=========================================================== Generating Tables of Descriptive Statistics with "memisc" =========================================================== Motivation ---------- *R* is well suited for statistical graphics, the application of advanced data analysis techniques, and Monte Carlo studies of estimators. However, it lacks support for the typical data management tasks as they arise in the social sciences as well as for the simple generation of desctiptive statistics. "memisc" facilitates not only typical data management tasks of survey researchers, but also the generation of descriptive statistics, as they are often a first step in serious social science data analysis. In particular it facilitates the creation of tables of percentages of other descriptive statistics broken down by subgroups in the data. This is mainly achieved by the function genTable, which is described in the following section. The section thereafter describes how tables thus created can be exported to LaTeX and HTML. Creating Tables of Descriptive Statistics ----------------------------------------- General table of descriptive statistics can be created using the function genTable(). The syntax of calls to this function is quite similar to that of the function xtabs(): The first argument (tagged formula) is a formula that determines the descriptive statistics used and by what groups they are computed. The left-hand side of the formula determines the statistics being computed. The right-hand side determines the grouping factor(s). The second argument is an optional data= argument that determines from which data frame or data set the descriptive statistics are to be computed. This is illustrated by the following example, which uses (like :ref:the page on item objects ) the GLES 2013 election study [1]_. In this example we first create a table of some descriptives of the age distribution of the respondents per German federal state: .. code-block:: r library(memisc) ZA5702 <- spss.system.file("Data/ZA5702_v2-0-0.sav") gles2013work <- subset(ZA5702, select=c( wave = survey, gender = vn1, byear = vn2c, bmonth = vn2b, intent.turnout = v10, turnout = n10, voteint.candidate = v11aa, voteint.list = v11ba, postal.vote.candidate = v12aa, postal.vote.list = v12ba, vote.candidate = n11aa, vote.list = n11ba, bula = bl )) gles2013work <- within(gles2013work,{ measurement(byear) <- "interval" measurement(bmonth) <- "interval" age <- 2013 - byear age[bmonth > 9] <- age[bmonth > 9] - 1 }) options(digits=3) age.tab <- genTable(c(Mean=mean(age), Std.dev=sd(age), Median=median(age))~bula, data=gles2013work) age.tab :: bula Baden-Wuerttemberg Bayern Berlin Brandenburg Bremen Hamburg Hessen Mean 54.5 54.4 52.8 59.7 60.4 51.5 56.9 Std.dev 18.9 18.9 19.8 19.3 11.5 18.7 18.5 Median 57.0 56.0 57.0 62.5 63.0 53.0 60.0 bula Mecklenburg-Vorpommern Niedersachsen Nordrhein-Westfalen Mean 57.0 55.1 53.9 Std.dev 19.2 18.4 19.1 Median 60.5 56.0 55.0 bula Rheinland-Pfalz Saarland Sachsen Sachsen-Anhalt Schleswig-Holstein