We will be using the BirdMove dataset available on canvas. This data consists of total flight distances (km) made by three individual birds that were fitted with GPS tracking devices. Birds were Rhinoceros Auklets tagged on Protection Island which is a seabird colony within the Salish Sea. Each row is a daily measurement of the total distance travelled by each of the three birds (columns: BirdID_1, BirdID_2, BirdID_3). Read the data into R setwd("~/Desktop/QSCI_381/lab_1") BirdMove = read.csv('BirdMove.csv') View(BirdMove) Question 1 1a) Given the information shown in the table, and your reading of the information about this data, is travel distance a quantitative or qualitative variable, AND what level of measurement does it belong to AND explain why (4 pts) 1b) Separately for each of the three birds find the shortest daily travel distance. This is known as the minimum of the data (3 pts) 1c) Separately for each of the three birds find the longest daily travel distance. This is known as the maximum of the data (3 pts) 1d) The difference between the maximum and minimum values in a data set is known as the range. What is the range in distances travelled for each bird (3 pts)? 1e) For each bird calculate the mean, the standard deviation, and coefficient of variation of distance travelled by each bird (Note: round your answer to 2 decimal places) (9 pts)

1f) Given your answer in (1e) which bird had the most variable travel distances? (2 pts) Question 2 In this question we will create a line graph of the distances travelled by the birds through time. Firstly, the date column in our data isn't formatted as a date (by default R reads in strings of text as just that, a string of text). However, we can re-format those strings as a date using the as.Date() function by following the commands below # The following code converts the date column to a date class # The format argument tells R how to interpret the string of txt BirdMove$date <- as.Date(BirdMove$date, format="%d/%m/%Y") Copy this code above into your R session and make sure that the dates look correct (they should appear as YYY-MM-DD). 2a) Create a line graph of travel distances through time for bird ID_2, plotting daily travel distance against date. When creating the plot add the following plot attributes by specifying the necessary arguments in the plot function (for more help with the plot function type ?plot in R): The default plot type in R is a scatterplot, we want a line graph instead. To do this, use the type="l" option. Label the x axis, using the xlab argument, "Date" Label the y axis, using the ylab argument, "Daily travel distance (km)" Change the y-axis range using the ylim argument so that the axis ranges from 0 to 250 Add a plot title using the main argument. Set the plot title to be a brief description of the information plotted followed by your name in parentheses Change the line width using the lwd function to 2 Take a screenshot of your graph and paste it below (6 pts) 2b) Now overlap the distances travelled by Bird ID_3 onto this figure using the lines() function, changing the color of the Bird ID_3 line to distinguish it from Bird ID_2, but keeping the same linewidth as before. Once the 2nd line is added, add a legend to tell the viewer which line belongs to which bird using the legend() function. (4 points) 2c) Comparing the distances travelled, provide a brief description of any major differences you see between the two patterns through time (2 pts)

Question 3 We will now investigate the distribution of data for Bird ID_1 by creating a frequency distribution and a histogram. 3a) Use Sturge's Rule to come up with class widths for the histogram of travel distances recorded for Bird ID_1 Sturge's rule: Bin width = ???????????????? ???????????????? 1 1.44 ln(????) Note: try the nrow function to tell you the size of the dataset, and the log function in R returns the natural log ln. Write your answer below, including your working. Round your answer to the nearest whole unit (4 pts) 3b) Using the class width (nearest whole unit) you got in 3a calculate the frequency distribution of travel distances made by Bird ID_1 Step 1: Find the class limits (set the lowest limit at 0) using the class width Step 2: Based on those classes, find the frequency of observations in each class using the following snippet, replacing LOWERLIMIT and UPPERLIMIT with the values for each class nrow(BirdMove[BirdMove$BirdID_1 >= LOWERLIMIT & BirdMove$BirdID_1 < UPPERLIMIT,]) Fill in the table below with information on class limits, midpoints and frequencies (make sure to check that all the frequencies sum to the total size of the dataset) (5 pts) 3c) Now, make a histogram of the travel distances made by Bird ID_1 using the hist function. Label the x-axis label with the appropriate variable name and include your name as the histogram title Paste your graph below (4 pts)

