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01_tidyingOriginalData.R
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#' ---
#' title: "ONS LAD 2019 data cleaning"
#' author: "Luis Chaves"
#' date: "Summer 2020"
#' output: html_document, pdf_document
#' ---
#' # Data cleaning report
#+ include = FALSE
knitr::opts_chunk$set(message = F, warning = F)
#' ## Load libraries
library(tidyverse)
library(lubridate)
library(readxl)
theme_set(theme_bw())
library(imputeTS)# time series imputation
library(naniar)# for missing data visualisation
library(knitr) # for rmarkdown rendering
debugSource('time_impute.R', echo = T) # time series wrapper imputation function
debugSource('updateCodes.R', echo = T)
#' ## Load Data
# deaths = read.csv('OriginalData/deathrecords2020.csv') # caveat: only 2020
# deaths = read.csv('OriginalData/DEATHS02010_2019.csv') # caveat: only by wider regions (around 13)
# -- not using for now as only 2020 deaths
lifeexpect = read.csv('OriginalData/lifeexpectancies.csv')
# population = read_xlsx('OriginalData/popestimates.xlsx',
# sheet = 'Dataset',
# range = cell_rows(c(3, 121433)))
population = read.csv('OriginalData/POPULATION2018_2019.csv')
suicides = read.csv('OriginalData/suicide2018.csv')
wellbeing = read.csv('OriginalData/wellbeing.csv')
work = read.csv('OriginalData/working.csv')
work_ts = read.csv('OriginalData/working_time_series.csv')
gdp = read_xlsx('OriginalData/regionalgrossdomesticproductgdplocalauthorities.xlsx',
sheet = 'Table 5',
range = cell_rows(c(2, 384)))
unemployment = read_xls('OriginalData/unemployment.xls',
range = 'A3:HA375', sheet = 3)
#' ## Clean data
#'
#' Here I take data cleaning on a case-by-case basis: tidying and imputing each data set at a time,
#' indepently of each other. A few comments that apply to all the ONS datasets: all data sets appear to have
#' redundant columns (as in duplicates), secondly for some of them the data corresponds to an interval and
#' for some others the data is available for every year.
#'
#' ### Deaths table (not used in the end)
# deaths table: some columns are duplicate so we remove those, we also change the week strings to week numbers
# str(deaths)
#
# deaths = deaths %>%
# select(-c(Data.Marking,calendar.years,week, cause.of.death, place.of.death, registration.or.occurrence)) %>%
# rename(Deaths = V4_1, year = time, areaCode = admin.geography,
# areaName = geography, deathCause = causeofdeath,
# deathPlace = placeofdeath, week = week.number) %>%
# mutate(week = as.integer(str_replace(week, 'week-', '')))
#' ### Life expectancy table
#' #### Visualise raw data
str(lifeexpect)
#' #### Modify initial data
#'
#' As there are many duplicated columns, we will delete those that are either duplicated or empty
#' and give better names to the ones we keep, as well as changing the type of numeric variables that
#' are registered as strings
lifeexpect = lifeexpect %>%
select(-c(Data_Marking,
two.year.intervals,
life.expectancy.variable,
birth.cohort)) %>%
rename(Value = V4_3,
lowerCI = lower.confidence.limit,
upperCI = upper.confidence.limit,
year = time,
areaCode = admin.geography,
areaName = geography,
LEvariable = lifeexpectancyvariable,
cohort = birthcohort) %>%
mutate(lowerCI = as.numeric(lowerCI),
upperCI = as.numeric(upperCI))
#' #### Modify year intervals to years
#' The life expectancy information is available in the year intervals `r{unique(lifeexpect$year)}`,
#' to be consistent with the datasets that are available for every year we turn intervals into years. We do this
#' by taking the upper value of each interval as the year column. e.g.: turning 2013-15 to 2015
#' and 2014-16 to 2016 and so forth
lifeexpect$year = as.numeric(lapply(strsplit(lifeexpect$year, '-'), '[[',1))+2
#' #### Check missing data with respect to several factors
#' Now we check missing data in the `Value` column by cohort, type of life expectancy variable and year
kable(table(is.na(lifeexpect$Value), lifeexpect$cohort)) # all cohorts with same amount of data
kable(table(is.na(lifeexpect$Value), lifeexpect$LEvariable)) # lifeexpectancy with the least missing data
kable(table(is.na(lifeexpect$Value), lifeexpect$year)) # 2017 with most missing data
#' #### Shortening strings that are too long
lifeexpect = lifeexpect %>%
mutate(LEvariable = ifelse(LEvariable == 'Disability-free life expectancy',
'DFLE',
ifelse(LEvariable == 'Healthy life expectancy',
'HLE',
'LE')),
cohort = ifelse(cohort == 'Females at age 65',
'FemAt65',
ifelse(cohort == 'Males at age 65',
'MaleAt65',
ifelse(cohort == 'Females at birth',
'FemAtBirth',
'MaleAtBirth'))))
#' #### Check number of entries with missing values for all years
#'
#' Here we wish to know how many entries, defined by unique combinations of the local authority code (`areaCode`),
#' the life expectancy(LE) variable (`LEvariable`, e.g. healthy LE, Disability-free LE...) and cohort (`cohort`, e.g. Male at birth,
#' Female at 65...) have no available values (aka all values missing) for all available years.
