# In this Critical Thinking Assignment, you will explore and summarize a dataset as well as create several visualizations. Explore breakfast cereals in the Cereals.csv (Links to an external site.) file

In this Critical Thinking Assignment, you will explore and summarize a dataset as well as create several visualizations. Explore breakfast cereals in the Cereals.csv (Links to an external site.) file by performing the following steps.

1. Follow the process under Summary Statistics in section 4.4 of our text.
2. Use the R code example shown in Table 4.3 and Table 4.4.
3. For your assignment submission, take screenshots of your entire R Studio window showing the successful execution of the data.frame() function to compute the following values. Your screenshots must include the system date and time.

1. mean
2. standard deviation
3. min
4. max
5. median
6. length
7. sum of missing values for each of the quantitative attributes

Hint: You can select a set of data frame columns in the sapply() function. First, determine the quantitative attribute columns. These are the columns you want to keep. You can either positively include these or negatively exclude the other columns. For example, the following code excludes columns 6, 7, and 8 from the mean calculation.sapply(cereals.df[,-c(6:8)], mean, na.rm=TRUE) The na.rm=TRUE eliminates missing values, which cause errors in mathematical calculations.

1. Plot a histogram for each of the quantitative variables by following the process under Distribution Plots: Boxplots and Histograms in section 3.3.
2. Use the R code example shown in Figure 3.2.
3. For your assignment submission, take a screenshot of your entire R Studio window showing the following items. Your screenshots must include the system date and time.

1. Your code for creating all the histograms
2. At least one histogram in the Plot window
5. Plot a side-by-side boxplot comparing calories in hot versus cold cereals by following the process under Distribution Plots: Boxplots and Histograms in section 3.3.
6. Use the R code example shown in Figure 3.2 and Figure 3.3.