206  Question

Schlüsselwörter

Aufgaben, Statistik, Prognose, Modellierung, R, Datenanalyse, Regression

In dieser Aufgaben analysieren wir den Datensatz diamonds, welcher Merkmale von Diamanten (wie Preis, Schliffart etc.) auflistet. Hier ist ein Blick in den Datensatz:

## Rows: 53,940
## Columns: 10
## $ carat   <dbl> 0.23, 0.21, 0.23, 0.29, 0.31, 0.24, 0.24, 0.26, 0.22, 0.23, 0.…
## $ cut     <ord> Ideal, Premium, Good, Premium, Good, Very Good, Very Good, Ver…
## $ color   <ord> E, E, E, I, J, J, I, H, E, H, J, J, F, J, E, E, I, J, J, J, I,…
## $ clarity <ord> SI2, SI1, VS1, VS2, SI2, VVS2, VVS1, SI1, VS2, VS1, SI1, VS1, …
## $ depth   <dbl> 61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, 59.4, 64…
## $ table   <dbl> 55, 61, 65, 58, 58, 57, 57, 55, 61, 61, 55, 56, 61, 54, 62, 58…
## $ price   <int> 326, 326, 327, 334, 335, 336, 336, 337, 337, 338, 339, 340, 34…
## $ x       <dbl> 3.95, 3.89, 4.05, 4.20, 4.34, 3.94, 3.95, 4.07, 3.87, 4.00, 4.…
## $ y       <dbl> 3.98, 3.84, 4.07, 4.23, 4.35, 3.96, 3.98, 4.11, 3.78, 4.05, 4.…
## $ z       <dbl> 2.43, 2.31, 2.31, 2.63, 2.75, 2.48, 2.47, 2.53, 2.49, 2.39, 2.…

diamonds ist Teil von Tidyverse (genauer gesagt dem Paket ggplot2).

Hier ist ein Überblick über die deskriptiven, univariaten Statistiken:

Data summary
Name diamonds
Number of rows 53940
Number of columns 10
_______________________
Column type frequency:
factor 3
numeric 7
________________________
Group variables None

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
cut 0 1 TRUE 5 Ide: 21551, Pre: 13791, Ver: 12082, Goo: 4906
color 0 1 TRUE 7 G: 11292, E: 9797, F: 9542, H: 8304
clarity 0 1 TRUE 8 SI1: 13065, VS2: 12258, SI2: 9194, VS1: 8171

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
carat 0 1 0.80 0.47 0.2 0.40 0.70 1.04 5.01 ▇▂▁▁▁
depth 0 1 61.75 1.43 43.0 61.00 61.80 62.50 79.00 ▁▁▇▁▁
table 0 1 57.46 2.23 43.0 56.00 57.00 59.00 95.00 ▁▇▁▁▁
price 0 1 3932.80 3989.44 326.0 950.00 2401.00 5324.25 18823.00 ▇▂▁▁▁
x 0 1 5.73 1.12 0.0 4.71 5.70 6.54 10.74 ▁▁▇▃▁
y 0 1 5.73 1.14 0.0 4.72 5.71 6.54 58.90 ▇▁▁▁▁
z 0 1 3.54 0.71 0.0 2.91 3.53 4.04 31.80 ▇▁▁▁▁

Betrachten Sie das Histogramm; welcher R-Code hat es erzeugt?

Für alle Diagramme gilt:

A

diamonds %>% 
  mutate(price = price/1000 %>% round) %>%
  ggplot() +
  aes(x = price) +
  geom_histogram()  +
  geom_vline(aes(xintercept = mean(price))) +
  theme_light() +
  facet_wrap( ~ cut, scales = "free")

B

diamonds %>% 
  drop_na(cut, price) %>% 
  filter(cut %in% c("Fair", "Premium", "Ideal")) %>% 
  mutate(price = price/1000 %>% round) %>% 
  ggplot() +
  aes(x = price) +
  geom_histogram()  +
  geom_vline(aes(xintercept = median(price))) +
  theme_light() +
  facet_wrap( ~ cut, scales = "free")

C

diamonds %>% 
  drop_na(cut, price) %>% 
  filter(cut %in% c("Fair", "Premium", "Ideal")) %>%      
  ggplot() +
  aes(x = price) +
  geom_histogram()  +
  geom_vline(aes(xintercept = median(price))) 
  facet_wrap(cut)

D

diamonds %>% 
  drop_na(cut, price) %>% 
  filter(cut %in% c("Fair", "Premium", "Ideal")) %>%      
  ggplot() +
  aes(x = price) +
  geom_histogram()  +
  geom_vline(aes(xintercept = median(price))) +
  theme_light() +
  facet_wrap( ~ cut, scales = "free")

206.1 Answerlist

  • Code-Beispiel A
  • Code-Beispiel B
  • Code-Beispiel C
  • Code-Beispiel D
  • Keine Antwort möglich

207 Solution

Code B passt zum Diagramm.

207.1 Answerlist

  • Falsch
  • Richtig
  • Falsch
  • Falsch
  • Falsch

208 Meta-information

exname: diamonds-histogram-rcode extype: schoice exsolution: 01000 exshuffle: TRUE expoints: 1