Replication Code
This file replicates the monthly report analysis. The entire dataset is quite big (greater than 390MB) and computationally intensive. This is the reason why we randomly select the 1% of firms registered on January 2021 for this replication code. To compare these business incorporations, we use the 1% of firms registered on January 2019. Note that results on this page significantly differ from the February 2021 analysis because of the smaller sample used.
We use the following packages.
packages <- c('tidyverse', 'naniar', 'haven', 'survey',
'data.table', 'lubridate', 'ggalt', 'cowplot','animation',
'patchwork', 'sp', 'scales', 'raster', 'rgeos', 'mapproj',
'rgdal', 'maptools', 'emojifont', 'nord', 'paletteer', 'plotly')
pkg_notinstall <- packages[!(packages %in% installed.packages()[,"Package"])]
lapply(pkg_notinstall, install.packages, dependencies = TRUE)
lapply(packages, library, character.only = TRUE)
if (!require(gpclib)) install.packages("gpclib", type="source");library(gpclib)
gpclibPermit() # Gives maptool permisssion to use gpclib
load.fontawesome()
Source of data
The data are retrieved from the UK business register, available from the Companies House (data here in .csv format).
Extract and prepare the data
For replication purposes, I randomly extract 1% of firms registered on January 2021 from the latest available register (released on Feb 1st, 2021).
# Create a temp. file
temp <- tempfile()
# Use `download.file()` to fetch the file into the temp. file
download.file("http://download.companieshouse.gov.uk/BasicCompanyDataAsOneFile-2021-02-01.zip",temp)
# Use unz() to extract the target file from temp. file
repD<- read.csv(unz(temp, "BasicCompanyDataAsOneFile-2021-02-01.csv"))
# Remove the temp file via 'unlink()'
unlink(temp)
# Make incorporation date as date format.
repD$IncorporationDate <- as.Date(repD$IncorporationDate, "%d/%m/%Y")
# select firms registered in January 2021
repD2021 <- repD[which(repD$IncorporationDate >= "2021-01-01" &
repD$IncorporationDate <= "2021-01-31"), ]
# randomly select the 1% of the resulting Jan2021 file
repD2021 <- repD2021 %>% slice_sample(prop = 0.01)
# keep only relevant columns to Report
repD2021 <- repD2021[c(2,10:15,27:30)]
# save resulting file as .csv (to be uploaded on Website)
write_csv(repD2021,'output/replicData2021.csv')
Download the resulting sample:
Analysis
Aggregate Analysis
Daily
Here we calculate how many new firms are registered daily.
n_incorp <- repD2021 %>%
group_by(IncorporationDate) %>%
count()
We find the corresponding day to each date and fill in with zeros those dates that aren’t included in the random sample.
# Rename IncorporationDate to day
n_incorp <- n_incorp %>% rename(day = IncorporationDate)
# what's the week day?
n_incorp$wkday <- weekdays(n_incorp$day, abbr = TRUE)
# change the max date here in new release!
total_days <- seq(ymd("2021-01-01"), ymd("2021-01-31"), by = "days")
# Instead of dropping NAs we replace them with 0.
total_days <- tibble(total_days) %>%
mutate(n = 0) %>%
rename(day = total_days)
fp <- full_join(total_days, n_incorp, by = "day")
fp <- fp %>% mutate(ni = replace_na(n.y, 0))
fp <- tibble(date = fp$day, ni = fp$ni, day = fp$wkday)
write.csv(fp, "output/repByDay.csv")
According to our timeline, we generate the following important milestone dates^[Given our random sample, here, I only use the start of lockdown-III]:
# Basic Dates
start_ldIII <- "2021-01-05"
We calculate the median firm registration according to the milestones. Note that the use of fp data frame decreases the median and the mean because of the zeros included. For the random sample, you may use the n_incorp data frame instead.
# Statistics
median_total <- median(fp$ni)
mean_total <- mean(fp$ni)
ldIII <- subset(fp, date >= start_ldIII)
median_during_ldIII <- median(ldIII$ni)
mean_during_ldIII <- mean(ldIII$ni)
# Put stats in a table
avg <- matrix(c(median_total, median_during_ldIII,
mean_total, mean_during_ldIII),
ncol=2,byrow=TRUE)
colnames(avg) <- c("Total","During Lockdown-III")
rownames(avg) <- c("Median", "Mean")
# print table
library(knitr)
library(kableExtra)
library(downloadthis)
avg %>%
kbl(caption = "Median and Mean of new daily companies Incorporations", align = "c", escape = F) %>%
kable_styling() %>%
kable_paper("hover", full_width = F) %>%
footnote(general_title ="", "Source: Authors' calculations based on Companies House [data](http://download.companieshouse.gov.uk/en_output.html)", footnote_as_chunk = T)
| Total | During Lockdown-III | |
|---|---|---|
| Median | 26.00000 | 28.00000 |
| Mean | 20.93548 | 22.33333 |
We plot as a histogram the number of daily registrations and overwrite the median values calculated above.