count = 0
combs = 0
for (reg in unique(lifeexpect$areaCode))
for (var in unique(lifeexpect$LEvariable))
for(coh in unique(lifeexpect$cohort))
{
combs = combs +1
if (all(is.na(lifeexpect %>% filter(areaCode == reg, LEvariable == var, cohort == coh) %>% select(Value)))){
count = count+1
}
}
cat(paste(count, 'combinations out of', combs, 'combinations have all missing entries'))
#' #### Missingness count by number of missing year entries
lifeexpect %>%
group_by(areaCode,
LEvariable,
cohort) %>%
summarise(NumberOfMissingEntries = sum(is.na(Value)),
ProprortionOfMissingEntries = paste0(round(mean(is.na(Value)),3)*100,' %')) %>%
group_by(NumberOfMissingEntries,
ProprortionOfMissingEntries) %>%
summarise(Count = n()) %>%
kable()
#' #### Time series imputation
#'
#' First we order the data by year, the time series works assuming the data is ordered.
lifeexpect = lifeexpect %>% arrange(areaCode, LEvariable, cohort, year)
lifeexpect = time_impute(lifeexpect, areaCode, LEvariable, cohort,
year_column = 'year')
#' #### Final check on missingness count
lifeexpect %>%
group_by(areaCode,
LEvariable,
cohort) %>%
summarise(NumberOfMissingEntries = sum(is.na(Value)),
ProportionOfMissingEntries = mean(is.na(Value))) %>%
group_by(NumberOfMissingEntries,
ProportionOfMissingEntries) %>%
summarise(Count = n()) %>%
kable()
#' #### Gather data in its final form
#'
#' We finally filter for the latest year (in this case 2017) and pivot the data to get a tidy format. We
#' also keep a copy of the tidy time-series data (just in case)
lifeexpect_time = lifeexpect %>%
pivot_wider(names_from = c(LEvariable, cohort),
values_from = c(Value, lowerCI, upperCI))
lifeexpect = lifeexpect_time %>%
filter(year == 2017)
str(lifeexpect)
#' #### Visualise missing data
vis_miss(lifeexpect)
#' We notice the variables DFLE and HLE have a lot of missingness hence we only keep the LE variables
lifeexpect_time = lifeexpect_time %>% select(year, areaCode, areaName, contains('_LE_'))
lifeexpect = lifeexpect %>% select(year, areaCode, areaName, contains('_LE_'))
#' #### Update old area codes
#'
#' Pass through custom function to update old codes, then combine the entries with matching area name
#' and area code
#'
#' ##### For lifeexpect
lifeexpect = updateCodes(lifeexpect)
combs_dup = lifeexpect %>%
group_by(areaCode, areaName, year) %>% summarise(duplicat = n()>1)
print(
lifeexpect[lifeexpect$areaCode %in%
combs_dup$areaCode[combs_dup$duplicat==T],c(2,3,6,7)])
#' This function combines entries by taking the mean when they are duplicated
for (row in which(combs_dup$duplicat==T)){
entry = combs_dup[row,]
lifeexpect[(lifeexpect$areaCode == entry$areaCode &
lifeexpect$areaName == entry$areaName &
lifeexpect$year == entry$year),
!colnames(lifeexpect) %in% c('areaCode','areaName', 'year')] =
t(apply(lifeexpect[(lifeexpect$areaCode == entry$areaCode &
lifeexpect$areaName == entry$areaName &
lifeexpect$year == entry$year),
!colnames(lifeexpect) %in% c('areaCode','areaName', 'year')],2,mean))
}
print(
lifeexpect[lifeexpect$areaCode %in%
combs_dup$areaCode[combs_dup$duplicat==T],c(2,3,6,7)])
#' Finally, select unique rows as we have generated duplicated entries in the previous step
lifeexpect = lifeexpect %>% distinct()
#'##### For lifeexpecttime
#'
lifeexpect_time = updateCodes(lifeexpect_time)
