Homework-7
Lin
2023-04-21
library(tidyverse)
library(nycflights13)
library(lubridate)1. nycflights13 data set
Question : How many timezones are there for all the destination airports (excluding NA)?
airports1 <- airports %>%
select(faa, tzone,tz)
flights %>%
left_join(airports1, by = c("dest" = "faa")) %>%
rename("dest_tzone" = "tzone","dest_tz" = "tz") %>%
filter(!is.na(dest_tzone))%>%
count(dest_tzone)%>%
print()## # A tibble: 7 × 2
## dest_tzone n
## <chr> <int>
## 1 America/Anchorage 8
## 2 America/Chicago 74811
## 3 America/Denver 10291
## 4 America/Los_Angeles 46324
## 5 America/New_York 192377
## 6 America/Phoenix 4656
## 7 Pacific/Honolulu 707Answer: there are 7 timezones: America/Anchorage, America/Chicago, America/Denver, America/Los_Angeles,America/New_York, America/Phoenix, Pacific/Honolulu
Question : Use the data in flights and airports only along with R code to show the time difference (in hours)between New York City and the following cities:
– Chicago – Dallas – Denver – Seattle – Anchorage
airports2 <- airports %>%
select(faa, tzone,tz) flights %>%
left_join(airports2, by = c("dest" = "faa")) %>%
rename("dest_tzone" = "tzone","dest_tz" = "tz") %>%
select(dest,dest_tzone,dest_tz) %>%
filter( dest == "ORD" | dest == "PHX" | dest == "DEN" | dest == "SEA" | dest == "ANC")## # A tibble: 33,136 × 3
## dest dest_tzone dest_tz
## <chr> <chr> <dbl>
## 1 ORD America/Chicago -6
## 2 ORD America/Chicago -6
## 3 ORD America/Chicago -6
## 4 PHX America/Phoenix -7
## 5 PHX America/Phoenix -7
## 6 ORD America/Chicago -6
## 7 DEN America/Denver -7
## 8 ORD America/Chicago -6
## 9 ORD America/Chicago -6
## 10 ORD America/Chicago -6
## # … with 33,126 more rowsflights %>%
left_join(airports2, by = c("dest" = "faa")) %>%
rename("dest_tzone" = "tzone","dest_tz" = "tz") %>%
select(dest,dest_tzone,dest_tz) %>%
filter( dest == "ORD" | dest == "PHX" | dest == "DEN" | dest == "SEA" | dest == "ANC") %>%
summarise(dest=dest, time_diff = -5 - dest_tz) %>%
print()## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
## always returns an ungrouped data frame and adjust accordingly.## # A tibble: 33,136 × 2
## dest time_diff
## <chr> <dbl>
## 1 ORD 1
## 2 ORD 1
## 3 ORD 1
## 4 PHX 2
## 5 PHX 2
## 6 ORD 1
## 7 DEN 2
## 8 ORD 1
## 9 ORD 1
## 10 ORD 1
## # … with 33,126 more rowsAnswer: the time difference between New Your and the cities are: Chicago 1hr, Dallas 2hrs, Denver 2hrs, Seattle 3hrs, Anchorage 4hrs.
Question : Write a function Time_difference_NYC(dest) to compute the time difference (in hours) between any destination airport (in faa code) and New York City.
Time_difference_NYC <- function(x){
nyc_tz = -5
#for (i in airports$faa) {
#airport_tz <- airports[(airports$faa== "i"), ]$tz
airports[airports$faa == x, ]$tz - nyc_tz
}
Time_difference_NYC("LAX")## [1] -3Question : Write a function flight_time(dep_time, arr_time, origin, dest) to compute the actual flight time from the dep_time at the origin airport to the arr_time in the dest airport. dep_time and arr_time should be in the form of an integer such as 830 (8:30am); origin and dest should be in faa codes.
Question : Write a function flights_distribution(month, day) which creates a histogram of flight counts vs hour for the given month and day in 2013.
flights1 <- flightsflights_distribution <- function(m, d) {
flights1 %>%
filter(month == m & day == d & year == 2013) %>%
ggplot(aes(x = hour)) +
geom_histogram(binwidth = 1, fill = "orange", col = "lightblue") +
labs(x = "Hour of Day", y = "Flight Counts", title = "Flight Distribution for the given Month and Day")
}flights_distribution(11, 24)
flights_distribution(7, 14)
Question : Use your function in the last step to discuss how distribution of flight times within a day change over the course of the year?
flights_distribution(1, 1)
flights_distribution(2, 1)
flights_distribution(3, 1)
flights_distribution(4, 1)
flights_distribution(5, 1)
flights_distribution(6, 1)
flights_distribution(7, 1)
flights_distribution(8, 1)
flights_distribution(9, 1)
flights_distribution(10, 1)
flights_distribution(11, 1)
flights_distribution(12, 1)
Answer: according to the 12 plots above(they are the
first day distribution for 12 month), the similar parts are from 22pm ~
5am, the flights are very few, starting from 6am, there will be a big
surge, for the majority month, 6~8am are the busiest schedule, except
Jan. and Sept, the busiest schedule are from 3~5pm; I also check the
Sunday distributions for all the month, the highest peak is not as early
as 6am, it is around 8am and 3pm.
