6-Data Visualization with ggplot2
If you and your group have any questions or get stuck as you work through this in-class exercise, please ask the instructor for assistance. Have fun!
The ggplot2 package is a popular system for creating data visualizations like plots, charts, graphs, etc.
In this activity, you will make a scatter plot, bar chart, and a line chart.
Before you start this activity, let’s give your RStudio session a fresh start. For that:
- Save your previous scripts by clicking on File > Save, or on the save icon on the top left. If needed, choose a folder to save it (probably the working directory you were working on in the previous activity) and give it a meaningful name.
- Close the script by clicking on File > Close or on the x next to the file name on the top left.
- Clean your R environment (i.e., remove all the objects) by clicking on the broom icon
on the top right and clicking yes on the pop-up window that appears. - Create a new script by clicking on File > New File > R Script, or on the New Script icon
on the top left.
1. Getting Ready
1.1 Prepare your working environment
You will use both the tidyverse and the janitor package in this activity. You should already have the tidyverse package installed on your computer for previous activities, but not the janitor package. So before you continue, make sure to install the janitor package by running the following code in your console:
install.packages("janitor")
⭐ Task 6-1
Prepare your working environment.
Prepare your working environment by loading the tidyverse and janitor packages (the ggplot2 package is part of the tidyverse package) and setting your working directory.
Check your code
# load packages
library(tidyverse)
library(janitor)
# set working directory
setwd("path-to-folder") # Remeber to substitute "path-to-folder" by the actual path to your folder
1.2 Load your data
From this link download the following data we have prepared for you to use in this activity. Save the file in your working directory.
⭐ Task 6-2
Read your data set.
- Data set file name:
flavors_of_cacao.csv - Name your dataframe:
chocolateData
Check your code
# read data
chocolateData <- read.csv("flavors_of_cacao.csv")
Hint: See Section 4 for instructions on importing a .csv file.
1.3 Check your data
⭐ Task 6-3
Preview the first 5 rows of your chocolate data.
Check your code
# Check data
chocolateData %>% head(5) # Preview first 5 lines of chocolateData
## Company SpecificBeanOrigin_BarName REF Review_Date Cocoa_Percent Company_Location Rating
## 1 A. Morin Agua Grande 1876 2016 63% France 3.75
## 2 A. Morin Kpime 1676 2015 70% France 2.75
## 3 A. Morin Atsane 1676 2015 70% France 3.00
## 4 A. Morin Akata 1680 2015 70% France 3.50
## 5 A. Morin Quilla 1704 2015 70% France 3.50
## Bean_Type Broad.Bean_Origin
## 1 Sao Tome
## 2 Togo
## 3 Togo
## 4 Togo
## 5 Peru
Another way to inspect your data is to use the str() function presented in section 4.
⭐ Task 6-4
See the structure of your data.
Check your code
# Check data
str(chocolateData)
## 'data.frame': 1795 obs. of 9 variables:
## $ Company : chr "A. Morin" "A. Morin" "A. Morin" "A. Morin" ...
## $ SpecificBeanOrigin_BarName: chr "Agua Grande" "Kpime" "Atsane" "Akata" ...
## $ REF : int 1876 1676 1676 1680 1704 1315 1315 1315 1319 1319 ...
## $ Review_Date : int 2016 2015 2015 2015 2015 2014 2014 2014 2014 2014 ...
## $ Cocoa_Percent : chr "63%" "70%" "70%" "70%" ...
## $ Company_Location : chr "France" "France" "France" "France" ...
## $ Rating : num 3.75 2.75 3 3.5 3.5 2.75 3.5 3.5 3.75 4 ...
## $ Bean_Type : chr " " " " " " " " ...
## $ Broad.Bean_Origin : chr "Sao Tome" "Togo" "Togo" "Togo" ...
We can see that the dataset is composed of 1795 observations of chocolates, where 9 variables have been measured. The result also shows you the names of the variables and the type of each variable. With this type of result, you can identify certain elements of your dataset that you might want to clean before starting with data visualization and analysis.
In the above example, you can see that variable names do not have a standardized format such as all lower caps, or using only “.” or “_” instead of spaces. Moreover, you can see that the percent of cocoa in each chocolate was read as a character because it contains the % sign, but you might want to make this into numeric values for data analysis. Next, we will see simple ways to clean your data.
1.4 Clean your data
After checking your dataset, you might encounter some errors that you want to correct in your data. Cleaning your data and making sure that it is in good shape for data visualization and analysis is an important step. Here we will go through some basic data cleaning steps.
⭐ Task 6-5
Standardize column names
Use the clean_names() function from the janitor package to automatically standardize column names formatting. The clean_names() function requires only one parameter: the dataframe name.
Hint: Remember to overwrite chocolateData with the object with the new column names, otherwise R will not save the new column names.
