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Community Contributions STAT GR 5293

13 Cheatsheet for multiple graphics

Guo Pei

13.1 Description

The cheatsheet includes a single formula sheet and a more detailed tutorial of implementing different plot types on several data sets. To be specific, it contains Histogram, Boxplot, Violin plot, Ridgeline plot, Q-Q plot, Bar chart, Cleveland dot plot, Scatterplot, Parallel coordinates plot, Biplot, Mosaic plot, Alluvial diagram and Heatmap.

For the formula sheet part, it contains nearly all formulas professor introduced in class and we used and met in the previous problem sets.

Link: https://github.com/gloria6661/5293_CC/blob/main/cheatsheet.pdf

For the implementation part, each figure is attached with code on how to draw it. For some types of plots, it lists more than one methods to draw.

13.2 Histogram

Data: iris

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

13.2.1 Frequency (count) histogram (ggplot2) 

ggplot(iris, aes(Sepal.Length)) +
  geom_histogram(color = "blue", fill = "lightblue", binwidth = .3) +
  theme_grey(14) +
  labs(title = "Histogram of Sepal Length", x = "Sepal Length")

13.2.2 Histograms with right closed / right open (base R) 

par(mfrow = c(1, 2))
# histogram with right closed
hist(iris$Sepal.Length, col = "lightblue", right = TRUE,
     breaks = 4, ylim = c(0, 60),
     main = "Histogram with right closed", xlab = "Sepal Length")
# histogram with right open
hist(iris$Sepal.Length, col = "lightblue", right = FALSE,
     breaks = 4, ylim = c(0, 60),
     main = "Histogram with right open", xlab = "Sepal Length")

13.2.3 Density histogram with density curve overlaid (ggplot2) 

ggplot(iris, aes(x = Sepal.Length, y = ..density..)) + 
  geom_histogram(binwidth = .5, color = "blue", fill = "lightblue", boundary = 0) +
  geom_density(color = "red") +
  labs(title = "Density Histogram", x = "Sepal Length")

13.2.4 Density histogram with density curve and theoretical normal curve overlaid (base R) 

# draw the density histogram
hist(iris$Sepal.Length, freq = FALSE, ylim = c(0, 0.5), 
     main = "Density Histogram", xlab = "Sepal Length")
# add density curve
lines(density(iris$Sepal.Length), col = 2)
# add normal curve
x <- seq(3, 9, length = 100) # x-axis grid
nc <- dnorm(x, mean = mean(iris$Sepal.Length), sd = sd(iris$Sepal.Length)) #normal curve
lines(x, nc, col = 3)

13.2.5 Cumulative frequency histogram 

g1 <- ggplot(iris, aes(x = Sepal.Length)) +
  geom_histogram(color = "blue", fill = "lightblue") +
  labs(title = "Frequency", x = "Sepal Length")
g2 <- ggplot(iris, aes(x = Sepal.Length)) +
  geom_histogram(aes(y = cumsum(..count..)),
                 color = "blue", fill = "lightblue") +
  labs(title = "Cumulative Frequency", x = "Sepal Length")
grid.arrange(g1, g2, nrow = 1)

13.3 Boxplot

13.3.1 Boxplot (base R) 

boxplot(Sepal.Length ~ Species, data = iris, horizontal = TRUE,
        main = "Sepal Length vs Species", ylab = "Sepal Length")

13.3.2 Boxplot (ggplot2) 

ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  geom_boxplot(varwidth = TRUE) +
  coord_flip() +
  labs(title = "Sepal Length vs Species", y = "Sepal Length")

13.4 Violin plot 

ggplot(iris, aes(x = Species,
                  y = Sepal.Length)) +
  geom_violin(adjust = 1.5) +
  coord_flip() +
  labs(title = "Violin Plot", y = "Sepal Length")

13.5 Ridgeline plot 

ggplot(iris, aes(x = Sepal.Length, y = reorder(Species, Sepal.Length, median))) +
  geom_density_ridges(fill = "blue", alpha = .5, scale = .95) +
  labs(title = "Ridgeline Plot", x = "Sepal Length", y = "Species")

13.6 Q-Q plot (quantile-quantile) 

qqnorm(iris$Sepal.Length)
qqline(iris$Sepal.Length, col = 2)

13.7 Bar chart

Data: TitanicSurvival

head(TitanicSurvival)
##                                 survived    sex     age passengerClass
## Allen, Miss. Elisabeth Walton        yes female 29.0000            1st
## Allison, Master. Hudson Trevor       yes   male  0.9167            1st
## Allison, Miss. Helen Loraine          no female  2.0000            1st
## Allison, Mr. Hudson Joshua Crei       no   male 30.0000            1st
## Allison, Mrs. Hudson J C (Bessi       no female 25.0000            1st
## Anderson, Mr. Harry                  yes   male 48.0000            1st

13.7.1 Ordinal data (sort in logical order of the categories) 

ggplot(TitanicSurvival, aes(passengerClass)) +
  geom_bar(fill = "cornflowerblue") +
  ggtitle("Passenger Class") +
  labs(title = "Passenger Class", x = "")

