COVID-19: Crescimento exponencial em Londres

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area_name

area_type

area_data

Area.name == area_name &

Area.type == area_type ,,

][,Specimen.date := as.Date(Specimen.date)

    ][,c(“Specimen.date”,”Daily.lab.confirmed.cases”)][

      order(Specimen.date)

      ]

area_data

data.table (Specimen.date = seq (

min (area_data[,Specimen.date]),

max (area_data[,Specimen.date]),

por = “1 dia”

)), todos = TRUE, por = “Specimen.date”)

setkey (area_data, Specimen.date)

setnafill (area_data, type = “const”, fill = 0,

cols = c (“Daily.lab.confirmed.cases”))

area_data[,roll_mean := frollmean(Daily.lab.confirmed.cases, n = 7, align = “right”)]

#################################################

############## Modelo exponencial ###########

#################################################

area_data[,increasing := c(rep(NA,7), roll_mean[-(1:7)]- roll_mean[-((.N-6):.N)]> 0)]

data final

Specimen.date[1], por = ”aumentando”]$ V1

data de início

aumentando == FALSE & Specimen.date

Specimen.date[1], por = ”aumentando”]$ V1

exp_lm_data start_date & Specimen.date

exp_lm_data[, days := 1:.N]

exp_lm

exp_lm_data[,fitted_numbers := exp(fitted.values(exp_lm))]

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predicted_data

predicted_data[,Specimen.date := min(exp_lm_data$Specimen.date)+ lubridate::days(days)]

predicted_data[,predicted_numbers := exp(predict.lm(exp_lm, predicted_data))]

##############################################

m_area_data

measure.vars = c (“Daily.lab.confirmed.cases”, ”roll_mean”))

exp_lm_data

id.vars = ”Specimen.date”,

measure.vars = c (“fit_numbers”, ”predicted_numbers”))

m_area_data

area_plot

geom_bar (data = subset (m_area_data, variable == “Daily.lab.confirmed.cases”),

stat = “identidade”) +

geom_line (data = subset (m_area_data, variable! = “Daily.lab.confirmed.cases”)) +

laboratórios (x = “Data da amostra”, y = “Número de casos confirmados”,

preenchimento = “”, cor = “”) +

scale_fill_manual (values ​​= c (“# ff0000 ″,” # 05d153 ″, ”# cad105 ″,” # 000000 ”),

labels = c (sprintf (“% s # casos confirmados diariamente”, area_name),

“Ajustado”, ”previsto”, ”média de 7 dias”)) +

scale_color_manual (values ​​= c (“# ff0000 ″,” # 05d153 ″, ”# cad105 ″,” # 000000 ”),

labels = c (sprintf (“% s # casos confirmados diariamente”, area_name),

“Ajustado”, ”previsto”, ”média de 7 dias”)) +

scale_x_date (date_breaks = “4 semanas”, date_labels = “% Y-% m-% d”) +

theme_bw ()% + replace% theme (legend.position = “top”,

legend.justification = “left”)



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