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Day 2.R
230
Day 2.R
@ -1,83 +1,5 @@
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library(tidyverse)
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#
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# fertilityplots
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#
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fertilityData <- structure(list(Country = structure(c(2L, 5L, 6L,
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9L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L), .Label = c("Abkhazia",
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"Afghanistan", "Akrotiri and Dhekelia", "Åland", "Albania",
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"Algeria", "American Samoa", "Andorra", "Angola", "Anguilla",
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"Antigua and Barbuda", "Argentina", "Armenia", "Aruba", "Australia",
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"Austria", "Azerbaijan", "Bahamas", "Bahrain", "Bangladesh",
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"Barbados", "Belarus", "Belgium", "Belize", "Benin", "Bermuda",
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"Bhutan", "Bolivia", "Bosnia and Herzegovina", "Botswana", "Brazil",
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"British Virgin Islands", "Brunei", "Bulgaria", "Burkina Faso",
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"Burundi", "Cambodia", "Cameroon", "Canada", "Cape Verde", "Cayman Islands",
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"Central African Republic", "Chad", "Channel Islands", "Chile",
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"China", "Christmas Island", "Cocos Island", "Colombia", "Comoros",
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"Congo, Dem. Rep.", "Congo, Rep.", "Cook Is", "Costa Rica", "Cote d'Ivoire",
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"Croatia", "Cuba", "Cyprus", "Czech Republic", "Czechoslovakia",
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"Denmark", "Djibouti", "Dominica", "Dominican Republic", "East Germany",
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"Ecuador", "Egypt", "El Salvador", "Equatorial Guinea", "Eritrea",
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"Eritrea and Ethiopia", "Estonia", "Ethiopia", "Faeroe Islands",
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"Falkland Is (Malvinas)", "Fiji", "Finland", "France", "French Guiana",
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"French Polynesia", "Gabon", "Gambia", "Georgia", "Germany",
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"Ghana", "Gibraltar", "Greece", "Greenland", "Grenada", "Guadeloupe",
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"Guam", "Guatemala", "Guernsey", "Guinea", "Guinea-Bissau", "Guyana",
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"Haiti", "Holy See", "Honduras", "Hong Kong, China", "Hungary",
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"Iceland", "India", "Indonesia", "Iran", "Iraq", "Ireland", "Isle of Man",
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"Israel", "Italy", "Jamaica", "Japan", "Jersey", "Jordan", "Kazakhstan",
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"Kenya", "Kiribati", "Kosovo", "Kuwait", "Kyrgyz Republic", "Lao",
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"Latvia", "Lebanon", "Lesotho", "Liberia", "Libya", "Liechtenstein",
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"Lithuania", "Luxembourg", "Macao, China", "Macedonia, FYR",
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"Madagascar", "Malawi", "Malaysia", "Maldives", "Mali", "Malta",
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"Marshall Islands", "Martinique", "Mauritania", "Mauritius",
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"Mayotte", "Mexico", "Micronesia, Fed. Sts.", "Moldova", "Monaco",
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"Mongolia", "Montenegro", "Montserrat", "Morocco", "Mozambique",
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"Myanmar", "Namibia", "Nauru", "Nepal", "Netherlands", "Netherlands Antilles",
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"New Caledonia", "New Zealand", "Ngorno-Karabakh", "Nicaragua",
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"Niger", "Nigeria", "Niue", "Norfolk Island", "North Korea",
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"North Yemen (former)", "Northern Cyprus", "Northern Mariana Islands",
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"Norway", "Oman", "Pakistan", "Palau", "Panama", "Papua New Guinea",
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"Paraguay", "Peru", "Philippines", "Pitcairn", "Poland", "Portugal",
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"Puerto Rico", "Qatar", "Reunion", "Romania", "Russia", "Rwanda",
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"Samoa", "San Marino", "Sao Tome and Principe", "Saudi Arabia",
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"Senegal", "Serbia", "Serbia and Montenegro", "Serbia excluding Kosovo",
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"Seychelles", "Sierra Leone", "Singapore", "Slovak Republic",
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"Slovenia", "Solomon Islands", "Somalia", "Somaliland", "South Africa",
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"South Korea", "South Ossetia", "South Yemen (former)", "Spain",
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"Sri Lanka", "St. Barthélemy", "St. Helena", "St. Kitts and Nevis",
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"St. Lucia", "St. Martin", "St. Vincent and the Grenadines",
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"St.-Pierre-et-Miquelon", "Sudan", "Suriname", "Svalbard", "Swaziland",
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"Sweden", "Switzerland", "Syria", "Taiwan", "Tajikistan", "Tanzania",
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"Thailand", "Timor-Leste", "Togo", "Tokelau", "Tonga", "Transnistria",
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"Trinidad and Tobago", "Tunisia", "Turkey", "Turkmenistan", "Turks and Caicos Islands",
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"Tuvalu", "Uganda", "Ukraine", "United Arab Emirates", "United Kingdom",
