library('tidyr')
library('readr')
library('dplyr')
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library('ggplot2')
library('lubridate')
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library('tidyverse')
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library('nycflights13')
late_flights <- flights %>%
filter(arr_delay > 5)
monthly_late_flights <- late_flights %>%
group_by(month) %>%
summarize(count = n())
print(monthly_late_flights)
## # A tibble: 12 × 2
## month count
## <int> <int>
## 1 1 8988
## 2 2 8119
## 3 3 9033
## 4 4 10544
## 5 5 8490
## 6 6 10739
## 7 7 11518
## 8 8 9649
## 9 9 5347
## 10 10 7628
## 11 11 7485
## 12 12 12291
total_flights_per_month <- flights %>%
group_by(month) %>%
summarise(total = n())
carrier_flights_per_month <- flights %>%
group_by(carrier, month) %>%
summarise(count = n())
## `summarise()` has grouped output by 'carrier'. You can override using the
## `.groups` argument.
carrier_percentage <- left_join(carrier_flights_per_month, total_flights_per_month, by = "month") %>%
mutate(percentage = paste0(round((count / total) * 100, 2), "%"))
spread_data <- carrier_percentage %>%
select(-count, -total) %>%
spread(key = month, value = percentage)
print(spread_data)
## # A tibble: 16 × 13
## # Groups: carrier [16]
## carrier `1` `2` `3` `4` `5` `6` `7` `8` `9` `10` `11`
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 9E 5.83% 5.85% 5.64% 5.33% 5.08% 5.09% 5.08% 4.96% 5.58% 5.79% 5.85%
## 2 AA 10.35% 10.09% 9.67% 9.61% 9.73% 9.76% 9.79% 9.74% 9.48% 9.4% 9.45%
## 3 AS 0.23% 0.22% 0.22% 0.21% 0.22% 0.21% 0.21% 0.21% 0.22% 0.21% 0.19%
## 4 B6 16.39% 16.44% 16.55% 15.94% 15.8… 16.3… 16.9… 16.8… 15.5… 15.1% 15.7…
## 5 DL 13.66% 13.8% 14.53% 14.44% 14.1… 14.6… 14.4… 14.7… 14.0… 14.1… 14.1…
## 6 EV 15.45% 15.34% 16.39% 16.1% 16.7… 15.7… 15.7… 15.5… 17.1… 16.9… 16.4%
## 7 F9 0.22% 0.2% 0.2% 0.2% 0.2% 0.19% 0.2% 0.19% 0.21% 0.2% 0.22%
## 8 FL 1.21% 1.19% 1.1% 1.1% 1.13% 0.89% 0.89% 0.9% 0.92% 0.82% 0.74%
## 9 HA 0.11% 0.11% 0.11% 0.11% 0.11% 0.11% 0.11% 0.11% 0.09% 0.07% 0.09%
## 10 MQ 8.41% 8.19% 7.82% 7.8% 7.93% 7.71% 7.68% 7.72% 8% 7.71% 7.54%
## 11 OO 0% <NA> <NA> <NA> <NA> 0.01% <NA> 0.01% 0.07% <NA> 0.02%
## 12 UA 17.17% 17.42% 17.24% 17.82% 17.2… 17.6… 17.2… 17.4… 17.0… 17.5… 17.8%
## 13 US 5.93% 6.22% 5.97% 6.1% 6.2% 6.15% 6.07% 6.07% 6.16% 6.39% 6.23%
## 14 VX 1.17% 1.09% 1.05% 1.64% 1.72% 1.7% 1.66% 1.67% 1.64% 1.63% 1.65%
## 15 WN 3.69% 3.65% 3.46% 3.46% 3.49% 3.64% 3.66% 3.57% 3.66% 3.78% 3.79%
## 16 YV 0.17% 0.19% 0.06% 0.13% 0.17% 0.17% 0.28% 0.22% 0.15% 0.23% 0.18%
## # ℹ 1 more variable: `12` <chr>
flights <- flights %>%
mutate(delay = dep_delay)
most_delayed_flights <- flights %>%
group_by(month) %>%
filter(delay == max(delay, na.rm = TRUE)) %>%
slice(1)
print(most_delayed_flights)
## # A tibble: 12 × 20
## # Groups: month [12]
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 9 641 900 1301 1242 1530
## 2 2013 2 10 2243 830 853 100 1106
## 3 2013 3 17 2321 810 911 135 1020
## 4 2013 4 10 1100 1900 960 1342 2211
## 5 2013 5 3 1133 2055 878 1250 2215
## 6 2013 6 15 1432 1935 1137 1607 2120
## 7 2013 7 22 845 1600 1005 1044 1815
## 8 2013 8 8 2334 1454 520 120 1710
## 9 2013 9 20 1139 1845 1014 1457 2210
## 10 2013 10 14 2042 900 702 2255 1127
## 11 2013 11 3 603 1645 798 829 1913
## 12 2013 12 5 756 1700 896 1058 2020
## # ℹ 12 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, time_hour <dttm>, delay <dbl>
# QUESTION 2
library(tidyr)
respons <- read_csv("multipleChoiceResponses1.csv")
## Rows: 16716 Columns: 47
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (46): LearningPlatformUsefulnessArxiv, LearningPlatformUsefulnessBlogs, ...
