# Load the needed packages
library(readr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(jsonlite)
library(httr)
# Load the needed data sets
data(cars)
Flying_Data <- read_csv("~/Desktop/On_Time_Performance.csv")## New names:
## • `` -> `...110`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)## Rows: 570131 Columns: 110## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (28): UniqueCarrier, Carrier, TailNum, Origin, OriginCityName, OriginSt...
## dbl (54): Year, Quarter, Month, DayofMonth, DayOfWeek, AirlineID, FlightNum...
## lgl (27): Div2WheelsOff, Div2TailNum, Div3Airport, Div3AirportID, Div3Airpo...
## date (1): FlightDate
##
## 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.# Use the median function to calculate the median from the speed column
median(cars$speed)## [1] 15# Use the structure function to find the size of the dataframe
str(Flying_Data)## spc_tbl_ [570,131 × 110] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Year : num [1:570131] 2018 2018 2018 2018 2018 ...
## $ Quarter : num [1:570131] 1 1 1 1 1 1 1 1 1 1 ...
## $ Month : num [1:570131] 1 1 1 1 1 1 1 1 1 1 ...
## $ DayofMonth : num [1:570131] 16 17 18 19 20 21 22 23 24 25 ...
## $ DayOfWeek : num [1:570131] 2 3 4 5 6 7 1 2 3 4 ...
## $ FlightDate : Date[1:570131], format: "2018-01-16" "2018-01-17" ...
## $ UniqueCarrier : chr [1:570131] "AA" "AA" "AA" "AA" ...
## $ AirlineID : num [1:570131] 19805 19805 19805 19805 19805 ...
## $ Carrier : chr [1:570131] "AA" "AA" "AA" "AA" ...
## $ TailNum : chr [1:570131] "N128AN" "N128AN" "N121AN" "N129AA" ...
## $ FlightNum : num [1:570131] 228 228 228 228 228 228 228 228 228 228 ...
## $ OriginAirportID : num [1:570131] 12892 12892 12892 12892 12892 ...
## $ OriginAirportSeqID : num [1:570131] 1289206 1289206 1289206 1289206 1289206 ...
## $ OriginCityMarketID : num [1:570131] 32575 32575 32575 32575 32575 ...
## $ Origin : chr [1:570131] "LAX" "LAX" "LAX" "LAX" ...
## $ OriginCityName : chr [1:570131] "Los Angeles, CA" "Los Angeles, CA" "Los Angeles, CA" "Los Angeles, CA" ...
## $ OriginState : chr [1:570131] "CA" "CA" "CA" "CA" ...
## $ OriginStateFips : chr [1:570131] "06" "06" "06" "06" ...
## $ OriginStateName : chr [1:570131] "California" "California" "California" "California" ...
## $ OriginWac : num [1:570131] 91 91 91 91 91 91 91 91 91 91 ...
## $ DestAirportID : num [1:570131] 12173 12173 12173 12173 12173 ...
## $ DestAirportSeqID : num [1:570131] 1217303 1217303 1217303 1217303 1217303 ...
## $ DestCityMarketID : num [1:570131] 32134 32134 32134 32134 32134 ...
## $ Dest : chr [1:570131] "HNL" "HNL" "HNL" "HNL" ...
## $ DestCityName : chr [1:570131] "Honolulu, HI" "Honolulu, HI" "Honolulu, HI" "Honolulu, HI" ...
## $ DestState : chr [1:570131] "HI" "HI" "HI" "HI" ...
## $ DestStateFips : chr [1:570131] "15" "15" "15" "15" ...
## $ DestStateName : chr [1:570131] "Hawaii" "Hawaii" "Hawaii" "Hawaii" ...
## $ DestWac : num [1:570131] 2 2 2 2 2 2 2 2 2 2 ...
## $ CRSDepTime : chr [1:570131] "2011" "2011" "2011" "2011" ...
## $ DepTime : chr [1:570131] "2010" "2003" "2008" "2010" ...
