Data
PromptBeginner5 minmarkdown
│ ├── data-wrangling.md # Subsetting traps
apply family
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General
PromptBeginner5 minmarkdown
If you're also someone who Googles `how to drop NA rows in R` every single time
this one's for you. 🫶
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Coding & Debugging
PromptBeginner5 minmarkdown
I'm a political science PhD candidate who uses R regularly but would never call myself *an R person*. I needed a Claude Code skill for base R — something without tidyverse
without ggplot2
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General
PromptBeginner5 minmarkdown
scaffold_analysis(project
outcome = outcome
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General
PromptBeginner5 minmarkdown
cat(sprintf(Created: %s\n
out_file))
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General
PromptBeginner5 minmarkdown
group
outcome
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General
PromptBeginner5 minmarkdown
project_name
project_name
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General
PromptBeginner5 minmarkdown
outcome
group
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General
PromptBeginner5 minmarkdown
group
group
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General
PromptBeginner5 minmarkdown
format(Sys.Date()
%%Y-%%m-%%d)
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Data
PromptBeginner5 minmarkdown
# boxplot(%s ~ %s
data = df
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General
PromptBeginner5 minmarkdown
par(mfrow = c(1
1))
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General
PromptBeginner5 minmarkdown
# png(%s_boxplot.png
width = 800
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General
PromptBeginner5 minmarkdown
plot(fit
which = 1) # residuals vs fitted
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General
PromptBeginner5 minmarkdown
par(mfrow = c(1
2))
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Data
PromptBeginner5 minmarkdown
# fit_aov <- aov(%s ~ %s
data = df)
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Data
PromptBeginner5 minmarkdown
# t.test(%s ~ %s
data = df)
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Data
PromptBeginner5 minmarkdown
fit <- lm(%s ~ %s
data = df)
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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
# df <- df[complete.cases(df)
] # drop rows with any NA
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General
PromptBeginner5 minmarkdown
cat(Dimensions:
dim(df)
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General
PromptBeginner5 minmarkdown
df <- read.csv(%s
stringsAsFactors = FALSE)
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