• Posted on October 26, 2023
• Libin

Assignment Sample on ARES40011 Research Methods and Data Analysis

INTRODUCTION

Animal welfare in simple terms can be explained as the relationships that people have with animals and the duty they have to assure that the animals under their care are treated humanely and responsibly. This Animal rights is essential because several animals suffer around the world as a result of their use for food, medicine and scientific advancement. Every animal deserves a good life in which they can receive the rewards of the three Domains. These three domains of animal welfare are nutrition, environment, health etc. This data analysis will cater the differences in the five domains. This software helps in detecting the current state of the animals and also analysis of the reason behind the death of the animals. Nutrition factors that involve animals to get sufficient balanced food and water. The environment factor enables the comfortness according to the space, temperature etc. The data analysis of this animal welfare overall provides the real picture of the condition of the animals. After analyzing the different data it will give the direction for the improvement in the situation of the animals that are required.

ANALYSIS

In this section the analysis of the project about animal welfare will be explained in detail. The project is based on data analysis on animal welfare. The data about various animals like cats, dogs, etc. are analyzed here. The data like their intake date, identification number, age of the animals, reason of death, type of diseases etc. are considered in this analysis (Brennan et al 2021). The next step is to choose the programming language in which the data analysis to be done. In this the R programming language is considered so that it can be analyzed in a most efficient way. “R programming language” the data in an efficient way in very less time. R programming is a language or software environment that is very useful for “statical analysis , graphics representation and reporting” etc. R is also having very effective storage and data handling service (Crespi et al 2018). It provides various types of tools that are used in data analysis. The graphical facility and display of the R software helps in analyzing the data in a  very efficient manner.

 

Figure 1: Code for importing dataset of different animals

(Source: RStudio)

This snip gives the information about the dataset that is used to analyze the present situation of different animals (Fernández-Mateo et 2020). The getwd function usually returns the file path that is representing the current working directory in the R process.

 

Figure 2: Dataset of different animals

(Source: RStudio)

This is the dataset that is imported in the RStudio for analysis. This dataset is based on the details of different animals (Grotto 2020). The details like their intake date identification chip number, animal name, age of animals, their reason of death, their intake reason etc are listed. A data set is a collection of connected, different elements of key information that may be retrieved separately, in combination, or handled as a super set. A set of data is structured into some kind of data structure (Heinl et al 2020). This above snip provides many internal aspects about the animals that can be analyzed or used in further study.

 

 

Figure 3: More information of dataset of different animals

(Source: RStudio)

This figure is providing the additional details related to animals which are also part of the analysis. In this the age of the animals, and the bread name of the same animal is provided. Which helps in detecting the age and the origin of that animal (Heise 2018). Also gives a fair idea about the average lifespan of the animal and their habitat.

 

Figure 4:True set of animals

(Source: RStudio)

This figure is the code for the “number of rows and the number of columns” that are present in the dataset. This code is the  overall summary of the dataframe.

Figure 5: Number of rows and columns in dataset

(Source: RStudio)

This output window is providing the data frame length as the “number of rows and the number of columns”.

 

 

 

Figure 6: Separation on basis of highest identification chip number

(Source: RStudio)

The above snip given the information about the identity of the animals. All the animals are provided with a unique identification number (Huettner et al 2021). This code is basically the code for implementing or to display the highest identification chip number among all the animal.

 

Figure 7: Output of highest identification chip number

(Source: RStudio)

The output screen provides the highest chip number that is allotted to the animal. This also provides the data of the animals who are having the same. The details of that animal are shown here (Jirkof et al 2019). The details like intakedate identification chip number, base color, specimen name etc.

 

Figure 8: Code for importing animal dataset on basis of movement date

(Source: RStudio)

This snip gives the code for importing the list of the animals with the specific movement date .

 

 

Figure 9: Output of dataset of animals

(Source: RStudio)

The output screen gives the set of animals who are having the movement date below the given date (Lai 2020). This movement date is the date in which the animal is moved from the shelter to another place. The identification number of the animals are given in the figure.

