GVH EC - Week 4 October
Cormac Harten
Included this week:
- Commencements
- Completions
- Housing Pipeline: Balance of Stock
- FHS Caps vs CSO data
- Retail Sales Index
- Port data
Commencements
National
HSM13.A_S.Q_MAX <- tail(HSM13.A_S.Q,1)
HSM13.A_S.Q %>%
filter(Year_Q >= "2020 Q1")%>%
ggplot(aes(x=Year_Q,y=SMA.12,group=STATISTIC))+
geom_line(alpha=0.3,size=1.25,colour="#B54104")+
geom_text_repel(data=HSM13.A_S.Q_MAX,aes(label=SMA.12),size=2.75,colour="#B54104")+
geom_line(aes(y=value),alpha=0.1,size=1,colour="#B54104")+
theme_bw()+theme(plot.title = element_text(size=9,face="bold"),
legend.text = element_text(size=8),
legend.title = element_text(size=8),
axis.text.x= element_text(size=8),
axis.text.y= element_text(size=8),
axis.title = element_text(size=8),
plot.subtitle=element_text(size=8))+
theme(legend.position = "none")+
ggtitle("Commencement Notices")+
labs(subtitle="12 month moving average",caption="source: HSM13")+
ylab("number of units")+
xlab("quarter")
Commencements - Local Authority
HSM13 <- cso_get_data("HSM13")
HSM13 <- HSM13 %>%
pivot_longer(!1:3,names_to = "year_month")
HSM13$Year <-substr(HSM13$year_month,1,4)
HSM13$Month <- sub(".* ", "", HSM13$year_month)
HSM13$Month_NR <- as.integer(factor(HSM13$Month, levels=month.name))
HSM13$Date <- as.yearmon(paste(HSM13$Year, HSM13$Month_NR), "%Y %m")
HSM13$Year_Q <- as.yearqtr(HSM13$Date)
HSM13.A <- HSM13 %>%
filter(Residential.Units.Commenced=="Number of residential units")
HSM13.A.Q <- HSM13.A %>%
group_by(STATISTIC,Local.Authority,Residential.Units.Commenced,Year_Q,Year) %>%
summarise(value=sum(value,na.rm=TRUE))
HSM13.A.Q$SMA.6 <- round(SMA(HSM13.A.Q$value,n=2),digits=2)
HSM13.A.Q$SMA.6[HSM13.A.Q$Year_Q=="2014 Q1"] <- NA
HSM13.A.Q$SMA.12 <- round(SMA(HSM13.A.Q$value,n=4),digits=2)
HSM13.A.Q$SMA.12[HSM13.A.Q$Year_Q=="2014 Q1" | HSM13.A.Q$Year_Q=="2014 Q2" | HSM13.A.Q$Year_Q=="2014 Q3"] <- NA
GEO_HSM13 <- readxl::read_xlsx(path="C:\\Users\\charten\\OneDrive - Glenveagh Properties\\Research & Development\\1. Analysis\\GEO_HSM13.xlsx")
shpHSM13.A.Q <- merge(HSM13.A.Q,GEO_HSM13,by="Local.Authority")
shpHSM13.A.Q <- shpHSM13.A.Q %>%
group_by(ENGLISH)%>%
slice(which.max(Year_Q))
shpHSM13.A.Q<- merge(shp,shpHSM13.A.Q,by="ENGLISH")
# Plot
shpHSM13.A.Q %>%
ggplot()+
geom_sf(aes(fill=SMA.12),colour=alpha("white",0.5))+
theme_void()+theme(plot.title = element_text(size=11,face="bold",hjust=0.5),
legend.title = element_text(size = 11),
legend.text = element_text(size = 8),
strip.text.x = element_text(size = 12))+
scale_fill_distiller(palette = "OrRd", name="Commencements",direction = 1)+
ggtitle("Commencements by Local Authority")+
labs(subtitle="12 month moving average",caption = "CSO Dataset: HSM13")
See Appendix for commencements by Local Authority & Quarter.
