---
title: Generating Tables of Descriptive Statistics
output: rmarkdown::html_vignette
vignette: >
% \VignetteIndexEntry{Generating Tables of Descriptive Statistics}
% \VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r,echo=FALSE,message=FALSE}
knitr::opts_chunk$set(comment=NA,eval=FALSE)
```
# 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.
Note that these examples require data not included in the package
(you need to register to [GESIS](https://www.gesis.org) to download the data).
The vignette code cannot be run without this additional data.
# 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, see `?item`) 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:
```r
library(memisc)
ZA5702 <- spss.system.file("Data/ZA5702_v2-0-0.sav",
ignore.scale.info=TRUE) # Because the measurement info in the file is wrong.
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 55 54 53 60 60 51 57
Std.dev 19 19 20 19 12 19 19
Median 57 56 57 62 63 53 60
bula
Mecklenburg-Vorpommern Niedersachsen Nordrhein-Westfalen
Mean 57 55 54
Std.dev 19 18 19
Median 60 56 55
bula
Rheinland-Pfalz Saarland Sachsen Sachsen-Anhalt Schleswig-Holstein
Mean 57 62 58 55 60
Std.dev 18 17 17 17 20
Median 60 65 60 56 65
bula
Thueringen
Mean 58
Std.dev 17
Median 60
```
This table does not look good, so we transprose it:
```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:
```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 messages:
1: In cases(postal.vote.candidate <- wave == 1 & intent.turnout == :
78 NAs created
2: In cases(postal.vote.list <- wave == 1 & intent.turnout == 6, 900 <- wave == :
78 NAs created
3: In recode(as.item(candidate.vote), `CDU/CSU` = 1 <- 1, SPD = 2 <- 4, :
recoding created 18 NAs
4: In recode(as.item(list.vote), `CDU/CSU` = 1 <- 1, SPD = 2 <- 4, :
recoding created 19 NAs
```
(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:
```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 Other No Vote N
Baden-Wuerttemberg 28 22 7 17 6 0.4 2.1 4.6 1.1 12 285
Bayern 36 18 6 11 5 0.0 2.4 4.0 2.0 16 451
Berlin 27 22 8 10 14 1.8 1.8 6.6 0.6 8 166
Brandenburg 20 23 2 6 19 0.6 0.6 2.5 1.2 25 162
Bremen 22 26 0 17 13 0.0 0.0 4.3 0.0 17 23
Hamburg 22 36 2 4 7 2.2 0.0 4.4 2.2 20 45
Hessen 42 26 3 8 4 0.0 0.5 3.0 0.0 12 200
Mecklenburg-Vorpommern 33 20 2 4 18 1.4 2.7 1.4 0.0 18 146
Niedersachsen 33 32 3 10 3 0.0 0.7 0.7 0.4 17 284
Nordrhein-Westfalen 33 31 3 11 4 0.4 2.3 1.8 0.7 13 563
Rheinland-Pfalz 39 21 2 6 9 1.6 0.8 3.9 1.6 15 127
Saarland 40 40 0 0 0 0.0 0.0 0.0 0.0 20 30
Sachsen 49 17 1 3 14 0.3 1.2 0.9 0.3 13 332
Sachsen-Anhalt 27 29 1 8 19 0.4 0.8 0.4 0.0 13 241
Schleswig-Holstein 28 26 4 9 4 0.0 0.0 5.2 0.9 22 116
Thueringen 35 16 2 3 22 1.2 0.0 2.4 0.8 18 245
```
It is of course also possible to create multi-dimensional tables, i.e. tables created by grouping by more than one factor:
```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:
```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()`
```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:
```r
show_html(ft,digits=c(1,1,0,1,1,0))
```
```{=html}
| Gender: |
| State | Voted: |
| 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 | | |
```
```r
show_html(ft,digits=c(1,1,0,1,1,0),show.titles=FALSE)
```
```{=html}
|
|
| 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 | | |
```
```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:
```r
# age.tab was created earlier
age.ftab <- ftable(age.tab,row.vars=2)
show_html(age.ftab,digits=1,show.titles=FALSE)
```
```{=html}
|
| 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:
```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}
```
[^1]: The [German Longitudinal Election
Study](https://www.gesis.org/gles/about-gles) is funded by the German National
Science Foundation (DFG) and carried out outin close cooperation with the
[DGfW](https://www.dgfw.info/), 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.