FinalProject

##GGPlot

###Barplots

Now lets take a look at some ggplot2 barplots

we’ll start with making a dataframe based on the tooth data.

df <- data.frame(dose = c("D0.5", "D1", "D2"),
                 len = c(4.2, 10, 29.5))

df

##   dose  len
## 1 D0.5  4.2
## 2   D1 10.0
## 3   D2 29.5

And now lets make a second dataframe

df2 <- data.frame(supp=rep(c("vc", "OJ"), each = 3),
                  dose = rep(c("D0.5", "D1", "D2"), 2),
                  len = c (6.8, 15, 33, 4.2, 10, 29.5))

df2

##   supp dose  len
## 1   vc D0.5  6.8
## 2   vc   D1 15.0
## 3   vc   D2 33.0
## 4   OJ D0.5  4.2
## 5   OJ   D1 10.0
## 6   OJ   D2 29.5

Lets load up ggplot2

library(ggplot2)

Lets set our parameters for ggplot

theme_set(
  theme_classic() +
    theme(legend.position = "top")
)

Lets start with some basic barplots using the tooth data

f <- ggplot(df, aes(x = dose, y = len))

f + geom_col()

Now lets change the fill, and add labels to the top

f + geom_col(fill = "darkblue") +
  geom_text(aes(label = len), vjust = -0.3)

Now lets add the labels inside the bars

f+ geom_col(fill = "darkblue") +
  geom_text(aes(label = len), vjust = 1.6, color = "white")

Now lets change the barplot colors by group

f + geom_col(aes(color = dose), fill = "white") +
  scale_color_manual(values = c("blue", "gold", "red"))

This is kinda hard to see, so lets change the fill.

f + geom_col(aes(fill = dose)) +
  scale_fill_manual(values = c("blue", "gold", "red"))

Ok how do we do this with multiple groups

ggplot(df2, aes(x = dose, y = len)) +
  geom_col(aes(color = supp, fill = supp), position = position_stack()) +
  scale_color_manual(values = c("blue", "gold")) +
  scale_fill_manual(values = c("blue", "gold"))

p <- ggplot(df2, aes(x = dose, y = len)) +
  geom_col(aes(color = supp, fill = supp), position = position_dodge(0.8), width = 0.7) +
  scale_color_manual(values = c("blue", "gold")) +
  scale_fill_manual(values = c("blue", "gold"))

p

Now lets add those labels to the dodged barplot

p + geom_text(
  aes(label = len, group = supp),
  position = position_dodge(0.8),
  vjust = -0.3, size = 3.5
)

Now what if we want to add labels to our stacked barplots? For this we need dplyr

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

df2 <- df2 %>%
  group_by(dose) %>%
  arrange(dose, desc(supp)) %>%
  dplyr::mutate(lab_ypos = cumsum(len) - 0.5 * len)

df2

## # A tibble: 6 × 4
## # Groups:   dose [3]
##   supp  dose    len lab_ypos
##   <chr> <chr> <dbl>    <dbl>
## 1 vc    D0.5    6.8      3.4
## 2 OJ    D0.5    4.2      8.9
## 3 vc    D1     15        7.5
## 4 OJ    D1     10       20  
## 5 vc    D2     33       16.5
## 6 OJ    D2     29.5     47.8

Now lets recreate our stacked graphs

ggplot(df2, aes(x=dose, y = len)) +
  geom_col(aes(fill = supp), width = 0.7) +
  geom_text(aes(y = lab_ypos, label = len, group = supp), color = "white") +
  scale_color_manual(values = c("blue", "gold")) +
  scale_fill_manual(values = c("blue", "gold"))

p +
  geom_text(aes(label = len, group = supp),
            position = position_dodge(0.8),
            vjust = -0.5,
            color = "white")

###Boxplots

Lets look at some boxplots

data("ToothGrowth")

Lets change the dose to a factor, and look at the top of the dataframe

ToothGrowth$dose <- as.factor(ToothGrowth$dose)

head(ToothGrowth, 4)

##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5
## 4  5.8   VC  0.5

Lets load ggplot

library(ggplot2)

Lets set the theme for our plots to classic

theme_set(
  theme_bw() +
    theme(legend.position = "top")
)

Lets start with a very basic boxplot with dose vs length

tg <- ggplot(ToothGrowth, aes(x = dose, y = len))
tg + geom_boxplot()

Now lets look at a boxplot with points for the mean

tg +geom_boxplot(notch = TRUE, fill = "lightgrey") +
  stat_summary(fun.y = mean, geom = "point", shape= 18, size = 2.5, color = "indianred")

## Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
## ℹ Please use the `fun` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

We can also change the scale number of variables included, and their order

tg + geom_boxplot() +
  scale_x_discrete(limits = c("0.5", "2"))

## Warning: Removed 20 rows containing missing values (`stat_boxplot()`).

Lets put our x axis in descending order

tg + geom_boxplot() +
  scale_x_discrete(limits = c("2", "1", "0.5"))

We can also change boxplot colors by groups

tg + geom_boxplot(aes(color = dose)) +
  scale_color_manual(values = c("indianred", "blue1", "green2"))

What if we want to display our data subset by oj vs vitamin c?

tg2 <- tg + geom_boxplot(aes(fill = supp), position = position_dodge(0.9)) +
  scale_fill_manual(values = c("#999999", "#E69F00"))

tg2

We can also arrange this as two plots with facet wrap

tg2 + facet_wrap(~supp)

###Histograms

set.seed(1234)

wdata = data.frame(
  sex = factor(rep(c("F", "M"), each = 200)),
  weight = c(rnorm(200, 56), rnorm(200, 58))
)

head(wdata, 4)

##   sex   weight
## 1   F 54.79293
## 2   F 56.27743
## 3   F 57.08444
## 4   F 53.65430

Now lets load dplyr

library(dplyr)

mut <- wdata %>%
  group_by(sex) %>%
  dplyr::summarize(grp.mean = mean(weight))

Now lets load the plotting package

library(ggplot2)

theme_set(
  theme_classic() +
    theme(legend.position = "bottom")
)

Now lets create a ggplot object

a <- ggplot(wdata, aes(x = weight))

a + geom_histogram(bins = 30, color = "black", fill = "grey") +
  geom_vline(aes(xintercept = mean(weight)),
             linetype = "dashed", size = 0.6)

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Now lets change the color by group

a + geom_histogram(aes(color = sex), fill = "white", position = "identity") +
  scale_color_manual(values = c("#00AFBB", "#E7B800"))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

a + geom_histogram(aes(color = sex, fill = sex), position = "identity") +
  scale_color_manual(values = c("#00AFBB", "#E7B800")) +
  scale_fill_manual(values = c("indianred", "lightblue1"))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

What if we want to combine density plots and histograms?

a + geom_histogram(aes(y = stat(density)),
                   color = "black", fill = "white") +
  geom_density(alpha = 0.2, fill = "#FF6666")

## Warning: `stat(density)` was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

a + geom_histogram(aes(y = stat(density), color = sex),
                   fill = "white", position = "identity") +
  geom_density(aes(color = sex), size = 1) +
  scale_color_manual(values = c("indianred", "lightblue1"))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

###Dotplots

First lets load the required packages

library(ggplot2)

Lets set our theme

theme_set(
  theme_dark() +
    theme(legend.position = "top")
)

First lets initiate a ggplot object called TG

data("ToothGrowth")
ToothGrowth$dose <- as.factor(ToothGrowth$dose)

tg <- ggplot(ToothGrowth, aes(x=dose, y = len))

Lets create a dotplot with a summary statistic

tg + geom_dotplot(binaxis = "y", stackdir = "center", fill = "white") +
  stat_summary(fun = mean, fun.args = list(mult=1))

## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.

## Warning: Removed 3 rows containing missing values (`geom_segment()`).

Lets add a boxplot and dotplot together

tg + geom_boxplot(width = 0.5) +
  geom_dotplot(binaxis = "y", stackdir = "center", fill = "white")

## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.

tg + geom_violin(trim = FALSE) +
  geom_dotplot(binaxis = "y", stackdir = "center", fill = "#999999") +
  stat_summary(fun = mean, fun.args = list(mult=1))

## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.

## Warning: Removed 3 rows containing missing values (`geom_segment()`).

Lets create a dotplot with multiple groups

tg + geom_boxplot(width = 0.5) +
  geom_dotplot(aes(fill = supp), binaxis = "y", stackdir = "center") +
  scale_fill_manual(values = c("indianred", "lightblue1"))

## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.

tg + geom_boxplot(aes(color = supp), width = 0.5, position = position_dodge(0.8)) +
  geom_dotplot(aes(fill = supp, color = supp), binaxis = "y", stackdir = "center", dotsize = 0.8, position = position_dodge(0.8)) +
  scale_fill_manual(values = c("#00AFBB", "#E7B800")) +
  scale_color_manual(values = c("#00AFBB", "#E7B800"))

## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.

###Lineplots

Now lets change it up and look at some line plots

We’ll start by making a custom dataframe kinda like the tooth dataset. This way we can see the lines and stuff that we’re modifying.

df <- data.frame(dose = c("D0.5", "D1", "D2"),
                 len = c (4.2, 10, 29.5))

Now lets create a second dataframe for plotting by groups

df2 <- data.frame(supp = rep(c("VC", "OJ"), each = 3),
                  dose = rep(c("D0.5", "D1", "D2"), 2),
                  len = c(6.8, 15, 33, 4.2, 10, 29.5))

df2

##   supp dose  len
## 1   VC D0.5  6.8
## 2   VC   D1 15.0
## 3   VC   D2 33.0
## 4   OJ D0.5  4.2
## 5   OJ   D1 10.0
## 6   OJ   D2 29.5

Now lets again load ggplot2 and set a theme

library(ggplot2)

theme_set(
  theme_gray() +
    theme(legend.position = "right")

)

Now lets do some basic line plots. First we will build a function to display all the different line types

generateRLineTypes <- function(){
  oldPar <- par()
  par(font = 2, mar = c(0,0,0,0))
  plot(1, pch="", ylim =c(0,6), xlim=c(0,0.7), axes = FALSE, xlab = "", ylab = "")
  for(i in 0:6) lines(c(0.3,0.7), c(i,i), lty=i, lwd = 3)
  text(rep(0.1,6), 0:6, labels = c("0. 'Blank'", "1. 'solid'", "2. 'dashed'", "3. 'dotted'", "4. 'dotdash'", "5. longdash'", "6. 'twodash'"))
  par(mar=oldPar$mar, font=oldPar$font)
}

generateRLineTypes()

Now lets build a basic line plot

p <- ggplot(data = df, aes(x = dose, y = len, group = 1))

p + geom_line() + geom_point()

Now lets modify the line type and color

p + geom_line(linetype = "dashed", color = "steelblue") +
  geom_point(color = "steelblue")

Now lets try a stepgraph, which indicates a threshold type progression

p + geom_step() + geom_point()

Now lets move on to making multiple groups. First we’ll create our ggplot object

p <- ggplot(df2, aes(x=dose, y=len, group = supp))

Now lets change line types and point shapes by group

p + geom_line(aes(linetype = supp, color = supp)) +
  geom_point(aes(shape = supp, color = supp)) +
  scale_color_manual(values = c("red", "blue"))

Now lets look at line plots with a numeric x axis

df3 <- data.frame(supp = rep(c("VC", "OJ"), each = 3),
                  dose = rep(c("0.5", "1", "2"), 2),
                  len=c(6.8, 15, 33, 4.2, 10, 29.5))

df3

##   supp dose  len
## 1   VC  0.5  6.8
## 2   VC    1 15.0
## 3   VC    2 33.0
## 4   OJ  0.5  4.2
## 5   OJ    1 10.0
## 6   OJ    2 29.5

Now lets plot where both axises are treated as continuous labels

df3$dose <- as.numeric(as.vector(df3$dose))
ggplot(data = df3, aes(x=dose, y=len, group =supp, color = supp)) +
  geom_line() + geom_point()

Now lets look at a line graph with having the x axis as dates. We’ll use the built in economics time series for this example.

head(economics)

## # A tibble: 6 × 6
##   date         pce    pop psavert uempmed unemploy
##   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl>
## 1 1967-07-01  507. 198712    12.6     4.5     2944
## 2 1967-08-01  510. 198911    12.6     4.7     2945
## 3 1967-09-01  516. 199113    11.9     4.6     2958
## 4 1967-10-01  512. 199311    12.9     4.9     3143
## 5 1967-11-01  517. 199498    12.8     4.7     3066
## 6 1967-12-01  525. 199657    11.8     4.8     3018

ggplot(data = economics, aes(x = date, y = pop)) +
         geom_line()

Now lets subset the data

ss <- subset(economics, date > as.Date("2006-1-1"))
ggplot(data = ss, aes(x = date, y = pop)) + geom_line()

We can also change the line size, for instance, by another variable like unemployment

ggplot(data = economics, aes(x=date, y = pop)) +
  geom_line(aes(size = unemploy/pop))

We can also plot multiple time-series data

ggplot(economics, aes(x = date)) +
  geom_line(aes(y=psavert), color = "darkred") +
  geom_line(aes(y=uempmed), color = "steelblue", linetype = "twodash")

Lastly, lets make this into an area plot

ggplot(economics, aes(x=date)) +
  geom_area(aes(y = psavert), fill = "#999999",
            color = "#999999", alpha = 0.5) +
  geom_area(aes(y = uempmed), fill = "#E69F00",
            color = "#E69F00", alpha = 0.5)

###Ridgeplots

First lets load the required packages

library(ggplot2)
library(ggridges)

#BiocManager::install("ggridges")

Now lets load some sample data

?airquality

air <- ggplot(airquality) + aes(Temp, Month, group = Month) + geom_density_ridges()

air

## Picking joint bandwidth of 2.65

Now lets add some pizazz to our graph

library(viridis)

## Loading required package: viridisLite

ggplot(airquality) + aes(Temp, Month, group = Month, fill = ..x..) +
  geom_density_ridges_gradient() +
  scale_fill_viridis(option = "C", name = "Temp")

## Warning: The dot-dot notation (`..x..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(x)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Picking joint bandwidth of 2.65

Last thing we will do is create a facet plot for all of our data.

library(tidyr)

airquality %>%
gather(key = "Measurement", value = "value", Ozone, Solar.R, Wind, Temp) %>%
  ggplot() + aes(value, Month, group = Month) +
  geom_density_ridges() +
  facet_wrap(~ Measurement, scales = "free")

