HOW TO ILLUSTRATE YOU GRAPH USING GGPLOT2
By: Ku Mohd Kalkausar
2026-06-26
Hola guys!
In this tutorial, you’ll learn new set of skills that called as ggplot2 . The ggplot2 is the powerful tools to illustrate the graph in a new dimension persepective.
Firstly, what do you need is your data. You need to find your data with:
library(readxl)
data <- readxl::read_excel("Data.xlsx")Lets say you wanna reconfirm your column’s name, you can write:
head(data)## # A tibble: 6 × 20
## Station Hours period abundance fiber fragment Film Pellet Transparent Black
## <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 PTB 1 SB 6.67 6.67 0 0 0 5 1.67
## 2 PTB 2 SB 5 5 0 0 0 0 3.33
## 3 PTB 3 SB 3.33 3.33 0 0 0 1.67 1.67
## 4 PTB 4 SB 5 5 0 0 0 1.67 0
## 5 PTB 5 SB 8.33 8.33 0 0 0 3.33 3.33
## 6 PTB 6 SB 0 0 0 0 0 0 0
## # ℹ 10 more variables: Red <dbl>, Blue <dbl>, Green <dbl>, Orange <dbl>,
## # Yellow <dbl>, Purple <dbl>, `<0.3` <dbl>, `0.3 - 0.5` <dbl>,
## # `0.5 - 1.0` <dbl>, `1.0 - 5.0` <dbl>If you wanna have some quick summarization of your data, you can write this:
summary(data)## Station Hours period abundance
## Length :96 Min. : 1.00 Length :96 Min. : 0.000
## N.unique : 4 1st Qu.: 6.75 N.unique : 2 1st Qu.: 3.330
## N.blank : 0 Median :12.50 N.blank : 0 Median : 6.670
## Min.nchar: 3 Mean :12.50 Min.nchar: 2 Mean : 7.587
## Max.nchar: 3 3rd Qu.:18.25 Max.nchar: 2 3rd Qu.:10.000
## Max. :24.00 Max. :25.000
## fiber fragment Film Pellet
## Min. : 0.000 Min. : 0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.: 1.670 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median : 4.165 Median : 0.000 Median :0.0000 Median :0.0000
## Mean : 4.410 Mean : 2.518 Mean :0.6427 Mean :0.0174
## 3rd Qu.: 6.670 3rd Qu.: 1.670 3rd Qu.:1.6700 3rd Qu.:0.0000
## Max. :15.000 Max. :20.000 Max. :6.6700 Max. :1.6700
## Transparent Black Red Blue
## Min. : 0.000 Min. : 0.000 Min. :0.000 Min. :0.0000
## 1st Qu.: 0.000 1st Qu.: 1.252 1st Qu.:0.000 1st Qu.:0.0000
## Median : 1.670 Median : 1.670 Median :0.000 Median :0.0000
## Mean : 3.247 Mean : 2.830 Mean :0.608 Mean :0.5734
## 3rd Qu.: 5.000 3rd Qu.: 3.748 3rd Qu.:1.670 3rd Qu.:1.6700
## Max. :13.330 Max. :11.670 Max. :3.330 Max. :3.3300
## Green Orange Yellow Purple
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.00000
## Mean :0.0174 Mean :0.0174 Mean :0.2609 Mean :0.03479
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.6700 Max. :1.6700 Max. :1.6700 Max. :1.67000
## <0.3 0.3 - 0.5 0.5 - 1.0 1.0 - 5.0
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0.000 Median : 0.000 Median : 1.670 Median : 1.670
## Mean : 1.337 Mean : 1.719 Mean : 2.153 Mean : 2.379
## 3rd Qu.: 1.670 3rd Qu.: 3.330 3rd Qu.: 3.330 3rd Qu.: 3.330
## Max. :13.330 Max. :10.000 Max. :10.000 Max. :13.330What if you want to check what kind of your data type is, you can see with:
str(data)## tibble [96 × 20] (S3: tbl_df/tbl/data.frame)
