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tidystata

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tidystata brings Stata-style data exploration and preparation to R. Inspect variables, summarise measurements, tabulate categories and prepare new columns. Select variables with tidyselect helpers and calculate by group with .by. The resulting tables work with ordinary R tools for filtering, joining and plotting.

Installation 

# install.packages("pak")
pak::pak("socimh/tidystata")

Core functions

findit(): find the R equivalent of a Stata command 

findit("reg, robust")
#> # Heteroskedasticity-robust standard errors [fixest]
#> Stata: regress y x1 x2, robust
#> R:     fixest::feols(y ~ x1 + x2, data = data, vcov = "hetero")
#> # Note: Match fixest covariance small-sample corrections and singleton handling with Stata; the same formula does not guarantee identical standard errors or retained observations.
#> 
#> # 17 more examples. To see them all: findit("reg, robust") |> print(n = Inf)
findit("egen rowmean")
#> # Average available values across columns [base]
#> Stata: egen z = rowmean(x y)
#> R:     data$z <- rowMeans(data[c('x','y')],na.rm=TRUE); data$z[is.nan(data$z)] <- NA_real_
#> 
#> # 59 more examples. To see them all: findit("egen rowmean") |> print(n = Inf)

A bare command such as findit(reg) shows one clear example; findit(reg) |> print(n = Inf) shows every match. Add variable names or hints with commas, as in findit(tab, sex, region) or findit(tab, gen). Every entry is listed by topic in Coming from Stata.

codebook(): describe every variable 

codebook(lifeexp)
#> # A tibble: 6 x 5
#>   variable  label                    type             n unique
#>   <chr>     <chr>                    <chr>        <int>  <int>
#> 1 region    Region                   double+label    68      3
#> 2 country   Country                  character       68     68
#> 3 popgrowth Avg. annual % growth     double          68     32
#> 4 lexp      Life expectancy at birth double          68     20
#> 5 gnppc     GNP per capita           double          67     64
#> 6 safewater Safe water               double          54     32

summ(): summary statistics

lexp:safewater selects the three consecutive variables from lexp through safewater, including both endpoints. Other tidyselect helpers such as ends_with() and matches(), or a Stata-style varlist(), also work.

summ(lifeexp, lexp:safewater, .by = region)
#> # A tibble: 9 x 9
#>   region               name      type      n unique   min    mean       sd   max
#>   <dbl+lbl>            <chr>     <chr> <int>  <int> <dbl>   <dbl>    <dbl> <dbl>
#> 1 1 [Europe & C. Asia] lexp      dbl      44     17    62    72.5     4.95    79
#> 2 1 [Europe & C. Asia] gnppc     dbl      43     42   170 11284.  12920.   46650
#> 3 1 [Europe & C. Asia] safewater dbl      42     25    36    81.6    18.6    100
#> 4 2 [North America]    lexp      dbl      14     13    57    69.7     6.14    79
#> 5 2 [North America]    gnppc     dbl      14     14   410  6294.   9080.   32040
#> 6 2 [North America]    safewater dbl       6      5    40    58      24.0     99
#> 7 3 [South America]    lexp      dbl      10      8    61    70.7     4.06    76
#> 8 3 [South America]    gnppc     dbl      10      9   960  3881    2432.    7960
#> 9 3 [South America]    safewater dbl       6      6    45    65.3    17.0     91

tab(), tab1() and tab2(): compare frequency tables

Use region and a second category derived from lifeexp: whether life expectancy is at least 70 years. region:lexp_70 selects these two columns.

tab() counts each observed combination, with percentages.

lifeexp_tab <- lifeexp |>
  transmute(region, lexp_70 = lexp >= 70)

tab(lifeexp_tab, region:lexp_70)
#> # A tibble: 6 x 7
#>   region               lexp_70     n percent   cum valid valid_cum
#>   <dbl+lbl>            <lgl>   <int>   <dbl> <dbl> <dbl>     <dbl>
#> 1 1 [Europe & C. Asia] FALSE      12   17.6   17.6 17.6       17.6
#> 2 1 [Europe & C. Asia] TRUE       32   47.1   64.7 47.1       64.7
#> 3 2 [North America]    FALSE       6    8.82  73.5  8.82      73.5
#> 4 2 [North America]    TRUE        8   11.8   85.3 11.8       85.3
#> 5 3 [South America]    FALSE       3    4.41  89.7  4.41      89.7
#> 6 3 [South America]    TRUE        7   10.3  100   10.3      100

tab1() returns a named list with a separate frequency table for each variable.

tab1(lifeexp_tab, region:lexp_70)
#> $region
#> # A tibble: 3 x 6
#>   value                    n percent   cum valid valid_cum
#>   <dbl+lbl>            <int>   <dbl> <dbl> <dbl>     <dbl>
#> 1 1 [Europe & C. Asia]    44    64.7  64.7  64.7      64.7
#> 2 2 [North America]       14    20.6  85.3  20.6      85.3
#> 3 3 [South America]       10    14.7 100    14.7     100  
#> 
#> $lexp_70
#> # A tibble: 2 x 6
#>   value     n percent   cum valid valid_cum
#>   <lgl> <int>   <dbl> <dbl> <dbl>     <dbl>
#> 1 FALSE    21    30.9  30.9  30.9      30.9
#> 2 TRUE     47    69.1 100    69.1     100

tab2() displays the joint counts as a cross-table, with region in rows and life expectancy category in columns.

tab2(lifeexp_tab, region:lexp_70)
#> # A tibble: 3 x 3
#>   `region \\ lexp_70`  `FALSE` `TRUE`
#>   <dbl+lbl>              <int>  <int>
#> 1 1 [Europe & C. Asia]      12     32
#> 2 2 [North America]          6      8
#> 3 3 [South America]          3      7

Learn more

tidystata also covers row calculations (row_mean()), panel operators (stata_lag()), correlations (corr() for complete cases and pwcorr() for pairwise samples), missing values (misstable()), keys and merging (isid(), stata_merge()) and binning (xtile()). See the guides below for examples and output conventions.

Code of Conduct

Please note that the tidystata project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.