Data science job salary
ilke
Veri setine erişmek için data-job-salary sitesini ziyaret edebilirsiniz.
1.Kütüphanelerin yüklenmesi
library(ggplot2)
library(tidyverse)
library(lubridate)
library(readxl)
library(funModeling)
library(gridExtra)
library(magrittr)
library(scales)
library(plotrix)
library(RColorBrewer)
library(readr)
library(maps)
library(highcharter)
library(dplyr)
library(tidyverse)
library(magrittr)
library(DataExplorer)
library(maps)
library(plotly)
library(DT)
library(tidytext)
library(gridExtra)
library(readxl)
library(ggplot2)
library(dplyr)
library(plotly)
library(tidyr)
library(d3Tree)2. Verisetini yükleme
getwd()## [1] "/home/ilke"setwd("/home/ilke/Downloads")
df<- read.csv("datascience_salaries (1).csv",sep=",", header=TRUE,stringsAsFactors = FALSE)3. Keşifci Veri Analizi(Veriseti genel yapısı hakkında bilgi edinme)
glimpse(df)## Rows: 1,171
## Columns: 7
## $ X <int> 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 17, 18, 20, 22…
## $ job_title <chr> "Data scientist", "Data scientist", "Data scientist",…
## $ job_type <chr> "Full Time", "Full Time", "Full Time", "Full Time", "…
## $ experience_level <chr> "Senior", "Senior", "Senior", "Senior", "Senior", "Se…
## $ location <chr> "New York City", "Boston", "London", "Boston", "New Y…
## $ salary_currency <chr> "USD", "USD", "USD", "USD", "USD", "USD", "USD", "USD…
## $ salary <int> 149000, 120000, 68000, 120000, 149000, 68000, 69000, …summary(df)## X job_title job_type experience_level
## Min. : 0.0 Length:1171 Length:1171 Length:1171
## 1st Qu.: 364.5 Class :character Class :character Class :character
## Median : 815.0 Mode :character Mode :character Mode :character
## Mean : 931.6
## 3rd Qu.:1504.5
## Max. :2259.0
## location salary_currency salary
## Length:1171 Length:1171 Min. : 30000
## Class :character Class :character 1st Qu.: 45000
## Mode :character Mode :character Median : 63000
## Mean : 64836
## 3rd Qu.: 68000
## Max. :228000plot_intro(df)
## 3.1 veri ön işleme/ temizleme
## [1] 0df$X <- NULL #gereksiz satır silme#%99unun maaşı USD türünden girili. Euro ve diğer olanları çıkardım.
df <-df %>%
filter(salary_currency == "USD")3.2 Genel istatistik/grafik
3.1.1 Sürekli Değişkenlerin Özet Bazı İstatistikleri
profiling_num(df)3.1.2 Genel Histogram
plot_num(df) #genel histogram
#veri setiyle ilgili genel bir önizleme için
d3tree(list(root = df2tree(rootname ='title',
struct = as.data.frame(df)),
layout = 'collapse'))ggplot(df,aes(job_title, fill=experience_level))+
geom_bar()+
coord_polar(theta = "y")
title_count<- df %>% group_by(job_title) %>% tally() %>% arrange(n, decreasing=T)
title_count## # A tibble: 5 × 2
## job_title n
## <chr> <int>
## 1 ML Ops 19
## 2 Big data 100
## 3 Machine learning 286
## 4 Data analyst 364
## 5 Data scientist 388#mesleklerde intern dağılımı nasıl?
#Big data'da intern olarak çalışan yok
ggplot(data = df) +
geom_bar(mapping = aes(x = job_title, fill = job_type), position = "dodge") + scale_fill_manual(values = c("blue","red"))+
theme_bw()
#mesleklere göre deneyimleri görebileceğimiz grafik
ggplot(data = df) +
geom_bar(mapping = aes(x = job_title, fill = experience_level), position = "dodge") + scale_fill_manual(values = c("yellow", "red", "blue","magenta"))+
theme_bw() 
#verisetine çalışma yeri olarak remote/no remote ifade edecek şekilde remote=0, no remote=1 olacak şekilde yeni bir sütuna eklendi.
ds <- df %>% mutate(remote = ifelse(grepl("remote",tolower(location) ), "1", "0"))#131 kişi remote olarak çalışmaktadır.
remote_counts <- count(ds, remote)
remote_counts## remote n
## 1 0 1026
## 2 1 131write.csv(ds, file = "/home/ilke/Downloads/ödev.csv", row.names = FALSE)
#remote eklenmiş halini kaydetme# çalışanların ½11'i remote olarak çalışmaktadır.
