All my work from the past week

week1/citibike.R

@@ -6,43 +6,80 @@ library(lubridate)
 ########################################
 
 # read one month of data
-trips <- read_csv('201402-citibike-tripdata.csv')
-
-# replace spaces in column names with underscores
-names(trips) <- gsub(' ', '_', names(trips))
-
-# convert dates strings to dates
-# trips <- mutate(trips, starttime = mdy_hms(starttime), stoptime = mdy_hms(stoptime))
-
-# recode gender as a factor 0->"Unknown", 1->"Male", 2->"Female"
-trips <- mutate(trips, gender = factor(gender, levels=c(0,1,2), labels = c("Unknown","Male","Female")))
 
 
 ########################################
 # YOUR SOLUTIONS BELOW
 ########################################
 
 # count the number of trips (= rows in the data frame)
+ summarize(trips, count = n())
 
 # find the earliest and latest birth years (see help for max and min to deal with NAs)
+max(trips$birth_year, na.rm = FALSE)
+#had issue with min that data model was not formatted with NA, instead it was \\N so figured out how to change it to NA so min could work   
+#my partner had a better solution as.numeric(trips$birthyear) which will automatically cast all non numerics as NA so the following is silly and overdramatic and pointless                                    
+trips <-  
+    mutate(trips, birth_year
+    = if_else(!str_detect(birth_year, 
+    "^[0-9]+$"), "NA", birth_year))
+min(trips$birth_year, na.rm = FALSE)
 
 # use filter and grepl to find all trips that either start or end on broadway
+filter(trips, grepl("Broadway", start_station_name)| grepl("Broadway", end_station_name))
 
 # do the same, but find all trips that both start and end on broadway
+filter(trips, grepl("Broadway", start_station_name) & grepl("Broadway", end_station_name))
 
 # find all unique station names
+trips |> distinct(start_station_name)
 
 # count the number of trips by gender, the average trip time by gender, and the standard deviation in trip time by gender
 # do this all at once, by using summarize() with multiple arguments
+summarize(group_by(trips, gender), 
+    count = n(), mean = mean(tripduration), 
+    sd = sd(tripduration))
 
 # find the 10 most frequent station-to-station trips
+trips |> group_by(start_station_name, end_station_name) |>
+     summarize(count = n()) |>
+      arrange(desc(count)) |>
+       head(n=10)
 
 # find the top 3 end stations for trips starting from each start station
+trips |> group_by(start_station_name, end_station_name) |>
+    summarize(count = n()) |>
+    group_by(start_station_name) |>
+    arrange(desc(count)) |>
+    mutate(rank = row_number()) |>
+    filter(rank <=  3) |>
+    arrange(start_station_name)
+#Jake's solution: trips |> 
+    #count(start_station_name, end_station_name) |>
+    #group_by(start_station_name) |>
+    #arrange(desc(n)) |>
+    #slice(1:3)
+
+
 
 # find the top 3 most common station-to-station trips by gender
+trips |> group_by(start_station_name, end_station_name, gender) |>
+     summarize(count = n()) |>
+     arrange(desc(count))|>
+     group_by(gender) |>
+     mutate(rank = row_number()) |>
+     filter(rank <=3) |>
+     arrange(gender)
+
 
 # find the day with the most trips
+trips |> group_by(as_date(starttime)) |> summarize(count = n()) |> arrange(desc(count)) |> head(n=1)
+
 # tip: first add a column for year/month/day without time of day (use as.Date or floor_date from the lubridate package)
 
+
 # compute the average number of trips taken during each of the 24 hours of the day across the entire month
 # what time(s) of day tend to be peak hour(s)?
+trips |> group_by(hour(starttime)) |> summarize(count =n(), average = count/n_distinct(as_date(starttime)))
+
+trips |> group_by(hour(starttime)) |> summarize(count =n(), average = count/n_distinct(as_date(starttime))) |> arrange(average) |> tail(n=1)

week1/citibike.sh

@@ -1,23 +1,23 @@
 #!/bin/bash
 #
-# add your solution after each of the 10 comments below
+# add your solution aftegr each of the 10 comments below
 #
 
 # count the number of unique stations
-
+cut -d, -f4 201402-citibike-tripdata.csv | sort | uniq -c | wc -l
 # count the number of unique bikes
-
+cut -d, -f12 201402-citibike-tripdata.csv | sort | uniq -c | wc -l
 # count the number of trips per day
-
+cut -d, -f2 201402-citibike-tripdata.csv | cut -d' ' -f1 |  sort | uniq -c
 # find the day with the most rides
-
+cut -d, -f2 201402-citibike-tripdata.csv | cut -d' ' -f1 |  sort | uniq -c | sort -r | head -1
 # find the day with the fewest rides
-
+cut -d, -f2 201402-citibike-tripdata.csv | cut -d' ' -f1 |  sort | uniq -c | sort | tail -n +2 | head -1
 # find the id of the bike with the most rides
-
-# count the number of rides by gender and birth year
-
+cut -d, -f12 201402-citibike-tripdata.csv | sort | uniq -c | sort -r | head -1
+# count the number f rides by gender and birth year
+cut -d, -f14,15 201402-citibike-tripdata.csv | tail -2 +2 | sort | uniq -c
 # count the number of trips that start on cross streets that both contain numbers (e.g., "1 Ave & E 15 St", "E 39 St & 2 Ave", ...)
-
-
+cut -d, -f5 201402-citibike-tripdata.csv | grep '.*[0-9].*&.*[0-9].*' | sort | uniq -c | awk '{sum += $1} END {print sum}'
 # compute the average trip duration
+awk -F, '{sum += $1; count++} END {print sum/count}' 201402-citibike-tripdata.csv