Layoffs dataset

hw1

challenge1

my name

dataset

ggplot2

Author

Manan Patel

Published

December 18, 2022

Code

library(tidyverse)
library(ggplot2)
library(lubridate)
library(readxl)
library(purrr)
library(dplyr)
library(stats)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

knitr::opts_chunk$set(echo = TRUE)

Introduction-

Layoffs or the termination of employment for a large number of workers and have become increasingly common in recent years especially since the COVID-19 pandemic started. Layoffs are typically counted when more than 50 employees are let go from a company for a period of at least 31 days. The occurrence of layoffs can cause anxiety and paranoia among remaining employees, especially if they are struggling to meet their performance goals or arrive to work on time. According to recent statistics, a total of 17 million layoffs have been reported in the United States. Many data has been collected and analyzed on layoffs in the US, including trends, demographics, and industry-specific information. We will examined the details of these statistics in order to provide a thorough understanding of the layoff situation in USA and rest of the world.

Nevertheless, tech companies around the world are struggling to deal with the economic downturn. A combination of factors such as reduced consumer spending, increased interest rates from central banks, and a strong US dollar has raised concerns about the possibility of a recession. In response to these challenges, some tech firms have started laying off employees. For example, Meta recently laid off 13% of its workforce, which is over 11,000 people. This data set was created in an effort to allow the Kaggle community to analyze the current challenges facing the tech industry and potentially uncover useful insights.

Research Questions-

A list of the top 10 companies that have laid off employees.
The top 3 companies that have laid off employees on a yearly basis.
The top 3 locations where the most layoffs have occurred on a yearly basis.
A list of the top 20 companies that have laid off a specific percentage of their employees.
The top 10 countries where the most layoffs have occurred.
The top 10 locations in the United States where the most layoffs have occurred.
The locations in India where the most layoffs have occurred.
The relationship between funds received and layoffs.
The stage of a company’s development at which the most layoffs have occurred.
The industry that has experienced the most layoffs.
The total number of layoffs on a yearly basis.
The number of layoffs that have occurred on a yearly basis according to country.

We will try our best to answer each on of the questions mentioned above, let’s get started-

Data-

Reading the dataset-

Code

# data frame
df <- read.csv("_data/layoffs.csv")
head(df)

   company      location  industry total_laid_off percentage_laid_off
1  Vedantu     Bengaluru Education            385                  NA
2    Plaid   SF Bay Area   Finance            260                0.20
3   Grover        Berlin    Retail             40                0.10
4 CircleCI   SF Bay Area   Product             NA                0.17
5     Doma   SF Bay Area   Finance            515                0.40
6 BuzzFeed New York City     Media            180                0.12
        date    stage       country funds_raised
1 2022-12-07 Series E         India          292
2 2022-12-07 Series D United States          734
3 2022-12-07  Unknown United States         2300
4 2022-12-07 Series F United States          315
5 2022-12-06      IPO United States          679
6 2022-12-06      IPO United States          696

Data Description-

company - This column refers to the name of the company that has experienced a layoff.
location - This column refers to the location of the layoff, which could be a city, state, or region.
industry - This column refers to the industry in which the company operates, such as tech, healthcare, or finance.
total_laid_off - This column refers to the total number of employees who were laid off by the company.
percentage_laid_off - This column refers to the percentage of the company’s workforce that was laid off.
date - This column refers to the date on which the layoff occurred.
stage - This column refers to the stage of funding that the company was in at the time of the layoff. This could be early-stage, late-stage, or publicly traded.
country - This column refers to the name of the country in which the company is located.
funds_raised - This column refers to the amount of funds that the company had raised, in millions of dollars, prior to the layoff. This information could be relevant in understanding the financial situation of the company at the time of the layoff.

