Skip to content

Latest commit

 

History

History
247 lines (180 loc) · 5.94 KB

Block3_notes.md

File metadata and controls

247 lines (180 loc) · 5.94 KB

Block 3: Descriptive statistics and visualisations

library(tidyverse)
## -- Attaching packages ------------------------------------------------- tidyverse 1.3.0 --

## v ggplot2 3.3.2     v purrr   0.3.4
## v tibble  3.0.3     v dplyr   1.0.0
## v tidyr   1.1.0     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.5.0

## -- Conflicts ---------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(here)
## here() starts at C:/Users/Kateryna/Documents/2020/IndependentStudy/Rstudio

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

mean(sales$floor_area_sqm)
## [1] 97.58903

median(sales$floor_area_sqm)
## [1] 96

# 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)
## [1] 67

3.3 Dispersion

"Range"
## [1] "Range"

max(sales$floor_area_sqm) - min(sales$floor_area_sqm)
## [1] 249

"Interquartile Range"
## [1] "Interquartile Range"

IQR(sales$floor_area_sqm)
## [1] 36

"Standard Deviation"
## [1] "Standard Deviation"

sd(sales$floor_area_sqm)
## [1] 24.22276

"Coefficient of variation"
## [1] "Coefficient of variation"

sd(sales$floor_area_sqm) / mean(sales$floor_area_sqm)
## [1] 0.2482119

"Kurtosis and Skewness from the 'e1071` library"
## [1] "Kurtosis and Skewness from the 'e1071` library"

kurtosis(sales$floor_area_sqm)
## [1] -0.1450646

skewness(sales$floor_area_sqm)
## [1] 0.2770161

"Summary"
## [1] "Summary"

summary(sales$floor_area_sqm)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   31.00   76.00   96.00   97.59  112.00  280.00

"Skim"
## [1] "Skim"

skim(sales$floor_area_sqm)
Name sales$floor_area_sqm
Number of rows 79100
Number of columns 1
_______________________
Column type frequency:
numeric 1
________________________
Group variables None

Data summary

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
data 0 1 97.59 24.22 31 76 96 112 280 ▃▇▁▁▁

3.4 Visualization

ggplot(sales, aes(x = floor_area_sqm)) +
  geom_histogram(binwidth = 5)

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)))

sales %>%
  filter(floor_area_sqm == 67) 
## # A tibble: 4,904 x 11
##    month      town  flat_type block street_name storey_range floor_area_sqm
##    <date>     <chr> <fct>     <chr> <chr>       <fct>                 <dbl>
##  1 2015-01-01 ANG ~ 3 ROOM    603   ANG MO KIO~ 07 TO 09                 67
##  2 2015-01-01 ANG ~ 3 ROOM    109   ANG MO KIO~ 01 TO 03                 67
##  3 2015-01-01 ANG ~ 3 ROOM    218   ANG MO KIO~ 07 TO 09                 67
##  4 2015-01-01 ANG ~ 3 ROOM    471   ANG MO KIO~ 07 TO 09                 67
##  5 2015-01-01 ANG ~ 3 ROOM    434   ANG MO KIO~ 07 TO 09                 67
##  6 2015-01-01 ANG ~ 3 ROOM    560   ANG MO KIO~ 07 TO 09                 67
##  7 2015-01-01 ANG ~ 3 ROOM    631   ANG MO KIO~ 07 TO 09                 67
##  8 2015-01-01 ANG ~ 3 ROOM    442   ANG MO KIO~ 10 TO 12                 67
##  9 2015-01-01 ANG ~ 3 ROOM    558   ANG MO KIO~ 10 TO 12                 67
## 10 2015-01-01 ANG ~ 3 ROOM    212   ANG MO KIO~ 10 TO 12                 67
## # ... with 4,894 more rows, and 4 more variables: flat_model <fct>,
## #   lease_commence_date <dbl>, remaining_lease <dbl>, resale_price <dbl>

  #%>% View()
ggplot(sales, aes(x = 1, y = floor_area_sqm)) +
  geom_boxplot()

ggplot(sales, aes(x = 1, y = floor_area_sqm)) +
  geom_violin()

ggplot(sales, aes(x = floor_area_sqm)) +
  geom_histogram(binwidth = 10) +
  facet_wrap(vars(flat_type), scales = "free_y")

ggplot(sales, aes(x = flat_type, y = floor_area_sqm)) +
  geom_violin()