General
PromptBeginner5 minmarkdown
plot(fit
which = 1) # residuals vs fitted
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General
PromptBeginner5 minmarkdown
par(mfrow = c(1
1))
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General
PromptBeginner5 minmarkdown
tapply(df$%s
df$%s
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General
PromptBeginner5 minmarkdown
tapply(df$%s
df$%s
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General
PromptBeginner5 minmarkdown
hist(df$%s
main = Distribution of %s
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General
PromptBeginner5 minmarkdown
cat(Dimensions:
dim(df)
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General
PromptBeginner5 minmarkdown
# df <- df[complete.cases(df)
] # drop rows with any NA
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General
PromptBeginner5 minmarkdown
out_file <- paste0(project_name
_analysis.R)
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General
PromptBeginner5 minmarkdown
df <- read.csv(%s
stringsAsFactors = FALSE)
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General
PromptBeginner5 minmarkdown
# scaffold_analysis(myproject
outcome = score
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General
PromptBeginner5 minmarkdown
dims = c(rows = n_row
cols = n_col)
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General
PromptBeginner5 minmarkdown
n_dup
n_dup / n_row * 100))
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General
PromptBeginner5 minmarkdown
pct <- round(cnt / n_row * 100
1)
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General
PromptBeginner5 minmarkdown
cat(sprintf( %-25s %5d (%.1f%%)\n
lbl
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General
PromptBeginner5 minmarkdown
cat(sprintf( ... and %d more levels\n
n_lv - top_n_levels))
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General
PromptBeginner5 minmarkdown
tbl <- sort(table(x
useNA = no)
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General
PromptBeginner5 minmarkdown
cat(sprintf(\n %s (%d unique values)\n
col
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General
PromptBeginner5 minmarkdown
show <- min(top_n_levels
n_lv)
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General
PromptBeginner5 minmarkdown
──────
───
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General
PromptBeginner5 minmarkdown
cat_cols <- names(df)[sapply(df
function(x) is.factor(x) | is.character(x))]
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General
PromptBeginner5 minmarkdown
min(x)
mean(x)
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General
PromptBeginner5 minmarkdown
Column
Min
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General
PromptBeginner5 minmarkdown
num_cols <- names(df)[sapply(df
is.numeric)]
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General
PromptBeginner5 minmarkdown
col
bar
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