1. Load the necessary packages such as ggplot2, dplyr, tidyr, and ggpmisc.

2. Import the data into R and convert it into a data frame.

3. Use the pivot_longer function from tidyr to reshape the data frame into long format, with separate columns for x and y values and a grouping variable for each data set.

4. Use the ggplot function from ggplot2 to create a basic plot.

5. Add layers to the plot using the aes function to specify the x and y variables and the color or linetype aesthetic to distinguish between data sets.

6. Use the stat_function function from ggplot2 to add curve fitting functions to the plot. This requires specifying the function and its parameters, as well as any relevant options such as color or linetype.

Example code:

library(ggplot2) library(dplyr) library(tidyr) library(ggpmisc)

import data

mydata <- read.csv("mydata.csv")

reshape data

mydatalong <- mydata %>% pivotlonger(cols = c("data1", "data2", "data3"), namesto = "group", valuesto = "y") %>% mutate(x = 1:n()) # create x values

plot data and add curve fitting

ggplot(mydatalong, aes(x = x, y = y, color = group)) + geompoint() + statsmooth(method = lm, formula = y ~ poly(x, 3), se = FALSE) + statpolyeq(formula = y ~ poly(x, 3), aes(label = paste(stat(eq.label), stat(adj.rr.label), sep = "*\", \"*")), parse = TRUE, label.x.npc = "right", label.y.npc = "top") + themebw() + labs(x = "X axis label", y = "Y axis label", color = "Data set")