 

                                      

 

Figure 10:Parameter matching of animal on basis of location

(Source: RStudio)

This snip gives the code from the animal dataset, to filter the animals based on the location. After searching the animals on the criteria given the list of all the animals who satisfy the criteria is presented (Mcloughlin et al 2019).

 

 Figure 11: Output of animals on location basis

(Source: RStudio)

This figure tells about the animals that are specifically located in the Lobby. This provides the details about the animals who are from the lobby (Noble et al 2018). The details like their shelter code, identification chip number, animal name, intake date etc. are provided.

 

 

Figure 12: Code for different animal on basis of age

(Source: RStudio)

This figure gives the code for segregating the animals based on the specific criteria (Normando et al 2018). The animals who are having movement type as foster are to be displayed here.

 

 

Figure 13: Output for forster movement of animal with age

(Source: RStudio)

This gives the details of the animals who have the movement type  as foster and the animal age in years greater than 25 years. This data gives a fair idea of the  age of the animals and also to justify werther they are adult or infant depending on their breed (Odermatt et al 2019). The overall  standards can also be set from the type of age groups of the animals. This provides the specific type of movement of the animals so that they can be put as one  group (Peter et al 2021). This group of the same movement type also helps in setting up a standard in the overall basis of the same group.

 

 

Figure 14: Code for plotting animal pie chart

(Source: RStudio)

This above snip is the code to plot the pie chart of the animals. This rainbow function is for the color pallet purpose, although there are many more types of color pallets available but this color pallet is more flexible than any other pallets (Sherwen et al 2018). The pie percent tag is for percentage conversion of the number of animals. This pie percent converts the total number of different animals in the percentage form. Now, the label tag is for the name or the labels for to represent the data in the pie chart.

 

 

Figure 15: Output code for pie chart

(Source: RStudio)

This figure is indicating the pie chart completion as null device 1. The png file name as animal chart is prepared and saved in the file section. Here the different animals list basically includes dog, cat, house rabbit and the rat. So, their numbers are also written and then it is converted into percent.

 

Figure 16: Animal pie chart with percentages

(Source: RStudio)

This is the final pie chart of different animals like dogs, cats, etc. This also gives the strength details of different animals in percentage form. The reading or analyzing part from the pie chart view is simple (Wilkes et al 2020). This pie chart represents  that the total number of dogs is equal to 50 percent. Cat is green in color and it is 39.2 percent and the remaining part is diversified by the house rabbit and the rat. In the top right corner the information about different colors is given. In this pie chart the green part analyzed the cat percentage. The red part indicates the number of the dogs, the blue part represent the house rabbit and the last violet part gives the percentage of the number of  rats that are present in this analysis.

 

 

Figure 17: Code for to find number of  cat died

(Source: RStudio)

In this above snip the code for the number of cats died is presented.

 

Figure 18: Data of cat died

(Source: RStudio)

This above figure represent the cat that are died in the analysis in a given period

 

Figure 19: Data of dog died

(Source: RStudio)

This snip is showing the code to differentiate the dogs who have died due to different reasons. This also helps in analyzing different conditions due to which the dogs are  dying. In this the animal in which the task is performed is dog and with the deceased reason.

 

Figure 20: Output of dog died

(Source: RStudio)

This snip gives information about the output of the dogs who have died. There can be different reasons due to which the dogs are dying. The reason can somewhat help in lifting the condition of the dogs (Wonneberger et al 2021). This also helps in deciding the proper facilities for the dogs so that the death  toll can be controlled or reduced.  The dogs who died are listed here with their unique chip number with all other necessary details related to them.

 

 

 

Figure 21: Code for number of deaths

(Source: RStudio)

This figure gives the code of the number of deaths of different animals. This also provides the details of different reasons for the death of the animals.

 

Figure 22: Output for number of deaths of animals

(Source: RStudio)

 

This above figure tells about the number of deaths of dogs, cats, house rabbits and rats. The bar graph gives the proper indication about the number of deaths.

