panelr: Wrangling and plotting panel data

Learning how to use the package

1 Declare panel data object

• panel_data object class: A modified tibble, which is itself a modified data.frame.

• Groupwise operations: panel_data frames are grouped by entity, so many operations (e.g., mean(), cumsum()) are performed by dplyr’s mutate()

• panel_data frames are in “long” format, in which each row is a unique combination of entity and time point.

• The package includes an example dataset called WageData, which comes from the Panel Study of Income Dynamics.

• Let’s see the first 14 observations

data("WageData")
glimpse(WageData)

## Observations: 4,165
## Variables: 14
## $ exp   <dbl> 3, 4, 5, 6, 7, 8, 9, 30, 31, 32, 33, 34, 35, 36, 6, 7, 8, 9, 10…
## $ wks   <dbl> 32, 43, 40, 39, 42, 35, 32, 34, 27, 33, 30, 30, 37, 30, 50, 51,…
## $ occ   <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ ind   <dbl> 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ south <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ smsa  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ ms    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, …
## $ fem   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ union <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, …
## $ ed    <dbl> 9, 9, 9, 9, 9, 9, 9, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12…
## $ blk   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ lwage <dbl> 5.56, 5.72, 6.00, 6.00, 6.06, 6.17, 6.24, 6.16, 6.21, 6.26, 6.5…
## $ t     <dbl> 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, …
## $ id    <dbl> 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, …

• The key columns are id and t. They indicate which respondent (i) and which time point (t) the row refers to.

• Let’s convert the data into a panel_data frame.

wages <- panel_data(WageData, id = id, wave = t)

• Show the new panel_data dataframe

wages

• panel_data() needs to know the id and wave columns so that it can protect them (and you) against accidentally being dropped, re-ordered, and so on.

  • It allows other panel data functions in the package to know entity and time indices without you having to state them every time.

• Note that the wages data are grouped by id and sorted by t within each id. So you can calculate group means and create lagged variables without concerns

• For more details about the panel_data() function: https://panelr.jacob-long.com/reference/panel_data.html

2 Create new variables

wages <- wages %>% 
  mutate(
    wks_mean = mean(wks),  # this is the person-level mean (based on the time variation)
    wks_lag = lag(wks),    # this will have a value of NA when t = 1
    wage =   exp(lwage),  #  the the anti log   
    cumu_wages = cumsum(exp(lwage)) # cumulative summation works within person
         ) 

• Understand what is happening

wages %>% 
  select(wks, wks_mean, wks_lag, wage, cumu_wages) 

• Note that id and t ride along even though we didn’t explicitly ask for them.

3 Describe panel

By default, it provies descriptive statistics for each variable in each year

describe_panel_by_year  <- summary(wages, union, lwage)
describe_panel_by_year %>% 
  kable() %>%
  kable_styling()

skim_type	skim_variable	t	n_missing	complete_rate	numeric.mean	numeric.sd	numeric.p0	numeric.p25	numeric.p50	numeric.p75	numeric.p100	numeric.hist
numeric	union	1	0	1	0.361	0.481	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▅
numeric	union	2	0	1	0.348	0.477	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▅
numeric	union	3	0	1	0.370	0.483	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▅
numeric	union	4	0	1	0.373	0.484	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▅
numeric	union	5	0	1	0.366	0.482	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▅
numeric	union	6	0	1	0.363	0.481	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▅
numeric	union	7	0	1	0.366	0.482	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▅
numeric	lwage	1	0	1	6.375	0.388	5.01	6.12	6.42	6.65	6.91	▁▂▃▇▇
numeric	lwage	2	0	1	6.465	0.363	5.01	6.24	6.53	6.75	6.91	▁▁▂▅▇
numeric	lwage	3	0	1	6.597	0.447	4.61	6.33	6.61	6.86	8.27	▁▂▇▃▁
numeric	lwage	4	0	1	6.696	0.441	5.08	6.44	6.71	6.96	8.52	▁▃▇▂▁
numeric	lwage	5	0	1	6.786	0.424	5.27	6.51	6.80	7.04	8.10	▁▂▇▅▁
numeric	lwage	6	0	1	6.864	0.424	5.66	6.60	6.91	7.11	8.16	▁▃▇▃▁
numeric	lwage	7	0	1	6.951	0.438	5.68	6.68	6.99	7.21	8.54	▁▅▇▂▁

• Stop getting per-year statistics by setting by.wave = FALSE

• For dataset with few entities, per-entity statistics can be obtained by setting by.wave = FALSE and by.id = TRUE.