Let’s have a look on the last few rows-

Code

# Get the last six rows of df
tail(df)

           company       location       industry total_laid_off
1747       Service    Los Angeles         Travel             NA
1748  HopSkipDrive    Los Angeles Transportation              8
1749   Panda Squad    SF Bay Area       Consumer              6
1750 Tamara Mellon    Los Angeles         Retail             20
1751      EasyPost Salt Lake City      Logistics             75
1752     Homebound    SF Bay Area    Real Estate             NA
     percentage_laid_off       date    stage       country funds_raised
1747                1.00 2020-03-16     Seed United States          5.1
1748                0.10 2020-03-13  Unknown United States         45.0
1749                0.75 2020-03-13     Seed United States          1.0
1750                0.40 2020-03-12 Series C United States         90.0
1751                  NA 2020-03-11 Series A United States         12.0
1752                  NA             Unknown United States        128.0

Get the class of each column in df-

Code

# sapply will get the class of each column
sapply(df, class)

            company            location            industry      total_laid_off 
        "character"         "character"         "character"           "integer" 
percentage_laid_off                date               stage             country 
          "numeric"         "character"         "character"         "character" 
       funds_raised 
          "numeric"

Checking for all possible NA and cleaning up the datasets-

Code

# Use sapply to apply a lambda function to each column in the df data frame
missing_counts <- sapply(df, function(x) {
  # Within the lambda function, we will use the sum and is.na functions to count the number of missing values in x
  sum(is.na(x))
})
# then finally we will print the resulting vector of missing value counts
missing_counts

            company            location            industry      total_laid_off 
                  0                   0                   0                 520 
percentage_laid_off                date               stage             country 
                576                   0                   0                   0 
       funds_raised 
                130

Code

# Use the drop_na function to remove rows with missing values from the df
df <- df %>% drop_na()
# Using sapply to apply a lambda function to each column in the df
missing_counts <- sapply(df, function(x) {
# Within the lambda function, use the sum and is.na functions to count the number of missing values in x
  sum(is.na(x))
})
# then finally we will print the resulting vector of missing value counts
missing_counts

            company            location            industry      total_laid_off 
                  0                   0                   0                   0 
percentage_laid_off                date               stage             country 
                  0                   0                   0                   0 
       funds_raised 
                  0

Let’s convert character columns to factor columns in df-

Code

# converting character to factor
df <- df %>% mutate_if(is.character,as.factor)
glimpse(df)

Rows: 859
Columns: 9
$ company             <fct> Plaid, Grover, Doma, BuzzFeed, Chipper Cash, Stash…
$ location            <fct> SF Bay Area, Berlin, SF Bay Area, New York City, S…
$ industry            <fct> Finance, Retail, Finance, Media, Finance, Finance,…
$ total_laid_off      <int> 260, 40, 515, 180, 50, 32, 20, 65, 30, 47, 110, 10…
$ percentage_laid_off <dbl> 0.200, 0.100, 0.400, 0.120, 0.125, 0.080, 0.080, 0…
$ date                <fct> 2022-12-07, 2022-12-07, 2022-12-06, 2022-12-06, 20…
$ stage               <fct> Series D, Unknown, IPO, IPO, Series C, Unknown, Se…
$ country             <fct> United States, United States, United States, Unite…
$ funds_raised        <dbl> 734, 2300, 679, 696, 302, 480, 165, 265, 103, 184,…

Now we will count the number of unique values in the company column of df-

Code

#counting the number of unique values
n_distinct(df$company)

[1] 762

Code

# Convert the date column to a year column and a month column
df$yrs <- strftime(df$date,'%Y') %>% as.factor()  # extract year from date and convert to a factor
df$mnt <- strftime(df$date,'%m') %>% as.factor()  # extract month from date and convert to a factor

Now let’s see the mean, median, and standard deviation of total_laid_off.

Code

#mean will calculates the average  
mean(df$total_laid_off)

[1] 218.8068

Code

#median will calculates the median
median(df$total_laid_off)

[1] 75

Code

#sd will calculates the standard deviation
sd(df$total_laid_off)

[1] 641.0096

Setup figure size-

Code

fig <- function(width, height) {
  # Set the width and height options for the plot
  options(repr.plot.width = width, repr.plot.height = height)
}
# Call it
fig(20, 10)