## $ DepDelay : num [1:570131] -1 -8 -3 -1 -10 -8 -8 0 71 -4 ...
## $ DepDelayMinutes : num [1:570131] 0 0 0 0 0 0 0 0 71 0 ...
## $ DepDel15 : num [1:570131] 0 0 0 0 0 0 0 0 1 0 ...
## $ DepartureDelayGroups: num [1:570131] -1 -1 -1 -1 -1 -1 -1 0 4 -1 ...
## $ DepTimeBlk : chr [1:570131] "2000-2059" "2000-2059" "2000-2059" "2000-2059" ...
## $ TaxiOut : num [1:570131] 24 18 14 17 17 17 24 23 26 18 ...
## $ WheelsOff : chr [1:570131] "2034" "2021" "2022" "2027" ...
## $ WheelsOn : chr [1:570131] "2358" "2348" "0006" "2352" ...
## $ TaxiIn : num [1:570131] 7 5 6 3 5 4 3 14 3 2 ...
## $ CRSArrTime : chr [1:570131] "0029" "0029" "0029" "0029" ...
## $ ArrTime : chr [1:570131] "0005" "2353" "0012" "2355" ...
## $ ArrDelay : num [1:570131] -24 -36 -17 -34 -32 -24 -12 -23 59 -30 ...
## $ ArrDelayMinutes : num [1:570131] 0 0 0 0 0 0 0 0 59 0 ...
## $ ArrDel15 : num [1:570131] 0 0 0 0 0 0 0 0 1 0 ...
## $ ArrivalDelayGroups : num [1:570131] -2 -2 -2 -2 -2 -2 -1 -2 3 -2 ...
## $ ArrTimeBlk : chr [1:570131] "0001-0559" "0001-0559" "0001-0559" "0001-0559" ...
## $ Cancelled : num [1:570131] 0 0 0 0 0 0 0 0 0 0 ...
## $ CancellationCode : chr [1:570131] NA NA NA NA ...
## $ Diverted : num [1:570131] 0 0 0 0 0 0 0 0 0 0 ...
## $ CRSElapsedTime : num [1:570131] 378 378 378 378 378 378 378 378 378 378 ...
## $ ActualElapsedTime : num [1:570131] 355 350 364 345 356 362 374 355 366 352 ...
## $ AirTime : num [1:570131] 324 327 344 325 334 341 347 318 337 332 ...
## $ Flights : num [1:570131] 1 1 1 1 1 1 1 1 1 1 ...
## $ Distance : num [1:570131] 2556 2556 2556 2556 2556 ...
## $ DistanceGroup : num [1:570131] 11 11 11 11 11 11 11 11 11 11 ...
## $ CarrierDelay : num [1:570131] NA NA NA NA NA NA NA NA 59 NA ...
## $ WeatherDelay : num [1:570131] NA NA NA NA NA NA NA NA 0 NA ...
## $ NASDelay : num [1:570131] NA NA NA NA NA NA NA NA 0 NA ...
## $ SecurityDelay : num [1:570131] NA NA NA NA NA NA NA NA 0 NA ...
## $ LateAircraftDelay : num [1:570131] NA NA NA NA NA NA NA NA 0 NA ...
## $ FirstDepTime : chr [1:570131] NA NA NA NA ...
## $ TotalAddGTime : num [1:570131] NA NA NA NA NA NA NA NA NA NA ...
## $ LongestAddGTime : num [1:570131] NA NA NA NA NA NA NA NA NA NA ...
## $ DivAirportLandings : num [1:570131] 0 0 0 0 0 0 0 0 0 0 ...
## $ DivReachedDest : num [1:570131] NA NA NA NA NA NA NA NA NA NA ...
## $ DivActualElapsedTime: num [1:570131] NA NA NA NA NA NA NA NA NA NA ...
## $ DivArrDelay : num [1:570131] NA NA NA NA NA NA NA NA NA NA ...
## $ DivDistance : num [1:570131] NA NA NA NA NA NA NA NA NA NA ...
## $ Div1Airport : chr [1:570131] NA NA NA NA ...
## $ Div1AirportID : num [1:570131] NA NA NA NA NA NA NA NA NA NA ...
## $ Div1AirportSeqID : num [1:570131] NA NA NA NA NA NA NA NA NA NA ...
## $ Div1WheelsOn : chr [1:570131] NA NA NA NA ...
## $ Div1TotalGTime : num [1:570131] NA NA NA NA NA NA NA NA NA NA ...
## $ Div1LongestGTime : num [1:570131] NA NA NA NA NA NA NA NA NA NA ...
## $ Div1WheelsOff : chr [1:570131] NA NA NA NA ...
## $ Div1TailNum : chr [1:570131] NA NA NA NA ...
## $ Div2Airport : chr [1:570131] NA NA NA NA ...
