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Below is a result from a factorial experiment on the effect of fertilizer types  A and B, watering amount  low, medium, and high, and watering frequency  low and high  on growth of a particular plant. The outcome is measured in the total growth in heights after 4 weeks.
a) (5 points) Using the deviation coding, create a model matrix for the first 12 rows of the data. Include the intercept column and all interaction terms.

I need Statistics homework help in a R programming homework solver to help me understand what is in the code. I found it on a website explaining how to plot the frequency of the missing values. On my side, it's hard to understand what the code does. Can you explain to me step by step what is happening in the code below?
getting table with missing values
df < read.table("https://archive.ics.uci.edu/ml/machinelearningdatabases/horsecolic/horsecolic.data")
df < data.frame(apply(df, 2, function(x) { ifelse(x=='?', NA, x)} ))add thresholding to function
missing_percentages < function(frame, color, cutoff_condition) {
res < apply(frame,2,pMiss)
par(mar=c(2,3,5,2))
mp < barplot(res, main='missing data by percentage\n', ylab='percentage missing (%)', col=ifelse(res >= cutoff_condition, color,'steelblue'), xaxt='n')
text(mp, par("usr")[3], labels = names(res), srt = 45, adj = c(1.1,1.1), xpd = TRUE, cex=.7)
} 
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