Counts by Site

#######################
#Scan frequency by site for each month 
ScannerXmonth<- as.data.frame(table(Month=Database$Month_YR, by=Database$Scanner))
names(ScannerXmonth)[names(ScannerXmonth) == "by"] <- "Scanner"

ScannerXmonth$Month<-factor(ScannerXmonth$Month, levels= c("Oct/17", "Nov/17", "Dec/17",
"Jan/18","Feb/18", "Mar/18", "Apr/18","May/18", "Jun/18","Jul/18","Aug/18","Sep/18","Oct/18", "Nov/18",  "Dec/18", "Jan/19","Feb/19", "Mar/19", "Apr/19","May/19", "Jun/19","Jul/19","Aug/19","Sep/19","Oct/19", "Nov/19",  "Dec/19", "Jan/20"))


Counts_by_Month<-ggplot(data=ScannerXmonth,
       aes(x =Month, y=Freq,  fill=Scanner))+
  geom_bar(stat = "identity")+
  ggtitle(label = "Scan Frequency by Month")+
  theme_minimal()+
  theme(plot.title = element_text(hjust = 0.5, 
        lineheight = 0.8, face = "bold"))+
        xlab("Month")+
        ylab("ScanFreq")+
   theme(axis.text.x = element_text(angle = 90))

ggplotly(Counts_by_Month)

*Hover over bars for counts

##          .y.        group1        group2            p   p.adj p.format p.signif
## 1 MPRAGE_cnr    NEU Prisma      KU Skyra 1.190553e-10 7.1e-10  1.2e-10     ****
## 2 MPRAGE_cnr    NEU Prisma PITT Prisma 1 1.060058e-04 5.3e-04  0.00011      ***
## 3 MPRAGE_cnr    NEU Prisma PITT Prisma 2 3.290925e-02 9.9e-02  0.03291        *
## 4 MPRAGE_cnr      KU Skyra PITT Prisma 1 4.913435e-04 2.0e-03  0.00049      ***
## 5 MPRAGE_cnr      KU Skyra PITT Prisma 2 5.530364e-01 1.0e+00  0.55304       ns
## 6 MPRAGE_cnr PITT Prisma 1 PITT Prisma 2 5.492695e-01 1.0e+00  0.54927       ns
##     method
## 1 Wilcoxon
## 2 Wilcoxon
## 3 Wilcoxon
## 4 Wilcoxon
## 5 Wilcoxon
## 6 Wilcoxon

IGNITE MRIQC Reports: T1 MPRAGE

Counts by Site