Let’s Analyse and Make Use of the Metadata
Now let’s see how we can use the metadata to better understand the data and make more informative plots.
Frequency Table
As expected, the frequency table displays the occurrence count of
each variable value. Now, let’s enhance the convenience of the frequency
table by utilizing the value labels associated with the variables. To
access the value labels, as explained in the preceding section, you can
utilize the base R function attributes(). Let’s proceed to
examine them now:
attributes(df$bap87)$labels_en
Does not apply No Answer Very good Good Satisfactory
-2 -1 1 2 3
Poor Bad
4 5 attributes(df$bap87)$labels_de
trifft nicht zu keine Angabe Sehr gut Gut
-2 -1 1 2
Zufriedenstellend Weniger gut Schlecht
3 4 5 table(factor(df$bap87, labels = names(attributes(df$bap87)$labels_en)))
Does not apply No Answer Very good Good Satisfactory
3 2 2 2 5
Poor Bad
2 3 Alternatively you can use the getmetadata_odf()-function to get the value labels:
To display the data in a language other than the default one, let’s try German by appending the respective language code to the attribute name. For example, you can use $labels_de to access the German language labels and present the information accordingly.
table(
factor(
df$bap87,
labels = names(attributes(df$bap87)$labels_de)
)
)
trifft nicht zu keine Angabe Sehr gut Gut
3 2 2 2
Zufriedenstellend Weniger gut Schlecht
5 2 3 Or using getmetadata_odf()-function:
Merge ODF-Datasets:
To merge ODF-datasets you should use the left_join(), right_join(), full_join(), and inner_join() from the dplyr-package instead of the the merge()-function to keep the attributes with the metadata of the merged datasets.
library(dplyr)
#similar to merge(df[,c(1:3,6)], df[,c(4:6)], by="name", all.x=T, all.y=F)
merged_df <- left_join(df[, c(1:3, 6)], df[, c(4:6)], by = "name")
#or
merged_df <- left_join(df[, c(1:3, 6)], df[, c(4:6)])Recoding a Variable
We want to display the table with only valid answers. Therefore, we
set the values -2 and -1 to NA.
Because we do not want to overwrite the original variable, we generate a
new one:
We check the attributes of the metadata and notice they are also copied from the original variable to the new one:
attributes(bap87_rec)
$name
[1] "bap87"
$label_en
[1] "Current Health"
$label_de
[1] "Gesundheitszustand gegenwärtig"
$description_en
[1] "Question: How would you describe your current health?"
$type
[1] "numeric"
$url
[1] "https://paneldata.org/soep-core/data/bap/bap87"
$labels_en
Does not apply No Answer Very good Good Satisfactory
-2 -1 1 2 3
Poor Bad
4 5
$labels_de
trifft nicht zu keine Angabe Sehr gut Gut
-2 -1 1 2
Zufriedenstellend Weniger gut Schlecht
3 4 5
$languages
[1] "en" "de"
$lang
[1] "en"
$label
[1] "Current Health"Now we can set the negative values to NA:
for (row in seq(1, length(bap87_rec))) {
if (!is.na(bap87_rec[row]) && bap87_rec[row] <= -1) {
bap87_rec[row] <- NA
}
}
table(bap87_rec, useNA = "ifany")
bap87_rec
1 2 3 4 5 <NA>
2 2 5 2 3 6 We notice that the copied values and value labels do not fit anymore:
attributes(bap87_rec)$labels_en
Does not apply No Answer Very good Good Satisfactory
-2 -1 1 2 3
Poor Bad
4 5 To change that, we’ll copy positions 3 to
7, retaining the desired range of values and their
respective value labels.
attributes(bap87_rec)$labels_en <-
unname(attributes(df$bap87)$labels_en)[3:7] # values
names(attributes(bap87_rec)$labels_en) <-
names(attributes(df$bap87)$labels_en)[3:7] # labels
attributes(bap87_rec)$labels_en
Very good Good Satisfactory Poor Bad
1 2 3 4 5 Do the same for the other language versions of the new recoded variable:
attributes(bap87_rec)$labels_de <-
unname(attributes(df$bap87)$labels_de)[3:7] # values
names(attributes(bap87_rec)$labels_de) <-
names(attributes(df$bap87)$labels_de)[3:7] # labelsWe do also notice that the variable name is not adequate. We replace
the name copied from the original variable with the new name
bap87_rec.
Now we generate the frequency table by using the variable as a factor variable.
Barplot
To create a barplot, we will utilize the recoded variable from the previous section. This example will demonstrate how to leverage metadata to create a more convenient and informative graph. By incorporating the metadata into the visualization, we can enhance the graph’s interpretability and provide a clearer understanding of the data.
barplot(
table(
factor(
bap87_rec,
labels = names(attributes(bap87_rec)$labels_en)
)
),
main = attributes(bap87_rec)$description_en, # title
xlab = paste0(
attributes(bap87_rec)$name, ": ", attributes(bap87_rec)$label), # label
sub = attributes(bap87_rec)$url, # subtitle
cex.main = 0.9, cex.names = 0.7, cex.sub = 0.8, cex.axis = 0.6,
cex.lab = 0.7 # font sizes
)
Drawing a barplot with the German description becomes effortless when dealing with dates that have multiple language versions of labels and descriptions. Simply append the language code to the end of the label attributes, and you’ll be able to generate the desired barplot with the German description:
barplot(
table(
factor(
bap87_rec,
labels = names(attributes(bap87_rec)$labels_de)
)
),
main = attributes(bap87_rec)$description_de, # title
xlab = paste0(
attributes(bap87_rec)$name, ": ", attributes(bap87_rec)$label_de), # label
sub = attributes(bap87_rec)$url, # subtitle
cex.main = 0.7, cex.names = 0.5, cex.sub = 0.8, cex.axis = 0.7,
cex.lab = 0.7 # font sizes
)