60 lines
1.5 KiB
R
60 lines
1.5 KiB
R
# ---- Clean Environment ----
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rm(list = ls())
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# Load Packages ----
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box::use(
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readr[read_csv],
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)
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# Load Data ----
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url <- "https://bit.ly/gacttCSV"
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ds_raw <- read_csv(url)
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# Count NAs ----
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nrow(ds_raw)
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na_count <- ds_raw |>
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dplyr::mutate(num_na = rowSums(is.na(ds))) |>
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dplyr::summarise(
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n = dplyr::n(),
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.by = num_na
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)
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# don't show this in blog but note that I did it
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ds_raw |>
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dplyr::summarise(dplyr::across(dplyr::everything(), ~ sum(is.na(.)), .names = "{.col}")) |>
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tidyr::pivot_longer(tidyr::everything()) |>
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dplyr::arrange(desc(value)) |>
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print(n = 50)
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# Clean Data ----
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ds <- ds_raw |>
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dplyr::select(
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ID = `Submission ID`,
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age = `What is your age?`,
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cups = `How many cups of coffee do you typically drink per day?`,
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where_drink = `Where do you typically drink coffee?`,
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brew_method = `How do you brew coffee at home?`,
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favorite = `What is your favorite coffee drink?`,
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additions = `Do you usually add anything to your coffee?`,
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style = `Before today's tasting, which of the following best described what kind of coffee you like?`,
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strength = `How strong do you like your coffee?`,
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roast_level = `What roast level of coffee do you prefer?`,
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why_drink = `Why do you drink coffee?`,
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taste = `Do you like the taste of coffee?`,
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gender = Gender,
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education_level = `Education Level`,
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ethnicity = `Ethnicity/Race`,
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employment = `Employment Status`,
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political_view = `Political Affiliation`
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) |>
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tidyr::drop_na()
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