Tuberculosis report from 2000

The Effect of Tuberculosis on the Globe

Introduction
Setting up my environment
Data cleaning
Data summary gaining insight
Exploratory Data Analysis
Inferences
Conclusion

Introduction

The data was gotten from WHO(world health organisation) http://www.who.int. The data was complex as three tables had to be joined together to be able take advantage of the full data set with several analysis carried out on the cases of tuberculosis, HIV-Tuberculosis cases and funding of Tuberculosis care all over the world.
** new- new cases ** sp - smear positive ** sn - smear negative ** ep - extra-plumotary ** f - female ** m - male ** u - unknown ** whoregion -

Setting up my environment

Notes: Setting up my R environment by loading needed packages like tidyverse, ggplot2, dpylr, rmarkdown, forcats, magrittr, readr, stringr, tibble and tidyr.

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

library(tidyverse)

## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──

## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.8     ✔ stringr 1.4.0
## ✔ tidyr   1.2.0     ✔ forcats 0.5.2
## ✔ readr   2.1.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(tidyr)
library(tibble)
library(stringr)
library(readr)
library(magrittr)

## 
## Attaching package: 'magrittr'
## 
## The following object is masked from 'package:purrr':
## 
##     set_names
## 
## The following object is masked from 'package:tidyr':
## 
##     extract

library(lubridate)

## 
## Attaching package: 'lubridate'
## 
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(ggplot2)
library(forcats)
library(knitr)

getting and loading the files into R

Notes: The file path was checked and initiated using the get function. The three different files were loaded into R and they where later joined together into a table for ease of use.

#To print the working directory file path
getwd()

## [1] "C:/Users/dell/Documents"

# to comfirm the existence of our file in the right path
file.exists("Excel/TB_notifications_2022-08-19.csv")

## [1] TRUE

file.exists("Excel/TB_outcomes_2022-08-20.csv")

## [1] TRUE

file.exists("Excel/TB_burden_countries_2022-08-19.csv")

## [1] TRUE

file.exists("Excel/TB_expenditure_utilisation_2022-08-19.csv")

## [1] TRUE

#with the help of the readr package, reading a csv file using the read_csv command
tuberculosis <- read_csv("Excel/TB_notifications_2022-08-19.csv")

## Rows: 8707 Columns: 198
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (5): country, iso2, iso3, iso_numeric, g_whoregion
## dbl (193): year, new_sp, new_sn, new_su, new_ep, new_oth, ret_rel, ret_taf, ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

tbhiv <- read_csv("Excel/TB_outcomes_2022-08-20.csv")

## Rows: 5539 Columns: 84
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (5): country, iso2, iso3, iso_numeric, g_whoregion
## dbl (79): year, rep_meth, new_sp_coh, new_sp_cur, new_sp_cmplt, new_sp_died,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

funding <- read_csv("Excel/TB_expenditure_utilisation_2022-08-19.csv")

## Rows: 860 Columns: 46
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (5): country, iso2, iso3, iso_numeric, g_whoregion
## dbl (41): year, exp_cpp_dstb, exp_cpp_mdr, exp_cpp_xdr, exp_cpp_tpt, exp_lab...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

estimates <- read_csv("Excel/TB_burden_countries_2022-08-19.csv")

## Rows: 4487 Columns: 50
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (5): country, iso2, iso3, iso_numeric, g_whoregion
## dbl (45): year, e_pop_num, e_inc_100k, e_inc_100k_lo, e_inc_100k_hi, e_inc_n...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Joining tables using the Join clause

#combining the four dataset
tuberculosis_join <- tuberculosis %>% 
  full_join(tbhiv, by = c("country", "year")) %>%
  full_join(estimates, by = c('country', "year")) %>%
  full_join(funding, by = c('country', "year"))