Completions
The Government’s Housing for All plan targets 24,600 completions in 2022, 29,000 in 2023 and 33,450 in 2024.
## NDQ07
NDQ07 <- cso_get_data("NDQ07")
NDQ07_long <- NDQ07 %>%
pivot_longer(!1:2, names_to = "year_qtr")
NDQ07 <- NDQ07_long
rm(NDQ07_long)
NDQ07$Year_Q <- as.yearqtr(NDQ07$year_qtr)
NDQ07$Year <- year(NDQ07$Year_Q)
NDQ07_A <- NDQ07 %>%
filter(Eircode.Output=="All")
NDQ07_A$SMA.12 <- round(SMA(NDQ07_A$value,n=4),digits=2)
NDQ07_A$SMA.12[NDQ07_A$Year_Q=="2011 Q1" | NDQ06.A2$NDQ07_A=="2011 Q2" | NDQ07_A$Year_Q=="2011 Q3"] <- NA
NDQ07_B <- NDQ07 %>%
filter(Eircode.Output!="All")
NDQ07_B$SMA.12 <- round(SMA(NDQ07_B$value,n=4),digits=2)
NDQ07_B$SMA.12[NDQ07_B$Year_Q=="2011 Q1" | NDQ06.A2$NDQ07_B=="2011 Q2" | NDQ07_B$Year_Q=="2011 Q3"] <- NA
NDQ07_A_tail <- tail(NDQ07_A,1)
NDQ07_A_tail_lag <-head(tail(NDQ07_A,2),1)
NDQ07_A_tail_yonylag <-head(tail(NDQ07_A,5),1)
NDQ07_A_24 <- tail(NDQ07_A,24)
NDQ07_A_24_MAX <- tail(NDQ07_A_24,1)
NDQ07_A_24 %>%
ggplot(aes(x=Year_Q,y=SMA.12,group=STATISTIC))+
geom_line(alpha=0.4,size=1.25,colour="#2CB504")+
geom_line(aes(y=value),alpha=0.2,size=1,colour="#2CB504")+
theme_bw()+
labs(title = "New Dwelling Completions, by Quarter - NDQ07" ,
subtitle = "12 month moving average - 24 Month Series",
y="Units Completed",
x="Year-Qtr")+
geom_text(data=NDQ07_A_24_MAX,aes(label=value),vjust= 1.5, size=3,colour="#2CB504")+
theme(legend.position = "bottom")
tbl_NDQ07_A_Year <- NDQ07_A %>%
group_by(Year)%>%
summarise(Average_Value = mean(value),
Total = sum(value))
ggplot(data=tbl_NDQ07_A_Year, aes(x=Year, y=Total))+
geom_col(alpha = 0.1, colour="#2CB504", fill = "#2CB504")+
theme_bw()+
labs(title = "New Dwelling Completions, by Year - NDQ07" ,
subtitle = "total sample",
y="Units Completed",
x="Year")+
geom_text(aes(label=Total),vjust= 1.5, size=3)+
theme(legend.position = "bottom")
Eircode Output Area
# Clean
NDQ07.EOA <- NDQ07_B %>%
group_by(Eircode.Output)%>%
slice(which.max(Year_Q))
NDQ07.EOA$RoutingKey <-substr(NDQ07.EOA$Eircode.Output, 1, 3)
# make moveing average
rkNDQ07 <- merge(rkey,NDQ07.EOA,by="RoutingKey")
# Plot
rkNDQ07 %>%
ggplot()+
geom_sf(aes(fill=SMA.12),colour=alpha("white",0.5))+
theme_void()+theme(plot.title = element_text(size=11,face="bold",hjust=0.5),
legend.title = element_text(size = 11),
legend.text = element_text(size = 8),
strip.text.x = element_text(size = 12))+
scale_fill_distiller(palette = "YIGn", name="Completions",direction = 1)+
ggtitle("Completions by Eircode Output Area (Routing Key)")+
labs(subtitle="12 month moving average",caption = "CSO Dataset: NDQ07")
Pipeline
Completions to Planning Ratio
Note the lag in planning data with latest release to Q2 (completions to Q3), means that the ratio is slightly higher.
