Fluent Essays

- Question answers   -  providing a  Visual representation and detailed explanation , screenshot the solution   -  recommended to use RStudio for all   - NO PLAGIARISM  - NEED PLAGIARISM REPORT - INTEXT CITATION  - QUESTION IS ATTACHED and SOURCE TOOAssessment 1 Basic Graphs with ggplot2 For assessment 1 you are to complete the problems by providing one of the following: 1. Visual representation of the information, 2. A detailed explanation of the answer, 3. Visual representation and detailed explanation. When providing a visual representation, screenshot the solution. It is recommended to use RStudio for all assessments. 1 – Run ggplot(data = mpg). What do you see? Import the necessary package, tidyverse, and load the mpg data from the ggplot2 package. 2 – How many rows are in mpg? How many columns? To get the dimensions of a data matrix, we can simply use the function ‘dim()’. 3 – What does the drv variable describe? Read the help for ?mpg to find out. 4 -Make a scatterplot of hwy vs cyl. 5 – What happens if you make a scatterplot of class vs drv? Why is the plot not useful? Link https://www.youtube.com/watch?v=nYR8DrJHLLQ https://www.youtube.com/watch?v=mcYcjH-1giM https://www.youtube.com/watch?v=N5gYo43oLE8 https://www.youtube.com/watch?v=hr2X7rmkprMGraphical Primitives Data Visualization with ggplot2 Cheat Sheet RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • [email protected] • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org • ggplot2 0.9.3.1 • Updated: 3/15 Geoms - Use a geom to represent data points, use the geom’s aesthetic properties to represent variables. Each function returns a layer. One Variable a + geom_area(stat = bin) x, y, alpha, color, fill, linetype, size b + geom_area(aes(y = ..density..), stat = bin) a + geom_density(kernel = gaussian) x, y, alpha, color, fill, linetype, size, weight b + geom_density(aes(y = ..county..)) a + geom_dotplot() x, y, alpha, color, fill a + geom_freqpoly() x, y, alpha, color, linetype, size b + geom_freqpoly(aes(y = ..density..)) a + geom_histogram(binwidth = 5) x, y, alpha, color, fill, linetype, size, weight b + geom_histogram(aes(y = ..density..)) Discrete b <- ggplot(mpg, aes(fl)) b + geom_bar() x, alpha, color, fill, linetype, size, weight Continuous a <- ggplot(mpg, aes(hwy)) Two Variables Continuous Function Discrete X, Discrete Y h <- ggplot(diamonds, aes(cut, color)) h + geom_jitter() x, y, alpha, color, fill, shape, size Discrete X, Continuous Y g <- ggplot(mpg, aes(class, hwy)) g + geom_bar(stat = identity) x, y, alpha, color, fill, linetype, size, weight g + geom_boxplot() lower, middle, upper, x, ymax, ymin, alpha, color, fill, linetype, shape, size, weight g + geom_dotplot(binaxis = y, stackdir = center) x, y, alpha, color, fill g + geom_violin(scale = area) x, y, alpha, color, fill, linetype, size, weight Continuous X, Continuous Y f <- ggplot(mpg, aes(cty, hwy)) f + geom_blank() f + geom_jitter() x, y, alpha, color, fill, shape, size f + geom_point() x, y, alpha, color, fill, shape, size f + geom_quantile() x, y, alpha, color, linetype, size, weight f + geom_rug(sides = bl) alpha, color, linetype, size f + geom_smooth(model = lm) x, y, alpha, color, fill, linetype, size, weight f + geom_text(aes(label = cty)) x, y, label, alpha, angle, color, family, fontface, hjust, lineheight, size, vjust Three Variables m + geom_contour(aes(z = z)) x, y, z, alpha, colour, linetype, size, weight seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2)) m <- ggplot(seals, aes(long, lat)) j <- ggplot(economics, aes(date, unemploy)) j + geom_area() x, y, alpha, color, fill, linetype, size j + geom_line() x, y, alpha, color, linetype, size j + geom_step(direction = hv) x, y, alpha, color, linetype, size Continuous Bivariate Distribution i <- ggplot(movies, aes(year, rating)) i + geom_bin2d(binwidth = c(5, 0.5)) xmax, xmin, ymax, ymin, alpha, color, fill, linetype, size, weight i + geom_density2d() x, y, alpha, colour, linetype, size i + geom_hex() x, y, alpha, colour, fill size e + geom_segment(aes( xend = long + delta_long, yend = lat + delta_lat)) x, xend, y, yend, alpha, color, linetype, size R For Data Science Cheat Sheet Tidyverse for Beginners Learn More R for Data Science Interactively at www.datacamp.com Tidyverse DataCamp Learn R for Data Science Interactively The tidyverse is a powerful collection of R packages that are actually data tools for transforming and visualizing data. All packages of the tidyverse share an underlying philosophy and common APIs. The core packages are: • ggplot2, which implements the grammar of graphics. You can use it to visualize your data. • dplyr is a grammar of data manipulation. You can use it to solve the most common data manipulation challenges. • tidyr helps you to create tidy data or data where each variable is in a column, each observation is a row end each value is a cell. • readr is a fast and friendly way to read rectangular data. • purrr enhances R’s functional programming (FP) toolkit by providing a complete and consistent set of tools for working with functions and vectors. • tibble is a modern re-imaginging of the data frame. • stringr provides a cohesive set of functions designed to make working with strings as easy as posssible • forcats provide a suite of useful tools that solve common problems with factors. You can install the complete tidyverse with: Then, load the core tidyverse and make it available in your current R session by running: Note: there are many other tidyverse packages with more specialised usage. They are not loaded automatically with library(tidyverse), so you’ll need to load each one with its own call to library(). ggplot2 > install.packages(tidyverse) > iris \%>\% Select iris data of species filter(Species==virginica) virginica > iris \%>\% Select iris data of species filter(Species==virginica, virginica and sepal length Sepal.Length > 6) greater than 6. dplyr Filter > library(tidyverse) Useful Functions Arrange Mutate Summarize > tidyverse_conflicts() Conflicts between tidyverse and other packages > tidyverse_deps() List all tidyverse dependencies > tidyverse_logo() Get tidyverse logo, using ASCII or unicode characters > tidyverse_packages() List all tidyverse packages > tidyverse_update() Update tidyverse packages Loading in the data > library(datasets) Load the datasets package > library(gapminder) Load the gapminder package > attach(iris) Attach iris data to the R search path filter() allows you to select a subset of rows in a data frame. > iris \%>\% Sort in ascending order of Graphical Primitives Data Visualization with ggplot2 Cheat Sheet RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • [email protected] • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org • ggplot2 0.9.3.1 • Updated: 3/15 Geoms - Use a geom to represent data points, use the geom’s aesthetic properties to represent variables. Each function returns a layer. One Variable a + geom_area(stat = bin) x, y, alpha, color, fill, linetype, size b + geom_area(aes(y = ..density..), stat = bin) a + geom_density(kernel = gaussian) x, y, alpha, color, fill, linetype, size, weight b + geom_density(aes(y = ..county..)) a + geom_dotplot() x, y, alpha, color, fill a + geom_freqpoly() x, y, alpha, color, linetype, size b + geom_freqpoly(aes(y = ..density..)) a + geom_histogram(binwidth = 5) x, y, alpha, color, fill, linetype, size, weight b + geom_histogram(aes(y = ..density..)) Discrete b <- ggplot(mpg, aes(fl)) b + geom_bar() x, alpha, color, fill, linetype, size, weight Continuous a <- ggplot(mpg, aes(hwy)) Two Variables Continuous Function Discrete X, Discrete Y h <- ggplot(diamonds, aes(cut, color)) h + geom_jitter() x, y, alpha, color, fill, shape, size Discrete X, Continuous Y g <- ggplot(mpg, aes(class, hwy)) g + geom_bar(stat = identity) x, y, alpha, color, fill, linetype, size, weight g + geom_boxplot() lower, middle, upper, x, ymax, ymin, alpha, color, fill, linetype, shape, size, weight g + geom_dotplot(binaxis = y, stackdir = center) x, y, alpha, color, fill g + geom_violin(scale = area) x, y, alpha, color, fill, linetype, size, weight Continuous X, Continuous Y f <- ggplot(mpg, aes(cty, hwy)) f + geom_blank() f + geom_jitter() x, y, alpha, color, fill, shape, size f + geom_point() x, y, alpha, color, fill, shape, size f + geom_quantile() x, y, alpha, color, linetype, size, weight f + geom_rug(sides = bl) alpha, color, linetype, size f + geom_smooth(model = lm) x, y, alpha, color, fill, linetype, size, weight f + geom_text(aes(label = cty)) x, y, label, alpha, angle, color, family, fontface, hjust, lineheight, size, vjust Three Variables m + geom_contour(aes(z = z)) x, y, z, alpha, colour, linetype, size, weight seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2)) m <- ggplot(seals, aes(long, lat)) j <- ggplot(economics, aes(date, unemploy)) j + geom_area() x, y, alpha, color, fill, linetype, size j + geom_line() x, y, alpha, color, linetype, size j + geom_step(direction = hv) x, y, alpha, color, linetype, size Continuous Bivariate Distribution i <- ggplot(movies, aes(year, rating)) i + geom_bin2d(binwidth = c(5, 0.5)) xmax, xmin, ymax, ymin, alpha, color, fill, linetype, size, weight i + geom_density2d() x, y, alpha, colour, linetype, size i + geom_hex() x, y, alpha, colour, fill size e + geom_segment(aes( xend = long + delta_long, yend = lat + delta_lat)) x, xend, y, yend, alpha, color, linetype, size Data Visualization with ggplot2 : : CHEAT SHEET ggplot2 is based on the grammar of graphics, the idea that you can build every graph from the same components: a data set, a coordinate system, and geoms—visual marks that represent data points. Basics GRAPHICAL PRIMITIVES a + geom_blank()
 (Useful for expanding limits) b + geom_curve(aes(yend = lat + 1,
 xend=long+1),curvature=1) - x, xend, y, yend, alpha, angle, color, curvature, linetype, size a + geom_path(lineend=butt, linejoin=round, linemitre=1)
 x, y, alpha, color, group, linetype, size a + geom_polygon(aes(group = group))
 x, y, alpha, color, fill, group, linetype, size b + geom_rect(aes(xmin = long, ymin=lat, xmax= long + 1, ymax = lat + 1)) - xmax, xmin, ymax, ymin, alpha, color, fill, linetype, size a + geom_ribbon(aes(ymin=unemploy - 900, ymax=unemploy + 900)) - x, ymax, ymin, alpha, color, fill, group, linetype, size + = To display values, map variables in the data to visual properties of the geom (aesthetics) like size, color, and x and y locations. + = data geom x = F · y = A coordinate system plot data geom x = F · y = A color = F size = A coordinate system plot Complete the template below to build a graph. required ggplot(data = mpg, aes(x = cty, y = hwy)) Begins a plot that you finish by adding layers to. Add one geom function per layer. 