Check your code
## standardize column names
chocolateData <- clean_names(chocolateData)
## view column names after standardization
str(chocolateData)
## 'data.frame': 1795 obs. of 9 variables:
## $ company : chr "A. Morin" "A. Morin" "A. Morin" "A. Morin" ...
## $ specific_bean_origin_bar_name: chr "Agua Grande" "Kpime" "Atsane" "Akata" ...
## $ ref : int 1876 1676 1676 1680 1704 1315 1315 1315 1319 1319 ...
## $ review_date : int 2016 2015 2015 2015 2015 2014 2014 2014 2014 2014 ...
## $ cocoa_percent : chr "63%" "70%" "70%" "70%" ...
## $ company_location : chr "France" "France" "France" "France" ...
## $ rating : num 3.75 2.75 3 3.5 3.5 2.75 3.5 3.5 3.75 4 ...
## $ bean_type : chr " " " " " " " " ...
## $ broad_bean_origin : chr "Sao Tome" "Togo" "Togo" "Togo" ...
⭐ Task 6-6
Fix percent values
Remove the percentage signs from the column cocoa_percent by converting the values to numbers using the functions mutate() and parse_number(). The parse_number() function takes in as a parameter a vector and drops all the non-numeric characters, transforming it into a numeric vector.
Hint: Remember to overwrite chocolateData with the object with the new column names, otherwise R will not save the new column names.
Check your code
# cleans % from cocoa_percent
chocolateData <- chocolateData %>% # get the dataframe
mutate( # identified that you want to "mutate" a variable
cocoa_percent = parse_number(cocoa_percent) # overwrite cocoa_percent variable after having used the parsed_number function
)
## view data frame after cleaning. Note how the cocoa_percent variable is now nueric
str(chocolateData)
## 'data.frame': 1795 obs. of 9 variables:
## $ company : chr "A. Morin" "A. Morin" "A. Morin" "A. Morin" ...
## $ specific_bean_origin_bar_name: chr "Agua Grande" "Kpime" "Atsane" "Akata" ...
## $ ref : int 1876 1676 1676 1680 1704 1315 1315 1315 1319 1319 ...
## $ review_date : int 2016 2015 2015 2015 2015 2014 2014 2014 2014 2014 ...
## $ cocoa_percent : num 63 70 70 70 70 70 70 70 70 70 ...
## $ company_location : chr "France" "France" "France" "France" ...
## $ rating : num 3.75 2.75 3 3.5 3.5 2.75 3.5 3.5 3.75 4 ...
## $ bean_type : chr " " " " " " " " ...
## $ broad_bean_origin : chr "Sao Tome" "Togo" "Togo" "Togo" ...
📍 Reminder! Save your work
2. Creating plots and charts in ggplot2
Here is some information about creating and formatting plots, common to all types we will look at in this activity. Don’t do anything yet!
The command to begin plots and charts are very similar. Let’s first look at the commonalities. For all of them, we will use the ggplot() function and a geometry function. ggplot() parameters are:
- The dataset used for the plot
data = datasetName - The aesthetic mappings. This specifies which column values are assigned to the x-axis, and which are assigned to the y-axis.
aes(x = columnForXAxis, y = columnForYAxis)
The geometry function is attached to the ggplot() function with + geom_ and is completed by the type of plot or chart:
- scatter plot or point plots:
+ geom_point() - bar charts:
geom_bar()orgeom_col() - line charts:
geom_line()
Plots will appear in the “Plot” tab (probably in the bottom right hand quadrant of your workspace).
2.1. Scatter Plots
Let’s apply the ggplot command above to create a scatter plot.
Definition - Scatter plot: A plot with two axes, each representing a different variable. Each individual observation is shown using a single point. The position of the point is determined by the value of the variables assigned to the x and y axes for that observation.
📍 Reminder! Save your work
⭐ Task 6-7
Make a scatter plot of the cocoa percentage and the rating a chocolate bar received.
- Using chocolate data :
chocolateData - X-axis = Cocoa percentage:
cocoa_percent - Y-axis = Rating a chocolate bar received:
rating
Check your code
ggplot(data = chocolateData, aes(x = cocoa_percent, y = rating)) +
geom_point() # then add a layer of points
Before we add details to our plot, we need to learn about the different components. Again, wait until the next task to do anything.
Definition - Fitted line: (aka. a ‘line of best fit’) is a line representing some function of x and y that has the best fit (or the smallest overall error) for the observed data.
Function for adding a smooth line to a plot: geom_smooth(method = "")
- method type specifies the type of smoothing to be used
Expand for more geom_smooth method types
- Linear Model (“lm”): fits a linear regression model, suitable for linear relationships.
