Question 1.
NUMBER1<- DATA%>%
filter(AveFK != "NA")%>%
mutate(AveFKFrequency=ifelse(AveFK<=1.49, "VERY POOR",
ifelse(AveFK<=2.49, "BELOW AVERAGE",
ifelse(AveFK<=3.49, "AVERAGE",
ifelse(AveFK<=4.49, "ABOVE AVERAGE", "EXCELLENT")))))%>%
group_by(School, AveFKFrequency)%>%
summarise(Frequency=n())%>%
mutate(Percentage=round(Frequency/sum(Frequency)*100,2))
## `summarise()` has grouped output by 'School'. You can override using the
## `.groups` argument.
NUMBER1
## # A tibble: 7 × 4
## # Groups: School [2]
## School AveFKFrequency Frequency Percentage
## <chr> <chr> <int> <dbl>
## 1 VCCS ABOVE AVERAGE 41 66.1
## 2 VCCS AVERAGE 7 11.3
## 3 VCCS EXCELLENT 14 22.6
## 4 VNHS ABOVE AVERAGE 68 70.8
## 5 VNHS AVERAGE 7 7.29
## 6 VNHS BELOW AVERAGE 2 2.08
## 7 VNHS EXCELLENT 19 19.8
Question 2.
NUMBER2<- DATA%>%
filter(AveSC != "NA")%>%
mutate(AveSCFrequency=ifelse(AveFK<=1.49, "VERY POOR",
ifelse(AveSC<=2.49, "BELOW AVERAGE",
ifelse(AveSC<=3.49, "AVERAGE",
ifelse(AveSC<=4.49, "ABOVE AVERAGE", "EXCELLENT")))))%>%
group_by(School, AveSCFrequency)%>%
summarise(Frequency=n())%>%
mutate(Percentage=round(Frequency/sum(Frequency)*100,2))
## `summarise()` has grouped output by 'School'. You can override using the
## `.groups` argument.
NUMBER2
## # A tibble: 8 × 4
## # Groups: School [2]
## School AveSCFrequency Frequency Percentage
## <chr> <chr> <int> <dbl>
## 1 VCCS ABOVE AVERAGE 38 61.3
## 2 VCCS AVERAGE 14 22.6
## 3 VCCS BELOW AVERAGE 1 1.61
## 4 VCCS EXCELLENT 9 14.5
## 5 VNHS ABOVE AVERAGE 58 60.4
## 6 VNHS AVERAGE 24 25
## 7 VNHS BELOW AVERAGE 1 1.04
## 8 VNHS EXCELLENT 13 13.5
Question 3.
NUMBER3<- DATA%>%
filter(AvePI != "NA")%>%
mutate(AvePIFrequency=ifelse(AvePI<=1.49, "VERY POOR",
ifelse(AvePI<=2.49, "BELOW AVERAGE",
ifelse(AvePI<=3.49, "AVERAGE",
ifelse(AvePI<=4.49, "ABOVE AVERAGE", "EXCELLENT")))))%>%
group_by(School, AvePIFrequency)%>%
summarise(Frequency=n())%>%
mutate(Percentage=round(Frequency/sum(Frequency)*100,2))
## `summarise()` has grouped output by 'School'. You can override using the
## `.groups` argument.
NUMBER3
## # A tibble: 9 × 4
## # Groups: School [2]
## School AvePIFrequency Frequency Percentage
## <chr> <chr> <int> <dbl>
## 1 VCCS ABOVE AVERAGE 39 62.9
## 2 VCCS AVERAGE 15 24.2
## 3 VCCS BELOW AVERAGE 1 1.61
## 4 VCCS EXCELLENT 7 11.3
## 5 VNHS ABOVE AVERAGE 49 51.0
## 6 VNHS AVERAGE 34 35.4
## 7 VNHS BELOW AVERAGE 7 7.29
## 8 VNHS EXCELLENT 5 5.21
## 9 VNHS VERY POOR 1 1.04
Question 4.
NUMBER4<- DATA%>%
filter(AveRR != "NA")%>%
mutate(AveRRFrequency=ifelse(AveRR<=1.49, "VERY POOR",
ifelse(AveRR<=2.49, "BELOW AVERAGE",
ifelse(AveRR<=3.49, "AVERAGE",
ifelse(AveRR<=4.49, "ABOVE AVERAGE", "EXCELLENT")))))%>%
group_by(School, AveRRFrequency)%>%
summarise(Frequency=n())%>%
mutate(Percentage=round(Frequency/sum(Frequency)*100,2))
