Suppose I have a dataframe containing Kentucky zip codes and values between -1 and 1. I want to plot these as a heatmap of Kentucky, where -1 represents the lowest color of the gradient and 1 represents the highest.
How can I do this in R? I'm using R Studio 1.3.959 (R version 3.6.3).
An option using tigris and sf. Note that this uses zip code tabulation areas, which are not a 1:1 match with zip codes. Zip codes follow streets and can't be made into sensible polygons: https://gis.stackexchange.com/a/2693/162034
library(tigris)
library(sf)
# download zipcode tabulation areas and state boundaries
zcta1 <- zctas(TRUE)
sts <- states(TRUE)
# subset zipcode data
zcta_ky <- st_intersection(zcta1, sts[sts$NAME == 'Kentucky', ])
# add random 0-1 field
zcta_ky$rand <- runif(nrow(zcta_ky))
#plot
plot(zcta_ky['rand'])
I am trying to learn R, and use the corrplot library to draw Y:City and X: Population graph. I wrote the below code:
When you look at the picture above, there are 2 columns City and population. When I run the code I get this error message:
Error in cor(Illere_Gore_Nufus) : 'x' must be numeric.
My excel data:
In general, correlation plot (Scattered plot) can be plotted only when you have two continuous variable. Correlation is a value that tells you how two continuous variables are linearly related. The Correlation value will always fall between -1 and 1, where correlation value of -1 depicts weak linear relationship and correlation value of 1 depicts strong linear relationship between the two variables. Correlation value of 0 says that there is no linear relationship between the two variables, however, there could be curvi-linear relationship between the two variables
For example
Area of the land Vs Price of the land
Here is the Data
The correlation value for this data is 0.896, which means that there is a strong linear correlation between Area of the land and Price of the land (Obviously!).
Scatter plot in R would look like this
Scatter plot
The R code would be
area<-c(650,785,880,990,1100,1250,1350,1800,2200,2800)
price<-c(250,275,280,290,350,340,400,335,420,460)
cor(area,price)
plot(area,price)
In Excel, for the same example, you can select the two columns, go to Insert > Scatter plot (under charts section)
Scatter plot
In your case, the information can be plotted in bar graph with city in y axis and population in x axis or vice versa!
Hope I have answered you query!
Some assumptions
You are asking how to do this in Excel, but your question is tagged R and Power BI (also RStudio, but that has been edited away), so I'm going to show you how to do this with R and Power BI. I'm also going to show you why you got that error message, and also why you would get an error message either way because your dataset is just not sufficient to make a correlation plot.
My answer
I'm assuming you would like to make a correlation plot of the population between the cities in your table. In that table you'd need more information than only one year for each city. I would check your data sources and see if you could come up with population numbers for, let's say, the last 10 years. In lack of the exact numbers for the cities in your table, I'm going to use some semi-made up numbers for the population in the 10 most populous countries (following your datastrutcture):
Country 2017 2016 2015 2014 2013
China 1415045928 1412626453 1414944844 1411445597 1409517397
India 1354051854 1340371473 1339431384 1343418009 1339180127
United States 326766748 324472802 325279622 324521777 324459463
Indonesia 266794980 266244787 266591965 265394107 263991379
Brazil 210867954 210335253 209297939 209860881 209288278
Pakistan 200813818 199761249 200253292 197655630 197015955
Nigeria 195875237 192568158 195757661 191728478 190886311
Bangladesh 166368149 165630262 165936711 166124290 164669751
Russia 143964709 143658415 143146914 143341653 142989754
Mexcio 137590740 137486490 136768870 137177870 136590740
Writing and debugging R code in Power BI is a real pain, so I would recommend installing R studio, write your little R snippets there, and then paste it into Power B.
The reason for your error message is that the function cor() onlyt takes numerical data as arguments. In your code sample the city names are given as arguments. And there are more potential traps in your code sample. You have to make sure that your dataset is numeric. And you have to make sure that your dataset has a shape that the cor() will accept.
Below is an R script that will do just that. Copy the data above, and store it in a file called data.xlsx on your C drive.
The Code
library(corrplot)
library(readxl)
# Read data
setwd("C:/")
data <- read_excel("data.xlsx")
# Set Country names as row index
rownames(data) <- data$Country
# Remove Country from dataframe
data$Country <- NULL
# Transpose data into a readable format for cor()
data <- data.frame(t(data))
# Plot data
corrplot(cor(data))
The plot
Power BI
In Power BI, you need to import the data before you use it in an R visual:
Copy this:
Country,2017,2016,2015,2014,2013
China,1415045928,1412626453,1414944844,1411445597,1409517397
India,1354051854,1340371473,1339431384,1343418009,1339180127
United States,326766748,324472802,325279622,324521777,324459463
Indonesia,266794980,266244787,266591965,265394107,263991379
Brazil,210867954,210335253,209297939,209860881,209288278
Pakistan,200813818,199761249,200253292,197655630,197015955
Nigeria,195875237,192568158,195757661,191728478,190886311
Bangladesh,166368149,165630262,165936711,166124290,164669751
Russia,143964709,143658415,143146914,143341653,142989754
Mexcio,137590740,137486490,136768870,137177870,136590740
Save it as countries.csv in a folder of your choosing, and pick it up in Power BI using
Get Data | Text/CSV, click Edit in the dialog box, and in the Power Query Editor, click Use First Row as headers so that you have this table in your Power Query Editor:
Click Close & Apply and make sure that you've got the data available under VISUALIZATIONS | FIELDS:
Click R under VISUALIZATIONS:
Select all columns under FIELDS | countries so that you get this setup:
Take parts of your R snippet that we prepared above
library(corrplot)
# Set Country names as row index
data <- dataset
rownames(data) <- data$Country
# Remove Country from dataframe
data$Country <- NULL
# Transpose data into a readable format for cor()
data <- data.frame(t(data))
# Plot data
corrplot(cor(data))
And paste it into the Power BI R script Editor:
Click Run R Script:
And you're gonna get this:
That's it!
If you change the procedure to importing data from an Excel file instead of a textfile (using Get Data | Excel , you've successfully combined the powers of Excel, Power BI and R to produce a scatterplot!
I hope this is what you were looking for!
I am new to R and am having trouble plotting a SpatialPointsDataFrame, with eventual hopes of creating minimum convex polygons. I've been trying for a few days but can't find anything to fix this problem.
I load my excel data as TXT. File has 3 columns (Latitude, Longitude, ID) and 549 rows of observations. Then I enter the following code:
# Create coordinates variable
coords <- cbind(LAT = as.numeric(as.character(multi$LAT)), LONG = as.numeric(as.character(multi$LONG)))
# Create the SpatialPointsDataFrame
multi_SPDF <- SpatialPointsDataFrame(coords, data = data.frame(multi$ID), proj4string = CRS("+init=epsg:4326"))
#Plot the points
plot(multi_SPDF)
When I enter this, it produces a plot that looks like this:
I made this code from a similar code found at this link: http://www.alex-singleton.com/R-Tutorial-Materials/7-converting-coordinates.pdf
If anyone is able to help me to make this work I would really appreciate it. Hopefully I provided all of the necessary information.
EDIT
In an attempt to provide a reproducible example, I extracted the head of the data to copy into the comment, as follows:
LAT LONG ID
1 -41.30853 174.7342 7
2 -41.30481 174.7353 6
3 -41.30681 174.7363 7
4 -41.30660 174.7360 10
5 -41.31400 174.7329 10
6 -41.31059 174.7350 6
When I ran the above code on these 6 rows alone, it produced a plot with points distributed vertically and horizontally (exactly what I wanted!)
However, the same code still does not work on my entire data set. So I think the problem may be in my excel file and not my code.
I usually went to the site :
http://gadm.org
to find the contours of the country I wanted to plot with R.
But today, this website is closed, and I don't know how to proceed to have a really accurate map...
When I try :
library(maps)
map("world", xlim=c(15,23), ylim=c(45.5,49))
The contours of the country are not very accurate...
I try to find an other website which could give the contours, but I didn't find.
Somebody can help me ?
Ok, let's say you want to plot France contours using data from Natural Earth:
# First download the data in your working directory
download.file("http://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_countries.zip", "countries.zip")
# Then unzip
unzip("countries.zip")
# Load maptools
library(maptools)
# Read in the shapefile
world <- readShapeSpatial("ne_10m_admin_0_countries.shp")
# Plot France
plot(world[world$ADMIN=="France",1])
# Column 1 is the id column, column "ADMIN" contains the country names
I would like to make a choropleth with the maps package in R. I have data which I have constructed to create bins and associate color names with those bins. Now, I need to use the col= argument to point the colors to the counties, in this example. How do I construct that argument? I would have thought that constructing a data frame would associate the county and color on the same line? Is that not true? So far I have the following
Example Data:
County | Value | Bin | Color
alamance | 100 | 1 | white
brunswick | 1000 | 2 | red
... through 100 counties
R code (which does not work):
library("maps")
DATA <- read.csv("~/Example_Data.csv")
DATA$County <- as.character(DATA$County)
DATA$Color <- as.character(DATA$Color)
NC <- map('county', 'north carolina', col= DATA$Color, Fill=TRUE)
So, after many iterations here is the essence of the solution. Instead of giving the R code which made it work (pretty bland), here are the rules that helped solve the problem.
The county.fips data included in the package has a column with all states and county names. This revealed the formatting of county name matches which are all lowercase, "state,county" with no spaces.
For the NC subset there are 102 entries, not 100, because Currituck County is subdivided into three entities. This was the source of most/all of the issues and was difficult to diagnose but easy to solve.
Solution 1 - Match a vector of colors to the vector of counties. 102 color entries IN THE PROPER ALPHA ORDER will produce a correctly resulting choropleth. Fastest, but also the least convenient if you were trying to do this for, say, all counties in the U.S.
Solution 2 - Add fips codes to original data and then match on fips. Since the county.fips file has Currituck entities listed as "north carolina,currituck:main", etc., this is still going to take some manipulation or finding an external fips reference. This is the method used in the maps() documentation, but which would have taken too long so I preferred the former. However, taking the time would allow you to approach a national dataset, for instance.