General
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General
PromptBeginner5 minmarkdownQuality: 24
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General
PromptBeginner5 minmarkdownQuality: 24
outcome
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General
PromptBeginner5 minmarkdownQuality: 24
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General
PromptBeginner5 minmarkdownQuality: 24
outcome
outcome
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General
PromptBeginner5 minmarkdownQuality: 24
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General
PromptBeginner5 minmarkdownQuality: 24
format(Sys.Date()
%%Y-%%m-%%d)
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Data
PromptBeginner5 minmarkdownQuality: 24
# boxplot(%s ~ %s
data = df
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General
PromptBeginner5 minmarkdownQuality: 24
# png(%s_boxplot.png
width = 800
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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
out_file <- paste0(project_name
_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(data_file)) data_file <- paste0(project_name
.csv)
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General
PromptBeginner5 minmarkdownQuality: 24
dims = c(rows = n_row
cols = n_col)
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