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10. Descriptive statistics.R
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10. Descriptive statistics.R
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setwd("E:\\Mousumi Ma'am (Project-1)\\8) Follow Up (Profile Plot) - Review")
library(Rmisc)
library(dplyr)
library(tidyverse)
#================ Data import ======================================================
pieD_IgG <- read.csv("Diabetes_IgG Cluster_Details_SC.csv")
pieD_tot <- read.csv("Diabetes_totalAb Cluster_Details_SC.csv")
pieK_IgG <- read.csv("Kidney_IgG Cluster_Details_SC.csv")
pieK_tot <- read.csv("Kidney_totalAb Cluster_Details_SC.csv")
#================ Diabetes cohort ==================================================
# Diabetes (Age)
summary(pieD_IgG$Age)
CI(pieD_IgG$Age, ci=0.95)
# Diabetes (IgG Z)
C1_D_IgG <- pieD_IgG %>%
filter(Clusters == "Cluster-1")
summary(C1_D_IgG$IgG_z1)
CI(C1_D_IgG$IgG_z1, ci=0.95)
summary(C1_D_IgG$IgG_z2)
CI(C1_D_IgG$IgG_z2, ci=0.95)
summary(C1_D_IgG$IgG_z3)
CI(C1_D_IgG$IgG_z3, ci=0.95)
C2_D_IgG <- pieD_IgG %>%
filter(Clusters == "Cluster-2")
summary(C2_D_IgG$IgG_z1)
CI(C2_D_IgG$IgG_z1, ci=0.95)
summary(C2_D_IgG$IgG_z2)
CI(C2_D_IgG$IgG_z2, ci=0.95)
summary(C2_D_IgG$IgG_z3)
CI(C2_D_IgG$IgG_z3, ci=0.95)
C3_D_IgG <- pieD_IgG %>%
filter(Clusters == "Cluster-3")
summary(C3_D_IgG$IgG_z1)
CI(C3_D_IgG$IgG_z1, ci=0.95)
summary(C3_D_IgG$IgG_z2)
CI(C3_D_IgG$IgG_z2, ci=0.95)
summary(C3_D_IgG$IgG_z3)
CI(C3_D_IgG$IgG_z3, ci=0.95)
# Diabetes (Total Ab Z)
C1_D_tot <- pieD_tot %>%
filter(Clusters == "Cluster-1")
summary(C1_D_tot$TotalAb_z1)
CI(C1_D_tot$TotalAb_z1, ci=0.95)
summary(C1_D_tot$TotalAb_z2)
CI(C1_D_tot$TotalAb_z2, ci=0.95)
summary(C1_D_tot$TotalAb_z3)
CI(C1_D_tot$TotalAb_z3, ci=0.95)
C2_D_tot <- pieD_tot %>%
filter(Clusters == "Cluster-2")
summary(C2_D_tot$TotalAb_z1)
CI(C2_D_tot$TotalAb_z1, ci=0.95)
summary(C2_D_tot$TotalAb_z2)
CI(C2_D_tot$TotalAb_z2, ci=0.95)
summary(C2_D_tot$TotalAb_z3)
CI(C2_D_tot$TotalAb_z3, ci=0.95)
C3_D_tot <- pieD_tot %>%
filter(Clusters == "Cluster-3")
summary(C3_D_tot$TotalAb_z1)
CI(C3_D_tot$TotalAb_z1, ci=0.95)
summary(C3_D_tot$TotalAb_z2)
CI(C3_D_tot$TotalAb_z2, ci=0.95)
summary(C3_D_tot$TotalAb_z3)
CI(C3_D_tot$TotalAb_z3, ci=0.95)
#================ Kidney cohort ====================================================
# Kidney (Age)
summary(pieK_IgG$Age)
CI(pieK_IgG$Age, ci=0.95)
# Kidney (IgG Z)
C1_K_IgG <- pieK_IgG %>%
filter(Clusters == "Cluster-1")
summary(C1_K_IgG$IgG_z1)
CI(C1_K_IgG$IgG_z1, ci=0.95)
summary(C1_K_IgG$IgG_z2)
CI(C1_K_IgG$IgG_z2, ci=0.95)
summary(C1_K_IgG$IgG_z3)
CI(C1_K_IgG$IgG_z3, ci=0.95)
C2_K_IgG <- pieK_IgG %>%
