create R scripts

scripts/01-EDA-pt1.R

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+########################################################
+# DATA UNDERSTANDING: EDA PART 1 - THE DATASET
+########################################################
+# ------------------------------------------------------
+# LOAD THE LIBRARIES
+# ------------------------------------------------------
+library(here)         # assess the file path
+library(DataExplorer) # EDA visualizations
+library(tidyverse)    # data wrangling
+library(kableExtra)   # write table
+
+
+# ------------------------------------------------------
+# OVERVIEW OF THE DATASET
+# ------------------------------------------------------
+# Load Data File
+df <- read_csv(here("dataset/transactions.csv"))
+
+
+# Quick overview of the dataset then transpose for better table display:
+# * index and column data types
+# * non-null values
+# * memory usage
+df_overview <- introduce(df) %>% t()          
+
+
+# turn the table into the Markdown format
+df_overview %>% kbl() %>% kable_styling(bootstrap_options = "striped", full_width = F)
+
+
+# inspect columns data type
+sapply(df, class) 
+
+# ------------------------------------------------------
+# MISSING VALUE
+# ------------------------------------------------------
+# Check for missing value in each column by plotting
+plot_missing(df)
+
+
+# convert character values of columns to upper case for better missing value inspection
+missing_df <- data.frame(
+  lapply(df, function(v) {
+  if (is.character(v)) return(toupper(v))
+  else return(v)
+}))
+
+
+# check if there is there is missing values assigned under new category
+## 1. date column
+sprintf(paste0("Is there any missing value observation categories in date column (T/F)?: ", 
+               missing_df[1] %in% c("NA","N/A","NULL","")))
+
+
+## 2. customer_id column
+sprintf(paste0("Is there any missing value observation categories in customer_id column (T/F)?: ", 
+               missing_df[1] %in% c("NA","N/A","NULL","")))
+
+
+# 3. Check for any transaction with zero values
+sprintf(paste0("How many rows contained 0 value in monthly transaction amount?: ", 
+               sum(df$monthly_amount==0)))
+
+
+# ------------------------------------------------------
+# DATA DISTRIBUTION: DATA SKEWNESS/IMBALANCE
+# ------------------------------------------------------
+
+# combine 2 plots into 1 plot
+par(mfrow=c(1,2))
+# plot data distribution 
+# 1. MONTHLY_AMOUNT group by INDUSTRY
+hist(df$industry,
+     main = "Trans by Industry", 
+     xlab="Industry", 
+     xlim = c(0,10), 
+     ylim=c(0,50000), 
+     las=0)
+
+## 2. MONTHLY_AMOUNT group by LOCATION
+hist(df$location,
+     main = "Trans by Location", 
+     xlab="Location", 
+     xlim = c(0,10), 
+     ylim=c(0,50000), 
+     las=0)
+
+# ------------------------------------------------------
+# DATA DISTRIBUTION: OUTLIERS
+# ------------------------------------------------------
+# combine 2 plots into 1 plot
+par(mfrow=c(1,2)) 
+# plot boxplot to check for outliers
+# 1. industry
+boxplot(monthly_amount~industry, 
+        data=df, 
+        main='Transaction vs. Industry',
+        xlab='Industry', 
+        ylab='Transaction Amount', 
+        horizontal=TRUE) + 
+  scale_fill_grey() + 
+  theme_classic()
+
+# 2. location
+boxplot(monthly_amount~location, 
+        data=df, 
+        main='Transaction vs. Location',
+        xlab='Location', 
+        ylab='Transaction Amount', 
+        horizontal=TRUE) + 
+  scale_fill_grey() + 
+  theme_classic()
+
+
+# ------------------------------------------------------
+# DATA TRANSFORMATION
+# ------------------------------------------------------
+# convert date column into the date format
+df$date <- as.Date(df$date,"%d/%m/%Y")
+
+
+# convert customer_id column into character format
+df$customer_id = as.character(df$customer_id, format = "")
+
+
+# convert location column into character format
+df$location <- as.character(df$location)
+
+
+# convert industry column into character format
+df$industry <- as.character(df$industry)
+
+
+# filter out value with 0 transaction amount
+df<-filter(df, monthly_amount!=0)
+
+
+# ------------------------------------------------------
+# VARIABLE CORRELATION
+# ------------------------------------------------------
+plot_correlation(df)
+