## $ Div2AirportID : num [1:570131] NA NA NA NA NA NA NA NA NA NA ...
## $ Div2AirportSeqID : num [1:570131] NA NA NA NA NA NA NA NA NA NA ...
## $ Div2WheelsOn : num [1:570131] NA NA NA NA NA NA NA NA NA NA ...
## $ Div2TotalGTime : num [1:570131] NA NA NA NA NA NA NA NA NA NA ...
## $ Div2LongestGTime : num [1:570131] NA NA NA NA NA NA NA NA NA NA ...
## $ Div2WheelsOff : logi [1:570131] NA NA NA NA NA NA ...
## $ Div2TailNum : logi [1:570131] NA NA NA NA NA NA ...
## $ Div3Airport : logi [1:570131] NA NA NA NA NA NA ...
## $ Div3AirportID : logi [1:570131] NA NA NA NA NA NA ...
## $ Div3AirportSeqID : logi [1:570131] NA NA NA NA NA NA ...
## $ Div3WheelsOn : logi [1:570131] NA NA NA NA NA NA ...
## $ Div3TotalGTime : logi [1:570131] NA NA NA NA NA NA ...
## $ Div3LongestGTime : logi [1:570131] NA NA NA NA NA NA ...
## $ Div3WheelsOff : logi [1:570131] NA NA NA NA NA NA ...
## $ Div3TailNum : logi [1:570131] NA NA NA NA NA NA ...
## $ Div4Airport : logi [1:570131] NA NA NA NA NA NA ...
## $ Div4AirportID : logi [1:570131] NA NA NA NA NA NA ...
## $ Div4AirportSeqID : logi [1:570131] NA NA NA NA NA NA ...
## $ Div4WheelsOn : logi [1:570131] NA NA NA NA NA NA ...
## $ Div4TotalGTime : logi [1:570131] NA NA NA NA NA NA ...
## $ Div4LongestGTime : logi [1:570131] NA NA NA NA NA NA ...
## [list output truncated]
## - attr(*, "spec")=
## .. cols(
## .. Year = col_double(),
## .. Quarter = col_double(),
## .. Month = col_double(),
## .. DayofMonth = col_double(),
## .. DayOfWeek = col_double(),
## .. FlightDate = col_date(format = ""),
## .. UniqueCarrier = col_character(),
## .. AirlineID = col_double(),
## .. Carrier = col_character(),
## .. TailNum = col_character(),
## .. FlightNum = col_double(),
## .. OriginAirportID = col_double(),
## .. OriginAirportSeqID = col_double(),
## .. OriginCityMarketID = col_double(),
## .. Origin = col_character(),
## .. OriginCityName = col_character(),
## .. OriginState = col_character(),
## .. OriginStateFips = col_character(),
## .. OriginStateName = col_character(),
## .. OriginWac = col_double(),
## .. DestAirportID = col_double(),
## .. DestAirportSeqID = col_double(),
## .. DestCityMarketID = col_double(),
## .. Dest = col_character(),
## .. DestCityName = col_character(),
## .. DestState = col_character(),
## .. DestStateFips = col_character(),
## .. DestStateName = col_character(),
## .. DestWac = col_double(),
## .. CRSDepTime = col_character(),
## .. DepTime = col_character(),
## .. DepDelay = col_double(),
## .. DepDelayMinutes = col_double(),
## .. DepDel15 = col_double(),
## .. DepartureDelayGroups = col_double(),
## .. DepTimeBlk = col_character(),
## .. TaxiOut = col_double(),
## .. WheelsOff = col_character(),
## .. WheelsOn = col_character(),
## .. TaxiIn = col_double(),
## .. CRSArrTime = col_character(),
## .. ArrTime = col_character(),
## .. ArrDelay = col_double(),
## .. ArrDelayMinutes = col_double(),
## .. ArrDel15 = col_double(),
## .. ArrivalDelayGroups = col_double(),
## .. ArrTimeBlk = col_character(),
## .. Cancelled = col_double(),
## .. CancellationCode = col_character(),
## .. Diverted = col_double(),
## .. CRSElapsedTime = col_double(),
## .. ActualElapsedTime = col_double(),
## .. AirTime = col_double(),
## .. Flights = col_double(),
## .. Distance = col_double(),
## .. DistanceGroup = col_double(),
## .. CarrierDelay = col_double(),
## .. WeatherDelay = col_double(),
## .. NASDelay = col_double(),
## .. SecurityDelay = col_double(),
## .. LateAircraftDelay = col_double(),
## .. FirstDepTime = col_character(),
## .. TotalAddGTime = col_double(),
## .. LongestAddGTime = col_double(),
## .. DivAirportLandings = col_double(),
## .. DivReachedDest = col_double(),
## .. DivActualElapsedTime = col_double(),
## .. DivArrDelay = col_double(),
## .. DivDistance = col_double(),
## .. Div1Airport = col_character(),
## .. Div1AirportID = col_double(),
## .. Div1AirportSeqID = col_double(),
## .. Div1WheelsOn = col_character(),
## .. Div1TotalGTime = col_double(),
## .. Div1LongestGTime = col_double(),
## .. Div1WheelsOff = col_character(),
## .. Div1TailNum = col_character(),
## .. Div2Airport = col_character(),
## .. Div2AirportID = col_double(),
## .. Div2AirportSeqID = col_double(),
## .. Div2WheelsOn = col_double(),
## .. Div2TotalGTime = col_double(),
## .. Div2LongestGTime = col_double(),
## .. Div2WheelsOff = col_logical(),
## .. Div2TailNum = col_logical(),
## .. Div3Airport = col_logical(),
## .. Div3AirportID = col_logical(),
## .. Div3AirportSeqID = col_logical(),
## .. Div3WheelsOn = col_logical(),
## .. Div3TotalGTime = col_logical(),
## .. Div3LongestGTime = col_logical(),
## .. Div3WheelsOff = col_logical(),
## .. Div3TailNum = col_logical(),
## .. Div4Airport = col_logical(),
## .. Div4AirportID = col_logical(),
## .. Div4AirportSeqID = col_logical(),
## .. Div4WheelsOn = col_logical(),
## .. Div4TotalGTime = col_logical(),
## .. Div4LongestGTime = col_logical(),
## .. Div4WheelsOff = col_logical(),
## .. Div4TailNum = col_logical(),
## .. Div5Airport = col_logical(),
## .. Div5AirportID = col_logical(),
## .. Div5AirportSeqID = col_logical(),
## .. Div5WheelsOn = col_logical(),
## .. Div5TotalGTime = col_logical(),
## .. Div5LongestGTime = col_logical(),
## .. Div5WheelsOff = col_logical(),
## .. Div5TailNum = col_logical(),
## .. ...110 = col_logical()
## .. )
## - attr(*, "problems")=<externalptr># The data is 570,131 x 110# Use the summary function to get the sum of rows with n/a
summary(Flying_Data$Div2WheelsOff)## Mode NA's
## logical 570131# Use Dplyr functions to group by carrier and calculate the average by carrier
Question_4 <- Flying_Data %>%
group_by(Carrier) %>%
summarise(
Average_delay = mean(DepDelay, na.rm = TRUE))
print(Question_4)## # A tibble: 18 × 2
## Carrier Average_delay
## <chr> <dbl>
## 1 9E 12.4
## 2 AA 6.93
## 3 AS -2.25
## 4 B6 20.4
## 5 DL 9.74
## 6 EV 13.6
## 7 F9 16.0
## 8 G4 10.4
## 9 HA 1.72
## 10 MQ 8.82
## 11 NK 5.61
## 12 OH 13.8
## 13 OO 15.1
## 14 UA 5.87
## 15 VX 2.83
## 16 WN 8.03
## 17 YV 8.86
## 18 YX 7.26# Load the data
DF <- fromJSON("https://min-api.cryptocompare.com/data/v2/histoday?fsym=BTC&tsym=USD&limit=100")
head(DF)## $Response