Data Cleaning

** Getting an overview of the data

# printing the structure of the data to have an overview of how the data looks like
str(tuberculosis)

## spec_tbl_df [8,707 × 198] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ country               : chr [1:8707] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
##  $ iso2                  : chr [1:8707] "AF" "AF" "AF" "AF" ...
##  $ iso3                  : chr [1:8707] "AFG" "AFG" "AFG" "AFG" ...
##  $ iso_numeric           : chr [1:8707] "004" "004" "004" "004" ...
##  $ g_whoregion           : chr [1:8707] "EMR" "EMR" "EMR" "EMR" ...
##  $ year                  : num [1:8707] 1980 1981 1982 1983 1984 ...
##  $ new_sp                : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn                : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_su                : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep                : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_oth               : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ ret_rel               : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ ret_taf               : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ ret_tad               : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ ret_oth               : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ newret_oth            : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_labconf           : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_clindx            : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ ret_rel_labconf       : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ ret_rel_clindx        : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ ret_rel_ep            : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ ret_nrel              : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ notif_foreign         : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ c_newinc              : num [1:8707] 71685 71554 41752 52502 18784 ...
##  $ new_sp_m04            : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_m514           : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_m014           : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_m1524          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_m2534          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_m3544          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_m4554          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_m5564          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_m65            : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_mu             : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_f04            : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_f514           : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_f014           : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_f1524          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_f2534          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_f3544          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_f4554          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_f5564          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_f65            : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sp_fu             : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_m04            : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_m514           : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_m014           : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_m1524          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_m2534          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_m3544          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_m4554          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_m5564          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_m65            : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_m15plus        : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_mu             : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_f04            : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_f514           : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_f014           : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_f1524          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_f2534          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_f3544          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_f4554          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_f5564          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_f65            : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_f15plus        : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_fu             : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_sexunk04       : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_sexunk514      : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_sexunk014      : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_sn_sexunk15plus   : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_m04            : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_m514           : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_m014           : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_m1524          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_m2534          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_m3544          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_m4554          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_m5564          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_m65            : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_m15plus        : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_mu             : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_f04            : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_f514           : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_f014           : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_f1524          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_f2534          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_f3544          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_f4554          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_f5564          : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_f65            : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_f15plus        : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_fu             : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_sexunk04       : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_sexunk514      : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_sexunk014      : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_sexunk15plus   : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_ep_sexunkageunk   : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ rel_in_agesex_flg     : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##  $ agegroup_option       : num [1:8707] NA NA NA NA NA NA NA NA NA NA ...
##   [list output truncated]
##  - attr(*, "spec")=
##   .. cols(
##   ..   country = col_character(),
##   ..   iso2 = col_character(),
##   ..   iso3 = col_character(),
##   ..   iso_numeric = col_character(),
##   ..   g_whoregion = col_character(),
##   ..   year = col_double(),
##   ..   new_sp = col_double(),
##   ..   new_sn = col_double(),
##   ..   new_su = col_double(),
##   ..   new_ep = col_double(),
##   ..   new_oth = col_double(),
##   ..   ret_rel = col_double(),
##   ..   ret_taf = col_double(),
##   ..   ret_tad = col_double(),
##   ..   ret_oth = col_double(),
##   ..   newret_oth = col_double(),
##   ..   new_labconf = col_double(),
##   ..   new_clindx = col_double(),
##   ..   ret_rel_labconf = col_double(),
##   ..   ret_rel_clindx = col_double(),
##   ..   ret_rel_ep = col_double(),
##   ..   ret_nrel = col_double(),
##   ..   notif_foreign = col_double(),
##   ..   c_newinc = col_double(),
##   ..   new_sp_m04 = col_double(),
##   ..   new_sp_m514 = col_double(),
##   ..   new_sp_m014 = col_double(),
##   ..   new_sp_m1524 = col_double(),
##   ..   new_sp_m2534 = col_double(),
##   ..   new_sp_m3544 = col_double(),
##   ..   new_sp_m4554 = col_double(),
##   ..   new_sp_m5564 = col_double(),
##   ..   new_sp_m65 = col_double(),
##   ..   new_sp_mu = col_double(),
##   ..   new_sp_f04 = col_double(),
##   ..   new_sp_f514 = col_double(),
##   ..   new_sp_f014 = col_double(),
##   ..   new_sp_f1524 = col_double(),
##   ..   new_sp_f2534 = col_double(),
##   ..   new_sp_f3544 = col_double(),
##   ..   new_sp_f4554 = col_double(),
##   ..   new_sp_f5564 = col_double(),
##   ..   new_sp_f65 = col_double(),
##   ..   new_sp_fu = col_double(),
##   ..   new_sn_m04 = col_double(),
##   ..   new_sn_m514 = col_double(),
##   ..   new_sn_m014 = col_double(),
##   ..   new_sn_m1524 = col_double(),
##   ..   new_sn_m2534 = col_double(),
##   ..   new_sn_m3544 = col_double(),
##   ..   new_sn_m4554 = col_double(),
##   ..   new_sn_m5564 = col_double(),
##   ..   new_sn_m65 = col_double(),
##   ..   new_sn_m15plus = col_double(),
##   ..   new_sn_mu = col_double(),
##   ..   new_sn_f04 = col_double(),
##   ..   new_sn_f514 = col_double(),
##   ..   new_sn_f014 = col_double(),
##   ..   new_sn_f1524 = col_double(),
##   ..   new_sn_f2534 = col_double(),
##   ..   new_sn_f3544 = col_double(),
##   ..   new_sn_f4554 = col_double(),
##   ..   new_sn_f5564 = col_double(),
##   ..   new_sn_f65 = col_double(),
##   ..   new_sn_f15plus = col_double(),
##   ..   new_sn_fu = col_double(),
##   ..   new_sn_sexunk04 = col_double(),
##   ..   new_sn_sexunk514 = col_double(),
##   ..   new_sn_sexunk014 = col_double(),
##   ..   new_sn_sexunk15plus = col_double(),
##   ..   new_ep_m04 = col_double(),
##   ..   new_ep_m514 = col_double(),
##   ..   new_ep_m014 = col_double(),
##   ..   new_ep_m1524 = col_double(),
##   ..   new_ep_m2534 = col_double(),
##   ..   new_ep_m3544 = col_double(),
##   ..   new_ep_m4554 = col_double(),
##   ..   new_ep_m5564 = col_double(),
##   ..   new_ep_m65 = col_double(),
##   ..   new_ep_m15plus = col_double(),
##   ..   new_ep_mu = col_double(),
##   ..   new_ep_f04 = col_double(),
##   ..   new_ep_f514 = col_double(),
##   ..   new_ep_f014 = col_double(),
##   ..   new_ep_f1524 = col_double(),
##   ..   new_ep_f2534 = col_double(),
##   ..   new_ep_f3544 = col_double(),
##   ..   new_ep_f4554 = col_double(),
##   ..   new_ep_f5564 = col_double(),
##   ..   new_ep_f65 = col_double(),
##   ..   new_ep_f15plus = col_double(),
##   ..   new_ep_fu = col_double(),
##   ..   new_ep_sexunk04 = col_double(),
##   ..   new_ep_sexunk514 = col_double(),
##   ..   new_ep_sexunk014 = col_double(),
##   ..   new_ep_sexunk15plus = col_double(),
##   ..   new_ep_sexunkageunk = col_double(),
##   ..   rel_in_agesex_flg = col_double(),
##   ..   agegroup_option = col_double(),
##   ..   newrel_m04 = col_double(),
##   ..   newrel_m59 = col_double(),
##   ..   newrel_m1014 = col_double(),
##   ..   newrel_m514 = col_double(),
##   ..   newrel_m014 = col_double(),
##   ..   newrel_m1519 = col_double(),
##   ..   newrel_m2024 = col_double(),
##   ..   newrel_m1524 = col_double(),
##   ..   newrel_m2534 = col_double(),
##   ..   newrel_m3544 = col_double(),
##   ..   newrel_m4554 = col_double(),
##   ..   newrel_m5564 = col_double(),
##   ..   newrel_m65 = col_double(),
##   ..   newrel_m15plus = col_double(),
##   ..   newrel_mu = col_double(),
##   ..   newrel_f04 = col_double(),
##   ..   newrel_f59 = col_double(),
##   ..   newrel_f1014 = col_double(),
##   ..   newrel_f514 = col_double(),
##   ..   newrel_f014 = col_double(),
##   ..   newrel_f1519 = col_double(),
##   ..   newrel_f2024 = col_double(),
##   ..   newrel_f1524 = col_double(),
##   ..   newrel_f2534 = col_double(),
##   ..   newrel_f3544 = col_double(),
##   ..   newrel_f4554 = col_double(),
##   ..   newrel_f5564 = col_double(),
##   ..   newrel_f65 = col_double(),
##   ..   newrel_f15plus = col_double(),
##   ..   newrel_fu = col_double(),
##   ..   newrel_sexunk04 = col_double(),
##   ..   newrel_sexunk514 = col_double(),
##   ..   newrel_sexunk014 = col_double(),
##   ..   newrel_sexunk15plus = col_double(),
##   ..   newrel_sexunkageunk = col_double(),
##   ..   rdx_data_available = col_double(),
##   ..   newinc_rdx = col_double(),
##   ..   rdxsurvey_newinc = col_double(),
##   ..   rdxsurvey_newinc_rdx = col_double(),
##   ..   rdst_new = col_double(),
##   ..   rdst_ret = col_double(),
##   ..   rdst_unk = col_double(),
##   ..   conf_rrmdr = col_double(),
##   ..   conf_mdr = col_double(),
##   ..   rr_sldst = col_double(),
##   ..   all_conf_xdr = col_double(),
##   ..   conf_rr_nfqr = col_double(),
##   ..   conf_rr_fqr = col_double(),
##   ..   unconf_rrmdr_tx = col_double(),
##   ..   conf_rrmdr_tx = col_double(),
##   ..   rrmdr_014_tx = col_double(),
##   ..   unconf_mdr_tx = col_double(),
##   ..   conf_mdr_tx = col_double(),
##   ..   conf_xdr_tx = col_double(),
##   ..   unconf_rr_nfqr_tx = col_double(),
##   ..   conf_rr_nfqr_tx = col_double(),
##   ..   conf_rr_fqr_tx = col_double(),
##   ..   mdrxdr_bdq_used = col_double(),
##   ..   mdrxdr_bdq_tx = col_double(),
##   ..   mdrxdr_alloral_used = col_double(),
##   ..   mdrxdr_alloral_tx = col_double(),
##   ..   mdrxdr_dlm_used = col_double(),
##   ..   mdrxdr_dlm_tx = col_double(),
##   ..   mdr_shortreg_used = col_double(),
##   ..   mdr_shortreg_tx = col_double(),
##   ..   mdr_tx_adverse_events = col_double(),
##   ..   mdr_alloral_short_used = col_double(),
##   ..   mdr_alloral_short_tx = col_double(),
##   ..   mdr_tx_adsm = col_double(),
##   ..   newrel_tbhiv_flg = col_double(),
##   ..   newrel_hivtest = col_double(),
##   ..   newrel_hivpos = col_double(),
##   ..   newrel_art = col_double(),
##   ..   tbhiv_014_flg = col_double(),
##   ..   newrel_hivtest_014 = col_double(),
##   ..   newrel_hivpos_014 = col_double(),
##   ..   newrel_art_014 = col_double(),
##   ..   hivtest = col_double(),
##   ..   hivtest_pos = col_double(),
##   ..   hiv_cpt = col_double(),
##   ..   hiv_art = col_double(),
##   ..   hiv_tbscr = col_double(),
##   ..   hiv_reg = col_double(),
##   ..   hiv_ipt = col_double(),
##   ..   hiv_reg_new = col_double(),
##   ..   hiv_ipt_reg_all = col_double(),
##   ..   hiv_reg_all = col_double(),
##   ..   hiv_tbdetect = col_double(),
##   ..   hiv_reg_new2 = col_double(),
##   ..   hiv_elig_all_tpt = col_double(),
##   ..   hiv_elig_all = col_double(),
##   ..   hiv_elig_new_tpt = col_double(),
##   ..   hiv_elig_new = col_double(),
##   ..   hiv_all_tpt = col_double(),
##   ..   hiv_all = col_double(),
##   ..   hiv_new_tpt = col_double(),
##   ..   hiv_new = col_double(),
##   ..   hiv_all_tpt_completed = col_double(),
##   ..   hiv_all_tpt_started = col_double()
##   .. )
##  - attr(*, "problems")=<externalptr>

