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#### Cycle Through Columns

KEY:

ds = dataset you are currently using.

Var = variable to be displayed

optional, etc. = where any optional commands belong

XYXY <- variable into which data is being saved

## Topics:

These code blurbs are designed to speed up the first pass-through of data analysis. They use for-loops to do a quick visual check of the state of your variables. Simply press "enter" to cycle through.

NOTE: Many of these visualization are dependent on the type of data you are examining. You may want to copy same-time variables (e.g., categorical, numeric) to type-specific data frames, in order to avoid errors.

## Bar Plots

Bar plots for categorical data.

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for (i in 1:length(XYXY)){

print(barplot(table(XYXY[,i]),

xlab = colnames(XYXY[i]),

axis.lty = "solid"))

}

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Anchor 1

## Histograms (with skew)

This will provide the histogram with skew value in the title. Note that skew comes from e1071, so you will need to download this package.

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library(e1071)

for (i in 1:length(XYXY)){

skewVal <- skewness(XYXY[,i])

print(histogram(XYXY[,i],

xlab = colnames(XYXY[i]),

main = paste0("Skewness = ", skewVal)))

}

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Anchor 2

## Normality (with Shapiro-Wilk test)

Shapiro-Wilk results appear in the title

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for (i in 1:length(XYXY)){

normVal <- shapiro.test(XYXY[,i])

qqnorm(XYXY[,i],

xlab = colnames(XYXY[,i]),

main = paste0("Shapiro-Wilk test = ", normVal\$p.value))

qqline(XYXY[,i])

}

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Anchor 3

## Scatter Plot

Note that the way this is set up, column 1 will always be on the y-axis, and the other columns will cycle along the x-axis. Adjust as needed.

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for(i in 2:length(XYXY)){

print(plot(XYXY[,1]~XYXY[,i],

ylab = colnames(XYXY),

xlab = colnames(XYXY[i])))