## [1] "Success"
##
## $Message
## [1] ""
##
## $HasWarning
## [1] FALSE
##
## $Type
## [1] 100
##
## $RateLimit
## named list()
##
## $Data
## $Data$Aggregated
## [1] FALSE
##
## $Data$TimeFrom
## [1] 1732579200
##
## $Data$TimeTo
## [1] 1741219200
##
## $Data$Data
## time high low open volumefrom volumeto close
## 1 1732579200 95007.52 90730.54 93019.38 77087.79 7150066319 91903.89
## 2 1732665600 97373.56 91757.22 91903.89 47765.92 4533983271 95957.51
## 3 1732752000 96672.28 94677.17 95957.51 24214.50 2311939500 95670.41
## 4 1732838400 98735.69 95391.39 95670.41 39884.80 3883366950 97510.92
## 5 1732924800 97514.26 96137.13 97510.92 12565.01 1215861239 96473.51
## 6 1733011200 97896.77 95752.22 96473.51 17120.23 1659820135 97276.47
## 7 1733097600 98219.29 94419.96 97276.47 52454.21 5040855659 95859.75
## 8 1733184000 96304.02 93590.91 95859.75 52781.20 5033493735 95928.37
## 9 1733270400 99226.36 94663.42 95928.37 59092.55 5727735676 98751.87
## 10 1733356800 104028.51 91741.97 98751.87 119875.78 12041439287 97053.82
## 11 1733443200 102088.57 96427.00 97053.82 56899.66 5653979066 99897.97
## 12 1733529600 100578.80 99025.63 99897.97 17306.94 1728102575 99926.38
## 13 1733616000 101430.60 98730.22 99926.38 17851.82 1785051035 101189.81
## 14 1733702400 101290.02 94567.01 101189.81 69981.57 6847958219 97338.34
## 15 1733788800 98316.66 94284.00 97338.34 68661.05 6621616095 96657.88
## 16 1733875200 101979.09 95730.76 96657.88 55047.48 5482015399 101203.07
## 17 1733961600 102598.04 99311.56 101203.07 47200.64 4765401240 100041.98
## 18 1734048000 101947.10 99232.64 100041.98 34639.86 3492131955 101429.78
## 19 1734134400 102653.76 100606.28 101429.78 16577.72 1683710017 101402.95
## 20 1734220800 105140.47 101228.16 101402.95 24799.66 2557994218 104424.54
## 21 1734307200 107829.08 103299.75 104424.54 60439.19 6398449052 106089.20
## 22 1734393600 108369.13 105308.99 106089.20 40879.26 4365460606 106140.14
## 23 1734480000 106502.15 99946.11 106140.14 75094.41 7727995790 100147.26
## 24 1734566400 102767.84 95555.55 100147.26 80969.49 8011759335 97381.10
## 25 1734652800 98131.87 92144.03 97381.10 84967.20 8141536427 97769.49
## 26 1734739200 99529.45 96379.46 97769.49 29797.52 2907358272 97223.39
## 27 1734825600 97387.01 94186.04 97223.39 29661.75 2842280144 95098.66
## 28 1734912000 96428.13 92378.53 95098.66 57639.30 5422281369 94771.64
## 29 1734998400 99439.54 93437.90 94771.64 40945.61 3967574826 98606.93
## 30 1735084800 99484.75 97568.85 98606.93 21801.56 2147049999 99356.06
## 31 1735171200 99888.75 95098.08 99356.06 38437.52 3704007593 95680.19
## 32 1735257600 97351.17 93270.34 95680.19 47149.30 4479654695 94170.09
## 33 1735344000 95542.25 94008.53 94170.09 14159.69 1339743546 95140.15
## 34 1735430400 95175.67 92850.44 95140.15 17921.90 1684134736 93564.85
## 35 1735516800 94910.24 91310.52 93564.85 56846.06 5287498462 92646.21
## 36 1735603200 96139.70 91894.97 92646.21 38897.74 3659994868 93391.98
## 37 1735689600 94953.50 92728.81 93391.98 19153.02 1798161468 94392.51
## 38 1735776000 97766.59 94197.85 94392.51 38283.30 3693985155 96900.96
## 39 