Deleting columns that are not required li>
Handling zero values li>
Checking and droping null values li>
Removing duplicate values li>
Transforming the data from wide to long using tidyr li>
Separating a column into several for simplicity li>
Adjusting and classifying the age data (Adding age range: 0-19, 20-34, 35-44, 45-65, 65+) li>
Filtered for year 2000 to 2020 li>
Changing values

tuber <- tuberculosis %>% 
  gather(key = type, value = cases, new_sp_m04:newrel_f65,-agegroup_option,
         -rel_in_agesex_flg, na.rm = TRUE) %>%
  select(country, g_whoregion, year, type, cases) %>%
  mutate(
    type = stringr::str_replace(type, "newrel", "new_rel")
  ) %>%
  separate(type, c("new", "var", "sexage")) %>%
  mutate(sexage = stringr::str_replace(sexage, "unk", "u")) %>%
  mutate(sexage = stringr::str_replace(sexage, "sexu", "u")) %>%
  separate(sexage, c("sex", "age"), sep = 1) %>%
  #filtering for only active cases i.e cases greater than one and from 2000 till 2020
  filter(cases > 0, year >= 2000) %>%
  
  # the 15plus values are removed from the data set because it is the combination of all ages from 15 and above thus causing the number of cases to be repeated and calculated repeatedly thereby providing an inaccurate analysis
  filter(age != "15plus")

tuber %>% head(10)