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"United Korea (former)\n", "United States", "Uruguay", "USSR",
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"Uzbekistan", "Vanuatu", "Venezuela", "Vietnam", "Virgin Islands (U.S.)",
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"Wallis et Futuna", "West Bank and Gaza", "West Germany", "Western Sahara",
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"Yemen", "Yugoslavia", "Zambia", "Zimbabwe"), class = "factor"),
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`1800` = c(7, 4.6, 6.99, 6.93, 5, 6.8, 7.8, 5.64, 6.5, 5.1,
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8.1, 5.9), `1801` = c(7, 4.6, 6.99, 6.93, 5, 6.8, 7.8, 5.64,
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6.48, 5.1, 8.1, 5.9), `1802` = c(7, 4.6, 6.99, 6.93, 4.99,
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6.8, 7.81, 5.64, 6.46, 5.1, 8.1, 5.9), `1803` = c(7, 4.6,
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6.99, 6.93, 4.99, 6.8, 7.81, 5.64, 6.44, 5.1, 8.1, 5.9),
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`1804` = c(7, 4.6, 6.99, 6.93, 4.99, 6.8, 7.81, 5.64, 6.42,
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5.1, 8.1, 5.9), `1805` = c(7, 4.6, 6.99, 6.93, 4.98, 6.8,
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7.82, 5.64, 6.4, 5.1, 8.1, 5.9)), .Names = c("Country",
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"1800", "1801", "1802", "1803", "1804", "1805"), row.names = c(NA,
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-12L), class = c("tbl_df", "tbl", "data.frame"))
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(fertilityTidy <- fertilityData %>%
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tidyr::pivot_longer(cols = -Country, names_to = "Year",
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values_to = "Fertility") %>%
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mutate(Year = as.integer(Year)))
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ggplot(fertilityTidy, aes(Year, Fertility, color = Country)) +
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geom_point() +
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geom_line()
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#
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#
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@ -95,5 +17,157 @@ iris |> # Data set
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scale_fill_viridis(discrete = T) # Auto scales colors for color blind friendliness
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df <- dplyr::filter(gapminder::gapminder, year == 1992)
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head(df)
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library(hrbrthemes)
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df |> ggplot(aes(gdpPercap,lifeExp,size=pop,color=continent)) +
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geom_point(alpha=0.5) +
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scale_size(range = c(1, 20)) +
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scale_x_log10()+
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# theme_ipsum() +
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scale_fill_viridis(discrete=TRUE, guide = "none", option="A") +
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theme(aspect.ratio = 0.8, legend.key.width = unit(3, "line"))+ # Setting plot ratio
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labs(title = "Gapminder for 1992",
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x = "Gross Domestic Product (log scale)",
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y = "Life Expectancy at birth (years)",
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color = "Continent", size = "Population")
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library(gganimate)
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ggplot(gapminder::gapminder,aes(gdpPercap,lifeExp,size=pop,color=continent)) +
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geom_point(alpha=0.5) +
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scale_size(range = c(1, 20)) +
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scale_x_log10()+
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# theme_ipsum() +
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scale_fill_viridis(discrete=TRUE, guide = "none", option="A") +
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theme(aspect.ratio = 0.8, legend.key.width = unit(3, "line"))+
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labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
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transition_time(year) +
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ease_aes('linear') # Does not combine the output photos
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ggplot(data = mpg) +
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geom_point(mapping = aes(x = displ, y = hwy, color = displ), color = "blue")
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sapply(mpg,is.factor)
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head(mpg)
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?mpg
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ggplot(data = mpg, aes(x = displ, y = hwy, color = displ, size = , shape = )) +
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geom_point()
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df <- data.frame(abc = 1, xyz = "a")
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df$x # Partial matching
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df[, "xyz"] # Giving vector
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df[, c("abc", "xyz")] # Giving data frame
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df <- tibble(abc = 1, xyz = "a")
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df$x
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df[, "xyz"] # Keeping data frame
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df[, c("abc", "xyz")]
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var<-"mpg"
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head(mtcars)
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mtcars[var]
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df <- tibble("1" = 1:4, "2" = 3:6)
