Week1
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library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.1.2## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.2 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## Warning: package 'ggplot2' was built under R version 4.1.2## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(openintro)## Warning: package 'openintro' was built under R version 4.1.2## Loading required package: airports## Warning: package 'airports' was built under R version 4.1.2## Loading required package: cherryblossom## Warning: package 'cherryblossom' was built under R version 4.1.2## Loading required package: usdata## Warning: package 'usdata' was built under R version 4.1.2Assignment 1:
Data for present day birth records in the United States
data('present', package='openintro')present## # A tibble: 63 x 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 1330869
## 7 1946 1691220 1597452
## 8 1947 1899876 1800064
## 9 1948 1813852 1721216
## 10 1949 1826352 1733177
## # ... with 53 more rowsglimpse(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~Some Exploration:
To access the data in a single column
present$boys## [1] 1211684 1289734 1444365 1508959 1435301 1404587 1691220 1899876 1813852
## [10] 1826352 1823555 1923020 1971262 2001798 2059068 2073719 2133588 2179960
## [19] 2152546 2173638 2179708 2186274 2132466 2101632 2060162 1927054 1845862
## [28] 1803388 1796326 1846572 1915378 1822910 1669927 1608326 1622114 1613135
## [37] 1624436 1705916 1709394 1791267 1852616 1860272 1885676 1865553 1879490
## [46] 1927983 1924868 1951153 2002424 2069490 2129495 2101518 2082097 2048861
## [55] 2022589 1996355 1990480 1985596 2016205 2026854 2076969 2057922 2057979#Exercise 1: to extract just the counts of girls birth record in US
count(present, "girls")## # A tibble: 1 x 2
## `"girls"` n
## <chr> <int>
## 1 girls 63Below is the vector prsentation of count
present$girls## [1] 1148715 1223693 1364631 1427901 1359499 1330869 1597452 1800064 1721216
## [10] 1733177 1730594 1827830 1875724 1900322 1958294 1973576 2029502 2074824
## [19] 2051266 2071158 2078142 2082052 2034896 1996388 1967328 1833304 1760412
## [28] 1717571 1705238 1753634 1816008 1733060 1588484 1528639 1537844 1531063
## [37] 1543352 1620716 1623885 1703131 1759642 1768966 1794861 1773380 1789651
## [46] 1832578 1831679 1858241 1907086 1971468 2028717 2009389 1982917 1951379
## [55] 1930178 1903234 1901014 1895298 1925348 1932563 1981845 1968011 1963747Data Visualization:
simple scatter plot of the number of girls birth record per year using ggplot
ggplot(data = present, aes(x = year, y = girls)) +
geom_point()
## simple line plot of the number of girls birth record per year in US using ggplot
ggplot(data = present, aes(x = year, y = girls)) +
geom_line()
## Exercise 2: From 1940 to 1960 there is increase in the number of girls born per year. ## Then from 1980 to 2000 there is a increase in the number of girls born per year.