## # A tibble: 10 × 8
##    country     g_whoregion  year new   var   sex   age   cases
##    <chr>       <chr>       <dbl> <chr> <chr> <chr> <chr> <dbl>
##  1 Afghanistan EMR          2010 new   sp    m     04        4
##  2 Afghanistan EMR          2011 new   sp    m     04        2
##  3 Albania     EUR          2006 new   sp    m     04        1
##  4 Albania     EUR          2008 new   sp    m     04        1
##  5 Angola      AFR          2011 new   sp    m     04      108
##  6 Angola      AFR          2012 new   sp    m     04       58
##  7 Argentina   AMR          2006 new   sp    m     04       19
##  8 Argentina   AMR          2007 new   sp    m     04       14
##  9 Argentina   AMR          2008 new   sp    m     04       11
## 10 Argentina   AMR          2009 new   sp    m     04        8

Adjusting and classifying the age data (Adding age range: 0-19, 20-34, 35-44, 45-65, 65+)

tuber$age[tuber$age == "04"] <- "0-19"
 
 tuber$age[tuber$age == "514"] <- "0-19"
 
 tuber$age[tuber$age == "014"] <- "0-19"
 
 tuber$age[tuber$age == "59"] <- "0-19"
 
 tuber$age[tuber$age == "1014"] <- "0-19"
 
 tuber$age[tuber$age == "1519"] <- "0-19"
 
 tuber$age[tuber$age == "1524"] <- "20-34"
 
 tuber$age[tuber$age == "2024"] <- "20-34"
 
 tuber$age[tuber$age == "2534"] <- "20-34"
 
 tuber$age[tuber$age == "3544"] <- "35-44"
 
 tuber$age[tuber$age == "4554"] <- "45-64"
 
 tuber$age[tuber$age == "5564"] <- "45-64"
 
 tuber$age[tuber$age == "65"] <- "65+"
 
 
 #changing the abbreviation of the g_whoregion for proper comprehension
 tuber$g_whoregion[tuber$g_whoregion == "AFR"] <- "Africa"
 
 tuber$g_whoregion[tuber$g_whoregion == "AMR"] <- "America"
 
 tuber$g_whoregion[tuber$g_whoregion == "EMR"] <- "Eastern Mediterrenian"
 
 tuber$g_whoregion[tuber$g_whoregion == "EUR"] <- "Europe"
 
 tuber$g_whoregion[tuber$g_whoregion == "SEA"] <- "South-East Asia"
 
 tuber$g_whoregion[tuber$g_whoregion == "WPR"] <- "Western Pacific"

kable(head(tuber))

country	g_whoregion	year	new	var	sex	age	cases
Afghanistan	Eastern Mediterrenian	2010	new	sp	m	0-19	4
Afghanistan	Eastern Mediterrenian	2011	new	sp	m	0-19	2
Albania	Europe	2006	new	sp	m	0-19	1
Albania	Europe	2008	new	sp	m	0-19	1
Angola	Africa	2011	new	sp	m	0-19	108
Angola	Africa	2012	new	sp	m	0-19	58

Data summary gaining insight

Note: The data was briefly summarized according to whoregion to show common trends of year, age and sex across regions

# showing the trend of tuberculosis cases from 1980 - 2020 for each WHO regions
 ggplot(data = tuber, mapping = aes(x = year, y = cases)) +
   geom_col() +
   facet_wrap(~g_whoregion)

# showing the distribution of tuberculosis cases according to age categories for each WHO regions
 ggplot(data = tuber, mapping = aes(x = age, y = cases)) +
   geom_col() +
   facet_wrap(~g_whoregion)

# showing the distribution of tuberculosis cases between male and female  
 ggplot(data = tuber, mapping = aes(x = sex, y = cases)) +
   geom_col() +
   facet_wrap(~g_whoregion)

tuber %>% 
   group_by(., g_whoregion) %>%
   summarise(., cases = sum(cases)) %>%
   arrange(., desc(cases)) %>% head()

## # A tibble: 6 × 2
##   g_whoregion              cases
##   <chr>                    <dbl>
## 1 South-East Asia       35196995
## 2 Western Pacific       20327430
## 3 Africa                18562209
## 4 Eastern Mediterrenian  6061551
## 5 Europe                 4418793
## 6 America                4004211

Explanatory Data Analysis

Question 1: What has been the trend of tuberculosis cases from 2000 to 2020?

Which year has the highest tuberculosis cases?

by_year <- tuber %>%
  group_by(.,year) %>%
  summarise(., total_cases = sum(cases))

kable(by_year)

year	total_cases
2000	1148524
2001	1237444
2002	1523369
2003	1860645
2004	2184958
2005	2364897
2006	3064892
2007	3933707
2008	3680748
2009	3932522
2010	4295276
2011	4390442
2012	4377419
2013	3422746
2014	5674335
2015	6041262
2016	6524581
2017	6536013
2018	7202478
2019	8237527
2020	6937404

plotting the trend of tuberculosis cases by year

by_year %>% 
  ggplot(aes (x = year)) +
  geom_line(aes(y = total_cases))

The year with the highest tuberculosis cases

#What year has the highest number of tuberculosis cases?
by_year %>%
  arrange(desc(total_cases)) %>%
  head(1)

## # A tibble: 1 × 2
##    year total_cases
##   <dbl>       <dbl>
## 1  2019     8237527

Question 2: What is the trend of tuberculosis cases in countries across the globe?

What are the 10 countries with the top tuberculosis cases? What are the 10 countries with the least tuberculosis cases?

# grouping cases by country
by_country <- tuber %>%
  group_by(.,country) %>%
  summarise(., total_cases = sum(cases))

What are the 10 countries with the top tuberculosis cases?