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df[["1"]]
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plot(df$`1`,df$`2`)
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df$`3` <- (df$`1`/df$`2`)
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### dplyr
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library(nycflights13)
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library(tidyverse)
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flights
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?flights
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flights |>
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filter(month == 1, day == 1)
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flights |>
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filter(arr_delay > 120)
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flights |>
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filter(dest %in% c("IAH", "HOU"))
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print(airlines)
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flights |>
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filter(carrier %in% c("UA", "AA", "DL"))
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flights |>
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filter(month %in% c(7:9))
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flights |>
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filter(arr_delay > 120 & dep_delay==0)
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flights |>
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filter(dep_time<=600)
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#
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# https://r4ds.had.co.nz/transform.html
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#
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flights |> arrange(!is.na(dep_time))
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flights |> arrange(desc(dep_delay),dep_time)
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flights |> mutate(speed=distance/air_time) |> arrange(desc(speed)) |> print(width=Inf)
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flights |> arrange(desc(distance))
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flights |> arrange(distance)
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# Selections
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flights |> select(all_of(c("dep_time", "dep_delay", "arr_time", "arr_delay")))
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flights |> select(dep_time, dep_delay, arr_time, arr_delay)
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flights |> select(dep_time, dep_delay, arr_time, arr_delay)
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flights |> select(match(c("dep_time", "dep_delay", "arr_time", "arr_delay"),names(flights)))
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flights |> select(any_of(vars))
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vars <- c("year", "month", "day", "dep_delay", "arr_delay")
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select(flights, contains("TIME", ignore.case = TRUE))
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# Mutate
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library(lubridate)
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flights |> mutate(
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dep_time_n =
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)
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min_trans <- function(x){
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ifelse(x== 2400, 0, x %/% 100*60 + x %% 100)
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}
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flights |> transmute(across(c(dep_time,sched_dep_time),min_trans))
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flights |> transmute(
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air_time = air_time,
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air_time_c = arr_time-dep_time # Differences due to time zones
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) |>
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slice_sample(n = 5e3) |>
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ggplot()+
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geom_point(aes(air_time,air_time_c))+
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geom_abline(color="red")
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flights |>
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transmute(
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sched_dep_time, dep_delay, dep_time
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)
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1:3+1:10
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142
Day 3.R
Normal file
142
Day 3.R
Normal file
@ -0,0 +1,142 @@
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library(tidyverse)
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library(nycflights13)
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# 5.6
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delays <- flights %>%
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group_by(dest) %>%
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summarise(
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count = n(),
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dist = mean(distance, na.rm = TRUE),
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delay = mean(arr_delay, na.rm = TRUE)
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) %>%
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filter(count > 20, dest != "HNL")
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ggplot(data = delays, mapping = aes(x = dist, y = delay)) +
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geom_point(aes(size = count), alpha = 1/3) +
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geom_smooth(se = FALSE)
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library(microbenchmark)
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microbenchmark::microbenchmark(
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flights |>
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rowwise() |> # loops rowwise, instead of vectorised.
