Global Inflation Analysis using R
This R notebook explores the Global Inflation dataset downloaded from Kaggle.com.
https://www.kaggle.com/datasets/sazidthe1/global-inflation-data
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.6
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.1 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.2
## ✔ purrr 1.2.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(stringr)
library(ggplot2)
library(tmap)
library(tmaptools)
library(sf)## Linking to GEOS 3.13.1, GDAL 3.11.4, PROJ 9.7.0; sf_use_s2() is TRUElibrary(geojsonsf)
library(rjson)
library(sp)
library(reshape2)##
## Attaching package: 'reshape2'
##
## The following object is masked from 'package:tidyr':
##
## smithsglobal.data <- read.csv(file = "global_inflation_data.csv", header = TRUE)
head(global.data, 10)## country_name indicator_name X1980
## 1 Afghanistan Annual average inflation (consumer prices) rate 13.4
## 2 Albania Annual average inflation (consumer prices) rate NA
## 3 Algeria Annual average inflation (consumer prices) rate 9.7
## 4 Andorra Annual average inflation (consumer prices) rate NA
## 5 Angola Annual average inflation (consumer prices) rate 46.7
## 6 Antigua and Barbuda Annual average inflation (consumer prices) rate 19.0
## 7 Argentina Annual average inflation (consumer prices) rate NA
## 8 Armenia Annual average inflation (consumer prices) rate NA
## 9 Aruba Annual average inflation (consumer prices) rate NA
## 10 Australia Annual average inflation (consumer prices) rate 10.1
## X1981 X1982 X1983 X1984 X1985 X1986 X1987 X1988 X1989 X1990 X1991 X1992
## 1 22.2 18.2 15.9 20.4 8.7 -2.1 18.4 27.5 71.5 47.4 43.8 58.19
## 2 NA NA NA NA NA NA NA NA NA -0.2 35.7 226.00
## 3 14.6 6.6 7.8 6.3 10.4 14.0 5.9 5.9 9.2 9.3 25.9 31.70
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 1.4 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 85.3 299.10
## 6 11.5 4.2 2.3 3.8 1.0 0.5 3.6 6.8 4.4 6.6 4.5 3.00
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA 3.6 3.1 4.0 5.8 5.6 3.90
## 10 9.5 11.4 10.0 4.0 6.7 9.1 8.5 7.3 7.6 7.2 3.3 1.00
## X1993 X1994 X1995 X1996 X1997 X1998 X1999 X2000 X2001 X2002 X2003
## 1 33.99 20.01 14.0 14.01 14.01 14.01 14.01 0.0 -43.4 51.93 35.66
## 2 85.00 22.60 7.8 12.70 33.20 20.60 0.40 0.0 3.1 5.20 2.40
## 3 20.50 29.00 29.8 18.70 5.70 5.00 2.60 0.3 4.2 1.40 4.30
## 4 NA NA NA NA NA NA NA NA NA 3.10 3.10
## 5 1379.50 949.80 2672.2 4146.00 221.50 107.40 248.20 325.0 152.6 108.90 98.20
## 6 3.10 6.50 2.7 3.00 0.40 3.30 1.10 -0.2 1.9 2.40 2.00
## 7 NA NA NA NA NA 0.90 -1.20 -0.9 -1.1 25.90 13.40
## 8 3731.80 5273.40 176.7 18.70 14.00 8.70 0.70 -0.8 3.2 1.00 4.70
## 9 5.20 6.30 3.4 3.20 3.00 1.90 2.30 4.1 2.9 3.30 3.70
## 10 1.80 1.90 4.6 2.70 0.20 0.90 1.40 4.5 4.4 3.00 2.70
## X2004 X2005 X2006 X2007 X2008 X2009 X2010 X2011 X2012 X2013 X2014 X2015
## 1 16.36 10.57 6.78 8.68 26.42 -6.81 2.18 11.8 6.44 7.39 4.67 -0.66
## 2 2.90 2.40 2.40 3.00 3.30 2.20 3.60 3.4 2.00 1.90 1.60 1.90
## 3 4.00 1.40 2.30 3.70 4.90 5.70 3.90 4.5 8.90 3.30 2.90 4.80
## 4 2.90 3.50 3.70 2.70 4.30 -1.20 1.70 2.6 1.50 0.50 -0.10 -1.10
## 5 43.50 23.00 13.30 12.20 12.50 13.70 14.50 13.5 10.30 8.80 7.30 9.20
## 6 2.00 2.10 1.80 1.40 5.30 -0.60 3.40 3.5 3.40 1.10 1.10 1.00
## 7 4.40 9.60 10.90 8.80 8.60 6.30 10.50 9.8 10.00 10.60 NA NA
## 8 6.90 0.60 2.90 4.40 9.10 3.50 8.20 7.5 2.50 5.70 3.00 3.70
## 9 2.50 3.40 3.60 5.40 9.00 -2.10 2.10 4.4 0.60 -2.40 0.40 0.50
## 10 2.30 2.70 3.60 2.40 4.30 1.80 2.90 3.4 1.70 2.50 2.50 1.50
## X2016 X2017 X2018 X2019 X2020 X2021 X2022 X2023 X2024
## 1 4.38 4.98 0.63 2.3 5.44 5.06 13.71 9.1 NA
## 2 1.30 2.00 2.00 1.4 1.60 2.00 6.70 4.8 4.0
## 3 6.40 5.60 4.30 2.0 2.40 7.20 9.30 9.0 6.8
## 4 -0.40 2.60 1.00 0.5 0.10 1.70 6.20 5.2 3.5
## 5 30.70 29.80 19.60 17.1 22.30 25.80 21.40 13.1 22.3
## 6 -0.50 2.40 1.20 1.4 1.10 1.60 7.50 5.0 2.9
## 7 NA 25.70 34.30 53.5 42.00 48.40 72.40 121.7 93.7
## 8 -1.40 1.20 2.50 1.4 1.20 7.20 8.60 3.5 4.0
## 9 -0.90 -1.00 3.60 3.9 -1.30 0.70 5.50 4.5 2.3
## 10 1.30 2.00 1.90 1.6 0.90 2.80 6.60 5.8 4.0count(global.data)## n
## 1 196countries <- fromJSON(file = "world-ash-ms.geojson")
countries <- geojson_sf(toJSON(countries))
countries <- subset(countries, st_is_valid(geometry) & name != "Antarctica")
countries[countries$continent == "Seven seas (open ocean)", c("continent")] <-
"Seven Seas"
head(countries[, c("continent", "admin", "adm0_a3", "geometry")])## Simple feature collection with 6 features and 3 fields
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: -87.67017 ymin: 8.070654 xmax: -63.00942 ymax: 20.09365
## Geodetic CRS: WGS 84
## continent admin adm0_a3 geometry
## 1 North America Costa Rica CRI POLYGON ((-82.56357 9.57666...
## 2 North America Nicaragua NIC POLYGON ((-83.15752 14.9930...
## 3 North America Saint Martin MAF POLYGON ((-63.01118 18.0689...
## 4 North America Sint Maarten SXM POLYGON ((-63.12305 18.0689...
## 5 North America Haiti HTI MULTIPOLYGON (((-71.77925 1...
## 6 North America Dominican Republic DOM POLYGON ((-71.76831 18.0391...Prepare Data
# Lookup ISO code for country name
iso_code_lookup <- function(d) {
d <- str_trim(d)
c <- as.data.frame(
subset(countries,
(iso_a3 != "-9999") & ((admin == d) | (name_long == d) |
(name_en == d) | (formal_en == d)) # | (sovereignt == d)))
)
)
c[1, "adm0_a3"]
}
# Normalize some country names to match with polygon boundaries
global.data$country_name[global.data$country_name=="China, People's Republic of"] <-
"China"
global.data$country_name[global.data$country_name=="Congo, Dem. Rep. of the"] <-
"Democratic Republic of the Congo"
global.data$country_name[global.data$country_name=="Congo, Republic of "] <-
"Republic of the Congo"
global.data$country_name[global.data$country_name=="Korea, Republic of"] <-
"Republic of Korea"
global.data$country_name[global.data$country_name=="Gambia, The"] <-
"Gambia"
global.data$country_name[global.data$country_name=="Kyrgyz Republic"] <-
"Kyrgyzstan"
global.data$country_name[global.data$country_name=="South Sudan, Republic of"] <-
"South Sudan"
global.data$country_name[global.data$country_name=="Hong Kong SAR"] <-
"Hong Kong S.A.R."
global.data$country_name[global.data$country_name=="Macao SAR"] <-
"Macao S.A.R"
global.data$country_name[global.data$country_name=="Türkiye, Republic of"] <-
"Turkey"
global.data$country_name[global.data$country_name=="Taiwan Province of China"] <-
"Taiwan"
global.data$country_name[global.data$country_name=="Bahamas, The"] <-
"The Bahamas"
global.data$country_name[global.data$country_name=="Lao P.D.R."] <-
"Laos"
global.data$country_name[global.data$country_name=="Micronesia, Fed. States of"] <-
"Federated States of Micronesia"
global.data$iso_a3 <- sapply(global.data$country_name, FUN = iso_code_lookup)
global.data[, c("country_name", "iso_a3")] %>%
subset(!is.na(iso_a3) & iso_a3 != "-99")## country_name iso_a3
## 1 Afghanistan AFG
## 2 Albania ALB
## 3 Algeria DZA
## 4 Andorra AND
## 5 Angola AGO
## 6 Antigua and Barbuda ATG
## 7 Argentina ARG
## 8 Armenia ARM
## 9 Aruba ABW
## 10 Australia AUS
## 11 Austria AUT
## 12 Azerbaijan AZE
## 13 The Bahamas BHS
## 14 Bahrain BHR
## 15 Bangladesh BGD
## 16 Barbados BRB
## 17 Belarus BLR
## 18 Belgium BEL
## 19 Belize BLZ
## 20 Benin BEN
## 21 Bhutan BTN
## 22 Bolivia BOL
## 23 Bosnia and Herzegovina BIH
## 24 Botswana BWA
## 25 Brazil BRA
## 26 Brunei Darussalam BRN
## 27 Bulgaria BGR
## 28 Burkina Faso BFA
## 29 Burundi BDI
## 30 Cabo Verde CPV
## 31 Cambodia KHM
## 32 Cameroon CMR
## 33 Canada CAN
## 34 Central African Republic CAF
## 35 Chad TCD
## 36 Chile CHL
## 37 China CHN
## 38 Colombia COL
## 39 Comoros COM
## 40 Democratic Republic of the Congo COD
## 41 Republic of the Congo COG
## 42 Costa Rica CRI
## 43 Croatia HRV
## 44 Cyprus CYP
## 45 Czech Republic CZE
## 46 Côte d'Ivoire CIV
## 47 Denmark DNK
## 48 Djibouti DJI
## 49 Dominica DMA
## 50 Dominican Republic DOM
## 51 Ecuador ECU
## 52 Egypt EGY
## 53 El Salvador SLV
## 54 Equatorial Guinea GNQ
## 55 Eritrea ERI
## 56 Estonia EST
## 57 Eswatini SWZ
## 58 Ethiopia ETH
## 59 Fiji FJI
## 60 Finland FIN
## 61 France FRA
## 62 Gabon GAB
## 63 Gambia GMB
## 64 Georgia GEO
## 65 Germany DEU
## 66 Ghana GHA
## 67 Greece GRC
## 68 Grenada GRD
## 69 Guatemala GTM
## 70 Guinea GIN
## 71 Guinea-Bissau GNB
## 72 Guyana GUY
## 73 Haiti HTI
## 74 Honduras HND
## 75 Hong Kong S.A.R. HKG
## 76 Hungary HUN
## 77 Iceland ISL
## 78 India IND
## 79 Indonesia IDN
## 80 Iran IRN
## 81 Iraq IRQ
## 82 Ireland IRL
## 83 Israel ISR
## 84 Italy ITA
## 85 Jamaica JAM
## 86 Japan JPN
## 87 Jordan JOR
## 88 Kazakhstan KAZ
## 89 Kenya KEN
## 90 Kiribati KIR
## 91 Republic of Korea KOR
## 92 Kosovo KOS
## 93 Kuwait KWT
## 94 Kyrgyzstan KGZ
## 95 Laos LAO
## 96 Latvia LVA
## 97 Lebanon LBN
## 98 Lesotho LSO
## 99 Liberia LBR
## 100 Libya LBY
## 101 Lithuania LTU
## 102 Luxembourg LUX
## 103 Macao S.A.R MAC
## 104 Madagascar MDG
## 105 Malawi MWI
## 106 Malaysia MYS
## 107 Maldives MDV
## 108 Mali MLI
## 109 Malta MLT
## 110 Marshall Islands MHL
## 111 Mauritania MRT
## 112 Mauritius MUS
## 113 Mexico MEX
## 114 Federated States of Micronesia FSM
## 115 Moldova MDA
## 116 Mongolia MNG
## 117 Montenegro MNE
## 118 Morocco MAR
## 119 Mozambique MOZ
## 120 Myanmar MMR
## 121 Namibia NAM
## 122 Nauru NRU
## 123 Nepal NPL
## 124 Netherlands NLD
## 125 New Zealand NZL
## 126 Nicaragua NIC
## 127 Niger NER
## 128 Nigeria NGA
## 129 North Macedonia MKD
## 130 Norway NOR
## 131 Oman OMN
## 132 Pakistan PAK
## 133 Palau PLW
## 134 Panama PAN
## 135 Papua New Guinea PNG
## 136 Paraguay PRY
## 137 Peru PER
## 138 Philippines PHL
## 139 Poland POL
## 140 Portugal PRT
## 141 Puerto Rico PRI
## 142 Qatar QAT
## 143 Romania ROU
## 144 Russian Federation RUS
## 145 Rwanda RWA
## 146 Saint Kitts and Nevis KNA
## 147 Saint Lucia LCA
## 148 Saint Vincent and the Grenadines VCT
## 149 Samoa WSM
## 150 San Marino SMR
## 151 Saudi Arabia SAU
## 152 Senegal SEN
## 153 Serbia SRB
## 154 Seychelles SYC
## 155 Sierra Leone SLE
## 156 Singapore SGP
## 157 Slovak Republic SVK
## 158 Slovenia SVN
## 159 Solomon Islands SLB
## 160 Somalia SOM
## 161 South Africa ZAF
## 162 South Sudan SDS
## 163 Spain ESP
## 164 Sri Lanka LKA
## 165 Sudan SDN
## 166 Suriname SUR
## 167 Sweden SWE
## 168 Switzerland CHE
## 169 Syria SYR
## 170 São Tomé and Príncipe STP
## 171 Taiwan TWN
## 172 Tajikistan TJK
## 173 Tanzania TZA
## 174 Thailand THA
## 175 Timor-Leste TLS
## 176 Togo TGO
## 177 Tonga TON
## 178 Trinidad and Tobago TTO
## 179 Tunisia TUN
## 180 Turkmenistan TKM
## 181 Tuvalu TUV
## 182 Turkey TUR
## 183 Uganda UGA
## 184 Ukraine UKR
## 185 United Arab Emirates ARE
## 186 United Kingdom GBR
## 187 United States USA
## 188 Uruguay URY
## 189 Uzbekistan UZB
## 190 Vanuatu VUT
## 191 Venezuela VEN
## 192 Vietnam VNM
## 193 West Bank and Gaza PSX
## 194 Yemen YEM
## 195 Zambia ZMB
## 196 Zimbabwe ZWEmelted.data <-
melt(
global.data,
id.vars = c("iso_a3", "country_name", "indicator_name"),
variable.name = "year",
value.name = "inflation_rate"
) %>%
#-- convert year to integer data type
mutate(
year = as.integer(str_replace(year, "X", ""))
) %>%
#-- remove indicator_name column
.[, -c(3)]
head(melted.data, 10)## iso_a3 country_name year inflation_rate
## 1 AFG Afghanistan 1980 13.4
## 2 ALB Albania 1980 NA
## 3 DZA Algeria 1980 9.7
## 4 AND Andorra 1980 NA
## 5 AGO Angola 1980 46.7
## 6 ATG Antigua and Barbuda 1980 19.0
## 7 ARG Argentina 1980 NA
## 8 ARM Armenia 1980 NA
## 9 ABW Aruba 1980 NA
## 10 AUS Australia 1980 10.1Exploratory Data Analysis
chart.data <- subset(melted.data, inflation_rate > 0) %>%
group_by(country_name) %>%
summarise(
avg_inflation = round(mean(inflation_rate), 1)
)
head(chart.data)## # A tibble: 6 × 2
## country_name avg_inflation
## <chr> <dbl>
## 1 Afghanistan 18.6
## 2 Albania 15.5
## 3 Algeria 8.5
## 4 Andorra 2.7
## 5 Angola 250.
## 6 Antigua and Barbuda 3.5chart.data <- subset(melted.data, inflation_rate > 0) %>%
group_by(country_name) %>%
summarise(
avg_inflation = round(mean(inflation_rate), 1)
)
hist(
chart.data$avg_inflation, col = "royalblue",
main = "Histogram of Average Inflation Rate by Country",
xlim = c(0, max(chart.data$avg_inflation) * 1.2),
xlab = "Average Inflation Rate",
ylim = c(0, 200)
)
grid(nx = NULL, ny = NULL)
chart.data <- subset(melted.data, inflation_rate > 0)
chart.data$inflation_rate <- ifelse(chart.data$inflation_rate > 100, 100,
chart.data$inflation_rate)
plot(
chart.data$inflation_rate ~ as.factor(chart.data$iso_a3),
las = 2,
cex.axis = 0.7,
main = "Distribution of Yearly Inflation per Country",
col = "royalblue", xlab = "", ylab = "Inflation Rate"
)
# Average inflation by year
chart.data <- subset(melted.data, inflation_rate > 0) %>%
group_by(year) %>%
summarise(avg_inflation = mean(inflation_rate))
plot(
x = chart.data$year, y = chart.data$avg_inflation, type = "l",
col = "royalblue", lwd = 2,
main = "Average Global Inflation per Year",
ylab = "Inflation Rate", xlab = ""
)
grid(nx = NULL, ny = NULL)
# Maximum inflation by year
chart.data <- subset(melted.data, inflation_rate > 0) %>%
group_by(year) %>%
summarise(max_inflation = max(inflation_rate))
plot(
x = chart.data$year, y = chart.data$max_inflation, type = "l",
col = "royalblue", lwd = 2,
main = "Maximum Global Inflation per Year",
ylab = "Inflation Rate", xlab = ""
)
grid(nx = NULL, ny = NULL)
# Countries with hyper-inflation rates
subset(
melted.data[, c("country_name", "year", "inflation_rate")],
inflation_rate > 250
) %>%
group_by(country_name) %>%
summarise(inflation_rate = max(inflation_rate)) %>%
arrange(desc(inflation_rate)) %>%
head(20)## # A tibble: 20 × 2
## country_name inflation_rate
## <chr> <dbl>
## 1 Venezuela 65374.
## 2 Democratic Republic of the Congo 23773.
## 3 Nicaragua 13110.
## 4 Bolivia 11750.
## 5 Peru 7482.
## 6 Armenia 5273.
## 7 Ukraine 4735.
## 8 Angola 4146
## 9 Turkmenistan 3102.
## 10 Brazil 2948.
## 11 Tajikistan 2601.
## 12 Belarus 2221.
## 13 Azerbaijan 1664
## 14 Kazakhstan 1662.
## 15 Russian Federation 1569.
## 16 Uzbekistan 1568.
## 17 Croatia 1518.
## 18 Kyrgyzstan 1086.
## 19 Bulgaria 1061.
## 20 Moldova 788.chart.data <-
subset(
melted.data[, c("country_name", "year", "inflation_rate")],
inflation_rate > 250
) %>%
group_by(country_name) %>%
summarise(inflation_rate = max(inflation_rate)) %>%
arrange(desc(inflation_rate)) %>%
head(20)
barplot(
chart.data$inflation_rate, col = "maroon",
names.arg = chart.data$country_name,
main = "Top 20 Countries with Hyper Inflation Rates",
las = 2,
ylim = c(0, 70000), #max(chart.data$inflation_rate) * 1.25),
cex.names = 0.7,
cex.axis = 0.8
)
grid(nx = NULL, ny = NULL)
# Maximum hyper-inflation rates per year
chart.data <-
subset(
melted.data[, c("country_name", "year", "inflation_rate")],
inflation_rate > 250
) %>%
group_by(year) %>%
summarise(inflation_rate = max(inflation_rate)) %>%
arrange(desc(inflation_rate)) %>%
head(20)
barplot(
chart.data$inflation_rate, col = "maroon",
names.arg = chart.data$year,
main = "Top 20 Years with Hyper Inflation Rates",
las = 2,
ylim = c(0, 70000),
cex.names = 0.7,
cex.axis = 0.8
)
grid(nx = NULL, ny = NULL)
Spatial Analysis
map.data <- merge(
countries[, c("adm0_a3", "continent", "geometry")],
melted.data,
by.x = "adm0_a3", by.y = "iso_a3"
)
head(map.data, 5)## Simple feature collection with 5 features and 5 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -70.06611 ymin: 12.423 xmax: -69.8957 ymax: 12.61411
## Geodetic CRS: WGS 84
## adm0_a3 continent country_name year inflation_rate
## 1 ABW North America Aruba 2003 3.7
## 2 ABW North America Aruba 2005 3.4
## 3 ABW North America Aruba 1994 6.3
## 4 ABW North America Aruba 2024 2.3
## 5 ABW North America Aruba 2010 2.1
## geometry
## 1 POLYGON ((-69.89912 12.452,...
## 2 POLYGON ((-69.89912 12.452,...
## 3 POLYGON ((-69.89912 12.452,...
## 4 POLYGON ((-69.89912 12.452,...
## 5 POLYGON ((-69.89912 12.452,...chart.data <- merge(
countries[, c("adm0_a3", "continent", "geometry")],
subset(melted.data, inflation_rate > 0),
by.x = "adm0_a3", by.y = "iso_a3"
)
plot(
inflation_rate ~ as.factor(continent), data = chart.data,
ylab = "Inflation Rate", xlab = "",
col = "royalblue",
main = "Inflation Distribution by Continent",
ylim = c(0, 100),
cex.axis = 0.7
)
# Range of Inflation Rate per country
calc_range <- function(x) {
d <- subset(melted.data, iso_a3 == x["adm0_a3"])
return(max(d$inflation_rate, na.rm = TRUE) -
min(d$inflation_rate, na.rm = TRUE))
}
# Standard deviation of inflation rate per country
calc_stdev <- function(x) {
d <- subset(melted.data, iso_a3 == x["adm0_a3"])
return(sd(d$inflation_rate, na.rm = TRUE))
}
# Variance of inflation rate per country
calc_var <- function(x) {
d <- subset(melted.data, iso_a3 == x["adm0_a3"])
return(var(d$inflation_rate, na.rm = TRUE))
}
map.data <- melted.data[, c("iso_a3", "inflation_rate")] %>%
group_by(iso_a3) %>%
summarise(
avg_inflation = round(mean(inflation_rate, na.rm = TRUE), 1)
) %>%
merge(
countries[, c("adm0_a3", "geometry")], .,
by.x = "adm0_a3", by.y = "iso_a3"
) %>%
mutate(
range_inflation = apply(., FUN = calc_range, MARGIN = 1),
stdev_inflation = apply(., FUN = calc_stdev, MARGIN = 1),
var_inflation = apply(., FUN = calc_var, MARGIN = 1)
)
as.data.frame(map.data)[, c("adm0_a3", "avg_inflation", "range_inflation",
"stdev_inflation", "var_inflation")] %>%
head(5)## adm0_a3 avg_inflation range_inflation stdev_inflation var_inflation
## 1 ABW 2.9 11.4 2.439423 5.950782e+00
## 2 AFG 15.3 114.9 19.013967 3.615309e+02
## 3 AGO 249.8 4144.6 749.345515 5.615187e+05
## 4 ALB 14.6 226.2 40.075300 1.606030e+03
## 5 AND 2.1 7.4 1.959229 3.838577e+00tm_shape(countries) +
tm_polygons(fill = "lightgray") +
tm_shape(map.data) +
tm_polygons(
fill = "avg_inflation",
fill.scale = tm_scale_continuous(
values = "viridis",
labels = c(0, 50, 100, 250, 1000, 5000)
),
fill.legend = tm_legend(
title = "Avg Rate",
position = tm_pos_out("right", "center")
)
) +
tm_title(
text = "Average Inflation Rate by Country",
position = tm_pos_out("center", "top")
)## [tip] Consider a suitable map projection, e.g. by adding `+ tm_crs("auto")`.
## This message is displayed once per session.
# Chart showing highest average inflation
chart.data <- map.data %>%
arrange(desc(avg_inflation)) %>%
head(20)
barplot(
chart.data$avg_inflation, col = "orange",
names.arg = chart.data$adm0_a3,
las = 2,
cex.axis = 0.8,
cex.names = 0.8,
main = "Top 20 Average Inflation Rate Countries",
ylim = c(0, 2500)
)
grid(nx = NULL, ny = NULL)
tm_shape(countries) +
tm_polygons(col = "lightgray") +
tm_shape(map.data) +
tm_polygons(
fill = "range_inflation",
fill.scale = tm_scale_continuous(
values = "viridis",
labels = c(0, 50, 100, 250, 1000, 5000, 70000)
),
fill.legend = tm_legend(
title = "Rate Range",
position = tm_pos_out("right", "center")
)
) +
tm_title("Inflation Rate Range by Country")## [plot mode] fit legend/component: Some legend items or map compoments do not
## fit well, and are therefore rescaled.
## ℹ Set the tmap option `component.autoscale = FALSE` to disable rescaling.
# Chart showing highest inflation rate range
chart.data <- map.data %>%
arrange(desc(range_inflation)) %>%
head(20)
barplot(
chart.data$range_inflation, col = "orange",
names.arg = chart.data$adm0_a3,
las = 2,
cex.axis = 0.8,
cex.names = 0.8,
main = "Top 20 Inflation Rate Range Countries",
ylim = c(0, 70000)
)
grid(nx = NULL, ny = NULL)
tm_shape(countries) +
tm_polygons(fill = "lightgray") +
tm_shape(map.data) +
tm_polygons(
fill = "stdev_inflation",
fill.scale = tm_scale_continuous(
values = "viridis",
labels = c(0, 25, 50, 75, 100, 250, 12000)
),
fill.legend = tm_legend(
title = "St Dev"
)
) +
tm_title(text = "Inflation Rate Standard Deviation by Country")## [plot mode] fit legend/component: Some legend items or map compoments do not
## fit well, and are therefore rescaled.
## ℹ Set the tmap option `component.autoscale = FALSE` to disable rescaling.
# Chart showing highest inflation rate standard deviation
chart.data <- map.data %>%
arrange(desc(stdev_inflation)) %>%
head(20)
barplot(
chart.data$stdev_inflation, col = "orange",
names.arg = chart.data$adm0_a3,
las = 2,
cex.axis = 0.8,
cex.names = 0.8,
main = "Top 20 Inflation Rate Standard Deviation Countries",
ylim = c(0, 12000)
)
grid(nx = NULL, ny = NULL)
chart.data <- subset(melted.data, inflation_rate > 0 & iso_a3 == "VEN")
chart.data## iso_a3 country_name year inflation_rate
## 191 VEN Venezuela 1980 21.4
## 387 VEN Venezuela 1981 16.2
## 583 VEN Venezuela 1982 9.6
## 779 VEN Venezuela 1983 6.2
## 975 VEN Venezuela 1984 12.2
## 1171 VEN Venezuela 1985 11.4
## 1367 VEN Venezuela 1986 11.5
## 1563 VEN Venezuela 1987 28.1
## 1759 VEN Venezuela 1988 29.5
## 1955 VEN Venezuela 1989 84.5
## 2151 VEN Venezuela 1990 40.7
## 2347 VEN Venezuela 1991 34.2
## 2543 VEN Venezuela 1992 31.4
## 2739 VEN Venezuela 1993 38.1
## 2935 VEN Venezuela 1994 60.8
## 3131 VEN Venezuela 1995 59.9
## 3327 VEN Venezuela 1996 99.9
## 3523 VEN Venezuela 1997 50.0
## 3719 VEN Venezuela 1998 35.8
## 3915 VEN Venezuela 1999 23.6
## 4111 VEN Venezuela 2000 16.2
## 4307 VEN Venezuela 2001 12.5
## 4503 VEN Venezuela 2002 22.4
## 4699 VEN Venezuela 2003 31.1
## 4895 VEN Venezuela 2004 21.7
## 5091 VEN Venezuela 2005 16.0
## 5287 VEN Venezuela 2006 13.7
## 5483 VEN Venezuela 2007 18.7
## 5679 VEN Venezuela 2008 31.4
## 5875 VEN Venezuela 2009 26.0
## 6071 VEN Venezuela 2010 28.2
## 6267 VEN Venezuela 2011 26.1
## 6463 VEN Venezuela 2012 21.1
## 6659 VEN Venezuela 2013 40.6
## 6855 VEN Venezuela 2014 62.2
## 7051 VEN Venezuela 2015 121.7
## 7247 VEN Venezuela 2016 254.9
## 7443 VEN Venezuela 2017 438.1
## 7639 VEN Venezuela 2018 65374.1
## 7835 VEN Venezuela 2019 19906.0
## 8031 VEN Venezuela 2020 2355.1
## 8227 VEN Venezuela 2021 1588.5
## 8423 VEN Venezuela 2022 186.5
## 8619 VEN Venezuela 2023 360.0
## 8815 VEN Venezuela 2024 200.0plot(
inflation_rate ~ as.factor(country_name),
data = chart.data,
main = "Venezuela Yearly Inflation Rate Distribution",
xlab = "", ylab = "Inflation Rate",
col = "maroon",
ylim = c(0, 500)
)
grid(nx = NULL, ny = NULL)
plot(
chart.data$year, chart.data$inflation_rate, type = "l", col = "maroon",
lwd = 2,
main = "Venezuela Inflation Rate per Year",
xlab = "", ylab = "Inflation Rate",
ylim = c(0, 500)
)
grid(nx = NULL, ny = NULL)
# Compare Venezuela with average of other South American countries
# and other countries globally
# Venezuela only
chart.data1 <-
subset(
melted.data,
inflation_rate > 0 & iso_a3 == "VEN"
) %>%
arrange(year)
# All Global Countries except Venezuela
chart.data2 <-
subset(
melted.data,
inflation_rate > 0 & iso_a3 != "VEN"
) %>%
group_by(year) %>%
summarise(
inflation_rate = mean(inflation_rate)
) %>%
arrange(year)
# All South American Countries except Venezuela
chart.data3 <-
subset(
melted.data,
inflation_rate > 0 & iso_a3 != "VEN"
) %>%
merge(
.,
as.data.frame(countries)[, c("adm0_a3", "continent")],
by.x = "iso_a3", by.y = "adm0_a3"
) %>%
subset(
.,
continent == "South America"
) %>%
group_by(year) %>%
summarise(
inflation_rate = mean(inflation_rate)
) %>%
arrange(year)
plot(
chart.data1$year, chart.data1$inflation_rate,
type = "l", col = "maroon",
lwd = 2,
main = "Venezuela vs Average Inflation Rate per Year",
xlab = "", ylab = "Inflation Rate",
ylim = c(0, 500)
)
lines(chart.data2$year, chart.data2$inflation_rate, col = "green", lwd = 2)
lines(chart.data3$year, chart.data3$inflation_rate, col = "blue", lwd = 2)
legend(
1995, 500, c("Global", "Venezuela", "South America"),
lwd = 2,
col = c("green", "maroon", "blue"),
cex = 0.75
)
grid(nx = NULL, ny = NULL)
Research Challenges
C1: Which countries have had consecutive years of inflation rates > 50%?
