Publications by U.S. Geological Survey/ The Nature Conservancy
Data608_final
1 Shiny app For my final project, I made a Shiny app that displays visualizations of real-time water quality data from the U.s. Geological Survey (USGS) for the geographic region of Long Island, New York. App Available :https://robertwelk.shinyapps.io/DATA608_final/?_ga=2.150054134.1761314644.1589728435-1274356216.1588363575 2 GitHub All files i...
3951 sym R (4359 sym/15 pcs)
DATA608_assignment1
Principles of Data Visualization and Introduction to ggplot2 I have provided you with data about the 5,000 fastest growing companies in the US, as compiled by Inc. magazine. lets read this in: inc <- read.csv("https://raw.githubusercontent.com/charleyferrari/CUNY_DATA_608/master/module1/Data/inc5000_data.csv", header= TRUE) Packages Used require...
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Document
1 R packages used 2 Read in tables Raw data from NWIS was read in to RStudio using the ‘dataRetrieval’ package created by the USGS. Data was then cleaned and structured using base R and dplyr, and saved as csv in the directory “X:/transfer/RobW/TNC_DO”. # get site.info table site.info <- read.csv("site_info.csv") #get DO time series ...
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TNC_DO
1 R packages used library(dataRetrieval) library(lubridate) library(tidyverse) library(plotly) library(devtools) library(stringr) 2 Data Prep and Cleaning 2.1 Site Information for years 2016-2017 2.1.1 import site information from NWIS (years 2016-2017 only) 2.2 Dissolved Oxygen Time Series Table (2016 and 2017) 2.3 2015 data ### Write ...
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DATA605_Final
1 Problem 1 1.1 Generate random variables Using R, generate a random variable X that has 10,000 random uniform numbers from 1 to N, where N can be any number of your choosing greater than or equal to 6. Then generate a random variable Y that has 10,000 random normal numbers with a mean of N+1/2. Assume the small letter “x” is estimated as th...
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DATA624 Assignment 7
6.2 Permeability a. Load data The dataset contain 165 observations (compounds) of 1107 binary molecular predictors. The target is permeability. library(AppliedPredictiveModeling) ## Warning: package 'AppliedPredictiveModeling' was built under R version 4.0.4 data("permeability") #dim(fingerprints) b. Filter Sparse Filter low frequency predicto...
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DATA624_Assignment3
6.2 The plastics data set consists of the monthly sales (in thousands) of product A for a plastics manufacturer for five years. a. Plot the time series of sales of product A. Can you identify seasonal fluctuations and/or a trend-cycle? For the five years of data, there are seasonal fluctuations, peaks in August/September and troughs in January/...
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DATA624_Assignment2
Exercise 3.1 For the following series, find an appropriate Box-Cox transformation in order to stabilize the variance. a) usnetelec For usnetelec, the BoxCox.lambda() function returns an optimal value of 0.517. By simply rounding down to 0.5, the transformation becomes much more interpretable, as a square root transformation. Upon comparing the o...
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DATA624_Assignment1
2.1 Use the help function to explore what the series gold, woolyrnq and gas represent. The gold time series provides daily morning gold prices in US dollars from 1 January 1985 – 31 March 1989. woolyrnq provides quarterly production of woollen yarn in Australia in tonnes from Mar 1965 – Sep 1994. gas provides Australian monthly gas productio...
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DATA624_Project1
library(tidyverse) library(timetk) library(lubridate) library(DataExplorer) library(ggfortify) library(forecast) library(caret) library(fpp) library(DMwR) library(imputeTS) library(zoo) library(kableExtra) setwd('C:/Users/robbj/OneDrive/CUNY SPS/DATA624/Project1') Part 1: ATM withdrawals Objective: Forecast cash withdrawals for 4 AT...
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