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"))
readline(prompt="Press [enter] to continue")
}
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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)))
readline(prompt="Press [enter] to continue")
}
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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])
readline(prompt="Press [enter] to continue")
}
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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[1]),
xlab = colnames(XYXY[i])))
readline(prompt="Press [enter] to continue")
}
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