My Blog: Sampling

Load the R packages we will use.

library(tidyverse)
library(moderndive)

Quiz Questions

Replace all the instances of SEE QUIZ. These are inputs from your moodle quiz.
Replace all the instances of ???. These are answers on your moodle quiz.
Run all the individual code chunks to make sure the answers in this file correspond with your moodle quiz.
After you check all your code chunks run then you can knit it. It won’t knit until the ??? are replaced.
The quiz assumes that you have watched the videos and worked through the examples in Chapter 7 of ModernDive.

Question:

7.2.4 in ModernDive with different sample sizes and repetitions

Make sure you have installed and loaded the tidyverse and ModernDive packages
Fill in the blanks
Put the command you see in the Rchunks in you Rmd file for this quiz.

Modify the code for comparing different sample sizes from the virtual bowl

Segment 1: Sample size = 26

1a.) Take 1180 samples of size of 26 instead of 1000 replicates of size 25 from the bowl dataset. Assign the output to virtual_samples_26

virtual_samples_26  <- bowl %>%
  rep_sample_n(size = 26, reps = 1180)

1b.) Compute resulting 1180 replicates of proportion red

Start with virtual_samples_26 THEN
group_by replicate THEN
Create variable red equal to the sum of all the red balls
Create variable prop_red equal to variable red / 26
Assign the output to virtual_prop_red_26

virtual_prop_red_26  <- virtual_samples_26  %>%
  group_by(replicate)  %>%
  summarize(red = sum(color == "red"))  %>%
  mutate(prop_red = red / 26)

1c.) Plot distribution of virtual_prop_red_26 via a histogram use labs to

Label x axis = “Proportion of 26 balls that were red”
Create title = “26”

ggplot(virtual_prop_red_26, aes(x = prop_red)) +
  geom_histogram(binwidth = 0.05, boundary = 0.4, color = "white") +
  labs(x = "Proportion of 26 balls that were red", title = "26")

Segment 2: Sample size = 55

2a.) Take 1180 samples of size of 55 instead of 1000 replicates of size 50. Assign the output to virtual_samples_55

virtual_samples_55  <- bowl %>%
  rep_sample_n(size = 55, reps = 1180)

2b.) Compute resulting 1180 replicates of proportion red

Start with virtual_samples_55 THEN
group_by replicate THEN
Create variable red equal to the sum of all the red balls
Create variable prop_red equal to the variable red / 55
Assign the output to `virtual_prop_red_55

virtual_prop_red_55  <- virtual_samples_55  %>%
  group_by(replicate)  %>%
  summarize(red = sum(color == "red"))  %>%
  mutate(prop_red = red / 55)

2c.) Plot distribution of virtual_prop_red_55 via a histogram uses labs to

Label x axis = “Proportion of 55 balls that were red”
Create title = “55”

ggplot(virtual_prop_red_55, aes(x = prop_red))  +
  geom_histogram(binwidth = 0.05, boundary = 0.4, color = "white") +
  labs(x = "Proportion of 55 balls that were red", title = "55")

**Segment 3: sample size = 110

3a.) Take 1180 samples of size of 110 instead of 1000 replicates of size 50. Assign the output to virtual_samples_110

virtual_samples_110  <- bowl %>%
  rep_sample_n(size = 110, reps = 1180)

3b.) Compute resulting 1180 replicates of proportion red

Start with virtual_samples_110 THEN
group_by replicate THEN
Create variable red equal to the sum of all the red balls
Create variable prop_red to variable red / 110
Assign the output to virtual_prop_red_110

virtual_prop_red_110  <- virtual_samples_110  %>%
  group_by(replicate) %>%
  summarize(red = sum(color == "red"))  %>%
  mutate(prop_red = red / 110)

3c.) Plot distribution of virtual_prop_red_110 via a histogram use labs to

Label x axis = “Proportion of 110 balls that were red”
Create title = “110”

ggplot(virtual_prop_red_110, aes(x = prop_red)) +
  geom_histogram(binwidth = 0.05, boundary = 0.4, color = "white") +
  labs(x = "Proportion of 110 balls that were red", title = "110")

ggsave(filename = "preview.png",
       path = here::here("_posts", "2022-04-12-sampling"))

Calculate the standard deviations for your three sets of 1180 values of prop_red using the standard deviation

n = 26

virtual_prop_red_26  %>%
  summarize(sd = sd(prop_red))

# A tibble: 1 × 1
      sd
   <dbl>
1 0.0930

n = 55

virtual_prop_red_55  %>%
  summarize(sd = sd(prop_red))

# A tibble: 1 × 1
      sd
   <dbl>
1 0.0649

n = 110

virtual_prop_red_110  %>%
  summarize(sd = sd(prop_red))

# A tibble: 1 × 1
      sd
   <dbl>
1 0.0454

The distribution with sample size, n = 110, has the smallest standard deviation (spread) around the estimated proportion of red balls