this slide give you a brief explanation about Exploratory Data Analysis on how to understand your characteristic of data

1. Making The Data Talk
in R
Andreas Chandra
linkedin.com/in/chandraandreas

2. Content
● Exploratory Data Analysis
● Data Visualization
○ Barplots
○ Boxplots
○ Plotcorr

3. Exploratory Data
Analysis

4. What is it?
is an approach to analyzing data sets to summarize
their main characteristics, often with visual
methods.
https://en.wikipedia.org/wiki/Exploratory_data_analysis

5. https://en.wikipedia.org/wiki/File:Data_visualization_process_v1.png

6. Got the point?

7. Summarization | Manipulation | Correlation | Data Visualization

8. Summarization

9. Using wine dataset

10. Summarization
Build in
summary()
str()
library(“Hmisc”)
describe()

12. Data Visualization

13. Data Visualization
Visualisasi data adalah istilah umum yang membantu anda memahami karakteristik data
dalam hal konteks visual, pola, trend, dan korelasi yang mungkin tidak terlihat dalam
data yang berbentuk tabel atau teks.

14. Histogram

15. Histogram
hist(df$alcohol, breaks=50)

18. Barplots

20. Barplots
set.seed(42)
# Making sample
nobs <- nrow(df) # 150 observations
sample <- train <- sample(nrow(df)) # 105 observations
df.Sample <- (df[sample, ])
# Load or install the gplots package
suppressWarnings(suppressMessages(if (!require(gplots)) install.packages("gplots")))
library("gplots")
# Preparing data
df.Sample$category <- as.factor(df.Sample$category)
x <- rbind(summary(na.omit(df.Sample$category)))
ord <- order(x[1, ], decreasing = TRUE)
# Build the barplot
bp <- barplot2(x[, ord], beside = TRUE, ylab = "Frequency", xlab = "type",
ylim = c(0, max(x) + 10), col = colorspace::rainbow_hcl(4))
text(bp, x[, ord] + 3, x[, ord])
# Adding a title
title(main = "Distribution of category")

21. Boxplots

23. Boxplots
boxplot(alcohol ~ category, data = df, ylab = "alcohol
(ml)",main = "Wine", boxwex = 0.5, col = colorspace::rainbow_hcl(4))

24. Plotcorr

26. Plotcorr
par(mfrow = c(1, 1), mar = c(4, 4, 2, 1))
suppressWarnings(suppressMessages(if (!require(ellipse))
install.packages("ellipse")))
library("ellipse")
# Build the correlations Matrix
corr <- cor(df[, 2:8])
# Colour the ellipses to emphasize the differences.
colors <- c("#A50F15", "#DE2D26", "#FB6A4A", "#FCAE91",
"#FEE5D9", "white", "#EFF3FF", "#BDD7E7", "#6BAED6",
"#3182BD", "#08519C")
# Plot the correlation matrix by ellipses
plotcorr(corr, col = colors)