A Guide to Qualitatively Analyzing Computing Code
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Step 2: Descriptive Codes

  • Step 1: Selecting a Unit of Analysis
  • Step 2: Descriptive Codes
  • Step 3: Discovering Emergent Themes
  • Optional: Comparing Across Students
  • Step 4: Digging Deeper

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  • Student A – Descriptive Codes
  • Student B – Descriptive Codes

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Step 2: Descriptive Codes

The second step in the qualitative analysis is creating qualitative codes for each unit of analysis (UOA).

The process of coding in qualitative research, where a researcher makes notes next to each UOA that are potentially relevant to addressing the research question. Codes act as labels, assigning “symbolic meaning” to each UOA (Miles, Huberman, and Saldaña 2020). In our context, each line of R code is a UOA, which is why we are “coding code.”

Miles, M. B., A. M. Huberman, and J. Saldaña. 2020. Qualitative Data Analysis. Thousand Oaks, CA: Sage.

The initial qualitative codes assigned to the units of analysis can be thought of as the “first cycle codes.” There are over 25 different methods for creating first cycle codes, each with a particular focus and purpose. In our paper, we discuss two specific methods of coding we believe are most relevant to investigating computing code: descriptive coding and in vivo coding.

For this research, I chose to use descriptive codes as I sought to describe the computing skills students were using in their research project. With in vivo coding, these descriptions would be required to take on the voice (code) of each student, which I felt constrained the possible descriptions available to me.


Student A – Descriptive Codes

If you are interested in seeing the original R script file from Student A, you can find that here. If while reading you wonder what the output of a specific statement is, I have reproduced Student A’s code using the penguins dataset (Horst, Hill, and Gorman 2020), available here.

Horst, Allison Marie, Alison Presmanes Hill, and Kristen B Gorman. 2020. Palmerpenguins: Palmer Archipelago (Antarctica) Penguin Data. https://allisonhorst.github.io/palmerpenguins/.
R CodeDescriptive Code
1str(PADataNoOutlier)inspect data
2str(PADataNoOutlierMultMeasure)inspect data
3names(RPMA2Growth)inspect data
4#upper anterior measurement Linear modelcode comment
5linearAnterior <- lm(PADataNoOutlier$Lipid ~ PADataNoOutlier$PSUA)fit a linear model, uses $ to select columns from dataframe
6summary(linearAnterior)views summary of lm object
7linearAnteriorinspects lm object
8with(PADataNoOutlier, plot(Lipid ~ PSUA, las = 1, col = ifelse(PADataNoOutlier$`Fork Length` <  280, "red", "black")))uses with() to attach dataframe, creates scatterplot, colors points based on conditional statement, rotates y-axis ticks
9abline(linearAnterior)adds linear trendline
10#Exponential functioncode comment
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Student B – Descriptive Codes

If you are interested in seeing the original R script file from Student A, you can find that here.

R CodeDescriptive Code
1rm(list = ls())cleaning working environment
2source("./Gas_Functions.R")loading functions written in another R script
3# Load data ####code comment
4load("***REDACTED***/gas")loading data, specifying full path to access data
5load("***REDACTED***/carboys")loading data, specifying full path to access data
6gas <- gas[!(substr(gas$sampleID,3,3) %in% c("b","c")), ]filtering rows, using brackets, relational statement (%in%), logical (!)
7gas$days <- as.numeric(gas$minutesSinceAmendment/(24*60))create new column, mutate existing column, change data type
8# Calculate molar fraction of N15-N2comment on code below
9RstN <- 0.003678create new variable
10R <- ((gas$delN2/1000)+1)*RstNcreate new variable, by mutating existing column and using previously defined variable 
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Step 1: Selecting a Unit of Analysis
Step 3: Discovering Emergent Themes
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