Lab 1
Marcela Rendon
9/9/2021
Intro to R and R Studio Lab 1
Install the necessary packages for this lab
# install.packages("tidyverse")
# install.packages("openintro")
# Added # after it was already installedAccess packages using library
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.4 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 2.0.1 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(openintro)## Loading required package: airports## Loading required package: cherryblossom## Loading required package: usdataDr. Arbuthnot’s Baptism Records
Load the data
arbuthnot <- arbuthnotSummary of the dataset
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
boys <- arbuthnot$boysExercise 1
girls <- arbuthnot$girls# Create a scatter plot
ggplot(data = arbuthnot, aes(x = year, y = girls)) +
geom_point()
# Create line graph
ggplot(data = arbuthnot, aes(x = year, y = girls)) +
geom_line()
Exercise 2
?ggplot# Can use R as calculator to find manually the total number of baptisms in 1629
5218 + 4863## [1] 10081# Faster way to calculate using data from above. Calculations below are for each year in the grid
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# Saving vector of total number of baptisms to generate plots. Saving it as permanent column in data frame using this code:
arbuthnot <- arbuthnot %>%
mutate(total = boys + girls)# Line plot of total number of baptisms per year:
ggplot(data = arbuthnot, aes(x = year, y = total)) +
geom_line()
# Manually calculate raito for every year
5218 / 4683## [1] 1.114243# Calculate same as above but for every year with R
arbuthnot <- arbuthnot %>%
mutate(boy_to_girl_ratio = boys / girls)# Compute the proportions of newborns that are boys in 1629 manually
5218 / (5218 + 4683)## [1] 0.5270175# Calculate same as above for all years with R
arbuthnot <- arbuthnot %>%
mutate(boy_ratio = boys / total)Exercise 3
# Generate a plot of the proportion of boys born over time
ggplot (data = arbuthnot, aes(x = year, y = boys/total)) +
geom_line()
# New variable more_boys tells whether the nymber of births of boys outnymbered that of girls:
arbuthnot <- arbuthnot %>%
mutate(more_boys = boys > girls)More Practice
# Find max and min values of columns
arbuthnot %>%
summarize(min = min(boys),
max = max(boys)
)## # A tibble: 1 × 2
## min max
## <int> <int>
## 1 2890 8426Exercise 4
Load ‘present’ data
present <- present
dim(present)## [1] 63 3Years 1940 - 2002 are included in this dataset Dimensions are 63x3 Variable names: year, boys, girls
Exercise 5
How do these counts compare to Arbuthnot’s? Are they of similar magnitude?
# present total and arbuthnot total to compare
present$boys + present$girls## [1] 2360399 2513427 2808996 2936860 2794800 2735456 3288672 3699940 3535068
## [10] 3559529 3554149 3750850 3846986 3902120 4017362 4047295 4163090 4254784
## [19] 4203812 4244796 4257850 4268326 4167362 4098020 4027490 3760358 3606274
## [28] 3520959 3501564 3600206 3731386 3555970 3258411 3136965 3159958 3144198
## [37] 3167788 3326632 3333279 3494398 3612258 3629238 3680537 3638933 3669141
## [46] 3760561 3756547 3809394 3909510 4040958 4158212 4110907 4065014 4000240
## [55] 3952767 3899589 3891494 3880894 3941553 3959417 4058814 4025933 4021726arbuthnot$boys + arbuthnot$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 14928Present is much larger since it captures birth for all of the US rather than just London
Exercise 6
# Add total column
present <- present %>%
mutate(total = boys + girls)# Generate a plot of the proportion of boys born over time
ggplot (data = present, aes(x = year, y = boys/total)) +
geom_point()
Proportion of boys born decreases over time
# Compare present ratio of boys to girls
present <- present %>%
mutate(more_boys = boys > girls)Arbuthnot’s observation is true as well in the US
Exercise 7
# Number of births per year in descending order:
present %>%
arrange(desc(total))## # A tibble: 63 × 5
## year boys girls total more_boys
## <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 1961 2186274 2082052 4268326 TRUE
## 2 1960 2179708 2078142 4257850 TRUE
## 3 1957 2179960 2074824 4254784 TRUE
## 4 1959 2173638 2071158 4244796 TRUE
## 5 1958 2152546 2051266 4203812 TRUE
## 6 1962 2132466 2034896 4167362 TRUE
## 7 1956 2133588 2029502 4163090 TRUE
## 8 1990 2129495 2028717 4158212 TRUE
## 9 1991 2101518 2009389 4110907 TRUE
## 10 1963 2101632 1996388 4098020 TRUE
## # … with 53 more rows1961 had highest number of births