Category: R

Hi, I need someone to help me correct some issues in my codes for a project pres

Hi, I need someone to help me correct some issues in my codes for a project presentation.
I want all the codes to run properly, then you to knit the file and send me the pdf and all the tables.
You can add a model evaluation table of all supervised models listing all the models and their ROC values.This an eample: Model <- c('Decision Tree-C5.0','Random Forest','kNN','SVM-vanilladot') Accuracy_percent <- c(88.57,88.32,88.29,88.00)
mytable<- data.frame(Model, Accuracy_percent) qplot(1:10, 1:10, geom = “blank”) + theme(line = element_blank(), text = element_blank()) + annotation_custom(grob = tableGrob(mytable))

Hi, I need someone to help me correct some issues in my codes for a project pres

Hi, I need someone to help me correct some issues in my codes for a project presentation.
I want all the codes to run properly, then you to knit the file and send me the pdf and all the tables.
You can add a model evaluation table of all supervised models listing all the models and their ROC values.This an eample: Model <- c('Decision Tree-C5.0','Random Forest','kNN','SVM-vanilladot') Accuracy_percent <- c(88.57,88.32,88.29,88.00) mytable<- data.frame(Model, Accuracy_percent) qplot(1:10, 1:10, geom = "blank") + theme(line = element_blank(), text = element_blank()) + annotation_custom(grob = tableGrob(mytable))

Hi! I have some issues in running my r studio codes because some errors arise: “

Hi! I have some issues in running my r studio codes because some errors arise: “Error in `roc_curve()`: ! Can’t rename variables in this context.” or “Error in roc.default(as.numeric(as.factor(test$attrition)), as.numeric(glm_pred)) : ‘” I need you to please run all codes, knit the codes and send me the pdf of the files and codes corrected. Add also a model evaluation table for the supervised models with all ROC values in comparison.
Thank you!

Please help me solve the two easy questions by using R. Thank you!
1. How can yo

Please help me solve the two easy questions by using R. Thank you!
1. How can you tell if an object is a tibble?
2. The following code defines a data frame: df <- data.frame(x= 1, y = "a") Try the following code and see the outputs: df$x, df[, ‘y’] Now change df to a tibble: new_df<-as_tibble(df) How to realize df$x, df[, ‘y’] for new_df using pipe?

Hi, please help me solve two simple questions related to R, based on the documen

Hi, please help me solve two simple questions related to R, based on the document attached below.
1. How can you tell if an object is a tibble?
2. The following code defines a data frame: df <- data.frame(x= 1, y = "a") Try the following code and see the outputs: df$x, df[, ‘y’] Now change df to a tibble: new_df<-as_tibble(df) How to realize df$x, df[, ‘y’] for new_df using pipe?

sample_PRE <- PRE (Hits ~ Runs, data = BaseballTimes) sdoPRE <- do(1000)*PRE(shu

sample_PRE <- PRE (Hits ~ Runs, data = BaseballTimes) sdoPRE <- do(1000)*PRE(shuffle(Hits)~ Runs, BaseballTimes) tally(sdoPRE > sample_PRE, format = “proportion”)
gf_histogram(~PRE, data = sdoPRE, fill = ~PRE > sample_PRE)%>%
gf_point(x=sample_PRE, y=0, color=”red”)%>%
gf_vline(xintercept=sample_PRE, color=”blue”)

I labelled everything clearly so please refer to what I have attached, the pdf w

I labelled everything clearly so please refer to what I have attached, the pdf with the questions is labeled, the other two zip folders have R codes and Class Content for your reference to answer the assignment, YOU NEED TO FOLLOW/REFER TO THE R CODES AND CLASS CONTENT TO ANSWER THE QUESTIONS DO NOT ANSWER BASED ON YOUR BEST GUESS.