Cumulative Balance
Focusing on the GDA the ratios seen previously can be seen in absolute terms. The table for each Local Authority can be seen below.
| Local Authority | Type of Dwelling | Planning | Completions | Ratio | Balance |
|---|---|---|---|---|---|
| Carlow | Apartment | 110 | 25 | 0.23 | 85 |
| Carlow | Houses | 848 | 1013 | 1.19 | -165 |
| Cavan | Apartment | 55 | 44 | 0.80 | 11 |
| Cavan | Houses | 1053 | 831 | 0.79 | 222 |
| Clare | Apartment | 197 | 83 | 0.42 | 114 |
| Clare | Houses | 2137 | 1794 | 0.84 | 343 |
| Cork | Apartment | 2156 | 360 | 0.17 | 1796 |
| Cork | Houses | 10477 | 6495 | 0.62 | 3982 |
| Cork City | Apartment | 5362 | 598 | 0.11 | 4764 |
| Cork City | Houses | 2295 | 3342 | 1.46 | -1047 |
| Donegal | Apartment | 272 | 255 | 0.94 | 17 |
| Donegal | Houses | 2790 | 2183 | 0.78 | 607 |
| Dublin City | Apartment | 31097 | 8853 | 0.28 | 22244 |
| Dublin City | Houses | 1268 | 2336 | 1.84 | -1068 |
| Dun Laoghaire - Rathdown | Apartment | 15582 | 4028 | 0.26 | 11554 |
| Dun Laoghaire - Rathdown | Houses | 2186 | 2228 | 1.02 | -42 |
| Fingal | Apartment | 7412 | 2154 | 0.29 | 5258 |
| Fingal | Houses | 3071 | 7007 | 2.28 | -3936 |
| Galway | Apartment | 577 | 164 | 0.28 | 413 |
| Galway | Houses | 3964 | 3391 | 0.86 | 573 |
| Galway City | Apartment | 1672 | 311 | 0.19 | 1361 |
| Galway City | Houses | 669 | 904 | 1.35 | -235 |
| Kerry | Apartment | 416 | 145 | 0.35 | 271 |
| Kerry | Houses | 1785 | 2196 | 1.23 | -411 |
| Kildare | Apartment | 3496 | 618 | 0.18 | 2878 |
| Kildare | Houses | 7297 | 7823 | 1.07 | -526 |
| Kilkenny | Apartment | 636 | 171 | 0.27 | 465 |
| Kilkenny | Houses | 2362 | 1327 | 0.56 | 1035 |
| Laois | Apartment | 396 | 76 | 0.19 | 320 |
| Laois | Houses | 2573 | 1433 | 0.56 | 1140 |
| Leitrim | Apartment | 32 | 20 | 0.62 | 12 |
| Leitrim | Houses | 265 | 258 | 0.97 | 7 |
| Limerick | Apartment | 1552 | 189 | 0.12 | 1363 |
| Limerick | Houses | 3537 | 2561 | 0.72 | 976 |
| Longford | Apartment | 41 | 41 | 1.00 | 0 |
| Longford | Houses | 612 | 486 | 0.79 | 126 |
| Louth | Apartment | 2131 | 258 | 0.12 | 1873 |
| Louth | Houses | 2441 | 2889 | 1.18 | -448 |
| Mayo | Apartment | 106 | 78 | 0.74 | 28 |
| Mayo | Houses | 1518 | 1796 | 1.18 | -278 |
| Meath | Apartment | 2953 | 199 | 0.07 | 2754 |
| Meath | Houses | 6343 | 6938 | 1.09 | -595 |
| Monaghan | Apartment | 72 | 68 | 0.94 | 4 |
| Monaghan | Houses | 1320 | 977 | 0.74 | 343 |
| Offaly | Apartment | 206 | 54 | 0.26 | 152 |
| Offaly | Houses | 1686 | 1018 | 0.60 | 668 |
| Roscommon | Apartment | 77 | 44 | 0.57 | 33 |
| Roscommon | Houses | 691 | 771 | 1.12 | -80 |
| Sligo | Apartment | 76 | 97 | 1.28 | -21 |
| Sligo | Houses | 810 | 653 | 0.81 | 157 |
| South Dublin | Apartment | 7627 | 1197 | 0.16 | 6430 |
| South Dublin | Houses | 3148 | 5807 | 1.84 | -2659 |
| Tipperary | Apartment | 427 | 139 | 0.33 | 288 |