combs_dup = lifeexpect_time %>%
group_by(areaCode, areaName, year) %>% summarise(duplicat = n()>1)
print(
lifeexpect_time[lifeexpect_time$areaCode %in%
combs_dup$areaCode[combs_dup$duplicat==T],c(3,6,7)])
#' This function combines entries by taking the mean when they are duplicated
for (row in which(combs_dup$duplicat==T)){
entry = combs_dup[row,]
lifeexpect_time[(lifeexpect_time$areaCode == entry$areaCode &
lifeexpect_time$areaName == entry$areaName &
lifeexpect_time$year == entry$year),
!colnames(lifeexpect_time) %in% c('areaCode','areaName', 'year')] =
t(apply(lifeexpect_time[(lifeexpect_time$areaCode == entry$areaCode &
lifeexpect_time$areaName == entry$areaName &
lifeexpect_time$year == entry$year),
!colnames(lifeexpect_time) %in% c('areaCode','areaName', 'year')],2,mean))
}
print(
lifeexpect_time[lifeexpect_time$areaCode %in%
combs_dup$areaCode[combs_dup$duplicat==T],c(3,6,7)])
#' Finally, select unique rows as we have generated duplicated entries in the previous step
lifeexpect_time = lifeexpect_time %>% distinct()
#' ### Population table
#'
#' Out if this dataset we will extract information regarding age statistics and population size
tail(population)
#' We notice below that the column containing the population estimates contains no missing values which is great
sum(is.na(population$v4_0)) # no missing values, thank god, no need for imputation
#' #### (un)select, filter, rename
population = population %>%
select(-c(calendar.years,
mid.year.pop.sex,
mid.year.pop.age))%>%
rename(areaName = geography,
areaCode = admin.geography,
Value = v4_0,
year = time)
#' ##### Update old codes
#'
#' In this case we update the codes before tidying because we are going to perform aggregations later
#'
population = updateCodes(population)
#' #### Visualise the age distribution
#'
#' Distribution of age coloured by gender for 9 random local authority districts
population %>%
filter(year == 2018) %>%
filter(sex != 'All',
age != 'Total',
areaName %in% sample(areaName, 9)) %>%
mutate(age = as.numeric(ifelse(age == '90+', 90, age))) %>%
ggplot(aes(x = age,
y = Value,
fill = sex))+
geom_col()+
facet_wrap(vars(areaName), scales = 'free')+
scale_x_continuous(breaks = seq(0,100, by = 20))
#' #### Check if all ages are represented
#'
#' Check if there are any holes in the age records
unique_ages = data.matrix(unique(population %>%
filter(age != 'Total') %>%
mutate(age = as.numeric(ifelse(age == '90+', 90, age))) %>%
select(age)))
all(unique_ages %in% c(0:90))
#' For population I can extract:
#' * age info:
#' * mean
#' * median
#' * mode: when calculating the mode, sometimes for some miracle
#' of the universe the frequency for a given age group and a given sex is the same
#' (See Wrexham in population df as they have 1015 for 46 and 47 y/o males), in these cases
#' I'm taking the mean for those cases (i.e. 46.5 in above example)
#' * stdev
#' * ~~range~~
#' * ~~max~~
#' * ~~min~~ in practice all LADs have at least one 0 year old and one 90+ year old
#' * age bins, number of people in each age bin
#'
#' * pop info:
#' * total
#' * total males
#' * total females
#'
#' All stratified by gender and not stratified
#'
#' We define the age bins arbitrarily to represent: children and teens (0-16 y/o), young adults (17-29 y/o),
#' adults (30-63 y/o), old (64-80) and very old (80+).
