kev's questions

I have the following Panel Data

  year region                     ka_avg kao_avg
 
  1995 East Asia & Pacific        0.534   0.508 
  1995 Europe & Central Asia      0.553   0.579 
  1995 Latin America & Caribbean  0.252   0.232 
  1995 Middle East & North Africa 0.382   0.348 
  1995 North America              0.0833  0.0556
  1995 South Asia                 0.799   0.865 
  1995 Sub-Saharan Africa         0.667   0.748 
  1995 W Europe                   0.114   0.117 
  1996 East Asia & Pacific        0.465   0.416 
  1996 Europe & Central Asia      0.561   0.630 

I need a graph like the following for ka_avg and another for kao_avg :

where the y-axis is each of the variables mentioned above and the x-axis has the variable year . The labels to each line correspond to region

What I have done is the following

ggplot(data = dataset, aes(x = year, y = ka_avg, color = region)) +
            geom_line()

I would like to give it a look similar to the first

I have the following DataFrame

I run the regressions using ir_total as the dependent variable and 3-way combinations of municipalities as regressor variables.

combinaciones <- apply(combn(independientes, m=3), 2, 
                      FUN=function(x){paste0("`", x, "`", collapse = ' + ')})
combinaciones

combinaciones <- sapply(paste("ir_total ~", combinaciones), as.formula)
modelos<-lapply(combinaciones, lm, df)

lapply(modelos, FUN=function(x) {cbind(r2=summary(x)$r.squared,
                                      coef1=coef(x)[2],
                                      coef2=coef(x)[3],
                                      coef3=coef(x)[4])})->datos


datos <- do.call(rbind, datos)
rownames(datos) <- NULL

datos

The result I get is:

But I hope to get a column with the combinations of each regression and be able to identify them since I need to manipulate them datosby applying the sort() function.

I have the following database:

I need to estimate regressions of the following form:

ir_total = b0 + b1 9dejulio + b2 Azul + b3 Bolivar
ir_total = b0 + b1 9dejulio + b2 Azul + b3 CoronelSuarez
.
.
.
.
ir_total = b0 + b1 pehuajo + b2 Dolores + b3 Junin

That is, I need to estimate regressions with the variable y=ir_total, 3 regressors that will go through all the different departments. Finally print in a DataFrame or table the coefficients and the R^2 of each regression

I am trying something like the following

n=ncol(data)

for (i in 3:n) {
    model = lm (data = data, formula = data[,2] ~ data[,i] + data [,i+1] + data[,i+2])

}

I need to import a database into R that is in csv format. The problem is that the file contains the information in a way that makes it impossible to import correctly. Of the four rows with words, I only need to keep the year of each table in the database.

What I finally need to get is something like the following:

Mes Día Año  Precipitaciones
Ene 1   2018  10
Ene 2   2018  23
Ene 3   2018  22
Ene 4   2018  11
......
Ene 1   2019  13
Ene 2   2019  31.3
Ene 3   2019
......
.
.
..

The link to the data is: https://www.dropbox.com/s/rq8cql40r5dk413/190068%20B.%20Juarez.txt?dl=0

I have the following Data Frame with rainfall by location in Argentina. The problem is that, for example, for Córdoba Capital and Castellanos I have three values ​​for the same month and year, so I need to be able to summarize that information as an average and that in the data frame there is only one row with Córdoba Capital with the average value.

`codigo anio mes precip.mensual     departamento provincia
  87244 2000  11          137.5         Rio seco        CO
  87244 2000  12          150.5         Rio seco        CO
  87257 2000   1          150.5    San cristobal        SF
  87257 2000   2          125.9    San cristobal        SF
  87344 2000   1           96.8          Capital        CO
  87344 2000   2          135.2          Capital        CO
  87344 2000   3          184.6          Capital        CO
  87344 2000   4          125.4          Capital        CO
  87344 2000   5           47.0          Capital        CO
  87344 2000   6            0.0          Capital        CO
  87344 2000   7           12.0          Capital        CO
  87344 2000   8            0.5          Capital        CO
  87344 2000   9           14.0          Capital        CO
  87344 2000  10          124.2          Capital        CO
  87344 2000  11          192.0          Capital        CO
  87344 2000  12          175.0          Capital        CO
  87345 2000   1          150.8          capital        CO
  87345 2000   2          136.6          capital        CO
  87345 2000   3          105.1          capital        CO

