library(paneldesc)6 For Stata users
In this example, the package functions are combined in such a way that the results obtained are more or less familiar to those users who are accustomed to using Stata.
6.1 Set up
Import the package.
6.2 Data import
Import the built-in dataset with simulated unbalanced panel data.
data(production)6.3 summarize
You can look at the simple descriptive statistics with summarize_numeric().
summarize_numeric(production)Analyzing all numeric variables: firm, year, sales, capital, labor| variable | count | mean | std | min | max |
|---|---|---|---|---|---|
| firm | 180 | 15.500 | 8.680 | 1.000 | 30.000 |
| year | 180 | 3.500 | 1.713 | 1.000 | 6.000 |
| sales | 154 | 69.756 | 46.804 | 8.321 | 336.853 |
| capital | 154 | 32.490 | 31.053 | 0.968 | 194.719 |
| labor | 154 | 79.329 | 73.687 | 4.097 | 419.848 |
6.4 xtset
You can set up a panel structure in advance so that you don’t have to do it later each time you use other functions. Note that if delta is supplied, the function checks for omitted time periods. If such periods exist, they will be taken into account when other functions work with this argument.
panel <- make_panel(production, index = c("firm", "year"), delta = 1)6.5 xtdes
xtdes command funcitonality can be reproduced by combining describe_dimensions(), describe_balance(), and describe_patterns() functions.
describe_dimensions(panel)| rows | entities | periods | variables |
|---|---|---|---|
| 180 | 30 | 6 | 5 |
describe_balance(panel, detail = TRUE)| dimension | mean | std | min | p5 | p25 | p50 | p75 | p95 | max |
|---|---|---|---|---|---|---|---|---|---|
| entities | 26.167 | 3.971 | 19 | 20.5 | 25.25 | 27 | 28.75 | 29.75 | 30 |
| periods | 5.233 | 0.935 | 3 | 4.0 | 4.25 | 6 | 6.00 | 6.00 | 6 |
describe_patterns(panel)| pattern | 1 | 2 | 3 | 4 | 5 | 6 | count | share |
|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 16 | 0.533 |
| 2 | 1 | 1 | 1 | 1 | 1 | 0 | 5 | 0.167 |
| 3 | 1 | 1 | 1 | 1 | 0 | 0 | 3 | 0.100 |
| 4 | 0 | 0 | 1 | 1 | 1 | 1 | 2 | 0.067 |
| 5 | 0 | 1 | 1 | 1 | 1 | 0 | 2 | 0.067 |
| 6 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0.033 |
| 7 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.033 |
6.6 xtsum
xtsum command funcitonality can be reproduced with decompose_numeric() function.
decompose_numeric(panel)Analyzing all numeric variables: sales, capital, labor| variable | dimension | mean | std | min | max | count |
|---|---|---|---|---|---|---|
| sales | overall | 69.756 | 46.804 | 8.321 | 336.853 | 154.000 |
| sales | between | NA | 29.776 | 25.772 | 159.197 | 30.000 |
| sales | within | NA | 35.862 | -28.397 | 247.412 | 5.133 |
| capital | overall | 32.490 | 31.053 | 0.968 | 194.719 | 154.000 |
| capital | between | NA | 13.969 | 8.671 | 75.083 | 30.000 |
| capital | within | NA | 27.701 | -22.444 | 152.126 | 5.133 |
| labor | overall | 79.329 | 73.687 | 4.097 | 419.848 | 154.000 |
| labor | between | NA | 44.023 | 24.606 | 175.731 | 30.000 |
| labor | within | NA | 59.561 | -77.709 | 323.445 | 5.133 |
6.7 xttab
xttab command funcitonality can be reproduced with decompose_factor() function.
decompose_factor(panel)Analyzing all factor variables: industry, ownership| variable | category | count_overall | share_overall | count_between | share_between | share_within |
|---|---|---|---|---|---|---|
| industry | Industry 1 | 63 | 0.401 | 13 | 0.433 | 0.918 |
| industry | Industry 2 | 45 | 0.287 | 11 | 0.367 | 0.809 |
| industry | Industry 3 | 49 | 0.312 | 10 | 0.333 | 0.917 |
| ownership | private | 76 | 0.484 | 16 | 0.533 | 0.898 |
| ownership | public | 55 | 0.350 | 13 | 0.433 | 0.813 |
| ownership | mixed | 26 | 0.166 | 7 | 0.233 | 0.724 |
6.8 xttrans
xttrans command funcitonality can be reproduced with summarize_transition() function.
summarize_transition(panel, select = "industry")23 rows with NA values in 'industry' removed.| from_to | Industry 1 | Industry 2 | Industry 3 |
|---|---|---|---|
| Industry 1 | 1.000 | 0.000 | 0.000 |
| Industry 2 | 0.054 | 0.919 | 0.027 |
| Industry 3 | 0.000 | 0.025 | 0.975 |