{r-1,2} library(readr) rus_unis <- read.csv("C:/Users/User/Downloads/rus_unis.csv", sep = ";") print(rus_unis)

{r-4} rus_unis <- read.csv("C:/Users/User/Downloads/rus_unis.csv", sep = ";") min(rus_unis$total_income) max(rus_unis$total_income) mean(rus_unis$total_income)

```{r-5} library(ggplot2)

rus_unis <- read.csv(“C:/Users/User/Downloads/rus_unis.csv”, sep = “;”) ggplot (rus_unis) + geom_histogram(aes(x = wos, fill = as.factor(year))) + theme_minimal() + labs (title =“Wos divided by year”, fill = “Year”, У = NULL) + theme(plot.title = element_text(face = “bold”, size = 20, vjust = 0.5))

#We can see that from the 2013 till 2017 the frequency of occurence has been decreasing for the values of WOS up to ~500.

```{r-5}
rus_unis <- read.csv("C:/Users/User/Downloads/rus_unis.csv", sep = ";")

ggplot (rus_unis) +
  geom_histogram(aes(x = scopus, fill = as.factor(year))) +
  theme_minimal() +
  labs (title ="Scopus divided by year",
  fill = "Year",
  У = NULL) +
theme(plot.title = element_text(face = "bold", size = 20, vjust = 0.5))

#Here we can see quite similar situation with the previous observation. We see the decreasing number of variables for values of Scopus up to ~500 for all the observed years. The max value we can see in 2014

```{r-5} rus_unis <- read.csv(“C:/Users/User/Downloads/rus_unis.csv”, sep = “;”)

ggplot (rus_unis) + geom_histogram(aes(x = rsci, fill = as.factor(year))) + theme_minimal() + labs (title =“rsci divided by year”, fill = “Year”, У = NULL) + theme(plot.title = element_text(face = “bold”, size = 20, vjust = 0.5))

#Here we can see the bigger number of values for RSCI variable. The frequency is decreasing till the 5000 of values for all the years.The max value is in 2016


``` r
rus_unis <- read.csv("C:/Users/User/Downloads/rus_unis.csv", sep = ";")

library(ggplot2)
ggplot(rus_unis)+
  geom_point(aes(x = wos, y = scopus))+
  labs(x = "wos", y = "scopus", 
       title = "Relationship between wos and scopus") +
  theme(plot.title = element_text(face = "bold", size = 20, vjust = 0.5),
        panel.grid.minor = element_line(color = "white"))

#We can see that the values of wos and scopus are similarry and correlatively frequent up to the meaning of 100, then values of bouth of the variables are decreasing (and less frequent).