Dairy industry analysis
2024-01-25
Introduction
For the analysis use two data sources. The first is per capita cheese consumption for selected varieties (annual), offered by the USDA dairy data site. The second is fatal injury data produced by the CDC and available on their Web-based Injury Statistics and Reporting System (WISQARS) site. In this analysis, explore the potential correlation between different kind of per capita cheese consumption and annual deaths by poisoning in the United States. After selecting “muenster” cheese type, final data set includes information on muenster consumption and injury-related mortality. After identifying a significant correlation between muenster and various causes of death, particularly poisoning, focus on this specific association. The subsequent visualization utilizes a scatter plot and a fitted linear regression line to illustrate the relationship between annual deaths by poisoning and muenster consumption.
Methodology
First new_death data frame is created by filtering the original deaths dataset based on specific criteria, renaming columns, and then reshaping the data using pivot_wider to arrange death counts by different injury mechanisms. Subsequently, the death_by_cheese data frame is created by selecting the relevant columns from the cheese dataset and performing an inner join with the previously created new_death data frame based on the “year” column. Finally, the code calculates the correlation matrix using the cor() function on the death counts for various injury mechanisms and the muenster cheese consumption.
## # A tibble: 6 × 12
## year `Cut/pierce` Drowning Fall `Fire/hot object or substance` Firearm
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2016 2823 4628 35862 3284 38658
## 2 2015 2531 4425 34488 3146 36252
## 3 2014 2609 3995 33018 3196 33594
## 4 2013 2576 4056 31240 3220 33636
## 5 2012 2648 4308 29776 2911 33563
## 6 2011 2587 4245 28360 3172 32351
## # ℹ 6 more variables: `Motor vehicle traffic` <dbl>,
## # `All Other Transport` <dbl>, Poisoning <dbl>, Suffocation <dbl>,
## # `All Other Specified` <dbl>, Unspecified <dbl>## muenster Cut/pierce Drowning Fall
## muenster 1.00000000 -0.04990773 0.51393834 0.9356425
## Cut/pierce -0.04990773 1.00000000 0.17063452 0.2206411
## Drowning 0.51393834 0.17063452 1.00000000 0.5567061
## Fall 0.93564250 0.22064112 0.55670608 1.0000000
## Fire/hot object or substance -0.83707186 -0.09090047 -0.53402721 -0.8667681
## Firearm 0.90453644 0.22761514 0.64881514 0.9257407
## Motor vehicle traffic -0.78593273 0.22880203 -0.36438044 -0.7506909
## All Other Transport -0.50103136 -0.31775277 -0.44540194 -0.6067667
## Poisoning 0.90497837 0.30911141 0.63334924 0.9791790
## Suffocation 0.93041389 0.17177657 0.54091944 0.9938990
## All Other Specified -0.37671471 -0.16056723 -0.01576699 -0.4647084
## Unspecified -0.30377829 -0.14550347 0.05042755 -0.3734979
## Fire/hot object or substance Firearm
## muenster -0.83707186 0.9045364
## Cut/pierce -0.09090047 0.2276151
## Drowning -0.53402721 0.6488151
## Fall -0.86676812 0.9257407
## Fire/hot object or substance 1.00000000 -0.7172177
## Firearm -0.71721766 1.0000000
## Motor vehicle traffic 0.88872560 -0.5418749
## All Other Transport 0.57250666 -0.4904084
## Poisoning -0.78997216 0.9589757
## Suffocation -0.86387501 0.9113808
## All Other Specified 0.52456683 -0.3167888
## Unspecified 0.52310835 -0.1104524
## Motor vehicle traffic All Other Transport
## muenster -0.7859327 -0.5010314
## Cut/pierce 0.2288020 -0.3177528
## Drowning -0.3643804 -0.4454019
## Fall -0.7506909 -0.6067667
## Fire/hot object or substance 0.8887256 0.5725067
## Firearm -0.5418749 -0.4904084
## Motor vehicle traffic 1.0000000 0.4476599
## All Other Transport 0.4476599 1.0000000
## Poisoning -0.6450346 -0.5999390
## Suffocation -0.7582158 -0.5886872
## All Other Specified 0.4785087 0.3578379
## Unspecified 0.5946724 0.5042059
## Poisoning Suffocation All Other Specified
## muenster 0.9049784 0.9304139 -0.37671471
## Cut/pierce 0.3091114 0.1717766 -0.16056723
## Drowning 0.6333492 0.5409194 -0.01576699
## Fall 0.9791790 0.9938990 -0.46470837
## Fire/hot object or substance -0.7899722 -0.8638750 0.52456683
## Firearm 0.9589757 0.9113808 -0.31678878
## Motor vehicle traffic -0.6450346 -0.7582158 0.47850867
## All Other Transport -0.5999390 -0.5886872 0.35783788
## Poisoning 1.0000000 0.9654581 -0.40415779
## Suffocation 0.9654581 1.0000000 -0.47571858
## All Other Specified -0.4041578 -0.4757186 1.00000000
## Unspecified -0.2982013 -0.3468012 0.52552447
## Unspecified
## muenster -0.30377829
## Cut/pierce -0.14550347
## Drowning 0.05042755
## Fall -0.37349794
## Fire/hot object or substance 0.52310835
## Firearm -0.11045238
## Motor vehicle traffic 0.59467240
## All Other Transport 0.50420589
## Poisoning -0.29820134
## Suffocation -0.34680116
## All Other Specified 0.52552447
## Unspecified 1.00000000- According to the correlation results , there are a few death incidents that correlate highly with muenster (fall, firearm, poisoning, suffocation).Since aim is to express analysis about a food product, choose poisoning.
Conclusion
The scatterplot and positively inclined regression line reveal a notable correlation coefficient of 0.90497 between per capita muenster cheese consumption and annual deaths by poisoning in the United States. This high positive correlation suggests a potential association between these two variables. From a practical standpoint, this finding raises intriguing questions about the intersection of dietary habits and public health outcomes. While correlation does not imply causation, the observed relationship prompts further investigation into the potential influence of muenster cheese consumption on poisoning-related fatalities.