Lab02 - Graphical Statistics
Jarad Niemi
2021-02-02
Setup
To follow along, use the lab02 code also run the following
Install package
install.packages("tidyverse")If the install fails, then run
install.packages(c("dplyr","tidyr","ggplot2"))Load packages
The installation only needs to be done once. But we will need to load the packages in every R session where we want to use them. To load the packages, use
library("dplyr")
library("tidyr")
library("ggplot2")alternatively, you can load the entire (not very big) tidyverse.
library("tidyverse")## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.0.5 v dplyr 1.0.3
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Constructing plots
The main purpose of the lab today is to construct plots using the ggplot2 R package. In order to construct these plots, we need to construct an appropriate data.frame and we will use dplyr to help us construct that data.frame.
Let’s use the built-in R data set airquality. Before we start plotting let’s take a quick look at the data.
dim(airquality)## [1] 153 6head(airquality)## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6tail(airquality)## Ozone Solar.R Wind Temp Month Day
## 148 14 20 16.6 63 9 25
## 149 30 193 6.9 70 9 26
## 150 NA 145 13.2 77 9 27
## 151 14 191 14.3 75 9 28
## 152 18 131 8.0 76 9 29
## 153 20 223 11.5 68 9 30summary(airquality)## Ozone Solar.R Wind Temp
## Min. : 1.00 Min. : 7.0 Min. : 1.700 Min. :56.00
## 1st Qu.: 18.00 1st Qu.:115.8 1st Qu.: 7.400 1st Qu.:72.00
## Median : 31.50 Median :205.0 Median : 9.700 Median :79.00
## Mean : 42.13 Mean :185.9 Mean : 9.958 Mean :77.88
## 3rd Qu.: 63.25 3rd Qu.:258.8 3rd Qu.:11.500 3rd Qu.:85.00
## Max. :168.00 Max. :334.0 Max. :20.700 Max. :97.00
## NA's :37 NA's :7
## Month Day
## Min. :5.000 Min. : 1.0
## 1st Qu.:6.000 1st Qu.: 8.0
## Median :7.000 Median :16.0
## Mean :6.993 Mean :15.8
## 3rd Qu.:8.000 3rd Qu.:23.0
## Max. :9.000 Max. :31.0
## For built in datasets, we can get more information by going to the help file.
?airqualityOne issue with this dataset is that the Month/Day columns don’t really provide us with a Date. Let’s create a new column that creates a real Date.
airquality <- airquality %>%
dplyr::mutate(Date = as.Date(paste("1973",Month,Day,sep="/"))) If you deal with dates a lot, you should check out the lubridate package.
Histogram
All ggplot2 graphics require a data.frame containing the data and this data.frame is always the first argument to a ggplot call. After this, we specify some aesthetics using the aes() function. Then, we tell ggplot2 what kind of graphics to construct.
ggplot(airquality, # data.frame containing the data
aes(x=Ozone)) + # a column name from the data.frame
geom_histogram() # create a histogram## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 37 rows containing non-finite values (stat_bin).
If you want to avoid the message, you can specify the number of bins to use.
ggplot(airquality, aes(x=Ozone)) +
geom_histogram(bins = 40)## Warning: Removed 37 rows containing non-finite values (stat_bin).
If you want plot on the density scale (so that you can compare to a pdf), use the following:
ggplot(airquality, aes(x=Ozone)) +
geom_histogram(aes(y=..density..), bins = 40)## Warning: Removed 37 rows containing non-finite values (stat_bin).
Histogram activity
Create a histogram of solar radiation on the density scale with 50 bins.
Click for solution
ggplot(airquality, aes(x=Solar.R)) +
geom_histogram(aes(y=..density..), bins = 50)## Warning: Removed 7 rows containing non-finite values (stat_bin).
Boxplots
The syntax for boxplots is similar except that the variable you are interest in is the y aesthetic.
ggplot(airquality,
aes(x=1,y=Ozone)) +
geom_boxplot() ## Warning: Removed 37 rows containing non-finite values (stat_boxplot).
Comparing boxplots
ggplot(airquality,
aes(x=Month, y=Ozone, group=Month)) +
geom_boxplot() ## Warning: Removed 37 rows containing non-finite values (stat_boxplot).
Boxplot activity
Create boxplots of wind speed by month. Bonus: See if you can google to find out how to swap the axes, i.e. have Month on the y-axis and Wind on the x-axis.
Click for solution
ggplot(airquality,
aes(x=Month, y=Wind, group=Month)) +
geom_boxplot(outlier.shape = NA, color='grey') +
theme_bw() +
coord_flip()
Scatterplot
At this point we can construct individual graphs for our 4 different response variables: Ozone, Solar.R, Wind, and Temp. Perhaps we want to understand the temporal variability for Ozone. We can use a scatterplot of Ozone vs Date.
ggplot(airquality, aes(x = Date, y = Ozone)) +
geom_point()## Warning: Removed 37 rows containing missing values (geom_point).
or if we wanted a line plot
ggplot(airquality, aes(x = Date, y = Ozone)) +
geom_line()
Notice that the line is disconnected wherever we have missing data.
Perhaps we want to understand the relationship between solar radiation and ozone.
ggplot(airquality, aes(x = Solar.R, y = Ozone)) +
geom_point()## Warning: Removed 42 rows containing missing values (geom_point).
Scatterplot activity
Create a scatterplot of wind speed versus temperature.
