Análisis del cambio de la temperatura del aire

Se analiza las tendencias del cambio de la temperatura desde el año 1960 hasta 2019 en cada región de los continentes del mundo.

Análisis del cambio de la temperatura

Paquetes

library(tidyverse)
library(plotly)
library(highcharter)
library(ggbeeswarm)
library(wesanderson)

Importar datos

La base de datos se descargó de FAOSTAT

key_crops <- read_csv("FAOSTAT_data_3-10-2021.csv")

key_crops

# A tibble: 10,384 x 14
   `Domain Code` Domain    `Area Code` Area   `Element Code` Element  
   <chr>         <chr>           <dbl> <chr>           <dbl> <chr>    
 1 ET            Temperat…        5101 Easte…           7271 Temperat…
 2 ET            Temperat…        5101 Easte…           7271 Temperat…
 3 ET            Temperat…        5101 Easte…           7271 Temperat…
 4 ET            Temperat…        5101 Easte…           7271 Temperat…
 5 ET            Temperat…        5101 Easte…           7271 Temperat…
 6 ET            Temperat…        5101 Easte…           7271 Temperat…
 7 ET            Temperat…        5101 Easte…           7271 Temperat…
 8 ET            Temperat…        5101 Easte…           7271 Temperat…
 9 ET            Temperat…        5101 Easte…           7271 Temperat…
10 ET            Temperat…        5101 Easte…           7271 Temperat…
# … with 10,374 more rows, and 8 more variables: Months Code <dbl>,
#   Months <chr>, Year Code <dbl>, Year <dbl>, Unit <chr>,
#   Value <dbl>, Flag <chr>, Flag Description <chr>

La base de datos fue adecuada para la respectiva visualización. Para ello, el siguiente código es pertinente.

fsdata <- key_crops%>%
  select(Area, Months, Year, Element, Value)%>%
  pivot_wider(names_from = Element,
              values_from = Value) %>%
  rename(Temperature="Temperature change", sd="Standard Deviation")

fsdata

# A tibble: 5,192 x 5
   Area           Months       Year Temperature    sd
   <chr>          <chr>       <dbl>       <dbl> <dbl>
 1 Eastern Africa Dec–Jan–Feb  1961       0.312 0.323
 2 Eastern Africa Dec–Jan–Feb  1962      -0.38  0.323
 3 Eastern Africa Dec–Jan–Feb  1963      -0.186 0.323
 4 Eastern Africa Dec–Jan–Feb  1964      -0.016 0.323
 5 Eastern Africa Dec–Jan–Feb  1965      -0.524 0.323
 6 Eastern Africa Dec–Jan–Feb  1966       0.238 0.323
 7 Eastern Africa Dec–Jan–Feb  1967       0.057 0.323
 8 Eastern Africa Dec–Jan–Feb  1968      -0.426 0.323
 9 Eastern Africa Dec–Jan–Feb  1969       0.051 0.323
10 Eastern Africa Dec–Jan–Feb  1970       0.289 0.323
# … with 5,182 more rows

Cambio de temperatura en America

america <- fsdata %>%
  filter(Area %in% c("South America", 
                        "Northern America", 
                        "Caribbean",
                        "Central America")
  )

p <- america %>%
  ggplot(aes(Year, Temperature, color = Months)) +
  geom_line() +
  facet_wrap(~Area, ncol = 2) +
  labs(x = "Year", y = "Temperature (°C)")

fig <- ggplotly(p)

fig

Atemyear <- america %>%
  group_by(Year, Area) %>%
  summarise(Tempyear = mean(Temperature))

hc <- hchart(Atemyear, "line",
              hcaes(x=Year, y=Tempyear, group = Area, color = Area)
              )
hc

pfs <- ggplot(america, aes(x=Area, y=Temperature, color = Area))+
  geom_quasirandom() +
  theme_minimal() +
  scale_color_manual(values = wes_palette("Darjeeling2")) +
  theme_minimal()

figfs <- ggplotly(pfs)

figfs

summ <- america %>%
  group_by(Area) %>%
  summarise(mean = mean(Temperature), SD = sd(Temperature)) %>%
  arrange(desc(mean))
summ