• For furthe details, see the documentio of the function: https://panelr.jacob-long.com/reference/summary.panel_data.html

4 Plot panel

4.1 Time series with overall trend

• The trend of log wages

wages %>% 
line_plot(lwage, 
          add.mean = TRUE, 
          alpha = 0.2)

• Plot a non-linear trend

wages %>% 
line_plot(lwage, 
          add.mean = TRUE,
          mean.function = "loess",
          alpha = 0.2)

4.2 Isolate time series of specific entities

• Import stata datset about exchange rates across countries and over time

penn <- import("http://www.stata-press.com/data/r13/pennxrate.dta")
head(penn)

• Declare panel dataset

penn <- panel_data(penn, id = country, wave = year)
penn

• Plot all trends

penn %>% 
line_plot(realxrate)

• Plot G7 countries by using subset.ids = filter(penn, g7 == 1)$country,

penn %>% 
  line_plot(realxrate, 
            overlay = FALSE,
            subset.ids = filter(penn, g7 == 1)$country, 
            add.mean = TRUE)

• Fit a non-linear trend

penn %>% 
  line_plot(realxrate, 
            overlay = FALSE,
            subset.ids = filter(penn, g7 == 1)$country, 
            add.mean = TRUE,
            mean.function = "loess"
            )

• For further details, see the documention of the function: https://panelr.jacob-long.com/reference/line_plot.html

5 Re-shape panel

• For further details, see the following vignette: https://panelr.jacob-long.com/articles/reshape.html

5.1 From long to wide

• Here is a long panel dataset stored as panel_data object

wages 

• Widen the data, which will leave us with one row for each id and a row for each t

widenPanel <- widen_panel(wages)
widenPanel

glimpse(widenPanel)