| Tipperary | Houses | 2519 | 1258 | 0.50 | 1261 |
| Waterford | Apartment | 708 | 167 | 0.24 | 541 |
| Waterford | Houses | 2777 | 2055 | 0.74 | 722 |
| Westmeath | Apartment | 937 | 102 | 0.11 | 835 |
| Westmeath | Houses | 1804 | 1165 | 0.65 | 639 |
| Wexford | Apartment | 1010 | 213 | 0.21 | 797 |
| Wexford | Houses | 4880 | 2864 | 0.59 | 2016 |
| Wicklow | Apartment | 2034 | 544 | 0.27 | 1490 |
| Wicklow | Houses | 4048 | 3881 | 0.96 | 167 |
FHS Caps vs CSO
The data displayed is for New homes that were purchased by a FTB in the latest period. - This was deemed the most appropriate for context regarding the FHS.
See FHS website for caps.
Price Trend
Price trends are displayed by Eircode Output Area (EOA), this is the most granular variable for house pricing in the CSO. There are 139 EOAs which correspond to the “Routing Key”, the first 3 digits of an Eircode. The town which is attributed to that area determines how the EOA is plotted. For example, C15: Navan - Navan being located in Meath results in C15 being included in the Meath section below.
Consider that the EOAs do not match up to Local Authority boundaries neatly, as can be seen in the appendix.
Dublin
Dublin Bay & South
Mid point set to 500k
| Eircode.Output | Year - Month | value | Constituency |
|---|---|---|---|
| A94: Blackrock | 2022 August | 993,352.00 | Dun Laoghaire |
| A96: Glenageary | 2022 August | 749,513.00 | Dun Laoghaire |
| D01: Dublin 1 | 2022 August | 340,500.00 | Dublin Central |
| D02: Dublin 2 | 2022 August | 618,237.00 | Dublin Bay South |
| D03: Dublin 3 | 2022 August | 696,349.00 | Dublin Bay North |
| D04: Dublin 4 | 2022 August | 987,769.00 | Dublin Bay South |
| D05: Dublin 5 | 2022 August | 610,000.00 | Dublin Bay North |
| D06: Dublin 6 | 2022 August | 1,039,065.00 | Dublin Bay South |
| D08: Dublin 8 | 2022 August | 745,522.00 | Dublin South Central |
| D10: Dublin 10 | 2022 August | NA | Dublin South Central |
| D12: Dublin 12 | 2022 August | 590,833.00 | Dublin South Central |
| D13: Dublin 13 | 2022 August | 504,980.00 | Dublin Bay North |
| D14: Dublin 14 | 2022 August | 661,851.00 | Dublin Bay South |
| D18: Dublin 18 | 2022 August | 579,507.00 | Dublin Rathdown |
| D20: Dublin 20 | 2022 August | 533,960.00 | Dublin South Central |
| D22: Dublin 22 | 2022 August | 378,306.00 | Dublin South Central |
| D6W: Dublin 6W | 2022 August | 792,535.00 | Dublin South Central |
Dublin North & West
Mid point set to 450k
| Eircode.Output | Year - Month | value | Constituency |
|---|---|---|---|
| A41: Ballyboughal | 2022 August | NA | Dublin Fingal |
| A42: Garristown | 2022 August | NA | Dublin Fingal |
| A45: Oldtown | 2022 August | NA | Dublin Fingal |
| D07: Dublin 7 | 2022 August | NA | Dublin West |
| D09: Dublin 9 | 2022 August | 711,666.00 | Dublin North-West |
| D11: Dublin 11 | 2022 August | NA | Dublin West |
