Add this code to set global options. Always initialize all of your packages up-front. (Professor J. Lecy)
# Upload and analyze dataset
# bikes <- readRDS("bikes.rds")
bikes <- readRDS(gzcon(url("https://github.com/lecy/CityBikeNYC/raw/master/DATA/bikes.rds")))
str(bikes) # 285552 rows## 'data.frame': 285552 obs. of 16 variables:
## $ tripduration : int 1346 363 346 182 969 496 152 1183 846 576 ...
## $ starttime : Factor w/ 36143 levels "1/1/2015 0:01",..: 1 2 3 3 4 5 5 6 7 8 ...
## $ stoptime : Factor w/ 36157 levels "1/1/2015 0:07",..: 11 2 4 1 9 5 3 15 10 8 ...
## $ start.station.id : int 455 434 491 384 474 512 498 405 450 160 ...
## $ start.station.name : Factor w/ 330 levels "1 Ave & E 15 St",..: 4 22 116 169 13 283 50 320 301 126 ...
## $ start.station.latitude : num 40.8 40.7 40.7 40.7 40.7 ...
## $ start.station.longitude: num -74 -74 -74 -74 -74 ...
## $ end.station.id : int 265 482 505 399 432 383 474 174 488 174 ...
## $ end.station.name : Factor w/ 330 levels "1 Ave & E 15 St",..: 257 268 16 196 153 177 13 117 290 117 ...
## $ end.station.latitude : num 40.7 40.7 40.7 40.7 40.7 ...
## $ end.station.longitude : num -74 -74 -74 -74 -74 ...
## $ bikeid : int 18660 16085 20845 19610 20197 20788 19006 17640 15691 17837 ...
## $ usertype : Factor w/ 2 levels "Customer","Subscriber": 2 2 2 2 2 2 2 2 2 2 ...
## $ birth.year : int 1960 1963 1974 1969 1977 1969 1972 1985 1991 1991 ...
## $ gender : int 2 1 1 1 1 2 1 2 1 1 ...
## $ ID : chr "455-265" "434-482" "491-505" "384-399" ...summary(bikes)## tripduration starttime stoptime
## Min. : 60.0 1/21/2015 8:48 : 53 1/23/2015 8:54: 52
## 1st Qu.: 334.0 1/23/2015 8:48 : 51 1/22/2015 9:02: 51
## Median : 504.0 1/20/2015 17:46: 47 1/15/2015 8:51: 47
## Mean : 654.3 1/22/2015 8:40 : 47 1/22/2015 8:50: 47
## 3rd Qu.: 772.0 1/23/2015 8:52 : 47 1/21/2015 8:55: 46
## Max. :43023.0 1/5/2015 8:49 : 47 1/21/2015 8:52: 45
## (Other) :285260 (Other) :285264
## start.station.id start.station.name start.station.latitude
## Min. : 72.0 8 Ave & W 31 St : 4157 Min. :40.68
## 1st Qu.: 308.0 Lafayette St & E 8 St : 3186 1st Qu.:40.72
## Median : 417.0 E 43 St & Vanderbilt Ave: 3139 Median :40.74
## Mean : 443.5 W 21 St & 6 Ave : 2988 Mean :40.74
## 3rd Qu.: 492.0 8 Ave & W 33 St : 2637 3rd Qu.:40.75
## Max. :3002.0 E 17 St & Broadway : 2542 Max. :40.77
## (Other) :266903
## start.station.longitude end.station.id
## Min. :-74.02 Min. : 72.0
## 1st Qu.:-74.00 1st Qu.: 307.0
## Median :-73.99 Median : 417.0
## Mean :-73.99 Mean : 440.3
## 3rd Qu.:-73.98 3rd Qu.: 492.0
## Max. :-73.95 Max. :3002.0
##
## end.station.name end.station.latitude
## 8 Ave & W 31 St : 3319 Min. :40.68
## W 41 St & 8 Ave : 3224 1st Qu.:40.72
## E 43 St & Vanderbilt Ave: 3128 Median :40.74
## Lafayette St & E 8 St : 3128 Mean :40.74
## W 21 St & 6 Ave : 2977 3rd Qu.:40.75
## E 17 St & Broadway : 2929 Max. :40.77
## (Other) :266847
## end.station.longitude bikeid usertype birth.year
## Min. :-74.02 Min. :14529 Customer : 5628 Min. :1899
## 1st Qu.:-74.00 1st Qu.:16355 Subscriber:279924 1st Qu.:1967
## Median :-73.99 Median :18148 Median :1977
## Mean :-73.99 Mean :18147 Mean :1975
## 3rd Qu.:-73.98 3rd Qu.:19903 3rd Qu.:1984
## Max. :-73.95 Max. :21690 Max. :1999
## NA's :5628
## gender ID
## Min. :0.000 Length:285552
## 1st Qu.:1.000 Class :character
## Median :1.000 Mode :character
## Mean :1.162
## 3rd Qu.:1.000
## Max. :2.000
## names(bikes)## [1] "tripduration" "starttime"
## [3] "stoptime" "start.station.id"
## [5] "start.station.name" "start.station.latitude"
## [7] "start.station.longitude" "end.station.id"
## [9] "end.station.name" "end.station.latitude"
## [11] "end.station.longitude" "bikeid"
## [13] "usertype" "birth.year"
## [15] "gender" "ID"Here I use dplyr package, deleting 13 columns and leaving only three - ID, start station longtitude and start station latitutide. The idea was to create two dataframes (the same one for end stations), then merge them into one dataframe by ID. I did it, but it looks like it messed up frequency of rides in every #root (frequency was multiplied by frequency - for instance, 20 rides in one root transformed into 400 rides
# Deleting all columns besides start station lat and long
# Start station
start.station <- bikes %>% as_tibble() %>%
mutate(tripduration = NULL, starttime = NULL, stoptime = NULL, start.station.id = NULL,
start.station.name = NULL, end.station.id = NULL, end.station.name = NULL,
end.station.latitude = NULL, end.station.longitude = NULL, bikeid = NULL, usertype = NULL,
birth.year = NULL, gender = NULL)
head(start.station, 10)## # A tibble: 10 × 3
## start.station.latitude start.station.longitude ID
## <dbl> <dbl> <chr>
## 1 40.75002 -73.96905 455-265
## 2 40.74317 -74.00366 434-482
## 3 40.74096 -73.98602 491-505
## 4 40.68318 -73.96596 384-399
## 5 40.74517 -73.98683 474-432
## 6 40.75007 -73.99839 512-383
## 7 40.74855 -73.98808 498-474
## 8 40.73932 -74.00812 405-174
## 9 40.76227 -73.98788 450-488
## 10 40.74824 -73.97831 160-174# Deleting all columns besides end station lat and long
# End station
end.station <- bikes %>% as_tibble() %>%
mutate(tripduration = NULL, starttime = NULL, stoptime = NULL, start.station.id = NULL,
start.station.name = NULL, start.station.latitude = NULL, start.station.longitude = NULL,
end.station.id = NULL, end.station.name = NULL, bikeid = NULL, usertype = NULL,
birth.year = NULL, gender = NULL)
head(end.station, 10)## # A tibble: 10 × 3
## end.station.latitude end.station.longitude ID
## <dbl> <dbl> <chr>
## 1 40.72229 -73.99148 455-265
## 2 40.73936 -73.99932 434-482
## 3 40.74901 -73.98848 491-505
## 4 40.68852 -73.96476 384-399
## 5 40.72622 -73.98380 474-432
## 6 40.73524 -74.00027 512-383
## 7 40.74517 -73.98683 498-474
## 8 40.73818 -73.97739 405-174
## 9 40.75646 -73.99372 450-488
## 10 40.73818 -73.97739 160-174# Merge data (long and lat columns for start and end station by id)
bikes.merged <- merge(start.station, end.station, by="ID")