#'
#' To calculate things like median and mode I actually 'explode' the age column with the `rep()` function
#' getting the count for each age from the `Value` column and then calculate the statistics in question
age_time = population %>%
filter(age != 'Total' & sex != 'All') %>%
mutate(age = as.numeric(ifelse(age == '90+', 90, age))) %>%
group_by(year,
areaCode,
areaName,
sex) %>%
summarise(TotalPeople = sum(Value),
MeanAge = sum(age*Value)/TotalPeople,
MedianAge = median(rep(age,Value)),
ModeAge = mean(age[which(Value == max(Value))]),
stdevAge = sd(rep(age,Value)),
NumberOfTeens = sum(Value[which(age<17)]),
NumberOfYoungAdults = sum(Value[which(age>=17 & age<30)]),
NumberOfAdults = sum(Value[which(age>=30 & age<64)]),
NumberOfOld = sum(Value[which(age>=64 & age<81)]),
NumberOfVeryOld = sum(Value[which(age>80)]))
age = age_time %>% filter(year == 2018)
age_time = age_time %>%
pivot_wider(names_from = sex,
values_from = c(TotalPeople,
MeanAge,
MedianAge,
ModeAge,
stdevAge,
NumberOfTeens,
NumberOfYoungAdults,
NumberOfAdults,
NumberOfOld,
NumberOfVeryOld)
)
age = age %>%
pivot_wider(names_from = sex,
values_from = c(TotalPeople,
MeanAge,
MedianAge,
ModeAge,
stdevAge,
NumberOfTeens,
NumberOfYoungAdults,
NumberOfAdults,
NumberOfOld,
NumberOfVeryOld)
)
#' #### Total(Female+Male) population statistics
#'
agetotal_time = population %>%
filter(age != 'Total' & sex != 'All') %>%
mutate(age = as.numeric(ifelse(age == '90+', 90, age))) %>%
group_by(year,
areaCode,
areaName) %>% # note we are not aggregating by sex
summarise(TotalPeople = sum(Value),
MeanAge = sum(age*Value)/TotalPeople,
MedianAge = median(rep(age,Value)),
ModeAge = mean(age[which(Value == max(Value))]),
stdevAge = sd(rep(age,Value)),
NumberOfTeens = sum(Value[which(age<17)]),
NumberOfYoungAdults = sum(Value[which(age>=17 & age<30)]),
NumberOfAdults = sum(Value[which(age>=30 & age<64)]),
NumberOfOld = sum(Value[which(age>=64 & age<81)]),
NumberOfVeryOld = sum(Value[which(age>80)]))
agetotal = agetotal_time %>% filter(year == 2018)
colnames(agetotal_time)[4:ncol(agetotal_time)] = paste0(
colnames(agetotal_time)[4:ncol(agetotal_time)], '_All')
colnames(agetotal)[4:ncol(agetotal)] = paste0(
colnames(agetotal)[4:ncol(agetotal)], '_All')
#'#### Merge into a single table
popclean_time = merge(age_time, agetotal_time, by = c('year','areaCode','areaName'))
popclean = merge(age, agetotal, by = c('year','areaCode','areaName'))
#' #### Update old area codes
#'
#' Pass through custom function to update old codes, then combine the entries with matching area name
#' and area code
#'
#' ##### For population table
popclean = updateCodes(popclean)
combs_dup = popclean %>%
group_by(areaCode, areaName,year) %>% summarise(duplicat = n()>1)
# In this case luckily we had no bad entries
print(
popclean[popclean$areaCode %in%
combs_dup$areaCode[combs_dup$duplicat==T],c(3,6,7)])
#' ##### For popclean_time table
popclean_time = updateCodes(popclean_time)
combs_dup = popclean_time %>%
group_by(areaCode, areaName,year) %>% summarise(duplicat = n()>1)
print(
popclean_time[popclean_time$areaCode %in%
combs_dup$areaCode[combs_dup$duplicat==T],c(3,6,7)])
#'### Suicides table
str(suicides)
#' #### Select, rename
suicides = suicides %>%
select(-calendar.years) %>%
rename(Value = V4_0,
year = time,
areaCode = admin.geography,
areaName = geography)
#' #### Again no NAs
sum(is.na(suicides$Value)) # no NAs good
#' According the command below all locations have data available for all years
sum(is.na(suicides %>%
pivot_wider(names_from = year, values_from = Value)))
#' #### Time-series plot of a few LADs
suicides %>%
filter(areaName %in% sample(areaName, 9)) %>%
ggplot(aes(x = year, y = Value))+
geom_point()+
facet_wrap(vars(areaName))
#' #### Update codes
#'
suicides = updateCodes(suicides)
combs_dup = suicides %>%
group_by(areaCode, areaName, year) %>% summarise(duplicat = n()>1)
print(
suicides[suicides$areaCode %in%
combs_dup$areaCode[combs_dup$duplicat==T],c(1)])
#' # No duplicates again!