Mean 57.2 61.9 58.3 54.7 60.0 Std.dev 18.2 17.3 16.7 17.1 19.9 Median 60.5 65.0 60.5 56.0 65.0 bula Thueringen Mean 57.8 Std.dev 17.4 Median 60.0 This table does not look good, so we transprose it: .. code-block:: r age.tab <- t(age.tab) age.tab :: bula Mean Std.dev Median Baden-Wuerttemberg 54.5 18.9 57.0 Bayern 54.4 18.9 56.0 Berlin 52.8 19.8 57.0 Brandenburg 59.7 19.3 62.5 Bremen 60.4 11.5 63.0 Hamburg 51.5 18.7 53.0 Hessen 56.9 18.5 60.0 Mecklenburg-Vorpommern 57.0 19.2 60.5 Niedersachsen 55.1 18.4 56.0 Nordrhein-Westfalen 53.9 19.1 55.0 Rheinland-Pfalz 57.2 18.2 60.5 Saarland 61.9 17.3 65.0 Sachsen 58.3 16.7 60.5 Sachsen-Anhalt 54.7 17.1 56.0 Schleswig-Holstein 60.0 19.9 65.0 Thueringen 57.8 17.4 60.0 In the next example we create a table of percentages of the second votes per federal state. First we have to prepare the data, though: .. code-block:: r gles2013work <- within(gles2013work,{ candidate.vote <- cases( wave == 1 & intent.turnout == 6 -> postal.vote.candidate, wave == 1 & intent.turnout %in% 4:5 -> 900, wave == 1 & intent.turnout %in% 1:3 -> voteint.candidate, wave == 2 & turnout == 1 -> vote.candidate, wave == 2 & turnout == 2 -> 900 ) list.vote <- cases( wave == 1 & intent.turnout == 6 -> postal.vote.list, wave == 1 & intent.turnout %in% 4:5 -> 900, wave == 1 & intent.turnout %in% 1:3 -> voteint.list, wave == 2 & turnout ==1 -> vote.list, wave == 2 & turnout ==2 -> 900 ) candidate.vote <- recode(as.item(candidate.vote), "CDU/CSU" = 1 <- 1, "SPD" = 2 <- 4, "FDP" = 3 <- 5, "Grüne" = 4 <- 6, "Linke" = 5 <- 7, "NPD" = 6 <- 206, "Piraten" = 7 <- 215, "AfD" = 8 <- 322, "Other" = 10 <- 801, "No Vote" = 90 <- 900, "WN" = 98 <- -98, "KA" = 99 <- -99 ) list.vote <- recode(as.item(list.vote), "CDU/CSU" = 1 <- 1, "SPD" = 2 <- 4, "FDP" = 3 <- 5, "Grüne" = 4 <- 6, "Linke" = 5 <- 7, "NPD" = 6 <- 206, "Piraten" = 7 <- 215, "AfD" = 8 <- 322, "Other" = 10 <- 801, "No Vote" = 90 <- 900, "WN" = 98 <- -98, "KA" = 99 <- -99 ) missing.values(candidate.vote) <- 98:99 missing.values(list.vote) <- 98:99 measurement(candidate.vote) <- "nominal" measurement(list.vote) <- "nominal" }) :: Warning in cases(postal.vote.candidate <- wave == 1 & intent.turnout == : conditions are not exhaustive :: Warning in cases(postal.vote.list <- wave == 1 & intent.turnout == 6, 900 <- wave == : conditions are not exhaustive (When the code is run, some warnings are issued, that indicate that the conditions are not exhaustive, that is, there are some observations for which none of the conditions in the call cases() are met. The corresponding elements of resulting vector will contain NA for these observations. In the present case this occurs with observations that have missing values in both intent.turnout and turnout.) After having set up the data, we get our table of percentages: .. code-block:: r vote.tab <- genTable(percent(list.vote)~bula, data=gles2013work) options(digits=1) t(vote.tab) :: bula CDU/CSU SPD FDP Grüne Linke NPD Piraten AfD Baden-Wuerttemberg 27.7 21.8 7.0 17.2 6.0 0.4 2.1 4.6 Bayern 36.4 17.7 5.5 