18 Jun 19

Tamaño del archivo: 8.19 MB IsoBuster Pro: versión profesional de uno de los programas más potentes para trabajar con imágenes de disco y recuperar el CD / DVD óptico. El programa le permite ver y extraer directamente archivos, pistas, sectores y sesiones de discos CD-i, CD-Text, VCD, SVCD, CD-ROM, CD-ROM XA, DVD y DVCD. La […]

17 May 19

Image Credit: Jennifer M., Chino Valley, AZ The author’s comments: I love K-pop and Big bang was one of my favorites  Hello my name is Halo, I am 28 years old and I moved from Ohio in America to South Korea. I moved to South Korea because I wanted to start new somewhere else so […]

12 May 19
Les 5 du Vin

There is a particularly interesting but unjustly neglected group of wines produced down near France’s mediterranean border with Spain. In the region known as the Roussillon, dry wines now dominate production, but there was a time when the major part of the considerable local production was fortified, as is Port for exemple. For understandable historical […]

04 May 19

Global Multi-Spot Welder Market report highlights the key features of the market in terms of periodic data, present market conditions, market needs along with a detailed examination of the key players involved in the market. Multi-Spot Welder The business report also offers vital aspects related with Multi-Spot Welder market and perform as a mandatory tool to industries […]

04 May 19

Global Inkjet Disc Printers Market report highlights the key features of the market in terms of periodic data, present market conditions, market needs along with a detailed examination of the key players involved in the market. Inkjet Disc Printers The business report also offers vital aspects related with Inkjet Disc Printers market and perform as a mandatory […]

16 Apr 19
Job Search Engine And Aggregation

Pasig.Experience in following technical skills, Microsoft: C#.NET, IIS, Visual Studio, SQL Server, SSRS, Windows Server OS 2008R2/2012; Linux/Red Hat, Oracle, SSH Secure Shell Client/FTP, Powersell and Unix shell scripting, (nice to have skills: MS Messaging Queue, WinSCP, LDAP Admin, Autosys job scheduler and cron, ImageTrac, iTran, Rimage and OPEx)Candidate must possess at least a Bachelor”s/College […]

08 Apr 19
Property Blog

The buyer is an entity tied to Equus Holdings Inc., an Edina based firm that owns several companies, including Rimage, a DVD publishing business. General Mills sells Golden Valley building near headquarters syndicated from https://singaporecondopage.wordpress.com/

21 Mar 19

My last post described my overall impressions from RAW, interestingly the hybrid issue has been in the news elsewhere a little, a topic I shall pick up again. To follow up I shall describe a few of my favourite wines without too much detail. If you want more technical details then the RAW site has […]

14 Mar 19

Senatorial candidates started their campaigns last February 12, 2019 – exactly 90 days before May 13, the election date. But even before the said date, candidates have started showing TV advertisements. According to Rappler, both administration and opposition bets aired their pre-campaign ads. A total of 11 senatorial candidates had TV ads during the commercial […]

12 Mar 19

Finding the scant remains of dinosaurs that roamed the Earth tens of millions of years ago obviously isn’t easy. Finding the remains of entirely new dinosaurs that nobody even knew existed? That’s even harder still, but researchers in Victoria, Australia did just that, and the newly documented herbivore would probably have been pretty adorable to […]

10 Feb 19
Little Shoe

Funky, sustainable, and delicious! What’s not to love about natural wine? I learned some useful insights during this Natural Reds tasting with Decant Be Serious.

08 Feb 19

Image Credit: Allison H., Vancouver, WA The author’s comments: D’aw, this is so sad… Anyway, ever since I obsessed over Loki, I wondered at how similar his and Luna’s stories were. So I wrote a little tragic fanfic, taking place right before Luna turns into Night Mare Moon. Why do I write such sad stuff? […]

27 Jan 19

Stacked Bar Chart for Rank Data At work on Friday, I was trying to figure out the best way to display some rank data. What I had were rankings from 1-5 for 10 factors considered most important in a job (such as Salary, Insurance Benefits, and the Opportunity to Learn), meaning each respondent chose and ranked the top 5 from those 10, and the remaining 5 were unranked by that respondent. Without even thinking about the missing data issue, I computed a mean rank and called it a day. (Yes, I know that ranks are ordinal and means are for continuous data, but my goal was simply to differentiate importance of the factors and a mean seemed the best way to do it.) Of course, then we noticed one of the factors had a pretty high average rank, even though few people ranked it in the top 5. Oops.

