General
PromptBeginner5 minmarkdown
cat(sprintf( Columns with NAs: %d of %d\n\n
length(cols_with_na)
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General
PromptBeginner5 minmarkdown
na_counts <- sapply(df
function(x) sum(is.na(x)))
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General
PromptBeginner5 minmarkdown
col
cls
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General
PromptBeginner5 minmarkdown
na_flag <- if (n_na == 0) " else sprintf( *** %d NAs (%.1f%%)
n_na
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General
PromptBeginner5 minmarkdown
pct <- round(n_na / n_row * 100
1)
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General
PromptBeginner5 minmarkdown
cat(sprintf( Rows: %d Columns: %d\n
n_row
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Data
PromptBeginner5 minmarkdown
check_data <- function(df
top_n_levels = 8) {
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General
PromptBeginner5 minmarkdown
# par(mfrow = c(2
2))
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General
PromptBeginner5 minmarkdown
# par(mfrow = c(1
1))
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General
PromptBeginner5 minmarkdown
# png(figure1_distributions.png
width = 1200
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Data
PromptBeginner5 minmarkdown
# saveRDS(df
data_clean.rds)
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General
PromptBeginner5 minmarkdown
par(mfrow = c(1
1))
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General
PromptBeginner5 minmarkdown
par(mfrow = c(2
2))
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Data
PromptBeginner5 minmarkdown
# fit_aov <- aov(outcome_var ~ group_var
data = df)
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General
PromptBeginner5 minmarkdown
# lmtest::coeftest(fit
vcov = sandwich::vcovHC(fit
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Data
PromptBeginner5 minmarkdown
t.test(outcome_var ~ group_var
data = df)
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General
PromptBeginner5 minmarkdown
par(mfrow = c(1
1))
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General
PromptBeginner5 minmarkdown
col = hcl.colors(20
RdBu
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General
PromptBeginner5 minmarkdown
cor_mat <- cor(df[num_cols]
use = complete.obs)
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General
PromptBeginner5 minmarkdown
col = red
lwd = 2)
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General
PromptBeginner5 minmarkdown
col = adjustcolor(steelblue
alpha.f = 0.5)
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General
PromptBeginner5 minmarkdown
plot(df$predictor
df$outcome_var
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Data
PromptBeginner5 minmarkdown
abline(lm(outcome_var ~ predictor
data = df)
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General
PromptBeginner5 minmarkdown
par(mfrow = c(2
2))
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