General
PromptBeginner5 minmarkdown
pct <- round(n_na / n_row * 100
1)
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General
PromptBeginner5 minmarkdown
# png(figure1_distributions.png
width = 1200
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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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General
PromptBeginner5 minmarkdown
# lmtest::coeftest(fit
vcov = sandwich::vcovHC(fit
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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
col = red
lwd = 2)
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General
PromptBeginner5 minmarkdown
cor_mat <- cor(df[num_cols]
use = complete.obs)
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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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General
PromptBeginner5 minmarkdown
par(mfrow = c(2
2))
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General
PromptBeginner5 minmarkdown
# prop.table(table(df$group
df$category)
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General
PromptBeginner5 minmarkdown
# table(df$group
df$category
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General
PromptBeginner5 minmarkdown
))
3)
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General
PromptBeginner5 minmarkdown
max = max(x
na.rm = TRUE)
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General
PromptBeginner5 minmarkdown
min = min(x
na.rm = TRUE)
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General
PromptBeginner5 minmarkdown
median = median(x
na.rm = TRUE)
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General
PromptBeginner5 minmarkdown
mean = mean(x
na.rm = TRUE)
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General
PromptBeginner5 minmarkdown
round(sapply(df[num_cols]
function(x) c(
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General
PromptBeginner5 minmarkdown
sd = sd(x
na.rm = TRUE)
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General
PromptBeginner5 minmarkdown
# df <- df[df$year >= 2010
]
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General
PromptBeginner5 minmarkdown
num_cols <- names(df)[sapply(df
is.numeric)]
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General
PromptBeginner5 minmarkdown
# df <- df[complete.cases(df[
c(outcome
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