So how could I present these results? One idea I had was a stacked bar chart, and it took a bit of data wrangling to do it. That is, the rankings were all in separate variables, but I want them all on the same chart. Basically, I needed to create a dataset with:

    1 variable to represent the factor being ranked

  • 1 variable to represent the ranking given (1-5, or 6 that I called “Not Ranked”)
  • 1 variable to represent the number of people giving that particular rank that particular factor

What I ultimately did was run frequencies for the factor variables, turn those frequency tables into data frames, and merged them together with rbind. I then created chart with ggplot. Here’s some code for a simplified example, which only uses 6 factors and asks people to rank the top 3.

First, let’s read in our sample dataset – note that these data were generated only for this example and are not real data:

## -- Attaching packages --------------------------------------------------------------------------------------------------------------------- tidyverse 1.2.1 --
## v ggplot2 3.0.0     v purrr   0.2.4
## v tibble 1.4.2 v dplyr 0.7.4
## v tidyr 0.8.0 v stringr 1.3.1
## v readr 1.1.1 v forcats 0.3.0
## Warning: package 'ggplot2' was built under R version 3.5.1
## -- Conflicts ------------------------------------------------------------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
ranks <- read_csv("C:/Users/slocatelli/Desktop/sample_ranks.csv", col_names = TRUE)
## Parsed with column specification:
## cols(
## RespID = col_integer(),
## Salary = col_integer(),
## Recognition = col_integer(),
## PTO = col_integer(),
## Insurance = col_integer(),
## FlexibleHours = col_integer(),
## OptoLearn = col_integer()
## )

This dataset contains 7 variables – 1 respondent ID and 6 variables with ranks on factors considered important in a job: salary, recognition from employer, paid time off, insurance benefits, flexible scheduling, and opportunity to learn. I want to run frequencies for these variables, and turn those frequency tables into a data frame I can use in ggplot2. I’m sure there are much cleaner ways to do this (and please share in the comments!), but here’s one not so pretty way:

salary <- as.data.frame(table(ranks$Salary))
salary$Name <- "Salary"
recognition <- as.data.frame(table(ranks$Recognition))
recognition$Name <- "Recognition by \nEmployer"
PTO <- as.data.frame(table(ranks$PTO))
PTO$Name <- "Paid Time Off"
insurance <- as.data.frame(table(ranks$Insurance))
insurance$Name <- "Insurance"
flexible <- as.data.frame(table(ranks$FlexibleHours))
flexible$Name <- "Flexible Schedule"
learn <- as.data.frame(table(ranks$OptoLearn))
learn$Name <- "Opportunity to \nLearn"

rank_chart <- rbind(salary, recognition, PTO, insurance, flexible, learn)
rank_chart$Var1 <- as.numeric(rank_chart$Var1)

With my not-so-pretty data wrangling, the chart itself is actually pretty easy:

ggplot(rank_chart, aes(fill = Var1, y = Freq, x = Name)) +
geom_bar(stat = "identity") +
labs(title = "Ranking of Factors Most Important in a Job") +
ylab("Frequency") +
xlab("Job Factors") +
scale_fill_continuous(name = "Ranking",
breaks = c(1:4),
labels = c("1","2","3","Not Ranked")) +
theme_bw() +

Based on this chart, we can see the top factor is Salary. Insurance is slightly more important than paid time off, but these are definitely the top 2 and 3 factors. Recognition wasn’t ranked by most people, but those who did considered it their #2 factor; ditto for flexible scheduling at #3. Opportunity to learn didn’t make the top 3 for most respondents.

22 Jan 19
Garner Insights Blogs

Garnerinsights.com has added a new report to its database Global Sticker Labels Flexo Printers Market Size, Status And Forecast 2019-2023 The Sticker Labels Flexo Printers Market research report provides a complete view of the market by assessing the impact of the technological advancements, changes in investment habits, and n-depth overview of Product Specification.This report is a valuable source […]

16 Jan 19
Life Hacks

I have a Lightroom library that contains some photos I classified myself and some other ones (in both cases, thousands) not yet classified. I want to automatically classify images based on the tags I assigned to the images I already classified. Most steps (I use Lightroom) are simple: write XMP files filter the files according […]