Practice Programming Assignment: swirl Lesson 5: Base Plotting System

| Nice colors, but still no title. Let's add one with the R command title. Use the argument main set equal to the string "Ozone and | Wind in New York City". boxplot(Ozone~Month,airquality,xlab="Month",ylab="Ozone (ppb)",col.axis="blue",col.lab="red",main="Ozone and Wind in New Yotk City") | That's not exactly what I'm looking for. Try again. Or, type info() for more …

Practice Programming Assignment: swirl Lesson 4: Plotting Systems

| Trying to install package ‘ggplot2’ now… also installing the dependencies ‘colorspace’, ‘labeling’, ‘munsell’, ‘RColorBrewer’, ‘gtable’, ‘lazyeval’, ‘scales’, ‘viridisLite’ package ‘colorspace’ successfully unpacked and MD5 sums checked package ‘labeling’ successfully unpacked and MD5 sums checked package ‘munsell’ successfully unpacked and MD5 sums checked package ‘RColorBrewer’ successfully unpacked and MD5 sums checked package ‘gtable’ successfully unpacked …

Exploratory Data Analysis – Practice Programming Assignment: swirl Lesson 3: Graphics Devices in R

| | 0% | Graphics_Devices_in_R. (Slides for this and other Data | Science courses may be found at github | https://github.com/DataScienceSpecialization/courses/. If | you care to use them, they must be downloaded as a zip file | and viewed locally. This lesson corresponds to | 04_ExploratoryAnalysis/Graphics_Devices_in_R.) … |== | 3% | As the title suggests, …

Getting and Cleaning Data Week #4 – Swirl #4– Dates and Times with Lubridate

Selection: 4 | Attempting to load lesson dependencies… | This lesson requires the ‘lubridate’ package. Would you like me to install it for you now? 1: Yes 2: No Selection: 1 | Trying to install package ‘lubridate’ now… package ‘lubridate’ successfully unpacked and MD5 sums checked | Package ‘lubridate’ loaded correctly! | | 0% | …

Getting and Cleaning Data Week #3 – Swirl #3– Tidying Data with tidyr

separate(res,sex_class,c("sex","class"),fill="right") grade sex class count 1 A male 1 3 2 B male 1 6 3 C male 1 7 4 D male 1 4 5 E male 1 1 6 A female 1 4 7 B female 1 4 8 C female 1 4 9 D female 1 0 10 E female 1 1 …

Getting and Cleaning Data Week #3 – Swirl #2– Grouping and Chaining with dplyr

We'll continue where we left off with RStudio's CRAN download log from July 8, 2014, which contains information on | roughly 225,000 R package downloads (http://cran-logs.rstudio.com/). … |========= | 8% | As with the last lesson, the dplyr package was automatically installed (if necessary) and loaded at the beginning of | this lesson. Normally, this …

Getting and Cleaning Data Week #3 – Swirl #1 – Manipulating Data with dplyr

Please choose a course, or type 0 to exit swirl. 1: Getting and Cleaning Data 2: R Programming 3: Take me to the swirl course repository! Selection: 1 | Please choose a lesson, or type 0 to return to course menu. 1: Manipulating Data with dplyr 2: Grouping and Chaining with dplyr 3: Tidying Data …

Getting and Cleaning Data Week 2 Quiz

1. Register an application with the Github API here https://github.com/settings/applications. Access the API to get information on your instructors repositories (hint: this is the url you want "https://api.github.com/users/jtleek/repos"). Use this data to find the time that the datasharing repo was created. What time was it created? This tutorial may be useful (https://github.com/hadley/httr/blob/master/demo/oauth2-github.r). You may also need …

R Programming Week 4 Programming Assignment 3: Hospital Quality

My solution after referring to online sources: 1. Plot the 30-day mortality rates for heart attack > outcome <- read.csv("outcome-of-care-measures.csv", colClasses = "character") > head(outcome) > outcome[, 11] <- as.numeric(outcome[, 11]) ## You may get a warning about NAs being introduced; that is okay > hist(outcome[, 11]) 2. Finding the best hospital in a state …

R Programming Swirl #14 – Base Graphics

| Would you like to continue with one of these lessons? 1: R Programming Base Graphics 2: No. Let me start something new. Selection: 1 | Load the included data frame cars with data(cars). data(cars) | Perseverance, that's the answer. |=========== | 9% | To fix ideas, we will work with simple data frames. Our …