 

 

Figure 23: Bar graph of animals

(Source: RStudio)

Bar graph is a graphical presentation of data using bars of different heights. In general “bar graphs” are used for financial purposes. The bar graph makes it simple to analyze a particular data and make decisions according to them. Here the bar graphs represent the number of deaths of dog, cat, house rabbit, and rat. On the “x-axis” the number of deaths are plotted and in the “y-axis” the names of the animals are represented. From this it can be easily concluded that the number of deaths of dogs are highest among all the animals and the deaths of rats is the lowest. This also states that the present scenario of the dogs is very delicate. Proper measures have to be taken to minimize the risk to the dogs and reduce the number of deaths. So, there  is a need for special attention to the dogs as their situation is the worst.

 

 

Figure 24: Animal died in foster

(Source: RStudio)

This snip gives the details of the code for which animals that have died due to different causes and having movement type is foster. The code helps to print the list of all the animals who have died due to various reasons. In this section the reason of the death is not specify but the data helps a lot in considering the overall perspective of different animals.

 

Figure 25: Output of  dataset of animals died in foster

(Source: RStudio)

This  figure presents the list of all the animals who died in the case study. The study only focuses on the movement type that is related to fostering. The details of the bread name, and shelter code is also shown with proper description (Yeh et al 2021). In this the additional details related to the animals are also included like intake reason, animal name etc. This helps not only getting the details of the proper reason for the condition but also helps in taking steps regarding the  situation.

 

Figure 26: Code to plot animal deceased reason 

(Source: RStudio)

In the above snip the the code part for to plot the deceased reason of the animals so that the  proper idea behind the death of the animals can be known.

 

Figure 27: Output of animal deceased reason

(Source: RStudio) 

The output of the deceased is shown on the above figure. After getting the proper number of the animals and the deceased reason of animals the pie chart is plotted. The plotted pie chart then saved as the animal deceased reason pie chart. Here also the rainbow function works for the color pallet. As this rainbow color pallet is widely used in this R software. The deceased reasons that are taken in this section are healthy, dead and the untreatable.

Figure 28: Pie chart of animal deceased reasons

(Source: RStudio)

The above figure represents the pie chart of the deceased animals. Here the green color takes the major part of the reason which represents the dead number percentage, the blue color shows the untreatable part and the last red part gives the information about health which are very few in number. The condition of the animal can be noticed  from this above pie chart.

CONCLUSION

In this section the whole overview of the project is concluded in a brief manner. First, the introduction about the project is presented. In the introduction part the overview of animal welfare is written. After the overview of the topic the analysis on animal welfare  is introduced. Here the software that is used in the analysis is R software.  R is chosen for the analysis because it has very effective storage and data handling service. In this R software variou types of tools for the data analysis are provided with the package. The graphical facility and display of the R software helps in analyzing the data in a  very effective  manner. The graphical facility of the R software is far better than any other software. After setting up the required setup for the R software then the analysis of the number of different animals is presented. The number of animals is represented in a pie chart which helps in deciding the number of animals in a pictorial or chart format. After that a brief analysis on the death count of the animals is concluded. This also gives the reason behind the death of the animals. It is analyzed in a bar graph format so that it gives a proper format to  analyze the number of deaths.  On the bar graph the  x-axis provides the  number of deaths and  the y-axis presents the name of the different animals. At last the pie chart of the deceased is formed or plotted. So, from the above analysis the proper situation related to animal welfare is analyzed in a simplifying manner.

 

 

 

 

 

 

 

 

 

 

 

 

 

Reference list

Journals

Brennan, M., Hennessy, T. and Dillon, E., 2021. Embedding animal welfare in sustainability assessment: an indicator approach. Irish Journal of Agricultural and Food Research.

Crespi, F., Formenti, F. and Congestri, F., 2018. Near infrared spectroscopy (NIRS) to attain reduction-refinement–respect, the three Rs towards ANIMAL WELFARE in preclinical research.