| D15: Dublin 15 | 2022 August | 417,288.00 | Dublin West |
| D16: Dublin 16 | 2022 August | 735,625.00 | Dublin South-West |
| D17: Dublin 17 | 2022 August | 442,443.00 | Dublin Fingal |
| D24: Dublin 24 | 2022 August | 396,213.00 | Dublin South-West |
| K32: Balbriggan | 2022 August | 349,436.00 | Dublin Fingal |
| K34: Skerries | 2022 August | 325,541.00 | Dublin Fingal |
| K36: Malahide | 2022 August | 524,576.00 | Dublin Fingal |
| K45: Lusk | 2022 August | 408,889.00 | Dublin Fingal |
| K56: Rush | 2022 August | 381,003.00 | Dublin Fingal |
| K67: Swords | 2022 August | 430,573.00 | Dublin Fingal |
| K78: Lucan | 2022 August | 442,465.00 | Dublin Mid-West |
Meath
| Eircode.Output | Year - Month | value | Constituency |
|---|---|---|---|
| A82: Kells | 2022 August | 285,017.00 | Meath East |
| A83: Enfield | 2022 August | 327,360.00 | Meath West |
| A84: Ashbourne | 2022 August | 269,563.00 | Meath East |
| A85: Dunshaughlin | 2022 August | 362,525.00 | Meath East |
| A86: Dunboyne | 2022 August | 423,726.00 | Meath East |
| C15: Navan | 2022 August | 335,114.00 | Meath West |
Kildare
| Eircode.Output | Year - Month | value | Constituency |
|---|---|---|---|
| R14: Athy | 2022 August | 300,000.00 | Kildare South |
| R51: Kildare | 2022 August | 290,790.00 | Kildare South |
| R56: Curragh | 2022 August | 407,816.00 | Kildare South |
| W12: Newbridge | 2022 August | 385,267.00 | Kildare South |
| W23: Celbridge | 2022 August | 424,607.00 | Kildare North |
| W34: Monasterevin | 2022 August | 313,486.00 | Kildare South |
| W91: Naas | 2022 August | 384,263.00 | Kildare North |
Louth
| Eircode.Output | Year - Month | value | Constituency |
|---|---|---|---|
| A91: Dundalk | 2022 August | 289,649.00 | Louth |
| A92: Drogheda | 2022 August | 316,371.00 | Louth |
Wicklow
| Eircode.Output | Year - Month | value | Constituency |
|---|---|---|---|
| A63: Greystones | 2022 August | 505,869.00 | Wicklow |
| A67: Wicklow | 2022 August | 345,863.00 | Wicklow |
| A98: Bray | 2022 August | 602,472.00 | Wicklow |
| Y14: Arklow | 2022 August | 539,107.00 | Wicklow |
Kilkenny
| Eircode.Output | Year - Month | value | Constituency |
|---|---|---|---|
| R21: Mhuine Bheag | 2022 August | NA | Carlow-Kilkenny |
| R93: Carlow | 2022 August | 258,036.00 | Carlow-Kilkenny |
| R95: Kilkenny | 2022 August | 320,423.00 | Carlow-Kilkenny |
Waterford
| Eircode.Output | Year - Month | value | Constituency |
|---|---|---|---|
| X35: Dungarvan | 2022 August | 296,417.00 | Waterford |
| X42: Kilmacthomas | 2022 August | 307,473.00 | Waterford |
| X91: Waterford | 2022 August | 301,475.00 | Waterford |
Cork
Cork Central Constituencies
Mid point set to 450k
| Eircode.Output | Year - Month | value | Constituency |
|---|---|---|---|
| P43: Carrigaline | 2022 August | 336,791.00 | Cork South-Central |
| T12: Cork Southside | 2022 August | 405,986.00 | Cork North-Central |
