library(tidyverse) # data science
library(fpp3) # tsClase_2
aus_accommodation# A tsibble: 592 x 5 [1Q]
# Key: State [8]
Date State Takings Occupancy CPI
<qtr> <chr> <dbl> <dbl> <dbl>
1 1998 Q1 Australian Capital Territory 24.3 65 67
2 1998 Q2 Australian Capital Territory 22.3 59 67.4
3 1998 Q3 Australian Capital Territory 22.5 58 67.5
4 1998 Q4 Australian Capital Territory 24.4 59 67.8
5 1999 Q1 Australian Capital Territory 23.7 58 67.8
6 1999 Q2 Australian Capital Territory 25.4 61 68.1
7 1999 Q3 Australian Capital Territory 28.2 66 68.7
8 1999 Q4 Australian Capital Territory 25.8 60 69.1
9 2000 Q1 Australian Capital Territory 27.3 60.9 69.7
10 2000 Q2 Australian Capital Territory 30.1 64.7 70.2
# ℹ 582 more rows
aus_accommodation_long <- aus_accommodation |>
pivot_longer(cols = Takings:CPI, names_to="variable",
values_to = "valor") |>
features(valor,
list(media = mean,
desv_std = sd,
minimo = min,
maximo = max,
mediana = median)
) #|>
#pivot_wider(names_from = variable, values_from = media:mediana)aus_accommodation_long# A tibble: 24 × 7
State variable media desv_std minimo maximo mediana
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Australian Capital Territory CPI 88.0 13.1 67 109. 87.2
2 Australian Capital Territory Occupancy 67.7 5.29 56.8 80.2 68.8
3 Australian Capital Territory Takings 44.8 13.2 22.3 71.1 45.7
4 New South Wales CPI 88.0 13.1 67 109. 87.2
5 New South Wales Occupancy 62.7 3.84 54.1 69.8 62.9
6 New South Wales Takings 575. 157. 316. 945. 571.
7 Northern Territory CPI 88.0 13.1 67 109. 87.2
8 Northern Territory Occupancy 60.8 10.4 41.8 79.8 60.4
9 Northern Territory Takings 53.8 21.7 22.0 118. 49.4
10 Queensland CPI 88.0 13.1 67 109. 87.2
# ℹ 14 more rows