Fernández-Mateo, J. and Franco-Barrera, A.J., 2020. Animal Welfare for Corporate Sustainability: The Business Benchmark on Farm Animal Welfare. Journal of Sustainability Research, 2(3).

Grotto, C.E., Wolf, T., Berkeley, E.V., Lee, S. and Ganswindt, A., 2020. Physiological measure of animal welfare in relation to semi-captive African Elephant (Loxodonta africana) interaction programs. African Zoology, 55(3), pp.245-249.

Heinl, C., Chmielewska, J., Olevska, A., Grune, B., Schönfelder, G. and Bert, B., 2020. Rethinking the incentive system in science: animal study registries: Preregistering experiments using animals could greatly improve transparency and reliability of biomedical studies and improve animal welfare. EMBO reports, 21(1), p.e49709.

Heise, H., Schwarze, S. and Theuvsen, L., 2018. II. 9 Economic effects of participation in animal welfare programmes: Does it pay off for farmers?. Tierwohl in der Nutztierhaltung: Eine Stakeholder-Analyse, p.261.

Huettner, T., Dollhaeupl, S., Simon, R., Baumgartner, K. and von Fersen, L., 2021. Activity budget comparisons using long-term observations of a group of bottlenose dolphins (Tursiops truncatus) under human care: Implications for animal welfare. Animals, 11(7), p.2107.

Jirkof, P., Rudeck, J. and Lewejohann, L., 2019. Assessing affective state in laboratory rodents to promote animal welfare—what is the progress in applied refinement research?. Animals, 9(12), p.1026.

Lai, Y. and Yue, C., 2020. Consumer Willingness to Pay for Organic and Animal Welfare Product Attributes: Do Experimental Results Align with Market Data?. Journal of Agricultural and Resource Economics.

Mcloughlin, M.P., Stewart, R. and McElligott, A.G., 2019. Automated bioacoustics: methods in ecology and conservation and their potential for animal welfare monitoring. Journal of the Royal Society Interface, 16(155), p.20190225.

Noble, D.W. and Nakagawa, S., 2018. Planned missing data design: stronger inferences, increased research efficiency and improved animal welfare in ecology and evolution. bioRxiv, p.247064.

Normando, S., Pollastri, I., Florio, D., Ferrante, L., Macchi, E., Isaja, V. and De Mori, B., 2018. Assessing animal welfare in animal-visitor interactions in zoos and other facilities. A pilot study involving giraffes. Animals, 8(9), p.153.

Odermatt, B., Keil, N. and Lips, M., 2019. Animal welfare payments and veterinary and insemination costs for dairy cows. Agriculture, 9(1), p.3.

Peter, K., John, V., George, G., Luke, H., Shawn, M. and Spencer, G., 2021. Impact of calf housing improvement and farmer training on finances, management and animal welfare perceptions of Kenyan smallholder dairy farmers. Journal of Development and Agricultural Economics, 13(2), pp.119-129.

Sherwen, S.L., Hemsworth, L.M., Beausoleil, N.J., Embury, A. and Mellor, D.J., 2018. An animal welfare risk assessment process for zoos. Animals, 8(8), p.130.

Whitehouse-Tedd, K., Wilkes, R., Stannard, C., Wettlaufer, D. and Cilliers, D., 2020. Reported livestock guarding dog-wildlife interactions: implications for conservation and animal welfare. Biological Conservation, 241, p.108249.

Wonneberger, A., Hellsten, I.R. and Jacobs, S.H., 2021. Hashtag activism and the configuration of counterpublics: Dutch animal welfare debates on Twitter. Information, Communication & Society, 24(12), pp.1694-1711.

Yeh, C.H. and Hartmann, M., 2021. To Purchase or Not to Purchase? Drivers of Consumers’ Preferences for Animal Welfare in Their Meat Choice. Sustainability 2021, 13, 9100. Sustainable Consumer Behavior and Food Marketing, p.213.