13.7.2 Nominal data (sort from highest to lowest count) 

ggplot(TitanicSurvival, aes(fct_infreq(passengerClass))) +
  geom_bar(fill = "cornflowerblue") +
  ggtitle("Passenger Class") +
  labs(title = "Passenger Class", x = "")

13.7.3 Bar chart with facets 

ggplot(data = TitanicSurvival, aes(x = passengerClass)) +
  geom_bar() +
  facet_wrap(~survived, ncol = 1, scales = "free_y") +
  labs(title = "Bar chart faceted by Survival Status",
       x = "Passenger Class", y = "")

13.7.4 Stacked bar chart 

ggplot(TitanicSurvival, aes(x = sex, fill = survived)) + 
    geom_bar()

13.7.5 Grouped bar chart 

ggplot(TitanicSurvival, aes(x = sex, fill = survived)) +
  geom_bar(position = "dodge")

13.7.6 Grouped bar chart with facets 

counts <- TitanicSurvival %>%
  group_by(sex, survived, passengerClass) %>%
  summarize(Freq = n()) %>%
  ungroup() %>%
  complete(sex, survived, passengerClass, fill = list(Freq = 0))
# draw the grouped bar chart
ggplot(counts, aes(x = sex, y = Freq, fill = survived)) +
  geom_col(position = "dodge") +
  facet_wrap(~passengerClass)

13.8 Cleveland dot plot 

TitanicSurvival1 <- TitanicSurvival %>%
  rownames_to_column(var = "name")
head(TitanicSurvival1)
##                              name survived    sex     age passengerClass
## 1   Allen, Miss. Elisabeth Walton      yes female 29.0000            1st
## 2  Allison, Master. Hudson Trevor      yes   male  0.9167            1st
## 3    Allison, Miss. Helen Loraine       no female  2.0000            1st
## 4 Allison, Mr. Hudson Joshua Crei       no   male 30.0000            1st
## 5 Allison, Mrs. Hudson J C (Bessi       no female 25.0000            1st
## 6             Anderson, Mr. Harry      yes   male 48.0000            1st

13.8.1 Cleveland dot plot 

ts1 <- TitanicSurvival1 %>%
  filter(!is.na(age) & passengerClass == "1st" & survived == "yes" & sex == "female" &
           age >= 30 & age <= 40)

ggplot(ts1,aes(x = age, y = fct_reorder(name, age))) +
  geom_point(color = "blue") +
  ylab("")

13.8.2 Cleveland dot plot with facets 

ts2 <- TitanicSurvival1 %>%
  filter(!is.na(age) & survived == "yes" & sex == "female" & age >= 30 & age <= 40)

ggplot(ts2, aes(x = age, y = reorder(name, age))) +
  geom_point(color = "blue") +
  facet_grid(.~reorder(passengerClass, -age, median)) +
  ylab("")

13.9 Scatterplot

Data: babies in the openintro package

head(babies)
## # A tibble: 6 × 8
##    case   bwt gestation parity   age height weight smoke
##   <int> <int>     <int>  <int> <int>  <int>  <int> <int>
## 1     1   120       284      0    27     62    100     0
## 2     2   113       282      0    33     64    135     0
## 3     3   128       279      0    28     64    115     1
## 4     4   123        NA      0    36     69    190     0
## 5     5   108       282      0    23     67    125     1
## 6     6   136       286      0    25     62     93     0

13.9.1 Scatterplot 

# draw the scatterplot
g <- ggplot(babies, aes(x = gestation, y = bwt)) +
  # adjust point size and add alpha blending
  geom_point(size = 1, alpha = .5)
g +
  # add the density contour lines
  geom_density_2d() +
  # add the linear model
  geom_smooth(method = 'lm', se = FALSE, col = 2)

13.9.2 Interactive scatterplot 

g1 <- ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_point()
ggplotly(g1)

13.9.3 Scatterplot matrix 

plot(iris[,1:4], col = "#00660030", pch = 19)

13.10 Parallel coordinates plot

Data: state.x77

mystates <- data.frame(state.x77) %>%
  rownames_to_column("State") %>%
  mutate(Region = factor(state.region))
head(mystates)
##        State Population Income Illiteracy Life.Exp Murder HS.Grad Frost   Area
## 1    Alabama       3615   3624        2.1    69.05   15.1    41.3    20  50708
## 2     Alaska        365   6315        1.5    69.31   11.3    66.7   152 566432
## 3    Arizona       2212   4530        1.8    70.55    7.8    58.1    15 113417
## 4   Arkansas       2110   3378        1.9    70.66   10.1    39.9    65  51945
## 5 California      21198   5114        1.1    71.71   10.3    62.6    20 156361
## 6   Colorado       2541   4884        0.7    72.06    6.8    63.9   166 103766
##   Region
## 1  South
## 2   West
## 3   West
## 4  South
## 5   West
## 6   West

