Challenge 5

challenge_5

pathogen_cost

Introduction to Visualization

Author

Jerin Jacob

Published

August 22, 2022

I am working on the Cost of Pathogens dataset for the year of 2018.

library(tidyverse)
library(ggplot2)
library(readxl)
library(dplyr)
library(stringr)
library(shadowtext)

Error in library(shadowtext): there is no package called 'shadowtext'

library(scales)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Read in data

Eventhough the file name says the data contains 15 pathogens, the actual dataset has 27 rows which means there are certain rows to be dropped. There are 3 columns/variable; the name of the pathogen, number of cases and the total cost caused by the pathogen in the year.

pathogen <- read_excel("_data/Total_cost_for_top_15_pathogens_2018.xlsx", skip = 5, col_names = c("Pathogen", "#Cases", "Cost"), n_max = 15)
#options(scipen=999)
#head(pathogen)
dim(pathogen)

[1] 15  3

#pathogen

Briefly describe the data

Vibrio vulnificus had the least number of cases, just 96 cases, in 2018 while Norovirus came first with 5461731 reported cases. Cyclospora Cayetanensis cost the least in total ($2571518) and Salmonella (non-typhoidal species) cost the most that totalled to 4142179161.

summary(pathogen)

   Pathogen             #Cases             Cost          
 Length:15          Min.   :     96   Min.   :2.572e+06  
 Class :character   1st Qu.:  26114   1st Qu.:7.007e+07  
 Mode  :character   Median :  86686   Median :3.133e+08  
                    Mean   : 594314   Mean   :1.171e+09  
                    3rd Qu.: 488139   3rd Qu.:2.374e+09  
                    Max.   :5461731   Max.   :4.142e+09

pathogen[which.min(pathogen$`#Cases`),]

# A tibble: 1 × 3
  Pathogen          `#Cases`       Cost
  <chr>                <dbl>      <dbl>
1 Vibrio vulnificus       96 359481557.

pathogen[which.max(pathogen$`#Cases`),]

# A tibble: 1 × 3
  Pathogen  `#Cases`        Cost
  <chr>        <dbl>       <dbl>
1 Norovirus  5461731 2566984191.

pathogen[which.min(pathogen$Cost),]

# A tibble: 1 × 3
  Pathogen                `#Cases`     Cost
  <chr>                      <dbl>    <dbl>
1 Cyclospora cayetanensis    11407 2571518.

pathogen[which.max(pathogen$Cost),]

# A tibble: 1 × 3
  Pathogen                           `#Cases`        Cost
  <chr>                                 <dbl>       <dbl>
1 Salmonella (non-typhoidal species)  1027561 4142179161.

Tidy Data (as needed)

The data has been mutated by adding a new variable called Average Cost per cases so that we can study how the cost per cases of each pathogen varies.

pathogen %>%
  mutate(Avg_Cost_per_case = Cost/`#Cases`)

# A tibble: 15 × 4
   Pathogen                                               #Case…¹   Cost Avg_C…²
   <chr>                                                    <dbl>  <dbl>   <dbl>
 1 Campylobacter spp. (all species)                        845024 2.18e9  2.58e3
 2 Clostridium perfringens                                 965958 3.84e8  3.98e2
 3 Cryptosporidium spp. (all species)                       57616 5.84e7  1.01e3
 4 Cyclospora cayetanensis                                  11407 2.57e6  2.25e2
 5 Listeria monocytogenes                                    1591 3.19e9  2.00e6
 6 Norovirus                                              5461731 2.57e9  4.70e2
 7 Salmonella (non-typhoidal species)                     1027561 4.14e9  4.03e3
 8 Shigella (all species)                                  131254 1.59e8  1.21e3
 9 Shiga toxin-producing Escherichia coli O157 (STEC O15…   63153 3.11e8  4.93e3
10 non-O157 Shiga toxin-producing Escherichia coli (STEC…  112752 3.17e7  2.81e2
11 Toxoplasma gondii                                        86686 3.74e9  4.32e4
12 Vibrio parahaemolyticus                                  34664 4.57e7  1.32e3
13 Vibrio vulnificus                                           96 3.59e8  3.74e6
14 Vibrio non-cholera species other than V. parahaemolyt…   17564 8.17e7  4.65e3
15 Yersinia enterocolitica                                  97656 3.13e8  3.21e3
# … with abbreviated variable names ¹`#Cases`, ²Avg_Cost_per_case

head(pathogen)

# A tibble: 6 × 3
  Pathogen                           `#Cases`        Cost
  <chr>                                 <dbl>       <dbl>
1 Campylobacter spp. (all species)     845024 2181485783.
2 Clostridium perfringens              965958  384277856.
3 Cryptosporidium spp. (all species)    57616   58394152.
4 Cyclospora cayetanensis               11407    2571518.
5 Listeria monocytogenes                 1591 3189686110.
6 Norovirus                           5461731 2566984191.

Univariate Visualizations

BLUE <- "#076fa2"
ggplot(pathogen) +
  geom_col(aes(Cost, Pathogen), fill = BLUE, width = .8)

ggplot(pathogen) +
  geom_col(aes(Cost, Pathogen), fill = BLUE, width = .8) +
  scale_x_continuous(trans = "log10")

ggplot(pathogen) +
  geom_col(aes(`#Cases`, Pathogen), fill = BLUE, width = .8)

ggplot(pathogen) +
  geom_col(aes(`#Cases`, Pathogen), fill = BLUE, width = .8) +
  scale_x_continuous(trans = "log10")

Bivariate Visualization(s)

ggplot(pathogen, aes(x=`#Cases`, y=Cost, label=Pathogen)) +
  geom_point() +
  scale_x_continuous(labels = scales::comma)+
  geom_text()

ggplot(pathogen, aes(x=`#Cases`, y=Cost, label=Pathogen)) +
  geom_point()+
  scale_x_continuous(trans = "log10", labels = scales::comma)+
  scale_y_continuous(trans = "log10", labels = scales::comma)+
  ggrepel::geom_label_repel()