DATA: Consumer Loans: Credit Cards and Other Revolving Plans, All Commercial Ban

DATA: Consumer Loans: Credit Cards and Other Revolving Plans, All Commercial Banks
(CCLACBW027NBOG)
Transform the dataset to monthly data and apply a SARIMA(p,d,q)(P,D,Q) model to the data
following those steps:
1- Import the data into R from FRED website.
2- After importing it into R, restrict the dataset to the period from April 2014 through end of 2019.
3- Split the data into training and testing set, where the training set covers the training period from
the first week in April 2014 until the last week of March 2019, and the testing set covers the
testing period from the first week of April 2019 until the end of the year.
4- Train your model on the training set then use the trained model to forecast during the testing
period and compare the results of the forecast to the testing set, according to the following
steps:
a. Identify the model parameters using ACF and PACF.
b. Identify the model parameters without having recourse to ACF and PACF.
c. Fit the model on the training data using auto.arima function in R.
d. Plot the model fit and the actual training set in one graph.
e. Use the trained model to create a forecast covering the testing period.
f. Plot the model’s forecast and testing dataset inn the same graph.
g. Test the model’s forecasting power for 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 periods
ahead, and create a plot that portrays the forecast accuracy for periods 1 through 12.

Please view attached pdf for practice work instructions. R should ONLY be used t

Please view attached pdf for practice work instructions. R should ONLY be used to complete questions 3 and 4. Questions 1 and 2 should be done by hand. The Excel datasheets attached are used to solve questions 3 and 4.

Learning Goal: I’m working on a r multi-part question and need the explanation a

Learning Goal: I’m working on a r multi-part question and need the explanation and answer to help me learn.
LEARNING OBJECTIVES:
Produce maps in a variety of styles and using a variety of mapping technologies
Make a cohesive argument through the production of a series of maps on a topic
DESCRIPTION:The major assignment in this course is to create an atlas. Your atlas should make a cohesive and coherent argument about some health-related topic/issue of your choice (broadly defined). Atlases should include a 2-3 page introduction and at least 5 individual maps, each accompanied by a one paragraph caption contextualizing the map. You are required to use at least two different mediums or technologies in creating your maps (ie. you cannot create all of your maps using QGIS; you cannot hand draw all of your maps).
DIRECTIONS:Choose a topic for your atlas. Topics must be related to health, though for the purposes of this class, health is very broadly defined so if you aren’t sure if a topic is health related, come talk to me about it. Considering that the atlas should make an argument, consider the argument you want to make with your topic or the question that you want your atlas to shed light on.
Create your maps. Remember that when put together, the maps should make some sort of coherent statement. It is up to you how broad or narrow that statement is which will help determine the types of maps you make. Your maps can be of all the same phenomenon at different scales or in different places; your maps can be of all different things (so long as you can link them together in the introduction). As the mapmaker and atlas designer, you have tremendous creative freedom to construct your argument however you like. You are welcome to come to office hours to get feedback on an outline of your atlas. Similarly, you will have a chance to submit individual maps for peer review twice during the quarter. I encourage you to submit maps in different styles for each review and to apply the feedback you get to the other maps you make. You are also always welcome to bring maps to office hours for direct feedback from me.
Write an introduction to your atlas. The introduction is your chance to lay out the argument you are making. The introduction should be 2-3 pages double spaced (or 1-2 pages single spaced), so you will have to be concise. This is your chance to make clear how the maps fit together and what ties your atlas together as a cohesive document. I encourage everyone to take a draft of their introduction to one of the writing centers on campus to help you make a strong, concise argument.
Write captions for each map. Each caption should be a paragraph (3-6 sentences). This is your chance to explain your thinking in each particular map and to articulate how the map fits into the broader argument.
Put the atlas together into a single document. Atlases should be submitted as a single PDF document (unless you have some reason as the atlas designer/mapmaker for why that is not its ideal presentation) on Canvas. Please take the time to make your atlases look professional. Ensure that your maps have scaled well and are readable in the PDF. The easiest way to do this is to import your maps (saved as JPEGs) into the written (Microsoft Word or other text editor) document, position them in order with their captions and then export the final document as a PDF. Because it is worth so much of your grade and aesthetic composition matters, make sure to open that PDF and double check that it looks the way you intend before submitting it.
Above is the assignment requirement and my topic of choice is: substance and abuse addiction by county