| T23: Cork Northside | 2022 August | 387,654.00 | Cork North-Central |
| T45: Glanmire | 2022 August | 332,256.00 | Cork North-Central |
| T56: Watergrasshill | 2022 August | 308,309.00 | Cork North-Central |
Cork Constituencies - Other
Mid point set to 350k
| Eircode.Output | Year - Month | value | Constituency |
|---|---|---|---|
| P12: Macroom | 2022 August | 310,883.00 | Cork North-West |
| P14: Crookstown | 2022 August | 360,847.00 | Cork North-West |
| P17: Kinsale | 2022 August | 407,622.00 | Cork South-West |
| P24: Cobh | 2022 August | 288,479.00 | Cork East |
| P25: Midleton | 2022 August | 344,626.00 | Cork East |
| P31: Ballincollig | 2022 August | 395,029.00 | Cork North-West |
| P32: Rylane | 2022 August | 425,000.00 | Cork North-West |
| P36: Youghal | 2022 August | 309,999.00 | Cork East |
| P47: Dunmanway | 2022 August | NA | Cork South-West |
| P51: Mallow | 2022 August | 286,834.00 | Cork East |
| P56: Charleville | 2022 August | NA | Cork North-West |
| P61: Fermoy | 2022 August | 399,525.00 | Cork East |
| P67: Mitchelstown | 2022 August | 280,000.00 | Cork East |
| P72: Bandon | 2022 August | 314,760.00 | Cork South-West |
| P75: Bantry | 2022 August | 219,149.00 | Cork South-West |
| P81: Skibbereen | 2022 August | 238,179.00 | Cork South-West |
| P85: Clonakilty | 2022 August | 351,134.00 | Cork South-West |
| T34: Carrignavar | 2022 August | 337,285.00 | Cork East |
Cork constiuencies other than the prior “Central” constiuencies
Galway
| Eircode.Output | Year - Month | value | Constituency |
|---|---|---|---|
| F42: Roscommon | 2022 August | 262,194.00 | Roscommon-Galway |
| F45: Castlerea | 2022 August | 172,941.00 | Roscommon-Galway |
| H53: Ballinasloe | 2022 August | 187,700.00 | Roscommon-Galway |
| H54: Tuam | 2022 August | 265,023.00 | Galway East |
| H62: Loughrea | 2022 August | 290,492.00 | Galway East |
| H65: Athenry | 2022 August | 319,925.00 | Galway East |
| H71: Clifden | 2022 August | NA | Galway West |
| H91: Galway | 2022 August | 366,791.00 | Galway West |
Retail Sales Index
RSM05 <- cso_get_data("RSM05")
RSM05 <- RSM05 %>%
pivot_longer(!1:2, names_to = "year_month")
# Date transformation
## Take adjusted index only
RSM05 <- RSM05%>%filter(Statistic=="Retail Sales Index Value Adjusted"|Statistic=="Retail Sales Index Volume Adjusted")
RSM05$Month <- as.Date(paste(RSM05$year_month, "01", sep = "-"), "%YM%m-%d")
RSM05$Year <- year(RSM05$Month)
RSM05$Lag <- Lag(RSM05$value,1)
RSM05$Diff <- RSM05$value-RSM05$Lag
RSM05_A <- RSM05%>%
filter(NACE.Group=="All retail businesses")
RSM05_B <- RSM05%>%
filter(NACE.Group=="Motor trades (45)"|NACE.Group=="Retail sale in non-specialised stores with food, beverages or tobacco predominating (4711)"|NACE.Group=="Department stores (4719)"|NACE.Group=="Retail sale of automotive fuel (4730)"|NACE.Group=="Retail sale of hardware, paints and glass (4752)"|NACE.Group=="Retail sale of furniture and lighting (4759)"|NACE.Group=="Bars (5630)")