#'#### Store time-series and most up-to-date one
#'
suicides_time = suicides %>% rename(NumberOfSuicides = Value)
suicides = suicides_time %>% filter(year == 2018)
#'### Wellbeing table
#'#### Evaluate content of table
tail(wellbeing)
#'#### Missing data
#'
#'We observe quite a lot of data missingness here
sum(is.na(wellbeing$V4_3))
mean(is.na(wellbeing$V4_3))
#'#### Select, rename, mutate
wellbeing = wellbeing %>%
select(-c(Data.Marking,
yyyy.yy,
wellbeing.measureofwellbeing,
wellbeing.estimate)) %>%
rename(Value = V4_3,
lowerCI = Lower.limit,
upperCI = Upper.limit,
year = time,
areaCode = admin.geography,
areaName = geography,
WellbeingMetric = allmeasuresofwellbeing,
EstimateBin = estimate) %>%
mutate(lowerCI = as.numeric(lowerCI),
upperCI = as.numeric(upperCI))
#' #### Turn year interval to year
#'
#' Same operation that we did for the `lifeexpect` table, here the intervals are only 2 years long though.
#' (e.g. 2014-15 --> 2015)
wellbeing$year = as.numeric(lapply(strsplit(wellbeing$year, '-'), '[[',1))+1
#' #### Variable selection
#'
#' The wellbeing data is very rich, this data was gathered by running a survey with 4 self-assesed
#' questions. For each question the user can score a value between 0 and 10. The `wellbeing` table
#' contains the average score for each category
#' (i.e. `r{paste(unique(wellbeing$wellbeing.measureofwellbeing), collapse= ', ')}`) as well as the proportion
#' of people in different bins (i.e. `r{paste(unique(wellbeing$estimate), collapse= ', ')}`). I believe the average
#' score is more interpretable than the bins and more concise, though there are some caveats to this. Read more
#' about the methodology behing these metrics in
#' [the ONS site](https://www.ons.gov.uk/peoplepopulationandcommunity/wellbeing/methodologies/personalwellbeingintheukqmi)
#'
wellbeing = wellbeing %>% filter(EstimateBin == 'Average (mean)')
#' #### Evaluate missingness after filtering
sum(is.na(wellbeing$Value)) # 117 missing values
#' #### Missingness by area and by wellbeing metric
#'
#' In this case if the output is 8, all entries are missing for all 8 years the variable was measured
wellbeing %>%
group_by(areaCode,
WellbeingMetric) %>%
summarise(NumberOfMissingEntries = sum(is.na(Value)),
ProportionOfMissingEntries = mean(is.na(Value))) %>%
group_by(NumberOfMissingEntries,
ProportionOfMissingEntries) %>%
summarise(Count = n()) %>%
kable()
#'#### Missingness by year
wellbeing %>%
group_by(year) %>%
summarise(NumberOfMissingEntries = sum(is.na(Value)),
ProportionOfMissingEntries = mean(is.na(Value))) %>%
kable()
#' #### Time-series plot
wellbeing %>%
filter(areaName %in% sample(areaName, 9)) %>%
ggplot(aes(x = year,
y = Value,
color = WellbeingMetric)) +
geom_point() +
facet_wrap(vars(areaName))
#' #### Edge-cases
#'
#' The LADs with all missingness are the City of london and the Isles of Scilly. These two often have
