Data
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t.test(%s ~ %s
data = df)
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General
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tapply(df$%s
df$%s
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General
PromptBeginner5 minmarkdownQuality: 24
cat(Dimensions:
dim(df)
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General
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df <- df[complete.cases(df)
] # drop rows with any NA
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General
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hist(df$%s
main = Distribution of %s
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General
PromptBeginner5 minmarkdownQuality: 24
outfile <- paste0(projectname
_analysis.R)
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General
PromptBeginner5 minmarkdownQuality: 24
df <- read.csv(%s
stringsAsFactors = FALSE)
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Data
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if (is.null(datafile)) datafile <- paste0(project_name
.csv)
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General
PromptBeginner5 minmarkdownQuality: 24
dims = c(rows = n_row
cols = n_col)
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General
PromptBeginner5 minmarkdownQuality: 24
scaffold_analysis(myproject
outcome = score
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General
PromptBeginner5 minmarkdownQuality: 24
n_dup
ndup / nrow * 100))
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General
PromptBeginner5 minmarkdownQuality: 24
cat(sprintf( %-25s %5d (%.1f%%)\n
lbl
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General
PromptBeginner5 minmarkdownQuality: 24
cat(sprintf( ... and %d more levels\n
nlv - topn_levels))
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General
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pct <- round(cnt / n_row * 100
1)
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General
PromptBeginner5 minmarkdownQuality: 24
show <- min(topnlevels
n_lv)
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General
PromptBeginner5 minmarkdownQuality: 24
tbl <- sort(table(x
useNA = no)
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General
PromptBeginner5 minmarkdownQuality: 24
cat(sprintf(\n %s (%d unique values)\n
col
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General
PromptBeginner5 minmarkdownQuality: 24
──────
───
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General
PromptBeginner5 minmarkdownQuality: 24
cat_cols <- names(df)[sapply(df
function(x) is.factor(x) | is.character(x))]
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General
PromptBeginner5 minmarkdownQuality: 24
min(x)
mean(x)
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General
PromptBeginner5 minmarkdownQuality: 24
col
bar
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General
PromptBeginner5 minmarkdownQuality: 24
Column
Min
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General
PromptBeginner5 minmarkdownQuality: 24
num_cols <- names(df)[sapply(df
is.numeric)]
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General
PromptBeginner5 minmarkdownQuality: 24
bar <- paste0(rep(█
bar_len)
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