Using ggplot to create a professional looking graph - r

Question

I have tried to use ggplot2 to create a professional-looking graph, but I have having some trouble with a lot of things. I would like to add color to the data points, add dates on the x-axis, and create a line of best fit or something similar if possible. I have been searching on Stack Exchange and Google in general to try and solve this problem but to no avail. I am using the "Civilian Labor Force Participation Rate: 20 years and over, Black or African American Men" from the Federal Reserve Bank of St. Louis (FRED).
I am using RStudio, and I imported the data from LNS11300031 and then used the read.csv() function to read it into RStudio. I initially used the plot() function to plot the data, but I want to use the ggplot() function to create a better looking graph, but when I create the graph the data points look very opaque, blurry, and cloudy, and there is no labels on the x-axis. I would like to add color and a line of best fit, but I do not know how to do that.
This is the code I used to create the graph with no x-axis labels:
ggplot(data = labor, mapping = aes(x = labor$DATE, y = labor$LNS11300031)) + geom_point(alpha = 0.1)
This is the graph that my code produced:
Here is some sample data (labor is the variable I used to store the data from the FRED site):
head(labor) DATE LNS11300031
1 1972-01-01 77.6
2 1972-02-01 78.3
3 1972-03-01 78.7
4 1972-04-01 78.6
5 1972-05-01 78.7
6 1972-06-01 79.4
I would like to change the variable name LNS11300031 to Labor Force Participation Rate
Additional information about the data:
str(labor)
'data.frame': 566 obs. of 2 variables:
$ DATE : Factor w/ 566 levels "1972-01-01","1972-02-01",..: 1 2 3 4 5 6 7 8 9 10 ...
$ LNS11300031: num 77.6 78.3 78.7 78.6 78.7 79.4 78.8 78.7 78.6 78.1 ...
I would like the code to create much clearer data points with color and a trend line, and be able to have an x-axis with the corresponding dates.

Answer 1

Here's a basic attempt to cover all 3 of your desired improvements:
Clearer points: don't set the alpha too low! A bit of alpha is good for overlapping points, but alpha = 0.1 makes them too blurry.
Colour: R understands simple colour names like "red", but also hex colour codes. Pick any colours you want.
Trend line: easy to add with stat_smooth(). I've used method='lm' which gives a straight linear regression line but there are more flexible alternatives.
Date labels on the x-axis: Make sure your DATE column is correctly set as a Date type, and use scale_x_date() to tweak the labels.
quantmod::getSymbols("LNS11300031", src="FRED")
# Your data is available from the quantmod package
labor = LNS11300031 %>%
as.data.frame() %>%
rownames_to_column(var = "DATE") %>%
# Make sure DATE is a Date column
mutate(DATE = as.Date(DATE))
# Generally, you don't use data$column syntax within ggplot,
# just give the column name
ggplot(data = labor, mapping = aes(x = DATE, y = LNS11300031)) +
geom_point(alpha = 0.7, colour = "#B07AA1") +
stat_smooth(method = "lm", colour = "#E15759", se = FALSE) +
scale_x_date(date_breaks = "5 years", date_labels = "%Y") +
theme_minimal()
Output: