Using for loop in r full#
Using for loop in r code#

# we merge the trade data with the codes data set in order to have the trade value matched to the reporter's country name, region and isCountryДЄBC_X_merged <- ABC_X_merged If you want to replicate this, make sure you replace ABC with your country's name and XYZ - with the code.ДЄBC_X_countries <- trade_data[trade_data $flow = 'E' &

Using for loop in r code#

For the moment we assume your country is ABC and the code is XYZ. #First, check 'codes' for your country's code. isCountry we use to exclude non-countries and region - to use the same color for the same regions. (If the two data sets had the same name, we do not need to specify by, by.x or by.y.)ДЇinally, we only keep the variables country, value, isCountry, region. by.x is for the first data set and by.y - for the second. We specify the names of the columns by which the data sets should be merged, since they have different names but essentially contain the same factor/categorical data. To use the merge command we first specify the data sets we want to merge - in this case CN_X_countries with codes. Head(CNX_merged) # country value isCountry region We start by creating a table with Chinese exports in 2016 to individual countries:Д¬NX_merged <- CNX_merged LetвЂ™s now show in a plot the top 10 countries China exported to in 2016. In Section 4, we will delve more into plots. Notice we join the all of the listed specifications with c, as we want them to appear together. We specify where in our plot we want the legend (e.g.В bottom left in this case), provide the names of the items in the legend: legend = c('Exports', 'Imports') and include the same specifications from the plot function for the colors: col=c(2,4) and the type of the line: lty=c(1,2).

Using for loop in r full#

( ?plot will also provide you with some options, but the full list is available in par.)ДЇor the second line we specify the data set variables to be used: CN_M, we make the line blue: col=4 and dashed: lty=2. ?par contains all the plot options available. We specify the color with col and a solid line with lty=1. We can tell R what coordinate system to use for the x-axis and y-axis, with xlim and ylim, respectively. We include a title main='China's exports and imports' and a y-axis label ylab='USD, trillions'. We specify first the plot for CN_X and select the variables years and trade value: CN_X. We combine both exports CN_X and imports CN_M into one plot by specifying the options for the first line and afterwards specifying lines() which adds a second line. We put the values of this data set in trillions: CN_M$value <- CN_M$value/1000000000000. We filter the trade_data by selecting only imports: trade_data$flow='I', the reporter as China: trade_data$reporter=924 and the partner as the world: trade_data$partner=1. We first create the data set for Chinese imports CN_M. Lines(CN_M, col= 4, lty= 2) #blue color, dashed line legend( 'bottomleft', Ylim= c( 1, 2.5), #adding y axis limits col= 2, #red color lty= 1 #solid line, see ?par for details

8.1 Calculating the regulatory distanceД¬N_M <- trade_data[trade_data $flow = 'I' &Д¬N_M $value <- CN_M $value / 1000000000000 plot(CN_X,.

7.4.4 Coverage ratios, Frequency index and Prevalence scores by broad sectors.

7.4 Calculating the Coverage ratio, Frequency index and Prevalence score.

7.2.1 Make NTM data binary by separating WLD and EUN into individual countries.

7.2 Uploading and cleaning the Non-Tariff Measures (NTMs) data for Viet Nam.

7.1 Uploading and cleaning the WITS trade data for Viet Nam in R.

7 Coverage ratio, Frequency index and Prevalence score for Viet Nam.

6 Non-Tariff Measures (NTMs) and incidence indicators, a simple case.

5.2 Poisson Pseudo-Maximum Likelihood Estimator (PPML).

5.1.4 STATA-like robust clustered standard errors.

5.1.3 Linear Gravity model with more variables and interaction terms.

4.3 Further specifications for graphs: adding axes titles and animation.

1.1 Declaring variables and arithmetics in R.

United Nations ESCAP Training on using R for trade analysis.