# Plot 1: Histogram with medians
dailyagg <- plot_ly(alpha = 0.5, y=fp$ni, x=fp$date, type = "bar", showlegend= F)
dailyagg <- dailyagg %>% layout(xaxis=list(type='auto')) %>%
add_segments(x = min(fp$date), xend= max(fp$date), y=median_total, yend=median_total, name = "Median (total)", showlegend=T) %>%
add_segments(x = min(fp$date), xend= max(fp$date), y=median_during_ldIII, yend=median_during_ldIII, name = "Median (during lockdown III)", showlegend=T) %>%
layout(title = 'Daily Company Registrations',
yaxis = list(title = "Number of registrations", showgrid = F))
dailyagg
Relative
For this case we see the relative daily registration during each week to the same week in 2019. To this end, first we assign the week number to each day in 2021. Week numbering starts from 1.
# week number in 2021
fp$week <- week(fp$date)
Following, we calculate the number of firms in January 2019 and replicate the analysis of the data frame above.
# calculate the number of fimrs registered in January 2019
#2019
n_incorp2019 <- repD2019 %>%
group_by(IncorporationDate) %>%
count()
# replicate the data frame analysis of 2021
n_incorp2019 <- n_incorp2019 %>% rename(day = IncorporationDate)
n_incorp2019$wkday <- weekdays(n_incorp2019$day, abbr = TRUE)
n_incorp2019$week <- week(n_incorp2019$day)
total_days2019 <- seq(ymd("2019-01-01"), ymd("2019-01-31"), by = "days")
total_days2019 <- tibble(total_days2019) %>%
mutate(n = 0) %>%
rename(day = total_days2019)
fp2019 <- full_join(total_days2019, n_incorp2019, by = "day")
fp2019 <- fp2019 %>% mutate(ni = replace_na(n.y, 0))
fp2019 <- tibble(date = fp2019$day, ni = fp2019$ni, day = fp2019$wkday, week = fp2019$week)
Aggregate the number of firms per week and calculate the ratio of registrations $$ \frac{\text{registrations }2021}{\text{registrations }2019} $$
# Aggregate the firms per week
Week2019 <- aggregate(fp2019$ni, by=list(week=fp2019$week), FUN=sum)
Week2021 <- aggregate(fp$ni, by=list(week=fp$week), FUN=sum)
# Calculate the ratio: registrations2021/registrations2019
Week2021$ratioTo2019 <- Week2021$x/Week2019$x
Plot the registrations ratio
# Plot 2: Ratio of registrations
RegRatio <- plot_ly(alpha = 0.5, y=Week2021$ratio, x = Week2021$week, type = 'scatter', mode = 'lines', name = "2020/2019") %>%
add_segments(x=min(Week2021$week), xend = max(Week2021$week), y=1, yend=1, line = list(dash = "dash"), showlegend=F)
RegRatio %>% layout(title = list(text = paste0('Company registrations Ratio',
'<br>',
'<sup>',
'(Same week of reference in 2019)',
'</sup>')),
xaxis = list(title= "Weeks", showgrid = F),
yaxis = list(title = "Ratio", showgrid = F))
In a panel which includes more months than January, it would make sense to compare the firms creation in a particular month relative to the same month in the base year (2019). To do so, we compare the median of January 2021 and January 2019.
mean_Jan2019 <- mean(fp2019$ni)
med_Jan2019 <- median(fp2019$ni)
mean_Jan2021 <- mean(fp$ni)
med_Jan2021 <- median(fp$ni)
We plot the median.
JanChange <- plot_ly(x=c("2019", "2021"), y=c(med_Jan2019, med_Jan2021), alpha=0.5, name = "Median January Registrations", type='bar', showlegend=F)
JanChange %>%
layout(title = "Daily median company registrations in January",
xaxis = list(title = "", showgrid=F),
yaxis = list(title = "", showgrid=F))
Regional Analysis
Postcode Area
We keep the first 1 or 2 letters of the postcode; this is the Postcode Area. Then, we calculate the new registrations by date and PC area and merge with the longitude and latitude of each area, found here.
