Visual_Analytics_NEC_All_Weeks_Solved_Week3_to_Week15_Solutions

According to the author, 'ggplot is an implementation of the grammar of graphics' which is a set of rules for producing visualizations of data. In this first plot, we will track the trajectory of life expectancy over time for each country in the data. 1. map year to x and lifeExp to y...

1. get the structure of the gss_sm dataframe. What is the data type of race, sex, region and income? What do the levels refer to? 2. create a graph that shows a count of religious preferences grouped by region 3. turn the region counts in percentages 4. use dodge2() to put the religious affiliat...

1. produce a scatterplot of the by_country data with the points colored by consent_law 2. Using facet_wrap() split the consent_law variable into two panels and rank the countries by donation rate within the panels 3. Use geom_pointrange() to create a dot and whisker plot showing the mean of donors...

1. Return to the visualization for Presidential Elections: Popular and Electoral College margins, subset by party, and use that to add color to your points. 2. Recreate figures 5.28 using functions from the dplyr library. 3. Using gss_sm data, calculate the mean and median number of children by de...

1. Using the gapminder data, create a plot comparing log(gdp PerCa with Life Exp and show three different smoothers in three different colors with a legend showing each smoother type. 2. In a paragraph compare and contrast the smoother types. LOESS, Cubic Spline, and OLS 3. Look at the gapminder...

1. load the broom library 2. use tidy() on the out dataframe to produce a new dataframe of component level information. Store the result in out_comp. 3. round all the columns to two decimal places using round_df(). 4. Produce a flipped scatter plot of Term v. Estimate 5. Produce a new tidy outpu...

1. Take a slice of the gapminder data showing only 1977 2. create a linear model of with lifeexp being the target of the log of gdpPercap. Save it in a variable called fit and show the summary. 3. Group the entire data set by continent and year, pipe it through the nest() function and store it in...

1. load the margins library 2. create a new column called called polviews_m to use Moderate as a reference category using relevel on the polviews column of the gss_sm data. 3. use glm() to create a model called out_bo using logistic regression of polviews_m with sex and race showing an interactio...

1. pipe the election data through the select() function to pick out the following columns - state, total_vote, r_points, pct_trump, party, census. Pipe that through sample() to see the first five rows. 2. Create a state level dotplot of election data except the District of Columbia faceted by regi...

1. look at the first six rows of the asasec dataset 2. plot members v revenue for 2014 in a scatterplot with a confidence interval 3. switch from loess to ols and add the Journal variable 4. show the first six rows of studebt 5. create a faceted comparison of the two distributions - percent of a...

Show meta data from the mpg dataframe using summary(). Show metadata from the gapminder dataframe assign ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp) to the variable 'p' find the structure of the p object. add () to the p object. Show p. replace () with h(). Show p. re...

According to the author, 'ggplot is an implementation of the grammar of graphics' which is a set of rules for producing visualizations of data. In this first plot, we will track the trajectory of life expectancy over time for each country in the data. 1. map year to x and lifeExp to y. 2. use g...