This worked example attempts to document a common workflow a user might follow when using the
fingertipsR provides users the ability to import data from the Fingertips website. Fingertips is a major repository of public health indicators in England. The site is structured in the following way:
This example demonstrates how you can plot healthy life expectancy and life expectancy by geographical regions for a given year of data that fingertips contains. So, where to start?
There is one function in the
fingertipsR package that imports data from the Fingertips API;
fingertips_data(). This function has the following inputs:
At least one of IndicatorID, DomainID or ProfileID must be complete. These fields relate to each other as described in the introduction. AreaTypeID is also required, and determines the geography for which data is imported. In this case we want County and Unitary Authority level. AreaCode needs completing if you are extracting data for a particular area or group of areas only. ParentAreaTypeID requires an area type code that the AreaTypeID maps to at a higher level of geography. For example, County and Unitary Authorities map to a higher level of geography called Government Office Regions. These mappings can be identified using the
area_types() function. If ignored, a ParentAreaTypeID will be chosen automatically.
Therefore, the inputs to the
fingertips_data function that we need to find out are the ID codes for:
We need to begin by calling the
There are two indicators we are interested in for this exercise. Without consulting the Fingertips website, we know approximately what they are called:
We can use the
indicators() function to return a list of all the indicators within Fingertips. We can then filter the name field for the term life expectancy (note, the IndicatorName field has been converted to lower case in the following code chunk to ensure matches will not be overlooked as a result of upper case letters).
inds <- indicators_unique() life_expectancy <- inds[grepl("life expectancy", tolower(inds$IndicatorName)),]
knitr::kable(life_expectancy, row.names = FALSE)
The two indicators we are interested in from this table are:
We can work out what the AreaTypeID codes we need using the function
area_types(). We've decided that we want to produce the graph at County and Unitary Authority level. From the section [Where to start] we need codes for AreaTypeID and ParentAreaTypeID.
areaTypes <- area_types()
DT::datatable(areaTypes, filter = "top", rownames = FALSE)
The table shows that the AreaTypeID for County and Unitary Authority level is 202. The third column, ParentAreaTypeID, shows the IDs of the area types that these map to. In the case of County and Unitary Authorities, these are:
knitr::kable(areaTypes[areaTypes$AreaTypeID == 202, ], row.names = FALSE)
ParentAreaTypeID is 6 by default for the
fingertips_data() function for
AreaTypeID of 202 (this value changes if different
AreaTypeIDs are entered), so we can stick with that in this example. Use the
area_types() function to understand more about how areas map to each other.
Finally, we can use the
fingertips_data() function with the inputs we have determined previously.
indicators <- c(90362, 90366) data <- fingertips_data(IndicatorID = indicators, AreaTypeID = 202)
pander::pandoc.table(tail(data), style="rmarkdown", split.tables = 90, keep.line.breaks = TRUE)
The data frame returned by
r ncol(data) variables. For this exercise, we are only interested in a few of them and for the time period 2012-14:
The data frame also contains data for the parent area, and for England, so we want to filter it to remove these too.
cols <- c("IndicatorID", "AreaCode", "ParentName", "Sex", "Timeperiod", "Value") area_type_name <- table(data$AreaType) # tally each group in the AreaType field area_type_name <- area_type_name[area_type_name == max(area_type_name)] # pick the group with the highest frequency area_type_name <- names(area_type_name) # retrieve the name data <- data[data$AreaType == area_type_name & data$Timeperiod == "2012 - 14", cols]
ggplot2 it is possible to plot the outputs.
library(ggplot2) ggplot(data, aes(x = reorder(ParentName, Value, median), y = Value, col = factor(IndicatorID))) + geom_boxplot(data = data[data$IndicatorID == 90366, ]) + geom_boxplot(data = data[data$IndicatorID == 90362, ]) + facet_wrap(~ Sex) + scale_colour_manual(name = "Indicator", breaks = c("90366", "90362"), labels = c("Life expectancy", "Healthy life expectancy"), values = c("#128c4a", "#88c857")) + labs(x = "Region", y = "Age", title = "Life expectancy and healthy life expectancy at birth \nfor Upper Tier Local Authorities within England regions (2012 - 2014)") + theme_bw() + theme(axis.text.x = element_text(angle = 45, hjust = 1))
The plot above makes use of the fields that are within the dataset by default when using the
fingertips_data() function. There is also a
deprivation_decile() function, which provides an indicator of deprivation for each geographical area (see
Not all indicators are available for every geography. To understand how indicators are mapped to different gegoraphies, there is a function
To understand more about what comprises each indicator, there is the
indicator_metadata() function, which provides the information on the definitions page of the Fingertips website.
nearest_neighbours() function provides groups of statistically similar area for some of the geographies that are available. The geographies these are available for, and their sources, are documented within the function documentation (
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