# First steps of data exploration and visualization with Tidyverse

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In this post, I will show you, how to use visualization and transformation for exploring your data in R. I will use several functions that come with Tidyverse package.

In general, there are two types of variables, categorical and continuous. In this section, I will show the best option to examine their distributions using the data from NHANES.

### Load the library and data:

library(tidyverse) library(RNHANES) d13 = nhanes_load_data("DEMO_H", "2013-2014") %>% transmute(SEQN=SEQN, wave=cycle, INDFMIN2, RIDRETH1) %>% left_join(nhanes_load_data("BMX_H", "2013-2014"), by="SEQN") %>% select(SEQN, wave, INDFMIN2, RIDRETH1, BMXBMI) %>% mutate( annincome = recode_factor(INDFMIN2, '1' = "lowest", '2' = "lowest", '3' = "lowest", '4' = "low", '5' = "low", '6' = "low", '7' = "medium", '8' = "medium", '9' = "medium", '10' = "high", '12' = "high", '13' = "high", '14' = "highest", '15' = "highest")) %>% filter(!is.na(BMXBMI), !is.na(annincome))

With the dataset created I will visualize the distribution using a bar chart.

ggplot(data = d13) + geom_bar(aes(annincome))

To see the exact number for each category, I can also calculate these values with `count()`

d13 %>% count(annincome)

For a continuous variable it is necessary to use the histogram. I chose to see how BMI is distributed in NHANES population for 2013, with `binwidth = 5`

, so cut the variable by 5 unit increase.

ggplot(data = d13) + geom_histogram(aes(BMXBMI), binwidth = 5)

Combining 'ggplot2' and 'dplyr', I can see the relevant values fo Bmi with the function `cut_width()`

by 5 unit increase)

d13 %>% count(cut_width(BMXBMI, 5))

To combine the information I showed previously in the same plot, for information about BMI and annual income I will use `geomfreqpoly()`

, and have the multiple histograms below.

ggplot(data = d13, aes(BMXBMI, color = annincome)) + geom_freqpoly(binwidth = 1)

### A categorical and a continuous variable

Now I am going to demonstrate a link of a continuous variable based on the other categorical variable using the boxplot.

ggplot(data = d13, aes(annincome, BMXBMI)) + geom_boxplot()

So for each box, the middle line is the median 50th percentile for each category. In my case, if I chose category medium for annual income the median of BMI is ~27. The upper and the lower line of the box shows 75th (BMI=31) percentile, and 25th (BMI=20) percentile and the distance between them is called the Interquartile Range.

### Two categorical variables

For two categorical variable, I need to visualize the relation between them, but I also would like to know the number of observations, so I will use 'geom_tile' and 'fill aesthetic' and have the graph below.

d13 %>% mutate(race = recode_factor(RIDRETH1, `1` = "Mexian American", `2` = "Hispanic", `3` = "Non-Hispanic, White", `4` = "Non-Hispanic, Black", `5` = "Others")) %>% count(race, annincome) %>% ggplot(aes(race, annincome)) + geom_tile(aes(fill = n))

### Two continuous variables

Below, I will see how do BMI and cholesterol come along with each other drawn in a scatterplot.

data13 = d13 %>% left_join(nhanes_load_data("TCHOL_H", "2013-2014"), by="SEQN") %>% select(SEQN, wave, INDFMIN2, RIDRETH1, BMXBMI, LBXTC) ggplot(data = data13) + geom_point(aes(BMXBMI, LBXTC))

Because the points overplot in the previous scatterplot, I can use 'alpha aesthetic' for a more useful graph.

ggplot(data = data13) + geom_point(aes(BMXBMI, LBXTC), alpha = 1/20)

Another way to visualize a relationship of two continuous variables is by using bins and treating one of the variables as a definite. Adding 'cut_number' will make the comparison fairer as there is the same number of points in each bin.

ggplot(data = data13, aes(BMXBMI, LBXTC)) + geom_boxplot(aes(group = cut_number(BMXBMI, 20)))

Hope this post will help you chose the right and best way to illustrate distribution and relations within and between variables.

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