My story about the water use in Africa by Miriam Kasebele

##GENERAL SUMMARIZED INTRODUCTION

This assignment report intends to answer SD Number 2, which states ‘End hunger, achieve food security and improved nutrition and promote sustainable agriculture’

In African context, the sure source of food to majority of household is Agriculture(Oluwatayo et al., 2016). One of the important entry point to end hunger in Africa is investment in Agricultrural innovations(Hanjra et al., 2009). Irrigation is one of the key innovations especially in the drier parts of the continent which rely heavily on rain-fed agriculture(Wanyama et al.,2018)

A more sustainable and uninterepted agricultural flow is essential to combat hunger in Africa. The indicator chosen to see the development towards the SD is amount of water withdrawn for use in agriculture. It follows therefore that, the more the water is withdrawn for irrigation, the stronger the food production from agriculture and the closer the countries are to achieving the SD goal as it would be expected enough food would be produced. Data show that, in the 1995 and 2000 not much was done interms of investment in Irrigation in Africa and therefore the color in different countries is the same. This may have been caused by less budget alocation to irrigationschemes by country govenments. However the level of investments kept changing with North and SouthAfrica showing more effort done in this area. In Tanzania for instance, the agricultural revolution that embraces such innovations was launched in 2009 under the theme’Kilimo Kwanza’(Agriculture first) aiming at producing agricultural reliant and hunger free country(Ngaiza, 2012) This is showed by the darker blue colors in the maps compared to the more lighter ones. However the pace at which the African countries is taking to achieve SD2 still needs to increase

In this exercise the two references provided “women in the world” and “mapping world development indicators” were used to get an inspiration of code writing and manipulation.

Relevant libraries were then run to call required functions. Then a series of codes were used to upload world data, exctract region specific data(Africa) and to map the data in a span of 18years to learn if there are anychanges.

irr_land <- read.csv("./Data/agricultural-land-irrigation.csv")
length(unique(irr_land$Code))

## [1] 119

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

nirr95 <- filter(irr_land, Year==1995)

nirr00 <- filter(irr_land, Year==2000)

nirr05 <- filter(irr_land, Year==2005)

nirr10 <- filter(irr_land, Year == 2010)

nirr13 <- filter(irr_land, Year == 2013)

names(nirr95) <-   c("Entity", "Code",   "Year",   "Irr_1995")

names(nirr00) <-   c("Entity", "Code",   "Year",   "Irr_2000")

names(nirr05) <-   c("Entity", "Code",   "Year",   "Irr_2005")

names(nirr10) <-   c("Entity", "Code",   "Year",   "Irr_2010")

names(nirr13) <-   c("Entity", "Code",   "Year",   "Irr_2013") 

library(rnaturalearth)
library(sf)

## Linking to GEOS 3.5.1, GDAL 2.1.3, PROJ 4.9.2

library(ggplot2)
library(ggiraph)
world <- sf::st_as_sf(rnaturalearth::countries110)

## str(world)
length(unique(world$iso_a3))

## [1] 175

library(sf)

nworld1 <-  left_join(world, nirr95, by = c('iso_a3' = 'Code')) 

nworld2 <-  left_join(nworld1, nirr00, by = c('iso_a3' = 'Code'))  

nworld3 <-   left_join(nworld2, nirr05, by = c('iso_a3' = 'Code')) 

nworld4 <-   left_join(nworld3, nirr10, by = c('iso_a3' = 'Code'))

nworld <-   left_join(nworld4, nirr13, by = c('iso_a3' = 'Code'))
                   
nworld %>%       st_transform(crs="+proj=laea +lon_0=18.984375")

(nworld$Irr_1995[is.na(nworld$Irr_1995)] <- 0)

## [1] 0

(nworld$Irr_2000[is.na(nworld$Irr_2000)] <- 0)

## [1] 0

(nworld$Irr_2005[is.na(nworld$Irr_2005)] <- 0)

## [1] 0

(nworld$Irr_2010[is.na(nworld$Irr_2010)] <- 0)

## [1] 0

(nworld$Irr_2013[is.na(nworld$Irr_2013)] <- 0)