# keep only the first 1 or 2 letters before the numbers in the Postcode
repD2021$PostCodeArea <- sub("^([[:alpha:]]*).*", "\\1", repD2021$RegAddress.PostCode)
# calculate the number of firms per PC and date of registration
n_incorp4 <-repD2021 %>%
group_by(IncorporationDate,PostCodeArea) %>%
count()
n_incorp4 <- n_incorp4 %>% rename(day = IncorporationDate)
n_incorp4$wkday <- weekdays(n_incorp4$day, abbr = TRUE)
n_incorp4$week <- week(n_incorp4$day)
# change the max date here in new release!
total_days4 <- seq(ymd("2021-01-01"), ymd("2021-01-31"), by = "days")
total_days4 <- tibble(total_days4) %>%
mutate(n = 0) %>%
rename(day = total_days4)
fp2 <- full_join(total_days4, n_incorp4, by = "day")
fp2 <- fp2 %>% mutate(ni = replace_na(n.y, 0))
fp2 <- tibble(date = fp2$day, ni = fp2$ni, PostcodeArea = fp2$PostCodeArea,
wkday = fp2$wkday, week = fp2$week)
# file with longitude and latitude of postcodes
areaPC <- read.csv("input/Postcodes summaryCLEAN.csv")
fp2Total <- merge(fp2,areaPC,by="PostcodeArea")
write.csv(fp2Total, "output/byDayPC.csv")
Perform the same analysis for 2019. Calculate the percentage change using the same reference week in January 2019. Finally, calculate the average change of the month and plot it in a map.
# perform the analysis for 2019
# keep only the first 1 or 2 letters before the numbers in the Postcode
repD2019$PostCodeArea <- sub("^([[:alpha:]]*).*", "\\1", repD2019$RegAddress.PostCode)
n_incorp4_2019 <-repD2019 %>%
group_by(IncorporationDate,PostCodeArea) %>%
count()
n_incorp4_2019 <- n_incorp4_2019 %>% rename(day = IncorporationDate)
n_incorp4_2019$wkday <- weekdays(n_incorp4_2019$day, abbr = TRUE)
n_incorp4_2019$week <- week(n_incorp4_2019$day)
total_days4_2019 <- seq(ymd("2019-01-01"), ymd("2019-01-31"), by = "days")
total_days4_2019 <- tibble(total_days4_2019) %>%
mutate(n = 0) %>%
rename(day = total_days4_2019)
full_period4_2019 <- full_join(total_days4_2019, n_incorp4_2019, by = "day")
full_period4_2019 <- full_period4_2019 %>% mutate(ni = replace_na(n.y, 0))
full_period4_2019 <- tibble(date = full_period4_2019$day, ni = full_period4_2019$ni, PostcodeArea = full_period4_2019$PostCodeArea,
wkday = full_period4_2019$wkday, week = full_period4_2019$week)
# Aggregate per week and postcode
mapWeek2021 <- aggregate(fp2Total$ni,
by=list(week=fp2Total$week, PostcodeArea=fp2Total$PostcodeArea),
FUN=sum)
mapWeek2019 <- aggregate(full_period4_2019$ni,
by=list(week=full_period4_2019$week, PostcodeArea=full_period4_2019$PostcodeArea),
FUN=sum)
mapWeek2021 <- mapWeek2021 %>% rename(x2021 = x)
mapWeek2019 <- mapWeek2019 %>% rename(x2019 = x)
# merge the 2 previous data frames with week and PC
mapWeek2021T <- merge(mapWeek2021, mapWeek2019, by=c("week","PostcodeArea"))
# calculate the percentage change between 2021 and 2019
mapWeek2021T$change <- (mapWeek2021T$x2021/mapWeek2021T$x2019 - 1) *100
# merge for longitude and latitude
mapWeek2021T <- merge(mapWeek2021T, areaPC,by="PostcodeArea")
# merge with countries
postcod2country <- read_csv("input/convertedPC2country.csv")
postcod2country <- postcod2country %>% rename (PostcodeArea = Postcode.area)
mapWeek2021T <- merge(mapWeek2021T, postcod2country, by="PostcodeArea")
# What to plot as MAP: Average change of January 2021 relative to January 2019 by Postcode Area
mapJan2021Ave <- mapWeek2021T %>%
group_by(PostcodeArea) %>%
summarise(avJan = mean(change))
## merge with countries
mapJan2021Ave <- merge(mapJan2021Ave, postcod2country, by="PostcodeArea")
mapJan2021Ave <- merge(mapJan2021Ave, areaPC, by="PostcodeArea")
Finally we plot the average change of new registrations on a map. The size of the bubbles regards the average change occurred in January 2021 relative to January 2019.
