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Introduction

In June 17, nice article for introducing new trial dataset were uploaded via R-bloggers.

iris, one of commonly used dataset for simple data analysis. but there is a little issue for using it. 

Too good.

Every data has well-structured and most of analysis method works with iris very well.

In reality, most of dataset is not pretty and requires a lot of pre-process to just start. This can be possible works in pre-process

Remove NAs.
Select meaningful features
Handle duplicated or inconsistent values.
or even, just loading the dataset. if is not well-structured like Flipkart-products

However, in this penguin dataset, you can try for this work. also there’s pre-processed data too.

For more information, see the page of palmerpenguins. 

There is a routine for me with brief data analysis. and today, I want to share them with this lovely penguins. 

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Contents

0. Load dataset and library on workspace.

library(palmerpenguins) # for data
library(dplyr) # for data-handling
library(corrplot) # for correlation plot
library(GGally) # for parallel coordinate plot
library(e1071) # for svm

data(penguins) # load pre-processed penguins 

palmerpenguins have 2 data penguins, penguins_raw , and as you can see from their name, penguins is pre-processed data.  

1. See the summary  and plot of Dataset

summary(penguins)
plot(penguins)

It seems species , island  and sex is categorical features.
and remaining for numerical features.

2. Set the format of feature

penguins$species <- as.factor(penguins$species)
penguins$island <- as.factor(penguins$island)
penguins$sex <- as.factor(penguins$sex)

summary(penguins)
plot(penguins)

and see summary and plot again. note that result of plot is same. 



There’s unwanted NA and . values in some features.

3. Remove not necessary datas ( in this tutorial, NA)

penguins <- penguins %>% filter(sex == 'MALE' | sex == 'FEMALE')
summary(penguins)

And here, I additionally defined color values for each penguins to see better plot result

# Green, Orange, Purple
pCol <- c('#057076', '#ff8301', '#bf5ccb')
names(pCol) <- c('Gentoo', 'Adelie', 'Chinstrap')
plot(penguins, col = pCol[penguins$species], pch = 19)

Now, plot results are much better to give insights.

Note that, other pre-process step may requires for different datasets.

4. See relation of categorical features

My first purpose of analysis this penguin is species
So, I will try to see relation between species and other categorical values

4-1. species, island

table(penguins$species, penguins$island)
chisq.test(table(penguins$species, penguins$island)) # meaningful difference

ggplot(penguins, aes(x = island, y = species, color = species)) +
  geom_jitter(size = 3) + 
  scale_color_manual(values = pCol) 

Wow, there’s strong relationship between species and island

– Adelie lives in every island
– Gentoo lives in only Biscoe
– Chinstrap lives in only Dream

4-2 & 4.3.
However, species and sex or sex and island did not show any meaningful relation.
You can try following codes. 

# species vs sex
table(penguins$sex, penguins$species)
chisq.test(table(penguins$sex, penguins$species)[-1,]) # not meaningful difference 0.916

# sex vs island
table(penguins$sex, penguins$island) # 0.9716
chisq.test(table(penguins$sex, penguins$island)[-1,]) # not meaningful difference 0.9716

5. See with numerical features

I will select numerical features. 
and see correlation plot and parallel coordinate plots.

# Select numericals
penNumeric <- penguins %>% select(-species, -island, -sex)

# Cor-relation between numerics

corrplot(cor(penNumeric), type = 'lower', diag = FALSE)

# parallel coordinate plots

ggparcoord(penguins, columns = 3:6, groupColumn = 1, order = c(4,3,5,6)) + 
  scale_color_manual(values = pCol)

plot(penNumeric, col = pCol[penguins$species], pch = 19)

and below are result of them.








lucky, every numeric features (even only 4) have meaningful correlation and there is trend with  their combination for species (See parallel coordinate plot)

6. Give statistical work on dataset.

In this step, I usually do linear modeling or svm to predict

6.1 linear modeling

species is categorical value, so it needs to be change to numeric value

set.seed(1234)
idx <- sample(1:nrow(penguins), size = nrow(penguins)/2)

# as. numeric
speciesN <- as.numeric(penguins$species)
penguins$speciesN <- speciesN

train <- penguins[idx,]
test <- penguins[-idx,]


fm <- lm(speciesN ~ flipper_length_mm + culmen_length_mm + culmen_depth_mm + body_mass_g, train)

summary(fm) 

It shows that, body_mass_g is not meaningful feature as seen in plot above ( it may explain gentoo, but not other penguins )

To predict, I used this code. however, numeric predict generate not complete value (like 2.123 instead of 2) so I added rounding step.

predRes <- round(predict(fm, test))
predRes[which(predRes>3)] <- 3
predRes <- sort(names(pCol))[predRes]

test$predRes <- predRes
ggplot(test, aes(x = species, y = predRes, color = species))+ 
  geom_jitter(size = 3) +
  scale_color_manual(values = pCol)

table(test$predRes, test$species)

Accuracy of basic linear modeling is 94.6%

6-2 svm

using svm is also easy step.

m <- svm(species ~., train)

predRes2 <- predict(m, test)
test$predRes2 <- predRes2

ggplot(test, aes(x = species, y = predRes2, color = species)) +
  geom_jitter(size = 3) +
  scale_color_manual(values = pCol)

table(test$species, test$predRes2)

and below are result of this code.





Accuracy of svm is 100%. wow.

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Conclusion

Today I introduced simple routine for EDA and statistical analysis with penguins.
That is not difficult that much, and shows good performances.

Of course, I skipped a lot of things like processing raw-dataset.
However I hope this trial gives inspiration for further data analysis.

Thanks.