DT <- read.csv("cema_internship_task_2023.csv")
head(DT,5) period county Total.Dewormed Acute.Malnutrition
1 Jan-23 Baringo County 3659 8
2 Jan-23 Bomet County 1580 NA
3 Jan-23 Bungoma County 6590 24
4 Jan-23 Busia County 7564 NA
5 Jan-23 Elgeyo Marakwet County 1407 NA
stunted.6.23.months stunted.0..6.months stunted.24.59.months diarrhoea.cases
1 471 34 380 2620
2 1 3 NA 1984
3 98 154 23 4576
4 396 143 111 2239
5 92 71 5 2739
Underweight.0..6.months Underweight.6.23.months Underweight.24.59.Months
1 85 739 731
2 41 86 16
3 231 315 120
4 251 608 125
5 57 104 21attach(DT)library(dplyr)
count(DT) n
1 1410library(tidyverse)
library(ggpubr)
library(rstatix)
SUMM <- DT %>%
group_by(county) %>%
get_summary_stats(Total.Dewormed, type = "mean_sd")
SUMM# A tibble: 47 × 5
county variable n mean sd
<chr> <fct> <dbl> <dbl> <dbl>
1 Baringo County Total.Dewormed 30 10597. 12659.
2 Bomet County Total.Dewormed 30 1656. 806.
3 Bungoma County Total.Dewormed 30 14638. 25747.
4 Busia County Total.Dewormed 30 4379. 1668.
5 Elgeyo Marakwet County Total.Dewormed 30 6774. 11168.
6 Embu County Total.Dewormed 30 8960. 13764.
7 Garissa County Total.Dewormed 30 10685. 7267.
8 Homa Bay County Total.Dewormed 30 7666. 16756.
9 Isiolo County Total.Dewormed 30 3425. 6157.
10 Kajiado County Total.Dewormed 30 16991. 32105.
# ℹ 37 more rowsSUMM2 <- DT %>%
group_by(county) %>%
get_summary_stats(Acute.Malnutrition, type = "mean_sd")
SUMM2# A tibble: 44 × 5
county variable n mean sd
<chr> <fct> <dbl> <dbl> <dbl>
1 Baringo County Acute.Malnutrition 28 8.82 10.1
2 Bomet County Acute.Malnutrition 6 1.83 1.17
3 Bungoma County Acute.Malnutrition 13 10.2 9.94
4 Busia County Acute.Malnutrition 15 5.8 6.7
5 Embu County Acute.Malnutrition 29 42.6 25.0
6 Garissa County Acute.Malnutrition 30 269. 224.
7 Homa Bay County Acute.Malnutrition 30 26.0 22.9
8 Isiolo County Acute.Malnutrition 30 64.4 50.4
9 Kajiado County Acute.Malnutrition 30 78.4 59.0
10 Kakamega County Acute.Malnutrition 26 24.2 15.7
# ℹ 34 more rowsSUMM3 <- DT %>%
group_by(county) %>%
get_summary_stats(stunted.6.23.months, type = "mean_sd")
SUMM3# A tibble: 47 × 5
county variable n mean sd
<chr> <fct> <dbl> <dbl> <dbl>
1 Baringo County stunted.6.23.months 30 262. 185.
2 Bomet County stunted.6.23.months 21 30.0 81.7
3 Bungoma County stunted.6.23.months 30 121. 69.7
4 Busia County stunted.6.23.months 30 339. 144.
5 Elgeyo Marakwet County stunted.6.23.months 30 80.0 48.6
6 Embu County stunted.6.23.months 30 264. 113.
7 Garissa County stunted.6.23.months 30 40.6 44.5
8 Homa Bay County stunted.6.23.months 30 144. 67.9
9 Isiolo County stunted.6.23.months 30 59.2 46.2
10 Kajiado County stunted.6.23.months 30 275. 148.
# ℹ 37 more rowsView(SUMM3)library(dplyr)
SUMM4<- group_by(DT, county) %>%
summarise(
count = n(),
mean = mean(stunted.0..6.months, na.rm = TRUE),
sd = sd(stunted.0..6.months, na.rm = TRUE),
median = median(stunted.0..6.months, na.rm = TRUE),
max = max(stunted.0..6.months, na.rm = TRUE),
min = min(stunted.0..6.months, na.rm = TRUE),
IQR = IQR(stunted.0..6.months, na.rm = TRUE)
)
SUMM4# A tibble: 47 × 8
county count mean sd median max min IQR
<chr> <int> <dbl> <dbl> <dbl> <int> <int> <dbl>
1 Baringo County 30 122. 119. 75.5 555 34 76.8
2 Bomet County 30 5.52 5.44 3 22 1 5
3 Bungoma County 30 95.3 76.2 82 404 32 37.8
4 Busia County 30 173. 145. 146. 883 53 77
5 Elgeyo Marakwet County 30 68.4 34.7 60.5 169 6 28.2
6 Embu County 30 94.1 55.9 90 348 4 35.5
7 Garissa County 30 25.7 44.7 9.5 208 1 12.8
8 Homa Bay County 30 120. 66.2 106. 378 44 70
9 Isiolo County 30 27.4 45.6 10 207 1 16
10 Kajiado County 30 167. 121. 108 484 51 151
