in this part we will cover same points such us;
The data set contains 563 variables in total
The dataset contains variables with different data types: name and city are categorical variables (object), typically representing text data. Variables such as age, height_cm, weight_kg, and passed_exam are numeric with decimal values (float64). Lastly, last_exam_date is also stored as text (object), likely representing date information that may require further formatting or conversion for time-based analysis. This variety of data types reflects the mixed nature of the dataset, combining categorical, numeric, and potentially temporal information.
In R, numeric variables represent quantitative data, such as numbers, and are used for mathematical operations, statistical analysis, and modeling. They can include integers or decimals, such as age, height, or income. Factor variables, on the other hand, represent categorical data, storing categories as integers with associated labels. They are used to classify data into distinct groups, such as gender, education level, or marital status. While numeric variables are used for continuous or discrete measurements, factor variables are ideal for qualitative classifications and are treated differently in analyses.
V106 <- SLDHS$V106
table(V106)
0 1 2 3
15287 1991 311 97
The variable V106 represents categorical data, likely related to education levels, with the following distribution: the majority (15,287) fall into category 0, which could represent “No education,” followed by 1,991 in category 1 (e.g., “Primary education”), 311 in category 2 (e.g., “Secondary education”), and 97 in category 3 (e.g., “Higher education”). This indicates that most individuals in the dataset have no formal education, with progressively fewer people in higher education levels, highlighting a potential educational disparity.
1 2 3 4 5 6323 3016 2054 2907 3386
1 2 3 4 5 0.358 0171 0.116 0.164 0.191
This indicates: * Approximately 52.9% of households are classified as poor, 11.6% fall into the middle category, and 36.1% are considered high-level when looking at the wealth index.
To calculate the correlation coefficient between the age of the household head (V151) and the number of living children (V201) in R, first ensure the dataset is loaded and extract the relevant variables. The cor() function is then used to compute the correlation between these two numeric variables, with the option use = “complete.obs” to exclude any missing data. The resulting correlation value, ranging from -1 to 1, indicates the strength and direction of the relationship—positive values suggest a direct relationship, while negative values imply an inverse relationship, with 0 indicating no correlation.
data\(poverty_status <- ifelse(data\)V190 %in% c(1, 2, 3), “poor”, ifelse(data$V190 %in% c(4, 5), “rich”, NA))
poverty_status <- SLDHS$poverty_status summary(poverty_status)
H http://127.0.0.1:18241/graphics/1067e3e9-0539-42cf-bb01-5462e0022df9.png
library(haven) new_data <- read_dta(“amoud universty/Course one/Exam/exam dic/new data.dta”) barplot(table(data$Poverty_status)
library(haven) new_data <- read_dta(“amoud universty/Course one/Exam/exam dic/new data.dta”) boxplot( V201,Poverty_status)
The dataset contains 563 variables, each with a specific data type suited for analysis. Numeric variables, such as age or income, are quantitative and support mathematical operations, while factor variables categorize data into distinct groups, like education level or marital status. The dataset’s structure was examined using str(), providing an overview of variable types and data composition. Cleaning efforts involved creating a new variable, poverty_status, to group households into “poor” (poorest, poorer, and middle wealth quintiles) and “rich” (richer and richest). This transformation allows for more focused analyses, particularly on wealth-based disparities.
Descriptive statistics provided key insights into variables like V136 (number of household members). The mean was 5, with a standard deviation of 2.48, indicating moderate variability across households. Frequency tables and proportions highlighted categorical distributions, such as education levels (V106) and wealth quintiles (V190), revealing that the majority had no formal education and approximately 52.9% of households were classified as “poor.” Correlation analysis between age (V151) and the number of living children (V201) using the cor() function helps explore relationships within the dataset, providing insights into demographic and familial trends.
Visualization techniques were employed to illustrate patterns and relationships effectively. A histogram of V136 revealed the distribution of household sizes, while a bar chart showed proportions of households in poverty categories. Additionally, a box plot compared the number of living children (V201) between poor and non-poor groups, uncovering disparities. Selecting appropriate visualization methods is crucial for simplifying data, avoiding misinterpretation, and communicating insights. By using these methods, the analysis provides a clear narrative that supports decision-making and highlights key socio-economic patterns.