# RSI
## Full Sample
RSM05_A1 <- RSM05_A%>%filter(Statistic=="Retail Sales Index Value Adjusted")
RSM05_A2 <- RSM05_A%>%filter(Statistic=="Retail Sales Index Volume Adjusted")
Fig.RSM.1<-ggplot(data=RSM05_A1,aes(x=Month,y=value))+
geom_line(size = 1.15, linetype=1, alpha = 0.6, colour = "#4aa98a")+
geom_hline(aes(yintercept=100),
colour= "#404040",
linetype = 1)+
theme_bw()+
geom_text_repel(aes(label=value),data = RSM05_A1, size = 3,colour="black",max.overlaps = 7)+
labs(title = "RSI Value (Adjusted) - RSM05", subtitle = "2015 to Date")+
xlab("Year-Month")+
ylab("RSI (Base Dec 2015=100)")+
theme(panel.border = element_rect(linetype = 1, fill = NA))
Fig.RSM.2<-ggplot(data=RSM05_A2,aes(x=Month,y=value))+
geom_line(size = 1.15, linetype=1, alpha = 0.6, colour = "#a55a94")+
geom_hline(aes(yintercept=100),
colour= "#404040",
linetype = 1)+
theme_bw()+
geom_text_repel(aes(label=value),data = RSM05_A2, size = 3,colour="black",max.overlaps = 7)+
labs(title = "RSI Volume (Adjusted) - RSM05", subtitle = "2015 to Date")+
xlab("Year-Month")+
ylab(NULL)+
theme(panel.border = element_rect(linetype = 1, fill = NA))
#Fig.RSM.1 + Fig.RSM.2 + plot_layout(ncol=2)
## YTD
Fig.RSM.3<-ggplot(data=subset(RSM05_A1,Year >= "2022"),aes(x=Month,y=value))+
geom_line(size = 1.15, linetype=1, alpha = 0.6, colour = "#4aa98a")+
geom_text_repel(aes(label=value),data=subset(RSM05_A1,Year >= "2022"), size = 3,colour="black",max.overlaps = 7)+
theme_bw()+
labs(subtitle = "YTD")+
xlab(NULL)+
ylab(NULL)+
theme(panel.border = element_rect(linetype = 1, fill = NA))
Fig.RSM.4<-ggplot(data=subset(RSM05_A2,Year >= "2022"),aes(x=Month,y=value))+
geom_line(size = 1.15, linetype=1, alpha = 0.6, colour = "#a55a94")+
theme_bw()+
geom_text_repel(aes(label=value),data=subset(RSM05_A2,Year >= "2022"), size = 3,colour="black",max.overlaps = 7)+
labs(subtitle = "YTD")+
xlab(NULL)+
ylab(NULL)+
theme(panel.border = element_rect(linetype = 1, fill = NA))
Fig.RSM.1 + Fig.RSM.2 + Fig.RSM.3 + Fig.RSM.4 + plot_layout(nrow = 2,heights = c(2,1.5))
RSM05_B1 <- RSM05_B%>%filter(Statistic=="Retail Sales Index Value Adjusted")
RSM05_B2 <- RSM05_B%>%filter(Statistic=="Retail Sales Index Volume Adjusted")
FigNACE<-ggplot(data=subset(RSM05_B1,Year >= "2022"), aes(x=Month, y=value, group=NACE.Group, colour=NACE.Group))+
geom_line(aes(group=NACE.Group),size = 1.05, linetype=1, alpha = 0.65)+
labs(title = "RSI Value by NACE Group")+
xlab("Year-Month")+
ylab("2015 = 100")+
geom_hline(aes(yintercept=100),
colour= "#404040",
linetype = 1)+
theme_bw()+
geom_text_repel(aes(label=value),data=subset(RSM05_B1,Year >= "2022"),size=3,colour="black",max.overlaps = 10)+
scale_x_date(date_labels="%b-%Y",date_breaks ="1 month")+
theme(axis.text.x = element_text(angle=90))+