#' no available data from the ONS
#'#### Time-series imputation
#' Order the data before imputation (just in case)
#'
wellbeing = wellbeing %>% arrange(areaCode, WellbeingMetric, year)
wellbeing = time_impute(wellbeing, areaCode, WellbeingMetric)
#' ##### Results (as expected)
wellbeing %>%
group_by(areaCode,
WellbeingMetric) %>%
summarise(NumberOfMissingEntries = sum(is.na(Value)),
ProportionOfMissingEntries = mean(is.na(Value))) %>%
group_by(NumberOfMissingEntries,
ProportionOfMissingEntries) %>%
summarise(Count = n()) %>%
kable()
#' (Updated) missingness by year
wellbeing %>%
group_by(year) %>%
summarise(NumberOfMissingEntries = sum(is.na(Value))) %>%
kable()
#' #### Gather data
#'
wellbeing_time = wellbeing %>%
mutate(Range = upperCI - lowerCI) %>%
select(Value,
Range,
year,
areaCode,
areaName,
WellbeingMetric) %>%
pivot_wider(names_from = WellbeingMetric,
values_from = c(Value, Range))
#' #### Update codes
#'
wellbeing_time = updateCodes(wellbeing_time)
combs_dup = wellbeing_time %>%
group_by(areaCode, areaName, year) %>% summarise(duplicat = n()>1)
print(
wellbeing_time[wellbeing_time$areaCode %in%
combs_dup$areaCode[combs_dup$duplicat==T],c(1,2,3,4)] %>%
arrange(areaCode, areaName,year), n = 100)
#' Many duplicates now
#'
#' #' This function combines entries by taking the __mean__ when they are duplicated
for (row in which(combs_dup$duplicat==T)){
entry = combs_dup[row,]
wellbeing_time[(wellbeing_time$areaCode == entry$areaCode &
wellbeing_time$areaName == entry$areaName &
wellbeing_time$year == entry$year),
!colnames(wellbeing_time) %in% c('areaCode','areaName', 'year')] =
t(apply(wellbeing_time[(wellbeing_time$areaCode == entry$areaCode &
wellbeing_time$areaName == entry$areaName &
wellbeing_time$year == entry$year),
!colnames(wellbeing_time) %in% c('areaCode','areaName', 'year')],2,mean))
}
print(
wellbeing_time[wellbeing_time$areaCode %in%
combs_dup$areaCode[combs_dup$duplicat==T],c(1,2,3,4)])
#' Finally, select unique rows as we have generated duplicated entries in the previous step
wellbeing_time = wellbeing_time %>% distinct()
wellbeing = wellbeing_time %>%
filter(year == 2019)
#'### Work table
#' The ASHE work table contains a column called coefficient of variation which is an estimate of the
#' variation within the sample and it helps evaluate whether the sample can be considered a good
#' representation of the population. Find explanation of the Coefficient of variation (CV) column
#' [here](https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/methodologies/annualsurveyofhoursandearningslowpayandannualsurveyofhoursandearningspensionresultsqmi)
#' Moreover, in this section we have two tables, `work` and `work_ts`, the 1st one contains info for 2019 and the second one contains
#' data from earlier years to enable us to impute the 2019 data.