Code

# Get the number of unique values in the location
print(length(unique(df$location)))

[1] 99

Code

# Get the unique values in the location
unique(df$location)

 [1] SF Bay Area     Berlin          New York City   Tel Aviv       
 [5] Boston          Burlington      Singapore       Los Angeles    
 [9] Jakarta         Sacramento      Buenos Aires    Bengaluru      
[13] London          Melbourne       Sao Paulo       Waterloo       
[17] Lagos           Dubai           Gurugram        Phoenix        
[21] Gothenburg      Toronto         Dublin          Seattle        
[25] Nairobi         Dover           Hamburg         San Diego      
[29] Logan           Tallin          Lehi            Columbus       
[33] Nebraska City   Copenhagen      Vancouver       Oslo           
[37] Stockholm       Pittsburgh      Montreal        San Luis Obispo
[41] Jerusalem       Austin          New Delhi       Belo Horizonte 
[45] Chicago         Salt Lake City  Bangkok         Raleigh        
[49] Portland        Bristol         Washington D.C. Indianapolis   
[53] Stamford        Curitiba        Mumbai          Boulder        
[57] Sydney          Detroit         Ottawa          Ferdericton    
[61] Dakar           Florianópolis   Philadelphia    Hong Kong      
[65] Beijing         Vienna          Atlanta         Dallas         
[69] Spokane         Chennai         Reno            Helsinki       
[73] Malmo           Kuala Lumpur    Bend            Mexico City    
[77] Cincinnati      Miami           Moscow          Shanghai       
[81] Non-U.S.        Nashville       Las Vegas       Edinburgh      
[85] Madison         Amsterdam       Santa Fe        Denver         
[89] Ahmedabad       Joinville       Zurich          Missoula       
[93] Minneapolis     Guadalajara     Blumenau        Milwaukee      
[97] Ann Arbor       Lisbon          Munich         
99 Levels: Ahmedabad Amsterdam Ann Arbor Atlanta Austin Bangkok ... Zurich

Code

# Get the number of unique values in the industry
print(length(unique(df$industry)))

[1] 28

Code

# Get the unique values in the industry
unique(df$industry)

 [1] Finance        Retail         Media          Security                     
 [6] Marketing      Food           Crypto         Education      Other         
[11] Consumer       Transportation Healthcare     Infrastructure Data          
[16] Sales          Fitness        Real Estate    Support        Logistics     
[21] Recruiting     Construction   HR             Product        Aerospace     
[26] Legal          Travel         Energy        
28 Levels:  Aerospace Construction Consumer Crypto Data Education ... Travel

Code

# Get the unique values in the stage
unique(df$stage)

 [1] Series D       Unknown        IPO            Series C       Series E      
 [6] Series B       Private Equity Series A       Series J       Series F      
[11] Acquired       Series H       Series G       Seed           Series I      
15 Levels: Acquired IPO Private Equity Seed Series A Series B ... Unknown

Code

# Total how many unique countries are there in the dataset
unique(df$country)

 [1] United States        Israel               Singapore           
 [4] Indonesia            Argentina            India               
 [7] United Kingdom       Australia            Germany             
[10] Brazil               Canada               Nigeria             
[13] Sweden               Ireland              Kenya               
[16] Estonia              Norway               Denmark             
[19] Thailand             Senegal              Hong Kong           
[22] China                United Arab Emirates Austria             
[25] Finland              Malaysia             Mexico              
[28] Russia               Seychelles           Netherlands         
[31] Switzerland          Portugal            
32 Levels: Argentina Australia Austria Brazil Canada China Denmark ... United States

Code

# Total how many unique companies are there in the dataset
length(unique(df$company))

[1] 762

Code

# Convert the 'date' column to a date data type
df$date <- as.Date(df$date)

# Extract the month and year from the 'date' column
df$month <- month(df$date)
df$year <- year(df$date)

Code

# Select rows that are duplicates
duplicate_rows <- df[duplicated(df),]

# Remove duplicate rows from the data frame
df <- df[!duplicated(df),]

Code

colSums(is.na(df))

            company            location            industry      total_laid_off 
                  0                   0                   0                   0 
percentage_laid_off                date               stage             country 
                  0                   0                   0                   0 
       funds_raised                 yrs                 mnt               month 
                  0                   0                   0                   0 
               year 
                  0

