9  Module 2.1: Loading Breeding Data - ICARDA Barley Example

9.1 Introduction to the Dataset

In this module, we’ll learn how to load typical phenotypic data into R. We’ll use a real-world example: data from a study on 275 barley accessions conducted at ICARDA in 2019. This dataset contains various measurements related to agronomic traits, grain quality, and morphological characteristics.

Why this dataset? * It’s representative of the kind of multi-trait data breeders work with. * It allows us to practice loading, inspecting, and performing basic summaries on realistic data. * This data comes from ICARDA’s valuable work in crop improvement for dry areas.

Column Descriptions (Partial List - full list would be in a data dictionary): * Taxa: The identifier for each barley accession (genotype). * Area: Grain area (e.g., mm²). * B_glucan: Beta-glucan content (%), a quality trait. * DTH: Days to Heading (days), an agronomic trait. * Fe: Iron content in grain (ppm), a nutritional trait. * FLA: Flag Leaf Area (cm²). * GY: Grain Yield (e.g., t/ha or kg/plot - units should always be known!). * PH: Plant Height (cm). * Protein: Grain protein content (%). * TKW: Thousand Kernel Weight (grams). * Zn: Zinc content in grain (ppm). * (And many others related to grain morphology and plant characteristics…)

Our goal is to load this data (which is typically stored in a file like a CSV or Excel sheet) into an R data frame so we can start analyzing it.

9.2 Setting Up: Libraries and File Path

First, we need to load the R packages that help us read data. Even though we have previously installed and loaded all packages we will need, in case you are only focusing on reading data, the readr package (part of tidyverse) is excellent for reading text files like CSVs.

Remember our RStudio Project setup! We will assume the data file is saved in the data/ subfolder of our project.

# Load the necessary libraries
# 'tidyverse' includes 'readr' (for read_csv) and 'dplyr' (for glimpse, etc.)
library(tidyverse) 

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.2     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

9.3 Reading the CSV File

Let’s say our barley data is stored in a CSV file named icarda_barley_2019_pheno.csv.

# Define the path to our data file (relative to the project root)
barley_data_file_path <- "data/icarda_barley_2019_pheno.csv"

# Check if the file exists (good practice!)
if (file.exists(barley_data_file_path)) {
  # Use read_csv() from the readr package to load the data
  barley_pheno_data <- read_csv(barley_data_file_path)
  
  print("ICARDA Barley Phenotype data loaded successfully!")
} else {
  print(paste("ERROR: File not found at:", barley_data_file_path))
  print("Please make sure 'icarda_barley_2019_pheno.csv' is in the 'data/example' folder.")
  # If the file isn't found, we'll create an empty placeholder to avoid later errors in the document
  barley_pheno_data <- tibble() # Creates an empty tibble (tidyverse data frame)
}

9.4 First Look: Inspecting the Loaded Data

It’s CRUCIAL to always inspect your data immediately after loading it to make sure it looks correct.

1. head(): Shows the first few rows (default is 6).

2. dim(): Shows the dimensions (number of rows, number of columns).

3. glimpse() (from dplyr): A great way to see column names, their data types, and the first few values. Better than str() for tibbles.

4. summary(): Provides basic summary statistics for each column (Min, Max, Mean, Median, Quartiles for numeric; counts for character/factor).

9.5 Understanding Data Types in Our Barley Data

When glimpse() runs, you’ll see types like: * Taxa: Should be <chr> (character) as it’s an identifier. * Area, B_glucan, DTH, GY, PH, etc.: Should mostly be <dbl> (double-precision numeric) as they are measurements.

If read_csv misinterprets a numeric column as character (e.g., if there’s a text entry like “missing” in a numeric column), you’ll need to clean that data or specify column types during import using the col_types argument in read_csv(). (We’ll cover data cleaning later).

9.6 Quick Summary of a Specific Trait

Let’s say we are interested in Grain Yield (GY).

