DataCamp Data Visualizations - Unit 9 - Data Visualization Principles Part 2

Exercise 1: Customizing plots - watch and learn

To make the plot on the right in the exercise from the last set of assessments, we had to reorder the levels of the states’ variables.

Redefine the state object so that the levels are re-ordered by rate.

Print the new object state and its levels (using levels) so you can see that the vector is now re-ordered by the levels.

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)
library(dslabs)
dat <- us_contagious_diseases %>%
filter(year == 1967 & disease=="Measles" & !is.na(population)) %>% mutate(rate = count / population * 10000 * 52 / weeks_reporting)
state <- dat$state 
rate <- dat$count/(dat$population/10000)*(52/dat$weeks_reporting)
state <- reorder(state, rate, FUN = mean)
levels(state)

##  [1] "Georgia"              "District Of Columbia" "Connecticut"         
##  [4] "Minnesota"            "Louisiana"            "New Hampshire"       
##  [7] "Maryland"             "Kansas"               "New York"            
## [10] "Pennsylvania"         "Rhode Island"         "Massachusetts"       
## [13] "Missouri"             "New Jersey"           "South Dakota"        
## [16] "Vermont"              "Delaware"             "Ohio"                
## [19] "Illinois"             "Michigan"             "Indiana"             
## [22] "North Carolina"       "South Carolina"       "Hawaii"              
## [25] "Maine"                "California"           "Florida"             
## [28] "Iowa"                 "Mississippi"          "Oklahoma"            
## [31] "Nebraska"             "Utah"                 "Alabama"             
## [34] "Kentucky"             "Wisconsin"            "Montana"             
## [37] "Virginia"             "Alaska"               "Tennessee"           
## [40] "Idaho"                "New Mexico"           "Arizona"             
## [43] "Nevada"               "Arkansas"             "Wyoming"             
## [46] "Colorado"             "West Virginia"        "Oregon"              
## [49] "Texas"                "North Dakota"         "Washington"

Exercise 2: Customizing plots - redefining

Now we are going to customize this plot a little more by creating a rate variable and reordering by that variable instead.

Add a single line of code to the definition of the dat table that uses mutate to reorder the states by the rate variable.

The sample code provided will then create a bar plot using the newly defined dat.

library(dplyr)
library(ggplot2)
library(dslabs)
data(us_contagious_diseases)
dat <- us_contagious_diseases %>% filter(year == 1967 & disease=="Measles" & count>0 & !is.na(population)) %>%
  mutate(rate = count / population * 10000 * 52 / weeks_reporting) %>%
  mutate(state = reorder(state, rate, FUN = mean))

dat %>% ggplot(aes(state, rate)) +
  geom_bar(stat="identity") +
  coord_flip()

Exercise 3: Showing the data and customizing plots

Say we are interested in comparing gun homicide rates across regions of the US. We see this plot:

library(dplyr) library(ggplot2) library(dslabs) data(“murders”) murders %>% mutate(rate = total/population*100000) %>% group_by(region) %>% summarize(avg = mean(rate)) %>% mutate(region = factor(region)) %>% ggplot(aes(region, avg)) + geom_bar(stat=“identity”) + ylab(“Murder Rate Average”) and decide to move to a state in the western region. What is the main problem with this interpretaion?

Possible Answers:

• The categories are ordered alphabetically.
• The graph does not show standard errors.
• It does not show all the data. We do not see the variability within a region and it’s possible that the safest states are not in the West.
• The Northeast has the lowest average.

Exercise 4: Making a box plot

To further investigate whether moving to the western region is a wise decision, let’s make a box plot of murder rates by region, showing all points.

Order the regions by their median murder rate by using mutate and reorder.

Make a box plot of the murder rates by region.

Show all of the points on the box plot.

library(dplyr)
library(ggplot2)
library(dslabs)
data("murders")
murders %>% mutate(rate = total/population*100000) %>%
  mutate(region = reorder(region, rate, FUN = median)) %>% 
  ggplot(aes(region,rate)) +
  geom_boxplot() + 
  geom_point()