head(bikes.merged, 10)## ID start.station.latitude start.station.longitude
## 1 116-116 40.74178 -74.0015
## 2 116-116 40.74178 -74.0015
## 3 116-116 40.74178 -74.0015
## 4 116-116 40.74178 -74.0015
## 5 116-116 40.74178 -74.0015
## 6 116-116 40.74178 -74.0015
## 7 116-116 40.74178 -74.0015
## 8 116-116 40.74178 -74.0015
## 9 116-116 40.74178 -74.0015
## 10 116-116 40.74178 -74.0015
## end.station.latitude end.station.longitude
## 1 40.74178 -74.0015
## 2 40.74178 -74.0015
## 3 40.74178 -74.0015
## 4 40.74178 -74.0015
## 5 40.74178 -74.0015
## 6 40.74178 -74.0015
## 7 40.74178 -74.0015
## 8 40.74178 -74.0015
## 9 40.74178 -74.0015
## 10 40.74178 -74.0015head(table(table(bikes.merged$ID)), 10)##
## 1 4 9 16 25 36 49 64 81 100
## 12932 6848 4400 3182 2355 1899 1543 1264 1162 983# 1 4 9 16 25 36 49 64 81 100
# 12932 6848 4400 3182 2355 1899 1543 1264 1162 983
# Group and summarize data
bikes.merged1 <- bikes.merged %>%
group_by(ID) %>%
summarise(n = n())
# unique(bikes.merged1)
# ID n
# <chr> <int>
# 1 116-116 400
# 2 116-127 100
# 3 116-128 1
# 4 116-147 16
# 5 116-151 16
# 6 116-153 169
# 7 116-157 1
# 8 116-160 9
# 9 116-167 1
# 10 116-168 1600
# ... with 44,063 more rowsAnother approach - not to merge two dataframes, but create the only one with dplyr. I deleted 11 columns with dplyr, but left five ones - ID, start station long and lat, end stat long and lat. I think this option is more reasonable. I do not need to merge dataframes, complicating the task and messing data.
stations <- bikes %>% as_tibble() %>%
mutate(tripduration = NULL, starttime = NULL, stoptime = NULL, start.station.id = NULL,
start.station.name = NULL, end.station.id = NULL, end.station.name = NULL, bikeid = NULL, usertype = NULL,
birth.year = NULL, gender = NULL)
head(stations, 10)## # A tibble: 10 × 5
## start.station.latitude start.station.longitude end.station.latitude
## <dbl> <dbl> <dbl>
## 1 40.75002 -73.96905 40.72229
## 2 40.74317 -74.00366 40.73936
## 3 40.74096 -73.98602 40.74901
## 4 40.68318 -73.96596 40.68852
## 5 40.74517 -73.98683 40.72622
## 6 40.75007 -73.99839 40.73524
## 7 40.74855 -73.98808 40.74517
## 8 40.73932 -74.00812 40.73818
## 9 40.76227 -73.98788 40.75646
## 10 40.74824 -73.97831 40.73818
## # ... with 2 more variables: end.station.longitude <dbl>, ID <chr>bikes2 <- stations %>%
group_by(ID) %>%
summarise(n = n())
# A tibble: 44,073 � 2
# ID n
# <chr> <int>
# 1 116-116 20
# 2 116-127 10
# 3 116-128 1
# 4 116-147 4
# 5 116-151 4
# 6 116-153 13
# 7 116-157 1
# 8 116-160 3
# 9 116-167 1
# 10 116-168 40
# ... with 44,063 more rows
head(table(table(stations$ID)), 10)##
## 1 2 3 4 5 6 7 8 9 10
## 12932 6848 4400 3182 2355 1899 1543 1264 1162 983