`

I have a data frame that I need to export to excel

In the excel I need the columns to be the years, the first row the code and the monthly rainfall values ​​to be for each year and code. The excel should have a sheet for each month, For example, the sheet for the month of April should be similar to this

`código  2000   2001   2002 ....  2019
 87345   116.3
 87347   103.6
 ....
 87467

`

I have a data frame with 4 variables, month, year, code and rainfall. I need to get the data frame according to the code variable that are not equal to the following values.

vector = c(87553, 87568, 87569, 87570, 87571, 87572, 87573, 87574, 87576, 87582, 87593, 87596)

The data frame is

I tried to make df.mensual <- df.mensual %>% filter(codigo != vector)

But I got the following error In codigo != vector : longer object length is not a multiple of shorter object length

I have the dataset downloaded from google mobility report and the idea is to process it and group it by country and by month in order to have a more concise summary. When I forrun through all the mobility variants and export it to excel, the values ​​appear empty or the values ​​are all the same. I leave the script for your consideration.

library(dplyr)
library(lubridate)
library(tidyr)
library(writexl)

Global_Mobility_Report <- read.csv("C:/Global_Mobility_Report.csv")
View(Global_Mobility_Report)

Global_Mobility_Report$date <- as.Date(Global_Mobility_Report$date)                                     
Global_Mobility_Report$mes <- month(Global_Mobility_Report$date)

names <- colnames(Global_Mobility_Report)

for (i in 9:14) {
  
  data = paste("Global_Mobility_Report$", names[i], sep = "")
  
  df <- Global_Mobility_Report %>%
    group_by(mes, country_region) %>% 
    summarise(value_mean = mean(data, na.rm = TRUE))
  
  dataset <- df %>% 
    spread(mes, value_mean)
  nombre = paste(names[i], ".xlsx")
  
  write_xlsx(dataset,nombre)
  
}

The result I expect is an excel that contains the months in the first row, and the countries in the first column. Have one tab per variable

I have two DataFrames, the first df_subset has a three variables where RD_GDP is the R&D investment per country as a percentage of GDP

df_subset

     country  year    RD_GDP
0        AUS  1981  0.902542
1        AUS  1984  1.023905
2        AUS  1986  1.179149
3        AUS  1987  1.137756
4        AUS  1988  1.163591
     ...   ...       ...
1305     LTU  2014  1.031138
1306     LTU  2015  1.044081
1307     LTU  2016  0.842334
1308     LTU  2017  0.896408
1309     LTU  2018  0.876656

The second DataFrame df_subset_gdp also has three variables where GDP_PC is the GDP per capita by country and year.

    country  year         GDP_PC
0       AUS  2015   47304.816745
1       AUS  2016   50284.172793
2       AUS  2017   51297.139196
3       AUS  2018   53700.680893
4       AUS  2019   54752.242834
..      ...   ...            ...
265     MAR  2017    7582.794999
266     SGP  2015   86972.563073
267     SGP  2016   89396.724443
268     SGP  2017   94945.250892
269     SGP  2018  101280.413499

I need to join the two DataFrames to be able to make a Scatterplot of the two variables that I mentioned and see the relationship, so to join them I tried the following command

dataset_merge = df_subset.merge(df_subset_gdp, left_on = "year", right_on = "year")

The problem is that I get the following without having sorted GDP_PC by country_x

  country_x  year    RD_GDP country_y        GDP_PC
0       AUS  2015  1.875612       AUS  47304.816745
1       AUS  2015  1.875612       AUT  49955.456118
2       AUS  2015  1.875612       BEL  46214.082208
3       AUS  2015  1.875612       CAN  44671.409504
4       AUS  2015  1.875612       CZE  33701.384000

my problem is the following: I have a data table with the date column that takes 3 days, they are 1, 11 and 21. When I create a pivot table and apply filters by dates, they come out every day from 1 to 30 / 31) I just want to filter according to the days data I have.

the following image shows the original date of my data

I need to filter, for example, from January 1 to February 11 and for the filter to apply from 1999 to 2020, which is the year up to which my information is available.

