50 lines
1.8 KiB
R
50 lines
1.8 KiB
R
options(scipen = 999)
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setwd("C:/Users/irina/Documents/DND/EOHI/eohi1")
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data <- read.csv("ehi1.csv")
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# Check the levels of the demo_edu variable
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print(levels(factor(data$demo_edu)))
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# Also show the unique values and their frequencies
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print("\nUnique values and frequencies:")
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print(table(data$demo_edu, useNA = "ifany"))
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# Recode demo_edu into 3 ordinal levels
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data$edu3 <- NA
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# HS_TS: High School and Trade School
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data$edu3[data$demo_edu %in% c("High School (or equivalent)", "Trade School (non-military)")] <- "HS_TS"
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# C_Ug: College and University - Undergraduate
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data$edu3[data$demo_edu %in% c("College Diploma/Certificate", "University - Undergraduate")] <- "C_Ug"
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# grad_prof: University - Graduate, University - PhD, and Professional Degree
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data$edu3[data$demo_edu %in% c("University - Graduate (Masters)", "University - PhD", "Professional Degree (ex. JD/MD)")] <- "grad_prof"
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# Convert to ordered factor
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data$edu3 <- factor(data$edu3,
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levels = c("HS_TS", "C_Ug", "grad_prof"),
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ordered = TRUE)
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# Check the recoded variable
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print(table(data$edu3, useNA = "ifany"))
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# Verify the recoding
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print(table(data$demo_edu, data$edu3, useNA = "ifany"))
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# Convert edu3 to numeric factor for correlations (1, 2, 3)
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# First ensure edu3 is a factor, then convert to numeric
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data$edu3 <- factor(data$edu3, levels = c("HS_TS", "C_Ug", "grad_prof"), ordered = TRUE)
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data$edu_num <- as.numeric(data$edu3)
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# Check the numeric conversion
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print(table(data$edu_num, useNA = "ifany"))
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# Verify the conversion
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print("\nCross-tabulation of factor vs numeric:")
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print(table(data$edu3, data$edu_num, useNA = "ifany"))
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# Note: To save the dataset, close any programs that have ehi1.csv open, then run:
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# write.csv(data, "ehi1.csv", row.names = FALSE) |