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library(openintro)
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Dr. Arbuthnot’s Baptism Records

Load the data

arbuthnot
## # A tibble: 82 × 3
##     year  boys girls
##    <int> <int> <int>
##  1  1629  5218  4683
##  2  1630  4858  4457
##  3  1631  4422  4102
##  4  1632  4994  4590
##  5  1633  5158  4839
##  6  1634  5035  4820
##  7  1635  5106  4928
##  8  1636  4917  4605
##  9  1637  4703  4457
## 10  1638  5359  4952
## # … with 72 more rows

See the data in the global environment

Name the data set with a backwards arrow and dash line “<-” The data will come up in the global environment and you can click on it. It will open the data in a new tab.

arbuthnot <- arbuthnot

View the data

glimpse(arbuthnot)
## Rows: 82
## Columns: 3
## $ year  <int> 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639…
## $ boys  <int> 5218, 4858, 4422, 4994, 5158, 5035, 5106, 4917, 4703, 5359, 5366…
## $ girls <int> 4683, 4457, 4102, 4590, 4839, 4820, 4928, 4605, 4457, 4952, 4784…

Some Exploration

arbuthnot$boys
##  [1] 5218 4858 4422 4994 5158 5035 5106 4917 4703 5359 5366 5518 5470 5460 4793
## [16] 4107 4047 3768 3796 3363 3079 2890 3231 3220 3196 3441 3655 3668 3396 3157
## [31] 3209 3724 4748 5216 5411 6041 5114 4678 5616 6073 6506 6278 6449 6443 6073
## [46] 6113 6058 6552 6423 6568 6247 6548 6822 6909 7577 7575 7484 7575 7737 7487
## [61] 7604 7909 7662 7602 7676 6985 7263 7632 8062 8426 7911 7578 8102 8031 7765
## [76] 6113 8366 7952 8379 8239 7840 7640

Exercise 1

arbuthnot$girls
##  [1] 4683 4457 4102 4590 4839 4820 4928 4605 4457 4952 4784 5332 5200 4910 4617
## [16] 3997 3919 3395 3536 3181 2746 2722 2840 2908 2959 3179 3349 3382 3289 3013
## [31] 2781 3247 4107 4803 4881 5681 4858 4319 5322 5560 5829 5719 6061 6120 5822
## [46] 5738 5717 5847 6203 6033 6041 6299 6533 6744 7158 7127 7246 7119 7214 7101
## [61] 7167 7302 7392 7316 7483 6647 6713 7229 7767 7626 7452 7061 7514 7656 7683
## [76] 5738 7779 7417 7687 7623 7380 7288

Data visualization

With ggplot():

The first argument is always the name of the dataset you wish to use for plotting. Next, you provide the variables from the dataset to be assigned to different aesthetic elements of the plot, such as the x and the y axes.

These commands will build a blank plot, with the variables you assigned to the x and y axes. Next, you need to tell ggplot() what type of visualization you would like to add to the blank template. You add another layer to the ggplot() by:

adding a “+” at the end of the line, to indicate that you are adding a layer then specify the geometric object to be used to create the plot.

Since we want to scatterplot, we use geom_point()

We can create a simple plot of the number of girls baptized per year with the following code:

ggplot(data = arbuthnot, aes(x = year, y = girls)) + 
  geom_point()

Create a line glaph of the number of girls baptized per year

ggplot(data = arbuthnot, aes(x = year, y = girls)) +
  geom_line()

Exercise 2 There is a trend. The number of girls baptised was on the decline from the 1960’s until about the 1950’s, then there was a steep increase in girls baptised.

Plot of the number of boys baptized per year (bonus practice)

ggplot(data = arbuthnot, aes(x = year, y = boys)) + 
  geom_point()

Find out what function does and how to use it

Information appears in the packages panel.