1735862400 98963.23 96021.74 96900.96 28427.30 2773288956 98135.80
## 40 1735948800 98757.16 97522.47 98135.80 9919.49 972181437 98213.69
## 41 1736035200 98818.40 97248.40 98213.69 9872.68 967941540 98346.97
## 42 1736121600 102530.34 97908.09 98346.97 46146.96 4654078183 102282.20
## 43 1736208000 102747.54 96112.95 102282.20 54319.14 5348079997 96942.47
## 44 1736294400 97251.53 92488.45 96942.47 57721.92 5480363249 95051.06
## 45 1736380800 95345.44 91197.56 95051.06 50519.28 4696698492 92548.12
## 46 1736467200 95845.36 92206.53 92548.12 55479.83 5217667771 94710.29
## 47 1736553600 94985.54 93826.56 94710.29 9594.23 905586720 94569.95
## 48 1736640000 95388.08 93675.70 94569.95 10865.93 1027482280 94505.67
## 49 1736726400 95894.16 89153.40 94505.67 67978.59 6274787648 94517.66
## 50 1736812800 97357.87 94324.09 94517.66 44347.27 4263471095 96526.87
## 51 1736899200 100719.11 96466.75 96526.87 43167.20 4261664752 100510.84
## 52 1736985600 100867.35 97275.38 100510.84 43426.87 4314930790 99979.43
## 53 1737072000 105926.06 99941.15 99979.43 54200.85 5613810480 104111.13
## 54 1737158400 104927.11 102234.37 104111.13 25366.61 2633222971 104431.52
## 55 1737244800 106317.18 99537.53 104431.52 41597.34 4325905602 101212.56
## 56 1737331200 109340.21 99449.75 101212.56 68840.35 7208305685 102148.74
## 57 1737417600 107252.77 100069.04 102148.74 84552.76 8798621119 106155.61
## 58 1737504000 106398.57 103321.48 106155.61 43996.49 4603973397 103669.08
## 59 1737590400 106865.30 101221.00 103669.08 94063.64 9801561615 103933.88
## 60 1737676800 107170.86 102751.09 103933.88 40201.93 4233037158 104854.78
## 61 1737763200 105282.15 104107.08 104854.78 11835.64 1239370332 104733.15
## 62 1737849600 105475.33 102487.80 104733.15 14118.63 1469434498 102576.93
## 63 1737936000 103230.24 97712.77 102576.93 94472.14 9486896779 102065.72
## 64 1738022400 103787.72 100221.08 102065.72 44096.37 4510250877 101284.47
## 65 1738108800 104796.98 101279.83 101284.47 47080.07 4842279589 103742.97
## 66 1738195200 106472.04 103297.81 103742.97 36659.17 3857857706 104739.58
## 67 1738281600 106101.91 101514.21 104739.58 42222.31 4378171064 102412.41
## 68 1738368000 102768.26 100270.22 102412.41 15691.73 1595801666 100619.87
## 69 1738454400 101436.90 96158.45 100619.87 56620.33 5562003742 97665.06
## 70 1738540800 102575.82 91142.87 97665.06 119095.77 11504010038 101451.28
## 71 1738627200 101800.32 95090.81 101451.28 63348.17 6271562967 97794.88
## 72 1738713600 99208.65 96175.47 97794.88 42695.62 4171560523 96633.71
## 73 1738800000 99180.75 95691.57 96633.71 44725.10 4349625341 96564.27
## 74 1738886400 100202.29 95630.04 96564.27 51939.12 5075285222 96532.73
## 75 1738972800 96906.35 95685.31 96532.73 12529.37 1207354074 96475.59
## 76 1739059200 97340.86 94752.27 96475.59 18638.48 1791686347 96485.60
## 77 1739145600 98363.35 95276.70 96485.60 28119.40 2731410652 97458.59
## 78 1739232000 98499.44 94852.37 97458.59 29002.62 2800036234 95781.05
## 79 1739318400 98127.45 94087.33 95781.05 40118.73 3853980408 97874.61
## 80 1739404800 98104.11 95225.08 