• "A picture is worth a thousand words”.

• a good visualization makes it easier to identify patterns and trends

  • provides a quick and effective way to communicate information

  • displays complex relationships among data

  • highlights interesting and compelling "stories" from the data

ggplot(data = Salaries,
       mapping = aes(x = yrs.service, 
                     y = salary)) +
  geom_point(color = "blue",
             size = 3,
             alpha = 1.5)

ggplot(data = Salaries,
       mapping = aes(x = yrs.service, 
                     y = salary)) +
  geom_point(color = "blue",
             size = 3,
             alpha = 1.5) +
  labs(x = "Years of Service",
       y = "Salary")

ggplot(data = Salaries,
       mapping = aes(x = yrs.service, 
                     y = salary)) +
  geom_point(color = "blue",
             size = 3,
             alpha = 1.5) +
  labs(x = "Years of Service",
       y = "Salary") +
  geom_smooth(method="lm",
              col = "red")

ggplot(data = Salaries,
       mapping = aes(x = yrs.service, 
                     y = salary)) +
  geom_point(color = "blue",
             size = 3,
             alpha = 1.5) +
  labs(x = "Years of Service",
       y = "Salary") +
  geom_smooth(method="lm",
              col = "red") +
  theme_classic()

ggplot(data = Salaries,
       mapping = aes(x = yrs.service, 
                     y = salary,
                     color = sex)) +
  geom_point(size = 3,
             alpha = 1.5) +
  labs(x = "Years of Service",
       y = "Salary") +
  geom_smooth(method="lm") +
  theme_classic()

ggplot(data = Salaries,
       mapping = aes(x = yrs.service, 
                     y = salary)) +
  geom_point(color = "lightblue",
             size = 3,
             alpha = 1.5) +
  labs(x = "Years of Service",
       y = "Salary") +
  geom_smooth(method="lm",
              col = "red") +
  facet_wrap(~sex)+
  theme_classic()

Salaries %>% 
  select(rank) %>% 
  ggplot(aes(x=rank)) +
  geom_bar(fill="lightblue")+
  labs(x="Rank", 
       y = "No. of faculty") +
  theme_classic()

Salaries %>% 
  select(rank) %>% 
  ggplot(aes(x=rank)) +
  geom_bar(fill="lightblue")+
  labs(x="Rank", 
       y = "No. of faculty") +
  coord_flip()+
  theme_classic()

Salaries %>% 
  select(rank) %>% 
  count(rank) %>% 
  mutate(p=round(n/sum(n)*100,1)) %>% 
  ggplot(aes(x = reorder(rank,p),
             y = p,
             label = p)) +
  geom_col(fill="lightblue", 
           col = "black") +
  geom_text(aes(label = p), 
            hjust=-0.5,
            size = 3.5) +
  labs(x = " ",
       y = "Percent (%)")+
  coord_flip() +
  theme_classic()

Salaries %>%
  drop_na(salary) %>% 
  group_by(rank) %>% 
  summarize(n = length(salary),
            Mean = mean(salary),
            SD = sd(salary)) %>% 
  mutate(SE = SD/sqrt(n)) %>% 
  as.data.frame() %>% 
  ggplot(aes(x=rank, y = Mean)) +
  geom_col(fill= "lightblue", 
           col="black", 
           width = 0.5) +
  geom_errorbar(aes(ymin = Mean-SE, 
                    ymax = Mean+SE),
                size = 0.7,
                width = 0.15) +
  theme_classic2()

Salaries %>% 
  select(discipline, rank, sex) %>% 
  group_by(sex) %>% 
  count(rank) %>% 
  as.data.frame() %>% 
  ggplot(aes(x=rank, 
             y = n, 
             fill=reorder(sex,-n))) +
  geom_bar(stat = "identity", 
           position = "stack", 
           width = 0.5) +
  coord_flip() +
  geom_text(aes(label = n),
            hjust=1.3,
            size = 3)+
  labs(x = "Rank",
       y = "No. of faculty",
       fill = "Sex") +
  theme_classic()

Salaries %>% 
  select(discipline, rank, sex) %>% 
  group_by(sex) %>% 
  count(rank) %>% 
  as.data.frame() %>% 
  ggplot(aes(x=rank, 
             y = n, 
             fill = sex)) +
  geom_bar(stat = "identity",
           position = "dodge",
           col = "black") +
  geom_text(aes(label = n),
            position = position_dodge(0.9),
            vjust = -1,
            size = 3)+
  labs(x = "Rank",
       y = "No. of faculty",
       fill = "Sex") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(0,300)) +
  theme_classic()

Salaries %>% 
  ggplot(aes(x = salary)) +
  geom_histogram(fill = "lightblue",
                 color = "black") +
  scale_y_continuous(expand = c(0,0)) +
  facet_wrap(~sex)

Salaries %>% 
  ggplot(aes(x = salary)) +
  geom_density(fill = "lightblue") +
  scale_y_continuous(expand = c(0,0)) +
  facet_wrap(~rank)

Salaries %>% 
  ggplot(aes(x = salary, 
             fill = rank, 
             color = rank)) +
  scale_y_continuous(expand = c(0,0)) +
  geom_density()

Salaries %>% 
  ggplot(aes(x = salary, 
             fill = rank, 
             color = rank)) +
  geom_boxplot() + 
  scale_y_continuous(expand = c(0,0)) +
  theme(axis.text.x=element_blank()) +
  coord_flip()