# ℹ 37 more rowsView(SUMM4)library(kableExtra)
kable(SUMM4)| county | count | mean | sd | median | max | min | IQR |
|---|---|---|---|---|---|---|---|
| Baringo County | 30 | 121.500000 | 119.124030 | 75.5 | 555 | 34 | 76.75 |
| Bomet County | 30 | 5.523809 | 5.437086 | 3.0 | 22 | 1 | 5.00 |
| Bungoma County | 30 | 95.266667 | 76.179431 | 82.0 | 404 | 32 | 37.75 |
| Busia County | 30 | 173.300000 | 145.228037 | 146.5 | 883 | 53 | 77.00 |
| Elgeyo Marakwet County | 30 | 68.433333 | 34.712299 | 60.5 | 169 | 6 | 28.25 |
| Embu County | 30 | 94.100000 | 55.929358 | 90.0 | 348 | 4 | 35.50 |
| Garissa County | 30 | 25.678571 | 44.724716 | 9.5 | 208 | 1 | 12.75 |
| Homa Bay County | 30 | 120.033333 | 66.228176 | 105.5 | 378 | 44 | 70.00 |
| Isiolo County | 30 | 27.379310 | 45.602642 | 10.0 | 207 | 1 | 16.00 |
| Kajiado County | 30 | 167.233333 | 120.566102 | 108.0 | 484 | 51 | 151.00 |
| Kakamega County | 30 | 220.433333 | 186.034235 | 161.5 | 787 | 33 | 169.25 |
| Kericho County | 30 | 33.142857 | 33.917890 | 23.0 | 134 | 1 | 32.00 |
| Kiambu County | 30 | 373.900000 | 136.202524 | 346.5 | 826 | 83 | 95.25 |
| Kilifi County | 30 | 328.633333 | 91.684799 | 335.0 | 558 | 145 | 117.25 |
| Kirinyaga County | 30 | 62.533333 | 46.506012 | 49.0 | 218 | 17 | 47.75 |
| Kisii County | 30 | 87.300000 | 100.394446 | 43.0 | 468 | 4 | 86.25 |
| Kisumu County | 30 | 96.200000 | 134.511735 | 68.0 | 784 | 6 | 46.00 |
| Kitui County | 30 | 175.000000 | 77.404446 | 176.5 | 431 | 35 | 61.00 |
| Kwale County | 30 | 135.000000 | 43.401891 | 124.5 | 245 | 31 | 44.50 |
| Laikipia County | 30 | 231.100000 | 235.376492 | 193.0 | 1457 | 90 | 57.50 |
| Lamu County | 30 | 10.148148 | 6.746530 | 9.0 | 31 | 2 | 5.00 |
| Machakos County | 30 | 43.166667 | 54.303796 | 23.0 | 287 | 2 | 45.50 |
| Makueni County | 30 | 106.266667 | 42.458039 | 101.5 | 274 | 48 | 42.00 |
| Mandera County | 30 | 85.633333 | 76.928755 | 77.5 | 445 | 12 | 59.75 |
| Marsabit County | 30 | 37.100000 | 22.884041 | 31.0 | 91 | 9 | 34.00 |
| Meru County | 30 | 124.000000 | 54.097613 | 117.5 | 295 | 42 | 46.25 |
| Migori County | 30 | 64.433333 | 54.254625 | 46.5 | 252 | 8 | 31.00 |
| Mombasa County | 30 | 156.566667 | 104.893602 | 137.0 | 615 | 58 | 78.00 |
| Muranga County | 30 | 265.166667 | 76.785453 | 250.0 | 463 | 140 | 97.25 |
| Nairobi County | 30 | 1228.533333 | 1342.443519 | 829.0 | 7900 | 513 | 463.75 |
| Nakuru County | 30 | 280.700000 | 73.649285 | 269.0 | 409 | 141 | 85.25 |
| Nandi County | 30 | 74.933333 | 43.759478 | 62.5 | 219 | 19 | 41.50 |
| Narok County | 30 | 85.533333 | 71.996999 | 52.5 | 265 | 17 | 89.00 |
| Nyamira County | 30 | 46.733333 | 23.752943 | 40.5 | 119 | 11 | 33.25 |
| Nyandarua County | 30 | 96.500000 | 53.274662 | 82.5 | 351 | 43 | 35.00 |
| Nyeri County | 30 | 178.600000 | 40.937800 | 169.0 | 284 | 104 | 51.25 |
| Samburu County | 30 | 35.333333 | 29.605840 | 26.0 | 122 | 1 | 31.25 |
| Siaya County | 30 | 25.066667 | 32.316253 | 10.0 | 146 | 2 | 23.00 |
| Taita Taveta County | 30 | 89.466667 | 44.575185 | 84.5 | 202 | 4 | 55.50 |
| Tana River County | 30 | 41.166667 | 107.404836 | 14.5 | 601 | 3 | 19.25 |
| Tharaka Nithi County | 30 | 88.433333 | 21.993233 | 95.0 | 121 | 28 | 29.50 |
| Trans Nzoia County | 30 | 130.966667 | 51.412787 | 127.5 | 307 | 9 | 54.50 |
| Turkana County | 30 | 239.466667 | 148.509081 | 200.0 | 558 | 65 | 246.25 |
| Uasin Gishu County | 30 | 191.633333 | 209.973969 | 128.0 | 849 | 45 | 64.50 |
| Vihiga County | 30 | 59.700000 | 50.087613 | 45.5 | 256 | 9 | 24.75 |
| Wajir County | 30 | 26.107143 | 44.588937 | 9.5 | 198 | 1 | 21.50 |
| West Pokot County | 30 | 37.866667 | 35.805734 | 27.5 | 143 | 4 | 41.25 |