Selvstudium: Brug R til at forudsige avocadopriser
I dette selvstudium præsenteres et helt til slut-eksempel på en Synapse Data Science-arbejdsproces i Microsoft Fabric. Den bruger R til at analysere og visualisere avocadopriser i USA til at bygge en model til maskinel indlæring, der forudsiger fremtidige avocadopriser.
I dette selvstudium beskrives disse trin:
- Indlæs standardbiblioteker
- Indlæs dataene
- Tilpas dataene
- Føj nye pakker til sessionen
- Analysér og visualiser dataene
- Oplære modellen
Få et Microsoft Fabric-abonnement. Du kan også tilmelde dig en gratis Prøveversion af Microsoft Fabric.
Brug oplevelsesskifteren i venstre side af startsiden til at skifte til Synapse Data Science-oplevelsen.
Åbn eller opret en notesbog. Du kan få mere at vide under Sådan bruger du Microsoft Fabric-notesbøger.
Angiv sprogindstillingen til SparkR (R) for at ændre det primære sprog.
Vedhæft din notesbog til et lakehouse. I venstre side skal du vælge Tilføj for at tilføje et eksisterende lakehouse eller for at oprette et lakehouse.
Brug biblioteker fra R-standardkørslen:
library(tidyverse)
library(lubridate)
library(hms)
Læs avocado priser fra en . CSV-fil, downloadet fra internettet:
df <- read.csv('https://synapseaisolutionsa.blob.core.windows.net/public/AvocadoPrice/avocado.csv', header = TRUE)
head(df,5)
Først skal du give kolonnerne mere brugervenlige navne.
# To use lowercase
names(df) <- tolower(names(df))
# To use snake case
avocado <- df %>%
rename("av_index" = "x",
"average_price" = "averageprice",
"total_volume" = "total.volume",
"total_bags" = "total.bags",
"amount_from_small_bags" = "small.bags",
"amount_from_large_bags" = "large.bags",
"amount_from_xlarge_bags" = "xlarge.bags")
# Rename codes
avocado2 <- avocado %>%
rename("PLU4046" = "x4046",
"PLU4225" = "x4225",
"PLU4770" = "x4770")
head(avocado2,5)
Rediger datatyperne, fjern uønskede kolonner, og tilføj det samlede forbrug:
# Convert data
avocado2$year = as.factor(avocado2$year)
avocado2$date = as.Date(avocado2$date)
avocado2$month = factor(months(avocado2$date), levels = month.name)
avocado2$average_price =as.numeric(avocado2$average_price)
avocado2$PLU4046 = as.double(avocado2$PLU4046)
avocado2$PLU4225 = as.double(avocado2$PLU4225)
avocado2$PLU4770 = as.double(avocado2$PLU4770)
avocado2$amount_from_small_bags = as.numeric(avocado2$amount_from_small_bags)
avocado2$amount_from_large_bags = as.numeric(avocado2$amount_from_large_bags)
avocado2$amount_from_xlarge_bags = as.numeric(avocado2$amount_from_xlarge_bags)
# Remove unwanted columns
avocado2 <- avocado2 %>%
select(-av_index,-total_volume, -total_bags)
# Calculate total consumption
avocado2 <- avocado2 %>%
mutate(total_consumption = PLU4046 + PLU4225 + PLU4770 + amount_from_small_bags + amount_from_large_bags + amount_from_xlarge_bags)
Brug installationen af den indbyggede pakke til at føje nye pakker til sessionen:
install.packages(c("repr","gridExtra","fpp2"))
Indlæs de nødvendige biblioteker.
library(tidyverse)
library(knitr)
library(repr)
library(gridExtra)
library(data.table)
Sammenlign konventionelle (ikke-organiske) avocadopriser efter område:
options(repr.plot.width = 10, repr.plot.height =10)
# filter(mydata, gear %in% c(4,5))
avocado2 %>%
filter(region %in% c("PhoenixTucson","Houston","WestTexNewMexico","DallasFtWorth","LosAngeles","Denver","Roanoke","Seattle","Spokane","NewYork")) %>%
filter(type == "conventional") %>%
select(date, region, average_price) %>%
ggplot(aes(x = reorder(region, -average_price, na.rm = T), y = average_price)) +
geom_jitter(aes(colour = region, alpha = 0.5)) +
geom_violin(outlier.shape = NA, alpha = 0.5, size = 1) +
geom_hline(yintercept = 1.5, linetype = 2) +
geom_hline(yintercept = 1, linetype = 2) +
annotate("rect", xmin = "LosAngeles", xmax = "PhoenixTucson", ymin = -Inf, ymax = Inf, alpha = 0.2) +
geom_text(x = "WestTexNewMexico", y = 2.5, label = "My top 5 cities!", hjust = 0.5) +
stat_summary(fun = "mean") +
labs(x = "US city",
y = "Avocado prices",
title = "Figure 1. Violin plot of nonorganic avocado prices",
subtitle = "Visual aids: \n(1) Black dots are average prices of individual avocados by city \n between January 2015 and March 2018. \n(2) The plot is ordered descendingly.\n(3) The body of the violin becomes fatter when data points increase.") +
theme_classic() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 25, vjust = 0.65),
plot.title = element_text(face = "bold", size = 15)) +
scale_y_continuous(lim = c(0, 3), breaks = seq(0, 3, 0.5))
Fokuser på Houston-området.
library(fpp2)
conv_houston <- avocado2 %>%
filter(region == "Houston",
type == "conventional") %>%
group_by(date) %>%
summarise(average_price = mean(average_price))
# Set up ts
conv_houston_ts <- ts(conv_houston$average_price,
start = c(2015, 1),
frequency = 52)
# Plot
autoplot(conv_houston_ts) +
labs(title = "Time plot: nonorganic avocado weekly prices in Houston",
y = "$") +
geom_point(colour = "brown", shape = 21) +
geom_path(colour = "brown")
Opret en prisforudsigelsesmodel for Området Houston baseret på AIMA (AutoRegressive Integrated Moving Average):
conv_houston_ts_arima <- auto.arima(conv_houston_ts,
d = 1,
approximation = F,
stepwise = F,
trace = T)
checkresiduals(conv_houston_ts_arima)
Vis en graf over prognoser fra Houston ARIMA-modellen:
conv_houston_ts_arima_fc <- forecast(conv_houston_ts_arima, h = 208)
autoplot(conv_houston_ts_arima_fc) + labs(subtitle = "Prediction of weekly prices of nonorganic avocados in Houston",
y = "$") +
geom_hline(yintercept = 2.5, linetype = 2, colour = "blue")