Data
PromptBeginner5 minmarkdown
Small Functional Analyst mode
Functional Analyst Mode
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Data
PromptBeginner5 minmarkdown
Functional Analyst
Act as a Senior Functional Analyst. Your role prioritizes correctness, clarity, traceability, and controlled scope, following UML2, Gherkin, and Agile/Scrum methodologies. Below are your core principl...
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Data
PromptBeginner5 minmarkdown
Probably the most useful standalone thing here. Source it and run `check_data(df)` on any data frame to get a formatted report of dimensions
NA counts
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Data
PromptBeginner5 minmarkdown
│ ├── statistics.md # Hypothesis tests
distributions
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Data
PromptBeginner5 minmarkdown
│ ├── visualization.md # par
layout
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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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Data
PromptBeginner5 minmarkdown
│ ├── data-wrangling.md # Subsetting traps
apply family
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Data
PromptBeginner5 minmarkdown
# boxplot(%s ~ %s
data = df
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Data
PromptBeginner5 minmarkdown
fit <- lm(%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_aov <- aov(%s ~ %s
data = df)
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Data
PromptBeginner5 minmarkdown
if (is.null(data_file)) data_file <- paste0(project_name
.csv)
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Data
PromptBeginner5 minmarkdown
check_data <- function(df
top_n_levels = 8) {
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Data
PromptBeginner5 minmarkdown
# saveRDS(df
data_clean.rds)
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Data
PromptBeginner5 minmarkdown
t.test(outcome_var ~ group_var
data = df)
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Data
PromptBeginner5 minmarkdown
# fit_aov <- aov(outcome_var ~ group_var
data = df)
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Data
PromptBeginner5 minmarkdown
abline(lm(outcome_var ~ predictor
data = df)
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Data
PromptBeginner5 minmarkdown
df <- read.csv(your_data.csv
stringsAsFactors = FALSE)
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Coding & Debugging
PromptBeginner5 minmarkdown
- `arrows`: `code = 1` (head at start)
`code = 2` (head at end
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Data
PromptBeginner5 minmarkdown
- Formula interface: `pairs(~ var1 + var2 + var3
data = df)`.
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Data
PromptBeginner5 minmarkdown
- Formula interface: `cor.test(~ x + y
data = df)` — note the `~` with no LHS.
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Data
PromptBeginner5 minmarkdown
- Default bandwidth: `bw = nrd0` (Silverman's rule of thumb). For multimodal data
consider `SJ` or `bcv`.
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Data
PromptBeginner5 minmarkdown
- Default kernel: `gaussian`. Range of density extends beyond data range (controlled by `cut`
default 3 bandwidths).
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Creative
PromptBeginner5 minmarkdown
- `nstart > 1` recommended (e.g.
`nstart = 25`): runs algorithm from multiple random starts
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