# Make sure the data and the 'GY' column exist
if (nrow(barley_pheno_data) > 0 && "GY" %in% names(barley_pheno_data)) {
  # Access the GY column
  yield_values <- barley_pheno_data$GY
  
  # Calculate some basic statistics
  mean_yield <- mean(yield_values, na.rm = TRUE) # na.rm=TRUE ignores missing values in calculation
  min_yield <- min(yield_values, na.rm = TRUE)
  max_yield <- max(yield_values, na.rm = TRUE)
  sd_yield <- sd(yield_values, na.rm = TRUE)

  print(paste("Average Grain Yield (GY):", round(mean_yield, 2)))
  print(paste("Minimum Grain Yield (GY):", round(min_yield, 2)))
  print(paste("Maximum Grain Yield (GY):", round(max_yield, 2)))
  print(paste("Standard Deviation of GY:", round(sd_yield, 2)))

  # How many accessions do we have yield data for (non-missing)?
  num_yield_obs <- sum(!is.na(yield_values))
  print(paste("Number of accessions with GY data:", num_yield_obs))
} else if (nrow(barley_pheno_data) > 0) {
  print("Column 'GY' not found in the loaded data.")
}

[1] "Average Grain Yield (GY): 1.19"
[1] "Minimum Grain Yield (GY): 0.5"
[1] "Maximum Grain Yield (GY): 2.47"
[1] "Standard Deviation of GY: 0.31"
[1] "Number of accessions with GY data: 275"

Exercise: 1. Load the icarda_barley_2019_pheno.csv file into R. 2. Use glimpse() to check the column names and data types. 3. Calculate and print the average Plant Height (PH) from the dataset. Remember to handle potential missing values (na.rm = TRUE).

This module has shown you the first critical step: getting your valuable field data into R. In the next modules, we’ll learn how to clean, manipulate, and visualize this data.

# Exercise
# Inspect data and data types
glimpse(barley_pheno_data)

Rows: 275
Columns: 24
$ Taxa        <chr> "G1", "G2", "G3", "G4", "G5", "G7", "G8", "G9", "G12", "G1…
$ Area        <dbl> 22.72177, 23.07877, 19.93612, 23.32083, 19.54859, 22.96829…
$ B_glucan    <dbl> 6.848584, 7.430943, 4.012621, 6.091926, 7.307811, 7.267738…
$ Circularity <dbl> 1.887915, 1.780070, 1.555492, 2.300471, 1.896487, 1.752493…
$ Diameter    <dbl> 5.366522, 5.403555, 5.034355, 5.420909, 4.981714, 5.422541…
$ DTH         <dbl> 75.89584, 70.17532, 74.19461, 74.39462, 77.66037, 72.65420…
$ Fe          <dbl> 29.67685, 31.59088, 34.15825, 31.44544, 30.31127, 30.61605…
$ FLA         <dbl> 16.586760, 7.966124, 7.592350, 18.753322, 16.532845, 18.40…
$ FLH         <dbl> 82.77116, 62.36145, 61.81653, 69.77661, 64.38496, 78.08801…
$ GpS         <dbl> 44.70886, 25.25222, 25.72167, 66.23116, 54.63221, 26.63211…
$ GWS         <dbl> 1.6156277, 1.0563208, 0.9516462, 2.5202010, 1.2044795, 1.0…
$ GY          <dbl> 1.3747605, 1.3735596, 0.9054266, 0.7614383, 0.8827177, 2.0…
$ HW          <dbl> 57.65487, 64.41055, 69.41048, 55.35590, 53.65940, 64.98411…
$ Length      <dbl> 10.542981, 10.106793, 8.565790, 11.920853, 9.781558, 9.968…
$ Length_Wid  <dbl> 3.313525, 3.013035, 2.524445, 3.833381, 3.305421, 2.978634…
$ PdH         <dbl> 91.09761, 63.65600, 68.75482, 69.50663, 64.10002, 80.59519…
$ PdL         <dbl> 8.1320940, 1.0341930, 6.5555050, 0.9639243, -0.6416490, 2.…
$ Perimeter   <dbl> 29.07010, 28.33654, 24.57919, 32.21194, 27.02846, 28.03643…
$ PH          <dbl> 96.76140, 70.56720, 77.16486, 79.22446, 71.78375, 88.70248…
$ Protein     <dbl> 14.06372, 14.16090, 15.23294, 14.34877, 14.46224, 14.31356…
$ SL          <dbl> 5.343668, 6.804813, 8.074976, 10.039594, 7.526454, 8.31457…
$ TKW         <dbl> 28.48964, 35.00164, 31.95561, 27.27054, 24.64983, 33.83401…
$ width       <dbl> 3.251696, 3.439623, 3.455571, 3.204069, 3.000305, 3.429043…
$ Zn          <dbl> 31.21179, 34.21217, 25.50724, 32.24918, 33.58773, 33.92710…

# Calculating average Plant Height
plant_heights <- barley_pheno_data$PH # extracting plant height column
mean_height <- mean(plant_heights, na.rm = TRUE)

# Printing average Plant Height
print(paste("Average Plant Height (PH):", round(mean_height, 2)))

[1] "Average Plant Height (PH): 74.63"