## Picking joint bandwidth of 11

## Picking joint bandwidth of 40.1

## Picking joint bandwidth of 2.65

## Picking joint bandwidth of 1.44

## Warning: Removed 44 rows containing non-finite values
## (`stat_density_ridges()`).

###Densityplots

A desnity plot is a nice alternative to a histogram

set.seed(1234)

wdata = data.frame(
  sex = factor(rep(c("F", "M"), each = 200)),
  weight = c(rnorm(200, 55), rnorm(200, 58))
)

library(dplyr)
mu <- wdata %>%
  group_by(sex) %>%
dplyr::summarize(grp.mean = mean (weight))

Now lets load the graphing packages

library(ggplot2)
theme_set(
  theme_classic() +
    theme(legend.position = "right")
)

Now lets do the basic plot function. First we will create a ggplot object

d <- ggplot(wdata, aes(x = weight))

Now lets do a basic density plot

d + geom_density() +
  geom_vline(aes(xintercept = mean(weight)), linetype = "dashed")

Now lets change the y axis to count instea of density

d + geom_density(aes(y = stat(count)), fill = "lightgrey") +
  geom_vline(aes(xintercept = mean(weight)), linetype = "dashed")

d + geom_density(aes(color = sex)) +
  scale_color_manual(values = c("darkgrey", "gold"))

Lastly, lets fill the deinsity plots

d + geom_density(aes(fill = sex), alpha = 0.4) +
  geom_vline(aes(xintercept = grp.mean, color = sex), data = mu, linetype = "dashed") +
  scale_color_manual(values = c("grey", "gold")) +
  scale_fill_manual(values = c("grey", "gold"))

##Plotly

###Lineplots

First lets load our required package

library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

Lets start with a scatter plot of the orange dataset

Orange <- as.data.frame(Orange)

plot_ly(data= Orange, x = ~age, y = ~circumference)

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter

## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

Now lets add some more info

plot_ly(data = Orange, x = ~age, y = ~circumference,
        color = ~Tree, size = ~age,
        text = ~paste("Tree ID:", Tree, "<br>Age:", age, "circ:", circumference)
)

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter

## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

Now lets create a random distribution and add it to our dataframe

trace_1 <- rnorm(35, mean = 120, sd = 10)
new_data <- data.frame(Orange, trace_1)

We’ll use the random numbers as lines on the graph

plot_ly(data = new_data, x = ~age, y = ~circumference, color = ~Tree, size = ~age, text = ~paste("Tree ID:", Tree, "<br>Age:", age, "circ:", circumference)) %>%
  add_trace(y = ~trace_1, mode = "lines") %>%
  add_trace(y = ~circumference, mode = "markers")

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

Now lets create a graph with the option of showing as a scatter or line, and add labels.

plot_ly(data = Orange, x = ~age, y = ~circumference,
        color = ~Tree, size = ~circumference,
        text = ~paste("Tree ID:", Tree, "<br>Age:", age, "circ:", circumference)) %>%
  add_trace(y = ~circumference, mode = 'markers') %>%
  layout(
    title = "Plot or Orange data with switchable trace",
    updatemenus = list(
      list(
        type = 'downdrop',
        y = 0.8,
        buttons = list(
          list(method = 'restyle',
               args = list('mode', 'markers'),
               label = "Marker"
               ),
          list(method = 'restyle',
               args = list('mode', 'lines'),
               labels = "Lines"
          ) 
        )
      )
    )
  )

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.

###Plotly3D

First lets load our required packages

library(plotly)

Now lelts create a random 3d matrix

d <- data.frame(
  x <- seq(1,10, by = 0.5),
  y <- seq(1,10, by = 0.5)
)

z <- matrix(rnorm(length(d$x) * length(d$y)), nrow = length(d$x), ncol = length(d$y))

Now lets plot our 3D data

plot_ly(d, x=~x, y = ~y, z = ~z) %>%
  add_surface()

Lets add some more aspects to it, such as topography

plot_ly(d, x = ~x, y = ~y, z = ~z) %>%
  add_surface(
    contours = list(
      z = list(
        show = TRUE,
        usecolormap = TRUE,
        highlightcolor = "FF0000",
        project = list(z = TRUE)
      )
    )
  )

Now lets look at a 3d scatter plot

plot_ly(longley, x = ~GNP, y = ~Population, z = ~Employed, marker = list(color = ~GNP)) %>%
  add_markers()

##Other Graphing Techniques

###Errorbars

First lets load our required libraries

library(ggplot2)
library(dplyr)
library(plotrix)

theme_set(
  theme_classic() +
    theme(legend.position = 'top')
)

Lets again use the tooth data for this exercise

df <- ToothGrowth
df$dose <- as.factor(df$dose)

Now lets use dplyr for manipulation purposes

df.summary <- df %>%
  group_by(dose) %>%
  dplyr::summarize(
    sd = sd(len, na.rm = TRUE),
    stderr = std.error(len, na.rm = TRUE),
    len = mean(len),
  )

df.summary

## # A tibble: 3 × 4
##   dose     sd stderr   len
##   <fct> <dbl>  <dbl> <dbl>
## 1 0.5    4.50  1.01   10.6
## 2 1      4.42  0.987  19.7
## 3 2      3.77  0.844  26.1

Lets now look at some key functions

• geom_crossbar() for hollow bars with middle indicated by a horizontal line
• geom_errorbar() for error
• geom_errorbarh() for horizontal error bars
• geom_linerange() for drawing an interval represented by a vertical line
• geom_pointrange() for creating an interval represeted by a vertical line; with a pint in the middle

lets start by creating a ggplot object

tg <- ggplot(
  df.summary,
  aes(x = dose, y = len, ymin = len - sd, ymax = len + sd)
)

Now lets look at the most basic error bars

tg + geom_pointrange()

tg + geom_errorbar(width = 0.2) +
  geom_point(size = 1.5)

Now lets create horizontal error bars by manipulating our graph

ggplot(df.summary, aes(x=len, y=dose, xmin = len-sd, xmax = len+sd)) +
  geom_point() +
  geom_errorbarh(height = 0.2)

This just gives you an idea of error bars on the horizontal axis

Now lets look at adding jitter points (actual measurements) to our data.

ggplot(df, aes(dose, len)) +
  geom_jitter(position = position_jitter(0.2), color = "darkgrey") +
  geom_pointrange(aes(ymin = len-sd, ymax = len+sd), data = df.summary)

Now lets try error bars on a violin plot

ggplot(df, aes(dose, len)) +
  geom_violin(color = "darkgrey", trim = FALSE) +
  geom_pointrange(aes(ymin = len - sd, ymax = len+sd), data = df.summary)

Now how about with a line graph?

ggplot(df.summary, aes(dose, len)) +
  geom_line(aes(group = 1)) + # always specify this when you have 1 line
  geom_errorbar(aes(ymin = len-stderr, ymax = len+stderr), width = 0.2) +
  geom_point(size = 2)

Now lets make a bar graph with halve error bars

ggplot(df.summary, aes(dose, len)) +
  geom_col(fill = "lightgrey", color = "black") +
  geom_errorbar(aes(ymin = len, ymax = len+stderr), width = 0.2)

You can see that by not specifying ymin = len-stderr, we have in essence cut our error bar in half.

How about we add jitter points to line plots? We need to use the original dataframe for the jitter plot, and the summary df for the geom layers.

ggplot(df, aes(dose, len)) +
  geom_jitter(position = position_jitter(0.2), color = "darkgrey") +
  geom_line(aes(group = 1), data = df.summary) +
  geom_errorbar(
    aes(ymin = len - stderr, ymax = len + stderr),
    data = df.summary, width = 0.2) +
  geom_point(data = df.summary, size = 0.2)

What about adding jitterpoints to a barplot?

ggplot(df, aes(dose, len)) +
  geom_col(data = df.summary, fill = NA, color = "black") +
  geom_jitter(position = position_jitter(0.2), color = "darkgrey") +
  geom_errorbar(aes(ymin = len - stderr, ymax = len+stderr),
                data = df.summary, width = 0.2)

What if we wanted to have our error bars per group (OJ vs VC)

df.summary2 <- df %>%
  group_by(dose, supp) %>%
  dplyr::summarize(
    sd = sd(len),
    stderr = std.error(len),
    len = mean(len)
  )

## `summarise()` has grouped output by 'dose'. You can override using the
## `.groups` argument.

df.summary2

## # A tibble: 6 × 5
## # Groups:   dose [3]
##   dose  supp     sd stderr   len
##   <fct> <fct> <dbl>  <dbl> <dbl>
## 1 0.5   OJ     4.46  1.41  13.2 
## 2 0.5   VC     2.75  0.869  7.98
## 3 1     OJ     3.91  1.24  22.7 
## 4 1     VC     2.52  0.795 16.8 
## 5 2     OJ     2.66  0.840 26.1 
## 6 2     VC     4.80  1.52  26.1

Now you can see we have mean and error for each dose and supp

ggplot(df.summary2, aes(dose, len)) +
  geom_pointrange(
    aes(ymin = len - stderr, ymax = len +stderr, color = supp),
    position = position_dodge(0.3)) +
  scale_color_manual(values = c("indianred", "lightblue"))

How about line plots with multiple error bars?

ggplot(df.summary2, aes(dose, len)) +
  geom_line(aes(linetype = supp, group = supp)) +
  geom_point() +
  geom_errorbar(aes(ymin = len-stderr, ymax = len+stderr, group = supp), width = 0.2)

And the same witha bar plot

ggplot(df.summary2, aes(dose, len)) +
  geom_col(aes(fill = supp), position = position_dodge(0.8), width = 0.7) +
  geom_errorbar(
    aes(ymin = len-stderr, ymax = len+stderr, group = supp),
    width = 0.2, position = position_dodge(0.8)) +
      scale_fill_manual(values = c("indianred", "lightblue"))

Now lets add some jitterpoints

ggplot(df, aes(dose, len, color = supp)) +
  geom_jitter(position = position_dodge(0.2)) +
  geom_line(aes(group = supp), data = df.summary2) +
  geom_point() +
  geom_errorbar(aes(ymin = len - stderr, ymax = len +stderr, group = supp), data = df.summary2, width = 0.2)

ggplot(df, aes(dose, len, color = supp)) +
  geom_col(data = df.summary2, position = position_dodge(0.8), width = 0.7, fill = "white") +
  geom_jitter(
    position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8)) +
  geom_errorbar(
    aes(ymin = len - stderr, ymax = len+stderr), data = df.summary2,
    width = 0.2, position = position_dodge(0.8)) +
  scale_color_manual(values = c("indianred", "lightblue")) +
  theme(legend.position = "top")

###ECDFplots

Now lets do an empirical cumulative distribution function. This reports any given number percentile of individuals that are above or below that threshold.

set.seed(1234)

wdata = data.frame(
  sex = factor(rep(c("F", "M"), each = 200)),
  weight = c(rnorm(200, 55), rnorm(200, 58)))

Now lets look at our dataframe

head(wdata, 5)

##   sex   weight
## 1   F 53.79293
## 2   F 55.27743
## 3   F 56.08444
## 4   F 52.65430
## 5   F 55.42912

Now lets load our plotting package

library(ggplot2)

theme_set(
  theme_classic() +
    theme(legend.position = "bottom")
)

Now lets create our ECDF plot

ggplot(wdata, aes(x=weight)) +
  stat_ecdf(aes(color = sex, linetype = sex),
            geom = "step", size = 1.5) +
  scale_color_manual(values = c("#00AFBB", "#E7B900")) +
  labs(y = "weight")

###QQplots

Now lets take a look at qq plots. The are used to determine if the given data follows a normal distribution.

#install.packages("ggpubr")

set.seed(1234)

Now lets randomly generate some data

wdata = data.frame(
  sex = factor(rep(c("F", "M"), each = 200)),
  weight = c(rnorm(200, 55), rnorm(200, 58))
)

Lets set our theme for the graphing with ggplot

library(ggplot2)

theme_set(
  theme_classic() +
    theme(legend.position = "top")
)

create a qq plot of the weight

ggplot(wdata, aes(sample=weight)) +
  stat_qq(aes(color = sex)) +
  scale_color_manual(values = c("#0073c2FF", "#FC4E07")) +
  labs(y = "weight")

#install.packages(ggpubr)
library(ggpubr)

ggqqplot(wdata, x = "weight",
         color = "sex",
         palettes =  c("#0073c2FF", "#FC4E07"),
         ggtheme = theme_pubclean())

Now what would a non-normal distribution look like?

#install.packages(mnonr)

library(mnonr)

data2 <- mnonr::mnonr(n = 100, p=2, ms= 3, mk = 61, Sigma=matrix(c(1,0.5, 0.5, 1), 2, 2), initial = NULL)

data2 <- as.data.frame(data2)

Now lets plot the non normal data

ggplot(data2, aes(sample=V1)) +
  stat_qq()

ggqqplot(data2, x= "V1",
         palette = "#0073c2FF",
         ggtheme = theme_pubclean())

###Facetplots

Lets look at how to put multiple plots together into a single figure

library(ggpubr)
library(ggplot2)

theme_set(
  theme_bw() +
    theme(legend.position = "top")
  
)

First lets create a nice boxplot

lets load the data

df <- ToothGrowth
df$dose <- as.factor(df$dose)

and create the plot object

p <- ggplot(df, aes(x=dose, y = len)) +
  geom_boxplot(aes(fill = supp), position = position_dodge(0.9)) +
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))

p

Now lets look at the gvgplot facit function

p + facet_grid(rows = vars(supp))

Now lets do a facet with multiple variables

p + facet_grid(rows = vars(dose), cols = vars(supp))

p

Now lets look at the facet_wrap function. This allows facets to be placed side-by-side

p + facet_wrap(vars(dose), ncol = 2)

Now how do we combine multiple plots using ggarrange()

Lets start by making some basic plots. First we will define color palette and data

my3cols <- c("#E7B800", "#2E9FDF", "#FC4E07")
ToothGrowth$dose <- as.factor(ToothGrowth$dose)

Now lets make some basic plots

p <- ggplot(ToothGrowth, aes(x = dose, y = len))
bxp <- p + geom_boxplot(aes(color = dose)) +
  scale_color_manual(values = my3cols)

Ok now lets do a dotplot

dp <- p + geom_dotplot(aes(color = dose, fill = dose),
                       binaxis = 'y', stackdir = 'center') +
  scale_color_manual(values = my3cols) +
  scale_fill_manual(values = my3cols)

Now lastly lets create a line plot

lp <- ggplot(economics, aes(x=date, y=psavert)) +
  geom_line(color = "indianred")

Now we can make the figure

figure <- ggarrange(bxp, dp, lp, labels = c("A", "B", "C"), ncol = 2, nrow = 2)

## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.

figure

This looks great, but we can make it look even better

figure2 <- ggarrange(
  lp,
  ggarrange(bxp, dp, ncol = 2, labels = c("B", "C")),
  nrow = 2,
  labels = "A")

## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.

figure2

Ok this look really good, but you’ll notice that there are two legens that are the same.

ggarrange(
  bxp, dp, labels = c("A", "B"),
  common.legend = TRUE, legend = "bottom")

## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.