Code

summary(df)

     company             location             industry   total_laid_off   
 Uber    :  4   SF Bay Area  :236   Finance       :124   Min.   :    3.0  
 Doma    :  3   New York City:101   Retail        : 72   1st Qu.:   35.0  
 Glossier:  3   Boston       : 40   Marketing     : 61   Median :   75.0  
 Intercom:  3   Los Angeles  : 34   Healthcare    : 60   Mean   :  218.2  
 Katerra :  3   Seattle      : 32   Transportation: 58   3rd Qu.:  160.0  
 Latch   :  3   Bengaluru    : 31   Food          : 50   Max.   :11000.0  
 (Other) :839   (Other)      :384   (Other)       :433                    
 percentage_laid_off      date                 stage              country   
 Min.   :0.0000      Min.   :2020-03-12   Series B:144   United States:561  
 1st Qu.:0.1000      1st Qu.:2020-05-03   IPO     :128   India        : 55  
 Median :0.1700      Median :2022-06-02   Series C:121   Canada       : 48  
 Mean   :0.2377      Mean   :2021-09-16   Unknown :115   Brazil       : 34  
 3rd Qu.:0.3000      3rd Qu.:2022-08-15   Series D:111   Israel       : 28  
 Max.   :1.0000      Max.   :2022-12-07   Series A: 64   Germany      : 27  
                                          (Other) :175   (Other)      :105  
  funds_raised         yrs           mnt          month             year     
 Min.   :     0.00   2020:331   04     :164   Min.   : 1.000   Min.   :2020  
 1st Qu.:    51.25   2021: 13   06     :140   1st Qu.: 4.000   1st Qu.:2020  
 Median :   153.50   2022:514   05     :110   Median : 6.000   Median :2022  
 Mean   :   880.37              11     :101   Mean   : 6.501   Mean   :2021  
 3rd Qu.:   425.25              07     : 84   3rd Qu.: 8.000   3rd Qu.:2022  
 Max.   :121900.00              08     : 73   Max.   :12.000   Max.   :2022  
                                (Other):186

Code

#creating a subset of df by filtering for rows where percentage_laid_off = 1
subset(df, percentage_laid_off == 1)