(3d) Based on the frequency distribution and the histogram, would you say the distribution of daily travel distances made by Bird ID_1 was symmetric, uniform, left-skewed or right-skewed, justifying your answer based on observed features. (

date BirdID_1 BirdID_2 BirdID_3 ######## 93.6 81.1 99.9 ######## 131.2 36.4 46.2 ######## 147.6 66.2 119.1 ######## 38.7 50.5 73.4 ######## 49.1 124.4 44.9 ######## 72.6 74 87.5 1/04/2019 82 60.2 69.9 2/04/2019 82.8 96 81.4 3/04/2019 78 156.7 149.5 4/04/2019 74.6 20.3 75.1 5/04/2019 71.3 50.9 37.1 6/04/2019 93 117 83.4 7/04/2019 118.1 109.6 44.1 8/04/2019 69.5 61 46.9 9/04/2019 80 45.2 70.2 ######## 162.3 83.1 56.1 ######## 85.4 102.9 111.8 ######## 72.1 31.2 103.8 ######## 70.6 86.5 87 ######## 59.9 82 100.2 ######## 59.1 81 93.4 ######## 65.8 84.7 87.9 ######## 57.6 173.4 157.9 ######## 70.3 85.4 51 ######## 53.9 59.3 224.7 ######## 66.7 45.7 23.6 ######## 59.7 83.4 71.9 ######## 57.5 71.7 75.9 ######## 51.2 118 31.8 ######## 56.7 114.9 24 ######## 110.2 61.8 49.2 ######## 50.3 62.5 39.1 ######## 70.6 92.7 75.3 ######## 41.4 72.2 92.2 ######## 75.9 86.7 63 ######## 66.7 68.5 51.1 1/05/2019 64.8 117.8 99.7 2/05/2019 98.2 63.5 68.7 3/05/2019 92.8 64.4 94.9 4/05/2019 48.8 75.4 41.7 5/05/2019 40.8 73.7 58.2 6/05/2019 67.4 118.6 73.1 7/05/2019 95.1 69.6 88.5 8/05/2019 65.2 105.5 245.6 9/05/2019 52.3 80 96.7 ######## 39.3 61.7 92.5 ######## 81.5 114.3 81.4 ######## 45.2 78.8 117.8 ######## 35.7 65.6 153.4 ######## 129 65.1 149.6 ######## 42.1 138.1 113.5

######## 51.5 97.3 115.7 ######## 93.2 153.9 56.6 ######## 74.2 114.3 150 ######## 72.3 218.2 79.2 ######## 75.7 151.7 50.6 ######## 49.5 167.2 82.9 ######## 105.5 28.8 93.7 ######## 121.3 10.8 78.9 ######## 58.9 33 120.6 ######## 61.3 42.8 49.5 ######## 62.9 23.5 116.3 ######## 59.5 17.2 28.7 ######## 59.6 28.5 83.5 ######## 71.5 32.4 99.8 ######## 53.5 36.1 106.3 ######## 70.7 16.3 109.8 1/06/2019 99.5 20 124.8 2/06/2019 95.6 23.1 98.3 3/06/2019 50.8 20.9 84.6 4/06/2019 73 32.1 123.1 5/06/2019 110.7 9 65.6 6/06/2019 79.8 17.3 79 7/06/2019 90.4 15.1 66.7 8/06/2019 68.4 11.6 33.4 9/06/2019 34.5 22.7 59.4 ######## 30.4 21.1 37.4 ######## 34.4 34.2 45 ######## 31.3 20.8 40.1 ######## 17.1 18 72.4 ######## 27.9 25.9 128.6 ######## 23.3 29.9 67.5 ######## 38.3 16.3 138.1 ######## 21.6 13.1 65 ######## 26.3 31 91.5 ######## 36.5 16.5 202.1 ######## 38.1 23 98.9 ######## 38 18.8 92.5 ######## 41.2 18.1 138.8 ######## 22.3 21.3 89.1 ######## 36.9 31.7 47.6 ######## 34.8 16.3 22.8 ######## 42.3 19.3 26.6 ######## 29.7 23.7 46.7 ######## 22.2 22.8 24.3 ######## 40.1 49.5 32.5 ######## 21 23.5 21 1/07/2019 24.4 12.6 51.8 2/07/2019 32.9 31.4 30 3/07/2019 43.7 36.6 19.7 4/07/2019 28.2 13.4 15.4 5/07/2019 37 23.9 27.2 6/07/2019 19.2 18.2 21.9

7/07/2019 13.9 19 24.8 8/07/2019 20 28.9 22.1 9/07/2019 47.5 20.4 28 ######## 43.5 32.2 19.3 ######## 22.4 39.9 25.5 ######## 27.6 16.2 27.9 ######## 30 23.1 27.1 ######## 23.2 19.2 10.8 ######## 26.5 26.7 45.3 ######## 21.2 11.6 15.4 ######## 21.8 23.1 27.2 ######## 19.4 11.6 40.3 ######## 36.3 28.8 33.1 ######## 21.4 31.4 26.1 ######## 29.6 24.8 33.6 ######## 17.6 21.1 25.3 ######## 15.7 13.8 22.7 ######## 25.3 20.7 22.3 ######## 15.5 11.4 29.7 ######## 21.4 17.9 27.9 ######## 21.4 12.3 26.1 ######## 32.6 13.6 36.7 ######## 41.3 17.8 28.1 ######## 38.9 10.3 30.6 ######## 44.8 11.4 25.6 1/08/2019 30.7 27.4 28.1 2/08/2019 20.7 59.1 30.4 3/08/2019 32.6 21.7 41.1 4/08/2019 29.8 35.3 13 5/08/2019 52.9 23.3 28.3 6/08/2019 39.4 17.1 37.2 7/08/2019 39.6 28.6 41.7 8/08/2019 32.1 28.5 66.5 9/08/2019 31.5 18.7 19.2 ######## 21.3 22.9 39.6 ######## 43.4 18.6 26 ######## 42.5 28.3 15.5 ######## 48.1 25.5 25.4 ######## 27.4 33.6 80 ######## 25 11.6 65.1 ######## 32.8 25.7 32.5

Year Day Hour Minutes Air Temp (C)BlocknumberLatitude Longitude Altitude HDOP Num_of_satsFix_time 14 199 22 30 26 151 14.16703 145.1631 21 1.1 9 91 14 197 3 0 25 79 14.16338 145.1579 11 1.2 6 137 14 197 22 0 26 101 14.16179 145.164 88 1.3 8 54 14 199 3 30 25 129 14.16748 145.1652 24 1.3 6 35 14 198 0 0 27 105 14.16019 145.1549 9 1 5 85 14 197 3 30 25 80 14.16328 145.1593 29 4 5 39 14 197 23 0 27 103 14.16293 145.1579 27 1.4 7 84 14 197 23 30 25 104 14.16011 145.1544 22 1.4 6 146 14 199 1 30 25 140 14.16688 145.163 21 1.5 5 105 14 199 3 0 26 128 14.16733 145.1633 29 1.5 6 180 14 200 6 0 25 166 14.15581 145.1537 112 1.5 6 144 14 199 1 0 26 139 14.16622 145.1637 25 1.5 9 122 14 199 23 0 28 152 14.16719 145.1616 1 1.5 5 41 14 199 21 30 25 149 14.16738 145.163 25 1.6 8 84 14 200 5 30 26 165 14.15501 145.1535 101 1.7 5 91 14 197 1 30 26 76 14.16315 145.1587 20 1.7 8 98 14 197 4 0 27 81 14.16333 145.1593 38 1.8 6 85 14 199 22 0 27 150 14.167 145.1631 24 1.8 6 49 14 199 2 0 25 141 14.16709 145.162 20 1.9 7 55 14 199 2 30 26 142 14.16759 145.1631 6528 2.1 3 41 14 197 2 30 28 78 14.16181 145.1569 26 2.1 5 180 14 197 4 30 26 82 14.16424 145.1593 26 2.2 4 180 14 197 2 0 26 77 14.16192 145.1569 46 2.9 4 180 14 197 1 0 31 75 14.16371 145.1598 18 3.2 4 180 14 200 6 30 26 167 14.15588 145.1538 114 3.7 4 180 14 197 22 30 26 102 14.16226 145.1609 6528 10.9 3 180 14 199 23 30 30 153 14.16913 145.161 6527 12.7 4 180