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 4021726Adding a new variable to the dataframe
present <- present %>%
mutate(total = boys + girls)
present## # A tibble: 63 x 4
## year boys girls total
## <dbl> <dbl> <dbl> <dbl>
## 1 1940 1211684 1148715 2360399
## 2 1941 1289734 1223693 2513427
## 3 1942 1444365 1364631 2808996
## 4 1943 1508959 1427901 2936860
## 5 1944 1435301 1359499 2794800
## 6 1945 1404587 1330869 2735456
## 7 1946 1691220 1597452 3288672
## 8 1947 1899876 1800064 3699940
## 9 1948 1813852 1721216 3535068
## 10 1949 1826352 1733177 3559529
## # ... with 53 more rowsExercise 3: To generate a plot of the proportion of boys born over time
present <- present %>%
mutate(boy_ratio = boys/total)
present## # A tibble: 63 x 5
## year boys girls total boy_ratio
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1940 1211684 1148715 2360399 0.513
## 2 1941 1289734 1223693 2513427 0.513
## 3 1942 1444365 1364631 2808996 0.514
## 4 1943 1508959 1427901 2936860 0.514
## 5 1944 1435301 1359499 2794800 0.514
## 6 1945 1404587 1330869 2735456 0.513
## 7 1946 1691220 1597452 3288672 0.514
## 8 1947 1899876 1800064 3699940 0.513
## 9 1948 1813852 1721216 3535068 0.513
## 10 1949 1826352 1733177 3559529 0.513
## # ... with 53 more rowsAnalysis: You can see below that there is a consistant fall pattern of boys birth ratio per year
ggplot(data = present, aes(x = year, y = boy_ratio)) +
geom_line()
ggplot(data = present, aes(x = year, y = boy_ratio)) +
geom_point() +
geom_smooth(method=lm) # add linear trend line## `geom_smooth()` using formula 'y ~ x'
# boys birth ratio per year is a downward slope from 1940 to 2000present <- present %>%
mutate(boy_to_girl_ratio = boys / girls)
present## # A tibble: 63 x 6
## year boys girls total boy_ratio boy_to_girl_ratio
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1940 1211684 1148715 2360399 0.513 1.05
## 2 1941 1289734 1223693 2513427 0.513 1.05
## 3 1942 1444365 1364631 2808996 0.514 1.06
## 4 1943 1508959 1427901 2936860 0.514 1.06
## 5 1944 1435301 1359499 2794800 0.514 1.06
## 6 1945 1404587 1330869 2735456 0.513 1.06
## 7 1946 1691220 1597452 3288672 0.514 1.06
## 8 1947 1899876 1800064 3699940 0.513 1.06
## 9 1948 1813852 1721216 3535068 0.513 1.05
## 10 1949 1826352 1733177 3559529 0.513 1.05
## # ... with 53 more rowsTo see the boolean output if the boys are more than girls
present <- present %>%
mutate(more_boys = boys > girls)
present## # A tibble: 63 x 7
## year boys girls total boy_ratio boy_to_girl_ratio more_boys
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 1940 1211684 1148715 2360399 0.513 1.05 TRUE
## 2 1941 1289734 1223693 2513427 0.513 1.05 TRUE
## 3 1942 1444365 1364631 2808996 0.514 1.06 TRUE
## 4 1943 1508959 1427901 2936860 0.514 1.06 TRUE
## 5 1944 1435301 1359499 2794800 0.514 1.06 TRUE
## 6 1945 1404587 1330869 2735456 0.513 1.06 TRUE
## 7 1946 1691220 1597452 3288672 0.514 1.06 TRUE
## 8 1947 1899876 1800064 3699940 0.513 1.06 TRUE
## 9 1948 1813852 1721216 3535068 0.513 1.05 TRUE
## 10 1949 1826352 1733177 3559529 0.513 1.05 TRUE
## # ... with 53 more rowspresent %>%
summarize(min = min(boys), max = max(boys))## # A tibble: 1 x 2
## min max
## <dbl> <dbl>
## 1 1211684 2186274Exercise 4:
What years are included in this data set?
present$year## [1] 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954
## [16] 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969
## [31] 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
## [46] 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
## [61] 2000 2001 2002Exercise 4:
How to get dimensions of a dataframe
glimpse(present)## Rows: 63
## Columns: 7
## $ year <dbl> 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948~
## $ boys <dbl> 1211684, 1289734, 1444365, 1508959, 1435301, 1404587~
## $ girls <dbl> 1148715, 1223693, 1364631, 1427901, 1359499, 1330869~
## $ total <dbl> 2360399, 2513427, 2808996, 2936860, 2794800, 2735456~
## $ boy_ratio <dbl> 0.5133386, 0.5131376, 0.5141926, 0.5138001, 0.513561~
## $ boy_to_girl_ratio <dbl> 1.054817, 1.053969, 1.058429, 1.056767, 1.055757, 1.~
## $ more_boys <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE~dim(present)## [1] 63 7Exercise 4:
What are the variable (column) names?
colnames(present)## [1] "year" "boys" "girls"
## [4] "total" "boy_ratio" "boy_to_girl_ratio"
## [7] "more_boys"Exercise 5:
How do these counts compare to Arbuthnot’s? Are they of a similar magnitude?
