Homework 2

Reading in Data

Code

library(tidyverse)
library(readxl)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Read in a dataset

First, we’ll read in the data from the Excel sheet monthly-listing.xlsx.

Source: https://www.bls.gov/web/wkstp/monthly-listing.xlsx

Code

  #Read in data
  strike <- read_excel("_data/monthly-listing.xlsx", skip=1)
  strike

Clean the data

Next, we’ll clean the data. First, we’ll remove redundant and unnecessary variables. Then, we’ll rename some variables with cumbersome or confusing titles.

Code

  # remove redundant and unnecessary variables
  strike <- strike %>%
    select("Organizations involved","States","Areas","Ownership","Union acronym","Work stoppage beginning date","Work stoppage ending date","Number of workers[2]","Days idle, cumulative for this work stoppage[3]")
  # rename variables
  strike <- strike %>%
    rename(
      "Employer"="Organizations involved",
      "Union"="Union acronym",
      "Start date"="Work stoppage beginning date",
      "End date"="Work stoppage ending date",
      "Workers"="Number of workers[2]",
      "Days struck"="Days idle, cumulative for this work stoppage[3]"
    )
  # view cleaned data
  strike

Provide a narrative about the data set

This data set shows data on work stoppages (strikes) from the Bureau of Labor Statistics, from 1993 to the present.

Variables

Employer shows the employer of the workers striking. This variable is categorical. States shows the state or state(s) in which the strike took place. This variable is categorical. Areas shows a more specific location of the strike, at a lower level than the state(s). This variable is categorical. Ownership tells what type of entity the employer is of the following options: private industry, local and/or state government. This variable is categorical. Union shows the acronym of the union to which the striking workers belonged. This variable is categorical. Start date shows the date on which the strike began. This column contains numerical, discrete, interval data. End date shows the date on which the strike ended. This column contains numerical, discrete, interval data. Workers shows the number of workers who went on strike. This column contains numerical, discrete, ratio data. Days struck shows the total number of hours of labor workers withheld during the strike. This is distinct from the difference between Start date and End date because, in some strikes, some workers go back to work before the strike ends. This column contains numerical, discrete, ratio data.

Some notable trends

Let’s see if anything jumps out when we create a new data frame, combining strikes that start in the same year. How have numbers of workers on strike and number of days struck changed over time in the past 30 years?

Code

  # select relevant variables
  strike_yr <- strike %>%
    select(
      "Start date",
      "Workers",
      "Days struck")
  # truncate start date to year
  strike_yr$"Start date" <- as.numeric(format(strike_yr$"Start date", "%Y"))
  # rename start date to year
  strike_yr <- strike_yr %>%
    rename("Year"="Start date") %>%
    rename("Days" = "Days struck")
  # condense rows by year and remove problematic rows
    strike_yr_sum <- strike_yr %>%
      filter(Year>1990) %>%
      group_by(Year) %>%
      summarize(
        Workers=sum(Workers, na.rm=TRUE),
        Days.Struck=sum(Days, na.rm=TRUE)
      )

First, let’s view the years in order from most workers on strike to least.

Code

  strike_yr_sum%>%
    arrange(desc(Workers))

Next, we’ll view the years in order from most days struck to least.

Code

  strike_yr_sum%>%
    arrange(desc(Days.Struck))

We can see that 2000 features high in both views. Let’s go back to the un-condensed data and filter to strikes in 2000 with more than 5000 workers, sorted from most to least workers involved, to look at the most significant contributors.

Code

  strike00 <- strike
# change date to year
  strike00$"Start date" <- as.numeric(format(strike00$"Start date", "%Y"))
  strike00 <- strike00 %>%
    rename("Year" = "Start date") %>%
# filter to 2000, large strikes
    filter(Year==2000,Workers>5000) %>%
    select(Union,Employer,States,Areas,Workers,"Days struck")
  strike00workers <- arrange(strike00, desc(Workers))
  strike00workers

Some more potential research questions

Has the average strike size and length changed over time?

How have strikes changed in length, frequency, and magnitude (number of workers and days struck) within the private sector and within the public sector, viewed separately?

Are there significant geographic trends?

Do large strikes in an area or industry tend to be followed by more strikes in that area or industry?