sum.data <- melted.data %>%
mutate(inflation_rate = coalesce(inflation_rate, 0)) %>%
arrange(iso_a3, year) %>%
mutate(
high_index = (inflation_rate > 50),
group_index =
cumsum(high_index != coalesce(lag(high_index), FALSE) |
(iso_a3 != coalesce(lag(iso_a3), "")))
) %>%
group_by(iso_a3, group_index) %>%
summarise(country_name = first(country_name),
min_inflation = min(inflation_rate),
max_inflation = max(inflation_rate),
high_index = first(high_index),
avg_inflation = round(mean(inflation_rate), 1),
min_year = min(year),
max_year = max(year),
years = n(),
.groups = "keep") %>%
subset(., (years > 1) & (high_index == TRUE), select = -c(2, 6))
sum.data## # A tibble: 67 × 8
## # Groups: iso_a3 [50]
## iso_a3 country_name min_inflation max_inflation avg_inflation min_year
## <chr> <chr> <dbl> <dbl> <dbl> <int>
## 1 AGO Angola 85.3 4146 830. 1991
## 2 ALB Albania 85 226 156. 1992
## 3 ARG Argentina 72.4 122. 95.9 2022
## 4 ARM Armenia 177. 5273. 3061. 1993
## 5 AZE Azerbaijan 412. 1664 1068. 1993
## 6 BGR Bulgaria 62.1 1061. 262. 1991
## 7 BLR Belarus 52.7 2221. 535. 1991
## 8 BLR Belarus 53.2 59.2 56.2 2011
## 9 BOL Bolivia 124. 11750. 2741. 1982
## 10 BRA Brazil 66 2948. 730. 1980
## # ℹ 57 more rows
## # ℹ 2 more variables: max_year <int>, years <int>C2: What is the most number of consecutive years of high inflation per country?
sum.data %>%
arrange(iso_a3, desc(years)) %>%
group_by(iso_a3) %>%
summarise(country_name = first(country_name),
years = first(years),
min_inflation = first(min_inflation),
avg_inflation = first(avg_inflation),
max_inflation = first(max_inflation),
.groups = "keep") %>%
arrange(desc(years), avg_inflation)## # A tibble: 50 × 6
## # Groups: iso_a3 [50]
## iso_a3 country_name years min_inflation avg_inflation max_inflation
## <chr> <chr> <int> <dbl> <dbl> <dbl>
## 1 BRA Brazil 16 66 730. 2948.
## 2 TUR Turkey 14 54.2 72.7 104.
## 3 AGO Angola 13 85.3 830. 4146
## 4 PER Peru 13 59.1 983. 7482.
## 5 BLR Belarus 11 52.7 535. 2221.
## 6 COD Democratic Republic o… 11 81.3 3069. 23773.
## 7 VEN Venezuela 11 62.2 8259. 65374.
## 8 URY Uruguay 10 54.1 74.7 112.
## 9 LBN Lebanon 8 50.1 137. 487.
## 10 NIC Nicaragua 8 117. 3754 13110.
## # ℹ 40 more rowsC3: Create a map showing maximum number of years of consecutive high inflation per country.
map.data <- sum.data %>%
arrange(iso_a3, desc(years)) %>%
group_by(iso_a3) %>%
summarise(
country_name = first(country_name),
years = first(years),
min_inflation = first(min_inflation),
avg_inflation = first(avg_inflation),
max_inflation = first(max_inflation),
.groups = "keep"
) %>%
arrange(desc(years), avg_inflation) %>%
merge(
countries, .,
by.x = "adm0_a3",
by.y = "iso_a3"
)
tm_shape(countries) +
tm_polygons(fill = "lightgray") +
tm_shape(map.data) +
tm_polygons(
fill = "years",
fill.scale = tm_scale_continuous(
values = "viridis"
),
fill.legend = tm_legend(
title = "Years"
)
) +
tm_title(
text = "Maximum Consecutive High Inflation Rate Years by Country",
position = tm_pos_out("center", "top")
)## [plot mode] fit legend/component: Some legend items or map compoments do not
## fit well, and are therefore rescaled.
## ℹ Set the tmap option `component.autoscale = FALSE` to disable rescaling.