Nazija Akter

Introduction

In this skills drill, you will be asked to practice the programming skills you have learned so far in order to investigate changes in smoking behavior over time in the general adult population.

The data you are analyzing is from the National Health Interview Survey (NHIS), a survey conducted annually since 1997 by the National Institutes of Health.

Step 1: Load Packages

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

library(readr)
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)

Step 2: Import Data

Import your data into R

data <- read_csv("/Users/Nazija/Downloads/Practice Skills Drill 1 Data.csv")

## Parsed with column specification:
## cols(
##   year = col_double(),
##   Behav_EverSmokeCigs_B = col_double(),
##   Behav_CigsPerDay_N = col_double(),
##   MentalHealth_MentalIllnessK6_C = col_character()
## )

Step 3: Preview Data

Preview the first 6 rows of your data

head(data)

## # A tibble: 6 x 4
##    year Behav_EverSmokeCigs_B Behav_CigsPerDay_N MentalHealth_MentalIllnessK6_C
##   <dbl>                 <dbl>              <dbl> <chr>                         
## 1  1997                     0                  0 Low Risk                      
## 2  1997                     0                  0 <NA>                          
## 3  1997                     1                  5 Low Risk                      
## 4  1997                     0                  0 Low Risk                      
## 5  1997                     0                  0 Low Risk                      
## 6  1997                     1                  0 MMD

Step 4: Avg Daily Cigarettes

Select the Behav_CigsPerDay_N and year variables from the data Rename the Behav_CigsPerDay_N variable to NumCigs filter to only keep those observations where year is greater than 1997 Calculate the mean of the NumCigs

  data%>%
  select(Behav_CigsPerDay_N, year)%>%
  rename(NumCigs = Behav_CigsPerDay_N)%>%
  filter(year > 1997)%>%
  summarize(avgNumCigs = mean(NumCigs))

## # A tibble: 1 x 1
##   avgNumCigs
##        <dbl>
## 1       2.71

Step 5: Avg Daily Cigarettes by Year

Select the year and Behav_CigsPerDay_N variables from the data Rename the Behav_CigsPerDay_N variable to NumCigs filter to only keep those observations where year is greater than 1997 Calculate the mean of the NumCigs by year

data%>%
  select(Behav_CigsPerDay_N, year)%>%
  rename(NumCigs = Behav_CigsPerDay_N)%>%
  filter(year > 1997)%>%
  group_by(year)%>%
  summarize(avgNumCigs = mean(NumCigs))

## `summarise()` ungrouping output (override with `.groups` argument)

## # A tibble: 19 x 2
##     year avgNumCigs
##    <dbl>      <dbl>
##  1  1998       3.83
##  2  1999       3.57
##  3  2000       3.48
##  4  2001       3.43
##  5  2002       3.29
##  6  2003       3.09
##  7  2004       2.92
##  8  2005       2.88
##  9  2006       2.74
## 10  2007       2.47
## 11  2008       2.67
## 12  2009       2.49
## 13  2010       2.32
## 14  2011       2.32
## 15  2012       2.23
## 16  2013       2.08
## 17  2014       1.99
## 18  2015       1.87
## 19  2016       1.95

Step 6: Interpretation

[Write your interpretation of the above output, here]

The data displays the average number of cigarettes used per day each year from 1998 to 2016. The average number of cigarettes consumed per day has steadily decreased since 1998, from an about 2.7 cigarettes per day to 2 cigarettes per day.

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

line chart
year on the x-axis
average cigarettes per day on the y-axis
vary the color of the line acording to the average cigarettes per day

plot<-data%>%
  select(Behav_CigsPerDay_N, year)%>%
  rename(NumCigs = Behav_CigsPerDay_N)%>%
  filter(year > 1997)%>%
  group_by(year)%>%
  summarize(avgNumCigs = mean(NumCigs))

## `summarise()` ungrouping output (override with `.groups` argument)

ggplot(plot, aes(x = year, y = avgNumCigs, color = avgNumCigs))+
  geom_line()

Step 8: Post to Rpubs

Post this to Rpubs & post the Rpubs URL on blackboard

Practice Skills Drill: Smoking Behavior