Evolution of Spanish inflation

Let’s examine the value of inflation in Spain since 2018. To filter the required data, I am using the year function from lubridate.

Here are some ggplot2 functionalities I have used in this plot:

• The red band covering the COVID lockdown period is made with geom_rect. I have set some value of alpha After this geom, I have added text with annotate.
• The evolution of inflation is presented with geom_line. I have changed width with size and also the color. This line is plotted after geom_rect to make it visible during the COVID lockdown.
• The start of the Ukraine conflict is presented with geom_vline. I have used linetype = "dashed" to present a dashed line. Again, I have used annotate to add text. If I wanted the line to start at zero, I would have used geom_segment instead of geom_vline.
• With scale_y_continuous I am presenting the inflation as percentage. That’s why I have divided inflation value by 100 with mutate at the beginning. I have also set y axis breaks. With scale_x_date, I am setting a scale break for each year.
• With theme_minimal I set the plot theme, place the legend with legend.position = "bottom" an annotate the plot with labs.

ipc_clasif %>%
  filter(year(periodo) >= 2018, grupo == "general", dato == "anual") %>%
  mutate(valor = valor / 100) %>%
  ggplot(aes(periodo, valor)) +
  geom_rect(aes(xmin = as.Date("2020-03-14"), xmax = as.Date("2020-06-21"), ymin = -0.02, ymax = 0.12), fill = "#FF9999", alpha = 0.05) +
  annotate("text", x= as.Date("2019-07-01"), y = 0.06, label = "COVID lockdown", size = 5) +
  geom_line(size = 1, color = "#606060") +
  geom_vline(xintercept = as.Date("2022-02-24"), color = "red", linetype = "dashed", size = 1) +
  annotate("text", x = as.Date("2021-07-01"), y = 0.1, label = "war on Ukraine", size = 5) +
  scale_y_continuous(labels = scales::percent, breaks = seq(-0.02, 0.12, 0.01)) +
  scale_x_date(breaks = scales::date_breaks("1 year"), date_labels = "%Y") +
  theme_minimal() +
  theme(legend.position = "bottom") +
  labs(title = "Evolution of Spanish inflation", x = "time", y = "yearly inflation", caption = "Source: INE")

We observe that inflation starts to ramp up at the beginning 2021, some months after the COVID lockdown. The events of Ukraine did not apparently affect inflation. Although yet in high values, it is decreasing since the beginning of 2022.

Evolution of prices of selected groups

Let’s see the groups with largest inflation in the reference period. To do so, I have calculated the maximum value of yearly inflation for each group, and ordered by decreasing value of this maximum:

ipc_ccaa %>%
  filter(region == "ES", year(periodo) >= 2018) %>%
  group_by(grupo) %>%
  summarise(max_value = max(valor)) %>%
  arrange(desc(max_value)) %>%
  slice(1:5) %>%
  kbl() %>%
  kable_styling(full_width = FALSE)

We can see which groups are G04 and G07 with ipc_clas groups:

ipc_clas_grupos %>%
  filter(codigo %in% c("G04", "G07")) %>%
  select(codigo, nombre) %>%
  kbl() %>%
  kable_styling(full_width = FALSE)

Here is the plot of housing and energy G04, transportation G07 and general index for the same period.

The tricks used here are similar to the plot above, with some differences:

• To make geom_rect to work correctly, I have set the color parameter. I have chosen the same value as fill.
• The ranges of the y axis need to be changed, as prices of groups have more variability than general inflation.
• Color lines and legend values are set with scale_color_manual.

ipc_clasif %>%
  filter(year(periodo) >= 2018, grupo %in% c("general", "G04", "G07"), dato == "anual") %>%
  mutate(valor = valor/100) %>%
  ggplot(aes(periodo, valor, color = grupo)) +
   geom_rect(aes(xmin = as.Date("2020-03-14"), xmax = as.Date("2020-06-21"), ymin = -0.1, ymax = 0.35), fill = "#FF9999", color = "#FF9999", alpha = 0.05) +
  annotate("text", x= as.Date("2019-07-01"), y = 0.15, label = "COVID lockdown", size = 5) +
  geom_line(size = 1) +
  geom_vline(xintercept = as.Date("2022-02-24"), color = "red", linetype = "dashed", size = 1) +
  annotate("text", x = as.Date("2021-07-01"), y = 0.3, label = "war on Ukraine", size = 5) +
  scale_y_continuous(labels = scales::percent) +
  scale_x_date(breaks = scales::date_breaks("1 year"), date_labels = "%Y") +
  scale_color_manual(values = c("#66CC00", "#0080FF", "#606060"), labels = c("energy", "transport", "general"), name = "group") +
  theme_minimal() +
  theme(legend.position = "bottom") +
  labs(title = "Evolution of Spanish inflation (groups)", x = "time", y = "yearly inflation", caption = "Source: INE")

Energy and transportation prices started growing in the middle of the lockdonn, at a higher rate than the global inflation index.