## Observations: 595
## Variables: 89
## $ id           <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1…
## $ fem          <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,…
## $ ed           <dbl> 9, 11, 12, 10, 16, 12, 12, 10, 16, 16, 12, 12, 12, 17, 1…
## $ blk          <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
## $ wks_mean     <dbl> 37.6, 31.6, 50.4, 47.9, 47.0, 45.9, 47.3, 49.6, 48.0, 43…
## $ exp_1        <dbl> 3, 30, 6, 31, 10, 26, 15, 23, 3, 3, 24, 21, 26, 15, 9, 1…
## $ wks_1        <dbl> 32, 34, 50, 52, 50, 44, 46, 51, 50, 49, 47, 47, 48, 45, …
## $ occ_1        <dbl> 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1,…
## $ ind_1        <dbl> 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,…
## $ south_1      <dbl> 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0,…
## $ smsa_1       <dbl> 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,…
## $ ms_1         <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1,…
## $ union_1      <dbl> 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,…
## $ lwage_1      <dbl> 5.56, 6.16, 5.65, 6.16, 6.44, 6.91, 6.13, 6.33, 6.55, 6.…
## $ wks_lag_1    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ wage_1       <dbl> 260, 475, 285, 472, 625, 998, 461, 562, 700, 600, 779, 7…
## $ cumu_wages_1 <dbl> 260, 475, 285, 472, 625, 998, 461, 562, 700, 600, 779, 7…
## $ exp_2        <dbl> 4, 31, 7, 32, 11, 27, 16, 24, 4, 4, 25, 22, 27, 16, 10, …
## $ wks_2        <dbl> 43, 27, 51, 46, 46, 47, 48, 50, 48, 47, 48, 46, 48, 45, …
## $ occ_2        <dbl> 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1,…
## $ ind_2        <dbl> 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,…
## $ south_2      <dbl> 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0,…
## $ smsa_2       <dbl> 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,…
## $ ms_2         <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1,…
## $ union_2      <dbl> 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,…
## $ lwage_2      <dbl> 5.72, 6.21, 6.44, 6.24, 6.62, 6.91, 6.17, 6.40, 6.55, 6.…
## $ wks_lag_2    <dbl> 32, 34, 50, 52, 50, 44, 46, 51, 50, 49, 47, 47, 48, 45, …
## $ wage_2       <dbl> 305, 500, 624, 512, 750, 998, 480, 604, 700, 625, 834, 7…
## $ cumu_wages_2 <dbl> 565, 975, 909, 984, 1375, 1996, 941, 1166, 1400, 1225, 1…
## $ exp_3        <dbl> 5, 32, 8, 33, 12, 28, 17, 25, 5, 5, 26, 23, 28, 17, 11, …
## $ wks_3        <dbl> 40, 33, 50, 46, 40, 47, 49, 50, 50, 46, 45, 47, 50, 45, …
## $ occ_3        <dbl> 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1,…
## $ ind_3        <dbl> 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,…
## $ south_3      <dbl> 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0,…
## $ smsa_3       <dbl> 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,…
## $ ms_3         <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1,…
## $ union_3      <dbl> 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,…
## $ lwage_3      <dbl> 6.00, 6.26, 6.55, 6.30, 6.63, 6.91, 6.21, 6.54, 6.80, 6.…
## $ wks_lag_3    <dbl> 43, 27, 51, 46, 46, 47, 48, 50, 48, 47, 48, 46, 48, 45, …
## $ wage_3       <dbl> 402, 525, 698, 545, 760, 1000, 499, 695, 900, 625, 900, …
## $ cumu_wages_3 <dbl> 967, 1500, 1607, 1529, 2135, 2996, 1440, 1861, 2300, 185…
## $ exp_4        <dbl> 6, 33, 9, 34, 13, 29, 18, 26, 6, 6, 27, 24, 29, 18, 12, …
## $ wks_4        <dbl> 39, 30, 52, 49, 50, 47, 46, 50, 48, 44, 45, 47, 48, 44, …
## $ occ_4        <dbl> 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1,…
## $ ind_4        <dbl> 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,…
## $ south_4      <dbl> 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0,…
## $ smsa_4       <dbl> 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,…
## $ ms_4         <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,…
## $ union_4      <dbl> 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,…
## $ lwage_4      <dbl> 6.00, 6.54, 6.60, 6.36, 6.98, 7.00, 6.31, 6.56, 6.91, 6.…
## $ wks_lag_4    <dbl> 40, 33, 50, 46, 40, 47, 49, 50, 50, 46, 45, 47, 50, 45, …
## $ wage_4       <dbl> 402, 695, 737, 578, 1078, 1100, 552, 708, 1000, 625, 100…
## $ cumu_wages_4 <dbl> 1369, 2195, 2344, 2107, 3213, 4096, 1992, 2569, 3300, 24…
## $ exp_5        <dbl> 7, 34, 10, 35, 14, 30, 19, 27, 7, 7, 28, 25, 30, 19, 13,…
## $ wks_5        <dbl> 42, 30, 52, 44, 47, 44, 47, 44, 48, 43, 47, 47, 48, 44, …
## $ occ_5        <dbl> 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1,…
## $ ind_5        <dbl> 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,…
## $ south_5      <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0,…
## $ smsa_5       <dbl> 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,…
## $ ms_5         <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,…
## $ union_5      <dbl> 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,…
## $ lwage_5      <dbl> 6.06, 6.70, 6.70, 6.47, 7.05, 7.07, 6.38, 6.59, 7.09, 6.…
## $ wks_lag_5    <dbl> 39, 30, 52, 49, 50, 47, 46, 50, 48, 44, 45, 47, 48, 44, …
## $ wage_5       <dbl> 429, 810, 809, 645, 1150, 1175, 587, 729, 1200, 680, 114…
## $ cumu_wages_5 <dbl> 1798, 3005, 3153, 2752, 4363, 5271, 2579, 3298, 4500, 31…
## $ exp_6        <dbl> 8, 35, 11, 36, 15, 31, 20, 28, 8, 8, 29, 26, 31, 20, 14,…
## $ wks_6        <dbl> 35, 37, 52, 52, 47, 45, 47, 51, 44, 34, 17, 47, 46, 44, …
## $ occ_6        <dbl> 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1,…
## $ ind_6        <dbl> 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,…
## $ south_6      <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0,…
## $ smsa_6       <dbl> 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,…
## $ ms_6         <dbl> 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,…
## $ union_6      <dbl> 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,…
## $ lwage_6      <dbl> 6.17, 6.79, 6.78, 6.56, 7.31, 7.52, 6.45, 6.82, 7.17, 6.…
## $ wks_lag_6    <dbl> 42, 30, 52, 44, 47, 44, 47, 44, 48, 43, 47, 47, 48, 44, …
## $ wage_6       <dbl> 480, 890, 879, 708, 1500, 1845, 630, 914, 1300, 745, 124…
## $ cumu_wages_6 <dbl> 2278, 3895, 4032, 3460, 5863, 7116, 3209, 4212, 5800, 39…
## $ exp_7        <dbl> 9, 36, 12, 37, 16, 32, 21, 29, 9, 9, 30, 27, 32, 21, 15,…
## $ wks_7        <dbl> 32, 30, 46, 46, 49, 47, 48, 51, 48, 40, 47, 46, 46, 44, …
## $ occ_7        <dbl> 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1,…
## $ ind_7        <dbl> 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,…
## $ south_7      <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0,…
## $ smsa_7       <dbl> 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,…
## $ ms_7         <dbl> 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,…
## $ union_7      <dbl> 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,…
## $ lwage_7      <dbl> 6.24, 6.82, 6.86, 6.62, 7.30, 7.34, 6.52, 6.89, 7.21, 6.…
## $ wks_lag_7    <dbl> 35, 37, 52, 52, 47, 45, 47, 51, 44, 34, 17, 47, 46, 44, …
## $ wage_7       <dbl> 515, 912, 954, 751, 1474, 1539, 680, 984, 1350, 845, 140…
## $ cumu_wages_7 <dbl> 2793, 4807, 4986, 4211, 7337, 8655, 3889, 5196, 7150, 47…

