Section 4 Summarizing the Data

Here we will make some visualizations to summarize the basic characteristics of our data.

We will need the following libraries:

library(ggplot2)
library(extrafont)

To customize the font of our plots, we will need the following lines of code:

# to change the font of the plots 
font_import()

## Importing fonts may take a few minutes, depending on the number of fonts and the speed of the system.
## Continue? [y/n]

loadfonts(device = "win")

4.1 Heart Rate Data

Let’s begin by taking a look at our heart rate data.

# now read in the cleaned CSV file 
points_out <- read.csv('data/all_hr.csv')

How many data points do we have?

## [1] 17054

We will first visualize the distribution of all heart rate values.

# create histogram of heart rate values 
ggplot(data = points_out)+
  geom_histogram(aes(x=points_out$hr), color='white', fill='grey')+ # add the histogram
  theme_minimal()+ # basic formatting
  xlab('Heart Rate')+ # change the x axis label
  ylab('Frequency')+ # change the y axis label
  theme(text = element_text(family='Times New Roman')) # change the font 

What is the mean and standard deviation?

## [1] "Mean: 122.753078456667"

## [1] "Standard deviation: 11.6308150361457"

Now let’s take a look at the spatial distribution of these points. We will create a hexbin plot that shows the count of data points within a given subset of the study area.

ggplot(data=points_out)+
  geom_hex(aes(x=points_out$X_coord, y=points_out$Y_coord), bins=15)+ # add in the hexbins 
  theme_minimal()+ # basic formatting
  xlab('Longitude')+ # change the x axis label
  ylab('Latitude')+ # change the y axis label
  theme(text = element_text(family='Times New Roman')) # change the font

4.2 Personal Comments

Now we can take a closer look at the personal comments data.

# read the file
notes <- read.csv('data/voice_notes.csv')

How many notes are there in total?

## [1] 36

Let’s visualize how these comments can be broken down across themes and positive/negative codes.

ggplot(data = notes)+
  geom_bar(aes(x=notes$Theme, fill=notes$Sentiment),  color='white')+ # add in the bars 
  coord_flip()+ # switch orientation of the axes to easier reading of the labels 
  theme_minimal()+ # basic formatting 
  xlab('Theme')+ # change the x axis label
  ylab('Frequency')+ # change the y axis label
  guides(fill=guide_legend(title='Impact on anxiety'))+ # change legend title 
  theme(text = element_text(family='Times New Roman')) # change the font