Click for solution
ggplot(airquality, aes(x = Temp, y = Wind)) +
geom_point()
Boxplots with scatterplots
Scatterplots don’t look so good when there are data points that overlap. For example, when plotting Ozone vs Month the points may overlap due to Month only having 5 values in the data set.
ggplot(airquality,
aes(x=Month, y=Ozone, group=Month)) +
geom_point() ## Warning: Removed 37 rows containing missing values (geom_point).
So, instead we will typically jitter the points a bit to remove the overlap, e.g.
ggplot(airquality,
aes(x=Month, y=Ozone, group=Month)) +
geom_jitter() ## Warning: Removed 37 rows containing missing values (geom_point).
Now, we can combine the boxplots we discussed earlier with scatterplots or jittered scatterplots, e.g.
ggplot(airquality,
aes(x=Month, y=Ozone, group=Month)) +
geom_boxplot(color='grey', # make the boxes not so obvious
outlier.shape = NA) + # remove outliers,
geom_point() + # because they get plotted here
theme_bw() # Change the theme to remove gray## Warning: Removed 37 rows containing non-finite values (stat_boxplot).## Warning: Removed 37 rows containing missing values (geom_point).
or
ggplot(airquality,
aes(x=Month, y=Ozone, group=Month)) +
geom_boxplot(color='grey', # make the boxes not so obvious
outlier.shape = NA) + # remove outliers,
geom_jitter() + # because they get plotted here
theme_bw() # Change the theme to remove gray## Warning: Removed 37 rows containing non-finite values (stat_boxplot).## Warning: Removed 37 rows containing missing values (geom_point).
Boxplot with scatterplot activity
Create a scatterplot of wind speed by month and add a boxplot for each month in the background.
Click for solution
ggplot(airquality,
aes(x=Month, y=Wind, group=Month)) +
geom_boxplot(outlier.shape = NA, color='grey') +
geom_jitter() +
theme_bw() +
coord_flip()
Converting data.frame from wide to long
If we want to put all the response variables on the same plot, we can color them. In order to do this, we will need to organize our data.frame into long format.
airquality_long <- airquality %>%
dplyr::select(-Month, -Day) %>% # Remove these columns
tidyr::gather(response, value, -Date)Take a look at the resulting data.frame.
dim(airquality)## [1] 153 7dim(airquality_long)## [1] 612 3head(airquality_long)## Date response value
## 1 1973-05-01 Ozone 41
## 2 1973-05-02 Ozone 36
## 3 1973-05-03 Ozone 12
## 4 1973-05-04 Ozone 18
## 5 1973-05-05 Ozone NA
## 6 1973-05-06 Ozone 28summary(airquality_long)## Date response value
## Min. :1973-05-01 Length:612 Min. : 1.00
## 1st Qu.:1973-06-08 Class :character 1st Qu.: 13.00
## Median :1973-07-16 Mode :character Median : 66.00
## Mean :1973-07-16 Mean : 80.06
## 3rd Qu.:1973-08-23 3rd Qu.: 91.00
## Max. :1973-09-30 Max. :334.00
## NA's :44table(airquality_long$response)##
## Ozone Solar.R Temp Wind
## 153 153 153 153ggplot(airquality_long,
aes(x = Date, y = value,
linetype = response,
color = response,
group = response)) +
geom_line()
Notice that the legend is automatically created. This is not something that is done in base R graphics.
Honestly, this doesn’t look very good, so it is better to facet the plot.
Faceted scatterplots
Facets are often a better way of representing multiple variables.
ggplot(airquality_long, aes(Date, value)) +
geom_point() +
facet_wrap(~response)## Warning: Removed 44 rows containing missing values (geom_point).
Since the axes are quite different for the different responses, we can allow them to vary in the different facets.
ggplot(airquality_long, aes(Date, value)) +
geom_line() +
facet_wrap(~response,scales="free_y")
Converting data.frame from long to wide
If we only had the long version of the data.frame, we can reconstruct the wide version by using the following
airquality2 <- airquality_long %>%
tidyr::spread(response, value)Customizing ggplot graphics
Sometimes it is helpful to save the plot as an R object so that it can be updated in the future. To save the plot, just use the assignment operator, e.g.
g <- ggplot(airquality2,
aes(x = Temp, y = Wind)) +
geom_point()
g # Then you can see the plot by just typing the object name
We would like this plot to be a bit more informative, so we will add some informative labels.
g <- g +
labs(x = "Temperature (F)",
y = "Wind speed (mph)",
title = "New York (May-September 1973)")
g
As you have seen before, we can also change the theme. I prefer the simple “bw” theme.
g <- g + theme_bw()
g
We can add a regression line.
g <- g + geom_smooth(method="lm")
g## `geom_smooth()` using formula 'y ~ x'
Alternatively, you can combine all the steps
ggplot(airquality2,
aes(x = Temp, y = Wind)) +
geom_point() +
geom_smooth(method = "lm") +
labs(x = "Temperature (F)",
y = "Wind speed (mph)",
title = "New York (May-September 1973)") +
theme_bw()## `geom_smooth()` using formula 'y ~ x'
Plot creation activity
Use the cars dataset to construct and customize a figure displaying the relationship between the stopping distance and speed of a car.
Click for solution
ggplot(cars,
aes(x=speed, y=dist)) +
geom_point() +
geom_smooth(method = "lm") +
labs(x = "Speed (mph)",
y = "Stopping distance (ft)",
title = "Stopping distance as a function of speed (1920s)") +
theme_bw()## `geom_smooth()` using formula 'y ~ x'
Saving ggplot graphics
If you want to save the plot, use the ggsave function, e.g.
ggsave(filename = "plot.png", plot = g, width = 5, height = 4)