97874.61 23246.76 2236344586 96632.78
## 81 1739491200 98869.28 96282.72 96632.78 20187.22 1966704818 97508.71
## 82 1739577600 97979.54 97245.24 97508.71 5831.48 569120592 97596.21
## 83 1739664000 97728.27 96069.15 97596.21 6632.76 643146269 96132.69
## 84 1739750400 97046.20 95226.61 96132.69 14565.31 1398128551 95790.31
## 85 1739836800 96736.69 93361.47 95790.31 26359.54 2502902526 95630.67
## 86 1739923200 96885.35 95032.61 95630.67 17048.51 1637124692 96640.99
## 87 1740009600 98763.14 96430.11 96640.99 21068.47 2057024375 98345.66
## 88 1740096000 99519.21 94775.25 98345.66 40404.88 3928905118 96150.27
## 89 1740182400 96973.86 95771.79 96150.27 11673.75 1126120706 96580.46
## 90 1740268800 96674.04 95255.75 96580.46 7473.29 718043734 96274.12
## 91 1740355200 96514.26 91349.77 96274.12 31216.13 2945422583 91536.97
## 92 1740441600 92540.35 85944.14 91536.97 73085.74 6473414679 88609.26
## 93 1740528000 89320.19 82199.13 88609.26 59111.39 5069737664 84128.38
## 94 1740614400 87008.08 82601.58 84128.38 46301.47 3929681160 84657.34
## 95 1740700800 85114.25 78203.72 84657.34 79539.18 6502539275 84322.57
## 96 1740787200 86537.62 83798.96 84322.57 18530.02 1577375765 86050.58
## 97 1740873600 95106.04 85047.82 86050.58 48038.07 4350642164 94275.76
## 98 1740960000 94415.65 85071.66 94275.76 65233.70 5844734403 86158.95
## 99 1741046400 88941.97 81449.43 86158.95 67017.22 5671376307 87255.42
## 100 1741132800 91001.98 86341.87 87255.42 38856.74 3457722669 90610.80
## 101 1741219200 92808.83 87818.33 90610.80 34741.78 3130644302 89229.85
## conversionType conversionSymbol
## 1 direct
## 2 direct
## 3 direct
## 4 direct
## 5 direct
## 6 direct
## 7 direct
## 8 direct
## 9 direct
## 10 direct
## 11 direct
## 12 direct
## 13 direct
## 14 direct
## 15 direct
## 16 direct
## 17 direct
## 18 direct
## 19 direct
## 20 direct
## 21 direct
## 22 direct
## 23 direct
## 24 direct
## 25 direct
## 26 direct
## 27 direct
## 28 direct
## 29 direct
## 30 direct
## 31 direct
## 32 direct
## 33 direct
## 34 direct
## 35 direct
## 36 direct
## 37 direct
## 38 direct
## 39 direct
## 40 direct
## 41 direct
## 42 direct
## 43 direct
## 44 direct
## 45 direct
## 46 direct
## 47 direct
## 48 direct
## 49 direct
## 50 direct
## 51 direct
## 52 direct
## 53 direct
## 54 direct
## 55 direct
## 56 direct
## 57 direct
## 58 direct
## 59 direct
## 60 direct
## 61 direct
## 62 direct
## 63 direct
## 64 direct
## 65 direct
## 66 direct
## 67 direct
## 68 direct
## 69 direct
## 70 direct
## 71 direct
## 72 direct
## 73 direct
## 74 direct
## 75 direct
## 76 direct
## 77 direct
## 78 direct
## 79 direct
## 80 direct
## 81 direct
## 82 direct
## 83 direct
## 84 direct
## 85 direct
## 86 direct
## 87 direct
## 88 direct
## 89 direct
## 90 direct
## 91 direct
## 92 direct
## 93 direct
## 94 direct
## 95 direct
## 96 direct
## 97 direct
## 98 direct
## 99 direct
## 100 direct
## 101 direct# Past the close data and use code to return the highest value
max(DF[["Data"]][["Data"]][["close"]])## [1] 106155.6# The formula returned that 106155.6 was the highest close value