## `summarise()` has grouped output by 'School'. You can override using the
## `.groups` argument.
NUMBER4
## # A tibble: 7 × 4
## # Groups: School [2]
## School AveRRFrequency Frequency Percentage
## <chr> <chr> <int> <dbl>
## 1 VCCS ABOVE AVERAGE 43 69.4
## 2 VCCS AVERAGE 11 17.7
## 3 VCCS EXCELLENT 8 12.9
## 4 VNHS ABOVE AVERAGE 67 69.8
## 5 VNHS AVERAGE 21 21.9
## 6 VNHS BELOW AVERAGE 3 3.12
## 7 VNHS EXCELLENT 5 5.21
library(rstatix)
Phase<- DATA1%>%
gather(key="Phase", value="CFUCount", "Adlay with wash","Adlay with milk","Adlay with milk and molasses")%>%
convert_as_factor(Phase)
Phase
## # A tibble: 192 × 11
## PhaseInterval Tempe…¹ ...6 ...7 ...8 ...9 ...10 ...11 ...12 Phase CFUCo…²
## <chr> <chr> <lgl> <lgl> <chr> <chr> <chr> <chr> <chr> <fct> <dbl>
## 1 3- day 25°C NA NA <NA> <NA> <NA> <NA> <NA> Adla… 759
## 2 3- day 25°C NA NA Conc… dip Day Weig… Color Adla… 736
## 3 3- day 25°C NA NA 1 15 4 <NA> <NA> Adla… 891
## 4 3- day 30°C NA NA 1 15 4 <NA> <NA> Adla… 573
## 5 3- day 30°C NA NA 1 15 4 <NA> <NA> Adla… 584
## 6 3- day 30°C NA NA 1 15 4 <NA> <NA> Adla… 482
## 7 3- day 35°C NA NA 1 30 4 <NA> <NA> Adla… 384
## 8 3- day 35°C NA NA 1 30 4 <NA> <NA> Adla… 264
## 9 3- day 35°C NA NA 1 30 4 <NA> <NA> Adla… 208
## 10 5-day 25°C NA NA 1 30 4 <NA> <NA> Adla… 888
## # … with 182 more rows, and abbreviated variable names ¹Temperature, ²CFUCount
Question 5.
NUMBER5<- Phase %>%
group_by(PhaseInterval)%>%
get_summary_stats(CFUCount, type="mean_sd")
NUMBER5
## # A tibble: 3 × 5
## PhaseInterval variable n mean sd
## <chr> <fct> <dbl> <dbl> <dbl>
## 1 3- day CFUCount 26 488. 264.
## 2 5-day CFUCount 27 284. 269.
## 3 7-day CFUCount 27 182. 271.
Question 6.
NUMBER6<- Phase %>%
group_by(Temperature)%>%
get_summary_stats(CFUCount, type="mean_sd")
NUMBER6
## # A tibble: 3 × 5
## Temperature variable n mean sd
## <chr> <fct> <dbl> <dbl> <dbl>
## 1 25°C CFUCount 27 480. 353.
## 2 30°C CFUCount 26 235. 194.
## 3 35°C CFUCount 27 229. 243.
Question 7.
NUMBER7<- Phase %>%
group_by(Phase)%>%
get_summary_stats(CFUCount, type="mean_sd")
NUMBER7
## # A tibble: 3 × 5
## Phase variable n mean sd
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 Adlay with milk CFUCount 26 190 232.
## 2 Adlay with milk and molasses CFUCount 27 293. 261.
## 3 Adlay with wash CFUCount 27 459. 323.
Question 8.
NUMBER8<- Phase %>%
group_by(PhaseInterval, Temperature)%>%
get_summary_stats(CFUCount, type="mean_sd")
NUMBER8
## # A tibble: 9 × 6
## PhaseInterval Temperature variable n mean sd
## <chr> <chr> <fct> <dbl> <dbl> <dbl>
## 1 3- day 25°C CFUCount 9 641. 244.
## 2 3- day 30°C CFUCount 8 347. 187.
## 3 3- day 35°C CFUCount 9 459. 282.
## 4 5-day 25°C CFUCount 9 460. 346.
## 5 5-day 30°C CFUCount 9 250. 218.
## 6 5-day 35°C CFUCount 9 142. 98.7
## 7 7-day 25°C CFUCount 9 340. 416.
## 8 7-day 30°C CFUCount 9 120. 108.
## 9 7-day 35°C CFUCount 9 85.4 104.
Question 9.
NUMBER9<- Phase %>%
group_by(Temperature,Phase)%>%
get_summary_stats(CFUCount, type="mean_sd")
NUMBER9
## # A tibble: 9 × 6
## Temperature Phase variable n mean sd
## <chr> <fct> <fct> <dbl> <dbl> <dbl>
## 1 25°C Adlay with milk CFUCount 9 243. 230.
## 2 25°C Adlay with milk and molasses CFUCount 9 416. 330.
## 3 25°C Adlay with wash CFUCount 9 783. 267.
## 4 30°C Adlay with milk CFUCount 8 84 57.6
## 5 30°C Adlay with milk and molasses CFUCount 9 193. 98.9
## 6 30°C Adlay with wash CFUCount 9 410. 213.
## 7 35°C Adlay with milk CFUCount 9 232. 309.
## 8 35°C Adlay with milk and molasses CFUCount 9 269. 271.
## 9 35°C Adlay with wash CFUCount 9 186. 135.
Question 10.
Refer to DATA Number 2
A <- c(34,58,14,24,1,1,9,13)
B <- c("ABOVE AVERAGE", "ABOVE AVERAGE", "AVERAGE", "AVERAGE", "BELOW AVERAGE","BELOW AVERAGE", "EXCELLENT", "EXCELLENT")
C = c("black", "yellow", "black", "yellow", "black", "yellow", "black", "yellow")
barplot(A, main = "Comparison of AveSCFrequency of VCCS & VNHS", names.arg = B,
xlab = "AveSCFrequency", ylab = "Frequency",
col = C)
legend("topright", c("VCCS", "VNHS"), cex = 0.8,
fill = C )
Refer to Data Number 5
X<- c(26,27,27)
Y<- c("3-day", "5-day", "7-day")
Z = c("khaki", "orange", "tan")
barplot(X, main = "Phase Interval of CFUcount", names.arg = Y,
xlab = "PhaseInterval", ylab = "CFUcount",
col = Z)