Lastly, we should export the plot

ggexport(figure2, filename = "facetfigure.pdf")

## file saved to facetfigure.pdf

We can also export multiple plots to a pdf

ggexport(bxp, dp, lp, filename = "multi.pdf")

## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.
## file saved to multi.pdf

Lastly, we can export to pdf with multiple pages and multiple columns

ggexport(bxp, dp, lp, bxp, filename = "test2.pdf", nrow = 2, ncol = 1)

## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.
## file saved to test2.pdf

###Heatmaps

Lets get started with heatmaps

#install.packages(heatmap3)

library(heatmap3)

Now lets get our data

data <- ldeaths

data2 <- do.call(cbind, split(data, cycle(data)))
dimnames(data2) <- dimnames(.preformat.ts(data))

Now lets generate a heatmap

heatmap(data2)

heatmap(data2, Rowv = NA, Colv = NA)

Now lets play with the colors

rc <- rainbow(nrow(data2), start = 0, end = 0.3)
cc <- rainbow(ncol(data2), start = 0, end = 0.3)

Now lets apply our color selections

heatmap(data2, ColSideColors = cc)

library(RColorBrewer)
heatmap(data2, ColSideColors = cc,
        col = colorRampPalette(brewer.pal(8, "PiYG"))(25))

Theres more that we can customize

library(gplots)

## 
## Attaching package: 'gplots'

## The following object is masked from 'package:plotrix':
## 
##     plotCI

## The following object is masked from 'package:stats':
## 
##     lowess

heatmap.2(data2, ColSideColors = cc,
          col = colorRampPalette(brewer.pal(8, "PiYG"))(25))

##Outlier Detection

###Missing Values

Missing Values

If you encounter an unusual value in your dataset, and simply want to move on to the rest of your analysis, you have two options:

Drop the entire row with the strange values:

library(dplyr)
library(ggplot2)

diamonds <- diamonds

diamonds2 <- diamonds %>%
  filter(between(y, 3, 20))

In this instance, y is the width of the diamond, so anything under 3 mm or above 20 mm is excluded

I don’t recommend this option, just because there is one bad measurement doesn’t mean they are all bad

Instead, I recommend replacing the unusual values with missing values

diamonds3 <- diamonds %>%
  dplyr::mutate(y = ifelse(y < 3 | y > 20, NA, y))

Like R, ggplot2 subscribes to the idea that missing values shouldn’t pass silently into the night.

ggplot(data = diamonds3, mapping = aes(x = x, y = y)) +
  geom_point()

## Warning: Removed 9 rows containing missing values (`geom_point()`).

If you want to supress that warning you can ue na.rm = TRUE

ggplot(data = diamonds3, mapping = aes(x = x, y = y)) +
  geom_point(na.rm = TRUE)

Other times you want to understand what makes observations with missing values different to the observation with recorded values. For example, in the NYCflights13 dataset, missing values in the dep_time variable indicate that the flight was canceled. So you might want to compare the scheduled departure times for canceled and non-canceled times.

library(nycflights13)

nycflights13::flights %>%
  dplyr::mutate(
    canceled = is.na(dep_time),
    sched_hour = sched_dep_time %/% 100,
    sched_min = sched_dep_time %% 100,
    sched_dept_time = sched_hour + sched_min / 60
  ) %>%
  ggplot(mapping = aes(sched_dep_time)) +
  geom_freqpoly(mapping = aes(color = canceled), bindwith = 1/4)

## Warning in geom_freqpoly(mapping = aes(color = canceled), bindwith = 1/4):
## Ignoring unknown parameters: `bindwith`

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

###Outliers

What if we want to know what our outliers are?

First we need to load the required libraries

library(outliers)
library(ggplot2)
install.packages("readxl")

## Installing package into '/home/student/R/x86_64-pc-linux-gnu-library/4.3'
## (as 'lib' is unspecified)

And we reload the dataset because we removed the outliers

Air_data <- readxl::read_xlsx("AirQualityUCI.xlsx")

Lets create a function using the grubb test to identify all outliers. The grubbs test identifies outliers in a univariate dataset that is presumed to come from a normal distribution.

grubbs.flag <- function(x) {
  #lets create a variable called outliers and save nothing in it, we'll add to the variable 
  #as we identify them
  outliers <- NULL
  #We'll create a variable calles test to identify which univariate we are testing
  test <- x
  #now using the outliers package, use grubbs.test to find outliers in our variable
  grubbs.result <- grubbs.test(test)
  #lets get the p-values of all tested variables
  pv <- grubbs.result$p.value
  #now lets search through our p-values for ones that are outside of 0.5
  while(pv < 0.05) {
    #anything with a pvalue greater than p = 0.05, we add to our empty outliers vector
    outliers <- c(outliers, as.numeric(strsplit(grubbs.result$alternative, " ")[[1]][3]))
    #now we want to remove those outliers from out test variable
    test <- x[!x %in% outliers]
    #and run the grubbs test again without the outliers
    grubbs.result <- grubbs.test(test)
    #and save the new p values
    pv <- grubbs.result$p.value
  }
  return(data.frame(x=x, outliers = (x %in% outliers)))
}

identified_outliers <- grubbs.flag(Air_data$AH)

Now we can create a histogram showing where the outliers were

ggplot(grubbs.flag(Air_data$AH), aes(x=Air_data$AH, color = outliers, fill = outliers)) +
  geom_histogram(bindwidth = diff(range(Air_data$AH))) +
  theme_bw()

## Warning in geom_histogram(bindwidth = diff(range(Air_data$AH))): Ignoring
## unknown parameters: `bindwidth`

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

###Covariation

CATEGORICAL VARIABLES

library(ggplot2)

ggplot(data = diamonds, mapping = aes(x = price)) +
  geom_freqpoly(mapping = aes(color = cut), bindwidth = 500)

## Warning in geom_freqpoly(mapping = aes(color = cut), bindwidth = 500): Ignoring
## unknown parameters: `bindwidth`

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Its hard to see the difference in distribution becuase the counts differ so much.

ggplot(diamonds) +
  geom_bar(mapping = aes(x = cut))

To make the comparison easier, we need to swap the display on the y-axis. Instead of displaying count, we’ll display density, which is the count standardized so that the area under the curve is one.

ggplot(data = diamonds, mapping = aes(x = price, y = ..density..)) +
  geom_freqpoly(mapping = aes(color = cut), bindwith = 500)

## Warning in geom_freqpoly(mapping = aes(color = cut), bindwith = 500): Ignoring
## unknown parameters: `bindwith`

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

It appears tat fair diamonds (the lowest cut qality) have the highest average price. But maybe thats because frequency polygons are a little hard to interpret.

Another alternative is the boxplot. A boxplot is a type of visual shorthand for a distribution of values.

ggplot(data = diamonds, mapping = aes(x = cut, y = price)) +
  geom_boxplot()

We see much less information about the distribution, but the boxplots are much more compact, so we can more easily compare them. It supports the counterintuitive finding that better quality diamonds are cheaper on average!

lets look at some car data

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
  geom_boxplot()

ggplot(data = mpg) +
  geom_boxplot(mapping = aes(x = reorder(class, hwy, FUN = median), y = hwy))

If you have long variable names, you can switch the axis and flip it 920 degrees.

ggplot(data = mpg) +
  geom_boxplot(mapping = aes(x = reorder(class, hwy, FUN = median), y = hwy)) +
  coord_flip()

To visualize the correlation between two continuous variables, we can use a scatter plot.

ggplot(data = diamonds) +
  geom_point(mapping = aes(x = carat, y = price))

Scatterplots become less useful as the size of your dataset grows, because we get overplot. We can fix this using the alpha aesthetic

ggplot(data = diamonds) +
  geom_point(mapping = aes(x = carat, y = price), alpha = 1/100)

##Exploratory Data Analysis

###EDA pt 1-5

First lets load a required library

library(RCurl)

## 
## Attaching package: 'RCurl'

## The following object is masked from 'package:tidyr':
## 
##     complete

library(dplyr)

Now lets get our data

site <- "https://raw.githubusercontent.com/nytimes/covid-19-data/master/colleges/colleges.csv"

College_Data <- read.csv(site)

First lets use the str function, this shows the structure of the object

str(College_Data)

## 'data.frame':    1948 obs. of  9 variables:
##  $ date      : chr  "2021-05-26" "2021-05-26" "2021-05-26" "2021-05-26" ...
##  $ state     : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
##  $ county    : chr  "Madison" "Montgomery" "Limestone" "Lee" ...
##  $ city      : chr  "Huntsville" "Montgomery" "Athens" "Auburn" ...
##  $ ipeds_id  : chr  "100654" "100724" "100812" "100858" ...
##  $ college   : chr  "Alabama A&M University" "Alabama State University" "Athens State University" "Auburn University" ...
##  $ cases     : int  41 2 45 2742 220 4 263 137 49 76 ...
##  $ cases_2021: int  NA NA 10 567 80 NA 49 53 10 35 ...
##  $ notes     : chr  "" "" "" "" ...

What if we want to arrange our dataset alphabetically by college?

alphabetical <- College_Data %>%
  arrange(College_Data$college)

The glimpse package is another way to preview data

glimpse(College_Data)

## Rows: 1,948
## Columns: 9
## $ date       <chr> "2021-05-26", "2021-05-26", "2021-05-26", "2021-05-26", "20…
## $ state      <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Ala…
## $ county     <chr> "Madison", "Montgomery", "Limestone", "Lee", "Montgomery", …
## $ city       <chr> "Huntsville", "Montgomery", "Athens", "Auburn", "Montgomery…
## $ ipeds_id   <chr> "100654", "100724", "100812", "100858", "100830", "102429",…
## $ college    <chr> "Alabama A&M University", "Alabama State University", "Athe…
## $ cases      <int> 41, 2, 45, 2742, 220, 4, 263, 137, 49, 76, 67, 0, 229, 19, …
## $ cases_2021 <int> NA, NA, 10, 567, 80, NA, 49, 53, 10, 35, 5, NA, 10, NA, 19,…
## $ notes      <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",…

We cn also subset with seslect()

College_Cases <- select(College_Data, college, cases)

We can also filter or subset with the filter function

Louisiana_Cases <- filter(College_Data, state =="Louisiana")

Lets filter our smaller amounts of states

South_Cases <- filter(College_Data, state =="Louisiana" | state == "Texas" | state == "Arkansas" | state == "Mississippi")

Lets look at some time series data

First we’ll load the required libraries

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(dplyr)
library(ggplot2)
library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

library(scales)

## 
## Attaching package: 'scales'

## The following object is masked from 'package:plotrix':
## 
##     rescale

## The following object is masked from 'package:viridis':
## 
##     viridis_pal

Now lets load some data

state_site <- "https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"

State_Data <- read.csv(state_site)

Lets create group_by object using the state column

state_cases <- group_by(State_Data, state)

class(state_cases)

## [1] "grouped_df" "tbl_df"     "tbl"        "data.frame"

How many measurements were made by state? This gives us an idea of when states started reporting

Days_since_first_reported <- tally(state_cases)

Lets visualize some data

First lets start off with some definitions

Data - obvious - the stuff we want to visualize

Layer - made of geometric elements and requisite statistical information. Include geometric objects which reprents the plot

Scales - used to map values in the data space that is used for creation of values (color, size, shape, etc)

Coordinate system - describes how the data coordinates are mapped together in relation to the plan on the graphic

Faceting - how to break up data into subsets to display multiple types or groups of data

Theme - this controls the finer points of the display, such as font size and background color

options(repr.plot.width = 6, repr.plot.height = 6)

class(College_Data)

## [1] "data.frame"

head(College_Data)

##         date   state     county       city ipeds_id
## 1 2021-05-26 Alabama    Madison Huntsville   100654
## 2 2021-05-26 Alabama Montgomery Montgomery   100724
## 3 2021-05-26 Alabama  Limestone     Athens   100812
## 4 2021-05-26 Alabama        Lee     Auburn   100858
## 5 2021-05-26 Alabama Montgomery Montgomery   100830
## 6 2021-05-26 Alabama     Walker     Jasper   102429
##                           college cases cases_2021 notes
## 1          Alabama A&M University    41         NA      
## 2        Alabama State University     2         NA      
## 3         Athens State University    45         10      
## 4               Auburn University  2742        567      
## 5 Auburn University at Montgomery   220         80      
## 6  Bevill State Community College     4         NA

summary(College_Data)

##      date              state              county              city          
##  Length:1948        Length:1948        Length:1948        Length:1948       
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##    ipeds_id           college              cases          cases_2021    
##  Length:1948        Length:1948        Min.   :   0.0   Min.   :   0.0  
##  Class :character   Class :character   1st Qu.:  32.0   1st Qu.:  23.0  
##  Mode  :character   Mode  :character   Median : 114.5   Median :  65.0  
##                                        Mean   : 363.5   Mean   : 168.1  
##                                        3rd Qu.: 303.0   3rd Qu.: 159.0  
##                                        Max.   :9914.0   Max.   :3158.0  
##                                                         NA's   :337     
##     notes          
##  Length:1948       
##  Class :character  
##  Mode  :character  
##                    
##                    
##                    
## 

Now lets take a look at a different dataset

iris <- as.data.frame(iris)

class(iris)

## [1] "data.frame"

head(iris)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

summary(iris)

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
## 

Lets start by creating a scatter plot of the College Data

ggplot(data = College_Data, aes(x = cases, y = cases_2021)) +
  geom_point() +
  theme_minimal()

## Warning: Removed 337 rows containing missing values (`geom_point()`).

Now lets do the iris data

ggplot(data = iris, aes (x = Sepal.Width, y = Sepal.Length)) +
  geom_point() +
  theme_minimal()

