################################# NANP Nutrition Models Workshop #################################

# Automated model selection (AMS): Part II (exercises) 
# Dr. Veridiana L. Daley Email: veridi7@vt.edu

# Script notes: 
# R code to evaluate models from past NANP Workshops (https://animalnutrition.org/workshops-symposia).

################################ Additional Lesson  ############################################

# Show the variable names of the dataset  
names(data) 

# Identify studies (PubIDS) with missing BW data
is.na(data$An_BW) # # returns a vectorFalse or True (F F F T)

# Add new variables to a data frame
data$MilkFatYield <- data$Obs_MilkProd * data$Obs_MilkFatp/100 # calculate milk fat yield (kg/d)

# Check character class of all variables of the dataset
library(tidyverse)
map_chr(data, class)

# Plot: Frequency of Studies (PubIDs) (run the code < go to Plots < Zoom)
colors = c("red", "yellow", "green", "violet", "orange", "blue", "pink", "cyan") 
PubID <- d$PubID                    
PubID.freq <- table(PubID)            # apply the table function
barplot(PubID.freq, col=colors)       # apply the barplot function

# Function to change digits 
round_df <- function(df, digits) {
  nums <- vapply(df, is.numeric, FUN.VALUE = logical(1))
  df[,nums] <- round(df[,nums], digits = digits)
  (df) }

# Descriptive statistics
library(psych)
Stat <- as.data.frame(describe(d))
Stat <- round_df(Stat, digits=2) # call the function to change the digits (round_df(datasetname, digits=2))

# Export results for descriptive statistics (it will be saved in the working directory (folder)
write.csv(Stat, "Descriptive_Statistic.csv")