- Locally Estimated Scatterplot Smoothing (“loess” or “lowess”): creates a smooth line through the plot by fitting simple models in a localized manner, which can handle non-linear relationships well. Ideal for smaller datasets
- Generalized Additive Models (“gam”): model complex, nonlinear trends in data. Ideal for larger datasets.
- Moving Average (“ma”): smooths data by creating an average of different subsets of the full dataset. It’s useful for highlighting trends in noisy data.
- Splines (“splines”): provide a way to smoothly interpolate between fixed points, creating a piecewise polynomial function. They are useful for fitting complex, flexible models to data.
- Robust Linear Model (“rlm”): Similar to linear models but less sensitive to outliers. It’s useful when your data contains outliers that might skew the results of a standard linear model.
- Fitted line:
method = "lm"
⭐ Task 6-8
Make another scatter plot of the cocoa percentage and the rating a chocolate bar received, with the following:
- A “line of best fit”
Remember:
- Using chocolate data:
chocolateData - X-axis = Cocoa percentage:
cocoa_percent - Y-axis = Rating a chocolate bar received:
rating - Line of best fit:
geom_smooth(method = "lm")
Check your code
ggplot(data = chocolateData, aes(x = cocoa_percent, y = rating)) +
geom_point() + # then add a layer of points
geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'
⭐ Task 6-9
Add descriptive axis labels and a title to your scatter plot.
We’re also going to add labels and custom colors using the labs() function and custom colors.
- Labels
+ labs(title = "", x = "", y = " ")
Check your code
# you can use the following labels or make your own.
ggplot(data = chocolateData, aes(x = cocoa_percent, y = rating)) +
geom_point() + # then add a layer of points
geom_smooth(method = "lm") +
labs(title = "Rating of Chocolate Bar by Cocoa Percentage", x = "Chocolate Bar Rating", y = "Cocoa Percentage")
## `geom_smooth()` using formula = 'y ~ x'
2.2. Bar Charts
A bar chart shows the relationship between a categorical variable (on the x-axis) and a numerical variable (on the y-axis).
A common type of bar plot is one that illustrates categories along the x-axis and the count of observations from each category on the y-axis.
For this type of data, the call for bar charts in ggplot2 geom_bar() makes the height of the bar proportional to the number of observations in each group of a categorical variable, so you only need to tell ggplot2 the variable you want to use on the x-axis of your bar chart, and it makes the calculations for the y-axis in the background.
For example, let’s make a bar chart that shows the number of chocolate bars that are made for different types of cacao beans.
⭐ Task 6-10
Create a basic bar chart.
Your chart will illustrate the number of bars of different types of beans that are being made.
- Use the
chocolateDataobject inside the ggplot call - Specify the variable
bean_typefor the x-axis - Use
+ geom_bar()to plot a bar chart
Check Your Code and Output
ggplot(chocolateData, aes(x = bean_type))+
geom_bar()
Output:
Hint: you do not need to specify anything for the y-axis in this case
You can see that the plot is hard to interpret, as there are many bean types that are not commonly made. In this case, it would be best to plot only the most common bean types. For that, we first need to create a new dataset that contains only the most common bean types.
To do that, we need to calculate the number of bars of each type that are being made. We can use the group_by() function that you learned in the previous section:
# Get the most common bean types
bars_per_type <- chocolateData %>% # get the dataframe
group_by(bean_type) %>% # group by bean type
count() # count the number of bars per bean_type
# check the new data frame
bars_per_type
## # A tibble: 12 × 2
## # Groups: bean_type [12]
## bean_type n
## <chr> <int>
## 1 Amazon 5
## 2 Beniano 3
## 3 Blend 41
## 4 BlendForasteroCriollo 1
## 5 CCN51 1
## 6 Criollo 213
## 7 EET 3
## 8 Forastero 195
## 9 ForasteroArriba 1
## 10 Matina 3
## 11 Nacional 5
## 12 Trinitario 436
Now, we want to get the list of the most common bean types. Looking at the data above, you could decide to use 10 as a threshold of a sufficient number of bars being produced.
# Get most common bean types
common_bean_types <- bars_per_type %>% # get the data
filter(n > 10) %>% # filters for rows where the variable n is larger than 10
pull(bean_type) # gets the column with bean type names
# check common bean types
common_bean_types
## [1] "Blend" "Criollo" "Forastero" "Trinitario"
There are four types of beans with more than 10 chocolate bears being produced. Finally, we can then filter the original dataset only for the rows with these bean types.
# Filter chocolateData to only include common beans
chocolateData_commonBeans <- chocolateData %>% # Get the data
filter(bean_type %in% common_bean_types) # Filter for rows where the value in
# variable bean_type is present in the vector common_bean_types
⭐ Task 6-11
Create a basic bar chart.
Now remake your bar chart, but only for the most common bean types.
