Exploratory data analysis of 2017 US Employment data using R

Exploratory data analysis of 2017 US Employment data using R

• 1.
  Data Science Exploratory dataanalysis of 2017 US Employment data using R – Use Case Chetan Khanzode
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  Data Source • Bureauof Labor Statistics (BLS)mission is the collection, analysis, and dissemination of essential economic information to support public and private decision-making. • Data from Quarterly Census of Employment and Wages for year 2017 https://www.bls.gov/ • 3.5 million rows and 38 columns
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  Data Science Process Source:data science cook book
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  R Packages Used •library(data.table) • library(plyr) • library(dplyr) • library(stringr) • library(ggplot2) • library(maps) • library(bit64) • library(RColorBrewer) • library(choroplethr)
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  Import the data Usefread function from the data.table package which is significantly faster
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  Merge the datawith associated codes and Titles
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  Map package data •Purpose is to look at the geographical distribution of wages across the US. • Map package has US map for both at the state-and county-levels and the data required to make the maps can be extracted. • Then align our employment data with the map data so that the correct data is represented at the right location on the map.
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  Map package data
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  Map package data state.fips$fips<- str_pad(state.fips$fips, width=2, pad="0“,side='left')
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  Map package data
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  Merge to maindataset Merged data sample to main data frame
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  Geospatial data visualization library(ggplot2) library(RColorBrewer) state_df<- map_data('state') county_df <- map_data('county') transform_mapdata <- function(x){ names(x)[5:6] <- c('state','county') for(u in c('state','county')){ x[,u] <- sapply(x[,u],MakeCap) } return(x) } state_df <- transform_mapdata(state_df) county_df <- transform_mapdata(county_df) chor <- left_join(county_df, d.cty) ggplot(chor, aes(long,lat, group=group))+ geom_polygon(aes(fill=wage))+ geom_path( color='white',alpha=0.5,size=0.2)+ geom_polygon(data=state_df, color='black',fill=NA)+ scale_fill_brewer(palette='PuRd')+ labs(x='',y='', fill='Avg Annual Pay by county')+ theme(axis.text.x=element_blank(), axis.text.y=element_blank(), axis.ticks.x=element_blank(), axis.ticks.y=element_blank()) chor <- left_join(state_df, d.state) ggplot(chor, aes(long,lat, group=group))+ geom_polygon(aes(fill=wage))+ geom_path( color='white',alpha=0.5,size=0.2)+ geom_polygon(data=state_df, color='black',fill=NA)+ scale_fill_brewer(palette='Spectral')+ labs(x='',y='', fill='Avg Annual Pay By State')+ theme(axis.text.x=element_blank(), axis.text.y=element_blank(), axis.ticks.x=element_blank(), axis.ticks.y=element_blank()) #The two functions filter and select are from dplyr. d.cty <- filter(ann2017full, agglvl_code==70)%>% select(state,county,abb, avg_annual_pay, annual_avg_emplvl)%>% mutate(wage=comDiscretize(avg_annual_pay), empquantile=comDiscretize(annual_avg_emplvl))
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  Avg Annual Payby County
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  Avg Annual Payby State
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  JOBS by Industry- NIACS d.sectors <- filter(ann2017full, industry_code %in% c(11,21,54,52), own_code==5, # Private sector agglvl_code == 74 # county-level ) %>% select(state,county,industry_code, own_code,agglvl_code, industry_title, own_title, avg_annual_pay, annual_avg_emplvl)%>% mutate(wage=comDiscretize(avg_annual_pay), emplevel=comDiscretize(annual_avg_emplvl)) d.sectors <- filter(d.sectors, !is.na(industry_code)) chor <- left_join(county_df, d.sectors) ggplot(chor, aes(long,lat,group=group))+ geom_polygon(aes(fill=emplevel))+ geom_polygon(data=state_df, color='black',fill=NA)+ scale_fill_brewer(palette='PuBu')+ facet_wrap(~industry_title, ncol=2, as.table=T)+ labs(fill='Avg Employment Level',x='',y='')+ theme(axis.text.x=element_blank(), axis.text.y=element_blank(), axis.ticks.x=element_blank(), axis.ticks.y=element_blank())
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  JOBS by Industry- NIACS
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  JOBS by Industry- NIACS
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  Thank You
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  References https://www.bls.gov/ https://cran.r-project.org/web/packages/data.table/vignettes/datatable-intro.html https://www.rdocumentation.org/packages/plyr/versions/1.8.4 https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html https://cran.r-project.org/web/packages/stringr/vignettes/stringr.html https://www.statmethods.net/advgraphs/ggplot2.html https://www.r-graph-gallery.com/map/ Practical data sciencebook