R Markdown

library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.4     ✓ dplyr   1.0.5
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(dplyr)
library(lubridate)
## 
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union
library(ggplot2)
data1<- read_csv("data1.csv")
## Parsed with column specification:
## cols(
##   Date = col_character(),
##   Factor = col_character(),
##   X1 = col_double()
## )
data2<- read_csv("data2.csv")
## Parsed with column specification:
## cols(
##   Date = col_character(),
##   Factor = col_character(),
##   X2 = col_double()
## )
data3<- read_csv("data3.csv")
## Parsed with column specification:
## cols(
##   Date = col_character(),
##   Factor = col_character(),
##   X3 = col_double()
## )

#converting tables to same date format

data1$Date <-dmy(data1$Date)
data2$Date <-mdy(data2$Date)
data3$Date <-dmy(data3$Date)

#merging tables into one by inner_join

data_use <- data1 %>% inner_join(data2, by=c("Date","Factor")) %>% inner_join(data3, by=c("Date","Factor"))

#Creating a new variable:Y, where Y = X1 +X2^2 + X3^3

data_use<- data_use %>% 
  mutate(Y=X1+X2^2 +X3^3)

#pulling data into the table: data_use

A <- data_use %>% filter(Factor=="A") %>% select(Y) %>% pull()
B <- data_use %>% filter(Factor=="B") %>% select(Y) %>% pull()
C <-data_use %>% filter(Factor=="C") %>% select(Y)%>% pull()
D <-data_use %>% filter(Factor=="D") %>% select(Y)%>%
pull()
cumsum_A <- cumsum(A)
cumsum_B <- cumsum_A[length(cumsum_A)] + cumsum(B)
cumsum_C <- cumsum_B[length(cumsum_B)] + cumsum(C)
cumsum_D <- cumsum_C[length(cumsum_C)] + cumsum(D)

data_use <- data.frame(Date=data_use$Date, A=cumsum_A, B=cumsum_B, C=cumsum_C, D=cumsum_D)

dat_use_l <- pivot_longer(data_use, -Date,names_to = "Factor", values_to = "Y")

#plot

ggplot(dat_use_l,aes(x=Date, y=Y, color=Factor))+
  geom_line()+
  labs(x="Cummulative Y", y="Y")