 qplot(x = cty, y = hwy, data = mpg, geom = “point) Creates a complete plot with given data, geom, and mappings. Supplies many useful defaults. last_plot() Returns the last plot ggsave(plot.png, width = 5, height = 5) Saves last plot as 5’ x 5’ file named plot.png in working directory. Matches file type to file extension. F M A F M A aesthetic mappings data geom LINE SEGMENTS b + geom_abline(aes(intercept=0, slope=1)) b + geom_hline(aes(yintercept = lat)) b + geom_vline(aes(xintercept = long)) common aesthetics: x, y, alpha, color, linetype, size b + geom_segment(aes(yend=lat+1, xend=long+1)) b + geom_spoke(aes(angle = 1:1155, radius = 1)) a <- ggplot(economics, aes(date, unemploy)) b <- ggplot(seals, aes(x = long, y = lat)) ONE VARIABLE continuous c <- ggplot(mpg, aes(hwy)); c2 <- ggplot(mpg) c + geom_area(stat = bin)
 x, y, alpha, color, fill, linetype, size c + geom_density(kernel = gaussian)
 x, y, alpha, color, fill, group, linetype, size, weight c + geom_dotplot() 
 x, y, alpha, color, fill c + geom_freqpoly() x, y, alpha, color, group, linetype, size c + geom_histogram(binwidth = 5) x, y, alpha, color, fill, linetype, size, weight c2 + geom_qq(aes(sample = hwy)) x, y, alpha, color, fill, linetype, size, weight discrete d <- ggplot(mpg, aes(fl)) d + geom_bar() 
 x, alpha, color, fill, linetype, size, weight e + geom_label(aes(label = cty), nudge_x = 1, nudge_y = 1, check_overlap = TRUE) x, y, label, alpha, angle, color, family, fontface, hjust, lineheight, size, vjust e + geom_jitter(height = 2, width = 2) 
 x, y, alpha, coloRStudio IDE : : CHEAT SHEET Write Code Pro Features RStudio® is a trademark of RStudio, Inc. • CC BY SA RStudio • [email protected] • 844-448-1212 • rstudio.com • Learn more at www.rstudio.com • RStudio IDE 0.99.832 • Updated: 2016-01 Turn project into package, Enable roxygen documentation with Tools > Project Options > Build Tools Roxygen guide at Help > Roxygen Quick Reference File > New Project > New Directory > R Package Share Project with Collaborators Active shared collaborators Select R Version Start new R Session in current project Close R Session in project JHT RStudio saves the call history, workspace, and working directory associated with a project. It reloads each when you re-open a project. Name of current project View(<data>) opens spreadsheet like view of data set Sort by values Filter rows by value or value range Search for value Viewer Pane displays HTML content, such as Shiny apps, RMarkdown reports, and interactive visualizations Stop Shiny app Publish to shinyapps.io, rpubs, RSConnect, … Refresh RStudio opens documentation in a dedicated Help pane Home page of helpful links Search within help file Search for help file GUI Package manager lists every installed package Click to load package with library(). Unclick to detach package with detach() Delete from library Install Packages Update Packages Create reproducible package library for your project RStudio opens plots in a dedicated Plots pane Navigate recent plots Open in window Export plot Delete plot Delete all plots Package version installed Examine variables in executing environment Open with debug(), browser(), or a breakpoint. RStudio will open the debugger mode when it encounters a breakpoint while executing code. Open traceback to examine the functions that R called before the error occurred Launch debugger mode from origin of error Click next to line number to add/remove a breakpoint. Select function in traceback to debug Highlighted line shows where execution has paused Run commands in environment where execution has paused Step through code one line at a time Step into and out of functions to run Resume execution Quit debug mode Open Shiny, R Markdown, knitr, Sweave, LaTeX, .Rd files and more in Source Pane Check spelling Render output Choose output format Choose output location Insert code chunk Jump to previous chunk Jump to next chunk Run selected lines Publish to server Show file outline Set knitr chunk options Run this and all previous code chunks Run this code chunk Jump to chunk RStudio recognizes that files named app.R, server.R, ui.R, and global.R belong to a shiny app Run app Choose location to view app Publish to shinyapps.io or server Manage publish accounts Access markdown guide at Help > Markdown Quick Reference Stage files: Show file diff Commit staged files