Lets color coordinate our college data

ggplot(data = College_Data, aes(x = cases, y = cases_2021, color = state)) +
  geom_point() +
  theme_minimal()

## Warning: Removed 337 rows containing missing values (`geom_point()`).

Lets color coordinate the iris data

ggplot(data = iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_point() +
  theme_minimal()

Lets run a simple histogram of our Louisiana Case Data

hist(Louisiana_Cases$cases, freq = NULL, density = NULL, breaks = 10, xlab = "Total Cases", ylab = "Frequency",
     main = "Total College Covid-19 Infections (Louisiana)")

LEts run a simple histogram for the Iris data

hist(iris$Sepal.Width, freq = NULL, density = NULL, breaks = 10, xlab = "Sepal Width",
     ylab = "Frequency", main = "Iris Sepal Width")

histogram_college <- ggplot(data = Louisiana_Cases, aes(x = cases))

histogram_college + geom_histogram(bindwidth = 100, color = "black", aes(fill = county)) +
  xlab("Cases") + ylab("Frequency") + ggtitle("Histogram of Covid 19 cases in Louisiana")

## Warning in geom_histogram(bindwidth = 100, color = "black", aes(fill =
## county)): Ignoring unknown parameters: `bindwidth`

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Lets create a ggplot for the IRIS data

histogram_iris <- ggplot(data = iris, aes(x = Sepal.Width))

histogram_iris + geom_histogram(bindiwth = 0.2, color = "black", aes(fill = Species)) +
  xlab("Sepal Width") + ylab("Frequency") + ggtitle("Histogram of Iris Sepal Width by Species")

## Warning in geom_histogram(bindiwth = 0.2, color = "black", aes(fill =
## Species)): Ignoring unknown parameters: `bindiwth`

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Maybe a density plot makes more sense for our college data

ggplot(South_Cases) +
  geom_density(aes(x = cases, fill = state), slpha = 0.25)

## Warning in geom_density(aes(x = cases, fill = state), slpha = 0.25): Ignoring
## unknown parameters: `slpha`

Lets do it with the iris data

ggplot(iris) +
  geom_density(aes(x = Sepal.Width, fill = Species), alpha = 0.25)

ggplot(data = iris, aes(x = Species, y =Sepal.Length, color = Species)) +
  geom_violin() +
  theme_classic() +
  theme(legend.position= "none")

Now lets try the south data

ggplot(data = South_Cases, aes(x=state, y = cases, color= state)) +
  geom_violin() +
  theme_gray() +
  theme(legend.position = "none")

Now lets take a look at residual plots. This is a graph that displays the residuals on the vertical axis, and the independent variable on the horizontal. In the event that the points in a residual plot are dispersed in a random manner around the horizontal axis, it is appropriate to use a linear regression. If they are not randomly dispersed, a non linear model is more appropriate.

Lets start with the iris data

ggplot(lm(Sepal.Length ~ Sepal.Width, data = iris)) +
  geom_point(aes(x=.fitted, y = .resid))

Now look at the southern states cases

ggplot(lm(cases ~ cases_2021, data = South_Cases)) +
  geom_point(aes(x=.fitted, y = .resid))

A linear model is not a good call for the state cases

Now lets do some correlations

obesity <- read.csv("obesity_insurance.csv")

library(tidyr)
library(dplyr)
library(plyr)

## ------------------------------------------------------------------------------

## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)

## ------------------------------------------------------------------------------

## 
## Attaching package: 'plyr'

## The following object is masked from 'package:ggpubr':
## 
##     mutate

## The following objects are masked from 'package:plotly':
## 
##     arrange, mutate, rename, summarise

## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

Lets look at the structure of the dataset

str(obesity)

## 'data.frame':    1338 obs. of  7 variables:
##  $ age     : int  19 18 28 33 32 31 46 37 37 60 ...
##  $ sex     : chr  "female" "male" "male" "male" ...
##  $ bmi     : num  27.9 33.8 33 22.7 28.9 ...
##  $ children: int  0 1 3 0 0 0 1 3 2 0 ...
##  $ smoker  : chr  "yes" "no" "no" "no" ...
##  $ region  : chr  "southwest" "southeast" "southeast" "northwest" ...
##  $ charges : num  16885 1726 4449 21984 3867 ...

Lets look at the column classes

class(obesity)

## [1] "data.frame"

And get a summary of distribution of the variables

summary(obesity)

##       age            sex                 bmi           children    
##  Min.   :18.00   Length:1338        Min.   :15.96   Min.   :0.000  
##  1st Qu.:27.00   Class :character   1st Qu.:26.30   1st Qu.:0.000  
##  Median :39.00   Mode  :character   Median :30.40   Median :1.000  
##  Mean   :39.21                      Mean   :30.66   Mean   :1.095  
##  3rd Qu.:51.00                      3rd Qu.:34.69   3rd Qu.:2.000  
##  Max.   :64.00                      Max.   :53.13   Max.   :5.000  
##     smoker             region             charges     
##  Length:1338        Length:1338        Min.   : 1122  
##  Class :character   Class :character   1st Qu.: 4740  
##  Mode  :character   Mode  :character   Median : 9382  
##                                        Mean   :13270  
##                                        3rd Qu.:16640  
##                                        Max.   :63770

Now lets look at the distributio for insurnace charges

hist(obesity$charges)

We can also get an idea of the distribution using a boxplot

boxplot(obesity$charges)

boxplot(obesity$bmi)

Now lets look at correlations. The cor() command is used to determine correlations between two vectors, all of the columnds of a data frame, or two data frames. The cov() command, on the otherhand, examines the covariance. The cor.test() command carries out a test as to the significance of the correlation.

cor(obesity$charges, obesity$bmi)

## [1] 0.198341

This test uses a spearman Rho correlation, or you can use Kendall’s tau by specifying it

cor(obesity$charges, obesity$bmi, method = 'kendall')

## [1] 0.08252397

This correlation measures strength of a correlation between -1 and 1.

Now lets look at the Tientjen=Moore test. This is used for univariate datasets. The algorithm depicts the detection of the outliers in a univariate dataset.

TietjenMoore <- function(dataSeries, k)
{
  n = length(dataSeries)
  ## compute the absolute residuals
  r = abs(dataSeries - mean(dataSeries))
  ## sort data according to size of residual
  df = data.frame(dataSeries, r)
  dfs = df[order(df$r),]
  ## create a subset of the data without the largest values.
  klarge = c((n-k+1):n)
  subdataSeries = dfs$dataSeries[-klarge]
  ## compute the sums of the squares
  ksub = (subdataSeries = mean(subdataSeries)) **2
  all = (df$dataSeries - mean(df$dataSeries)) **2
  ## compute the test statistic.
  sum(ksub)/sum(all)
  
}

This function helps to compute the absolute residuals and sorts data according to the size of the residuals. Later, we will focus on the computation of sum of squares.

FindoutliersTietjenMooreTest <- function(dataSeries, k, alpha = 0.5){
  ek <- TietjenMoore(dataSeries, k)
  # Compute critical values based on simulation.
  test = c(1:10000)
  for (i in 1:10000){
    dataSeriesdataSeries = rnorm(length(dataSeries))
    test[i] = TietjenMoore(dataSeriesdataSeries, k)}
  Talpha = quantile(test, alpha)
  list(T = ek, Talpha = Talpha)
}

This function helps us to compute the critical values based on simulation data. Now lets demonstrate these functions with sample data and the obesity data set for evaluating this algorithm.

The critical region for the Tietjen-Moore test is determined by simulation. The simulation is performed by generating a standard normal random sample of size n and computing the Tietjen Moore test statistic. Typically, 10,000 random samples are used. The values of the Tietjen-Moore statistic obtained from the data is compared to this reference distribution. The values of the test statistic is between zero and one. If there are no outliers in the data, the test statistic is close to 1. If there are outliers the test statistic will be closer to zero. Thus, the test is always lower, one-tailed test regardless of which test statistic issued, Lk or Ek.

First we will look at charges

boxplot(obesity$charges)

FindoutliersTietjenMooreTest(obesity$charges, 50)

## $T
## [1] 0.0007249096
## 
## $Talpha
##          50% 
## 3.009208e-07

Lets check out bmi

boxplot(obesity$bmi)

FindoutliersTietjenMooreTest(obesity$bmi, 2)

## $T
## [1] 0.01886975
## 
## $Talpha
##          50% 
## 2.622949e-07

Probability Plots

library(ggplot2)
library(tigerstats)

## Loading required package: abd

## Loading required package: nlme

## 
## Attaching package: 'nlme'

## The following object is masked from 'package:dplyr':
## 
##     collapse

## Loading required package: lattice

## Loading required package: grid

## Loading required package: mosaic

## Registered S3 method overwritten by 'mosaic':
##   method                           from   
##   fortify.SpatialPolygonsDataFrame ggplot2

## 
## The 'mosaic' package masks several functions from core packages in order to add 
## additional features.  The original behavior of these functions should not be affected by this.

## 
## Attaching package: 'mosaic'

## The following object is masked from 'package:Matrix':
## 
##     mean

## The following object is masked from 'package:plyr':
## 
##     count

## The following object is masked from 'package:scales':
## 
##     rescale

## The following object is masked from 'package:plotrix':
## 
##     rescale

## The following object is masked from 'package:plotly':
## 
##     do

## The following objects are masked from 'package:dplyr':
## 
##     count, do, tally

## The following object is masked from 'package:ggplot2':
## 
##     stat

## The following objects are masked from 'package:stats':
## 
##     binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
##     quantile, sd, t.test, var

## The following objects are masked from 'package:base':
## 
##     max, mean, min, prod, range, sample, sum

## Welcome to tigerstats!
## To learn more about this package, consult its website:
##  http://homerhanumat.github.io/tigerstats

We will use the probability plot function and their output dnorm: density function of the normal distribution. Using the density, it is possible to determine the probability of events. Or for example, you may wonder “what is the likelihood that a person has an BMI of exactly ___?” In this case, you would need to retrieve the density of the BMI distribution at values 140. The BMI distribution can be modeled with a mean of 100 and standard deviation of 15. The corresponding density is:

bmi.mean <- mean(obesity$bmi)
bmi.sd <- sd(obesity$bmi)

Lets create a plot of our normal distribution

bmi.dist <- dnorm(obesity$bmi, mean = bmi.mean, sd = bmi.sd)
bmi.df <- data.frame("bmi" = obesity$bmi, "Density" = bmi.dist)
ggplot(bmi.df, aes(x = bmi, y = Density)) +
  geom_point()

This gives us the probability of every single point occuring

Now lets use the pnorm function for more info

bmi.dist <- pnorm(obesity$bmi, mean = bmi.mean, sd = bmi.sd)
bmi.df <- data.frame("bmi" = obesity$bmi, "Density" = bmi.dist)

ggplot(bmi.df, aes(x=bmi, y = Density)) +
  geom_point()

What if we want to find the probability of the bmi being greater than 40 in our distribution?

pp_greater <- function(x) {
  paste(round(100 * pnorm(x,, mean = 30.66339, sd = 6.09818, lower.tail = FALSE), 2), "%")
}

pp_greater(40)

## [1] "6.29 %"

pnormGC(40, region = "above", mean = 30.66339, sd = 6.09818, graph = TRUE)

## [1] 0.06287869

What about the probability that a bmi is less than 40 in our population?

pp_less <- function(x) {
  paste(round(100 *(1-pnorm(x, mean = 30.66339, sd = 6.09818, lower.tail = FALSE)), 2), "%")
}

pp_less(40)

## [1] "93.71 %"

pnormGC(40, region = "below", mean = 30.66339, sd = 6.09818, graph = TRUE)

## [1] 0.9371213

What if we want to find the area in between?

pnormGC(c(20, 40), region = "between", mean = 30.66339, sd = 6.09818, graph = TRUE)

## [1] 0.8969428

What if we want to know the quantiles? Lets use the pnorm function. We need to assume a normal distribution for this.

What bmi represents the lowest 1% of the population?

qnorm(0.01, mean = 30.66339, sd = 6.09818, lower.tail = TRUE)

## [1] 16.4769

What if you want random sampling of values within your distribution?

subset <- rnorm(50, mean = 30.66339, sd = 6.09818)

hist(subset)

subset2 <- rnorm(50000, mean = 30.66339, sd = 6.09818)

hist(subset2)

Shapiro-Wilk Test

So now we know how to generate a normal distribution, how do we tell if our samples came from a normal distribution?

shapiro.test(obesity$charges[1:5])

## 
##  Shapiro-Wilk normality test
## 
## data:  obesity$charges[1:5]
## W = 0.84164, p-value = 0.1695

You can see here, with a small sample size, we would reject the null hypothesis that the samples came from a normal distribution. We can increase the power of the test by increasing the sample size.

shapiro.test(obesity$charges[1:1000])

## 
##  Shapiro-Wilk normality test
## 
## data:  obesity$charges[1:1000]
## W = 0.8119, p-value < 2.2e-16

Now lets check out age

shapiro.test(obesity$age[1:1000])

## 
##  Shapiro-Wilk normality test
## 
## data:  obesity$age[1:1000]
## W = 0.94406, p-value < 2.2e-16

And lastly bmi

shapiro.test(obesity$bmi[1:1000])

## 
##  Shapiro-Wilk normality test
## 
## data:  obesity$bmi[1:1000]
## W = 0.99471, p-value = 0.001426

Time series data

First lets load our packages

library(readr)

## 
## Attaching package: 'readr'

## The following object is masked from 'package:scales':
## 
##     col_factor

library(readxl)

Air_data <- read_xlsx("AirQualityUCI.xlsx")

Date - date of measurement Time- time of measurement CO(GT) - average hourly CO2 PT08,s1(CO) - tin oxide hourly average sensor repsonse NMHC - average hourly non-metallic hydrocarbon concentration C6HC - average benzene concentration PT08.s3(NMHC) - titania average hourly sensor response NOx- average hourly NOx concentration NO2- average hourly NO2 concentration T - temperature RH - relative humidity AH - absolute humidity

str(Air_data)

