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library(tidyverse)
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library(ggthemes)
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library(ggrepel)
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library(xts)
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library(tsibble)
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datas <- read.csv("C:\\Users\\karth\\Downloads\\Child Growth and Malnutrition.csv")
view(datas)
typeof(datas$Year.period)
## [1] "character"
datas1 <- datas
datas1$Median.Year <- as.Date(datas1$Median.Year, format = "%Y")
view(datas1)

Time column - Median Year Response Variable - Stunting

datas1$Median.Year[is.na(datas1$Median.Year)]<-mean(datas1$Median.Year,na.rm=TRUE)
datas1$Stunting[is.na(datas1$Stunting)]<-mean(datas1$Stunting,na.rm=TRUE)
datas2 <- datas1 |>
  group_by(Median.Year) |>
  summarise(height = mean(Stunting))
view(datas2)
height_ts <- as_tsibble(datas2, index = Median.Year, key = height)
height_ts
## # A tsibble: 58 x 2 [1.58324837684631e-08D]
## # Key:       height [57]
##    Median.Year height
##    <date>       <dbl>
##  1 1969-11-12    3.7 
##  2 1971-11-12    7.09
##  3 1984-11-12   10.8 
##  4 1981-11-12   12.3 
##  5 2021-11-12   20.7 
##  6 2019-11-12   21.0 
##  7 2017-11-12   21.7 
##  8 1990-11-12   22.3 
##  9 2012-11-12   22.3 
## 10 2014-11-12   23.0 
## # ℹ 48 more rows
height_ts |>
  drop_na() |>
  filter_index("2001" ~ "2022") |>
  ggplot() +
  geom_point(mapping = aes(x = Median.Year, y = height)) + 
  labs(title = "Height Deficit in the 21st Century") +
  theme_hc()

height_ts |>
  drop_na() |>
  filter_index("1971" ~ "2000") |>
  ggplot() +
  geom_point(mapping = aes(x = Median.Year, y = height)) + 
  labs(title = "Height Deficit from 1971 to 2000") +
  theme_hc()

From the above 2 graphs, we see that the height deficit has gone down by the years, from the start when the data has been recorded. A clear indication of the improving health in the countries

lr <- lm(height ~ Median.Year, data = height_ts)
summary(lr)
## 
## Call:
## lm(formula = height ~ Median.Year, data = height_ts)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.119  -6.031  -3.405   2.953  40.696 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.182e+01  1.784e+00   17.83   <2e-16 ***
## Median.Year 2.244e-06  1.877e-05    0.12    0.905    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.58 on 56 degrees of freedom
## Multiple R-squared:  0.0002552,  Adjusted R-squared:  -0.0176 
## F-statistic: 0.0143 on 1 and 56 DF,  p-value: 0.9053

The coefficient and the intercept tell us that there is a downward trend for the average deficit in height over the years

pacf(height_ts$height, ci = 0.95, na.action = na.exclude, xlab = "height lag", main = "PACF")