2)
mean(Numb)## [1] 12.5Numb - mean(Numb)## [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 12.53 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
- Enter these numbers into a vector called miles. (2)
miles = c(65311, 65624, 65908, 66219, 66499, 66821, 67145, 67447)- Use the function diff() to determine the number of miles between fill-ups. (2)
?diffdiff (miles)## [1] 313 284 311 280 322 324 302- What is the maximum, minimum, and mean number of miles between fill-ups? (3)
summary(miles)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 65311 65837 66359 66372 66902 674474 Create the following sequences using the seq() and rep() functions as appropriate.
- “a”, “a”, “a”, “a” (2)
rep("a", 4)## [1] "a" "a" "a" "a"- The odd numbers in the interval from 1 to 100 (2)
rev(100:1)## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100seq(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- 1, 1, 1, 2, 2, 2, 3, 3, 3 (2)
rep = c(rep(1,3), rep(2,3), rep(3,3))
rep## [1] 1 1 1 2 2 2 3 3 3- 1, 1, 1, 2, 2, 3 (2)
repd = c(rep(1,3), rep(2,2), rep(3,1))
repd## [1] 1 1 1 2 2 3- 1, 2, 3, 4, 5, 4, 3, 2, 1 (3) Hint: Use the c() function.
seq = c(rep(1:5), rep(4:1))
seq## [1] 1 2 3 4 5 4 3 2 15 Read the monthly precipitation dataset from my website (https://moraviansoundscapes.music.fsu.edu/sites/g/files/upcbnu1806/files/Media/Sciuchetti/ALMonthlyP.txt).
library(ggplot2)input = ("https://moraviansoundscapes.music.fsu.edu/sites/g/files/upcbnu1806/files/Media/Sciuchetti/ALMonthlyP.txt")
DRIPDROP = read.table(input, na.string = "-9.900", header = TRUE)- What are the wettest and driest values during the month of January? (2)
sort (DRIPDROP$Jan, decreasing = TRUE)## [1] 13.09 12.16 11.61 11.12 10.38 10.19 9.87 9.75 9.66 8.99 8.67 8.61
## [13] 8.44 8.40 8.00 7.66 7.60 7.41 7.37 7.24 7.11 7.07 7.02 7.00
## [25] 6.97 6.87 6.79 6.75 6.74 6.74 6.55 6.46 6.41 6.38 6.27 6.21
## [37] 6.11 6.11 6.04 5.92 5.89 5.84 5.79 5.73 5.71 5.70 5.51 5.47
## [49] 5.46 5.40 5.33 5.31 5.28 5.25 4.96 4.94 4.94 4.92 4.88 4.85
## [61] 4.79 4.75 4.74 4.74 4.74 4.73 4.65 4.62 4.49 4.38 4.37 4.36
## [73] 4.32 4.25 4.24 4.23 4.16 4.13 4.08 4.02 4.01 3.99 3.85 3.73
## [85] 3.71 3.68 3.64 3.61 3.60 3.50 3.47 3.41 3.41 3.29 3.18 3.16
## [97] 3.07 3.02 3.01 2.99 2.97 2.87 2.86 2.84 2.77 2.73 2.70 2.68
## [109] 2.64 2.64 2.62 2.51 2.48 2.48 2.47 2.45 2.31 2.29 2.17 2.14
## [121] 1.94 1.72 1.70 1.57 0.80range (DRIPDROP$Jan)## [1] 0.80 13.09- Sort the February rainfall values from wettest to driest. (2)
sort (DRIPDROP$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 0.76range (DRIPDROP$Feb)## [1] 0.76 13.35- Compute the variance of the March rainfall values. (2)
var(DRIPDROP$Mar)## [1] 7.41769- What is the 95th percentile value of April rainfall? (2)
quantile(DRIPDROP$Apr, probs = c(.95))## 95%
## 10.21- Create a time series graph of April rainfall. (4)
ggplot(DRIPDROP, aes(x = Year, y = Apr)) + geom_line() + ylab("April Rainfall (in)")