library(paneldesc)7 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.
7.1 Set up
Import the package.
7.2 Data import
Import the built-in dataset with simulated unbalanced panel data.
data(production)7.3 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 | 68.402 | 45.025 | 11.999 | 292.850 |
| capital | 154 | 33.152 | 32.044 | 2.030 | 160.085 |
| labor | 154 | 76.883 | 74.150 | 5.972 | 579.024 |
7.4 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)7.5 xtdes
xtdes command functionality can be reproduced by combining describe_dimensions(), describe_balance(), and describe_patterns() functions.
describe_dimensions(panel)| rows | entities | periods | variables |
|---|---|---|---|
| 180 | 30 | 6 | 6 |
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 |
7.6 xtsum
xtsum command functionality 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 | 68.402 | 45.025 | 11.999 | 292.850 | 154.000 |
| sales | between | NA | 29.060 | 34.263 | 166.364 | 30.000 |
| sales | within | NA | 34.127 | -12.444 | 234.297 | 5.133 |
| capital | overall | 33.152 | 32.044 | 2.030 | 160.085 | 154.000 |
| capital | between | NA | 17.414 | 9.019 | 74.225 | 30.000 |
| capital | within | NA | 27.072 | -25.567 | 149.329 | 5.133 |
| labor | overall | 76.883 | 74.150 | 5.972 | 579.024 | 154.000 |
| labor | between | NA | 41.068 | 31.021 | 190.645 | 30.000 |
| labor | within | NA | 61.202 | -58.040 | 483.217 | 5.133 |
7.7 xttab
xttab command functionality can be reproduced with decompose_factor() function.
decompose_factor(panel)Analyzing all factor variables: industry, ownership, region| 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 | 80 | 0.510 | 17 | 0.567 | 0.894 |
| ownership | public | 36 | 0.229 | 9 | 0.300 | 0.787 |
| ownership | mixed | 41 | 0.261 | 10 | 0.333 | 0.772 |
| region | west | 38 | 0.242 | 7 | 0.233 | 1.000 |
| region | east | 40 | 0.255 | 8 | 0.267 | 1.000 |
| region | north | 36 | 0.229 | 7 | 0.233 | 1.000 |
| region | south | 43 | 0.274 | 8 | 0.267 | 1.000 |
7.8 xttrans
xttrans command functionality 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 |
7.9 reshape
reshape command functionality can be reproduced with make_wide() and make_long() functions.
wide <- make_wide(panel, select = c("sales", "capital", "labor", "industry", "ownership")) Static variables: region
Reshaped variables: sales_1, sales_2, sales_3, sales_4, sales_5, sales_6
capital_1, capital_2, capital_3, capital_4, capital_5, capital_6
labor_1, labor_2, labor_3, labor_4, labor_5, labor_6
industry_1, industry_2, industry_3, industry_4, industry_5, industry_6
ownership_1, ownership_2, ownership_3, ownership_4, ownership_5, ownership_6 long <- make_long(wide, select = c("sales", "capital", "labor", "industry", "ownership")) Static variables: region
Reshaped variables: sales, capital, labor, industry, ownership 7.10 xtdata
xtdata command functionality can be partially reproduced with make_demeaned() function. Particularly, xtdata, fe is reproduced by passing only firm identifier to the function.
firm_demeaned <- make_demeaned(production, group = "firm")Demeaning numeric variables: year, sales, capital, laborUnlike Stata command, make_demeaned() allows you to demean variables not only within entities, but using various grouping patterns. For example, when data frame has panel attributes, it will be demeaned using both entities and time identifiers. Grouping by more than two variables is also available.
firm_year_demeaned <- make_demeaned(panel)Demeaning numeric variables: sales, capital, laborxtdata, be and xtdata, re functionality is not reproduced directly. However, with some additional efforts, xtdata, be can be replicated with make_mundlak() and summarize_numeric() as both these functions allow to calculate within-group means.