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Standardize data.frame for comparable Machine Learning prediction and visualization

Source: R/data_transform.R
data_transform.Rd

Often economic and other Machine Learning data are of different units or sizes making either estimation, interpretation or visualization difficult. The solution to these issues can be handled if the data can be transformed into unitless or data of similar magnitude. This is what data_transform is set to do. It is simple and straight forward to use.

Usage

data_transform(data, method, MARGIN = 2)

Arguments

data

A data.frame with numeric data for transformation. All columns in the data are transformed

method

The type of transformation. There three options. 1 is for log transformation, 2 is for min-max transformation and 3 is for mean-SD transformation.

MARGIN

Option to either transform the data 2 == column-wise or 1 == row-wise. Defaults to column-wise transformation if no option is indicated.

Value

This function returns the output of the data transformation process as

tata_transformed

A new data.frame containing the transformed values

Examples


library(Dyn4cast)
# View the data without transformation

data0 <- Transform %>%
pivot_longer(!X, names_to = "Factors", values_to = "Data")

ggplot(data = data0, aes(x = X, y = Data, fill = Factors, color = Factors)) +
  geom_line() +
  scale_fill_brewer(palette = "Set1") +
  scale_color_brewer(palette = "Set1") +
  labs(y = "Data", x = "Series", color = "Factors") +
  theme_bw(base_size = 12)


# Example 1: Transformation by min-max method.
# You could also transform the `X column` but is is better not to.

data1 <- data_transform(Transform[, -1], 1)
data1 <- cbind(Transform[, 1], data1)
data1 <- data1 %>%
  pivot_longer(!X, names_to = "Factors", values_to = "Data")

ggplot(data = data1, aes(x = X, y = Data, fill = Factors, color = Factors)) +
  geom_line() +
  scale_fill_brewer(palette = "Set1") +
  scale_color_brewer(palette = "Set1") +
  labs(y = "Data", x = "Series", color = "Factors") +
  theme_bw(base_size = 12)


# Example 2: `log` transformation

data2 <- data_transform(Transform[, -1], 2)
data2 <- cbind(Transform[, 1], data2)
data2 <- data2 %>%
  pivot_longer(!X, names_to = "Factors", values_to = "Data")

ggplot(data = data2, aes(x = X, y = Data, fill = Factors, color = Factors)) +
  geom_line() +
  scale_fill_brewer(palette = "Set1") +
  scale_color_brewer(palette = "Set1") +
  labs(y = "Data", x = "Series", color = "Factors") +
  theme_bw(base_size = 12)


# Example 3: `Mean-SD` transformation

data3 <- data_transform(Transform[, -1], 3)
data3 <- cbind(Transform[, 1], data3)
data3 <- data3 %>%
  pivot_longer(!X, names_to = "Factors", values_to = "Data")

ggplot(data = data3, aes(x = X, y = Data, fill = Factors, color = Factors)) +
  geom_line() +
  scale_fill_brewer(palette = "Set1") +
  scale_color_brewer(palette = "Set1") +
  labs(y = "Data", x = "Series", color = "Factors") +
  theme_bw(base_size = 12)