10.6 5.3 0.0 2.4 4.0 Berlin 26.5 22.3 8.4 10.2 13.9 1.8 1.8 6.6 Brandenburg 20.4 22.8 2.5 5.6 18.5 0.6 0.6 2.5 Bremen 21.7 26.1 0.0 17.4 13.0 0.0 0.0 4.3 Hamburg 22.2 35.6 2.2 4.4 6.7 2.2 0.0 4.4 Hessen 42.0 26.5 3.0 8.5 4.0 0.0 0.5 3.0 Mecklenburg-Vorpommern 32.9 19.9 2.1 4.1 17.8 1.4 2.7 1.4 Niedersachsen 32.7 32.4 3.2 9.5 3.2 0.0 0.7 0.7 Nordrhein-Westfalen 32.7 31.3 3.4 10.7 3.7 0.4 2.3 1.8 Rheinland-Pfalz 39.4 21.3 1.6 6.3 8.7 1.6 0.8 3.9 Saarland 40.0 40.0 0.0 0.0 0.0 0.0 0.0 0.0 Sachsen 49.4 16.6 1.2 3.3 14.2 0.3 1.2 0.9 Sachsen-Anhalt 27.0 29.5 1.2 8.3 19.1 0.4 0.8 0.4 Schleswig-Holstein 28.4 25.9 4.3 9.5 4.3 0.0 0.0 5.2 Thueringen 35.1 15.9 1.6 2.9 22.4 1.2 0.0 2.4 bula Other No Vote N Baden-Wuerttemberg 1.1 12.3 285.0 Bayern 2.0 16.0 451.0 Berlin 0.6 7.8 166.0 Brandenburg 1.2 25.3 162.0 Bremen 0.0 17.4 23.0 Hamburg 2.2 20.0 45.0 Hessen 0.0 12.5 200.0 Mecklenburg-Vorpommern 0.0 17.8 146.0 Niedersachsen 0.4 17.3 284.0 Nordrhein-Westfalen 0.7 13.1 563.0 Rheinland-Pfalz 1.6 15.0 127.0 Saarland 0.0 20.0 30.0 Sachsen 0.3 12.7 332.0 Sachsen-Anhalt 0.0 13.3 241.0 Schleswig-Holstein 0.9 21.6 116.0 Thueringen 0.8 17.6 245.0 It is of course also possible to create multi-dimensional tables, i.e. tables created by grouping by more than one factor: .. code-block:: r gles2013work <- within(gles2013work,{ # We relabel the items, since they are originally in German labels(turnout) <- c("Yes, voted"=1, "No, did not vote"=2) labels(gender) <- c("Male"=1,"Female"=2) }) genTable(percent(turnout)~gender+bula, data=gles2013work) :: , , bula = Baden-Wuerttemberg gender Male Female Yes, voted 88 85 No, did not vote 12 15 N 90 61 , , bula = Bayern gender Male Female Yes, voted 85 80 No, did not vote 15 20 N 89 129 , , bula = Berlin gender Male Female Yes, voted 100 85 No, did not vote 0 15 N 38 52 , , bula = Brandenburg gender Male Female Yes, voted 83 77 No, did not vote 17 23 N 36 62 , , bula = Bremen gender Male Female Yes, voted 91 80 No, did not vote 9 20 N 11 5 , , bula = Hamburg gender Male Female Yes, voted 88 76 No, did not vote 12 24 N 16 21 , , bula = Hessen gender Male Female Yes, voted 91 81 No, did not vote 9 19 N 66 48 , , bula = Mecklenburg-Vorpommern gender Male Female Yes, voted 84 72 No, did not vote 16 28 N 32 47 , , bula = Niedersachsen gender Male Female Yes, voted 88 83 No, did not vote 12 17 N 75 70 , , bula = Nordrhein-Westfalen gender Male Female Yes, voted 90 82 No, did not vote 10 18 N 148 158 , , bula = Rheinland-Pfalz gender Male Female Yes, voted 84 85 No, did not vote 16 15 N 43 34 , , bula = Saarland gender Male Female Yes, voted 91 72 No, did not vote 9 28 N 11 18 , , bula = Sachsen gender Male Female Yes, voted 88 88 No, did not vote 12 12 N 103 73 , , bula = Sachsen-Anhalt gender Male Female Yes, voted 89 81 No, did not vote 11 19 N 63 73 , , bula = Schleswig-Holstein gender Male Female Yes, voted 89 85 No, did not vote 11 15 N 37 33 , , bula = Thueringen gender Male Female Yes, voted 91 71 No, did not vote 9 29 N 70 73 Formatting Tables of Descriptive Statistics ------------------------------------------- The results of genTable() are objects of class "table" so that they can be re-arranged into a "flattened" table by the function ftable. To demonstrate this, we continue the previous example: .. code-block:: r gt <- genTable(percent(turnout)~gender+bula, data=gles2013work) # We beautify the table a bit ... names(dimnames(gt)) <- c("Voted","Gender","State") gt <- dimrename(gt,"Yes, voted"="Yes", "No, did not vote"="No") ftable(gt,col.vars = c("Gender","Voted")) :: Gender Male Female Voted Yes No N Yes No N State Baden-Wuerttemberg 88 12 90 85 15 61 Bayern 85 15 89 80 20 129 Berlin 100 0 38 85 15 52 Brandenburg 83 17 36 77 23 62 Bremen 91 9 11 80 20 5 Hamburg 88 12 16 76 24 21 Hessen 91 9 66 81 19 48 Mecklenburg-Vorpommern 84 16 32 72 28 47 Niedersachsen 88 12 75 83 17 70 Nordrhein-Westfalen 90 10 148 82 18 158 Rheinland-Pfalz 84 16 43 85 15 34 Saarland 91 9 11 72 28 18 Sachsen 88 12 103 88 12 73 Sachsen-Anhalt 89 11 63 81 19 73 Schleswig-Holstein 89 11 37 85 15 33 Thueringen 91 9 70 71 29 73 Arranging the cells of a table using ftable() improves the appearance of the results of genTable() on screen, but to include the results into a word processor document or a LaTeX file, further facilities are needed and provided by "memisc". To include the flattened table into a LaTeX document, one can convert and store it in the appropriate format using toLatex() and writeLines() .. code-block:: r ft <- ftable(gt,col.vars = c("Gender","Voted")) lt <- toLatex(ft,digits=c(1,1,0,1,1,0)) writeLines(lt,con="Voted2013-GenderState.tex") For HTML output, one can use show_html() (e.g. for inclusion in "knitr" documents) and write_html(), both functions being based on format_html(). Here we continue the example to demonstate this: .. code-block:: r show_html(ft,digits=c(1,1,0,1,1,0)) .. raw:: html
 Gender: Male Female State Voted: Yes No N Yes No N Baden-Wuerttemberg 87 . 8 12 . 2 90 85 . 2 14 . 8 61 Bayern 85 . 4 14 . 6 89 79 . 8 20 . 2 129 Berlin 100 . 0 0 . 0 38 84 . 6 15 . 4 52 Brandenburg 83 . 3 16 . 7 36 77 . 4 22 . 6 62 Bremen 90 . 9 9 . 1 11 80 . 0 20 . 0 5 Hamburg 87 . 5 12 . 5 16 76 . 2 23 . 8 21 Hessen 90 . 9 9 . 1 66 81 . 2 18 . 8 48 Mecklenburg-Vorpommern 84 . 4 15 . 6 32 72 . 3 27 . 7 47 Niedersachsen 88 . 0 12 . 0 75 82 . 9 17 . 1 70 Nordrhein-Westfalen 89 . 9 10 . 1 148 82 . 3 17 . 7 158 Rheinland-Pfalz 83 . 7 16 . 3 43 85 . 3 14 . 7 34 Saarland 90 . 9 9 . 1 11 72 . 2 27 . 8 18 Sachsen 88 . 3 11 . 7 103 87 . 7 12 . 3 73 Sachsen-Anhalt 88 . 9 11 . 1 63 80 . 8 19 . 2 73 Schleswig-Holstein 89 . 2 10 . 8 37 84 . 8 15 . 2 33 Thueringen 91 . 4 8 . 6 70 71 . 2 28 . 8 73
.. code-block:: r show_html(ft,digits=c(1,1,0,1,1,0),show.titles=FALSE) .. raw:: html
