# Load necessary libraries
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(janitor)
##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.test
#Load the data
df <- read.csv("Data/Epi_Task_Data.csv") %>% clean_names()
head(df)
## year epi_week county age_group ili_percentage population
## 1 2023 1 Nairobi 0-4yrs 6.1 3452
## 2 2024 1 Nairobi 0-4yrs 3.2 3658
## 3 2023 2 Nairobi 0-4yrs 4.5 3043
## 4 2024 2 Nairobi 0-4yrs 4.9 2765
## 5 2023 3 Nairobi 0-4yrs 4.6 6975
## 6 2024 3 Nairobi 0-4yrs 3.8 609
#Descriptive analysis (mean ILI percentage per county per year)
mean_ili <- df %>%
group_by(county, year) %>%
summarise(mean_ili = mean(ili_percentage, na.rm = TRUE)) %>%
tidyr::pivot_wider(names_from = year, values_from = mean_ili)
## `summarise()` has grouped output by 'county'. You can override using the
## `.groups` argument.
print(mean_ili)
## # A tibble: 7 × 3
## # Groups: county [7]
## county `2023` `2024`
## <chr> <dbl> <dbl>
## 1 Kakamega 4.06 4.54
## 2 Kiambu 3.90 3.70
## 3 Kisumu 4.07 3.72
## 4 Machakos 3.38 3.98
## 5 Mombasa 3.90 3.99
## 6 Nairobi 4.22 3.99
## 7 Nakuru 3.78 4.53
#Plot weekly ILI trend
weekly_trend <- df %>%
group_by(epi_week, county) %>%
summarise(mean_ili = mean(ili_percentage, na.rm = TRUE))
## `summarise()` has grouped output by 'epi_week'. You can override using the
## `.groups` argument.
ggplot(weekly_trend, aes(x = epi_week, y = mean_ili, color = county)) +
geom_line() +
labs(title = "Weekly ILI Trend by County",
x = "Epidemiological Week",
y = "Mean ILI Percentage") +
facet_wrap(~ county) +
theme_minimal()
#Compute incidence rate per 100,000 population
selected <- df %>% filter(county %in% c("Nairobi", "Kakamega", "Kisumu"))
selected <- selected %>%
mutate(ili_cases = (ili_percentage / 100) * population)
incidence <- selected %>%
group_by(county) %>%
summarise(total_cases = sum(ili_cases, na.rm = TRUE),
total_pop = sum(population)) %>%
mutate(incidence_per_100k = (total_cases / total_pop) * 1e5)
print(incidence)
## # A tibble: 3 × 4
## county total_cases total_pop incidence_per_100k
## <chr> <dbl> <int> <dbl>
## 1 Kakamega 9268. 228303 4060.
## 2 Kisumu 7573. 196715 3850.
## 3 Nairobi 6814. 164774 4135.
# One-way ANOVA
subset_data <- df %>% filter(county %in% c("Nairobi", "Kakamega", "Kisumu"))
anova_result <- aov(ili_percentage ~ county, data = subset_data)
summary(anova_result)
## Df Sum Sq Mean Sq F value Pr(>F)
## county 2 3.28 1.641 0.868 0.422
## Residuals 117 221.16 1.890
#check pairwise differences
TukeyHSD(anova_result)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = ili_percentage ~ county, data = subset_data)
##
## $county
## diff lwr upr p adj
## Kisumu-Kakamega -0.405 -1.1348068 0.3248068 0.3884043
## Nairobi-Kakamega -0.195 -0.9248068 0.5348068 0.8015505
## Nairobi-Kisumu 0.210 -0.5198068 0.9398068 0.7738045