In-class-5

Author

Daisy

Published

February 26, 2023

pacman::p_load(plotly,tidyverse,ggiraph,ggstatsplot,corrplot,ellipse,corrgram,ggpubr,patchwork,seriation, dendextend, heatmaply)

package 'tidyr' successfully unpacked and MD5 sums checked
package 'ggpubr' successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\LYX\AppData\Local\Temp\Rtmpk73dEO\downloaded_packages

wine <- read_csv("wine_quality.csv")

pairs(wine[,1:11])

pairs(wine[,2:12])

ggstatsplot::ggcorrmat(
  data = wine, 
  cor.vars = 1:11)

ggstatsplot::ggcorrmat(
  data = wine, 
  cor.vars = 1:11,
  ggcorrplot.args = list(outline.color = "black", 
                         hc.order = TRUE,
                         tl.cex = 10),
  title    = "Correlogram for wine dataset",
  subtitle = "Four pairs are no significant at p < 0.05"
)

ggplot.component = list(
    theme(text=element_text(size=5),
      axis.text.x = element_text(size = 8),
      axis.text.y = element_text(size = 8)))

grouped_ggcorrmat(
  data = wine,
  cor.vars = 1:11,
  grouping.var = type,
  type = "robust",
  p.adjust.method = "holm",
  plotgrid.args = list(ncol = 2),
  ggcorrplot.args = list(outline.color = "black", 
                         hc.order = TRUE,
                         tl.cex = 10),
  annotation.args = list(
    tag_levels = "a",
    title = "Correlogram for wine dataset",
    subtitle = "The measures are: alcohol, sulphates, fixed acidity, citric acid, chlorides, residual sugar, density, free sulfur dioxide and volatile acidity",
    caption = "Dataset: UCI Machine Learning Repository"
  )
)

wine.cor <- cor(wine[, 1:11])

corrplot(wine.cor)

corrplot(wine.cor, 
         method = "ellipse")

corrplot(wine.cor, 
         method = "ellipse", 
         type="lower")

corrplot(wine.cor, 
         method = "ellipse", 
         type="lower",
         diag = FALSE,
         tl.col = "black")

corrplot.mixed(wine.cor, 
               lower = "ellipse", 
               upper = "number",
               tl.pos = "lt",
               diag = "l",
               tl.col = "black")

wine.sig = cor.mtest(wine.cor, conf.level= .95)

corrplot(wine.cor,
         method = "number",
         type = "lower",
         diag = FALSE,
         tl.col = "black",
         tl.srt = 45,
         p.mat = wine.sig$p,
         sig.level = .05)

corrplot.mixed(wine.cor, 
               lower = "ellipse", 
               upper = "number",
               tl.pos = "lt",
               diag = "l",
               order="AOE",
               tl.col = "black")

corrplot(wine.cor, 
         method = "ellipse", 
         tl.pos = "lt",
         tl.col = "black",
         order="hclust",
         hclust.method = "ward.D",
         addrect = 3)

pop_data <- read_csv("respopagsex2000to2018_tidy.csv")

agpop_mutated <- pop_data %>%
  mutate(`Year` = as.character(Year))%>%
  pivot_wider(names_from=AG, values_from=Population) %>% ##########
  mutate(YOUNG = rowSums(.[4:8]))%>%
  mutate(ACTIVE = rowSums(.[9:16]))  %>%
  mutate(OLD = rowSums(.[17:21])) %>%
  mutate(TOTAL = rowSums(.[22:24])) %>%
  filter(Year == 2018)%>%
  filter(TOTAL > 0)

ggtern::ggtern(data=agpop_mutated,aes(x=YOUNG,y=ACTIVE, z=OLD)) +
  geom_point()

ggtern::ggtern(data=agpop_mutated, aes(x=YOUNG,y=ACTIVE, z=OLD)) +
  geom_point() +
  labs(title="Population structure, 2015")

  ggtern::theme_rgbw()

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label <- function(txt) {
  list(
    text = txt, 
    x = 0.1, y = 1,
    ax = 0, ay = 0,
    xref = "paper", yref = "paper", 
    align = "center",
    font = list(family = "serif", size = 15, color = "white"),
    bgcolor = "#b3b3b3", bordercolor = "black", borderwidth = 2
  )
}

axis <- function(txt) {
  list(
    title = txt, tickformat = ".0%", tickfont = list(size = 10)
  )
}

ternaryAxes <- list(
  aaxis = axis("Young"), 
  baxis = axis("Active"), 
  caxis = axis("Old")
)

plot_ly(
  agpop_mutated, 
  a = ~YOUNG, 
  b = ~ACTIVE, 
  c = ~OLD, 
  color = I("black"), 
  type = "scatterternary"
) %>%
  layout(
    annotations = label("Ternary Markers"), 
    ternary = ternaryAxes
  )

wh <- read_csv("WHData-2018.csv")

row.names(wh) <- wh$Country

wh1 <- dplyr::select(wh, c(3, 7:12))
wh_matrix <- data.matrix(wh)

wh_heatmap <- heatmap(wh_matrix,
                      Rowv=NA, Colv=NA)

wh_heatmap <- heatmap(wh_matrix)

heatmaply(normalize(wh_matrix[, -c(1, 2, 4, 5)]))

heatmaply(normalize(wh_matrix[, -c(1, 2, 4, 5)]),
          dist_method = "euclidean",
          hclust_method = "ward.D")

wh_d <- dist(normalize(wh_matrix[, -c(1, 2, 4, 5)]), method = "euclidean")
dend_expend(wh_d)[[3]]

  dist_methods hclust_methods     optim
1      unknown         ward.D 0.6137851
2      unknown        ward.D2 0.6289186
3      unknown         single 0.4774362
4      unknown       complete 0.6434009
5      unknown        average 0.6701688
6      unknown       mcquitty 0.5020102
7      unknown         median 0.5901833
8      unknown       centroid 0.6338734