str(work)
#'#### Select, rename, mutate
work = work %>%
select(-c(Data.Marking,
calendar.years,
ashe.working.pattern,
ashe.sex,
ashe.statistics,
ashe.workplace.or.residence,
ashe.hours.and.earnings,
ashe.working.pattern)) %>%
rename(Value = V4_2,
year = time,
areaCode = admin.geography,
areaName = geography,
CoefficientOfVariation = CV) %>%
mutate(CoefficientOfVariation = as.numeric(CoefficientOfVariation))
#'#### Explore a few columns
#'
unique(work$hoursandearnings)
unique(work$workplaceorresidence)
unique(work$workingpattern)
unique(work$statistics)
#' #### Many, many, many statistics available
#'
#' Too many stats are available as seen above, we are only interested in a few. We select mean, median,
#' 10th and 90th percentile
work = work %>%
filter(statistics %in% c('Mean', 'Median', '10th percentile', '90th percentile'),
workplaceorresidence == 'Workplace')
#' #### Select earnings metric
#'
#' From the above command we see that hourly pay has the least missingness so we'll go
#' with that one
work %>%
group_by(hoursandearnings) %>%
summarise(NumberOfMissingEntries = mean(is.na(Value))) %>%
kable()
#' Now we evaulate the missingess of each earning metric per statistic to see if
#' some combination of these is missing at an unacceptable rate
work %>%
group_by(hoursandearnings, statistics) %>%
summarise(NumberOfMissingEntries = mean(is.na(Value))) %>%
ggplot(aes(x = hoursandearnings,
y = statistics,
fill = NumberOfMissingEntries,
label = round(NumberOfMissingEntries,2)))+
geom_tile()+
geom_text(color = 'white')+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
#' this last graph reveals that the 90th and the 10th percentile stats have too much
#' missingess hence I am dropping them, it also show how Hourly pay is the
#' most covered stat
#'
#' Based on previous graph I decided to choose hourly pay
#' and mean and median only
work = work %>%
filter(hoursandearnings == 'Hourly pay - Gross',
statistics %in% c('Median', 'Mean'))
#' Given that now the `hoursandearnings` and the `worplaceorresidence` columns contain
#' unique values only I drop them
work = work %>% select(-c(hoursandearnings, workplaceorresidence))
#' #### Work time-series table
#' ##### Select, rename, mutate
work_ts = work_ts %>%
select(-c(Data_Marking,
calendar.years,
ashe.working.pattern,
ashe.sex,
ashe.statistics,
ashe.workplace.or.residence,
ashe.hours.and.earnings,
ashe.working.pattern)) %>%
rename(Value = V4_2,
year = time,
areaCode = admin.geography,
areaName = geography,
CoefficientOfVariation = CV) %>%
mutate(CoefficientOfVariation = as.numeric(CoefficientOfVariation))
#' Filter the same way as for the `work` dataframe
#'
work_ts = work_ts %>%
filter(statistics %in% c('Mean', 'Median', '10th percentile', '90th percentile'),
workplaceorresidence == 'Workplace')
work_ts = work_ts %>%
filter(hoursandearnings == 'Hourly pay - Gross',
statistics %in% c('Median', 'Mean'))
work_ts = work_ts %>% select(-c(hoursandearnings, workplaceorresidence))
#' ##### Check for any disparity between the areas covered by both of these dataframes
#'
unique(work$areaName[!(work$areaName %in% work_ts$areaName)])
#' Now merge work (data from 2019 only) and work_ts (2016 through 2018)
work = rbind(work, work_ts)
#' #### Start exploring dataset
#'
#' I believe not all towns have entries for all years
#' in theory there should be 4 entries (2016-19) per combination of
#' areacode, stat, sex and working pattern
work %>%
group_by(areaCode,
statistics,
sex,
workingpattern) %>%
summarise(NumberOfYearsWithAvailableData = n()) %>% ## this takes the numbers
# of entries per unique combination of the variables in the group_by
group_by(NumberOfYearsWithAvailableData) %>%
summarise(Count = n()) %>%
kable()
#' As you can see in the above table for some combinations
#' there are only 3 available variables hence 3 missing variables
#' represent 100% where as in other cases 4 missing varialbes represent
#' 100% of variables
#'
work %>%
group_by(areaCode,
statistics,
sex,
workingpattern) %>%
summarise(NumberOfMissingEntries = sum(is.na(Value)),
ProportionOfMissingEntries = mean(is.na(Value))) %>%
group_by(NumberOfMissingEntries,
ProportionOfMissingEntries) %>%
summarise(Count = n()) %>%
kable()
#' Missingness by year
work %>%
group_by(year) %>%
summarise(NumberOfMissingEntries = sum(is.na(Value)),
ProportionOfMissingEntries = mean(is.na(Value))) %>%
kable()
