General
PromptBeginner5 minmarkdown
col = red
lwd = 2)
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Explore
Find agent skills by outcome
122,416 skills indexed with the new KISS metadata standard.
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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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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
sd = sd(x
na.rm = TRUE)
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General
PromptBeginner5 minmarkdown
mean = mean(x
na.rm = TRUE)
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General
PromptBeginner5 minmarkdown
# df <- df[complete.cases(df[
c(outcome
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General
PromptBeginner5 minmarkdown
num_cols <- names(df)[sapply(df
is.numeric)]
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General
PromptBeginner5 minmarkdown
round(sapply(df[num_cols]
function(x) c(
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General
PromptBeginner5 minmarkdown
# labels = c(18-25
26-45
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General
PromptBeginner5 minmarkdown
# df <- df[df$year >= 2010
]
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General
PromptBeginner5 minmarkdown
# breaks = c(0
25
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General
PromptBeginner5 minmarkdown
# df$gender <- ifelse(df$gender == 1
Male
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General
PromptBeginner5 minmarkdown
print(table(df[[col]]
useNA = ifany))
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General
PromptBeginner5 minmarkdown
# df$date <- as.Date(df$date
format = %Y-%m-%d)
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General
PromptBeginner5 minmarkdown
cat(sprintf(Duplicate rows: %d\n
n_dup))
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General
PromptBeginner5 minmarkdown
cat_cols <- names(df)[sapply(df
function(x) is.character(x) | is.factor(x))]
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