## $ Station : chr [1:96] "PTB" "PTB" "PTB" "PTB" ...
## $ Hours : num [1:96] 1 2 3 4 5 6 13 14 15 16 ...
## $ period : chr [1:96] "SB" "SB" "SB" "SB" ...
## $ abundance : num [1:96] 6.67 5 3.33 5 8.33 0 10 8.33 5 8.33 ...
## $ fiber : num [1:96] 6.67 5 3.33 5 8.33 0 8.33 8.33 5 5 ...
## $ fragment : num [1:96] 0 0 0 0 0 0 0 0 0 1.67 ...
## $ Film : num [1:96] 0 0 0 0 0 0 1.67 0 0 1.67 ...
## $ Pellet : num [1:96] 0 0 0 0 0 0 0 0 0 0 ...
## $ Transparent: num [1:96] 5 0 1.67 1.67 3.33 0 0 1.67 3.33 1.67 ...
## $ Black : num [1:96] 1.67 3.33 1.67 0 3.33 0 5 3.33 1.67 5 ...
## $ Red : num [1:96] 0 0 0 1.67 0 0 1.67 0 0 0 ...
## $ Blue : num [1:96] 0 1.67 0 1.67 1.67 0 3.33 1.67 0 1.67 ...
## $ Green : num [1:96] 0 0 0 0 0 0 0 0 0 0 ...
## $ Orange : num [1:96] 0 0 0 0 0 0 0 0 0 0 ...
## $ Yellow : num [1:96] 0 0 0 0 0 0 0 1.67 0 0 ...
## $ Purple : num [1:96] 0 0 0 0 0 0 0 0 0 0 ...
## $ <0.3 : num [1:96] 0 0 0 0 0 0 1.67 0 0 1.67 ...
## $ 0.3 - 0.5 : num [1:96] 0 1.67 0 0 1.67 0 1.67 0 0 1.67 ...
## $ 0.5 - 1.0 : num [1:96] 3.33 1.67 0 1.67 5 0 5 3.33 0 1.67 ...
## $ 1.0 - 5.0 : num [1:96] 3.33 1.67 3.33 3.33 1.67 0 1.67 5 5 3.33 ...For data dimension, you can write:
dim(data)## [1] 96 20To see the total number of rows:
nrow(data) ## [1] 96To see the total number of column:
ncol(data) ## [1] 20Also, if you wanna see the total of column, you can use this:
length(data) ## [1] 20To see the names for each columns:
names(data) ## [1] "Station" "Hours" "period" "abundance" "fiber"
## [6] "fragment" "Film" "Pellet" "Transparent" "Black"
## [11] "Red" "Blue" "Green" "Orange" "Yellow"
## [16] "Purple" "<0.3" "0.3 - 0.5" "0.5 - 1.0" "1.0 - 5.0"Did you know that ggplot2 only works with long format data. Mostly we used wide format data with all data will be filled under specific column. But ‘ggplot’ cant perform that way. So, we need to transform first. To do that, we are using ‘tidyverse’ package.
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.3 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsdata_long <- data %>% pivot_longer(cols = c(abundance, fiber, fragment, Film, Pellet, Transparent, Black, Red, Blue, Green, Orange, Yellow, Purple, "<0.3", "0.3 - 0.5", "0.5 - 1.0", "1.0 - 5.0"), names_to = "Parameter", values_to = "Concentration")Now, we check does our column is differs from the original format (wide format).
names(data_long)## [1] "Station" "Hours" "period" "Parameter"
## [5] "Concentration"We start with the basic one, which is the base:
ggplot(data_long, aes(x=Station, y = Concentration, fill = Station))
Now, we slowly add up the base with graph. In ‘ggplot()’, we called this as geom. For example geom_bar, geom_boxplot, geom_line, etc. In this one, we are trying to perform boxplot first.
ggplot(data_long, aes(x=Station, y = Concentration, fill = Station)) + geom_boxplot()
What if we try for the geom_violin?
ggplot(data_long, aes(x=Station, y = Concentration, fill = Station)) +geom_violin()
What if we try for the geom_barplot?
ggplot(data_long, aes(x=Station, y = Concentration, fill = Station)) +geom_bar(position = "stack", stat = "identity")
It is interesting right? There’s lot out there and you can find the codes in https://r-graph-gallery.com/
Lets continue with out box plot. Before this we just perform the overall box plot. But, if you take a look deeper, you might find the data set have 16 different variables? Before we proceed, lets us tackle the main issue first, which is the outlier.To do that we must change the outlier shape first. You may choose from 1 - 30. The shape will be differ for each number. Please choose accordingly. In this case, im gonna use 21 since it can fill with colour.
ggplot(data_long, aes(x=Station, y = Concentration, fill = Station)) + geom_boxplot(outlier.shape = 21)
Lets say you wanna try shape = 15, then it’ll looks like this.
ggplot(data_long, aes(x=Station, y = Concentration, fill = Station)) + geom_boxplot(outlier.shape = 15)
Next, we set for the outlier’s colour. We’ll gonna give “purple” inside of the outlier
ggplot(data_long, aes(x=Station, y = Concentration, fill = Station)) + geom_boxplot(outlier.shape = 21, outlier.fill = "purple")
Now, we set the size of the outlier itself. To see the change, lets try size = 5
ggplot(data_long, aes(x=Station, y = Concentration, fill = Station)) + geom_boxplot(outlier.shape = 21, outlier.fill = "purple", outlier.size = 5)
You can see the differences right? So, use the size according the article’s format. But, it seems that the intensity of the outlier’s colour way to high right? How about we loose that by changing the the transparency. The transprency range from 0 - 1. Lets try 0.3.
ggplot(data_long, aes(x=Station, y = Concentration, fill = Station)) + geom_boxplot(outlier.shape = 21, outlier.fill = "purple", outlier.size = 5, outlier.alpha = 0.3)
Nice right? But back to our previous discussion, this data set have 16 different variable but in this graph, we cant see any result for each individual parameter. So how we can do that? No worries, we just use facet to resolve the issue. Lets continue
ggplot(data_long, aes(x=Station, y = Concentration, fill = Station)) + geom_boxplot(outlier.shape = 21, outlier.fill = "purple", outlier.size = 5, outlier.alpha = 0.3) + facet_wrap(~Parameter)
There you go. Now you can see each individual parameter like in your data set. But the outlier size seems big and we need to make it smaller.
ggplot(data_long, aes(x=Station, y = Concentration, fill = Station)) + geom_boxplot(outlier.shape = 21, outlier.fill = "purple", outlier.size = 3, outlier.alpha = 0.3) + facet_wrap(~Parameter)
Wait a bit, can’t you see each individual parameter has a similar scale in y-axis? How to fix that? Chill out, we sort it out together by adding scales.
ggplot(data_long, aes(x=Station, y = Concentration, fill = Station)) + geom_boxplot(outlier.shape = 21, outlier.fill = "purple", outlier.size = 3, outlier.alpha = 0.3) + facet_wrap(~Parameter, scales = "free_y")