#' #### Time-series imputation
#'
#' Make sure data is ordered by year
work = work %>% arrange(areaCode, statistics, sex, workingpattern, year)
work = time_impute(work, cols_to_impute = c('Value', 'CoefficientOfVariation'),
areaCode, statistics, sex, workingpattern)
#' #### Results: (updated) missingness
work %>%
group_by(areaCode,
statistics,
sex,
workingpattern) %>%
summarise(NumberOfMissingEntries = sum(is.na(Value)),
ProportionOfMissingEntries = mean(is.na(Value))) %>%
group_by(NumberOfMissingEntries,
ProportionOfMissingEntries) %>%
summarise(Count = n())
work %>%
group_by(year) %>%
summarise(NumberOfMissingEntries = sum(is.na(Value)),
ProportionOfMissingEntries = mean(is.na(Value))) %>%
kable()
#' #### Edge cases
#'
#' Finally we gotta be careful with those entries where records ends in 2018,
#' we have to characterise them
work %>%
group_by(areaName) %>%
summarise(latestAvailableYear = max(year)) %>%
group_by(latestAvailableYear) %>%
summarise(Count = n()) %>%
kable()
#' Note: interestingly, by area code you get slightly different numbers than grouping by areaName
work %>%
group_by(areaCode) %>%
summarise(latestAvailableYear = max(year)) %>%
group_by(latestAvailableYear) %>%
summarise(Count = n()) %>%
kable()
#' Check count per number of available values
work %>%
group_by(areaCode,
areaName,
statistics,
sex,
workingpattern) %>%
summarise(NumberOfValues = n()) %>%
group_by(NumberOfValues) %>%
summarise(Count = n()) %>%
kable()
#' Check also which area names have several latest years available
tst = work %>% group_by(areaCode, areaName) %>% summarise(latest = max(year))
tab = table(tst$areaName)
tab[tab>1]
#' Ok, so some codes have been updated which is fine, for example see Dorset
#' code from 2018 corresponds to one that has been deprecated, what we'll do now
#' is for each area name choose a single area code (the area code corresponding to the
#' latest year)
#'
#' #### Get entries for which latest available data is from 2019
work %>% group_by(areaCode, areaName) %>% summarise(year = max(year)) %>% filter(year == 2018)
#' it further appears that many areaName for which latest figures come
#' from 2018 are so called 'abolished' now (i.e. inactive), see
#' [Suffolk Coastal](https://en.wikipedia.org/wiki/Suffolk_Coastal) or
#' [East Dorset](https://en.wikipedia.org/wiki/East_Dorset)
#' So I have not check all the 14 entries which have missing data, the robust
#' way to do this would be filtering with nspl (unclear what I meant in this comment)
length(unique(work$areaName))
length(unique(work$areaCode))
#' A final problem which is not really a problem is the fact that because the work
#' table has data on regions(also UK-wide, England-wide...) as well as local authorities, If
#' a region happens to be called the same than a LA then it'll appear twice (e.g. West Midlands
#' E1200005 - Region code, E1100005 - LA code)
#' #### Gather final data
#'
work_time = work %>%
rename(Pay = Value,
CoV = CoefficientOfVariation) %>%
pivot_wider(names_from = c(statistics,
workingpattern,
sex),
values_from = c(Pay,
CoV))
#'#### Update codes
work_time = updateCodes(work_time)
combs_dup = work_time %>%
group_by(areaCode, areaName, year) %>% summarise(duplicat = n()>1)
print(
work_time[work_time$areaCode %in%
combs_dup$areaCode[combs_dup$duplicat==T],c(1,2,3,4)] %>%
arrange(areaCode, areaName,year), n = 100)
#' Many duplicates now
#'
#' This function combines entries by taking the __mean__ when they are duplicated
for (row in which(combs_dup$duplicat==T)){
entry = combs_dup[row,]
work_time[(work_time$areaCode == entry$areaCode &
work_time$areaName == entry$areaName &
work_time$year == entry$year),
!colnames(work_time) %in% c('areaCode','areaName', 'year')] =
t(apply(work_time[(work_time$areaCode == entry$areaCode &
work_time$areaName == entry$areaName &
work_time$year == entry$year),
!colnames(work_time) %in% c('areaCode','areaName', 'year')],2,mean))
}
print(
work_time[work_time$areaCode %in%
combs_dup$areaCode[combs_dup$duplicat==T],c(1,2,3,4)])
#' Finally, select unique rows as we have generated duplicated entries in the previous step
work_time = work_time %>% distinct()
work = work_time %>% filter(year == 2019)
#'### GDP table
str(gdp)
#'#### No NAs yay
sum(is.na(gdp$`2018`)) ## good news
#'#### Rename, pivot, mutate
gdp = gdp %>% rename(Region = `NUTS1 Region`,
areaCode = `LA code`,
areaName = `LA name`) %>%