The Data consists of the amount of tick vaccine sold in Australia for beef cattle between 1/7/13 and 30/9/13 in two forms: chilled trivalent and frozen trivalent.this vaccine protects cattle against the tick fever that is caused by Babesia bovis, Babesia bigemina and Anaplasma marginale from ticks.The trivalent vaccine contains live doses of tick fever organisms, which helps provide immunity to the tick fever infection usually for life. Source: Agriculture and Fisheries, Queensland Government

The raw Data

library(readxl)
Tick_data <- read_excel("~/Uni/Stats/Assignments/Tick data.xlsx")
## New names:
## * `` -> ...4
## * `` -> ...5
Tick_data
## # A tibble: 26 x 5
##    Region           Type     Sales ...4    ...5
##    <chr>            <chr>    <dbl> <lgl>  <dbl>
##  1 Brisbane.Moreton Chilled  22880 NA        NA
##  2 Brisbane.Moreton Frozen     475 NA    300095
##  3 WideBay.Burnett  Chilled  46635 NA     18975
##  4 WideBay.Burnett  Frozen     500 NA        NA
##  5 CentralQLD       Chilled 169965 NA    306700
##  6 CentralQLD       Frozen    9000 NA     12370
##  7 NorthQLD         Chilled  10005 NA        NA
##  8 NorthQLD         Frozen       0 NA    319070
##  9 Far North QLD    Chilled   3245 NA        NA
## 10 Far North QLD    Frozen       0 NA        NA
## # ... with 16 more rows
library(readxl)
Chilled_vs_Frozen <- read_excel("~/Uni/Stats/Assignments/Chilled vs Frozen.xlsx")
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
totnum= Tick_data %>%
  group_by(Type) %>%
summarise(Sum=sum(Sales), Mean=mean(Sales), Min=min(Sales), Median=median(Sales), Sd=sd(Sales), IQR=IQR(Sales))
totnum 
## # A tibble: 2 x 7
##   Type       Sum   Mean   Min Median     Sd   IQR
##   <chr>    <dbl>  <dbl> <dbl>  <dbl>  <dbl> <dbl>
## 1 Chilled 300095 23084.    10   9245 46132. 22105
## 2 Frozen   18975  1460.     0      0  3351.   475
totnum= Tick_data %>%
  group_by(Region) %>%
summarise(Sum=sum(Sales), Mean=mean(Sales), Min=min(Sales), Median=median(Sales), Sd=sd(Sales), IQR=IQR(Sales))
totnum
## # A tibble: 13 x 7
##    Region                   Sum    Mean   Min  Median        Sd     IQR
##    <chr>                  <dbl>   <dbl> <dbl>   <dbl>     <dbl>   <dbl>
##  1 Brisbane.Moreton       23355 11678.    475 11678.   15843.   11202. 
##  2 CentralQLD            178965 89482.   9000 89482.  113819.   80482. 
##  3 DarlingDowns.West QLD  11785  5892.      0  5892.    8333.    5892. 
##  4 Far North QLD           3245  1622.      0  1622.    2295.    1622. 
##  5 NorthQLD               10005  5002.      0  5002.    7075.    5002. 
##  6 NorthWes QLD           32210 16105    9000 16105    10048.    7105  
##  7 NSW                      775   388.      0   388.     548.     388. 
##  8 NT                      9245  4622.      0  4622.    6537.    4622. 
##  9 SA                        10     5       0     5        7.07     5  
## 10 TAS                       10     5       0     5        7.07     5  
## 11 VIC                       95    47.5     0    47.5     67.2     47.5
## 12 WA                      2235  1118.      0  1118.    1580.    1118. 
## 13 WideBay.Burnett        47135 23568.    500 23568.   32622.   23068.
summary(Tick_data$Sales)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##      0.0      0.0    487.5  12271.9   9183.8 169965.0
library(ggplot2)
ggplot(Tick_data, aes(x= Region, y= Sales, fill= Type))+ geom_bar(position="dodge", stat= "identity")+ labs(y="Number of Vaccines Sold")+ scale_y_continuous(limits= c(0,170000), breaks= seq(0,170000,10000), expand= c(0,0)) + theme(axis.text.x = element_text(face="bold", color="#333333", size=8, angle=90), axis.text.y = element_text(face="bold", color="#333333", size=8, angle=0))

Figure 1: This graph shows the amount of Chilled and Frozen tick vaccine sold in Australia over the year 2013. It also shows that QLD has the biggest intake of vaccines meaning they must have the most cases of tick fever, also the frozen vaccines are much less popular than the chilled vaccines.

Chilledsales <- sum(22880,46635,169965,10005,3245,23210,11785,775,9245,10,10,95,2235)
Frozensales <- sum(Tick_data$Sales)-Chilledsales

histdata <- c(Chilledsales, Frozensales)

barplot(histdata, xlab= "Type of Vaccine", ylab="Sales", space = 0, names.arg = c("Chilled", "Frozen"), col=rainbow(2))

Figure 2: This figure shows the difference between the Chilled and Frozen vaccines sold in Australia. It is evident that the Chilled vaccine is much more popular than the Frozen vaccine.

boxplot(Tick_data$Sales, ylab="Sales")

Figure 3: The above box plot is a representation of the sales and the outliers of the Chilled vaccine in Central QLD and Widebay.

Conclusion

In this data set the mean (12271.9) is larger than the median (487.5) meaning that it is positively skewed. The interquartile range is 9183.8.

Plotting this data set of different tick vaccines sold in Australia it has shown that the vaccines are predominantly sold into QLD with 96% of the vaccine sales and only 4% to every other state. the data also shows that 94% of the vaccines sold are chilled whereas only 6% of the vaccines sold are frozen, this is also evident as the frozen vaccines were only sold in QLD.