Block3_notes.Rmd

---
title: "Block 3: Descriptive statistics and visualisations"
output: github_document
---
```{r}
library(tidyverse)
library(here)
library(e1071)
library(skimr)
#library(lubridate)
#library(forcats)
#library(ggplot2)
sales <- readRDS(here::here("data/sales.rds"))
```
## Chapter3: 2 different types of descriptive statistics: measures of the central tendency and measures of dispersion. 
https://02522-cua.github.io/lecturenotes/descriptive-statistics-and-visualisations.html#assignment-monday-february-17th-2359

### 3.2 Central Tendency
```{r}
mean(sales$floor_area_sqm)
median(sales$floor_area_sqm)

# from https://stackoverflow.com/a/25635740
manual_mode <- function(x, na.rm = FALSE) { # we don't use 'mode' as a function name because it already exists
  if (na.rm) {
    x <- x[!is.na(x)]
  }
  ux <- unique(x)
  return(ux[which.max(tabulate(match(x, ux)))])
}

manual_mode(sales$floor_area_sqm)
```
### 3.3 Dispersion
```{r}
"Range"
max(sales$floor_area_sqm) - min(sales$floor_area_sqm)

"Interquartile Range"
IQR(sales$floor_area_sqm)

"Standard Deviation"
sd(sales$floor_area_sqm)

"Coefficient of variation"
sd(sales$floor_area_sqm) / mean(sales$floor_area_sqm)

"Kurtosis and Skewness from the 'e1071` library"
kurtosis(sales$floor_area_sqm)
skewness(sales$floor_area_sqm)
"Summary"
summary(sales$floor_area_sqm)
"Skim"
skim(sales$floor_area_sqm)
```
### 3.4 Visualization
```{r}
ggplot(sales, aes(x = floor_area_sqm)) +
  geom_histogram(binwidth = 5)
```

```{r}
ggplot(sales, aes(x = floor_area_sqm)) +
  geom_histogram(aes(y = ..density..), binwidth = 5) +
  stat_function(fun = dnorm, args = list(mean = mean(sales$floor_area_sqm), sd = sd(sales$floor_area_sqm)))
```

```{r}
sales %>%
  filter(floor_area_sqm == 67) 
  #%>% View()
```

```{r}
ggplot(sales, aes(x = 1, y = floor_area_sqm)) +
  geom_boxplot()
```

```{r}
ggplot(sales, aes(x = 1, y = floor_area_sqm)) +
  geom_violin()
```
```{r}
ggplot(sales, aes(x = floor_area_sqm)) +
  geom_histogram(binwidth = 10) +
  facet_wrap(vars(flat_type), scales = "free_y")
```

```{r}
ggplot(sales, aes(x = flat_type, y = floor_area_sqm)) +
  geom_violin()
```