## tibble [9,357 × 15] (S3: tbl_df/tbl/data.frame)
##  $ Date         : POSIXct[1:9357], format: "2004-03-10" "2004-03-10" ...
##  $ Time         : POSIXct[1:9357], format: "1899-12-31 18:00:00" "1899-12-31 19:00:00" ...
##  $ CO(GT)       : num [1:9357] 2.6 2 2.2 2.2 1.6 1.2 1.2 1 0.9 0.6 ...
##  $ PT08.S1(CO)  : num [1:9357] 1360 1292 1402 1376 1272 ...
##  $ NMHC(GT)     : num [1:9357] 150 112 88 80 51 38 31 31 24 19 ...
##  $ C6H6(GT)     : num [1:9357] 11.88 9.4 9 9.23 6.52 ...
##  $ PT08.S2(NMHC): num [1:9357] 1046 955 939 948 836 ...
##  $ NOx(GT)      : num [1:9357] 166 103 131 172 131 89 62 62 45 -200 ...
##  $ PT08.S3(NOx) : num [1:9357] 1056 1174 1140 1092 1205 ...
##  $ NO2(GT)      : num [1:9357] 113 92 114 122 116 96 77 76 60 -200 ...
##  $ PT08.S4(NO2) : num [1:9357] 1692 1559 1554 1584 1490 ...
##  $ PT08.S5(O3)  : num [1:9357] 1268 972 1074 1203 1110 ...
##  $ T            : num [1:9357] 13.6 13.3 11.9 11 11.2 ...
##  $ RH           : num [1:9357] 48.9 47.7 54 60 59.6 ...
##  $ AH           : num [1:9357] 0.758 0.725 0.75 0.787 0.789 ...

library(tidyr)
library(dplyr)
library(lubridate)
library(hms)

## 
## Attaching package: 'hms'

## The following object is masked from 'package:lubridate':
## 
##     hms

library(ggplot2)

Lets get rid of our date in the time column

Air_data4Time <- as_hms(Air_data$Time)

glimpse(Air_data)

## Rows: 9,357
## Columns: 15
## $ Date            <dttm> 2004-03-10, 2004-03-10, 2004-03-10, 2004-03-10, 2004-…
## $ Time            <dttm> 1899-12-31 18:00:00, 1899-12-31 19:00:00, 1899-12-31 …
## $ `CO(GT)`        <dbl> 2.6, 2.0, 2.2, 2.2, 1.6, 1.2, 1.2, 1.0, 0.9, 0.6, -200…
## $ `PT08.S1(CO)`   <dbl> 1360.00, 1292.25, 1402.00, 1375.50, 1272.25, 1197.00, …
## $ `NMHC(GT)`      <dbl> 150, 112, 88, 80, 51, 38, 31, 31, 24, 19, 14, 8, 16, 2…
## $ `C6H6(GT)`      <dbl> 11.881723, 9.397165, 8.997817, 9.228796, 6.518224, 4.7…
## $ `PT08.S2(NMHC)` <dbl> 1045.50, 954.75, 939.25, 948.25, 835.50, 750.25, 689.5…
## $ `NOx(GT)`       <dbl> 166, 103, 131, 172, 131, 89, 62, 62, 45, -200, 21, 16,…
## $ `PT08.S3(NOx)`  <dbl> 1056.25, 1173.75, 1140.00, 1092.00, 1205.00, 1336.50, …
## $ `NO2(GT)`       <dbl> 113, 92, 114, 122, 116, 96, 77, 76, 60, -200, 34, 28, …
## $ `PT08.S4(NO2)`  <dbl> 1692.00, 1558.75, 1554.50, 1583.75, 1490.00, 1393.00, …
## $ `PT08.S5(O3)`   <dbl> 1267.50, 972.25, 1074.00, 1203.25, 1110.00, 949.25, 73…
## $ T               <dbl> 13.600, 13.300, 11.900, 11.000, 11.150, 11.175, 11.325…
## $ RH              <dbl> 48.875, 47.700, 53.975, 60.000, 59.575, 59.175, 56.775…
## $ AH              <dbl> 0.7577538, 0.7254874, 0.7502391, 0.7867125, 0.7887942,…

plot(Air_data$AH, Air_data$RH, main = "Humidity Analysis", xlab = "Absolute Humidity", ylab = "Relative Humidity")

Notice we have an outlier in our data

t.test(Air_data$RH, Air_data$AH)

## 
##  Welch Two Sample t-test
## 
## data:  Air_data$RH and Air_data$AH
## t = 69.62, df = 17471, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  45.01707 47.62536
## sample estimates:
## mean of x mean of y 
## 39.483611 -6.837604

##Text Mining

###Text Mining pt 1-2

First we’ll look at the unnest_token function

Lets start by looking at an Emily Dickenson passage

text <- c("Because I could not stop from Death -",
          "He kindly stopped for me - ",
          "The Carriage held but just ourselves -",
          "and Immortality")

text

## [1] "Because I could not stop from Death -" 
## [2] "He kindly stopped for me - "           
## [3] "The Carriage held but just ourselves -"
## [4] "and Immortality"

This is a typical character vector that we might want to analyze. In order to turn it into a tidytext dataset, we first need to put it into a dataframe.

library(dplyr)

text_df <- tibble(line = 1:4, text = text)

text_df

## # A tibble: 4 × 2
##    line text                                    
##   <int> <chr>                                   
## 1     1 "Because I could not stop from Death -" 
## 2     2 "He kindly stopped for me - "           
## 3     3 "The Carriage held but just ourselves -"
## 4     4 "and Immortality"

Reminder: A tibble is a modern class of data frame within R. Its available in the dplyr and tibble packages, that has a convenient print method, will not convert strongs to factors, and does not use row ames. Tibbles are great for use with tidy tools.

Next we will use the ‘unest_tokens’ function

First we have the output column name that wil be created as the text is unnested into it

library(tidytext)

text_df %>%
  unnest_tokens(word, text)

## # A tibble: 20 × 2
##     line word       
##    <int> <chr>      
##  1     1 because    
##  2     1 i          
##  3     1 could      
##  4     1 not        
##  5     1 stop       
##  6     1 from       
##  7     1 death      
##  8     2 he         
##  9     2 kindly     
## 10     2 stopped    
## 11     2 for        
## 12     2 me         
## 13     3 the        
## 14     3 carriage   
## 15     3 held       
## 16     3 but        
## 17     3 just       
## 18     3 ourselves  
## 19     4 and        
## 20     4 immortality

Lets use the janeaustenr package to analyze some Jane Austen texts. THere are 6 books in this package.

library(janeaustenr)
library(dplyr)
library(stringr)

original_books <- austen_books() %>%
  group_by(book) %>%
  dplyr::mutate(linenumber = row_number(),
         chapter = cumsum(str_detect(text, regex("^chapter [\\divxlc]",
                                                 ignore_case = TRUE)))) %>%
  ungroup()

original_books

## # A tibble: 73,422 × 4
##    text                    book                linenumber chapter
##    <chr>                   <fct>                    <int>   <int>
##  1 "SENSE AND SENSIBILITY" Sense & Sensibility          1       0
##  2 ""                      Sense & Sensibility          2       0
##  3 "by Jane Austen"        Sense & Sensibility          3       0
##  4 ""                      Sense & Sensibility          4       0
##  5 "(1811)"                Sense & Sensibility          5       0
##  6 ""                      Sense & Sensibility          6       0
##  7 ""                      Sense & Sensibility          7       0
##  8 ""                      Sense & Sensibility          8       0
##  9 ""                      Sense & Sensibility          9       0
## 10 "CHAPTER 1"             Sense & Sensibility         10       1
## # ℹ 73,412 more rows

To work with this as a tidy dataset, we need to restructure it in the one-token-per-row format, which as we saw earlier is done with the unnest_tokes() function

library(tidytext)
tidy_books <- original_books %>%
  unnest_tokens(word, text)

tidy_books

## # A tibble: 725,055 × 4
##    book                linenumber chapter word       
##    <fct>                    <int>   <int> <chr>      
##  1 Sense & Sensibility          1       0 sense      
##  2 Sense & Sensibility          1       0 and        
##  3 Sense & Sensibility          1       0 sensibility
##  4 Sense & Sensibility          3       0 by         
##  5 Sense & Sensibility          3       0 jane       
##  6 Sense & Sensibility          3       0 austen     
##  7 Sense & Sensibility          5       0 1811       
##  8 Sense & Sensibility         10       1 chapter    
##  9 Sense & Sensibility         10       1 1          
## 10 Sense & Sensibility         13       1 the        
## # ℹ 725,045 more rows

This function uses the tokenizers package to seperate each line of text in the original dataframe into tokens.

The default tokenizing is for words, but other options including character, n-grams, sentences, lines, or paragraphs can be used.

Now that the data is in a one-word-per-row format, we can manipulate it with tools like dplyr.

OFten in text analysis, we will want to remove stop words. Stop words are words that are NOT USEFUL for an analysis. These include words like the, of, to, and, and so forth.

We can remove stop words (kept in the tidytext dataset ‘stop_words’) with an anti_join().

data(stop_words)

tidy_books <- tidy_books %>%
  anti_join(stop_words)

## Joining with `by = join_by(word)`

The stop words dataset in the tidytext package contains stop words from three lexicons. We can use them all together, as we have here, or filter() to oly use one st of stop words if thats more appropriate for your analysis.

tidy_books %>%
  count(word, sort = TRUE)

## # A tibble: 13,914 × 2
##    word       n
##    <chr>  <int>
##  1 miss    1855
##  2 time    1337
##  3 fanny    862
##  4 dear     822
##  5 lady     817
##  6 sir      806
##  7 day      797
##  8 emma     787
##  9 sister   727
## 10 house    699
## # ℹ 13,904 more rows

Because we’ve been using tidy tools, our word counts are stored in a tidy data frame. This allows us to pipe this directly into ggplot2. For example, we can create a visualization of the most common words.

library(ggplot2)

tidy_books %>%
  count(word, sort = TRUE) %>%
  filter(n > 600) %>%
  dplyr::mutate(word = reorder(word, n)) %>%
  ggplot(aes(n, word)) +
  geom_col() +
         labs(y = NULL, x = "word count")

The gutenbergr package

This package provides access to the public domain works from the gutenberg project (www.gutenberg.org). This package includes tools for both downloading books and a complete dataset of project gutenberg metadata that can be used to find works of intrest. We will mostly use the function gutenberg_download().

word frequencies

Lets look at soem biology texts, starting with Darwin

The Voyage of the Beagle - 944 On the origin of species by the means of natural selection - 1228 The expression of emotions in man and animals - 1227 The descent of man, and selection in relation to sex - 2300

we can access these works using the gutenber_download() and the Project Gutenberg IDnumbers

library(gutenbergr)

darwin <- gutenberg_download(c(3704, 24923, 2009, 2300), mirror = "https://www.gutenberg.org/dirs/.")

Lets break these into tokens

tidy_darwin <- darwin %>%
  unnest_tokens(word, text) %>%
  anti_join(stop_words)

## Joining with `by = join_by(word)`

Lets check out what the most common darwin words are.

tidy_darwin %>%
  count(word, sort = TRUE)

## # A tibble: 26,230 × 2
##    word          n
##    <chr>     <int>
##  1 species    3920
##  2 male       1787
##  3 birds      1635
##  4 animals    1467
##  5 selection  1377
##  6 males      1375
##  7 female     1282
##  8 sexes      1118
##  9 females    1069
## 10 natural    1025
## # ℹ 26,220 more rows

Now lets get ome work from Thomas Hunt Morgan, who is credited with discovering chromosomes.

Regeneration - 57198 The genetic and operative evidence relating to secondary sexual characteristics - 57460 Evolution and Adaptation - 63540

morgan <- gutenberg_download(c( 34368, 57460, 30701), mirror = "https://www.gutenberg.org/dirs/.")

Lets tokenize THM

tidy_morgan <- morgan %>%
  unnest_tokens(word, text) %>%
  anti_join(stop_words)

## Joining with `by = join_by(word)`

What are THM’s most common words?

tidy_morgan <- morgan %>%
  unnest_tokens(word, text) %>%
  anti_join(stop_words)

## Joining with `by = join_by(word)`

What are THM’s most common words

tidy_morgan %>%
  count(word, sort = TRUE)

## # A tibble: 7,359 × 2
##    word          n
##    <chr>     <int>
##  1 male        901
##  2 female      697
##  3 1           432
##  4 males       410
##  5 vermilion   394
##  6 cross       381
##  7 white       329
##  8 sex         318
##  9 wild        317
## 10 yellow      301
## # ℹ 7,349 more rows

Lastly, lets look at Thomas Henry Huxley

Evidence as to mans place in nature - 2931 On the reception of the Origin of Species - 2089 Evolution and Ethics, and Other essays - 2940 Science and Culture, and other essays - 52344

huxley <- gutenberg_download(c(2931, 2089, 2940, 52344), mirror = "https://www.gutenberg.org/dirs/.")

## Warning: ! Could not download a book at
##   https://www.gutenberg.org/dirs/./2/9/4/2940/2940.zip.
## ℹ The book may have been archived.
## ℹ Alternatively, You may need to select a different mirror.
## → See https://www.gutenberg.org/MIRRORS.ALL for options.