13.10.1 Static parallel coordinates plot 

ggparcoord(mystates, columns = 2:9, alphaLines = .5, 
           scale = "uniminmax", splineFactor = 10, groupColumn = 10) +
  geom_vline(xintercept = 2:8, color = "lightblue")

13.10.2 Interactive parallel coordinates plot 

mystates %>%
  arrange(Region) %>%
  parcoords(
    rownames = FALSE,
    brushMode = "1D-axes",
    reorderable = TRUE,
    queue = TRUE,
    alpha = .5,
    color = list(
      colorBy = "Region",
      colorScale = "scaleOrdinal",
      colorScheme = "schemeCategory10"
      ),
    withD3 = TRUE,
    width = 800,
    height = 600
    )

13.11 Biplot

Data: attributes.xls
(http://www.econ.upf.edu/~michael/attributes.xls)

ratings <- data.frame(country = c("Italy","Spain","Croatia","Brazil","Russia",
                                  "Germany","Turkey","Morocco","Peru","Nigeria",
                                  "France","Mexico","SouthAfrica"),
                      living_standard = c(7,7,5,5,6,8,5,4,5,2,8,2,4),
                      climate = c(8,9,6,8,2,3,8,7,6,4,4,5,4),
                      food = c(9,9,6,7,2,2,9,8,6,4,7,5,5),
                      security = c(5,5,6,3,3,8,3,2,3,2,7,2,3),
                      hospitality = c(3,2,5,2,7,7,1,1,4,3,9,3,3),
                      infrastructure = c(7,8,6,3,6,9,3,2,4,2,8,3,3))
head(ratings)
##   country living_standard climate food security hospitality infrastructure
## 1   Italy               7       8    9        5           3              7
## 2   Spain               7       9    9        5           2              8
## 3 Croatia               5       6    6        6           5              6
## 4  Brazil               5       8    7        3           2              3
## 5  Russia               6       2    2        3           7              6
## 6 Germany               8       3    2        8           7              9

13.11.1 Principal components analysis (PCA) 

pca <- prcomp(ratings[,2:7], scale. = TRUE)  
summary(pca)
## Importance of components:
##                          PC1    PC2     PC3     PC4     PC5     PC6
## Standard deviation     1.854 1.4497 0.43959 0.39052 0.27517 0.19778
## Proportion of Variance 0.573 0.3503 0.03221 0.02542 0.01262 0.00652
## Cumulative Proportion  0.573 0.9232 0.95544 0.98086 0.99348 1.00000

13.11.2 Biplot 

draw_biplot(ratings, fix_sign = FALSE)

13.11.3 Biplot with calibrated axis and projection lines 

draw_biplot(ratings, "climate",  project = TRUE)

13.12 Mosaic plot

13.12.1 Mosaic plot with one variable 

counts1 <- TitanicSurvival %>%
  group_by(sex, survived) %>%
  summarize(Freq = n())
mosaic(~sex, direction = "v", counts1)

13.12.2 Mosaic plot with two variables 

mosaic(survived ~ sex, counts1, direction = c("v", "h"))

13.12.3 Mosaic plot with three variables 

mosaic(survived ~ passengerClass + sex, counts, direction = c("v", "v", "h"),
       rot_labels = c(0,0,0,90))

13.12.4 Mosaic pairs plot 

pairs(table(TitanicSurvival[,c(1,2,4)]), highlighting = 2)

13.13 Alluvial diagram

Data: Yamaguchi87 in the vcdExtra package

head(Yamaguchi87)
##    Son Father Country Freq
## 1 UpNM   UpNM      US 1275
## 2 LoNM   UpNM      US  364
## 3  UpM   UpNM      US  274
## 4  LoM   UpNM      US  272
## 5 Farm   UpNM      US   17
## 6 UpNM   LoNM      US 1055
ggplot(Yamaguchi87, aes(y = Freq, axis1 = Father, axis2 = Son)) +
  geom_flow(aes(fill = Father), width = 1/12) +
  geom_stratum(width = 1/12, fill = "grey80", color = "black") +
  geom_label(stat = "stratum", aes(label = after_stat(stratum))) +
  scale_x_discrete(limit = c("Father", "Son"), expand = c(.05, .05)) +
  scale_y_continuous(expand = c(.01, 0)) +
  guides(fill = FALSE)

13.14 Heatmap

13.14.1 Hexagonal heatmap 

ggplot(babies, aes(x = gestation, y = bwt)) +
  geom_hex()

13.14.2 Square heatmap 

ggplot(babies, aes(x = gestation, y = bwt)) +
  geom_bin_2d()

13.14.3 Heatmap with facets 

mydata <- Yamaguchi87 %>%
  group_by(Country, Father) %>% 
  mutate(Total = sum(Freq)) %>%
  ungroup()
ggplot(mydata, aes(x = Father, y = Son)) +
  geom_tile(aes(fill = Freq/Total), color = "white") +
  coord_fixed() +
  facet_wrap(~Country) +
  scale_fill_distiller(palette = "RdBu")