# selecting the top 10 country with the highest tuberculosis cases through all
# the years
top_10 <- by_country %>%
  arrange(desc(total_cases)) %>%
  head(10)
kable(top_10)

country	total_cases
India	21337820
China	13048274
Indonesia	6311768
South Africa	5179958
Pakistan	3717044
Philippines	3567369
Bangladesh	3137372
Democratic Republic of the Congo	1782554
Myanmar	1611582
Russian Federation	1526742

  # a plot of the most country with the lowest tuberculosis cases
  ggplot(top_10, aes(total_cases, fct_reorder(country, total_cases))) +
  geom_point()

#### What are the 10 countries with the least tuberculosis cases?

# selecting the least 10 country with the least tuberculosis cases through all
# the years
least_10 <- by_country %>%
  arrange(total_cases) %>%
  head(10)
kable(least_10)

country	total_cases
San Marino	1
Tokelau	2
Anguilla	3
Montserrat	4
Monaco	5
Niue	5
British Virgin Islands	6
Cook Islands	19
Bermuda	20
Curaçao	28

#a plot of the least country with the lowest tuberculosis cases
ggplot(least_10, aes(total_cases, fct_reorder(country, total_cases))) +
geom_point()

Question 3: What is the pattern of tuberculosis cases across age groups and sex

checking for pattern across age groups and noting age groups that has the highest frequency of tuberculosis cases

# grouping data by age_group 
by_age_group <- tuber %>%
  group_by(.,age) %>%
  summarise(., total_cases = sum(cases))

kable(by_age_group)

age	total_cases
0-19	9931668
20-34	31620389
35-44	15195618
45-64	22407587
65+	8896305
u	519622

# a plot of the age group that frequently occurs
ggplot(by_age_group, aes(x = age, y = total_cases)) +
  geom_col()

It is shown the the middle aged 20-64 has the highest case of tuberculosis

checking the sex with the highest cases of tuberculosis

by_sex <- tuber %>%
  group_by(.,sex) %>%
  summarise(., total_cases = sum(cases)) %>%
  arrange(., desc(total_cases))

kable(by_sex)

sex	total_cases
m	54847196
f	33455750
u	268243

# a plot of the sex that frequently occurs
ggplot(by_sex, aes(x = sex, y = total_cases)) +
  geom_col()

Question 4: Tuberculosis cases with HIV across the globe

Selecting TBHIV cases from the general table ‘tuberculosis_join’

tbhiv1 <- select(tuberculosis_join, country, year,
                 success_tbhiv_treatment = tbhiv_succ, failed_tbhiv_treatment = tbhiv_fail,
                 tbhiv_death = tbhiv_died,
                 tbhiv_lost) %>% 
  drop_na() %>%
  mutate(tbhiv_total = success_tbhiv_treatment + failed_tbhiv_treatment + tbhiv_death + tbhiv_lost)

Country with the most TBHIV cases all over the world for the year 2019

tbhiv1 %>%
  group_by(.,country, year) %>%
  summarise(., total_tbhiv_cases = sum(tbhiv_total)) %>%
  filter(., total_tbhiv_cases > 0, year == 2019)%>%
  arrange(desc(total_tbhiv_cases)) %>%
  head(10)

## `summarise()` has grouped output by 'country'. You can override using the
## `.groups` argument.

## # A tibble: 10 × 3
## # Groups:   country [10]
##    country                      year total_tbhiv_cases
##    <chr>                       <dbl>             <dbl>
##  1 South Africa                 2019             95969
##  2 India                        2019             35419
##  3 Mozambique                   2019             31753
##  4 Kenya                        2019             20980
##  5 United Republic of Tanzania  2019             18792
##  6 Zambia                       2019             16002
##  7 Uganda                       2019             15709
##  8 Zimbabwe                     2019             12257
##  9 Nigeria                      2019             12214
## 10 Russian Federation           2019             10635

Note: 8 out of the 10 countries with the most TBHIV cases were African countries and a huge amount of the countries are in the southern part of the continent

Viewing countries with the highest TBHIV rate (2019)

# viewing countries with the most percentage for year 2019 of hiv to tuberculosis cases estimates
tbhiv1 %>% 
  full_join(by_year_country, by = c("country", "year")) %>%
  select(., country, year, total_cases, tbhiv_total) %>%
  drop_na()%>%
  mutate(., hivtb_rate = (tbhiv_total/total_cases)*100) %>%
  arrange(desc(hivtb_rate)) %>% 
  filter(., hivtb_rate < 100, total_cases > 100000, year == 2019)

## # A tibble: 12 × 5
##    country       year total_cases tbhiv_total hivtb_rate
##    <chr>        <dbl>       <dbl>       <dbl>      <dbl>
##  1 South Africa  2019      226005       95969    42.5   
##  2 Kenya         2019      111049       20980    18.9   
##  3 Nigeria       2019      126611       12214     9.65  
##  4 Brazil        2019      111332        6462     5.80  
##  5 Myanmar       2019      184951        9509     5.14  
##  6 Viet Nam      2019      104124        2643     2.54  
##  7 Indonesia     2019      629171       10370     1.65  
##  8 India         2019     2883264       35419     1.23  
##  9 China         2019      837860        6143     0.733 
## 10 Philippines   2019      530626        1516     0.286 
## 11 Pakistan      2019      373759         662     0.177 
## 12 Bangladesh    2019      303925         117     0.0385

South Africa is the country with the most TBHIV rate all over the world as at 2019 with a TBHIV to total tuberculosis cases rate at 42.5%, Kenya was the second highest with 18.9% and Nigeria third with 9.65%

checking for the trend of TBHIV cases from 2012 till 2019

tbhiv_by_year <- tbhiv1 %>%
  group_by(.,year) %>%
  summarise(., tbhiv_total = sum(tbhiv_total))

kable(tbhiv_by_year)

year	tbhiv_total
2012	363207
2013	360953
2014	392464
2015	446016
2016	403686
2017	416406
2018	380606
2019	390368

# plotting total tuberculosis and HIV cases against year
tbhiv_by_year %>% ggplot(aes(x = year))+
  geom_point(aes(y = tbhiv_total))+
  geom_line(aes(y = tbhiv_total))

Note: The year with the highest TBHIV rate is 2015 and it has been falling since then

Question 5: What is the tuberculosis mortality rate across the globe?