# Plot 4a: Map with bubbles. Size of bubble denotes the perc. change
# Download the Outline of the UK
UKareas <- raster::getData('GADM', country='GBR', level = 3) %>%
fortify()
aveMap <-
ggplot() +
geom_polygon(data = UKareas,
aes(long, lat, group = group),
fill="grey", alpha=0.4) +
geom_point(data = mapJan2021Ave,
aes(x = Longitude, y = Latitude,
size= avJan, label = Area.covered),
shape = 21,
fill = "steelblue",
alpha = .6) +
ylim(50,59) + coord_map() +
labs(title = "Average change of New Incorporations",
subtitle = "Change measured between January 20201 and January 2019") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
panel.background = element_rect(fill = NA, colour = NA),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
ggplotly(aveMap)
Country
We perform an analysis by country. Earlier we merged the postcode areas to countries. To proceed with the analysis, we exclude London from England so that it does not drive the results. To do so, we define the areas of London. We aggreggate firm registration by country and week and find the percentage change between January 2021 and January 2019.
# Define London areas
london <- c("Central London", "East London",
"North London", "South East London",
"South West London", "West London")
# not include function
'%!in%' <- function(x,y)!('%in%'(x,y))
# only LDN
mapWeek2021TonlyLDN <- subset(mapWeek2021T, Area.covered %in% london)
# exclude LDN
mapWeek2021TnoLDN <- subset(mapWeek2021T, Area.covered %!in% london)
# aggregate firm registration LDN by country
AggregateonlyLDN2021 <- aggregate(list(mapWeek2021TonlyLDN$x2021,mapWeek2021TonlyLDN$x2019),
by=list(week=mapWeek2021TonlyLDN$week,
Country=mapWeek2021TonlyLDN$Country),
FUN=sum)
colnames(AggregateonlyLDN2021)[3] <- "Jan2021"
colnames(AggregateonlyLDN2021)[4] <- "Jan2019"
# calculate the percentage change
AggregateonlyLDN2021$change <- ((AggregateonlyLDN2021$Jan2021/AggregateonlyLDN2021$Jan2019)-1)*100
# replace country name to LDN
AggregateonlyLDN2021$Country <- sub("England", "London", AggregateonlyLDN2021$Country)
AggregateexclLDN2021 <- aggregate(list(mapWeek2021TnoLDN$x2021,mapWeek2021TnoLDN$x2019),
by=list(week=mapWeek2021TnoLDN$week,
Country=mapWeek2021TnoLDN$Country),
FUN=sum)
colnames(AggregateexclLDN2021)[3] <- "Jan2021"
colnames(AggregateexclLDN2021)[4] <- "Jan2019"
# calculate the percentage change
AggregateexclLDN2021$change <- ((AggregateexclLDN2021$Jan2021/AggregateexclLDN2021$Jan2019-1)*100)
# replace the name of the country to England (excl. LDN)
AggregateexclLDN2021$Country <- sub("England", "England (excl. Ldn)", AggregateexclLDN2021$Country)
Finally we plot the percentage change per week in a timelines plot.
# Plot 5: by country and LDN
Country <- plot_ly (alpha=0.5, x=AggregateexclLDN2021$week,
y=AggregateexclLDN2021$change,
type = 'scatter', mode = 'line',
color = AggregateexclLDN2021$Country)
Country <- Country %>%
add_trace(y = AggregateonlyLDN2021$change,
x = AggregateonlyLDN2021$week,
name = 'London', type='scatter', mode = 'lines',
color = AggregateonlyLDN2021$Country)
Country %>%
layout(title = list(text = paste0('Change of company registrations by Country',
'<br>',
'<sup>',
'(Same week of reference in 2019)',
'</sup>')),
xaxis = list(title= "Weeks", showgrid = F),
yaxis = list(title = "% change", showgrid = F))
London
London areas present an heterogeneous firm registration. Here, we replicate the analysis above focusing only in the different areas in London.