filter(Clusters == "Cluster-2")
summary(C2_K_IgG$IgG_z1)
CI(C2_K_IgG$IgG_z1, ci=0.95)
summary(C2_K_IgG$IgG_z2)
CI(C2_K_IgG$IgG_z2, ci=0.95)
summary(C2_K_IgG$IgG_z3)
CI(C2_K_IgG$IgG_z3, ci=0.95)
C3_K_IgG <- pieK_IgG %>%
filter(Clusters == "Cluster-3")
summary(C3_K_IgG$IgG_z1)
CI(C3_K_IgG$IgG_z1, ci=0.95)
summary(C3_K_IgG$IgG_z2)
CI(C3_K_IgG$IgG_z2, ci=0.95)
summary(C3_K_IgG$IgG_z3)
CI(C3_K_IgG$IgG_z3, ci=0.95)
# Kidney (Total Ab Z)
C1_K_tot <- pieK_tot %>%
filter(Clusters == "Cluster-1")
summary(C1_K_tot$TotalAb_z1)
CI(C1_K_tot$TotalAb_z1, ci=0.95)
summary(C1_K_tot$TotalAb_z2)
CI(C1_K_tot$TotalAb_z2, ci=0.95)
summary(C1_K_tot$TotalAb_z3)
CI(C1_K_tot$TotalAb_z3, ci=0.95)
C2_K_tot <- pieK_tot %>%
filter(Clusters == "Cluster-2")
summary(C2_K_tot$TotalAb_z1)
CI(C2_K_tot$TotalAb_z1, ci=0.95)
summary(C2_K_tot$TotalAb_z2)
CI(C2_K_tot$TotalAb_z2, ci=0.95)
summary(C2_K_tot$TotalAb_z3)
CI(C2_K_tot$TotalAb_z3, ci=0.95)
C3_K_tot <- pieK_tot %>%
filter(Clusters == "Cluster-3")
summary(C3_K_tot$TotalAb_z1)
CI(C3_K_tot$TotalAb_z1, ci=0.95)
summary(C3_K_tot$TotalAb_z2)
CI(C3_K_tot$TotalAb_z2, ci=0.95)
summary(C3_K_tot$TotalAb_z3)
CI(C3_K_tot$TotalAb_z3, ci=0.95)
#================ Date Conversion and Calculation ==================================
# Format fixing
pieD_IgG$Date_antibody_cecking1 <- gsub('\\.', '-', pieD_IgG$Date_antibody_cecking1)
pieD_IgG$Date_antibody_cecking2 <- gsub('\\.', '-', pieD_IgG$Date_antibody_cecking2)
pieD_IgG$Date_antibody_cecking3 <- gsub('\\.', '-', pieD_IgG$Date_antibody_cecking3)
pieD_tot$Date_antibody_cecking1 <- gsub('\\.', '-', pieD_tot$Date_antibody_cecking1)
pieD_tot$Date_antibody_cecking2 <- gsub('\\.', '-', pieD_tot$Date_antibody_cecking2)
pieD_tot$Date_antibody_cecking3 <- gsub('\\.', '-', pieD_tot$Date_antibody_cecking3)
pieK_IgG$Date_antibody_cecking1 <- gsub('\\.', '-', pieK_IgG$Date_antibody_cecking1)
pieK_IgG$Date_antibody_cecking2 <- gsub('\\.', '-', pieK_IgG$Date_antibody_cecking2)
pieK_IgG$Date_antibody_cecking3 <- gsub('\\.', '-', pieK_IgG$Date_antibody_cecking3)
pieK_tot$Date_antibody_cecking1 <- gsub('\\.', '-', pieK_tot$Date_antibody_cecking1)
pieK_tot$Date_antibody_cecking2 <- gsub('\\.', '-', pieK_tot$Date_antibody_cecking2)
pieK_tot$Date_antibody_cecking3 <- gsub('\\.', '-', pieK_tot$Date_antibody_cecking3)
class(pieD_IgG$Date_antibody_cecking1)
pieD_IgG$Date_antibody_cecking1 <- as.Date(pieD_IgG$Date_antibody_cecking1, format('%d-%m-%Y'))
pieD_IgG$Date_antibody_cecking2 <- as.Date(pieD_IgG$Date_antibody_cecking2, format('%d-%m-%Y'))
pieD_IgG$Date_antibody_cecking3 <- as.Date(pieD_IgG$Date_antibody_cecking3, format('%d-%m-%Y'))
