1. Import, Plot, Summarize, and Save Data Using the Bureau of Labor Statistics data, choose a dataset that interest you. Then generate summary statistics for 2 variables, provide 6 graphs some of the...

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1.

Import, Plot, Summarize, and Save Data



Using the Bureau of Labor Statistics data, choose a dataset that interest you. Then generate summary statistics for 2 variables, provide 6 graphs some of the features (e.g., histograms, box plots, density plots, etc.) of several variables, summaries of graphs and save the data locally as CSV files.



2.

Explore Some Bivariate Relations



Use the same dataset within the same website to explore some bivariate relations (e.g. bivariate plot, correlation, table cross table etc.)



3.

Organize a Data Report



Generate a summary report. Make sure to include: summary for every variable, structure and type of data elements, discuss four results of your data.




Number of nonfatal occupational injuries and illnesses involving days away from work by industry and selected natures of injury or illness, private industry. Using R studio, use the CD_r1_2017.xlsx data set for the following analysis 1. Import, Plot, Summarize, and Save Data Using the Bureau of Labor Statistics data, choose a dataset that interest you. Then generate summary statistics for 2 variables, provide 6 graphs some of the features (e.g., histograms, box plots, density plots, etc.) of several variables, summaries of graphs and save the data locally as CSV files. 2. Explore Some Bivariate Relations Use the same dataset within the same website to explore some bivariate relations (e.g. bivariate plot, correlation, table cross table etc.) 3. Organize a Data Report Generate a summary report. Make sure to include: summary for every variable, structure and type of data elements, discuss four results of your data. Provide the results in RMarkdown = Word, I need both formats for review please, the .RMD and word.doc outputs.
Answered Same DaySep 03, 2021

Answer To: 1. Import, Plot, Summarize, and Save Data Using the Bureau of Labor Statistics data, choose a...

Pritam answered on Sep 06 2021
137 Votes
---
title: "Untitled"
author: "Unknown"
date: "6 September 2019"
output:
word_document: default
pdf_doc
ument: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Required libraries:
```{r, warning=FALSE, message=FALSE}
library(readxl)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggcorrplot)
library(gridExtra)
```
# Import, Plot, summarize and save data:
```{r, warning=FALSE, message=FALSE}
cdr12017_sfyfso0o <- read_excel("cdr12017-sfyfso0o.xlsx", skip = 4)
#View(cdr12017_sfyfso0o)
d1 = cdr12017_sfyfso0o
colnames(d1) = c("Industry","NAICS_code","Total_cases","sprains_strains_tears",
"Fractures","Total","Cuts_lacerations","Punctures",
"Bruise_contusions",
"Heat_burns","chemical_burns","Amputations",
"Carpal_tunnel_syndrome",
"Tendonitis","Total_trauma","with_sprains","with_fractures",
"soreness_pain","other_natures")
# changing the character type to numeric
d1[,2:19] = as.data.frame(sapply(d1[,2:19], as.numeric))
# Brief observation of the...
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