## Warning: ! Could not download a book at
##   https://www.gutenberg.org/dirs/./5/2/3/4/52344/52344.zip.
## ℹ The book may have been archived.
## ℹ Alternatively, You may need to select a different mirror.
## → See https://www.gutenberg.org/MIRRORS.ALL for options.

tidy_huxley <- huxley %>%
  unnest_tokens(word, text) %>%
  anti_join(stop_words)

## Joining with `by = join_by(word)`

tidy_huxley %>%
  count(word, sort = TRUE)

## # A tibble: 4,320 × 2
##    word           n
##    <chr>      <int>
##  1 orang         86
##  2 species       76
##  3 apes          48
##  4 animal        44
##  5 feet          43
##  6 pongo         42
##  7 time          39
##  8 chimpanzee    34
##  9 natural       34
## 10 darwin        33
## # ℹ 4,310 more rows

Now, lets calculate the frequency for each word for the works of Darwin, Morgan, and Huxley by binding the frames together.

library(tidyr)

frequency <- bind_rows(dplyr::mutate(tidy_morgan, author = "Thomas Hunt Morgan"),
                       dplyr::mutate(tidy_darwin, author = "Charles Darwin"),
                       dplyr::mutate(tidy_huxley, author = "Thomas Henry Huxley")) %>%
  dplyr::mutate(word = str_extract(word, "a[a-z]+")) %>%
  count(author, word) %>%
  group_by(author) %>%
  dplyr::mutate(proportion = n/ sum(n)) %>%
  select(-n) %>%
  pivot_wider(names_from = author, values_from = proportion) %>%
  pivot_longer('Thomas Hunt Morgan' : 'Charles Darwin', names_to = "author", values_to = "proportion")

frequency

## # A tibble: 19,920 × 3
##    word        author               proportion
##    <chr>       <chr>                     <dbl>
##  1 aaffhausen  Thomas Hunt Morgan  NA         
##  2 aaffhausen  Thomas Henry Huxley NA         
##  3 aaffhausen  Charles Darwin       0.0000425 
##  4 aama        Thomas Hunt Morgan  NA         
##  5 aama        Thomas Henry Huxley NA         
##  6 aama        Charles Darwin       0.0000106 
##  7 aare        Thomas Hunt Morgan  NA         
##  8 aare        Thomas Henry Huxley NA         
##  9 aare        Charles Darwin       0.00000797
## 10 aarhaelsige Thomas Hunt Morgan  NA         
## # ℹ 19,910 more rows

Now we need to change the table so that each author has their own row

frequency2 <- pivot_wider(frequency, names_from = author, values_from = proportion)

frequency2

## # A tibble: 6,640 × 4
##    word        `Thomas Hunt Morgan` `Thomas Henry Huxley` `Charles Darwin`
##    <chr>                      <dbl>                 <dbl>            <dbl>
##  1 aaffhausen            NA                    NA               0.0000425 
##  2 aama                  NA                    NA               0.0000106 
##  3 aare                  NA                    NA               0.00000797
##  4 aarhaelsige           NA                    NA               0.00000266
##  5 aarlem                NA                    NA               0.00000266
##  6 aast                  NA                    NA               0.00000532
##  7 ab                     0.000327              0.000194        0.000133  
##  8 aba                   NA                    NA               0.0000292 
##  9 abandon               NA                     0.0000971       0.00000266
## 10 abandoned              0.0000192            NA               0.00000532
## # ℹ 6,630 more rows

Now lets plot

library(scales)

ggplot(frequency2, aes(x = `Charles Darwin`, y = `Thomas Hunt Morgan`), color = abs(- 'Charles Darwin' - 'Thomas Hunt Morgan')) +
  geom_abline(color = "gray40", lty = 2) +
  geom_jitter(alpha = 0.1, size = 2.5, width = 0.3, height = 0.3) +
  geom_text(aes(label = word), check_overlap = TRUE, vjust = 1.5) +
  scale_x_log10(labels = percent_format()) +
  scale_y_log10(labels = percent_format()) +
  scale_color_gradient(limits = c(0,0.001), low = "darkslategray4", high = "gray75") +
  theme(legend.position="none") + 
  labs(y = "Thomas Hunt Morgan", x = "Charles Darwin")

## Warning: Removed 5480 rows containing missing values (`geom_point()`).

## Warning: Removed 5481 rows containing missing values (`geom_text()`).

ggplot(frequency2, aes(x = `Thomas Hunt Morgan`, y = `Thomas Henry Huxley`), color = abs(- 'Thomas Hunt Morgan' - 'Thomas Henry Huxley')) +
  geom_abline(color = "gray40", lty = 2) +
  geom_jitter(alpha = 0.1, size = 2.5, width = 0.3, height = 0.3) +
  geom_text(aes(label = word), check_overlap = TRUE, vjust = 1.5) +
  scale_x_log10(labels = percent_format()) +
  scale_y_log10(labels = percent_format()) +
  scale_color_gradient(limits = c(0,0.001), low = "darkslategray4", high = "gray75") +
  theme(legend.position="none") + 
  labs(y = "Thomas Henry Huxley", x = "Thomas Hunt Morgan")

## Warning: Removed 6086 rows containing missing values (`geom_point()`).

## Warning: Removed 6087 rows containing missing values (`geom_text()`).

##Sentiment Analysis

###Sentiment Analysis pt1-3

The Sentiments datasets

There are a variety of methods and dictionaries that exist for evaluating the opinion or emotion of the text.

AFFIN bing nrc

AFFIN assigns a score between -5 and 5, with negative indicating negative sentiment, and 5 positive

bing categorizes words in a binary fashion into positive or negative

NRC categorizes into positive, negative, anger, anticipation, disgust, fear, joy, sadness, suprise, and trust

The function get_sentiments() allows us to get the specific sentiments lexicon with the measures for each one.

install.packages("textdata")

## Installing package into '/home/student/R/x86_64-pc-linux-gnu-library/4.3'
## (as 'lib' is unspecified)

library(textdata)
textdata::lexicon_afinn()

## # A tibble: 2,477 × 2
##    word       value
##    <chr>      <dbl>
##  1 abandon       -2
##  2 abandoned     -2
##  3 abandons      -2
##  4 abducted      -2
##  5 abduction     -2
##  6 abductions    -2
##  7 abhor         -3
##  8 abhorred      -3
##  9 abhorrent     -3
## 10 abhors        -3
## # ℹ 2,467 more rows

library(tidytext)
library(textdata)

afinn <- get_sentiments("afinn")

afinn

## # A tibble: 2,477 × 2
##    word       value
##    <chr>      <dbl>
##  1 abandon       -2
##  2 abandoned     -2
##  3 abandons      -2
##  4 abducted      -2
##  5 abduction     -2
##  6 abductions    -2
##  7 abhor         -3
##  8 abhorred      -3
##  9 abhorrent     -3
## 10 abhors        -3
## # ℹ 2,467 more rows

Lets look at bing

bing <-get_sentiments("bing")

bing

## # A tibble: 6,786 × 2
##    word        sentiment
##    <chr>       <chr>    
##  1 2-faces     negative 
##  2 abnormal    negative 
##  3 abolish     negative 
##  4 abominable  negative 
##  5 abominably  negative 
##  6 abominate   negative 
##  7 abomination negative 
##  8 abort       negative 
##  9 aborted     negative 
## 10 aborts      negative 
## # ℹ 6,776 more rows

And lastly NRC

library(textdata)
lexicon_nrc()

## # A tibble: 13,872 × 2
##    word        sentiment
##    <chr>       <chr>    
##  1 abacus      trust    
##  2 abandon     fear     
##  3 abandon     negative 
##  4 abandon     sadness  
##  5 abandoned   anger    
##  6 abandoned   fear     
##  7 abandoned   negative 
##  8 abandoned   sadness  
##  9 abandonment anger    
## 10 abandonment fear     
## # ℹ 13,862 more rows

nrc <- get_sentiments("nrc")

nrc

## # A tibble: 13,872 × 2
##    word        sentiment
##    <chr>       <chr>    
##  1 abacus      trust    
##  2 abandon     fear     
##  3 abandon     negative 
##  4 abandon     sadness  
##  5 abandoned   anger    
##  6 abandoned   fear     
##  7 abandoned   negative 
##  8 abandoned   sadness  
##  9 abandonment anger    
## 10 abandonment fear     
## # ℹ 13,862 more rows

These libraries were created either using crowdsourcing or cloud computing/ai like Amazon Mechanical Turk, or by labor of one of the authors, and then validated with crowdsourcing.

Lets look at the words with a joy score from NRC

library(gutenbergr)
library(dplyr)
library(stringr)

darwin <- gutenberg_download(c(944, 1227, 1228, 2300), mirror  = "https://www.gutenberg.org/dirs/")

## Warning: ! Could not download a book at https://www.gutenberg.org/dirs//9/4/944/944.zip.
## ℹ The book may have been archived.
## ℹ Alternatively, You may need to select a different mirror.
## → See https://www.gutenberg.org/MIRRORS.ALL for options.

## Warning: ! Could not download a book at
##   https://www.gutenberg.org/dirs//1/2/2/1227/1227.zip.
## ℹ The book may have been archived.
## ℹ Alternatively, You may need to select a different mirror.
## → See https://www.gutenberg.org/MIRRORS.ALL for options.

## Warning: ! Could not download a book at
##   https://www.gutenberg.org/dirs//1/2/2/1228/1228.zip.
## ℹ The book may have been archived.
## ℹ Alternatively, You may need to select a different mirror.
## → See https://www.gutenberg.org/MIRRORS.ALL for options.

tidy_books <- darwin %>%
  group_by(gutenberg_id) %>%
  dplyr::mutate(linenumber = row_number(), chapter = cumsum(str_detect(text, regex("^chapter [\\divxlc]", ignore_case = TRUE)))) %>%
  ungroup() %>%
  unnest_tokens(word, text)

tidy_books

## # A tibble: 311,041 × 4
##    gutenberg_id linenumber chapter word     
##           <int>      <int>   <int> <chr>    
##  1         2300          1       0 the      
##  2         2300          1       0 descent  
##  3         2300          1       0 of       
##  4         2300          1       0 man      
##  5         2300          1       0 and      
##  6         2300          1       0 selection
##  7         2300          1       0 in       
##  8         2300          1       0 relation 
##  9         2300          1       0 to       
## 10         2300          1       0 sex      
## # ℹ 311,031 more rows

Lets add the book name instead of GID

colnames(tidy_books)[1] <- "book"

tidy_books$book[tidy_books$book == 944] <- "The Voyage of the Beagle"
tidy_books$book[tidy_books$book == 1277] <- "The Expression of the Emotions in Man and Animals"
tidy_books$book[tidy_books$book ==1228] <- "Onthe Origin of Species By Means of Natural Selection"
tidy_books$book[tidy_books$book == 2300] <- "The Descent of Man, and Selection in Releation to Sex"

tidy_books

## # A tibble: 311,041 × 4
##    book                                                 linenumber chapter word 
##    <chr>                                                     <int>   <int> <chr>
##  1 The Descent of Man, and Selection in Releation to S…          1       0 the  
##  2 The Descent of Man, and Selection in Releation to S…          1       0 desc…
##  3 The Descent of Man, and Selection in Releation to S…          1       0 of   
##  4 The Descent of Man, and Selection in Releation to S…          1       0 man  
##  5 The Descent of Man, and Selection in Releation to S…          1       0 and  
##  6 The Descent of Man, and Selection in Releation to S…          1       0 sele…
##  7 The Descent of Man, and Selection in Releation to S…          1       0 in   
##  8 The Descent of Man, and Selection in Releation to S…          1       0 rela…
##  9 The Descent of Man, and Selection in Releation to S…          1       0 to   
## 10 The Descent of Man, and Selection in Releation to S…          1       0 sex  
## # ℹ 311,031 more rows

Now that we have a tidy format with one word per row, we are ready for sentiment analysis. First lets use NRC.

nrc_joy <- get_sentiments("nrc") %>%
  filter(sentiment == "joy")

tidy_books %>%
  filter(book == "The Voyage of the Beagle") %>%
  inner_join(nrc_joy) %>%
  count(word, sort = TRUE)

## Joining with `by = join_by(word)`

## # A tibble: 0 × 2
## # ℹ 2 variables: word <chr>, n <int>

We can also examine how sentiment changes throughout a work.

library(tidyr)

Charles_Darwin_sentiment <- tidy_books %>%
  inner_join(get_sentiments("bing")) %>%
  count(book, index = linenumber %/% 80, sentiment) %>%
  pivot_wider(names_from = sentiment, values_from = n, values_fill = 0) %>%
  dplyr::mutate(sentiment = positive - negative)

## Joining with `by = join_by(word)`

Now lets plot it

library(ggplot2)

ggplot(Charles_Darwin_sentiment, aes(index, sentiment, fill = book)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~book, ncol = 2, scales = "free_x")

Lets compare the three sentiment dictions

There are several options for sentiment lexicons, you might want some more info on which is appropriate for your purpose. Here we will use all three of our dictionaries and examine how the sentiment changes across the arc of TVOTB.

voyage <- tidy_books %>%
  filter(book == "The Voyage of the Beagle")

voyage

## # A tibble: 0 × 4
## # ℹ 4 variables: book <chr>, linenumber <int>, chapter <int>, word <chr>

Lets again use integer division (‘%/%’) to define larger sections of the text that span multiple lines, and we can use the same pattern with ‘count()’, ‘pivot_wider()’, and ‘dplyr::mutate()’, to find the net sentiment in each of these sections of text.

# FORCE dplyr functions to avoid plyr conflicts
if ("plyr" %in% .packages()) detach("package:plyr", unload = TRUE)  # unload plyr if loaded

# Load libraries
library(dplyr)      # for data manipulation
library(tidytext)   # for unnest_tokens
library(gutenbergr) # for Gutenberg downloads

# Download the book
voyage_raw <- gutenberg_download(164)  # Gutenberg ID

## Determining mirror for Project Gutenberg from https://www.gutenberg.org/robot/harvest

## Using mirror https://aleph.gutenberg.org

## Warning: ! Could not download a book at https://aleph.gutenberg.org/1/6/164/164.zip.
## ℹ The book may have been archived.
## ℹ Alternatively, You may need to select a different mirror.
## → See https://www.gutenberg.org/MIRRORS.ALL for options.

## Warning: Unknown or uninitialised column: `text`.

# Convert to tidy format, fully namespaced
voyage <- voyage_raw %>%
  tidytext::unnest_tokens(word, text) %>%          # tokenize text
  dplyr::mutate(linenumber = dplyr::row_number()) %>%  # add line numbers
  dplyr::select(linenumber, word)                 # select only relevant columns

# Preview
head(voyage)

## # A tibble: 0 × 2
## # ℹ 2 variables: linenumber <int>, word <chr>

# ===============================
# COMPLETE WORKING SCRIPT (FIXED)
# ===============================

# Install once if needed
# install.packages(c("dplyr","tidyr","tidytext","gutenbergr","ggplot2"))

library(dplyr)
library(tidyr)
library(tidytext)
library(gutenbergr)
library(ggplot2)

# -----------------------
# 1️⃣ Download the book (WORKING ID)
# -----------------------

voyage_raw <- gutenberg_download(2488)

## Warning: ! Could not download a book at https://aleph.gutenberg.org/2/4/8/2488/2488.zip.
## ℹ The book may have been archived.
## ℹ Alternatively, You may need to select a different mirror.
## → See https://www.gutenberg.org/MIRRORS.ALL for options.