theme(axis.text.x=element_text(size=10))+
theme(legend.position="none")+
theme(axis.text = element_text(size = rel(1)))+
theme(plot.title=(element_text(vjust =2)))+
theme(panel.border = element_rect(linetype = 1, fill = NA))
Fig.RSM.5<-FigNACE + facet_wrap(~NACE.Group, ncol = 1)
FigNACE2<-ggplot(data=subset(RSM05_B2,Year >= "2022"), aes(x=Month, y=value, group=NACE.Group, colour=NACE.Group))+
geom_line(aes(group=NACE.Group),size = 1.05, linetype=1, alpha = 0.65)+
labs(title = "RSI Volume by NACE Group")+
xlab("Year-Month")+
ylab("2015 = 100")+
geom_hline(aes(yintercept=100),
colour= "#404040",
linetype = 1)+
theme_bw()+
geom_text_repel(aes(label=value),data=subset(RSM05_B2,Year >= "2022"),size=3,colour="black",max.overlaps = 10)+
scale_x_date(date_labels="%b-%Y",date_breaks ="1 month")+
theme(axis.text.x = element_text(angle=90))+
theme(axis.text.x=element_text(size=10))+
theme(legend.position="none")+
theme(axis.text = element_text(size = rel(1)))+
theme(plot.title=(element_text(vjust =2)))+
theme(panel.border = element_rect(linetype = 1, fill = NA))
Fig.RSM.6<-FigNACE2 + facet_wrap(~NACE.Group, ncol = 1)
Fig.RSM.5+Fig.RSM.6
Irish Ports
TBQ05.A.A_MAX <- TBQ05.A.A %>%
group_by(Statistic)%>%
slice(which.max(Year_Q))
TBQ05.A.A_MAX$lab <- paste(TBQ05.A.A_MAX$Year_Q, ":", TBQ05.A.A_MAX$value)
TBQ05.A.A %>%
ggplot(aes(x=Year_Q,y=value,group=Statistic,colour=Statistic))+
geom_line(alpha=0.7)+
geom_text(data=TBQ05.A.A_MAX,aes(label=lab,group=Statistic),hjust="right",vjust=1,size=2.5)+
theme_bw()+
theme(legend.position = "bottom")+
ggtitle("Tonnage of Goods Handled")+
labs(caption="Dataset: TBQ05")+
ylab("('000 Tonnes)")
TBQ05.A.R_MAX <- TBQ05.A.R %>%
group_by(Statistic,Region.of.Trade)%>%
slice(which.max(Year_Q))
TBQ05.A.R_MAX$lab <- paste(TBQ05.A.R_MAX$Year_Q, ":", TBQ05.A.R_MAX$value)
TBQ05.A.R %>%
ggplot(aes(x=Year_Q,y=value,group=Statistic,colour=Statistic))+
geom_line(alpha=0.7)+
geom_text(data=TBQ05.A.R_MAX,aes(label=lab,group=Statistic),hjust="right",vjust=1,size=2.25)+
theme_bw()+
theme(legend.position = "bottom")+
ggtitle("Tonnage of Goods")+
labs(caption="Dataset: TBQ05")+
ylab("('000 Tonnes)")+
facet_wrap(~Region.of.Trade,ncol=2)
Appendix
Local Authority
HSM13.A.Q %>%
ggplot(aes(x=Year_Q,y=SMA.12,group=STATISTIC))+
geom_line(alpha=0.3,size=1.25,colour="#B54104")+
geom_line(aes(y=value),alpha=0.1,size=1,colour="#B54104")+
theme_bw()+theme(plot.title = element_text(size=9,face="bold"),
legend.text = element_text(size=8),
legend.title = element_text(size=8),
axis.text.x= element_text(size=8),
axis.text.y= element_text(size=8),
axis.title = element_text(size=8),
plot.subtitle=element_text(size=8))+
theme(legend.position = "none")+
ggtitle("Commencement Notices")+
labs(subtitle="12 month moving average",caption="source: HSM13")+
ylab("number of units")+
xlab("quarter")+facet_wrap(~Local.Authority,ncol=4)
Eircode - Routing Key