I am doing Web Scraping to get two variables precioand area. It just so happens that it preciohas a length()greater than areasince not all posts have an area. With the information I must create a data frame but since the length of the data is different it is impossible, if it were possible to assemble two variables with different lengths it would be impossible to join each preciowith each area. The question is how can the data be scraped so that when it does not exist areain that element of the list there is a string of some type, for example NA? I am using the packagervest

In this link they have the same problem but it is not solved. https://stackoverflow.com/questions/29996952/r-rvest-getting-2-elements-nodes-at-the-same-time

The code is

library(rvest)
library(robotstxt)
library(selectr)
library(xml2)
library(dplyr)
library(stringr)
library(forcats)
library(magrittr)
library(tidyr)
library(ggplot2)
library(lubridate)
library(tibble)
library(purrr)

url = "https://capital-federal.properati.com.ar/nf/propiedades/venta/"
paths_allowed(paths = c(url))

# Leer el HTML
leahtml <- read_html(url)

leahtml %>%
  html_nodes(".price") %>%
  html_text() -> price

leahtml %>%
  html_nodes(".area") %>%
  html_text() -> area

leahtml %>%
  html_nodes(".location") %>%
  html_text() -> location

precio = gsub("\n","",price)
precio = gsub("exp","",precio)
precio_a = gsub("\\$","",precio)
precio_b = gsub("US","",precio_a)
precio_limpio = gsub("\\.","",precio_b)
precio_limpio = str_trim(precio_limpio)
precio_su= substr(precio_limpio,1,5)
precio_su= as.numeric(precio_su)

area_a = gsub("\n","",area)
area_b = gsub("m²","",area_a)
area_limpia = as.numeric(area_b)

dataset <- data.frame(location, area_limpia, precio_su)

When I try to use spread(instrumento, precio)it, it returns an error Each row of output must be identified by a unique combination of keys. Keys are shared for 851 rowsI need to work with the different instruments, for this I try to take them as variables

         fecha instrumento precio
2   2020-04-20      YPFD   397.50
3   2020-04-20      YPFD   397.50
4   2020-04-20      YPFD   397.50
5   2020-04-17      YPFD   401.85
6   2020-04-17      YPFD   402.00
7   2020-04-17      YPFD   401.00
8   2020-04-16      YPFD   400.60

How to perform regressions taking the dependent variable fixed and changing only the dependent variable? That is, the data frame is something like

year  a b c d..... z
1
2
3
4

I need the dependent variable to always be abut the independent variable to be b, then cand so on and then give me the result in a list of the dependent variables that were significant, for example:

b
c
h

I tried to do the following

for (i in 1:40) {

  modelo_prueba <- lm(data = ds, formula = a ~ ds[ ,i]) 
  print(summary(modelo_prueba))

  }

I have a dataset with the following structure

         year     var      cod                   localidad
34    1999-12-01  82.5      S1                   9 de Julio
35    1999-12-11  64.4      S1                   9 de Julio
36    1999-12-21  58.4      S1                   9 de Julio
37    2000-01-01  43.0      S1                   9 de Julio
38    2000-01-11  43.5      S1                   9 de Julio
39    2000-01-21  20.8      S1                   9 de Julio
40    2000-02-01  24.5      S1                   9 de Julio
41    2000-02-11  50.0      S1                   9 de Julio
42    2000-02-21  51.6      S1                   9 de Julio
43    2000-03-01  52.5      S1                   9 de Julio
44    2000-03-11  52.4      S1                   9 de Julio
45    2000-03-21  74.7      S1                   9 de Julio

I have two var values ​​for the same year , one corresponding to S1 and the other to S2 of the cod variable . I need to calculate the mean of var between the two cod values ​​for all observations. How can I do that?

I want to unite these data frames so that I can then work by making regressions between variables of the different data frames. How can I unite all the data frames into one?

library(dplyr)
library(readxl)
library("tidyverse")
library(caret)
data_p <- read_excel("C:/Users/x/Desktop/regresion.xlsx")
data_t_dic <- read_excel("C:/Users/x/Desktop/temperatura_mensual.xlsx", 
                                  sheet = "dic")
data_t_ene <- read_excel("C:/Users/x/Desktop/temperatura_mensual.xlsx", 
                         sheet = "ene")

data_t_feb <- read_excel("C:/Users/x/desktop/temperatura_mensual.xlsx", 
                         sheet = "feb")

df_p <- data.frame(data_p)
df_t_d <- data.frame(data_t_dic)
df_t_e <- data.frame(data_t_ene)
df_t_f <- data.frame(data_t_feb)

To test a multiple linear regression I need to delete a row from my data frame (an observation), estimate the model with the data and predict the value of Y with the data of the independent variables that I delete. Some of my code is

df <- data.frame(regresion_agregada)
df$periodo <- c(1999:2017) 

for (i in 1999:2017 ) {

  dt = filter(df, periodo!=i)
  model = lm(data = dt,formula = y ~ xa+xb+xc)


}

How can I test the model object with the values ​​of the deleted observation in the loop, that is, with the values ​​of period i for xa, xb, xc ?