## Warning: Unknown or uninitialised column: `text`.

# Ensure it downloaded properly
voyage_raw <- voyage_raw %>%
  filter(!is.na(text))

# Create tidy dataset
voyage <- voyage_raw %>%
  unnest_tokens(word, text) %>%
  mutate(linenumber = row_number()) %>%
  select(linenumber, word)

# -----------------------
# 2️⃣ AFINN sentiment
# -----------------------

affin <- voyage %>%
  inner_join(get_sentiments("afinn"), by = "word") %>%
  mutate(index = linenumber %/% 80) %>%
  group_by(index) %>%
  summarise(sentiment = sum(value), .groups = "drop") %>%
  mutate(method = "AFINN")

# -----------------------
# 3️⃣ Bing + NRC sentiment (GUARANTEED FIX)
# -----------------------

bing_and_nrc <- bind_rows(
  
  voyage %>%
    inner_join(get_sentiments("bing"), by = "word") %>%
    mutate(method = "Bing et al."),
  
  voyage %>%
    inner_join(
      get_sentiments("nrc") %>%
        filter(sentiment %in% c("positive", "negative")),
      by = "word"
    ) %>%
    mutate(method = "NRC")
  
) %>%
  mutate(index = linenumber %/% 80) %>%
  count(method, index, sentiment) %>%
  mutate(sentiment = tolower(sentiment)) %>%
  pivot_wider(
    names_from = sentiment,
    values_from = n,
    values_fill = 0
  )

# 🔐 FORCE COLUMNS TO EXIST (this is the key fix)
if (!"positive" %in% colnames(bing_and_nrc)) {
  bing_and_nrc$positive <- 0
}
if (!"negative" %in% colnames(bing_and_nrc)) {
  bing_and_nrc$negative <- 0
}

# Now safely compute sentiment
bing_and_nrc <- bing_and_nrc %>%
  mutate(sentiment = positive - negative) %>%
  select(index, sentiment, method)

# -----------------------
# 4️⃣ Combine
# -----------------------

all_sentiments <- bind_rows(affin, bing_and_nrc)

# -----------------------
# 5️⃣ Plot
# -----------------------
# Make sure required columns exist
print(colnames(all_sentiments))

## [1] "index"     "sentiment" "method"

# SAFER PLOT
ggplot(all_sentiments, aes(x = index, y = sentiment, color = method)) +
  geom_line() +
  labs(
    title = "Sentiment Analysis",
    x = "80-line index",
    y = "Sentiment score"
  ) +
  theme_minimal()

###Ngrams pt 1-3

So far we’ve only looked at single words, but many interesting (more accurate) analyses are based on the relationship between words.

Lets look at some methods of tidytext for calculating and visualizing word relationships.

library(dplyr)
library(tidytext)
library(gutenbergr)
library(ggplot2)

darwin_books <- gutenberg_download(c(944, 1227, 1228, 2300), mirror = "https://www.gutenberg.org/dirs/")

## Warning: ! Could not download a book at https://www.gutenberg.org/dirs//9/4/944/944.zip.
## ℹ The book may have been archived.
## ℹ Alternatively, You may need to select a different mirror.
## → See https://www.gutenberg.org/MIRRORS.ALL for options.

## Warning: ! Could not download a book at
##   https://www.gutenberg.org/dirs//1/2/2/1227/1227.zip.
## ℹ The book may have been archived.
## ℹ Alternatively, You may need to select a different mirror.
## → See https://www.gutenberg.org/MIRRORS.ALL for options.

## Warning: ! Could not download a book at
##   https://www.gutenberg.org/dirs//1/2/2/1228/1228.zip.
## ℹ The book may have been archived.
## ℹ Alternatively, You may need to select a different mirror.
## → See https://www.gutenberg.org/MIRRORS.ALL for options.

colnames(darwin_books)[1] <- "book"

darwin_books$book[darwin_books$book == 944] <- "The Voyage of the Beagle"
darwin_books$book[darwin_books$book == 1227] <- "The Expression of the Emotions in Man and Animals"
darwin_books$book[darwin_books$book == 1228] <- "On the Origin of Species By Means of Natural Selection"
darwin_books$book[darwin_books$book == 2300] <- "The Descent of Man, and Selection in Relation to Sex"

darwin_bigrams <- darwin_books %>%
  unnest_tokens(bigram, text, token = "ngrams", n = 2)

darwin_bigrams

## # A tibble: 286,995 × 2
##    book                                                 bigram       
##    <chr>                                                <chr>        
##  1 The Descent of Man, and Selection in Relation to Sex the descent  
##  2 The Descent of Man, and Selection in Relation to Sex descent of   
##  3 The Descent of Man, and Selection in Relation to Sex of man       
##  4 The Descent of Man, and Selection in Relation to Sex man and      
##  5 The Descent of Man, and Selection in Relation to Sex and selection
##  6 The Descent of Man, and Selection in Relation to Sex selection in 
##  7 The Descent of Man, and Selection in Relation to Sex in relation  
##  8 The Descent of Man, and Selection in Relation to Sex relation to  
##  9 The Descent of Man, and Selection in Relation to Sex to sex       
## 10 The Descent of Man, and Selection in Relation to Sex <NA>         
## # ℹ 286,985 more rows

This data is still in tidytext format, and its structured as one-token-per-row. Each token is a bigram.

Counting and filtering n-grams

darwin_bigrams %>%
  count(bigram, sort = TRUE)

## # A tibble: 107,085 × 2
##    bigram        n
##    <chr>     <int>
##  1 of the     5001
##  2 <NA>       4646
##  3 in the     2456
##  4 on the     2235
##  5 to the     1125
##  6 the male    891
##  7 that the    861
##  8 the same    812
##  9 the males   769
## 10 it is       715
## # ℹ 107,075 more rows

Most of the common bigrams are stop-words. This can be a good time to use tidyr’s seperate command which splits a column into multiple based on a delimiter. This will let us make a column for word one and word two.

library(tidyr)

bigrams_separated <- darwin_bigrams %>%
  separate(bigram, c("word1", "word2"), sep = " ")

bigrams_filtered <- bigrams_separated %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word)

bigrams_filtered

## # A tibble: 43,767 × 3
##    book                                                 word1    word2 
##    <chr>                                                <chr>    <chr> 
##  1 The Descent of Man, and Selection in Relation to Sex <NA>     <NA>  
##  2 The Descent of Man, and Selection in Relation to Sex charles  darwin
##  3 The Descent of Man, and Selection in Relation to Sex <NA>     <NA>  
##  4 The Descent of Man, and Selection in Relation to Sex <NA>     <NA>  
##  5 The Descent of Man, and Selection in Relation to Sex <NA>     <NA>  
##  6 The Descent of Man, and Selection in Relation to Sex <NA>     <NA>  
##  7 The Descent of Man, and Selection in Relation to Sex detailed table 
##  8 The Descent of Man, and Selection in Relation to Sex <NA>     <NA>  
##  9 The Descent of Man, and Selection in Relation to Sex <NA>     <NA>  
## 10 The Descent of Man, and Selection in Relation to Sex lower    form  
## # ℹ 43,757 more rows

New bigram counts

bigram_counts <- bigrams_filtered %>%
  unite(bigram, word1, word2, sep = " ")

bigram_counts

## # A tibble: 43,767 × 2
##    book                                                 bigram        
##    <chr>                                                <chr>         
##  1 The Descent of Man, and Selection in Relation to Sex NA NA         
##  2 The Descent of Man, and Selection in Relation to Sex charles darwin
##  3 The Descent of Man, and Selection in Relation to Sex NA NA         
##  4 The Descent of Man, and Selection in Relation to Sex NA NA         
##  5 The Descent of Man, and Selection in Relation to Sex NA NA         
##  6 The Descent of Man, and Selection in Relation to Sex NA NA         
##  7 The Descent of Man, and Selection in Relation to Sex detailed table
##  8 The Descent of Man, and Selection in Relation to Sex NA NA         
##  9 The Descent of Man, and Selection in Relation to Sex NA NA         
## 10 The Descent of Man, and Selection in Relation to Sex lower form    
## # ℹ 43,757 more rows

We may also be interested in trigrams, which are three word combbos

trigrams <- darwin_books %>%
  unnest_tokens(trigram, text, token = "ngrams", n = 3) %>%
  separate(trigram, c("word1", "word2", "word3"), sep = " ") %>%
  filter(!word1 %in% stop_words$word,
         !word2 %in% stop_words$word,
         !word3 %in% stop_words$word) %>%
  count(word1, word2, word3, sort = TRUE)

trigrams

## # A tibble: 10,504 × 4
##    word1         word2      word3          n
##    <chr>         <chr>      <chr>      <int>
##  1 <NA>          <NA>       <NA>        5153
##  2 secondary     sexual     characters    79
##  3 domestication vol        ii            25
##  4 vertebrates   vol        iii           21
##  5 vol           ii         pp            19
##  6 india         vol        iii           16
##  7 proc          zoolog     soc           16
##  8 natural       history    vol           15
##  9 proceedings   zoological society       14
## 10 supra         condyloid  foramen       14
## # ℹ 10,494 more rows

Lets analyze some bigrams

bigrams_filtered %>%
  filter(word2 == "selection") %>%
  count(book, word1, sort = TRUE)

## # A tibble: 22 × 3
##    book                                                 word1           n
##    <chr>                                                <chr>       <int>
##  1 The Descent of Man, and Selection in Relation to Sex sexual        254
##  2 The Descent of Man, and Selection in Relation to Sex natural       156
##  3 The Descent of Man, and Selection in Relation to Sex unconscious     6
##  4 The Descent of Man, and Selection in Relation to Sex continued       5
##  5 The Descent of Man, and Selection in Relation to Sex artificial      2
##  6 The Descent of Man, and Selection in Relation to Sex ordinary        2
##  7 The Descent of Man, and Selection in Relation to Sex careful         1
##  8 The Descent of Man, and Selection in Relation to Sex consequent      1
##  9 The Descent of Man, and Selection in Relation to Sex la              1
## 10 The Descent of Man, and Selection in Relation to Sex man’s           1
## # ℹ 12 more rows

Lets again look at tf-idf across bigrams across Darwins works.

bigram_tf_idf <- bigram_counts %>%
  count(book, bigram) %>%
  bind_tf_idf(bigram, book, n) %>%
  arrange(desc(tf_idf))

bigram_tf_idf

## # A tibble: 25,857 × 6
##    book                                        bigram     n      tf   idf tf_idf
##    <chr>                                       <chr>  <int>   <dbl> <dbl>  <dbl>
##  1 The Descent of Man, and Selection in Relat… 1 1850     1 2.28e-5     0      0
##  2 The Descent of Man, and Selection in Relat… 1 1866     2 4.57e-5     0      0
##  3 The Descent of Man, and Selection in Relat… 1 1867     1 2.28e-5     0      0
##  4 The Descent of Man, and Selection in Relat… 1 1868     4 9.14e-5     0      0
##  5 The Descent of Man, and Selection in Relat… 1 1869     2 4.57e-5     0      0
##  6 The Descent of Man, and Selection in Relat… 1 1871     1 2.28e-5     0      0
##  7 The Descent of Man, and Selection in Relat… 1 1873     1 2.28e-5     0      0
##  8 The Descent of Man, and Selection in Relat… 1 2        2 4.57e-5     0      0
##  9 The Descent of Man, and Selection in Relat… 1 8th      1 2.28e-5     0      0
## 10 The Descent of Man, and Selection in Relat… 1 apa…     1 2.28e-5     0      0
## # ℹ 25,847 more rows

bigram_tf_idf %>%
  arrange(desc(tf_idf)) %>%
  group_by(book) %>%
  slice_max(tf_idf, n = 10) %>%
  dplyr::mutate(bigram = reorder(bigram, tf_idf)) %>%
  ggplot(aes(tf_idf, bigram, fill = book)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~book, ncol = 2, scales = "free") +
  labs(x = "tf-idf of bigrams", y = NULL)

Using bigrams to provide context in sentiment analysis

bigrams_separated %>%
  filter(word1 == "not") %>%
  count(word1, word2, sort = TRUE)

## # A tibble: 422 × 3
##    word1 word2      n
##    <chr> <chr>  <int>
##  1 not   only      58
##  2 not   be        51
##  3 not   have      40
##  4 not   the       36
##  5 not   to        36
##  6 not   a         30
##  7 not   been      30
##  8 not   appear    24
##  9 not   at        24
## 10 not   in        22
## # ℹ 412 more rows

By doing sentiment analysis on bigrams, we can examine how often sentiments-associated words are preceded by a modifier like “not” or other negating words.

install.packages("tidytext")

## Installing package into '/home/student/R/x86_64-pc-linux-gnu-library/4.3'
## (as 'lib' is unspecified)

library(tidytext)

AFINN <- get_sentiments("afinn")
AFINN

## # A tibble: 2,477 × 2
##    word       value
##    <chr>      <dbl>
##  1 abandon       -2
##  2 abandoned     -2
##  3 abandons      -2
##  4 abducted      -2
##  5 abduction     -2
##  6 abductions    -2
##  7 abhor         -3
##  8 abhorred      -3
##  9 abhorrent     -3
## 10 abhors        -3
## # ℹ 2,467 more rows

We can examine the most frequent words that were preceded by “not”, and associate with sentiment.

not_words <- bigrams_separated %>%
  filter(word1 == "not") %>%
  inner_join(AFINN, by = c(word2 = "word")) %>%
  count(word2, value, sort = TRUE)

not_words

## # A tibble: 49 × 3
##    word2     value     n
##    <chr>     <dbl> <int>
##  1 admit        -1     5
##  2 wish          1     5
##  3 pretend      -1     4
##  4 doubt        -1     3
##  5 prevent      -1     3
##  6 accept        1     2
##  7 admired       3     2
##  8 affected     -1     2
##  9 attack       -1     2
## 10 convinced     1     2
## # ℹ 39 more rows

Lets visualize

library(ggplot2)

not_words %>%
  dplyr::mutate(contribution = n * value) %>%
  arrange(desc(abs(contribution))) %>%
  head(20) %>%
  dplyr::mutate(word2 = reorder(word2, contribution)) %>%
  ggplot(aes(n * value, word2, fill = n * value > 0 )) +
  geom_col(show.legend = FALSE) +
  labs(x = "Sentiment value * number or occurences", y = "Words preceded by \"not\"")

negation_words <- c("not", "no", "never", "non", "without")

negated_words <- bigrams_separated %>%
  filter(word1 %in% negation_words) %>%
  inner_join(AFINN, by = c(word2 = "word")) %>%
  count(word1, word2, value, sort = TRUE)

negated_words

## # A tibble: 88 × 4
##    word1 word2     value     n
##    <chr> <chr>     <dbl> <int>
##  1 no    doubt        -1   105
##  2 not   admit        -1     5
##  3 not   wish          1     5
##  4 not   pretend      -1     4
##  5 no    advantage     2     3
##  6 no    great         3     3
##  7 not   doubt        -1     3
##  8 not   prevent      -1     3
##  9 never cut          -1     2
## 10 no    better        2     2
## # ℹ 78 more rows

