General
PromptBeginner5 minmarkdownQuality: 24
plot(fit
which = 1) # residuals vs fitted
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General
PromptBeginner5 minmarkdownQuality: 24
par(mfrow = c(1
1))
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Data
PromptBeginner5 minmarkdownQuality: 24
fit_aov <- aov(%s ~ %s
data = df)
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General
PromptBeginner5 minmarkdownQuality: 24
par(mfrow = c(1
2))
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Data
PromptBeginner5 minmarkdownQuality: 24
fit <- lm(%s ~ %s
data = df)
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General
PromptBeginner5 minmarkdownQuality: 24
tapply(df$%s
df$%s
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Data
PromptBeginner5 minmarkdownQuality: 24
t.test(%s ~ %s
data = df)
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General
PromptBeginner5 minmarkdownQuality: 24
tapply(df$%s
df$%s
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General
PromptBeginner5 minmarkdownQuality: 24
cat(Dimensions:
dim(df)
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General
PromptBeginner5 minmarkdownQuality: 24
df <- df[complete.cases(df)
] # drop rows with any NA
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General
PromptBeginner5 minmarkdownQuality: 24
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
PromptBeginner5 minmarkdownQuality: 24
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
PromptBeginner5 minmarkdownQuality: 24
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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