Code to loop through a series of dataframe applying an identical analysis to each and storing the regression [lm()] model results in a list

replicated_analysis.R

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# A student has five imputed datasets.  She wanted to know the #
# easiest way to repeat the analysis with each dataset and     #
# store the results.  This may not be the "easiest" way, but   #
# it is a way                                                  #
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#
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# Create five toy datasets to demonstrate the approach         #
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#
data("Loblolly")  # This data set is part of the core R distribution

DF1 <- Loblolly[sample(1:84, 84, replace=T), 1:2]  # create a random resample of the Loblolly dataframe
DF2 <- Loblolly[sample(1:84, 84, replace=T), 1:2]  # create a random resample of the Loblolly dataframe
DF3 <- Loblolly[sample(1:84, 84, replace=T), 1:2]  # create a random resample of the Loblolly dataframe
DF4 <- Loblolly[sample(1:84, 84, replace=T), 1:2]  # create a random resample of the Loblolly dataframe
DF5 <- Loblolly[sample(1:84, 84, replace=T), 1:2]  # create a random resample of the Loblolly dataframe
rm(Loblolly)  # rremove the original Loblolly data frame

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# Start the actual code for looping through the datasets       #
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#
# You need a list of the names of the relevant datasets.  You could use the ls()
# command at this point, but it is safer to list them by hand
myDF <- c("DF1", "DF2", "DF3", "DF4", "DF5")  # A vector of the names of all the dataframe

all_results <- list()  # An empty list to store the results of the lm models

for( i in myDF){  # Loop through each of the dataframes 
  print(i)
  model_i <- lm(height ~ age, data = get(i))  # "get()" is the key function ... get the object with the name i
  all_results[i] <- list(model_i)  # store the results
}

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# Use "summary()" to show an example of the data stored in     #
# element of the "all_results" list                            #
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#
summary(all_results$DF1)  # Could be used for DF1..5, all the model information is stored in the list