Check Your Code and Output
ggplot(chocolateData_commonBeans, aes(x = bean_type))+
geom_bar()
Output:
Hint: use the newly created object chocolateData_commonBeans.
Another type of bar chart is the stacked bar chart. A stacked bar chart shows two dimensions (i.e., categorical variables) of data. Each bar will represent one category type, and each bar will be chopped into sections which represent a second category type.
⭐ Task 6-12
Create a stacked bar chart.
To add a second dimension,
- following the same command as the bar chart above, modify it by:
- adding the parameter
fill = factor2nametoaes(), where ‘factor2name’ is the second variable’s column name. - setting the parameter of
geom_bar()toposition="stack"
- adding the parameter
For this task, use company_location as the second variable that will chop the bars of the most common bean types into sections.
Check Your Code and Output
ggplot(chocolateData_commonBeans, aes(x = bean_type, fill = company_location)) +
geom_bar(position = "stack")
So far, we have looked at bar charts that plot the count of observations in different categories in the y-axis. But if we want the y-axis to show the values of actual variables in your data? For that situation, you can use the geom_col() function.
For example, imagine you want to plot the average rating for the different types of beans. First, you would need to calculate the average rating per bean type. To do this, you can use the group_by() and summarise() functions your learned in the previous section:
chocolateData_commonBeans_rating <- chocolateData_commonBeans %>% # get the dataset
group_by(bean_type) %>% # group by bean type
summarise( # summarise a variable for each bean type
mean_rating = mean(rating) # the summary is the mean rating
)
# see the results
chocolateData_commonBeans_rating
## # A tibble: 4 × 2
## bean_type mean_rating
## <chr> <dbl>
## 1 Blend 3.35
## 2 Criollo 3.27
## 3 Forastero 3.11
## 4 Trinitario 3.25
Then, you can use this new dataset to plot your bar chart.
⭐ Task 6-13
Create a bar chart using geom_col().
Use the object chocolateData_commonBeans_rating and the function geom_col() to plot a bar chart showing the average rating per bean type.
Check Your Code and Output
ggplot(chocolateData_commonBeans_rating, aes(x = bean_type, y = mean_rating)) +
geom_col()
- Hint: you need to specify a variable for the y-axis when using
geom_col()
2.3. Line Charts
To create a line chart, let’s start first by creating a new variable that we might want to plot in a line chart. In this case, let’s assume we are interested in seeing how the average chocolate rating varies through the years.
⭐ Task 6-14
Create an object with the mean chocolate rating by year.
Using piping, create a new object, meanRatingByYear
- base data:
chocolateData - group_by:
review_date - use
summarise()and calculate the mean of the rating variables inside the summarise
Check Your Code
meanRatingByYear <- chocolateData %>%
group_by(review_date) %>%
summarise(rating = mean(rating))
# Now see the object created
meanRatingByYear
Your output will be:
## # A tibble: 12 × 2
## review_date rating
## <dbl> <dbl>
## 1 2006 3.28
## 2 2007 3.30
## 3 2008 3.05
## 4 2009 3.13
## 5 2010 3.20
## 6 2011 3.27
## 7 2012 3.21
## 8 2013 3.28
## 9 2014 3.24
## 10 2015 3.28
## 11 2016 3.26
## 12 2017 3.5
- Hint: this will be very similar to when you calculated the mean rating by bean type above.
Now we are ready to make our line chart!
⭐ Task 6-15
Create a line chart using the mean chocolate rating by year.
Here we’ll make a line chart to show how the mean rating of chocolate has changed by year.
- Your base data will be the mean rating table you just created
- the x-axis value will be the review date
- the y-axis will be the rating
- the geom type is
geom_line(), with no parameter
After the geom type, you might want to add a line of code to make sure the x-axis label contains the actual years. For that, you can use the scale_x_continuous function, which take as the parameter breaks the vector of points to create axis breaks. To use the function, you have to use + scale_x_continuous(breaks = vectorofbreaks) at the end of your plot code.
Check Your Code
ggplot(meanRatingByYear, aes(x = review_date, y = rating)) +
geom_line() +
scale_x_continuous(
breaks = meanRatingByYear$review_date # Use actual review dates for breaks
)
⭐ Task 6-16
Style your line chart.
Using the same chart you just made, add some stylistic features and modifications.
- rename the x label to “Review Date”
- rename the y label to “Rating”
- Add a title using
ggtitle(): “Change in Rating Over Time”
Check Your Code
ggplot(meanRatingByYear, aes(x = review_date, y = rating)) +
geom_line() +
scale_x_continuous(
breaks = meanRatingByYear$review_date # Use actual review dates for breaks
) +
labs(
x = "Review Date",
y = "Rating",
title = "Change in Rating Over Time"
)
Congratulations! Now you know how to use ggplot2 to plot scatter plots, bar charts and line charts!
📍 Reminder! Save your work