# calculation the estimated mortality recorded and the rate of mortality to the 
# total tuberculosis cases
tb_mortality <- select(tuberculosis_join, country, e_pop_num, e_mort_num) %>%
  group_by(., country) %>%
  drop_na() %>%
  summarise(., total_population = max(e_pop_num, na.rm = TRUE)
            , tb_death = mean(e_mort_num, na.rm = TRUE)) %>%
  full_join(by_country, by = "country") %>%
  mutate(., mortality_rate = (tb_death/total_cases)*100) %>%
  drop_na() %>%
  arrange(., desc(mortality_rate))

kable(tb_mortality)

country	total_population	tb_death	total_cases	mortality_rate
Anguilla	15002	1.047619e+00	3	34.9206349
Mozambique	31255435	2.376190e+04	120779	19.6738711
Serbia & Montenegro	10101170	3.780000e+02	2710	13.9483395
Nigeria	206139587	1.382857e+05	1373968	10.0646969
Central African Republic	4829764	9.380952e+03	120664	7.7744417
Ghana	31072945	1.647619e+04	231460	7.1183749
South Sudan	11193729	6.730000e+03	95267	7.0643560
United Republic of Tanzania	59734213	6.595238e+04	1014853	6.4987127
Cameroon	26545864	1.809524e+04	279837	6.4663494
Malawi	19129955	1.479048e+04	235457	6.2816039
Guinea-Bissau	1967998	2.052381e+03	33490	6.1283397
Equatorial Guinea	1402985	8.500000e+02	14133	6.0142928
Nepal	29136808	2.261905e+04	426709	5.3008133
Lao People’s Democratic Republic	7275556	4.261905e+03	81363	5.2381362
Lesotho	2142252	6.990476e+03	135129	5.1731872
Kenya	53771300	6.509524e+04	1476253	4.4094907
Papua New Guinea	8947027	6.323810e+03	144143	4.3871777
Grenada	112519	1.952381e+00	45	4.3386243
Somalia	15893219	9.819048e+03	226946	4.3266009
Côte d’Ivoire	26378275	1.375714e+04	320104	4.2977104
Angola	32866268	2.052381e+04	484618	4.2350490
Ethiopia	114963583	5.547619e+04	1403111	3.9537991
Gambia	2416664	6.061905e+02	15357	3.9473235
Madagascar	27691019	1.271429e+04	326929	3.8890052
Gabon	2225728	2.414286e+03	62658	3.8531165
Congo	5518092	4.180952e+03	110152	3.7956209
Eswatini	1160164	3.306190e+03	88105	3.7525571
Niger	24206636	5.028571e+03	136131	3.6939209
Eritrea	3546427	9.442857e+02	26391	3.5780596
Comoros	869595	5.638095e+01	1623	3.4738726
Burundi	11890781	3.804762e+03	112033	3.3961082
Sudan	44053386	9.619048e+03	283706	3.3904985
Liberia	5057677	3.200000e+03	94522	3.3854552
Myanmar	54409794	5.452381e+04	1611582	3.3832476
Mauritania	4649660	1.154286e+03	34233	3.3718509
Saint Kitts and Nevis	53192	1.142857e+00	34	3.3613445
Zambia	18383956	1.814286e+04	540084	3.3592658
Burkina Faso	20903278	2.447619e+03	73031	3.3514796
Namibia	2540916	4.800000e+03	145488	3.2992412
Chad	16425859	4.866667e+03	149455	3.2562756
United Arab Emirates	9890400	5.071429e+01	1615	3.1402034
Barbados	287371	2.523810e+00	83	3.0407344
Democratic Republic of the Congo	89561404	5.414286e+04	1782554	3.0373754
Turkmenistan	6031187	9.304762e+02	31503	2.9536114
Afghanistan	38928341	1.252381e+04	424368	2.9511673
Dominica	71991	2.523810e+00	87	2.9009305
Guinea	13132792	5.214286e+03	185716	2.8076664
Timor-Leste	1318442	9.210526e+02	33995	2.7093768
India	1380004385	5.642857e+05	21337820	2.6445331
Uganda	45741000	1.861905e+04	722953	2.5754161
Thailand	69799978	2.042857e+04	793538	2.5743659
Sierra Leone	7976985	4.657143e+03	182622	2.5501543
South Africa	59308690	1.308571e+05	5179958	2.5262202
Botswana	2351625	2.438095e+03	101160	2.4101376
Senegal	16743930	2.995238e+03	125159	2.3931464
Ukraine	48838058	1.009524e+04	432085	2.3364010
Bangladesh	164689383	7.328571e+04	3137372	2.3358950
Curaçao	164100	6.363636e-01	28	2.2727273
Mali	20250834	2.076190e+03	97439	2.1307592
Djibouti	988002	5.195238e+02	24853	2.0903867
Benin	12123198	1.366667e+03	66849	2.0444085
Zimbabwe	14862927	1.154762e+04	567028	2.0365165
Algeria	43851043	2.957143e+03	149070	1.9837277
Sao Tome and Principe	219161	4.614286e+01	2412	1.9130538
Togo	8278737	8.190476e+02	43522	1.8819163
Antigua and Barbuda	97928	1.285714e+00	69	1.8633540
Yemen	29825968	2.357143e+03	127975	1.8418776
Indonesia	273523621	1.114286e+05	6311768	1.7654098
Iceland	341250	3.190476e+00	191	1.6704064
Libya	6871287	4.357143e+02	26284	1.6577168
Viet Nam	97338583	2.252381e+04	1369950	1.6441337
Saint Vincent and the Grenadines	110947	2.714286e+00	171	1.5873016
Saudi Arabia	34813867	9.585714e+02	62551	1.5324638
Russian Federation	146404890	2.307143e+04	1526742	1.5111544
Belarus	9871635	7.833333e+02	57333	1.3662870
Vanuatu	307150	2.452381e+01	1813	1.3526646
Azerbaijan	10139175	7.828571e+02	58866	1.3298970
Netherlands Antilles	198662	7.000000e-01	53	1.3207547
Pakistan	220892331	4.885714e+04	3717044	1.3144085
Saint Lucia	183629	2.809524e+00	216	1.3007055
Wallis and Futuna Islands	15098	4.285714e-01	33	1.2987013
Dominican Republic	10847904	8.385714e+02	66243	1.2659019
Rwanda	12952209	1.170952e+03	93814	1.2481638
Guyana	786559	1.469048e+02	11908	1.2336644
Bolivia (Plurinational State of)	11673029	1.833333e+03	151430	1.2106804
Puerto Rico	3670308	1.614286e+01	1337	1.2073940
Chile	19116209	5.533333e+02	45840	1.2070971
Estonia	1399111	5.914286e+01	4949	1.1950466
Haiti	11402533	3.052381e+03	265507	1.1496424
Ecuador	17643060	9.900000e+02	88066	1.1241569
occupied Palestinian territory, including east Jerusalem	5101416	5.142857e+00	459	1.1204482
Latvia	2384150	1.587619e+02	14293	1.1107668
Finland	5540718	5.857143e+01	5301	1.1049128
Japan	128555196	3.914286e+03	360088	1.0870359
Bhutan	771612	1.514286e+02	14613	1.0362593
Cambodia	16718971	5.357143e+03	520488	1.0292539
France	65273512	7.980952e+02	78081	1.0221376
Uzbekistan	33469199	3.080952e+03	301911	1.0204836
Hungary	10220509	1.811429e+02	18091	1.0012871
Greece	11234993	8.380952e+01	8410	0.9965461