pieD_tot$Date_antibody_cecking1 <- as.Date(pieD_tot$Date_antibody_cecking1, format('%d-%m-%Y'))
pieD_tot$Date_antibody_cecking2 <- as.Date(pieD_tot$Date_antibody_cecking2, format('%d-%m-%Y'))
pieD_tot$Date_antibody_cecking3 <- as.Date(pieD_tot$Date_antibody_cecking3, format('%d-%m-%Y'))
pieK_IgG$Date_antibody_cecking1 <- as.Date(pieK_IgG$Date_antibody_cecking1, format('%d-%m-%Y'))
pieK_IgG$Date_antibody_cecking2 <- as.Date(pieK_IgG$Date_antibody_cecking2, format('%d-%m-%Y'))
pieK_IgG$Date_antibody_cecking3 <- as.Date(pieK_IgG$Date_antibody_cecking3, format('%d-%m-%Y'))
pieK_tot$Date_antibody_cecking1 <- as.Date(pieK_tot$Date_antibody_cecking1, format('%d-%m-%Y'))
pieK_tot$Date_antibody_cecking2 <- as.Date(pieK_tot$Date_antibody_cecking2, format('%d-%m-%Y'))
pieK_tot$Date_antibody_cecking3 <- as.Date(pieK_tot$Date_antibody_cecking3, format('%d-%m-%Y'))
class(pieD_IgG$Date_antibody_cecking1)
# Calculate Differences & assign the time interval values to a new column
library(Rmisc)
# pieD_IgG (t2-t1)
pieD_IgG$t2_t1 <- pieD_IgG$Date_antibody_cecking2 - pieD_IgG$Date_antibody_cecking1
pieD_IgG$t2_t1 <- round(pieD_IgG$t2_t1/30)
pieD_IgG$t2_t1 <- as.numeric(pieD_IgG$t2_t1)
CL1_1_D_IgG <- pieD_IgG %>%
filter(Clusters == "Cluster-1")
CL2_1_D_IgG <- pieD_IgG %>%
filter(Clusters == "Cluster-2")
CL3_1_D_IgG <- pieD_IgG %>%
filter(Clusters == "Cluster-3")
summary(CL1_1_D_IgG$t2_t1)
CI(CL1_1_D_IgG$t2_t1, ci=0.95)
summary(CL2_1_D_IgG$t2_t1)
CI(CL2_1_D_IgG$t2_t1, ci=0.95)
summary(CL3_1_D_IgG$t2_t1)
CI(CL3_1_D_IgG$t2_t1, ci=0.95)
# pieD_IgG (t3-t2)
pieD_IgG$t3_t2 <- pieD_IgG$Date_antibody_cecking3 - pieD_IgG$Date_antibody_cecking2
pieD_IgG$t3_t2 <- round(pieD_IgG$t3_t2/30)
pieD_IgG$t3_t2 <- as.numeric(pieD_IgG$t3_t2)
CL1_2_D_IgG <- pieD_IgG %>%
filter(Clusters == "Cluster-1")
CL2_2_D_IgG <- pieD_IgG %>%
filter(Clusters == "Cluster-2")
CL3_2_D_IgG <- pieD_IgG %>%
filter(Clusters == "Cluster-3")
summary(CL1_2_D_IgG$t3_t2)
CI(CL1_2_D_IgG$t3_t2, ci=0.95)
summary(CL2_2_D_IgG$t3_t2)
CI(CL2_2_D_IgG$t3_t2, ci=0.95)
summary(CL3_2_D_IgG$t3_t2)
CI(CL3_2_D_IgG$t3_t2, ci=0.95)
# pieD_tot (t2-t1)
pieD_tot$t2_t1 <- pieD_tot$Date_antibody_cecking2 - pieD_tot$Date_antibody_cecking1
pieD_tot$t2_t1 <- round(pieD_tot$t2_t1/30)
pieD_tot$t2_t1 <- as.numeric(pieD_tot$t2_t1)
CL1_1_D_tot <- pieD_tot %>%
filter(Clusters == "Cluster-1")
CL2_1_D_tot <- pieD_tot %>%
filter(Clusters == "Cluster-2")
CL3_1_D_tot <- pieD_tot %>%
filter(Clusters == "Cluster-3")
summary(CL1_1_D_tot$t2_t1)
CI(CL1_1_D_tot$t2_t1, ci=0.95)
summary(CL2_1_D_tot$t2_t1)
CI(CL2_1_D_tot$t2_t1, ci=0.95)
summary(CL3_1_D_tot$t2_t1)
CI(CL3_1_D_tot$t2_t1, ci=0.95)
# pieD_tot (t3-t2)
pieD_tot$t3_t2 <- pieD_tot$Date_antibody_cecking3 - pieD_tot$Date_antibody_cecking2
