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 deprivation for a given year of data that fingertips contains. So, where to start?
There is one function in the
fingertipsR package that extracts 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. AreaCode needs completing if you are extracting data for a particular area or group of areas only. AreaTypeID determines the geography to extract the data for. In this case we want County and Unitary Authority level. ParentAreaTypeID requires an area type code that the AreaTypeID maps to at a higher level of geography. For example, when combining groups of County and Unitary Authorities it is possible to create 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$IndicatorName)),] knitr::kable(life_expectancy, row.names = FALSE) #note, this line will only work in a markdown file (*.Rmd). It presents the table for a report
The two indicators we are interested in from this table are:
We can work out what the AreaTypeID codes we are interested in using the function
area_types(). We've decided that we want to produce the graph at County and Unitary Authority level. From the section [Where to start] we need codes for AreaTypeID and ParentAreaTypeID.
areaTypes <- area_types() DT::datatable(areaTypes, filter = "top", rownames = FALSE) #note, this line will only work in a markdown file (*.Rmd). It presents the table for a report
The table shows that the AreaID for County and Unitary Authority level is 102. The third column, ParentAreaTypeID, shows the IDs of the area types that these map to. In the case of County and Unitary Authorities, these are:
knitr::kable(areaTypes[areaTypes$AreaTypeID == 102, c("ParentAreaTypeID","ParentAreaTypeName")], row.names = FALSE) #note, this line will only work in a markdown file (*.Rmd). It presents the table for a report
ParentAreaTypeID is 6 by default for the
fingertips_data() function for
AreaTypeID of 102 (though it 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 = 102) pander::pandoc.table(tail(data), style="rmarkdown", split.tables = 90, keep.line.breaks = TRUE) #note, this line will only work in a markdown file (*.Rmd). It presents the table for a report
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:
cols <- c("IndicatorID", "AreaCode", "Sex", "Timeperiod", "Value") data <- data[data$AreaType == "County & UA" & data$Timeperiod == "2012 - 14", cols]
We want to plot life expectancy against deprivation information. As deprivation is a notable cause of health inequalities, the
deprivation_deciles() function has been provided to allow easy access to this information. This is populated from the Department for Communities and Local Government Indices of Multiple Deprivation (IMD). Note, there is only information for General Practices, upper and lower tier local authorities (AreaTypeID = 7, 102 and 101 respectively). IMD has only been produced for the years 2010 and 2015 (for the latter areas) as well as 2011 and 2012 for General Practices.
dep <- deprivation_decile(AreaTypeID = 102, Year = 2015) DT::datatable(dep, filter = "top", rownames = FALSE) #note, this line will only work in a markdown file (*.Rmd). It presents the table for a report
Now we need to merge this with the main dataset:
# merge deprivation onto data data <- merge(data, dep, by.x = "AreaCode", by.y = "AreaCode", all.x = TRUE) # remove NA values data <- data[complete.cases(data),] DT::datatable(data, filter = "top", rownames = FALSE) #note, this line will only work in a markdown file (*.Rmd). It presents the table for a report
ggplot2 it is possible to plot the outputs.
library(ggplot2) p <- ggplot(data, aes(x = IMDscore, y = Value, col = factor(IndicatorID))) p <- p + geom_point() + geom_smooth(se = FALSE, method = "loess") + facet_wrap(~ Sex) + scale_colour_manual(name = "Indicator", breaks = c("90366", "90362"), labels = c("Life expectancy", "Healthy life expectancy"), values = c("#128c4a", "#88c857")) + scale_x_reverse() + labs(x = "IMD deprivation", y = "Age", title = "Life expectancy and healthy life expectancy at birth \nfor Upper Tier Local Authorities (2012 - 2014)") + theme_bw() print(p)
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