Tajikistan	9537642	8.738095e+02	88628	0.9859294
Lithuania	3501842	2.742857e+02	28097	0.9762100
Turks and Caicos Islands	38718	4.761905e-01	49	0.9718173
Croatia	4428075	9.980952e+01	10464	0.9538372
Republic of Moldova	4202659	5.776190e+02	60595	0.9532454
Jamaica	2961161	1.890476e+01	2002	0.9442938
Suriname	586634	2.066667e+01	2217	0.9321906
Guatemala	17915567	5.090476e+02	54672	0.9310938
Panama	4314768	2.671429e+02	29156	0.9162535
Cabo Verde	555988	3.528571e+01	4059	0.8693204
Portugal	10604066	3.876190e+02	44802	0.8651825
Italy	60673694	4.347619e+02	50359	0.8633251
Aruba	106766	9.047619e-01	105	0.8616780
North Macedonia	2083458	5.319048e+01	6343	0.8385697
Honduras	9904608	4.880952e+02	58232	0.8381908
Philippines	109581085	2.942857e+04	3567369	0.8249377
Bahamas	393248	7.380952e+00	895	0.8246874
Mauritius	1271767	1.895238e+01	2322	0.8162093
Bosnia and Herzegovina	3765422	1.635238e+02	20175	0.8105269
Mexico	128932753	3.128571e+03	389743	0.8027268
Kazakhstan	18776707	2.295714e+03	288492	0.7957636
Trinidad and Tobago	1399491	3.342857e+01	4250	0.7865546
Peru	32971846	3.166667e+03	405028	0.7818390
Greenland	56968	7.000000e+00	907	0.7717751
Belize	397621	1.390476e+01	1818	0.7648384
Iraq	40222503	1.104762e+03	145762	0.7579218
Costa Rica	5094114	6.419048e+01	8867	0.7239255
Norway	5421242	3.233333e+01	4490	0.7201188
Marshall Islands	59194	2.204762e+01	3087	0.7142086
Micronesia (Federated States of)	115021	1.723810e+01	2416	0.7134973
Venezuela (Bolivarian Republic of)	30081827	9.666667e+02	139174	0.6945742
Solomon Islands	686878	4.928571e+01	7115	0.6927015
Poland	38556699	7.961905e+02	115385	0.6900294
Kyrgyzstan	6524191	7.090476e+02	102769	0.6899431
Nicaragua	6624554	2.480952e+02	36682	0.6763405
Armenia	3069597	1.391429e+02	20906	0.6655642
Colombia	50882884	1.519048e+03	230162	0.6599906
Serbia	9193818	1.542500e+02	23736	0.6498568
New Caledonia	285491	4.476191e+00	691	0.6477844
Sri Lanka	21413250	1.010476e+03	159305	0.6343029
Slovenia	2078932	1.823810e+01	2939	0.6205544
Austria	9006400	5.785714e+01	9390	0.6161570
Uruguay	3473727	8.914286e+01	14634	0.6091489
Bulgaria	7997951	2.087619e+02	34331	0.6080857
Paraguay	7132530	2.766667e+02	45569	0.6071379
Sint Maarten (Dutch part)	42882	1.818182e-01	30	0.6060606
Fiji	896444	3.128571e+01	5229	0.5983116
French Polynesia	280904	5.333333e+00	900	0.5925926
Slovakia	5459643	4.109524e+01	6944	0.5918093
China, Macao SAR	649342	3.661905e+01	6336	0.5779521
Spain	47084242	5.247619e+02	91350	0.5744520
Mongolia	3278292	3.828571e+02	66759	0.5734914
Germany	83783945	4.361905e+02	76671	0.5689119
Kiribati	119446	4.185714e+01	7376	0.5674775
Seychelles	98340	1.428571e+00	257	0.5558644
Czechia	10708982	5.971429e+01	10832	0.5512766
Sweden	10099270	5.119048e+01	9327	0.5488418
Ireland	4937796	3.295238e+01	6056	0.5441278
Romania	22137423	1.607619e+03	296657	0.5419117
Andorra	84461	5.238095e-01	97	0.5400098
Nauru	10834	6.190476e-01	116	0.5336617
Northern Mariana Islands	58412	4.095238e+00	777	0.5270577
Morocco	36910558	2.785714e+03	530361	0.5252487
Tonga	105697	1.333333e+00	258	0.5167959
Brazil	212559409	7.542857e+03	1488878	0.5066135
Lebanon	6859408	5.414286e+01	10731	0.5045462
Republic of Korea	51269183	3.023810e+03	600809	0.5032897
Tuvalu	11792	1.952381e+00	391	0.4993302
United States of America	331002647	9.514286e+02	192395	0.4945183
Palau	19861	1.095238e+00	224	0.4889456
Malaysia	32365998	1.847619e+03	382075	0.4835750
China	1439323774	6.280952e+04	13048274	0.4813627
Canada	37742157	1.331429e+02	27800	0.4789311
Denmark	5792203	2.647619e+01	5563	0.4759337
Belgium	11589616	7.457143e+01	16070	0.4640412
Argentina	45195777	8.595238e+02	185463	0.4634476
Luxembourg	625976	2.285714e+00	494	0.4626952
Egypt	102334403	7.119048e+02	158614	0.4488285
Cyprus	1207361	3.380952e+00	755	0.4478083
Israel	8655541	3.533333e+01	7941	0.4449482
Iran (Islamic Republic of)	83992953	7.623810e+02	177944	0.4284387
Maldives	540542	9.904762e+00	2326	0.4258281
Brunei Darussalam	437483	1.685714e+01	3963	0.4253632
United Kingdom of Great Britain and Northern Ireland	67886004	4.709524e+02	113020	0.4166983
Guam	168783	6.428571e+00	1546	0.4158196
Netherlands	17134873	6.390476e+01	15594	0.4098035
Tunisia	11818618	1.657143e+02	41880	0.3956884
Switzerland	8654618	3.485714e+01	8880	0.3925354
Cuba	11339255	5.209524e+01	13321	0.3910760
Georgia	4362184	2.342857e+02	60364	0.3881216
New Zealand	4822233	1.809524e+01	5207	0.3475175
Türkiye	84339067	7.809524e+02	228680	0.3415045
Jordan	10203140	2.204762e+01	6639	0.3320925
Cayman Islands	65720	1.428571e-01	44	0.3246753
El Salvador	6486201	1.328571e+02	41875	0.3172708
Oman	5106622	1.938095e+01	6177	0.3137600
Australia	25499881	6.438095e+01	21080	0.3054125
Malta	441539	2.666667e+00	900	0.2962963
Bahrain	1701583	9.142857e+00	3107	0.2942664
China, Hong Kong SAR	7496988	2.285714e+02	83901	0.2724299
Singapore	5850343	8.533333e+01	31895	0.2675445
Bermuda	66260	4.761900e-02	20	0.2380952
Albania	3129701	1.404762e+01	7744	0.1814000
Kuwait	4270563	2.247619e+01	12708	0.1768665
Montenegro	628062	2.250000e+00	1574	0.1429479
Samoa	198410	2.047619e+00	1687	0.1213764
American Samoa	59684	4.761900e-02	45	0.1058201
Syrian Arab Republic	21362541	4.419048e+01	63208	0.0699128
Qatar	2881060	5.809524e+00	8619	0.0674037
British Virgin Islands	30237	0.000000e+00	6	0.0000000
Cook Islands	19094	0.000000e+00	19	0.0000000
Monaco	39244	0.000000e+00	5	0.0000000
Montserrat	4999	0.000000e+00	4	0.0000000
Niue	1902	0.000000e+00	5	0.0000000
San Marino	33938	0.000000e+00	1	0.0000000
Tokelau	1550	0.000000e+00	2	0.0000000