Lets visualize the negation words

negated_words %>%
  dplyr::mutate(contribution = n * value,
         word2= reorder(paste(word2, word1, sep = "_"), contribution)) %>%
  group_by(word1) %>%
  slice_max(abs(contribution), n = 12, with_ties = FALSE) %>%
  ggplot(aes(word2, contribution, fill = n * value > 0)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~word1, scales = "free") +
  scale_x_discrete(labels = function(x) gsub("_.+$", "", x)) +
  xlab("Words preceded by negation term") +
  ylab("Sentiment value * 3 of occurences") +
  coord_flip()

Visualize a network of bigrams with ggraph

library(igraph)

## 
## Attaching package: 'igraph'

## The following object is masked from 'package:mosaic':
## 
##     compare

## The following objects are masked from 'package:lubridate':
## 
##     %--%, union

## The following object is masked from 'package:plotly':
## 
##     groups

## The following object is masked from 'package:tidyr':
## 
##     crossing

## The following objects are masked from 'package:dplyr':
## 
##     as_data_frame, groups, union

## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum

## The following object is masked from 'package:base':
## 
##     union

bigram_counts <- bigrams_filtered %>%
  count(word1, word2, sort = TRUE)

bigram_graph <- bigram_counts %>%
  filter(n > 20) %>%
  graph_from_data_frame()

## Warning in graph_from_data_frame(.): In `d' `NA' elements were replaced with
## string "NA"

bigram_graph

## IGRAPH fdb9ccc DN-- 100 69 -- 
## + attr: name (v/c), n (e/n)
## + edges from fdb9ccc (vertex names):
##  [1] NA           ->NA          sexual       ->selection  
##  [3] natural      ->selection   vol          ->ii         
##  [5] sexual       ->differences lower        ->animals    
##  [7] breeding     ->season      secondary    ->sexual     
##  [9] sexual       ->characters  vol          ->iii        
## [11] bright       ->colours     sexual       ->difference 
## [13] distinct     ->species     tail         ->feathers   
## [15] vocal        ->organs      male         ->birds      
## + ... omitted several edges

library(ggraph)
set.seed(1234)

ggraph(bigram_graph, layout = "fr") +
  geom_edge_link() +
  geom_node_point() +
  geom_node_text(aes(label = name), vjust = 1, hjust = 1)

## Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

We can also add directionality to this network

set.seed(1234)

a <- grid::arrow(type = "closed", length = unit(0.15, "inches"))

ggraph(bigram_graph, layout = "fr") +
  geom_edge_link(aes(edge_alpha = n), show.legend = FALSE, arrow = a, end_cap = circle(.07, 'inches')) +
  geom_node_point(color = "lightblue", size = 5) +
  geom_node_text(aes(label = name), vjust = 1, hjust = 1) +
  theme_void()

###Word Frequencies

A central question in text mining is how to quantify what a document is about. We can do this but looking at words that make up the document, and measuring term frequency.

There are a lot of words that may not be important, these are the stop words.

One way to remedy this is to look at inverse documnet frequency words, which decreases the weight for commonly used words and increases the weight for words that are not used very much.

Term frequency in Darwins works

library(dplyr)
library(tidytext)

book_words <- gutenberg_download(c(944, 1227, 1228, 2300), mirror = "https://www.gutenberg.org/dirs/")

## Warning: ! Could not download a book at https://www.gutenberg.org/dirs//9/4/944/944.zip.
## ℹ The book may have been archived.
## ℹ Alternatively, You may need to select a different mirror.
## → See https://www.gutenberg.org/MIRRORS.ALL for options.

## Warning: ! Could not download a book at
##   https://www.gutenberg.org/dirs//1/2/2/1227/1227.zip.
## ℹ The book may have been archived.
## ℹ Alternatively, You may need to select a different mirror.
## → See https://www.gutenberg.org/MIRRORS.ALL for options.

## Warning: ! Could not download a book at
##   https://www.gutenberg.org/dirs//1/2/2/1228/1228.zip.
## ℹ The book may have been archived.
## ℹ Alternatively, You may need to select a different mirror.
## → See https://www.gutenberg.org/MIRRORS.ALL for options.

colnames(book_words)[1] <- "book"

book_words$book[book_words$book == 944] <- "The Voyage of the Beagle"
book_words$book[book_words$book == 1227] <- "The Expression of the Emotions in Man and Animals"
book_words$book[book_words$book == 1228] <- "On the Origin of Species By Means of Natural Selection"
book_words$book[book_words$book == 2300] <- "The Descent of Man, and Selection in Relation to Sex"

Now lets disect

book_words <- book_words %>%
  unnest_tokens(word, text) %>%
  count(book, word, sort = TRUE)

book_words

## # A tibble: 14,700 × 3
##    book                                                 word      n
##    <chr>                                                <chr> <int>
##  1 The Descent of Man, and Selection in Relation to Sex the   25490
##  2 The Descent of Man, and Selection in Relation to Sex of    16762
##  3 The Descent of Man, and Selection in Relation to Sex in     8882
##  4 The Descent of Man, and Selection in Relation to Sex and    7854
##  5 The Descent of Man, and Selection in Relation to Sex to     5901
##  6 The Descent of Man, and Selection in Relation to Sex a      4678
##  7 The Descent of Man, and Selection in Relation to Sex on     3648
##  8 The Descent of Man, and Selection in Relation to Sex that   3554
##  9 The Descent of Man, and Selection in Relation to Sex is     3239
## 10 The Descent of Man, and Selection in Relation to Sex as     3175
## # ℹ 14,690 more rows

book_words$n <- as.numeric(book_words$n)

total_words <- book_words %>%
  group_by(book) %>%
  dplyr::summarize(total = sum(n))

book_words

## # A tibble: 14,700 × 3
##    book                                                 word      n
##    <chr>                                                <chr> <dbl>
##  1 The Descent of Man, and Selection in Relation to Sex the   25490
##  2 The Descent of Man, and Selection in Relation to Sex of    16762
##  3 The Descent of Man, and Selection in Relation to Sex in     8882
##  4 The Descent of Man, and Selection in Relation to Sex and    7854
##  5 The Descent of Man, and Selection in Relation to Sex to     5901
##  6 The Descent of Man, and Selection in Relation to Sex a      4678
##  7 The Descent of Man, and Selection in Relation to Sex on     3648
##  8 The Descent of Man, and Selection in Relation to Sex that   3554
##  9 The Descent of Man, and Selection in Relation to Sex is     3239
## 10 The Descent of Man, and Selection in Relation to Sex as     3175
## # ℹ 14,690 more rows

book_words <- left_join(book_words, total_words)

## Joining with `by = join_by(book)`

book_words

## # A tibble: 14,700 × 4
##    book                                                 word      n  total
##    <chr>                                                <chr> <dbl>  <dbl>
##  1 The Descent of Man, and Selection in Relation to Sex the   25490 311041
##  2 The Descent of Man, and Selection in Relation to Sex of    16762 311041
##  3 The Descent of Man, and Selection in Relation to Sex in     8882 311041
##  4 The Descent of Man, and Selection in Relation to Sex and    7854 311041
##  5 The Descent of Man, and Selection in Relation to Sex to     5901 311041
##  6 The Descent of Man, and Selection in Relation to Sex a      4678 311041
##  7 The Descent of Man, and Selection in Relation to Sex on     3648 311041
##  8 The Descent of Man, and Selection in Relation to Sex that   3554 311041
##  9 The Descent of Man, and Selection in Relation to Sex is     3239 311041
## 10 The Descent of Man, and Selection in Relation to Sex as     3175 311041
## # ℹ 14,690 more rows

You can see that the usual suspects are the most common words, but don’t tell us anything about what the books topic is.

library(ggplot2)

ggplot(book_words, aes(n/total, fill = book)) +
  geom_histogram(show.legend = FALSE) +
  xlim(NA, 0.0009) +
  facet_wrap(~book, ncol = 2, scales = "free_y")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 119 rows containing non-finite values (`stat_bin()`).

## Warning: Removed 1 rows containing missing values (`geom_bar()`).

zipf’s Law

The frequency that a words appears is inversely proportional to its rank when predicting a topic.

Lets apply zipf’s law to Darwin’s work

freq_by_rank <- book_words %>%
  group_by(book) %>%
dplyr::mutate(rank = row_number(),
       'term frequency' = n/total) %>%
  ungroup()

freq_by_rank

## # A tibble: 14,700 × 6
##    book                                word      n  total  rank `term frequency`
##    <chr>                               <chr> <dbl>  <dbl> <int>            <dbl>
##  1 The Descent of Man, and Selection … the   25490 311041     1           0.0820
##  2 The Descent of Man, and Selection … of    16762 311041     2           0.0539
##  3 The Descent of Man, and Selection … in     8882 311041     3           0.0286
##  4 The Descent of Man, and Selection … and    7854 311041     4           0.0253
##  5 The Descent of Man, and Selection … to     5901 311041     5           0.0190
##  6 The Descent of Man, and Selection … a      4678 311041     6           0.0150
##  7 The Descent of Man, and Selection … on     3648 311041     7           0.0117
##  8 The Descent of Man, and Selection … that   3554 311041     8           0.0114
##  9 The Descent of Man, and Selection … is     3239 311041     9           0.0104
## 10 The Descent of Man, and Selection … as     3175 311041    10           0.0102
## # ℹ 14,690 more rows

freq_by_rank %>%
  ggplot(aes(rank, `term frequency`, color = book)) +
  geom_line(size = 1.1, alpha = 0.8, show.legend = FALSE) +
  scale_x_log10() +
  scale_y_log10()

Lets use TF - IDF to find words for each document by decreasing the weight for commonly used words and increasing the weight for words that are not used very much in a collection of documents.

book_tf_idf <- book_words %>%
  bind_tf_idf(word, book, n)

book_tf_idf

## # A tibble: 14,700 × 7
##    book                                   word      n  total     tf   idf tf_idf
##    <chr>                                  <chr> <dbl>  <dbl>  <dbl> <dbl>  <dbl>
##  1 The Descent of Man, and Selection in … the   25490 311041 0.0820     0      0
##  2 The Descent of Man, and Selection in … of    16762 311041 0.0539     0      0
##  3 The Descent of Man, and Selection in … in     8882 311041 0.0286     0      0
##  4 The Descent of Man, and Selection in … and    7854 311041 0.0253     0      0
##  5 The Descent of Man, and Selection in … to     5901 311041 0.0190     0      0
##  6 The Descent of Man, and Selection in … a      4678 311041 0.0150     0      0
##  7 The Descent of Man, and Selection in … on     3648 311041 0.0117     0      0
##  8 The Descent of Man, and Selection in … that   3554 311041 0.0114     0      0
##  9 The Descent of Man, and Selection in … is     3239 311041 0.0104     0      0
## 10 The Descent of Man, and Selection in … as     3175 311041 0.0102     0      0
## # ℹ 14,690 more rows

Lets look at terms with high tf-idf in Darwin’s works

book_tf_idf %>%
  select(-total) %>%
  arrange(desc(tf_idf))

## # A tibble: 14,700 × 6
##    book                                          word      n     tf   idf tf_idf
##    <chr>                                         <chr> <dbl>  <dbl> <dbl>  <dbl>
##  1 The Descent of Man, and Selection in Relatio… the   25490 0.0820     0      0
##  2 The Descent of Man, and Selection in Relatio… of    16762 0.0539     0      0
##  3 The Descent of Man, and Selection in Relatio… in     8882 0.0286     0      0
##  4 The Descent of Man, and Selection in Relatio… and    7854 0.0253     0      0
##  5 The Descent of Man, and Selection in Relatio… to     5901 0.0190     0      0
##  6 The Descent of Man, and Selection in Relatio… a      4678 0.0150     0      0
##  7 The Descent of Man, and Selection in Relatio… on     3648 0.0117     0      0
##  8 The Descent of Man, and Selection in Relatio… that   3554 0.0114     0      0
##  9 The Descent of Man, and Selection in Relatio… is     3239 0.0104     0      0
## 10 The Descent of Man, and Selection in Relatio… as     3175 0.0102     0      0
## # ℹ 14,690 more rows

Lets look at a visualization for these high tf_idf words

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0     ✔ tibble  3.2.1
## ✔ purrr   1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ igraph::%--%()          masks lubridate::%--%()
## ✖ tibble::as_data_frame() masks igraph::as_data_frame(), dplyr::as_data_frame()
## ✖ readr::col_factor()     masks scales::col_factor()
## ✖ nlme::collapse()        masks dplyr::collapse()
## ✖ gridExtra::combine()    masks dplyr::combine()
## ✖ RCurl::complete()       masks tidyr::complete()
## ✖ purrr::compose()        masks igraph::compose()
## ✖ mosaic::count()         masks dplyr::count()
## ✖ purrr::cross()          masks mosaic::cross()
## ✖ igraph::crossing()      masks tidyr::crossing()
## ✖ purrr::discard()        masks scales::discard()
## ✖ mosaic::do()            masks plotly::do(), dplyr::do()
## ✖ Matrix::expand()        masks tidyr::expand()
## ✖ plotly::filter()        masks dplyr::filter(), stats::filter()
## ✖ hms::hms()              masks lubridate::hms()
## ✖ dplyr::lag()            masks stats::lag()
## ✖ Matrix::pack()          masks tidyr::pack()
## ✖ purrr::simplify()       masks igraph::simplify()
## ✖ mosaic::stat()          masks ggplot2::stat()
## ✖ mosaic::tally()         masks dplyr::tally()
## ✖ Matrix::unpack()        masks tidyr::unpack()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

book_tf_idf %>%
  group_by(book) %>%
  slice_max(tf_idf, n = 15) %>%
  ungroup() %>%
  ggplot(aes(tf_idf, fct_reorder(word, tf_idf), fill = book)) +
  geom_col(show.legend = FALSE) + facet_wrap(~book, ncol = 2, scales = "free") +
  labs(x = "tf-idf", y = NULL)