CS计算机代考程序代写 data science Intro to Data Science

Intro to Data Science
In-Class Exercise 13: Basic Programming
1. In the Texas reservoirs example from the lecture, the loop is indexed directly over the years from 1971 to 2020. Reprogram the loop to index it over the values 1:50. To recall, the data is read in as follows:
2. Load the Corpus Christi radiosonde record, CorpusCristi.rda. It will appear in your global environment as CorpusChristi. In the in-class exercises for lecture 11, you plotted the wind speed over time at pressure level 925. Create a plot where you overlay the wind speed over the year with each pressure level plotted in a different color. The range on the y-axis should be set to be between 0 and 70.
3. Answer the following questions about the plot that you created in the prior question.
• Do the strongest winds occur in the upper or lower atmosphere? Note, 1000 mb is closest to the earth’s surface, and 7 mb is farthest away.
• What time of year are the winds the weakest?
• Does the spike due to the hurricane on August 26, 2017 affect all of the pressure levels?
• Would the spike in wind speeds due to the hurricane be considered a global or a local
outlier?
• Use the which.max command to find the observation whose wind speed is the highest.
What is this wind speed, and at what pressure does it occur, and on what date does it occur?
4. In the first data collection asking students to “Mark one random spot inside of the square,” the square was completely empty. For a second experiment, a landmark dot was placed in the center of the square, and students were asked again to “Mark one random spot inside of the square,” as seen in the figure below.
The data can be loaded using the code below. Plot the data, and comment on whether or not it appears that the presence of a dot in the center of the square caused students to choose points elsewhere. Alpha-blending for plotting the points will be essential here.
suppressMessages(library(lubridate))
www <- "https://www.waterdatafortexas.org/reservoirs/statewide.csv" water <- read.csv(file=www, header=T, skip=29) water_year <- year(water\$date) suppressMessages(library(spatstat)) data <- read.csv(file="dot_experiment_data.csv", header=T) center <- which(data\$type=="centerDot") #Rescaling data to be on the unit square x2 <- data\$x[center]/13.8 y2 <- data\$y[center]/13.8 1 Mark one random spot inside of the square. Data collection survey with landmark dot in the center. 2 5. Create an object of type ppp using the code below. What proportion of the points are within 0.1 units of the center of the square? The center of the square is (0.5, 0.5). You will need to use the distance formula. 6. In this question, you will do a Monte Carlo study in which you compute the proportion of points within 0.1 units of the center of a square. You will repeat this many times and compare the results with the observed data. Complete the following steps: • Create an empty variable to hold a value. • Create a for() loop to iterate through 1000 times. • Inside the for() loop, simulate a dataset with 340 observations distributed randomly in the unit square using the command sim_dat <- runifpoint(nn, nsim = 1). You can extract the x and y coordinates using sim_dat\$x\$ and sim_dat\$y. • Compute and save the proportion of points that are within 0.1 units of the center. • Plot a histogram of these proportions. • Overlay the proportion of points that were observed in the data with a red vertical line. • Is the proportion in the observed data similar to or different than points that are randomly distributed in the square? win <- owin(c(0, 1), c(0, 1)) data_dot <- ppp(x2, y2, window = win) # Warning: data contain duplicated points 3