1 Executive Summary

The aim of this report is to investigate the sales of two vaccines for the cattle disease ‘Tick fever’, commonly known as ‘Red Water’. This disease is caused by the transmission of the parasites Babesia bovis, Babesia bigemina, and Anaplasma marginale to the blood by the cattle tick, and can be potentially fatal to cattle, causing weakness, depression, and a lack of appetite in affected cattle. The vaccines being investigated come in two forms: chilled trivalent vaccine and frozen trivalent vaccine, have been sold across Australia in varying quantities. Our data gives clear insights into the amount of vaccines exported across Australia, with Queensland having the highest amount of vaccines imported, likely to be as a result of the large meet industries seen within Queensland along with the tropical climate increasing tick populations. However, all states producing meet within Australia require the vaccine meaning it is a necessity for any meet farmers throughout the country. As shown in the data set we can see the chilled vaccines are much more common, likely due to the fact that the vaccines are used straight away on the cattle populations to reduce the impact of the sickness, frozen vaccines are bought as a precaution in case an outbreak occurs. The data is clear and concise making it easy to read and depict the trends within the data set, this allows us to investigate and analyse the data in order to formulate our findings accurately.

2 Dataset (IDA)

2.1 Initial Data Analysis

ticks= read.csv("~/Desktop/Tick.csv")

Looking at the top 5 rows of data

head(ticks)
##             Region    Type  Sales
## 1 Brisbane.Moreton Chilled  22880
## 2 Brisbane.Moreton  Frozen    475
## 3  WideBay.Burnett Chilled  46635
## 4  WideBay.Burnett  Frozen    500
## 5       CentralQLD Chilled 169965
## 6       CentralQLD  Frozen   9000

Size of data

dim(ticks)
## [1] 26  3

R’s classification of data

class(ticks)
## [1] "data.frame"

R’s classification of variables

str(ticks)
## 'data.frame':    26 obs. of  3 variables:
##  $ Region: Factor w/ 13 levels "Brisbane.Moreton",..: 1 1 13 13 2 2 5 5 4 4 ...
##  $ Type  : Factor w/ 2 levels "Chilled","Frozen": 1 2 1 2 1 2 1 2 1 2 ...
##  $ Sales : int  22880 475 46635 500 169965 9000 10005 0 3245 0 ...
sapply(ticks, class)
##    Region      Type     Sales 
##  "factor"  "factor" "integer"

2.2 Background to data

The data is sourced from the Agriculture and Fisheries sector of the Queensland Government, and includes the amount of tick vaccine sold for beef cattle in Australia between 1/7/13-30/9/13. The data is valid as it comes from the reliable source of the government, meaning that it can be used in this investigation. The data is limited in that it does not give any insight as to the different prices/effectiveness of the tick vaccine, making it difficult to determine why one vaccine is preferred over the other. Each row of the data represents a different region within Australia, while the columns look at the type of vaccine (chilled or frozen) and the sales.

2.3 Classification of variables

In this dataset, the independent variable is the region of Australia, while the dependent variable is the sales of tick vaccine.

2.4 Stakeholders

This data affects many individuals, greatly affecting those involved in the agricultural business. Cattle farmers who have access to this data are able to see what vaccine other farmers within their region are using, allowing them to determine which type of vaccine may be best for their own cattle, ultimately leading to the increased health of their livestock and therefore increased revenue. The data is also of use to companies producing the vaccine, allowing for the approximation of future sales and allowing for the appropriate amounts of each vaccine to be produced.

3 Research Questions

3.1 Question 1: Were frozen or chilled vaccines more widely sold?

aggregate(ticks$Sales, by = list(Category = ticks$Type), FUN = sum)
##   Category      x
## 1  Chilled 300095
## 2   Frozen  18975
Chilled=300095
Frozen=18975
slices <-c(300095,18975)
lbls <-c("Chilled", "Frozen")
pie(slices, labels= lbls, main= "Pie Chart of Chilled and Frozen Vaccine Sales")

It can be seen from the pie chart that chilled vaccines were much more widely sold than frozen vaccines, with the total chilled vaccines coming in at 300095 while frozen vaccine sales only reached 18975 across Australia during the time period. This demonstrates the popularity of chilled vaccines over frozen vaccines, which could be due to a variety of reasons including effectiveness, accessibility, ease of storage, and price. This information is useful as it demonstrates the demand for the different types of vaccine, allowing for future planning on production to be carried out based on the current sale trends. While chilled vaccines were heavily favoured over frozen vaccines, the amount of frozen vaccines was still significant, demonstrating the lasting value in producing it.

