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 the page on item
objects) the GLES 2013 election study1. In this
example we first create a table of some descriptives of the age
distribution of the respondents per German federal state:
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:
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:
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:
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:
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:
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()
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:
show_html(ft,digits=c(1,1,0,1,1,0))
| 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 | |||||
show_html(ft,digits=c(1,1,0,1,1,0),show.titles=FALSE)
| 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 | ||||
# 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:
# age.tab was created earlier
age.ftab <- ftable(age.tab,row.vars=1)
show_html(age.ftab,digits=1,show.titles=FALSE)
| 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:
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:
- 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.