# A tibble: 4 x 3
  Area              mean    SD
  <chr>            <dbl> <dbl>
1 Northern America 0.523 0.872
2 Caribbean        0.489 0.514
3 South America    0.453 0.469
4 Central America  0.379 0.508

Cambio de temperatura en Europe

Europe <- fsdata %>%
  filter(Area %in% c("Eastern Europe", 
                        "Northern Europe", 
                        "Southern Europe",
                        "Western Europe")
  )

p1 <- Europe %>%
  ggplot(aes(Year, Temperature, color = Months)) +
  geom_line() +
  facet_wrap(~Area, ncol = 2) +
  labs(x = "Year", y = "Temperature (°C)")


fig1 <- ggplotly(p1)

fig1

Etemyear <- Europe %>%
  group_by(Year, Area) %>%
  summarise(Tempyear = mean(Temperature))

hc1 <- hchart(Etemyear, "line",
              hcaes(x=Year, y=Tempyear, group = Area, color = Area)
              )
hc1

pfs1 <- ggplot(Europe, aes(x=Area, y=Temperature, color = Area))+
  geom_quasirandom() +
  theme_minimal() +
  scale_color_manual(values = wes_palette("Darjeeling2")) +
  theme_minimal()

figfs1 <- ggplotly(pfs1)

figfs1

summ1 <- Europe %>%
  group_by(Area) %>%
  summarise(mean = mean(Temperature), SD = sd(Temperature)) %>%
  arrange(desc(mean))

summ1

# A tibble: 4 x 3
  Area             mean    SD
  <chr>           <dbl> <dbl>
1 Eastern Europe  0.757 1.13 
2 Western Europe  0.679 1.13 
3 Northern Europe 0.597 1.30 
4 Southern Europe 0.568 0.837

Cambio de temperatura en Asia

asia <- fsdata %>%
  filter(Area %in% c("Eastern Asia", 
                        "South-eastern Asia",
                        "Western Asia")
  )

p3 <- asia %>%
  ggplot(aes(Year, Temperature, color = Months)) +
  geom_line() +
  facet_wrap(~Area, ncol = 2) +
  labs(x = "Year", y = "Temperature (°C)")


fig3 <- ggplotly(p3)

fig3

Astemyear <- asia %>%
  group_by(Year, Area) %>%
  summarise(Tempyear = mean(Temperature))

hc2 <- hchart(Astemyear, "line",
              hcaes(x=Year, y=Tempyear, group = Area, color = Area)
              )
hc2

pfs2 <- ggplot(asia, aes(x=Area, y=Temperature, color = Area))+
  geom_quasirandom() +
  theme_minimal() +
  scale_color_manual(values = wes_palette("Darjeeling2")) +
  theme_minimal()

figfs2 <- ggplotly(pfs2)

figfs2

summ2 <- asia %>%
  group_by(Area) %>%
  summarise(mean = mean(Temperature), SD = sd(Temperature)) %>%
  arrange(desc(mean))

summ2

# A tibble: 3 x 3
  Area                mean    SD
  <chr>              <dbl> <dbl>
1 Eastern Asia       0.562 0.768
2 Western Asia       0.447 0.860
3 South-eastern Asia 0.354 0.431

