606 lab1
IvanTikhonov
2022-09-04
install.packages(“tidyverse”) install.packages(“openintro”) install.packages(“dplyr”)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(openintro)## Loading required package: airports
## Loading required package: cherryblossom
## Loading required package: usdatadata(arbuthnot)
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
## # ℹ Use `print(n = ...)` to see more rowsglimpse(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…arbuthnot$boys## [1] 5218 4858 4422 4994 5158 5035 5106 4917 4703 5359 5366 5518 5470 5460 4793
## [16] 4107 4047 3768 3796 3363 3079 2890 3231 3220 3196 3441 3655 3668 3396 3157
## [31] 3209 3724 4748 5216 5411 6041 5114 4678 5616 6073 6506 6278 6449 6443 6073
## [46] 6113 6058 6552 6423 6568 6247 6548 6822 6909 7577 7575 7484 7575 7737 7487
## [61] 7604 7909 7662 7602 7676 6985 7263 7632 8062 8426 7911 7578 8102 8031 7765
## [76] 6113 8366 7952 8379 8239 7840 76401.What command would you use to extract just the counts of girls baptized? Try it!
arbuthnot$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 7288ggplot(data = arbuthnot,aes(x = year, y = girls)) +
geom_point()
ggplot(data = arbuthnot, aes(x = year, y = girls)) +
geom_line()
?ggplot## starting httpd help server ... done2.Is there an apparent trend in the number of girls baptized over the years? How would you describe it? (To ensure that your lab report is comprehensive, be sure to include the code needed to make the plot as well as your written interpretation.)
There is a positive linear trend in the number of girls baptized over the years. The trend is positive which means each year more girls are being baptized than the previous year.
5218 + 4683## [1] 9901arbuthnot <- arbuthnot %>%
mutate(total = boys + girls)ggplot(data = arbuthnot, aes(x = year, y = total)) +
geom_line()
5218 / 4683## [1] 1.114243arbuthnot <- arbuthnot %>%
mutate(boy_to_girl_ratio = boys / girls)5218 / (5218 + 4683)## [1] 0.5270175arbuthnot <- arbuthnot %>%
mutate(boy_to_girl_ratio = boys / girls)
arbuthnot <- arbuthnot %>%
mutate(boy_ratio = boys / total)
arbuthnot <- arbuthnot %>%
mutate(girl_ratio = girls / total)
arbuthnot %>%
ggplot(aes(year, boy_to_girl_ratio)) +
geom_line() +
xlab("Year") + ylab("Baptized ratio (boys to girls)") +
ggtitle("Baptized children, born in London. Recorded by Dr. John Arbuthnot.")
3.Now, generate a plot of the proportion of boys born over time. What do
you see?
arbuthnot <- arbuthnot %>%
mutate(more_boys = boys > girls)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 ask if the number of births of boys outnumber that of girls in each year with the expression
data('present', package='openintro')
arbuthnot %>%
summarize(min = min(boys), max = max(boys))## # A tibble: 1 × 2
## min max
## <int> <int>
## 1 2890 84264.What years are included in this data set? What are the dimensions of the data frame? What are the variable (column) names?
glimpse(present)## Rows: 63
## Columns: 3
## $ year <dbl> 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950…
## $ boys <dbl> 1211684, 1289734, 1444365, 1508959, 1435301, 1404587, 1691220, 1…
## $ girls <dbl> 1148715, 1223693, 1364631, 1427901, 1359499, 1330869, 1597452, 1…head(present)## # A tibble: 6 × 3
## year boys girls
## <dbl> <dbl> <dbl>
## 1 1940 1211684 1148715
## 2 1941 1289734 1223693
## 3 1942 1444365 1364631
## 4 1943 1508959 1427901
## 5 1944 1435301 1359499
## 6 1945 1404587 1330869tail(present)## # A tibble: 6 × 3
## year boys girls
## <dbl> <dbl> <dbl>
## 1 1997 1985596 1895298
## 2 1998 2016205 1925348
## 3 1999 2026854 1932563
## 4 2000 2076969 1981845
## 5 2001 2057922 1968011
## 6 2002 2057979 1963747dim(present)## [1] 63 3names(present)## [1] "year" "boys" "girls"5.How do these counts compare to Arbuthnot’s? Are they of a similar magnitude?
present <- present %>%
mutate(more_boys = boys > girls)6.Make a plot that displays the proportion of boys born over time. What do you see? Does Arbuthnot’s observation about boys being born in greater proportion than girls hold up in the U.S.? Include the plot in your response. Hint: You should be able to reuse your code from Exercise 3 above, just replace the dataframe name.
present <- present %>%
mutate(boy_to_girl_ratio = boys / girls)
present %>%
ggplot(aes(year, boy_to_girl_ratio)) +
geom_line()
7.In what year did we see the most total number of births in the U.S.?
Hint: First calculate the totals and save it as a new variable. Then,
sort your dataset in descending order based on the total column. You can
do this interactively in the data viewer by clicking on the arrows next
to the variable names. To include the sorted result in your report you
will need to use two new functions: arrange (for sorting). We can
arrange the data in a descending order with another function: desc (for
descending order). The sample code is provided below.
present <- present %>%
mutate(total = boys + girls)
present <- present %>%
arrange(desc(total))
head(present, 10)## # A tibble: 10 × 6
## year boys girls more_boys boy_to_girl_ratio total
## <dbl> <dbl> <dbl> <lgl> <dbl> <dbl>
## 1 1961 2186274 2082052 TRUE 1.05 4268326
## 2 1960 2179708 2078142 TRUE 1.05 4257850
## 3 1957 2179960 2074824 TRUE 1.05 4254784
## 4 1959 2173638 2071158 TRUE 1.05 4244796
## 5 1958 2152546 2051266 TRUE 1.05 4203812
## 6 1962 2132466 2034896 TRUE 1.05 4167362
## 7 1956 2133588 2029502 TRUE 1.05 4163090
## 8 1990 2129495 2028717 TRUE 1.05 4158212
## 9 1991 2101518 2009389 TRUE 1.05 4110907
## 10 1963 2101632 1996388 TRUE 1.05 4098020ggplot(present, aes(year, total)) +
geom_line()
present %>%
arrange(desc(total))## # A tibble: 63 × 6
## year boys girls more_boys boy_to_girl_ratio total
## <dbl> <dbl> <dbl> <lgl> <dbl> <dbl>
## 1 1961 2186274 2082052 TRUE 1.05 4268326
## 2 1960 2179708 2078142 TRUE 1.05 4257850
## 3 1957 2179960 2074824 TRUE 1.05 4254784
## 4 1959 2173638 2071158 TRUE 1.05 4244796
## 5 1958 2152546 2051266 TRUE 1.05 4203812
## 6 1962 2132466 2034896 TRUE 1.05 4167362
## 7 1956 2133588 2029502 TRUE 1.05 4163090
## 8 1990 2129495 2028717 TRUE 1.05 4158212
## 9 1991 2101518 2009389 TRUE 1.05 4110907
## 10 1963 2101632 1996388 TRUE 1.05 4098020
## # … with 53 more rows
## # ℹ Use `print(n = ...)` to see more rows