Introduction

In this skills drill, you will be asked to practice the programming skills you have learned so far in order to investigate differences in work absenteeism between cocaine users and non-cocaine users.

The data you are analyzing is from the National Survey of Drug Use and Health (NSDUH), a survey conducted annually since 1971 by the Substance Abuse and Mental Health Services Adminsitration. You will be studying the following two variables:

cocaine_month: Did this respondent report using cocaine in the past 30 days? (Yes/No)
SelectiveLeave: # of times in the past 30 days where the respondent skipped work because they “just didnt want to be there”

Step 1: Load Packages

Load the packages necessary to (1)import, (2)manipulate, and (3)visualize data.

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)

## Warning in file(con, "r"): cannot open file '/var/db/timezone/zoneinfo/
## +VERSION': No such file or directory

library(readr)

Step 2: Import Data

Import your data into R

data<-read_csv("/Users/chelsyrodriguez/Downloads/SkillsDrill1Data.csv")

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   .default = col_double(),
##   marij_month = col_character(),
##   cocaine_month = col_character(),
##   pharmamonth = col_character()
## )
## ℹ Use `spec()` for the full column specifications.

Step 3: Preview Data

Preview the first 6 rows of your data

head(data)

## # A tibble: 6 x 47
##   marij_ever marij_month marij_year cocaine_ever cocaine_month cocaine_year
##        <dbl> <chr>            <dbl>        <dbl> <chr>                <dbl>
## 1          1 Used Marij…          1            1 Did not use …            0
## 2          0 Did not us…          0            0 Did not use …            0
## 3          1 Did not us…          0            0 Did not use …            0
## 4          0 Did not us…          0            0 Did not use …            0
## 5          0 Did not us…          0            0 Did not use …            0
## 6          0 Did not us…          0            0 Did not use …            0
## # … with 41 more variables: crack_ever <dbl>, crack_month <dbl>,
## #   crack_year <dbl>, heroin_ever <dbl>, heroin_month <dbl>, heroin_year <dbl>,
## #   hallucinogen_ever <dbl>, hallucinogen_month <dbl>, hallucinogen_year <dbl>,
## #   inhalant_ever <dbl>, inhalant_month <dbl>, inhalant_year <dbl>,
## #   meth_ever <dbl>, meth_month <dbl>, meth_year <dbl>, painrelieve_ever <dbl>,
## #   painrelieve_month <dbl>, painrelieve_year <dbl>, tranq_ever <dbl>,
## #   tranq_month <dbl>, tranq_year <dbl>, stimulant_ever <dbl>,
## #   stimulant_month <dbl>, stimulant_year <dbl>, sedative_ever <dbl>,
## #   sedative_month <dbl>, sedative_year <dbl>, anydrugever <dbl>,
## #   pharmamonth <chr>, nonpharmamonth <dbl>, nonpharmamonth_nomj <dbl>,
## #   anydrugmonth <dbl>, anydrugyear <dbl>, anydrugever_nomj <dbl>,
## #   anydrugmonth_nomj <dbl>, anydrugyear_nomj <dbl>, countofdrugs_ever <dbl>,
## #   countofdrugs_month <dbl>, countofdrugs_year <dbl>, SelectiveLeave <dbl>,
## #   SkipSick <dbl>

Step 4: Avg Days Skipped Work

Select the cocaine_month and SelectiveLeave variables from the data Rename the SelectiveLeave variable to DaysSkippedWork filter to only keep those observations where DaysSkippedWork is less than than 31 Calculate the mean of the DaysSkippedWork

data%>%
select(cocaine_month,SelectiveLeave) %>%
rename(DaysSkippedWork = SelectiveLeave) %>%
filter(DaysSkippedWork<31)%>%
summarize(AvgDaysSkippedWork = mean(DaysSkippedWork))

## # A tibble: 1 x 1
##   AvgDaysSkippedWork
##                <dbl>
## 1              0.327

Step 5: Avg Days Skipped Work by Cocaine Use

data %>%
select(cocaine_month,SelectiveLeave) %>%
rename(DaysSkippedWork = SelectiveLeave) %>%
filter(DaysSkippedWork<31) %>%
group_by(cocaine_month) %>%
summarize(AvgDaysSkippedWork = mean(DaysSkippedWork))

## # A tibble: 2 x 2
##   cocaine_month                    AvgDaysSkippedWork
## * <chr>                                         <dbl>
## 1 Did not use Cocaine Past 30 days              0.322
## 2 Used Cocaine Past 30 days                     0.85

Step 6: Interpretation

We can indicate that those who did not use cocaine for the past 30 days didn’t call out of work as much. Those who did used cocaine within that last 30 days skipped work several times.

Step 7: Visualization

Copy the code from step 5, and paste into this code chunk. Add onto the code to create a visualization which shows a

column (col) chart
Cocaine use on the x-axis
average days of selective work absence on the y-axis
vary the fill of the line acording to the average days of selective work absence.

data %>%
select(cocaine_month,SelectiveLeave) %>%
rename(DaysSkippedWork = SelectiveLeave) %>%
filter(DaysSkippedWork<31) %>%
group_by(cocaine_month) %>%
summarize(AvgDaysSkippedWork = mean(DaysSkippedWork)) %>%
ggplot()+
  geom_col(aes(x = cocaine_month,y = AvgDaysSkippedWork,color = AvgDaysSkippedWork))

Extra Credit:

(1pt) In the following code chunk, produce the table which compares average absenteeism between marijuana users & non-users (marij_month). Be sure to filter to only keep those observations where DaysSkippedWork is less than than 31.

data %>%
select(marij_month,SelectiveLeave) %>%
rename(DaysSkippedWork = SelectiveLeave) %>%
filter(DaysSkippedWork<31) %>%
group_by(marij_month) %>%
summarize(AvgDaysSkippedWork = mean(DaysSkippedWork))

## # A tibble: 2 x 2
##   marij_month                        AvgDaysSkippedWork
## * <chr>                                           <dbl>
## 1 Did not use Marijuana Past 30 days              0.290
## 2 Used Marijuana Past 30 days                     0.591

(0.5pt) In the following code chunk, produce a column chart to visualize the average absenteeism for marijuana users vs. non-users

data %>%
select(marij_month,SelectiveLeave) %>%
rename(DaysSkippedWork = SelectiveLeave) %>%
filter(DaysSkippedWork<31) %>%
group_by(marij_month) %>%
summarize(AvgDaysSkippedWork = mean(DaysSkippedWork)) %>%
ggplot()+
  geom_col(aes(x = marij_month,y = AvgDaysSkippedWork,color = AvgDaysSkippedWork))

(0.5pt) Interpret the result of your analysis of the relationship between marijuana use & absenteeism. Based on the results we can indicate that users who’ve used marijuana for the past 30 days are more likely to skip or call out work than those who don’t use marijuana.
# Step 8: Post to Rpubs Post this to Rpubs & post the Rpubs URL on blackboard

Practice Skills Drill: Drug Use and Work Absenteeism