Cross-tabulation is one of the most useful analytical tools and a mainstay of the market research industry. Cross-tabulation analysis, also known as contingency table analysis, is most often used to analyze categorical (nominal measurement scale) data.
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.7 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(stringr)
library(gmodels)
library(ggplot2)I will like to release the commands so that you can go ahead to practice them …..if any issue arises, you are very free to contact me for further discussions.
ID = seq(1:2500)
set.seed(234)
Age = sample(c("0 - 5", "6 - 14", "15 - 24", "25 - 50", "51 - 64", "65+ "), 2500, replace = TRUE) #View(Age)
set.seed(234)
Gender = sample(c("Male", "Female"), 2500, replace = TRUE) #View(Gender)
set.seed(234)
Country = sample(c("Nigeria", "Ghana", "South Africa", "Botswana", "United Kingdom", "Austria"), 2500, replace = TRUE) #View(Country)
set.seed(234)
Health_Status = sample(c("Poor", "Fair", "Okay"), 2500, replace = TRUE)
# View(Health_Status)
Survey = data.frame(Age, Gender, Country, Health_Status)
View(Survey)
head(Survey, 50) ## Age Gender Country Health_Status
## 1 0 - 5 Male Nigeria Poor
## 2 6 - 14 Male Ghana Okay
## 3 65+ Female Austria Fair
## 4 6 - 14 Female Ghana Fair
## 5 6 - 14 Female Ghana Fair
## 6 51 - 64 Female United Kingdom Fair
## 7 0 - 5 Male Nigeria Poor
## 8 25 - 50 Female Botswana Poor
## 9 25 - 50 Male Botswana Okay
## 10 65+ Female Austria Fair
## 11 65+ Female Austria Okay
## 12 15 - 24 Male South Africa Okay
## 13 6 - 14 Female Ghana Fair
## 14 6 - 14 Male Ghana Okay
## 15 51 - 64 Male United Kingdom Fair
## 16 0 - 5 Female Nigeria Fair
## 17 65+ Male Austria Poor
## 18 15 - 24 Female South Africa Poor
## 19 15 - 24 Female South Africa Fair
## 20 0 - 5 Male Nigeria Okay
## 21 51 - 64 Female United Kingdom Okay
## 22 25 - 50 Male Botswana Poor
## 23 51 - 64 Female United Kingdom Poor
## 24 65+ Female Austria Poor
## 25 6 - 14 Male Ghana Fair
## 26 15 - 24 Male South Africa Fair
## 27 6 - 14 Male Ghana Okay
## 28 6 - 14 Male Ghana Fair
## 29 6 - 14 Female Ghana Fair
## 30 25 - 50 Male Botswana Fair
## 31 65+ Female Austria Okay
## 32 15 - 24 Female South Africa Okay
## 33 15 - 24 Female South Africa Fair
## 34 15 - 24 Female South Africa Okay
## 35 51 - 64 Male United Kingdom Okay
## 36 65+ Female Austria Okay
## 37 6 - 14 Female Ghana Okay
## 38 0 - 5 Female Nigeria Poor
## 39 0 - 5 Male Nigeria Fair
## 40 51 - 64 Male United Kingdom Okay
## 41 0 - 5 Female Nigeria Okay
## 42 65+ Female Austria Fair
## 43 51 - 64 Male United Kingdom Poor
## 44 6 - 14 Female Ghana Poor
## 45 6 - 14 Male Ghana Poor
## 46 0 - 5 Male Nigeria Okay
## 47 65+ Male Austria Okay
## 48 0 - 5 Male Nigeria Poor
## 49 6 - 14 Female Ghana Fair
## 50 0 - 5 Female Nigeria Okay