3.2 Question 2: Which region bought the most tick vaccines in total?

table1=aggregate(ticks$Sales, by = list(Category = ticks$Region), FUN = sum)
table1
##                 Category      x
## 1       Brisbane.Moreton  23355
## 2             CentralQLD 178965
## 3  DarlingDowns.West QLD  11785
## 4          Far North QLD   3245
## 5               NorthQLD  10005
## 6           NorthWes QLD  32210
## 7                    NSW    775
## 8                     NT   9245
## 9                     SA     10
## 10                   TAS     10
## 11                   VIC     95
## 12                    WA   2235
## 13       WideBay.Burnett  47135
plot(table1, main = "Tick vaccines sold in different regions across Australia")
mtext(side = 1, text = "Region in Australia", line = 4)
mtext(side = 2, text = "Number of vaccines sold", line = 3)

From this data analysis, it can be seen that Central Queensland had the highest number of sales in buying tick vaccines, either chilled or frozen. The Queensland regions in general were much higher than other states across Australia, which could be due to a number of factors including increased cattle populations in this state and increased tick populations (and therefore disease prevalence) in these states. Queensland provides the largest portion of beef products across Australia, which largely affects these results as the populations that need to be treated are much larger than other regions, and the importance of treating these cattle populations is very high due to Australia’s dependence on Queensland. Tasmania and South Australia had significantly less total vaccine sales, with only 10 each. This is interesting, as South Australia has the largest working cattle station, Anna Creek Station. However, the lack of tick vaccine sales could be due to reduced tick populations in these areas due to their lower climate.

4 Related Research Article

Our related research article comes from the Queensland Government Bureau of Statistics, and it encompasses the issues involved with tick fever in cattle corresponding to the data we have presented above. The article orchestrates how tick fever is detrimental to livestock and has a tendency to kill upwards of 5% of all cattle within an outbreak period. The article also depicts how after an outbreak is recorded, live cattle exports are stopped for 6-12 months depending on the severity of the outbreak. Furthermore, the outbreak can cause bulls to become infertile, pregnant cattle to abort calves and reduce milk production or even dry cattle up for a whole cycle. Statistics have been used to gain this knowledge of the effects tick fever has not only on the cattle but the farmers and local economies as a result. This provides valuable insights into our data eventuating the importance for the use of vaccines within the farming community throughout Australia. Further insights are seen in the risk management section of the article explaining the importance of early intervention with vaccines to eradicate the issue before a spread begins. Overall this article provides a clear correlation with our data and explains the importance of vaccines to reduce the impact of this sickness among cattle as well as reduce the economic burden on local communities.

5 References

Business Queensland (2018). Managing tick fever in cattle. Retrieved 20 October, 2018, from https://www.business.qld.gov.au/industries/farms-fishing-forestry/agriculture/livestock/cattle/managing-tick-fever

6 Personal reflection on group work

480385301: The way I contributed was researching background information on the data initially, before helping in the coding and data analysis. We completed all parts of the assignment together in order to achieve the best result possible, with both of us contributing our thoughts and suggestions for each section so that the report was comprehensive and detailed. What I learnt about group work was that it requires good communication skills between all members of the team, and that it relies on everyone putting in equal amounts of effort in order for a good result to be achieved. 470391626: I contributed to this assignment through picking which data set to use, before as a group we carried out research on the data and area of investigation. As a team we worked well together, offering feedback and support to one another in each part of the task. What I learnt about group work was that being prepared is essential for success, and that communication between us was key in order to create a report we were proud of.