• For further details, see the documention of the function: https://panelr.jacob-long.com/reference/widen_panel.html

5.2 From wide to long

• Is is importnat to know how many waves there are, which variables change over time, and how the time-varying variables are labeled.

• Let’s load a wide dataset

data("teen_poverty")
glimpse(teen_poverty) 

## Observations: 1,151
## Variables: 28
## $ id        <dbl> 22, 75, 92, 96, 141, 161, 220, 229, 236, 240, 245, 249, 255…
## $ pov1      <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,…
## $ mother1   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
## $ spouse1   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ inschool1 <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,…
## $ hours1    <dbl> 21, 8, 30, 19, 0, 0, 6, 0, 0, 18, 0, 0, 0, 12, 0, 19, 25, 2…
## $ pov2      <dbl> 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0,…
## $ mother2   <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,…
## $ spouse2   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
## $ inschool2 <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0,…
## $ hours2    <dbl> 15, 0, 27, 54, 6, 15, 8, 32, 20, 0, 0, 0, 23, 0, 0, 20, 30,…
## $ pov3      <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0,…
## $ mother3   <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1,…
## $ spouse3   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
## $ inschool3 <dbl> 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0,…
## $ hours3    <dbl> 3, 0, 24, 0, 0, 37, 6, 0, 0, 0, 0, 30, 23, 0, 0, 0, 20, 55,…
## $ pov4      <dbl> 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1,…
## $ mother4   <dbl> 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1,…
## $ spouse4   <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,…
## $ inschool4 <dbl> 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0,…
## $ hours4    <dbl> 0, 4, 31, 26, 0, 0, 12, 15, 40, 85, 0, 0, 58, 0, 0, 27, 38,…
## $ age       <dbl> 16, 17, 16, 17, 16, 17, 17, 16, 17, 16, 16, 16, 16, 17, 16,…
## $ black     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,…
## $ pov5      <dbl> 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,…
## $ mother5   <dbl> 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1,…
## $ spouse5   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,…
## $ inschool5 <dbl> 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,…
## $ hours5    <dbl> 0, 0, 0, 36, 0, 0, 0, 20, 40, 90, 27, 0, 25, 12, 0, 46, 0, …

• The long_panel() needs to know what the waves are called (1, 2, 3, …), where the wave label is in the variable name (beginning or end), and whether the label has prefixes or suffixes (e.g., “W1_variable” has a “W” prefix and “_” suffix).

• We have no prefix/suffix, the label is at the end, and the labels go from 1 to 5.

teen_poverty %>% 
  long_panel(
    label_location = "end", 
    periods = 1:5
    )

• For further details, see the documention of the function: https://panelr.jacob-long.com/reference/long_panel.html

6 References

• https://jacob-long.com/post/panelr-intro/

• https://panelr.jacob-long.com/reference/index.html

• https://cran.r-project.org/web/packages/panelr/index.html

7 Run it in Rcloud

• https://rstudio.cloud/project/849033

END