ggplot(data, aes(Month,Temp, fill = Month)) + ggtitle("Varying Temperatures thoughout the Months") + xlab("Months") + ylab("Temperatures") + geom_boxplot() How do I reorder/arrange the boxplot from least to most(lowest to Highest) using R
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ggplot(data, aes(Month,Temp, fill = Month)) +
ggtitle("Varying Temperatures thoughout the Months") +
xlab("Months") +
ylab("Temperatures") +
geom_boxplot()
How do I reorder/arrange the boxplot from least to most(lowest to Highest) using R
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- Arrays Write a for loop to print all elements in courseGrades, following each element with a space (including the last). Print forwards, then backwards. End each loop with a newline. Ex: If courseGrades = {7, 9, 11, 10}, print: 7 9 11 10 10 11 9 7 Hint: Use two for loops. Second loop starts with i = NUM_VALS - 1. (Notes)Note: These activities may test code with different test values. This activity will perform two tests, both with a 4-element array (int courseGrades[4]). See "How to Use zyBooks".Also note: If the submitted code tries to access an invalid array element, such as courseGrades[9] for a 4-element array, the test may generate strange results. Or the test may crash and report "Program end never reached", in which case the system doesn't print the test case that caused the reported message. #include <iostream>using namespace std; int main() {const int NUM_VALS = 4;int courseGrades[NUM_VALS];int i; for (i = 0; i < NUM_VALS; ++i) {cin >> courseGrades[i];} /* Your…Q5) Run the code below to check on the model behavior for different polynomials (2,3, 5,10,20). Comment on the generated figure. In [26]: # Hyperparam initialization eta = 0.25 epochs = 500000 # This will take a while but you can set it to 10000. Poly_degree_values = [2, 3, 5, 10, 20] # Initializing plot plt.title( "Regression Lines for Different polynomials") plt.scatter(data.GranulesDiameter, data.Beachslope, label='Traning Data') # iterating over different alpha values (This is going to take a while) for i in Poly_degree_values: polynomial x = GeneratePolynomialFeatures (X, i) thetaInit - np.zeros( (polynomial_x.shape[1],1)) theta, losses = gradientDescent (polynomial_x, Y, thetaInit, eta, epochs) poly = PolynomialFeatures (i) plot SimpleNonlinearRegression line (theta, X, poly) plt.legend(Poly_degree_values) plt.show() Regression Lines for Different polynomials 2 25 10 20 20 15 10 0.2 0.3 0.4 0.5 0.6 0.7 0.8 In [271: # Write your response hereWe know that the size of the previous question's struct point_st is 8 bytes. In general, what are the size and alignment rules for a struct?
- Fill in the loop table with the correct values, Each row should represent the values of the variables at the line with the comment LOCATION. You don't necessarily need to fill out all of the rows on the provided fable. If you don't fill up the whole table, that isn't necessarily wrong! 1 - 2 a - 10 name - 'AL' while i < 9: if name 'AL': 1: 2 nam AL intinl else: if name < 'NAPOLEON': name - 'NASH elif name < 'NATE': name - 'SAM' else: name- 'AL ' # LOCATIONradius_mean gives the average size of the cells in terms of the radius of circles drawn around each cell in the biopsy. texture_mean provides the amount of variation in light to dark values in an image of the cells. sb.regplot (data = cancer, x = "radius_mean", y = "texture_mean", "lowess = True, line_kws = {"color": "orange"}) --INSERT-- 40 texture_mean 35 20 15 10 10 15 20 radius_mean 25 Provide a brief explanation of what is displayed in this plot. Be sure to comment on the orange line and how it expresses the conditional mean of "texture_mean" for different values of "radius_mean."def print_table( values: tuple[float, ) -> None: ], drag_coeff: float, increments: int, step: float 3 4 Parameters: 6 values (tuple[float, ...]): mass, force, ref_area, density, init_velocity, lift_velocity, start_position, time_inc drag_coeff (float): The drag coefficient. increments (int): The number of drag coefficients displayed. step (float): The difference between each drag coefficient. 8 9 10 11 12 Returns: 13 None 14 II II 15 For this function you need to compute the distance before lift-off for a range of drag coefficients and then you need to print these results in a table. The drag coefficient of an aeroplane has a significant impact on the plane's ability to lift-off. If the drag coefficient is sufficiently high, the plane will not actually be able to generate enough speed to lift off. In this task you will write a function which will explore this phenomenon.
- keys = ['name', 'last', 'job_title', 'email']Faculty2 = Faculty = dict.fromkeys(keys)print(Faculty2) Use the dictionaries that you created for faculty members. Be sure that all the dictionaries have the same keys: name, last name, etc. If there are keys or values missing, add them. If you do not have the value, you can use 'n/a' or 'TBD'. Make a list of the dictionaries. Loop the list to print all the information available to each faculty member. In Python lang please.3. In file 'R-Factor-Basics.docx', page 4, use the factor() command to modify the column dat$Group so that the control group is plotted last.eAlter the attached code so that the bar chart can be up to 25 categories, and can be sorted ascending or descending. In this assignment an array of Category structs are used to store both the category name (label) and the category value. The Category struct is provided below along with changes to the prototypes from assignment 3. The get_longest_category_name function has been removed because after sorting, the last or first element in the cats array will be the longest length label. Use this information in create_bar_chart and the asc Boolean value where true is sort ascending and false is sort descending. struct Category { std::string label; double value;}; Update the attached code to include the following in global scope: #define CATEGORIES 25int num_categories = 5; Change the attached code's functions to the following: //Ask user how many categories, up to a max of 25int how_many_categories();void get_category(Category cats[CATEGORIES]);void get_values(Category…
- def get_nearest_station(my_latitude: float, my_longitude: float, stations: List['Station']) -> int: """Return the id of the station from stations that is nearest to the location given by my_latidute and my_longitude. In the case of a tie, return the ID of the last station in stations with that distance. Preconditions: len(stations) > 1 >>> get_nearest_station(43.671134, -79.325164, SAMPLE_STATIONS) 7571 >>> get_nearest_station(43.674312, -79.299221, SAMPLE_STATIONS) 7486 """# TODO y_values = display(y_values) todo_check([ (y_values.shape == (120,),'y_values does not have the correct shape of (120,)'), (np.all(np.isclose(y_values,…# Create Custom Transformer Create a custom transformer, just as we did in the lecture video entitled "Custom Transformers", that performs two computations: 1. Adds an attribute to the end of the numerical data (i.e. new last column) that is equal to $\frac{x_1^3}{x_5}$ for each observation. In other words, for each instance, you will cube the $x_1$ column and then divide by the $x_5$ column. 2. Drops the entire $x_4$ feature column if the passed function argument `drop_x4` is `True` and doesn't drop the column if `drop_x4` is `False`. (See further instructions below.) You must name your custom transformer class `Assignment4Transformer`. Your class should include an input parameter called `drop_x4` with a default value of `True` that deletes the $x_4$ feature column when its value is `True`, but preserves the $x_4$ feature column when you pass a value of `False`. This transformer will be used in a pipeline. In that pipeline, an imputer will be run *before* this transformer. Keep…