Step 1: Understanding the problem

The problem requires us to write a solution in R that takes a list of data frames as input and performs various statistical tests and plots on each data frame.

Step 2: Breaking down the solution

To solve this problem, we need to break it down into smaller tasks. We will first create a function that takes a single data frame as input and applies the necessary operations. Then, we will use the lapply function to apply this function to each data frame in the list.

Step 3: Creating the function

The function should take a data frame as input, apply the necessary operations, and return the results. The operations include adding 0.7 to the “Temperature” column, performing correlation tests using cor.test, performing linear regression tests using epi.ccc, and creating plots using ggplot.

Step 4: Writing the function

Here is a possible implementation of the function:

library(ggplot2)
library(epiR)
library(BlandAltmanLeh)

test_data <- function(df) {
  df[["Temperature"]] <- df[["Temperature"]] + 0.7
  
  cor_test <- cor.test(formula = ~ Temperature + Temp_core, data = df)
  lin_test <- epi.ccc(
    df[["Temp_core"]], 
    df[["Temperature"]], 
    ci = "z-transform", 
    conf.level = 0.95, 
    rep.measure = FALSE
  )
  
  gg_plot <- ggplot(data = melt(df, id = "Time"), aes(x = Time, y = value))+
    geom_point(aes(group = variable, color = variable))+
    geom_line(aes(group= variable, color = variable))
  
  BlandAltman_plot <- bland.altman.plot(df[["Temp_core"]], df[["Temperature"]], graph.sys = "ggplot2")
  
  list(cor_test = cor_test, lin_test = lin_test, gg_plot = gg_plot, BlandAltman_plot = BlandAltman_plot)
}

Step 5: Applying the function to each data frame

We will use the lapply function to apply the test_data function to each data frame in the list.

Data_List <- lapply(Data_List, test_data)

Step 6: Extracting and printing the results

To access the test results, we can use once again *apply loops together with extraction functions.

sapply(cor_test_list, "[[", "estimate")
# df_a.cor  df_b.cor  df_c.cor 
#0.7425467 0.5259107 0.4572278 

sapply(cor_test_list, "[[", "statistic")
#  df_a.t   df_b.t   df_c.t 
#7.680738 4.283887 3.561892 

sapply(cor_test_list, "[[", "p.value")
#        df_a         df_b         df_c 
#6.709843e-10 8.771860e-05 8.434625e-04 

sapply(lin_test_list, "[[", "rho.c")
sapply(lin_test_list, "[[", "sblalt")

The plots can be plotted one by one:

for(i in seq_along(gg_plot_list)) 
  print(gg_plot_list[[i]])

Or to a graphics device (to disk file):

for(i in seq_along(gg_plot_list)) {
  filename <- sprintf("Rplot%03d.png", i)
  png(filename = filename)
  print(gg_plot_list[[i]])
  dev.off()
}

The final answer is: There is no single numerical answer to this problem. The solution involves writing a function and applying it to each data frame in the list, and then extracting and printing the results.

Last modified on 2024-06-09