Read in file from source.

MALARIA <- readr::read_csv("https://raw.githubusercontent.com/sadia-perveen/Project-2/master/Child-health_malaria_CoD_Feb-2018_WHO_MCEE_-1.csv")
## Warning: Missing column names filled in: 'X1' [1], 'X2' [2], 'X3' [3],
## 'X4' [4], 'X5' [5], 'X6' [6], 'X7' [7], 'X8' [8], 'X9' [9], 'X10' [10],
## 'X11' [11], 'X12' [12], 'X13' [13], 'X14' [14]
## Parsed with column specification:
## cols(
##   X1 = col_character(),
##   X2 = col_character(),
##   X3 = col_character(),
##   X4 = col_character(),
##   X5 = col_character(),
##   X6 = col_character(),
##   X7 = col_character(),
##   X8 = col_character(),
##   X9 = col_character(),
##   X10 = col_character(),
##   X11 = col_character(),
##   X12 = col_character(),
##   X13 = col_character(),
##   X14 = col_character()
## )
head(MALARIA)
## # A tibble: 6 x 14
##   X1     X2     X3    X4    X5    X6    X7    X8    X9    X10   X11   X12  
##   <chr>  <chr>  <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 <NA>   <NA>   <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
## 2 Estim~ <NA>   <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
## 3 Last ~ <NA>   <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
## 4 <NA>   <NA>   <NA>  Tota~ Tota~ Neon~ Post~ Unde~ Neon~ Post~ Unde~ % ne~
## 5 iso3   Count~ year  nnd   pnd   neo8  post8 ufiv~ rneo8 rpos~ rufi~ fneo8
## 6 <NA>   Global 2000  4,00~ 6,04~ -     639,~ 639,~ -     5     5     0%   
## # ... with 2 more variables: X13 <chr>, X14 <chr>

Data cleanup: 1. Get rid on first column, its not needed. 2. Update values x2 and x3 row 4 based values in row 5. 3. Drop all columns/rows with na’s using drop_na function. 4. Make the first row values in the dataset as the column names. 5. Get rid of 1st two rows using slice. 6. Replace all ‘-’ with 0 using mutate_if function.

MALARIA <- select(MALARIA, -c(X1))

MALARIA$X2[4] <- MALARIA$X2[5]
MALARIA$X3[4] <- MALARIA$X3[5]
                            
MALARIA <- MALARIA %>% drop_na()

names(MALARIA) <- as.character(unlist(MALARIA[1,]))

MALARIA <- slice(MALARIA, 3:n())

MALARIA <- mutate_if(MALARIA, 
                is.character, 
                str_replace_all, pattern = "-", replacement = "0")

Generate some comparison data, in this case total deaths neanatal and post neonatal based on country for all years. Dividing totals by 10000 to make the numbers readable.

DEATHS_COUNTRY <- MALARIA %>%
  group_by(`Country/area name`) %>%
  summarise(neoNatal = sum(as.integer(gsub(",", "", `Total Neonatal deaths`)))/10000, 
            postNeoNatal = sum(as.integer(gsub(",", "", `Total Post-Neonatal deaths`)))/10000) %>%
  arrange(`Country/area name`)

Plot the data in a barplot.

barplot(t(as.matrix(DEATHS_COUNTRY[, 2:3])),
        beside = TRUE,
        names.arg = DEATHS_COUNTRY$`Country/area name`,
        legend.text = TRUE,
        las=2, 
        cex.names=.5,
        main = "Deaths NeoNatal/Post Neo Natal By Contry (2000-2016)",
        xlab = "Countries",
        ylab = "# Deaths/10000")