                company      location       industry total_laid_off
58  Deliveroo Australia     Melbourne           Food            120
89             Planetly        Berlin          Other            200
112        Fifth Season    Pittsburgh           Food            100
137            Playdots New York City       Consumer             65
163          Kitty Hawk   SF Bay Area      Aerospace            100
181        Simple Feast    Copenhagen           Food            150
199               Reali   SF Bay Area    Real Estate            140
256              Metigy        Sydney      Marketing             75
273              Soluto      Tel Aviv        Support            120
306  Butler Hospitality New York City           Food           1000
327         WanderJaunt   SF Bay Area         Travel             85
331           Crejo.Fun     Bengaluru      Education            170
375           SummerBio   SF Bay Area     Healthcare            101
415         The Grommet        Boston         Retail             40
436               Udayy      Gurugram      Education            100
458         BeyondMinds      Tel Aviv           Data             65
482                SEND        Sydney           Food            300
493               Ahead   SF Bay Area     Healthcare             44
521             Katerra   SF Bay Area   Construction           2434
525               Hubba       Toronto         Retail             45
527          Pocketmath     Singapore      Marketing             21
531    Bridge Connector     Nashville     Healthcare            154
543               Eatsy     Singapore           Food             20
546   Buy.com / Rakuten   SF Bay Area         Retail             87
562                Dark   SF Bay Area        Product              6
594            Bluprint        Denver      Education            137
600         Stay Alfred       Spokane         Travel            221
615               Deliv   SF Bay Area         Retail            669
625                Jump New York City Transportation            500
670          TutorMundi     Sao Paulo      Education              4
722                Atsu       Seattle Infrastructure              6
816             Amplero       Seattle      Marketing             17
820                HOOQ     Singapore       Consumer            250
834         Consider.co   SF Bay Area          Other             13
856            Help.com        Austin        Support             16
    percentage_laid_off       date    stage       country funds_raised  yrs mnt
58                    1 2022-11-15      IPO     Australia    1700.0000 2022  11
89                    1 2022-11-04 Acquired       Germany       5.0000 2022  11
112                   1 2022-10-28 Series B United States      35.0000 2022  10
137                   1 2022-10-13 Acquired United States      10.0000 2022  10
163                   1 2022-09-21  Unknown United States       1.0000 2022  09
181                   1 2022-09-07  Unknown       Denmark     173.0000 2022  09
199                   1 2022-08-24 Series B United States     117.0000 2022  08
256                   1 2022-07-31 Series B     Australia      18.0000 2022  07
273                   1 2022-07-24 Acquired        Israel      18.0000 2022  07
306                   1 2022-07-08 Series B United States      50.0000 2022  07
327                   1 2022-07-01 Series B United States      26.0000 2022  07
331                   1 2022-06-30     Seed         India       3.0000 2022  06
375                   1 2022-06-20  Unknown United States       7.0000 2022  06
415                   1 2022-06-09 Acquired United States       5.0000 2022  06
436                   1 2022-06-01     Seed         India       2.0000 2022  06
458                   1 2022-05-23 Series A        Israel      16.0000 2022  05
482                   1 2022-05-04     Seed     Australia       3.0000 2022  05
493                   1 2022-04-14  Unknown United States       9.0000 2022  04
521                   1 2021-06-01  Unknown United States    1600.0000 2021  06
525                   1 2021-02-01 Series B        Canada      61.0000 2021  02
527                   1 2021-01-20  Unknown     Singapore      20.0000 2021  01
531                   1 2020-11-17 Series B United States      45.0000 2020  11
543                   1 2020-08-08     Seed     Singapore       0.9755 2020  08
546                   1 2020-07-30 Acquired United States      42.4000 2020  07
562                   1 2020-06-23     Seed United States       3.0000 2020  06
594                   1 2020-05-26 Acquired United States     108.0000 2020  05
600                   1 2020-05-20 Series B United States      62.0000 2020  05
615                   1 2020-05-13 Series C United States      80.0000 2020  05
625                   1 2020-05-07 Acquired United States      11.0000 2020  05
670                   1 2020-04-24 Series A        Brazil       2.0000 2020  04
722                   1 2020-04-10  Unknown United States       1.0000 2020  04
816                   1 2020-03-29 Series B United States      25.0000 2020  03
820                   1 2020-03-27  Unknown     Singapore      95.0000 2020  03
834                   1 2020-03-26     Seed United States       5.0000 2020  03
856                   1 2020-03-16     Seed United States       6.0000 2020  03
    month year
58     11 2022
89     11 2022
112    10 2022
137    10 2022
163     9 2022
181     9 2022
199     8 2022
256     7 2022
273     7 2022
306     7 2022
327     7 2022
331     6 2022
375     6 2022
415     6 2022
436     6 2022
458     5 2022
482     5 2022
493     4 2022
521     6 2021
525     2 2021
527     1 2021
531    11 2020
543     8 2020
546     7 2020
562     6 2020
594     5 2020
600     5 2020
615     5 2020
625     5 2020
670     4 2020
722     4 2020
816     3 2020
820     3 2020
834     3 2020
856     3 2020

Code

head(df, 2)

  company    location industry total_laid_off percentage_laid_off       date
1   Plaid SF Bay Area  Finance            260                 0.2 2022-12-07
2  Grover      Berlin   Retail             40                 0.1 2022-12-07
     stage       country funds_raised  yrs mnt month year
1 Series D United States          734 2022  12    12 2022
2  Unknown United States         2300 2022  12    12 2022

Code

colSums(is.na(df))

            company            location            industry      total_laid_off 
                  0                   0                   0                   0 
percentage_laid_off                date               stage             country 
                  0                   0                   0                   0 
       funds_raised                 yrs                 mnt               month 
                  0                   0                   0                   0 
               year 
                  0

Code

dim(df)

[1] 858  13

Code

# subset the data by selecting rows where company = Lyft
df[df$company == "Lyft", ]

    company    location       industry total_laid_off percentage_laid_off
93     Lyft SF Bay Area Transportation            700                0.13
283    Lyft SF Bay Area Transportation             60                0.02
650    Lyft SF Bay Area Transportation            982                0.17
          date stage       country funds_raised  yrs mnt month year
93  2022-11-03   IPO United States         4900 2022  11    11 2022
283 2022-07-20   IPO United States         4900 2022  07     7 2022
650 2020-04-29   IPO United States         4900 2020  04     4 2020

This will show all the company names which are their multiple times.

