library(readr)
airbnb <- read_csv("airbnb.csv")## Rows: 48895 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): name, host_name, neighbourhood_group, neighbourhood, room_type
## dbl (10): id, host_id, latitude, longitude, price, minimum_nights, number_o...
## date (1): last_review
##
## 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.View(airbnb)Exercise I
MEAN FUNCTION FOR REVIEWS
mean_number_of_reviews <- function(airbnb) {
mean_reviews <- mean(airbnb$number_of_reviews)
return(mean_reviews)
}
mean_reviews <- mean_number_of_reviews(airbnb)
print(mean_reviews)## [1] 23.27447MEAN FUNCTION FOR PRICE
mean_price <- function(airbnb) {
mean_price <- mean(airbnb$price)
return(mean_price)
}
mean_price <- mean_price(airbnb)
print(mean_price)## [1] 152.7207Let’s validate the results with decribe function, but first, mark psych package.
library(psych)
describe(airbnb)## Warning in FUN(newX[, i], ...): ningún argumento finito para min; retornando
## Inf## Warning in FUN(newX[, i], ...): ningun argumento finito para max; retornando
## -Inf## vars n mean sd median
## id 1 48895 19017143.24 10983108.39 19677284.00
## name* 2 48879 23963.73 13811.31 23928.00
## host_id 3 48895 67620010.65 78610967.03 30793816.00
## host_name* 4 48874 5428.50 3228.86 5315.00
## neighbourhood_group* 5 48895 2.68 0.74 3.00
## neighbourhood* 6 48895 108.11 68.74 95.00
## latitude 7 48895 40.73 0.05 40.72
## longitude 8 48895 -73.95 0.05 -73.96
## room_type* 9 48895 1.50 0.55 1.00
## price 10 48895 152.72 240.15 106.00
## minimum_nights 11 48895 7.03 20.51 3.00
## number_of_reviews 12 48895 23.27 44.55 5.00
## last_review 13 38843 NaN NA NA
## reviews_per_month 14 38843 1.37 1.68 0.72
## calculated_host_listings_count 15 48895 7.14 32.95 1.00
## availability_365 16 48895 112.78 131.62 45.00
## trimmed mad min max
## id 19188061.30 14689959.59 2539.00 36487245.00
## name* 23962.96 17712.62 1.00 47894.00
## host_id 54170438.55 40836605.41 2438.00 274321313.00
## host_name* 5399.50 4194.28 1.00 11452.00
## neighbourhood_group* 2.61 1.48 1.00 5.00
## neighbourhood* 106.81 88.96 1.00 221.00
## latitude 40.73 0.05 40.50 40.91
## longitude -73.96 0.04 -74.24 -73.71
## room_type* 1.48 0.00 1.00 3.00
## price 121.43 68.20 0.00 10000.00
## minimum_nights 3.58 2.97 1.00 1250.00
## number_of_reviews 12.45 7.41 0.00 629.00
## last_review NaN NA Inf -Inf
## reviews_per_month 1.06 0.92 0.01 58.50
## calculated_host_listings_count 1.50 0.00 1.00 327.00
## availability_365 96.50 66.72 0.00 365.00
## range skew kurtosis se
## id 36484706.00 -0.09 -1.23 49669.87
## name* 47893.00 0.00 -1.20 62.47
## host_id 274318875.00 1.21 0.17 355509.26
## host_name* 11451.00 0.06 -1.16 14.61
## neighbourhood_group* 4.00 0.37 -0.11 0.00
## neighbourhood* 220.00 0.26 -1.26 0.31
## latitude 0.41 0.24 0.15 0.00
## longitude 0.53 1.28 5.02 0.00
## room_type* 2.00 0.42 -0.97 0.00
## price 10000.00 19.12 585.59 1.09
## minimum_nights 1249.00 21.83 853.95 0.09
## number_of_reviews 629.00 3.69 19.53 0.20
## last_review -Inf NA NA NA
## reviews_per_month 58.49 3.13 42.49 0.01
## calculated_host_listings_count 326.00 7.93 67.54 0.15
## availability_365 365.00 0.76 -1.00 0.60Validated