Data
PromptBeginner5 minmarkdownQuality: 22
> This is acceptable. It names the feature (map)
the data used (location)
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Showing 24 of 5,925Categories: Data, Openclaw
Data
PromptBeginner5 minmarkdownQuality: 22
> This is acceptable. It names the feature (map)
the data used (location)
1
Data
PromptBeginner5 minmarkdownQuality: 26
Academic analyst and exam pattern extractor
ROLE: Act as an expert academic analyst and exam pattern extractor.
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Data
PromptBeginner5 minmarkdownQuality: 26
Expert Legal Analyst in Tax and Commercial Law
Act as a legal expert with extensive experience in tax law and commercial law. You are known for your top-tier capabilities in corporate compliance and dispute resolution. Your task is to:
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Data
PromptBeginner5 minmarkdownQuality: 26
Academic analyst and exam pattern extractor
ROLE: Act as an expert academic analyst and exam pattern extractor.
0
Data
PromptBeginner5 minmarkdownQuality: 26
Expert Legal Analyst in Tax and Commercial Law
Act as a legal expert with extensive experience in tax law and commercial law. You are known for your top-tier capabilities in corporate compliance and dispute resolution. Your task is to:
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Data
PromptBeginner5 minmarkdownQuality: 26
Ultra-micro Functional Analyst Prompt
Act as a senior functional analyst: work in phases, state all assumptions, preserve existing behaviour, no UML/Gherkin/specs without explicit approval, be direct and analytical.
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Data
PromptBeginner5 minmarkdownQuality: 22
Small Functional Analyst mode
Functional Analyst Mode
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Data
PromptBeginner5 minmarkdownQuality: 26
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 minmarkdownQuality: 26
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 minmarkdownQuality: 22
│ ├── statistics.md # Hypothesis tests
distributions
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Data
PromptBeginner5 minmarkdownQuality: 22
│ ├── visualization.md # par
layout
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Data
PromptBeginner5 minmarkdownQuality: 22
│ ├── data-wrangling.md # Subsetting traps
apply family
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Data
PromptBeginner5 minmarkdownQuality: 22
# boxplot(%s ~ %s
data = df
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Data
PromptBeginner5 minmarkdownQuality: 22
fit <- lm(%s ~ %s
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 22
# t.test(%s ~ %s
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 22
# fit_aov <- aov(%s ~ %s
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 22
if (is.null(data_file)) data_file <- paste0(project_name
.csv)
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Data
PromptBeginner5 minmarkdownQuality: 22
check_data <- function(df
top_n_levels = 8) {
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Data
PromptBeginner5 minmarkdownQuality: 22
# saveRDS(df
data_clean.rds)
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Data
PromptBeginner5 minmarkdownQuality: 22
t.test(outcome_var ~ group_var
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 22
# fit_aov <- aov(outcome_var ~ group_var
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 22
abline(lm(outcome_var ~ predictor
data = df)
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Data
PromptBeginner5 minmarkdownQuality: 22
df <- read.csv(your_data.csv
stringsAsFactors = FALSE)
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