50 lines
1.5 KiB
R
50 lines
1.5 KiB
R
options(scipen = 999)
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library(dplyr)
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setwd("C:/Users/irina/Documents/DND/EOHI/eohi1")
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df <- read.csv("ehi1.csv")
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data <- df %>%
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select(eohiDGEN_mean, ehi_global_mean, demo_sex, demo_age_1, edu3, AOT_total, CRT_correct, CRT_int, bs_28, bs_easy, bs_hard, cal_selfActual, cal_global) %>%
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filter(demo_sex != "Prefer not to say")
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print(colSums(is.na(data)))
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print(sapply(data, class))
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# Create dummy variable for sex (0 = Male, 1 = Female)
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data$sex_dummy <- ifelse(data$demo_sex == "Female", 1, 0)
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# Verify the dummy coding
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print(table(data$demo_sex, data$sex_dummy))
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#descriptives
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# Descriptives for age
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print(summary(data$demo_age_1))
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print(sd(data$demo_age_1, na.rm = TRUE))
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# Center demo_age_1 (subtract the mean)
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data$age_centered <- data$demo_age_1 - mean(data$demo_age_1, na.rm = TRUE)
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# Verify the centering
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print(summary(data$age_centered))
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# Descriptives for sex (frequency table)
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print(table(data$demo_sex))
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print(prop.table(table(data$demo_sex)))
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# Descriptives for sex dummy variable
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print(table(data$sex_dummy))
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# Convert edu3 to numeric factor for correlations (1, 2, 3)
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# First ensure edu3 is a factor, then convert to numeric
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data$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(table(data$edu3, data$edu_num, useNA = "ifany")) |