?ggplot()

R as a big calculator

5218 + 4683
## [1] 9901
arbuthnot$boys + arbuthnot$girls
##  [1]  9901  9315  8524  9584  9997  9855 10034  9522  9160 10311 10150 10850
## [13] 10670 10370  9410  8104  7966  7163  7332  6544  5825  5612  6071  6128
## [25]  6155  6620  7004  7050  6685  6170  5990  6971  8855 10019 10292 11722
## [37]  9972  8997 10938 11633 12335 11997 12510 12563 11895 11851 11775 12399
## [49] 12626 12601 12288 12847 13355 13653 14735 14702 14730 14694 14951 14588
## [61] 14771 15211 15054 14918 15159 13632 13976 14861 15829 16052 15363 14639
## [73] 15616 15687 15448 11851 16145 15369 16066 15862 15220 14928

What you will see is a list of 82 numbers. These numbers appear as a list, because we are working with vectors rather than a data frame. Each number represents the sum of how many boys and girls were baptized that year. You can take a look at the first few rows of the boys and girls columns to see if the calculation is right.

Adding a new variable to the data frame

We are interested in using this new vector of the total number of baptisms to generate some plots, so we’ll want to save it as a permanent column in our data frame. We can do this using the following code:

arbuthnot <- arbuthnot %>%
  mutate(total = boys + girls)

“Take the arbuthnot dataset and pipe it into the mutate function. Mutate the arbuthnot data set by creating a new variable called total that is the sum of the variables called boys and girls. Then assign the resulting dataset to the object called arbuthnot, i.e. overwrite the old arbuthnot dataset with the new one containing the new variable.”

This is equivalent to going through each row and adding up the boys and girls counts for that year and recording that value in a new column called total.

Where is the new variable?

When you make changes to variables in your dataset, click on the name of the dataset again to update it in the data viewer.

You’ll see that there is now a new column called total that has been tacked onto the data frame. The special symbol <- performs an assignment, taking the output of the piping operations and saving it into an object in your environment. In this case, you already have an object called arbuthnot in your environment, so this command updates that data set with the new mutated column.

You can make a line plot of the total number of baptisms per year with the following code:

ggplot(data = arbuthnot, aes(x = year, y = total)) + 
  geom_line()

Calculate the ratio of number of boys to the number of girls baptized

Alternatively, you could calculate this ratio for every year by acting on the complete boys and girls columns, and then save those calculations into a new variable named boy_to_girl_ratio:

arbuthnot <- arbuthnot %>%
  mutate(boy_to_girl_ratio = boys / girls)

Exercise 3 Generate a plot of the proportion of boys born over time. What do you see?

ggplot(data = arbuthnot, aes(x = year, y = boy_to_girl_ratio)) + 
  geom_point()

Making comparisons in R

Finally, in addition to simple mathematical operators like subtraction and division, you can ask R to make comparisons like greater than, >, less than, <, and equality, ==. For example, we can create a new variable called more_boys that tells us whether the number of births of boys outnumbered that of girls in each year with the following code:

You can ask R to make comparisons like greater than, >, less than, <, and equality, ==. For example, we can create a new variable called more_boys that tells us whether the number of births of boys outnumbered that of girls in each year with the following code:

arbuthnot <- arbuthnot %>%
  mutate(more_boys = boys > girls)

This command adds a new variable to the arbuthnot data frame containing the values of either TRUE if that year had more boys than girls, or FALSE if that year did not (the answer may surprise you). This variable contains a different kind of data than we have encountered so far. All other columns in the arbuthnot data frame have values that are numerical (the year, the number of boys and girls). Here, we’ve asked R to create logical data, data where the values are either TRUE or FALSE. In general, data analysis will involve many different kinds of data types, and one reason for using R is that it is able to represent and compute with many of them.

More Practice

Find minimum and maximum amount of boy births in a year To find the minimum and maximum values of columns, you can use the functions min() and max() within a summarize() call, which you will learn more about in the following lab.

arbuthnot %>%
  summarize(min = min(boys),
            max = max(boys)
            )
## # A tibble: 1 × 2
##     min   max
##   <int> <int>
## 1  2890  8426
knitr::include_graphics("/Users/asiadowner/Downloads/Abstract.jpg")