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4-Data Visualization with ggplot2 |
5 |
Workshop Activities |
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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.
- Package names:
- tidyverse
- ggthemes
- janitor
{::options parse_block_html='true' /}
Check Your Code
install.packages("ggthemes") #then, as always, type 'enter' or 'return' to submit the command for execution
install.packages("janitor")
library(ggthemes) #Do not wrap library() parameter string in quotes
library(janitor){::options parse_block_html='false'/}
Hint: wrap the package name in "" quotations
- Do not wrap
the library() parameter in "" quotations
More about ggthemes here{:target=“_blank”}. More about janitor here{:target=“_blank”}.
- Data set file name:
flavors_of_cacao.csv(unless you changed the filename after downloading) - Name your variable:
chocolateData - Clean the column header names using
clean_names()where the parameter is chocolateData (leave parentheses blank if piping) - Remove first (empty) row using
filter(ref != "REF")
{::options parse_block_html='true' /}
Check Your Code
#if your file cannot be found, enter `getwd()` into your console and it will tell you the file path you should most likely use. If you cannot find the file, use Option a.
chocolateData <- read_csv("Desktop/flavors_of_cacao.csv") %>%
clean_names() %>% #Clean the column header names
filter(ref != "REF")
#If you get a column specification error, add `, show_col_types = FALSE` as to a parameter read_csv()
#e.g. chocolateData <- read_csv("Desktop/flavors_of_cacao.csv", show_col_types = FALSE)## Rows: 1795 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): Company, SpecificBeanOrigin_BarName, Cocoa_Percent, Company_Locatio...
## dbl (3): REF, Review_Date, Rating
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
{::options parse_block_html='false'/} Hint: See Activity 3, Task 3.1 for instructions on importing a csv file.
{::options parse_block_html='true' /}
Check Your Code
#preview first 5 lines of chocolateData
chocolateData %>% head(5)## # A tibble: 5 × 9
## company specific_bean_origin_bar_name ref review_date cocoa_percent
## <chr> <chr> <dbl> <dbl> <chr>
## 1 A. Morin Agua Grande 1876 2016 63%
## 2 A. Morin Kpime 1676 2015 70%
## 3 A. Morin Atsane 1676 2015 70%
## 4 A. Morin Akata 1680 2015 70%
## 5 A. Morin Quilla 1704 2015 70%
## # ℹ 4 more variables: company_location <chr>, rating <dbl>, bean_type <chr>,
## # broad_bean_origin <chr>
{::options parse_block_html='false'/}
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 is assigned
to the x axis, and which is 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() - line
charts: geom_line()
Plots will appear in the “Plot” tab (probably in the bottom right hand quadrant of your workspace).
First things first, we need to quickly clean up our dataframe for scatter plots. Copy and paste the following code into your console, and execute to imort and prepare our data.
#remove the percentage signs from the column cocoa_percent by converting the values to numbers
chocolateData$cocoa_percent <- parse_number(chocolateData$cocoa_percent)
#make sure the data type of each column is correct.
chocolateData <- type_convert(chocolateData)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## company = col_character(),
## specific_bean_origin_bar_name = col_character(),
## company_location = col_character(),
## bean_type = col_character(),
## broad_bean_origin = col_character()
## )
#You can ignore the Column Specification comment in the output. It indicates the column specification, which describes the data types of various columns after conversion, and shows that several columns have been confirmed as character columns.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 showing 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.
- Using chocolate data :
chocolateData - X-axis = Cocoa percentage:
cocoa_percent - Y-axis = Rating a chocolate bar received:
rating
{::options parse_block_html='true' /}
Check Your Code
ggplot(data = chocolateData, aes(x = cocoa_percent, y = rating)) +
geom_point() # then add a layer of points{::options parse_block_html='false'/}
Output
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 2.1.2: Make another scatter plot of the cocoa percentage and the rating a chocolate bar received, with the following:
-
A “line of best fit”
-
Informative x and y axis labels
-
A title
- 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")
- Using chocolate data :
{::options parse_block_html='true' /}
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'
{::options parse_block_html='false'/}
We’re also going to add labels and custom colors using the labs()
function and custom colors.