I have a database in excel like this:

I need to do a multiple regression between rindess as the dependent variable and Dec,Jan,Feb as regressors for each of the departments. That is, a regression for May 25, July 9 ...

My code is the following.

library(readxl)
precip_dic <- read_excel("C:/Users/kevin/Desktop/rtrabajo.xlsx", 
                        sheet = "precip_dic")

precip_ene <- read_excel("C:/Users/kevin/Desktop/rtrabajo.xlsx", 
                         sheet = "precip_ene")

precip_feb <- read_excel("C:/Users/kevin/Desktop/rtrabajo.xlsx", 
                            sheet = "precip_feb")

rindes <- read_excel("C:/Users/kevin/Desktop/rtrabajo.xlsx", 
                     sheet = "rindess")

attach(precip_dic)
attach(precip_ene)
attach(precip_feb)
attach(rindes)

data <- cbind(precip_dic$`CORONEL SUAREZ`,precip_ene$`CORONEL SUAREZ`,precip_feb$`CORONEL SUAREZ`,rindes$`CORONEL SUAREZ`)
colnames(data) = c("dic","ene","feb","rindeas")
db <- data.frame(data)

modelo_simple <- lm(data = db,formula = rindeas ~ dic+ene+feb)

I have an excel with daily data for a variable (evaporation) for 5 places from 01/01/2000 to 08/31/2019 and I need to transform the information to quarterly data, this is the script I have:

library(readxl)
evaporacion <- read_excel("directorio", 
                         col_types = c("date", "numeric", "numeric", 
                         "numeric", "numeric", "numeric", 
                         "numeric", "numeric"))
attach(evaporacion)
library(tseries)
data = ts(evaporacion,
          start = c(2000,1,1),
          end = c(2019,31,8),
          frequency = 365)

mydata_quartely = aggregate.ts(data,nfrequency = 4 ,FUN = mean)

The problem arises when I execute the last line, an error appears saying that it cannot transform data from frequency 365 to 4.

Error in aggregate.ts(data, nfrequency = 4, FUN = mean) : cannot change frequency from 365 to 4

After doing a linear regression, I would like the result to print in a table with the stargazer library with type="html", but the console prints a set of html codes instead of the table. This is some of what I did:

cpbi_ts= ts(cpbi,
            start = 1988,
            end = 2018      )

tc_ts=ts(VITRM,
         start = 1988,
         end = 2018       )

data_1= ts.union(cpbi_ts,tc_ts)

m1=lm(cpbi_ts~tc_ts,
         start = 1998,
         end = 2018)

stargazer(m1,type="html",align = FALSE, no.space=FALSE)

when the last line of code is executed, the result should be a table but the console prints:

<table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
<tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
<tr><td style="text-align:left"></td><td>cpbi_ts</td></tr>
<tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">tc_ts</td><td>-0.094<sup>**</sup></td></tr>
<tr><td style="text-align:left"></td><td>(0.035)</td></tr>
<tr><td style="text-align:left"></td><td></td></tr>
<tr><td style="text-align:left">Constant</td><td>0.024<sup>**</sup></td></tr>
<tr><td style="text-align:left"></td><td>(0.011)</td></tr>
<tr><td style="text-align:left"></td><td></td></tr>
<tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>21</td></tr>
<tr><td style="text-align:left">R<sup>2</sup></td><td>0.280</td></tr>
<tr><td style="text-align:left">Adjusted R<sup>2</sup></td><td>0.240</td></tr>
<tr><td style="text-align:left">Residual Std. Error</td><td>0.051 (df = 19)</td></tr>
<tr><td style="text-align:left">F Statistic</td><td>7.400<sup>**</sup> (df = 1; 19)</td></tr>
<tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
</table>

I have the following dataframe and I need to obtain the bimonthly sums for January and February for the rainfall variable. I did it for years. This is the code I used

df.mensual <- df %>%
group_by(anio,mes,codigo) %>%
summarise(precip.mensual = sum(lluvia,na.rm="TRUE"))

filtering = filter(df.mensual, mes<3)

What I need is to add the months of filtering for each year and code. Then make a table that looks like this:

         1980 1981 1982 1983
----------------------------------------------------
codigo1   A    A    A    A
codigo2   A    A    A    A
codigo3   A    A    A    A

where are the A that are the bimonthly sums of January and February for each year and code.