visualising countries with the highest tb mortality rate and has tuberculosis cases of over 100000

# visualising countries with the highest tb mortality rate and has tuberculosis
# cases of over 100000
tb_mortality %>% 
  filter(total_cases > 100000) %>%
  head(10)

## # A tibble: 10 × 5
##    country                     total_population tb_death total_cases mortality…¹
##    <chr>                                  <dbl>    <dbl>       <dbl>       <dbl>
##  1 Mozambique                          31255435   23762.      120779       19.7 
##  2 Nigeria                            206139587  138286.     1373968       10.1 
##  3 Central African Republic             4829764    9381.      120664        7.77
##  4 Ghana                               31072945   16476.      231460        7.12
##  5 United Republic of Tanzania         59734213   65952.     1014853        6.50
##  6 Cameroon                            26545864   18095.      279837        6.47
##  7 Malawi                              19129955   14790.      235457        6.28
##  8 Nepal                               29136808   22619.      426709        5.30
##  9 Lesotho                              2142252    6990.      135129        5.17
## 10 Kenya                               53771300   65095.     1476253        4.41
## # … with abbreviated variable name ¹mortality_rate

Note: All top 10 countries with highest tuberculosis mortality rate are African countries. This shows the lack and access to quality health care services in virtually all countries in Africa.

#worldwide tuberculosis death rate 
tb_mortality %>% 
  summarise(., total_cases = sum(total_cases, na.rm = TRUE),
            total_death = sum(tb_death, na.rm = TRUE)) %>% 
  mutate(., rate = (total_death/total_cases)*100)

## # A tibble: 1 × 3
##   total_cases total_death  rate
##         <dbl>       <dbl> <dbl>
## 1    87203222    1925091.  2.21

Note: The global tuberculosis mortality rate is 2.21

Conclusion

After thoroughly analyzing the WHO data set and answering questions through visualizations, below are the insight gotten from the data by the Exploratory analysis carried out on the data.
#### The first question
“What has been the trend of tuberculosis cases from 2000 to 2020?” reveals the tuberculosis trends throughout all the years. I was seen that from 2000 till 2020 the trend has been progressing upward but a drastic drop was recorded in 2013. 2019 is the year with the highest ever recorded tuberculosis cases ever.
#### The second question
“What is the trend of tuberculosis cases in countries across the globe?” gave us insight to know countries that are heavy burdened with tuberculosis, we further checked the least burdened countries with tuberculosis. The top and least ten countries with tuberculosis cases were analyze and plotted for easy visualization. The nine out of ten countries with the most tuberculosis cases were Asian countries this is due to the large population in the region.
#### The third question
“What is the pattern of tuberculosis cases across age groups and sex” reveals the age group that are predominant. It was gathered that what causes tuberculosis is the weakening of the immune system of individuals by the bacteria Mycobacterium tuberculosis. Mid-aged individuals (20-64) mostly engage in activities and habit such as smoking, alcohol intake and substance abuse that weakens the human immune system. Checking the total cases amongst male and females. The male gender are likely possible of engaging in health hazarduous activities than their female conterpart. #### The fourth question
“Tuberculosis cases with HIV across the globe”

Tuberculosis report from 2000 - 2020

Josiah Paul

2022-09-24