data <- c(131, 1026)
data_percent <- prop.table(data) * 100
library(ggplot2)
data_df <- data.frame(remote = c("Remote", "No remote"), count = c(131, 1026))
ggplot(data = data_df, aes(x = "", y = count, fill = remote)) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y", start = 0) +
ggtitle("Location Pie Chart") +
scale_fill_manual(values = c("#00BFC4", "#F8766D")) +
theme_void() +
labs(fill = "location", title = "Location Pie Chart") +
geom_text(aes(label = paste0(round(data_percent), "%")), position = position_stack(vjust = 0.5))
#en çok çalışılan lokasyonlara bakalım
top_10_locations <- df %>% group_by(location) %>%
summarise(count=n()) %>%
arrange(desc(count)) %>%
top_n(10)
top_10_locations## # A tibble: 10 × 2
## location count
## <chr> <int>
## 1 London 71
## 2 Remote 49
## 3 San Francisco 43
## 4 Bengaluru 34
## 5 Bangkok 32
## 6 New York City 32
## 7 Paris 32
## 8 Berlin 27
## 9 United States 26
## 10 Toronto 19ggplot(data = top_10_locations, aes(x = location, y = count, colour = location)) +
geom_bar(stat = "identity") +
ggtitle("Lokasyon ve Count") +
xlab("Lokasyon") +
ylab("Count")
#Çalışma tipine göre sıklıklar ve maaş istatistikleri
salary_by_jobtype <- df %>% group_by(job_type) %>%
summarise(count= n(),
min_salary=min(salary),
max_salary=max(salary),
mean_salary=mean(salary)) %>%
arrange(desc(mean_salary)) %>%
mutate(rank = rank(-mean_salary))
salary_by_jobtype## # A tibble: 2 × 6
## job_type count min_salary max_salary mean_salary rank
## <chr> <int> <int> <int> <dbl> <dbl>
## 1 Full Time 1122 30000 228000 65360. 1
## 2 Internship 35 30000 135000 54629. 2#mesleğe göre meslek sıklıkları ve maaş istatistikleri
salary_by_job <- df %>% group_by(job_title) %>%
summarise(count= n(),
min_salary=min(salary),
max_salary=max(salary),
mean_salary=mean(salary),
median_salary=median(salary)) %>%
arrange(desc(mean_salary)) %>%
mutate(rank = rank(-mean_salary))
salary_by_job## # A tibble: 5 × 7
## job_title count min_salary max_salary mean_salary median_salary rank
## <chr> <int> <int> <int> <dbl> <dbl> <dbl>
## 1 ML Ops 19 35000 228000 81263. 62000 1
## 2 Machine learning 286 30000 228000 68503. 51000 2
## 3 Data scientist 388 30000 196000 67454. 68000 3
## 4 Data analyst 364 30000 225000 60973. 63000 4
## 5 Big data 100 30000 178000 57440 53500 5#deneyim seviyesine göre maaş istatistikleri
salary_by_exp <- df %>% group_by(experience_level) %>% summarise(count= n(),
min_salary=min(salary),
max_salary=max(salary),
mean_salary=mean(salary)) %>%
arrange(desc(mean_salary)) %>%
mutate(rank = rank(-mean_salary))
salary_by_exp## # A tibble: 4 × 6
## experience_level count min_salary max_salary mean_salary rank
## <chr> <int> <int> <int> <dbl> <dbl>
## 1 Executive 13 41000 175000 76077. 1
## 2 Senior 719 30000 228000 75403. 2
## 3 Mid 299 30000 160000 51813. 3
## 4 Entry 126 30000 140000 36111. 4#meslek ve çalışma tiplerine göre maaş ortalamaları
a <- df %>%
select(job_title, salary, job_type) %>%
group_by(job_title, job_type) %>%
summarise(avg_income = mean(salary))
a## # A tibble: 10 × 3
## # Groups: job_title [5]
## job_title job_type avg_income
## <chr> <chr> <dbl>
## 1 Big data Full Time 57717.
## 2 Big data Internship 30000
## 3 Data analyst Full Time 61210.
## 4 Data analyst Internship 48857.
## 5 Data scientist Full Time 67936
## 6 Data scientist Internship 53538.
## 7 Machine learning Full Time 69168.
## 8 Machine learning Internship 54538.
## 9 ML Ops Full Time 78278.
## 10 ML Ops Internship 135000#remote çalışma durumuna göre maaş istatistikleri
salary_by_remote <- ds %>% group_by(remote) %>%
summarise(count= n(),
min_salary=min(salary),
max_salary=max(salary),
mean_salary=mean(salary)) %>%
arrange(desc(mean_salary)) %>%
mutate(rank = rank(-mean_salary))
salary_by_remote## # A tibble: 2 × 6
## remote count min_salary max_salary mean_salary rank
## <chr> <int> <int> <int> <dbl> <dbl>
## 1 1 131 30000 225000 73008. 1
## 2 0 1026 30000 228000 64018. 2#meslek ve deneyimlere göre maaşın saçılım grafiği
saçılım<- plot_ly(ds , x= ~experience_level , y = ~salary , z= ~job_title , color = ~job_title , text = ~salary ) %>%
add_markers() %>%
layout(
scene = list(xaxis = list(title = "LEVEL"),
yaxis= list(title = "SALARY"),
zaxis = list (title = "TİTLE"))
)
saçılımp1 <- plot_ly(ds, y = ~salary, color = ~experience_level, colors = c('#F8766D', '#00BA38','#619CFF'), legendgroup = ~job_title) %>%
add_boxplot(x = ~job_title)
p1#mesleklere göre remote/no remeto maaş
ggplot(ds, aes(x = remote, y = salary, fill =job_title, colour = job_title)) +
geom_boxplot(outlier.colour = NA) + xlab("remote/no remote") + ylab("salary") 