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mutate(arr_time2 = arr_time + 1) |>
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ungroup() # Reverses the rowwise
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flights |>
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mutate(arr_time2 = arr_time + 1)
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)
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# 5.6.1 Exercises
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flights |>
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group_by(flight) |>
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summarise(med = median(arr_delay, na.rm=TRUE)) |>
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filter(med==-15)
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flights |>
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group_by(flight) |>
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summarise(med = median(arr_delay, na.rm=TRUE)) |>
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filter(med==15)
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flights |>
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filter(arr_delay==10) |>
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group_by(flight) |>
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summarise(n=n()) |>
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filter(n >= 10) |>
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arrange(desc(n))
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flights |>
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group_by(flight) |>
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summarise(early = mean(arr_delay >= 0, na.rm = T),
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n = n())
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not_cancelled <- flights %>%
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filter(!is.na(dep_delay), !is.na(arr_delay))
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not_cancelled %>% count(dest)
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flights |>
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filter(!is.na(dep_delay), !is.na(arr_delay)) |>
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group_by(dest) |>
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summarise(n= n())
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flights |>
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filter(!is.na(dep_delay), !is.na(arr_delay)) |>
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group_by(tailnum) |>
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summarise(n = sum(distance))
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not_cancelled %>% count(tailnum, wt = distance)
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flights |>
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(\(.) filter(., complete.cases(.))) ()
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flights |>
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group_by(year, month, day) |>
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summarise(canc = sum(is.na(dep_delay)),
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n = n()) |>
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arrange(desc(canc))
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flights |>
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group_by(year, month, day) |>
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summarise(canc = mean(is.na(dep_delay)), # calculates the proportion of NAs per day
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del = mean(dep_delay, na.rm=TRUE)) |>
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ggplot(aes(canc,del)) +
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geom_point() +
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geom_smooth() +
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theme_bw(18)
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# 5.7.1 Exercises
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flights |>
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group_by(tailnum) |>
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filter(sum(!is.na(arr_delay))>1) |>
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summarise(m_del = max(arr_delay, na.rm = TRUE)) |>
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slice_max(m_del, n = 1)
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flights |>
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group_by(hour) |>
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summarise(mean_del=mean(arr_delay , na.rm = TRUE)) |>
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ggplot(aes(hour,mean_del)) +
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geom_point() +
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geom_smooth()
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flights %>%
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group_by(dest) %>%
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mutate(prop_delay = arr_delay / sum(arr_delay, na.rm = TRUE)) %>%
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relocate(prop_delay)
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# Relational data
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airports |>
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right_join(flights |>
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group_by(dest) |>
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summarise(avg_del = mean(arr_delay,na.rm=TRUE)),
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c("faa" = "dest")) |>
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ggplot(aes(lon, lat, size = avg_del, color = avg_del)) +
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borders("state") +
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geom_point(alpha=.6) +
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coord_quickmap()+
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theme_bw(18)+
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scale_color_viridis_c(direction = -1)
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library(lubridate)
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flights |>
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group_by(tailnum) |>
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summarise(avg_del = mean(arr_delay,na.rm=TRUE)) |>
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left_join(planes |> select(tailnum, year),
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c("tailnum")) |>
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(\(.) filter(.,complete.cases(.))) () |>
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ggplot(aes(2014-year, avg_del, color = avg_del)) +
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geom_point(alpha=.6)+
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theme_bw(18)+
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scale_color_viridis_c(direction = -1) +
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geom_smooth() +
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theme(aspect.ratio = 0.8, legend.key.width = unit(3, "line"))+ # Setting plot ratio
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labs(y="Mean delay", x="Age")
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weather
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