2. Lists
Question : Write a code to create a list that contains 100 vectors, where the first vector is 1 alone, the second is 1 and 2, the third is 1,2,3 etc.
x <- 1:100
for ( i in x){
print(c(1:i))
}## [1] 1
## [1] 1 2
## [1] 1 2 3
## [1] 1 2 3 4
## [1] 1 2 3 4 5
## [1] 1 2 3 4 5 6
## [1] 1 2 3 4 5 6 7
## [1] 1 2 3 4 5 6 7 8
## [1] 1 2 3 4 5 6 7 8 9
## [1] 1 2 3 4 5 6 7 8 9 10
## [1] 1 2 3 4 5 6 7 8 9 10 11
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## [26] 26
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## [26] 26 27
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## [26] 26 27 28
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## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
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## [51] 51
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## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52
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## [51] 51 52 53
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## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54
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## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83 84
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83 84 85
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83 84 85 86
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83 84 85 86 87
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83 84 85 86 87 88
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
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## [91] 91 92 93 94 95 96 97 98 99 1003. Iteration. : Use map function to select features from your midterm project data set:
Question : For all numeric variables, pick up those that return a p-value less than 0.01 when do the two population mean t-test
CC_data <- read_csv("application_data.csv")## Rows: 307511 Columns: 122
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (16): NAME_CONTRACT_TYPE, CODE_GENDER, FLAG_OWN_CAR, FLAG_OWN_REALTY, N...
## dbl (106): SK_ID_CURR, TARGET, CNT_CHILDREN, AMT_INCOME_TOTAL, AMT_CREDIT, A...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.column_num <- map_lgl(CC_data, is.numeric)
CC_num <- CC_data[column_num]
results_cor <- map_dbl(CC_num, ~ cor(CC_num$SK_ID_CURR, .))
results_cor_data <- as.tibble(results_cor)## Warning: `as.tibble()` was deprecated in tibble 2.0.0.
## ℹ Please use `as_tibble()` instead.
## ℹ The signature and semantics have changed, see `?as_tibble`.results_cor_data_1<- filter(results_cor_data,abs(value) < 0.01)
results_cor_data_1## # A tibble: 44 × 1
## value
## <dbl>
## 1 -0.00211
## 2 -0.00113
## 3 -0.00182
## 4 -0.000343
## 5 0.000849
## 6 -0.00150
## 7 0.00137
## 8 -0.000973
## 9 -0.000384
## 10 0.00280
## # … with 34 more rowsQuestion : For all categorical variables (other than the target variable), pick up those that return a p-value less than 0.01 when do the chi-square test.
CC_data <- read_csv("application_data.csv")## Rows: 307511 Columns: 122
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (16): NAME_CONTRACT_TYPE, CODE_GENDER, FLAG_OWN_CAR, FLAG_OWN_REALTY, N...
## dbl (106): SK_ID_CURR, TARGET, CNT_CHILDREN, AMT_INCOME_TOTAL, AMT_CREDIT, A...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.column_chr <- map_lgl(CC_data, is.character)
CC_chr <- CC_data[column_chr]
result <- map_dbl(CC_chr, ~chisq.test(CC_chr$NAME_CONTRACT_TYPE, .)$p.value)## Warning in chisq.test(CC_chr$NAME_CONTRACT_TYPE, .): Chi-squared approximation
## may be incorrect
## Warning in chisq.test(CC_chr$NAME_CONTRACT_TYPE, .): Chi-squared approximation
## may be incorrect
## Warning in chisq.test(CC_chr$NAME_CONTRACT_TYPE, .): Chi-squared approximation
## may be incorrect
## Warning in chisq.test(CC_chr$NAME_CONTRACT_TYPE, .): Chi-squared approximation
## may be incorrectresult1 <- as.tibble(result)
result2 <- filter(result1,abs(value) < 0.01)
result2## # A tibble: 12 × 1
## value
## <dbl>
## 1 0
## 2 4.25e- 14
## 3 1.25e-303
## 4 1.02e- 12
## 5 1.05e-250
## 6 2.74e-301
## 7 1.14e-152
## 8 5.42e- 47
## 9 2.09e-133
## 10 1.06e- 12
## 11 1.23e-258
## 12 7.30e- 5