Sampling and point estimates

Sampling in R

Richie Cotton

Data Evangelist at DataCamp

Estimating the population of France

A map of France.

A census asks every household how many people live there.

There are lots of people in France

A map of France with icons of people.

Censuses are really expensive!

Sampling households

A map of France with icons of people, some of which are highlighted.

It's cheaper to ask a small number of households and use statistics to make estimates about the whole population.

Working with a subset of the whole population is called sampling.

Population vs. sample

The population is the complete dataset.

It doesn't have to refer to people.
You typically don't know what the whole population is.

The sample is the subset of data you calculate on.

Coffee rating dataset

total_cup_points	variety	country_of_origin	aroma	flavor	aftertaste	body	balance
90.58	NA	Ethiopia	8.67	8.83	8.67	8.50	8.42
89.92	Other	Ethiopia	8.75	8.67	8.50	8.42	8.42
...	...	...	...	...	...	...	...
73.75	NA	Vietnam	6.75	6.67	6.5	6.92	6.83

Each row represents 1 coffee.
1138 rows.
We'll treat this as the population.

Points vs. flavor: population

pts_vs_flavor_pop <- coffee_ratings %>% 
  select(total_cup_points, flavor)

dim(pts_vs_flavor_pop)

1338    2

     total_cup_points flavor
1               90.58   8.83
2               89.92   8.67
3               89.75   8.50
4               89.00   8.58
...              ...     ...
1335            78.08   7.67
1336            77.17   7.33
1337            75.08   6.83
1338            73.75   6.67

Points vs. flavor: 10 row sample

pts_vs_flavor_samp <- coffee_ratings %>% 
  select(total_cup_points, flavor) %>%
  slice_sample(n = 10)

dim(pts_vs_flavor_samp)

10  2

   total_cup_points flavor
1             82.25   7.58
2             83.50   7.67
3             80.50   7.17
4             79.33   7.17
5             83.83   7.58
6             84.17   7.75
7             83.67   8.17
8             81.92   7.50
9             82.67   7.58
10            83.42   7.67

Base-R sampling

Use slice_sample() for data frames, and sample() for vectors.

cup_points_samp <- sample(coffee_ratings$total_cup_points, size = 10)

88.25 83.83 83.17 82.67 84.67 83.42 73.67 86.00 81.58 80.92

Population parameters & point estimates

A population parameter is a calculation made on the population dataset.

mean(pts_vs_flavor_pop$total_cup_points)

82.15

A point estimate or sample statistic is a calculation made on the sample dataset.

mean(cup_points_samp)

82.82

Point estimates with dplyr

pts_vs_flavor_pop %>% 
  summarize(mean_flavor = mean(flavor))

  mean_flavor
1       7.526

pts_vs_flavor_samp %>% 
  summarize(mean_flavor = mean(flavor))

  mean_flavor
1       7.716

Let's practice!

Sampling in R