LondonByAreas2021 <- aggregate(list(mapWeek2021TonlyLDN$x2021,
mapWeek2021TonlyLDN$x2019),
by=list(week=mapWeek2021TonlyLDN$week,
AreaCovered=mapWeek2021TonlyLDN$Area.covered),
FUN=sum)
colnames(LondonByAreas2021)[3] <- "Jan2021"
colnames(LondonByAreas2021)[4] <- "Jan2019"
LondonByAreas2021$change <- ((LondonByAreas2021$Jan2021/LondonByAreas2021$Jan2019)-1)*100
And we plot it as before.
# Plot 6: by London areas
LDN <- plot_ly (alpha=0.5, x=LondonByAreas2021$week, y=LondonByAreas2021$change,
type = 'scatter', mode = 'line',
color = LondonByAreas2021$AreaCovered)
LDN %>%
layout(title = list(text = paste0('Change of company registrations by London Areas',
'<br>',
'<sup>',
'(Same week of reference in 2019)',
'</sup>')),
xaxis = list(title= "Weeks", showgrid = F),
yaxis = list(title = "% change", showgrid = F))
Sectoral Analysis
To examine each sector we pay attention to the first SIC code reported by each firm. We keep only the numerical part. We end up with a 5-digit code. To merge it with the ONS Sections, we keep only the first 2 digits. For the assignment of the 2-digit SIC code to ONS sections, we follow this. Then, we calculate how many new firms per day and sector are registered. We repeat the analysis for 2019. Finally, we calculate the percentage change as before.
# Keep only the code from SIC code text 1
repD2021$SIC5dg1<- as.numeric(gsub("([0-9]+).*$", "\\1", repD2021$SICCode.SicText_1)) # pattern is by finding a set of numbers in the start and capturing them
# keep only the first 2 digits if more than 2
repD2021$SIC2dg1 <- repD2021$SIC5dg1/1000
# Drop decimals in SIC2digit
repD2021$SIC2dg1 <- as.integer(repD2021$SIC2dg1)
# Convert SIC2dig to subsectors and sectors
# This table is retrieved, following this code: https://github.com/ygalanak/UKsicConverter
convert <- read.csv('input/sic2007conversion.csv')
convert <- convert %>% rename (Sector = Section)
repD2021Total <- merge(repD2021, convert, by="SIC2dg1")
# calculate how many new firms by day and sector?
n_incorp3 <- repD2021Total %>%
group_by(IncorporationDate, Sector) %>%
count()
n_incorp3 <- n_incorp3 %>% rename(day = IncorporationDate)
n_incorp3$wkday <- weekdays(n_incorp3$day, abbr = TRUE)
n_incorp3$week <- week(n_incorp3$day)
# change the max date here in new release!
total_days3 <- seq(ymd("2021-01-01"), ymd("2021-01-31"), by = "days")
total_days3 <- tibble(total_days3) %>%
mutate(n = 0) %>%
rename(day = total_days3)
full_period3 <- full_join(total_days3, n_incorp3, by = "day")
full_period3 <- full_period3 %>% mutate(ni = replace_na(n.y, 0))
full_period3 <- tibble(date = full_period3$day, ni = full_period3$ni, Sector = full_period3$Sector,
wkday = full_period3$wkday, week = full_period3$week)
# merge with conversion file for the name of each section
full_period3Total <- merge(full_period3,convert[,c("Sector", "Section.name")],by="Sector") #merge for sector names
# Remove duplicates based on week columns
full_period3 <- full_period3 %>%
distinct(Sector, date, week, .keep_all = TRUE)
write.csv(full_period3, "output/byDaySector.csv")
# replicate steps above for 2019
# Keep only the code from SIC
repD2019$SIC5dg1<- as.numeric(gsub("([0-9]+).*$", "\\1", repD2019$SICCode.SicText_1)) # pattern is by finding a set of numbers in the start and capturing them
repD2019$SIC2dg1 <- repD2019$SIC5dg1/1000
# Drop decimals in SIC2digit
repD2019$SIC2dg1 <- as.integer(repD2019$SIC2dg1)
repD2019Total <- merge(repD2019, convert, by="SIC2dg1")
# calculate how many new firms in Jan2019
n_incorp3_2019 <- repD2019Total %>%
group_by(IncorporationDate, Sector) %>%
count()
n_incorp3_2019 <- n_incorp3_2019 %>% rename(day = IncorporationDate)
n_incorp3_2019$wkday <- weekdays(n_incorp3_2019$day, abbr = TRUE)
n_incorp3_2019$week <- week(n_incorp3_2019$day)