Evolution of prices of electricity and gas

We may be interested in the evolution of gas and electricity. We know that they are in GO45, so we look for these groups with a regular expression using grepl.

ipc_clas_grupos %>%
  filter(grepl("^G045", codigo)) %>%
  select(codigo, nombre) %>%
  kbl() %>%
  kable_styling(full_width = FALSE)

Here is the evolution of prices of gas G04521 and electricity G04510:

ipc_clasif %>%
  filter(year(periodo) >= 2018, grupo %in% c("general", "G04510", "G04521"), dato == "anual") %>%
  mutate(valor = valor/100) %>%
  ggplot(aes(periodo, valor, color = grupo)) +
  geom_rect(aes(xmin = as.Date("2020-03-14"), xmax = as.Date("2020-06-21"), ymin = -0.2, ymax = 1.2), fill = "#FF9999", color = "#FF9999", alpha = 0.05) +
  annotate("text", x= as.Date("2019-07-01"), y = 0.4, label = "COVID lockdown", size = 5) +
  geom_line(size=1) +
  geom_vline(xintercept = as.Date("2022-02-24"), color = "red", linetype = "dashed", size = 1) +
  annotate("text", x = as.Date("2021-07-01"), y = 0.8, label = "war on Ukraine", size = 5) +
  scale_y_continuous(labels = scales::percent) +
  scale_x_date(breaks = scales::date_breaks("1 year"), date_labels = "%Y") +
  scale_color_manual(values = c("#66CC00", "#0080FF", "#606060"), labels = c("electricity", "gas", "general"), name = "group") +
  theme_minimal() +
  theme(legend.position = "bottom") +
  labs(title = "Evolution of Spanish inflation (gas and electricity)", x = "time", y = "yearly inflation", caption = "Source: INE")

Although the recent events in Ukraine would suggest a rise in the price of gas, it has grown more than electricity prices. Gas and electricity prices started growing earlier than the war in Ukraine. Some people argue, though, that INE considers the evolution of prices or regulated market only. This could reduce the impact of electricity prices in inflation.

References

• The ESdata package. https://github.com/jmsallan/ESdata
• Instituto Nacional de Estadística. INEbase. Lista completa de operaciones. https://www.ine.es/dyngs/INEbase/listaoperaciones.htm Accessed 2022-11-12.
• Rodriguez Asensio, D. (2022). Qué está pasando (y qué no) con la inflación en España. https://www.libremercado.com/2022-11-06/daniel-rodriguez-asensio-que-esta-pasando-y-que-no-con-la-inflacion-en-espana-6950646/ Accessed 2022-11-06

Session info

## R version 4.2.2 (2022-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Linux Mint 19.2
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
## 
## locale:
##  [1] LC_CTYPE=es_ES.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=es_ES.UTF-8        LC_COLLATE=es_ES.UTF-8    
##  [5] LC_MONETARY=es_ES.UTF-8    LC_MESSAGES=es_ES.UTF-8   
##  [7] LC_PAPER=es_ES.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] kableExtra_1.3.4 lubridate_1.8.0  ESdata_0.1.0     forcats_0.5.2   
##  [5] stringr_1.4.1    dplyr_1.0.10     purrr_0.3.5      readr_2.1.3     
##  [9] tidyr_1.2.1      tibble_3.1.8     ggplot2_3.3.6    tidyverse_1.3.1 
## 
## loaded via a namespace (and not attached):
##  [1] svglite_2.1.0     assertthat_0.2.1  digest_0.6.30     utf8_1.2.2       
##  [5] R6_2.5.1          cellranger_1.1.0  backports_1.4.1   reprex_2.0.2     
##  [9] evaluate_0.17     highr_0.9         httr_1.4.4        blogdown_1.9     
## [13] pillar_1.8.1      rlang_1.0.6       readxl_1.4.1      rstudioapi_0.13  
## [17] jquerylib_0.1.4   rmarkdown_2.14    labeling_0.4.2    webshot_0.5.3    
## [21] munsell_0.5.0     broom_1.0.1       compiler_4.2.2    modelr_0.1.9     
## [25] xfun_0.34         pkgconfig_2.0.3   systemfonts_1.0.4 htmltools_0.5.3  
## [29] tidyselect_1.1.2  bookdown_0.26     fansi_1.0.3       viridisLite_0.4.1
## [33] crayon_1.5.2      tzdb_0.3.0        dbplyr_2.2.1      withr_2.5.0      
## [37] grid_4.2.2        jsonlite_1.8.3    gtable_0.3.0      lifecycle_1.0.3  
## [41] DBI_1.1.2         magrittr_2.0.3    scales_1.2.1      cli_3.4.1        
## [45] stringi_1.7.8     farver_2.1.1      fs_1.5.2          xml2_1.3.3       
## [49] bslib_0.3.1       ellipsis_0.3.2    generics_0.1.2    vctrs_0.5.0      
## [53] tools_4.2.2       glue_1.6.2        hms_1.1.2         fastmap_1.1.0    
## [57] yaml_2.3.6        colorspace_2.0-3  rvest_1.0.3       knitr_1.40       
## [61] haven_2.5.1       sass_0.4.1