As you can see there is amajor difference in the magnitude of means
# arbuthnot data mean is:
mean(arbuthnot$boys + arbuthnot$girls)## [1] 11441.74# present data mean is:
mean(present$boys + present$girls)## [1] 3679515Exercise 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
present <- present %>%
mutate(girl_ratio = girls/total)
present## # A tibble: 63 x 8
## year boys girls total boy_ratio boy_to_girl_rat~ more_boys girl_ratio
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl>
## 1 1940 1211684 1148715 2360399 0.513 1.05 TRUE 0.487
## 2 1941 1289734 1223693 2513427 0.513 1.05 TRUE 0.487
## 3 1942 1444365 1364631 2808996 0.514 1.06 TRUE 0.486
## 4 1943 1508959 1427901 2936860 0.514 1.06 TRUE 0.486
## 5 1944 1435301 1359499 2794800 0.514 1.06 TRUE 0.486
## 6 1945 1404587 1330869 2735456 0.513 1.06 TRUE 0.487
## 7 1946 1691220 1597452 3288672 0.514 1.06 TRUE 0.486
## 8 1947 1899876 1800064 3699940 0.513 1.06 TRUE 0.487
## 9 1948 1813852 1721216 3535068 0.513 1.05 TRUE 0.487
## 10 1949 1826352 1733177 3559529 0.513 1.05 TRUE 0.487
## # ... with 53 more rowsggplot(data = present, aes(x = year, y = girl_ratio)) +
geom_point() +
geom_smooth(method=lm) # add linear trend line## `geom_smooth()` using formula 'y ~ x'
# Analysis: you can see that the girls birth ratio increases from 1940 to 2000
# Whereas the boys birth ratio is decreasing from 1940 to 2000Exercise 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.
present <- present %>%
mutate(total = boys + girls)
present## # A tibble: 63 x 8
## year boys girls total boy_ratio boy_to_girl_rat~ more_boys girl_ratio
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl>
## 1 1940 1211684 1148715 2360399 0.513 1.05 TRUE 0.487
## 2 1941 1289734 1223693 2513427 0.513 1.05 TRUE 0.487
## 3 1942 1444365 1364631 2808996 0.514 1.06 TRUE 0.486
## 4 1943 1508959 1427901 2936860 0.514 1.06 TRUE 0.486
## 5 1944 1435301 1359499 2794800 0.514 1.06 TRUE 0.486
## 6 1945 1404587 1330869 2735456 0.513 1.06 TRUE 0.487
## 7 1946 1691220 1597452 3288672 0.514 1.06 TRUE 0.486
## 8 1947 1899876 1800064 3699940 0.513 1.06 TRUE 0.487
## 9 1948 1813852 1721216 3535068 0.513 1.05 TRUE 0.487
## 10 1949 1826352 1733177 3559529 0.513 1.05 TRUE 0.487
## # ... with 53 more rowstotal_desc <- present %>%
arrange(desc(total))
total_desc## # A tibble: 63 x 8
## year boys girls total boy_ratio boy_to_girl_rat~ more_boys girl_ratio
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl>
## 1 1961 2186274 2082052 4268326 0.512 1.05 TRUE 0.488
## 2 1960 2179708 2078142 4257850 0.512 1.05 TRUE 0.488
## 3 1957 2179960 2074824 4254784 0.512 1.05 TRUE 0.488
## 4 1959 2173638 2071158 4244796 0.512 1.05 TRUE 0.488
## 5 1958 2152546 2051266 4203812 0.512 1.05 TRUE 0.488
## 6 1962 2132466 2034896 4167362 0.512 1.05 TRUE 0.488
## 7 1956 2133588 2029502 4163090 0.513 1.05 TRUE 0.487
## 8 1990 2129495 2028717 4158212 0.512 1.05 TRUE 0.488
## 9 1991 2101518 2009389 4110907 0.511 1.05 TRUE 0.489
## 10 1963 2101632 1996388 4098020 0.513 1.05 TRUE 0.487
## # ... with 53 more rowsggplot(data = present, aes(x = year, y = total)) +
geom_line()
# 1960 is the year where you can see the most total number of births in the U.S.Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.