pieD_tot$t3_t2 <- round(pieD_tot$t3_t2/30)
pieD_tot$t3_t2 <- as.numeric(pieD_tot$t3_t2)
CL1_2_D_tot <- pieD_tot %>%
filter(Clusters == "Cluster-1")
CL2_2_D_tot <- pieD_tot %>%
filter(Clusters == "Cluster-2")
CL3_2_D_tot <- pieD_tot %>%
filter(Clusters == "Cluster-3")
summary(CL1_2_D_tot$t3_t2)
CI(CL1_2_D_tot$t3_t2, ci=0.95)
summary(CL2_2_D_tot$t3_t2)
CI(CL2_2_D_tot$t3_t2, ci=0.95)
summary(CL3_2_D_tot$t3_t2)
CI(CL3_2_D_tot$t3_t2, ci=0.95)
# pieK_IgG (t2-t1)
pieK_IgG$t2_t1 <- pieK_IgG$Date_antibody_cecking2 - pieD_tot$Date_antibody_cecking1
pieK_IgG$t2_t1 <- round(pieK_IgG$t2_t1/30)
pieK_IgG$t2_t1 <- as.numeric(pieK_IgG$t2_t1)
CL1_1_K_IgG <- pieK_IgG %>%
filter(Clusters == "Cluster-1")
CL2_1_K_IgG <- pieK_IgG %>%
filter(Clusters == "Cluster-2")
CL3_1_K_IgG <- pieK_IgG %>%
filter(Clusters == "Cluster-3")
summary(CL1_1_K_IgG$t2_t1)
CI(CL1_1_K_IgG$t2_t1, ci=0.95)
summary(CL2_1_K_IgG$t2_t1)
CI(CL2_1_K_IgG$t2_t1, ci=0.95)
summary(CL3_1_K_IgG$t2_t1)
CI(CL3_1_K_IgG$t2_t1, ci=0.95)
# pieK_IgG (t3-t2)
pieK_IgG$t3_t2 <- pieK_IgG$Date_antibody_cecking3 - pieD_tot$Date_antibody_cecking2
pieK_IgG$t3_t2 <- round(pieK_IgG$t3_t2/30)
pieK_IgG$t3_t2 <- as.numeric(pieK_IgG$t3_t2)
CL1_2_K_IgG <- pieK_IgG %>%
filter(Clusters == "Cluster-1")
CL2_2_K_IgG <- pieK_IgG %>%
filter(Clusters == "Cluster-2")
CL3_2_K_IgG <- pieK_IgG %>%
filter(Clusters == "Cluster-3")
summary(CL1_2_K_IgG$t3_t2)
CI(CL1_2_K_IgG$t3_t2, ci=0.95)
summary(CL2_2_K_IgG$t3_t2)
CI(CL2_2_K_IgG$t3_t2, ci=0.95)
summary(CL3_2_K_IgG$t3_t2)
CI(CL3_2_K_IgG$t3_t2, ci=0.95)
# pieK_tot (t2-t1)
pieK_tot$t2_t1 <- pieK_tot$Date_antibody_cecking2 - pieD_tot$Date_antibody_cecking1
pieK_tot$t2_t1 <- round(pieK_tot$t2_t1/30)
pieK_tot$t2_t1 <- as.numeric(pieK_tot$t2_t1)
CL1_1_K_tot <- pieK_tot %>%
filter(Clusters == "Cluster-1")
CL2_1_K_tot <- pieK_tot %>%
filter(Clusters == "Cluster-2")
CL3_1_K_tot <- pieK_tot %>%
filter(Clusters == "Cluster-3")
summary(CL1_1_K_tot$t2_t1)
CI(CL1_1_K_tot$t2_t1, ci=0.95)
summary(CL2_1_K_tot$t2_t1)
CI(CL2_1_K_tot$t2_t1, ci=0.95)
summary(CL3_1_K_tot$t2_t1)
CI(CL3_1_K_tot$t2_t1, ci=0.95)
# pieK_tot (t3-t2)
pieK_tot$t3_t2 <- pieK_tot$Date_antibody_cecking3 - pieD_tot$Date_antibody_cecking2
pieK_tot$t3_t2 <- round(pieK_tot$t3_t2/30)
pieK_tot$t3_t2 <- as.numeric(pieK_tot$t3_t2)
CL1_2_K_tot <- pieK_tot %>%
filter(Clusters == "Cluster-1")
CL2_2_K_tot <- pieK_tot %>%
filter(Clusters == "Cluster-2")
CL3_2_K_tot <- pieK_tot %>%
filter(Clusters == "Cluster-3")
summary(CL1_2_K_tot$t3_t2)
CI(CL1_2_K_tot$t2_t1, ci=0.95)
summary(CL2_2_K_tot$t3_t2)
CI(CL2_2_K_tot$t2_t1, ci=0.95)
summary(CL3_2_K_tot$t3_t2)
CI(CL3_2_K_tot$t2_t1, ci=0.95)