 Male Female Yes No N Yes No N Baden-Wuerttemberg 87 . 8 12 . 2 90 85 . 2 14 . 8 61 Bayern 85 . 4 14 . 6 89 79 . 8 20 . 2 129 Berlin 100 . 0 0 . 0 38 84 . 6 15 . 4 52 Brandenburg 83 . 3 16 . 7 36 77 . 4 22 . 6 62 Bremen 90 . 9 9 . 1 11 80 . 0 20 . 0 5 Hamburg 87 . 5 12 . 5 16 76 . 2 23 . 8 21 Hessen 90 . 9 9 . 1 66 81 . 2 18 . 8 48 Mecklenburg-Vorpommern 84 . 4 15 . 6 32 72 . 3 27 . 7 47 Niedersachsen 88 . 0 12 . 0 75 82 . 9 17 . 1 70 Nordrhein-Westfalen 89 . 9 10 . 1 148 82 . 3 17 . 7 158 Rheinland-Pfalz 83 . 7 16 . 3 43 85 . 3 14 . 7 34 Saarland 90 . 9 9 . 1 11 72 . 2 27 . 8 18 Sachsen 88 . 3 11 . 7 103 87 . 7 12 . 3 73 Sachsen-Anhalt 88 . 9 11 . 1 63 80 . 8 19 . 2 73 Schleswig-Holstein 89 . 2 10 . 8 37 84 . 8 15 . 2 33 Thueringen 91 . 4 8 . 6 70 71 . 2 28 . 8 73
.. code-block:: r # Writing into a HTML file ... write_html(ft,digits=c(1,1,0,1,1,0),show.titles=FALSE, file="Voted2013-GenderState.html") Continuing another example: .. code-block:: r # age.tab was created earlier age.ftab <- ftable(age.tab,row.vars=1) show_html(age.ftab,digits=1,show.titles=FALSE) .. raw:: html
 Mean Std.dev Median Baden-Wuerttemberg 54 . 5 18 . 9 57 . 0 Bayern 54 . 4 18 . 9 56 . 0 Berlin 52 . 8 19 . 8 57 . 0 Brandenburg 59 . 7 19 . 3 62 . 5 Bremen 60 . 4 11 . 5 63 . 0 Hamburg 51 . 5 18 . 7 53 . 0 Hessen 56 . 9 18 . 5 60 . 0 Mecklenburg-Vorpommern 57 . 0 19 . 2 60 . 5 Niedersachsen 55 . 1 18 . 4 56 . 0 Nordrhein-Westfalen 53 . 9 19 . 1 55 . 0 Rheinland-Pfalz 57 . 2 18 . 2 60 . 5 Saarland 61 . 9 17 . 3 65 . 0 Sachsen 58 . 3 16 . 7 60 . 5 Sachsen-Anhalt 54 . 7 17 . 1 56 . 0 Schleswig-Holstein 60 . 0 19 . 9 65 . 0 Thueringen 57 . 8 17 . 4 60 . 0
Of course we can also export to LaTeX: .. code-block:: r toLatex(age.ftab,digits=1,show.titles=FALSE) :: \begin{tabular}{llD{.}{.}{1}D{.}{.}{1}D{.}{.}{1}} \toprule && \multicolumn{1}{c}{Mean}&\multicolumn{1}{c}{Std.dev}&\multicolumn{1}{c}{Median}\\ \midrule Baden-Wuerttemberg && 54.5 & 18.9 & 57.0\\ Bayern && 54.4 & 18.9 & 56.0\\ Berlin && 52.8 & 19.8 & 57.0\\ Brandenburg && 59.7 & 19.3 & 62.5\\ Bremen && 60.4 & 11.5 & 63.0\\ Hamburg && 51.5 & 18.7 & 53.0\\ Hessen && 56.9 & 18.5 & 60.0\\ Mecklenburg-Vorpommern && 57.0 & 19.2 & 60.5\\ Niedersachsen && 55.1 & 18.4 & 56.0\\ Nordrhein-Westfalen && 53.9 & 19.1 & 55.0\\ Rheinland-Pfalz && 57.2 & 18.2 & 60.5\\ Saarland && 61.9 & 17.3 & 65.0\\ Sachsen && 58.3 & 16.7 & 60.5\\ Sachsen-Anhalt && 54.7 & 17.1 & 56.0\\ Schleswig-Holstein && 60.0 & 19.9 & 65.0\\ Thueringen && 57.8 & 17.4 & 60.0\\ \bottomrule \end{tabular} After formatting with LaTeX, this may look like this: .. image:: age-ftab.png :alt: LaTeX output example :width: 450px .. [1] The German Longitudinal Election Study __ is funded by the German National Science Foundation (DFG) and carried out outin close cooperation with the DGfW __, German Society for Electoral Studies. Principal investigators are Hans Rattinger (University of Mannheim, until 2014), Sigrid Roßteutscher (University of Frankfurt), Rüdiger Schmitt-Beck (University of Mannheim), Harald Schoen (Mannheim Centre for European Social Research, from 2015), Bernhard Weßels (Social Science Research Center Berlin), and Christof Wolf (GESIS – Leibniz Institute for the Social Sciences, since 2012). Neither the funding organisation nor the principal investigators bear any responsibility for the example code shown here.