## [1] 0

world.centers <- st_centroid(nworld)

world.spdf <- methods::as(nworld, 'Spatial')
world.spdf@data$id <- row.names(world.spdf@data)

world.tidy <- broom::tidy(world.spdf)

## Regions defined for each Polygons

world.tidy <- dplyr::left_join(world.tidy, world.spdf@data, by='id')
africa.tidy <- dplyr::filter(world.tidy, region_un=='Africa')

library(sf)
library(ggplot2)
library(hrbrthemes)

## NOTE: Either Arial Narrow or Roboto Condensed fonts are *required* to use these themes.

##       Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and

##       if Arial Narrow is not on your system, please see http://bit.ly/arialnarrow

library(colormap)
irr95 <- ggplot(africa.tidy) +
  geom_polygon_interactive(
    color='black',
    aes(long, lat, group=group, fill=(Irr_1995),
        tooltip=sprintf("%s<br/>%s",iso_a3,Irr_1995))) +
 hrbrthemes::theme_ipsum() +
  colormap::scale_fill_colormap(
    colormap=colormap::colormaps$freesurface_blue, reverse = T) +
  labs(title='Share of Irrigated land in Africa in 1995', subtitle='As a percentage of total farmed land',
       caption='Source: Our World in Data.')

##widgetframe::frameWidget(ggiraph(code=print(g4)))
ggiraph(code=print(irr95))

library(colormap)
irr10 <- ggplot(africa.tidy) +
  geom_polygon_interactive(
    color='black',
    aes(long, lat, group=group, fill=(Irr_2000),
        tooltip=sprintf("%s<br/>%s",iso_a3,Irr_2000))) +
 hrbrthemes::theme_ipsum() +
  colormap::scale_fill_colormap(
    colormap=colormap::colormaps$freesurface_blue, reverse = T) +
  labs(title='Share of Irrigated land in Africa in 2000', subtitle='As a percentage of total farmed land',
       caption='Source: Our World in Data.')

##widgetframe::frameWidget(ggiraph(code=print(g4)))
ggiraph(code=print(irr10))

library(colormap)
irr05 <- ggplot(africa.tidy) +
  geom_polygon_interactive(
    color='black',
    aes(long, lat, group=group, fill=(Irr_2005),
        tooltip=sprintf("%s<br/>%s",iso_a3,Irr_2005))) +
 hrbrthemes::theme_ipsum() +
  colormap::scale_fill_colormap(
    colormap=colormap::colormaps$freesurface_blue, reverse = T) +
  labs(title='Share of Irrigated land in Africa in 2005', subtitle='As a percentage of total farmed land',
       caption='Source: Our World in Data.')

##widgetframe::frameWidget(ggiraph(code=print(g4)))
ggiraph(code=print(irr05))

library(colormap)
irr10 <- ggplot(africa.tidy) +
  geom_polygon_interactive(
    color='black',
    aes(long, lat, group=group, fill=(Irr_2010),
        tooltip=sprintf("%s<br/>%s",iso_a3,Irr_2010))) +
 hrbrthemes::theme_ipsum() +
  colormap::scale_fill_colormap(
    colormap=colormap::colormaps$freesurface_blue, reverse = T) +
  labs(title='Share of Irrigated land in Africa in 2010', subtitle='As a percentage of total farmed land',
       caption='Source: Our World in Data.')

##widgetframe::frameWidget(ggiraph(code=print(g4)))
ggiraph(code=print(irr10))

library(colormap)
irr13 <- ggplot(africa.tidy) +
  geom_polygon_interactive(
    color='black',
    aes(long, lat, group=group, fill=(Irr_2013),
        tooltip=sprintf("%s<br/>%s",iso_a3,Irr_2013))) +
 hrbrthemes::theme_ipsum() +
  colormap::scale_fill_colormap(
    colormap=colormap::colormaps$freesurface_blue, reverse = T) +
  labs(title='Share of Irrigated land in Africa in 2013', subtitle='As a percentage of total farmed land',
       caption='Source: Our World in Data.')

##widgetframe::frameWidget(ggiraph(code=print(g4)))
ggiraph(code=print(irr13))