https://juanpacheco.netlify.app/post/ Recent content in Posts on Juan Pacheco Hugo -- gohugo.io en-us Sat, 26 Mar 2022 00:00:00 +0000 https://juanpacheco.netlify.app/post/power-bi-dash/ Sat, 26 Mar 2022 00:00:00 +0000 https://juanpacheco.netlify.app/post/power-bi-dash/ This example shows a Dashboard in Power BI using Netflix information 2016-2022. This example shows a Dashboard in Power BI using Commercial Information. https://juanpacheco.netlify.app/post/sql/ Tue, 01 Mar 2022 00:00:00 +0000 https://juanpacheco.netlify.app/post/sql/ SQL SQL - CONCAT FUNCTION SELECT usertype, CONCAT(start_station_name, "to", end_station_name) AS route, COUNT(*) AS num_trip, ROUND(AVG(CAST(tripduration as int64)/60),2) AS duration, FROM `bigquery-public-data.new_york.citibike_trips` GROUP BY start_station_name, end_station_name, usertype ORDER BY num_trip DESC LIMIT 10 Function Usage Example CONCAT A function that adds strings together to create new text strings that can be used as unique keys CONCAT (‘Google’, ‘.com’); CONCAT_WS A function that adds two or more strings together with a separator CONCAT_WS (‘ . https://juanpacheco.netlify.app/post/data-manipulation-with-dplyr/ Thu, 13 May 2021 00:00:00 +0000 https://juanpacheco.netlify.app/post/data-manipulation-with-dplyr/ R Packages we gonna use dplyr, ggplot2, plotly, readxl and knitr in this example. Functions that were used: read_excel(): for read excel files. filter(): in order to make subsets of our data. mutate(): To create new variables. as.factor(): To convert a variable in factor. glimpse similar to str(): applied to a dataframe. group_by(): takes an existing tbl and converts it into a grouped tbl where operations are performed “by group”. https://juanpacheco.netlify.app/post/ggplot/ Sat, 08 May 2021 00:00:00 +0000 https://juanpacheco.netlify.app/post/ggplot/ We gonna use dplyr, gapminder, RColorBrewer, ggplot2 libraries in this example. Let’s load gapminder data frame and see colnames. data("gapminder") colnames(gapminder) ## [1] "country" "continent" "year" "lifeExp" "pop" "gdpPercap" gapminder is a data frame with 9 variables and more than 10 thousand observations. In variables we have country names, continent, year, life expectancy, population and gross domestic product per capita. gapminder %>% head() ## # A tibble: 6 x 6 ## country continent year lifeExp pop gdpPercap ## <fct> <fct> <int> <dbl> <int> <dbl> ## 1 Afghanistan Asia 1952 28. https://juanpacheco.netlify.app/post/2021-05-08-introduction-to-financial-time-series-in-r/ Sat, 08 May 2021 00:00:00 +0000 https://juanpacheco.netlify.app/post/2021-05-08-introduction-to-financial-time-series-in-r/ Financial series This post brings a quick overview of financial series in R. It’s common analyze time series in advanced economics or finance courses. Some courses and depending of university use a particular software for this purpose, from Excel to high-level programming languages like Stata, R or Python. In this case, I’m going to show how to get the financial series from Yahoo Finance by two methods. First, I’ll do it within a long code using quantmond library, after that I gonna use tidyquant library to reduce the previous code and automatize it. https://juanpacheco.netlify.app/post/argentina/ Sat, 08 May 2021 00:00:00 +0000 https://juanpacheco.netlify.app/post/argentina/ #packages for (x in c("dplyr", "ggplot2","readxl","plotly" ,"psych", "ggfortify")) { library(x, character.only = TRUE) } Macroeconomic variables datos<- read_excel("/Users/Juan Alfredo.DESKTOP-ETJ31JC/Documents/Universidad/Termino 2020 2S/R Samples/Pagina/PIB Argentina_1.xlsx") dt<- datos[,c(2,4,10,13)] dt %>% mutate(m= import/PIB) %>% mutate(x= export/PIB) ## # A tibble: 114 x 6 ## PIB import export ITCRM m x ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 391657. 55833. 45376. NA 0.143 0.116 ## 2 437818.