# change the max date here in new release!
total_days3_2019 <- seq(ymd("2019-01-01"), ymd("2019-01-31"), by = "days")
total_days3_2019 <- tibble(total_days3_2019) %>%
mutate(n = 0) %>%
rename(day = total_days3_2019)
full_period3_2019 <- full_join(total_days3_2019, n_incorp3_2019, by = "day")
full_period3_2019 <- full_period3_2019 %>% mutate(ni = replace_na(n.y, 0))
full_period3_2019 <- tibble(date = full_period3_2019$day, ni = full_period3_2019$ni,
wkday = full_period3_2019$wkday, Sector = full_period3_2019$Sector, week = full_period3_2019$week)
dffp3Total2019 <- merge(full_period3_2019,convert[,c("Sector", "Section.name")],by="Sector") #merge for sic names
# Remove duplicates based on week columns
dffp3Total2019 <- dffp3Total2019 %>%
distinct(Sector, date, week, .keep_all = TRUE)
# aggregate by week and sic
regWeek2021 <- aggregate(full_period3$ni,
by=list(week=full_period3$week, Sector=full_period3$Sector),
FUN=sum)
regWeek2019 <- aggregate(dffp3Total2019$ni,
by=list(week=dffp3Total2019$week, Sector=dffp3Total2019$Sector),
FUN=sum)
regWeek2019<- regWeek2019 %>%
group_by(Sector) %>%
arrange(week, .by_group = TRUE)
# calculate percentage change
regWeek2021$change <- (regWeek2021$x-regWeek2019$x)/regWeek2019$x *100
regWeek2021 <- merge(regWeek2021, convert[,c("Sector", "Section.name")],by="Sector")
# Remove duplicates based on week columns
regWeek2021 <- regWeek2021 %>%
distinct(Sector, week, .keep_all = TRUE)
We plot the percentage change per week for each sector. Note that this graph is not included in the monthly report. Instead, we will create a Shiny App to see each section and subsection per week.
# Plot 7: All sectors in January 2021/2019
Sec <- plot_ly(alpha=0.5, x=regWeek2021$week, y=regWeek2021$change,
color = regWeek2021$Section.name,
type = 'scatter', mode = 'line', showlegend =F)
Sec %>%
layout(autosize = T, xaxis = list(showgrid = F),
yaxis = list(title = "% change to 2019", showgrid = F),
title = list(text = paste0('Change in company registrations',
'<br>',
'<sup>',
'(Same week of reference in 2019)',
'</sup>')))
We see the daily registrations within a particular month and compare it between 2019 and 2021. We calculate the monthly registrations by sector as earlier.
# Daily registrations in a month by sector
DsecJan2021 <- aggregate(full_period3$ni, by=list(Sector=full_period3$Sector), FUN=sum)
colnames(DsecJan2021)[2] <- "Jan2021"
dffp3Jan2019 <- subset(dffp3Total2019, date >="2019-01-01" & date<="2019-01-31")
DsecJan2019 <- aggregate(dffp3Jan2019$ni, by=list(Sector=dffp3Jan2019$Sector), FUN=sum)
colnames(DsecJan2019)[2] <- "Jan2019"
DsecJan <- merge(DsecJan2021, DsecJan2019, by="Sector")
DsecJan <- merge(DsecJan, convert[,c("Sector", "Section.name")],by="Sector")
# Remove duplicates based on week columns
DsecJan <- DsecJan %>%
distinct(Sector, .keep_all = TRUE)
write.csv(DsecJan, "output/bySectorCompareALL.csv")
Finally, we plot the daily registrations in bar graph.
# Plot 8: Daily registrations by sector, January total
DSJan <- plot_ly(alpha = 0.5, x=DsecJan$Section.name, y=DsecJan$Jan2019, name = "2019", type = 'bar')
DSJan %>%
add_trace(alpha = 0.5, x=DsecJan$Section.name, y=DsecJan$Jan2021, name = "2021", type = 'bar') %>%
layout(autosize = T, xaxis = list(tickfont = list(size = 7), tickwidth=3,tickangle = 90),
yaxis = list(title = "# of registations in January", showgrid = F),
title = list(text = paste0('Daily company registrations in January by sector')))