- Labels
+ labs(title = "", x = "", y = " ")
{::options parse_block_html='true' /}
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"){::options parse_block_html='false'/}
Output:
## `geom_smooth()` using formula = 'y ~ x'
First things first, we need to quickly clean up our dataframe for bar charts. Copy and paste the following code into your console, and execute.
chocolateData$bean_type_simplified <- word(chocolateData$bean_type, 1)
chocolateData$bean_type_simplified <- gsub('[[:punct:]]', '', chocolateData$bean_type_simplified)
chocolateData$bean_type_simplified <- trimws(chocolateData$bean_type_simplified)
chocolateData <- chocolateData %>%
filter(str_detect(bean_type_simplified, "\\S")) # This ensures the string contains at least one non-whitespace character
commonBeanTypes <- chocolateData %>%
select(bean_type_simplified) %>%
group_by(bean_type_simplified) %>%
count() %>%
filter(n > 20) %>%
ungroup() %>%
mutate(bean_type_simplified = as.factor(bean_type_simplified))
# Filter chocolateData to only include common beans
chocolateData_commonBeans <- chocolateData %>%
filter(bean_type_simplified %in% commonBeanTypes$bean_type_simplified)A bar chart illustrates categories along the x axis and the count of observations from each category on the y axis.
To make a bar chart, you need the data (categories, and values relevate to those categories), and the categories the data will be separated by (each representing one bar).
The first 5 rows of the bars made of common beans:
## # A tibble: 5 × 10
## company specific_bean_origin_bar_name ref review_date cocoa_percent
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 A. Morin Carenero 1315 2014 70
## 2 A. Morin Sur del Lago 1315 2014 70
## 3 A. Morin Puerto Cabello 1319 2014 70
## 4 A. Morin Madagascar 1011 2013 70
## 5 A. Morin Chuao 1015 2013 70
## # ℹ 5 more variables: company_location <chr>, rating <dbl>, bean_type <chr>,
## # broad_bean_origin <chr>, bean_type_simplified <chr>
The bars will represent the following categories:
## # A tibble: 4 × 2
## bean_type_simplified n
## <fct> <int>
## 1 Blend 41
## 2 Criollo 213
## 3 Forastero 195
## 4 Trinitario 436
With the code above, you now have:
- A dataset
chocolateData_commonBeans: containing the chocolate bars made with the most common beans - A vector
commonBeanTypeslist of the common bean types, which will be used as the categories for the x-axis.
Your chart will illustrate the frequency that chocolate bars are being made in different countries.
- Country bar was made in:
broad_bean_origin
{::options parse_block_html='true' /}
Check Your Code
ggplot(chocolateData_commonBeans, aes(x = chocolateData_commonBeans$bean_type_simplified)) + geom_bar(){::options parse_block_html='false'/}
Hint: geom type = “bar”
Output:
A stacked bar chart shows two dimensions (variables) of data. Each bar will represent one variable, and each bar will be chopped into sections which represent a second variable.
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
{::options parse_block_html='true' /}
Check Your Code
ggplot(chocolateData_commonBeans, aes(x = bean_type_simplified, fill = company_location)) +
geom_bar(position = "stack"){::options parse_block_html='false'/}
Output:
Using piping, create a new variable, meanRatingByYear
- base data:
chocolateData - group_by:
review_date - use
summarise()- the parameter is
rating=mean(rating)
- the parameter is
{::options parse_block_html='true' /}
Check Your Code
meanRatingByYear <- chocolateData %>% group_by(review_date)%>%summarise(rating=mean(rating)){::options parse_block_html='false'/}
Your output will be:
Then convert “review_date to Date class by entering
meanRatingByYear$review_date <- as.integer(meanRatingByYear$review_date)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
line, with no parameter
After the geom type, add:
ggplot(meanRatingByYear, aes(x = review_date, y = rating)) + geom_line()+ scale_x_continuous(breaks = meanRatingByYear$review_date, labels = as.character(meanRatingByYear$review_date))
{::options parse_block_html='true' /}
Check Your Code
ggplot(meanRatingByYear, aes(x = review_date, y = rating)) +
geom_line()+ scale_x_continuous(
breaks = meanRatingByYear$review_date, # Use actual review dates for breaks
labels = as.character(meanRatingByYear$review_date) # Convert to character to avoid decimals
){::options parse_block_html='false'/}
Output:
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
{::options parse_block_html='true' /}
Check Your Code
ggplot(meanRatingByYear, aes(x = review_date, y = rating)) +
geom_line() +
scale_x_continuous(
breaks = meanRatingByYear$review_date, # Use actual review dates for breaks
labels = as.character(meanRatingByYear$review_date) # Convert to character to avoid decimals
) +
labs(
x = "Review Date",
y = "Rating",
title = "Change in Rating Over Time"
) {::options parse_block_html='false'/}
Output:
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