Cambio de temperatura en Africa

africa <- fsdata %>%
  filter(Area %in% c("Eastern Africa", 
                        "Middle Africa", 
                        "Northern Africa",
                        "Southern Africa",
                        "Western Africa")
  )

p4 <- africa %>%
  ggplot(aes(Year, Temperature, color = Months)) +
  geom_line() +
  facet_wrap(~Area, ncol = 2) +
  labs(x = "Year", y = "Temperature (°C)")


fig4 <- ggplotly(p4)

fig4

Aftemyear <- africa %>%
  group_by(Year, Area) %>%
  summarise(Tempyear = mean(Temperature))

hc3 <- hchart(Aftemyear, "line",
              hcaes(x=Year, y=Tempyear, group = Area, color = Area)
              )
hc3

pfs3 <- ggplot(africa, aes(x=Area, y=Temperature, color = Area))+
  geom_quasirandom() +
  theme_minimal() +
  scale_color_manual(values = wes_palette("Darjeeling2")) +
  theme_minimal()

figfs3 <- ggplotly(pfs3)

figfs3

summ3 <- africa %>%
  group_by(Area) %>%
  summarise(mean = mean(Temperature), SD = sd(Temperature)) %>%
  arrange(desc(mean))

summ3

# A tibble: 5 x 3
  Area             mean    SD
  <chr>           <dbl> <dbl>
1 Western Africa  0.596 0.624
2 Northern Africa 0.548 0.743
3 Eastern Africa  0.447 0.499
4 Southern Africa 0.436 0.630
5 Middle Africa   0.396 0.494

Cambio de temperatura en Oceania

oceania <- fsdata %>%
  filter(Area %in% c("Melanesia", 
                        "Micronesia", 
                        "Polynesia",
                        "Australia and New Zealand")
  )

p5 <- oceania %>%
  ggplot(aes(Year, Temperature, color = Months)) +
  geom_line() +
  facet_wrap(~Area, ncol = 2) +
  labs(x = "Year", y = "Temperature (°C)")


fig5 <- ggplotly(p5)

fig5

Otemyear <- oceania %>%
  group_by(Year, Area) %>%
  summarise(Tempyear = mean(Temperature))

hc4 <- hchart(Otemyear, "line",
              hcaes(x=Year, y=Tempyear, group = Area, color = Area)
              )
hc4

pfs4 <- ggplot(oceania, aes(x=Area, y=Temperature, color = Area))+
  geom_quasirandom() +
  theme_minimal() +
  scale_color_manual(values = wes_palette("Darjeeling2")) +
  theme_minimal()

figfs4 <- ggplotly(pfs4)

figfs4

summ4 <- oceania %>%
  group_by(Area) %>%
  summarise(mean = mean(Temperature), SD = sd(Temperature)) %>%
  arrange(desc(mean))

summ4

# A tibble: 4 x 3
  Area                       mean    SD
  <chr>                     <dbl> <dbl>
1 Australia and New Zealand 0.428 0.572
2 Polynesia                 0.389 0.431
3 Melanesia                 0.332 0.424
4 Micronesia                0.241 0.346

Conclusión

• En el continente Americano la temperatura más elvedada que cambió fue 0.523° C con una variación ±0.872° C, la cual es superior a la temperatura media. Esta información corresponde a América del norte. La tendencia de las temperaturas en el continente Americano ya superó 1° C durante los últimos cinco años.

• En el continente Europeo, Europa del este liderá el ránking de cambios de la temperatura con 0.757 ±1.13° C. De acuerdo a las tendencias, dos regiones de este continente ya superaron los 2° C.

• Asia oriental liderá el cambio de temperatura en el continente Asiático con 0.562° C y una variación de ±0.768° C. La tendencia del cambio de temperatura de este continente estaría por superar los 1.5° C. Sin embargo, en el Asia Occidental ya se tuvo registros superiores a los 2° C.

• En el continente Africano, la región de África occidental lidera el cambio de temperatura con 0.596 ±0.624° C. Los cambios de temperatura en este continente ya superon los 1.5° C. Sin embargo, ya existe un registro que supera 2° C.

• Australia y Nueva Zelanda son líderes en los registros de cambio de temperatura con 0.428 ±0.572° C en el continente de Oceania. Las tendencias del cambio de temperatura aún no superaron los 1.5° C.