## dbl (1): Age
##
## 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.
result <- respons %>%
select(starts_with("LearningPlatformUsefulness")) %>%
gather(key = "learning_platform", value = "usefulness", na.rm = TRUE) %>%
mutate(learning_platform = gsub('LearningPlatformUsefulness', '', learning_platform)) %>%
count(learning_platform, usefulness)
print(result)
## # A tibble: 54 × 3
## learning_platform usefulness n
## <chr> <chr> <int>
## 1 Arxiv Not Useful 37
## 2 Arxiv Somewhat useful 1038
## 3 Arxiv Very useful 1316
## 4 Blogs Not Useful 45
## 5 Blogs Somewhat useful 2406
## 6 Blogs Very useful 2314
## 7 College Not Useful 101
## 8 College Somewhat useful 1405
## 9 College Very useful 1853
## 10 Communities Not Useful 16
## # ℹ 44 more rows
atleastuseful <- respons %>%
gather(key = "learning_platform", value = "usefulness", starts_with("LearningPlatformUsefulness")) %>%
filter(!is.na(usefulness)) %>%
mutate(learning_platform = gsub("LearningPlatformUsefulness", "", learning_platform)) %>%
count(learning_platform, name = "count") %>%
left_join(
respons %>%
gather(key = "learning_platform", value = "usefulness", starts_with("LearningPlatformUsefulness")) %>%
filter(!is.na(usefulness) & usefulness != "Not Useful") %>%
mutate(learning_platform = gsub("LearningPlatformUsefulness", "", learning_platform)) %>%
count(learning_platform, name = "at_least_useful"),
by = "learning_platform"
) %>%
mutate(
tot = ifelse(is.na(at_least_useful), count, at_least_useful),
perc_usefulness = tot / count
) %>%
select(learning_platform, tot, count, perc_usefulness)
atleastuseful
## # A tibble: 18 × 4
## learning_platform tot count perc_usefulness
## <chr> <int> <int> <dbl>
## 1 Arxiv 2354 2391 0.985
## 2 Blogs 4720 4765 0.991
## 3 College 3258 3359 0.970
## 4 Communities 1126 1142 0.986
## 5 Company 940 981 0.958
## 6 Conferences 2063 2182 0.945
## 7 Courses 5945 5992 0.992
## 8 Documentation 2279 2321 0.982
## 9 Friends 1530 1581 0.968
## 10 Kaggle 6527 6583 0.991
## 11 Newsletters 1033 1089 0.949
## 12 Podcasts 1090 1214 0.898
## 13 Projects 4755 4794 0.992
## 14 SO 5576 5640 0.989
## 15 Textbook 4112 4181 0.983
## 16 TradeBook 324 333 0.973
## 17 Tutoring 1394 1426 0.978
## 18 YouTube 5125 5229 0.980
atleastuseful %>%
mutate(
learning_platform = fct_reorder(learning_platform, perc_usefulness, .desc = TRUE),
perc_usefulness = as.numeric(perc_usefulness)
) %>%
ggplot(aes(y = learning_platform, yend = learning_platform, x = 0, xend = perc_usefulness)) +
geom_segment(color = "pink") +
geom_point(aes(x = perc_usefulness), color = "pink", size = 3) +
scale_x_continuous(labels = scales::percent_format()) +
coord_flip() +
labs(
title = "Percentage of Usefulness by Learning Platform",
x = "Percent findings at least somewhat useful",
y = "Learning platform"
)