Now, we will check the data, we will do more cleaning and re-factoring.

Code

head(df, n = 2)

  company    location industry total_laid_off percentage_laid_off       date
1   Plaid SF Bay Area  Finance            260                 0.2 2022-12-07
2  Grover      Berlin   Retail             40                 0.1 2022-12-07
     stage       country funds_raised  yrs mnt month year
1 Series D United States          734 2022  12    12 2022
2  Unknown United States         2300 2022  12    12 2022

Code

colSums(is.na(df))

            company            location            industry      total_laid_off 
                  0                   0                   0                   0 
percentage_laid_off                date               stage             country 
                  0                   0                   0                   0 
       funds_raised                 yrs                 mnt               month 
                  0                   0                   0                   0 
               year 
                  0

Code

df <- df %>% drop_na()
df %>% sapply(function(x)sum(is.na(x)))

            company            location            industry      total_laid_off 
                  0                   0                   0                   0 
percentage_laid_off                date               stage             country 
                  0                   0                   0                   0 
       funds_raised                 yrs                 mnt               month 
                  0                   0                   0                   0 
               year 
                  0

Code

df <- df %>% mutate_if(is.character,as.factor)
glimpse(df)

Rows: 858
Columns: 13
$ company             <fct> Plaid, Grover, Doma, BuzzFeed, Chipper Cash, Stash…
$ location            <fct> SF Bay Area, Berlin, SF Bay Area, New York City, S…
$ industry            <fct> Finance, Retail, Finance, Media, Finance, Finance,…
$ total_laid_off      <int> 260, 40, 515, 180, 50, 32, 20, 65, 30, 47, 110, 10…
$ percentage_laid_off <dbl> 0.200, 0.100, 0.400, 0.120, 0.125, 0.080, 0.080, 0…
$ date                <date> 2022-12-07, 2022-12-07, 2022-12-06, 2022-12-06, 2…
$ stage               <fct> Series D, Unknown, IPO, IPO, Series C, Unknown, Se…
$ country             <fct> United States, United States, United States, Unite…
$ funds_raised        <dbl> 734, 2300, 679, 696, 302, 480, 165, 265, 103, 184,…
$ yrs                 <fct> 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 20…
$ mnt                 <fct> 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 11…
$ month               <dbl> 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 11…
$ year                <dbl> 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 20…

Code

table(df$year)


2020 2021 2022 
 331   13  514

Visualization-

Code

par(bg = "lightgray")
# group the data by country and count the number of unique companies
layoff_by_country <- df %>% group_by(country) %>% summarize(no_of_companies = n_distinct(company)) %>%
  arrange(desc(no_of_companies)) %>% head(10)

# create the bar plot using barplot
barplot(layoff_by_country$no_of_companies, main = "Number of companies did layoffs by Country",
        xlab = "Country", ylab = "Number of companies", col = "green",
        names.arg = layoff_by_country$country, las = 2)

This code first groups the data by country and counts the number of unique companies in each group. It then arranges the resulting data frame in descending order based on the number of companies and selects the top 10 rows. Finally, it creates a bar plot using the barplot function, with the number of companies on the y-axis and the country on the x-axis. The plot is titled Number of Companies Undertook Lay Offs by Country.

In the dataset, there is a company based in the United States that laid off 11,000 employees, which is the highest number of layoffs for any company in the dataset. The size of layoffs is very high in both the United States and India, but surprisingly, it is very low in China which is not even in top 10 compared to the United States and India.

Code

par(bg = "lightgray")
# group the data by location and count the number of unique companies
layoff_by_city <- df %>% group_by(location) %>% summarize(no_of_companies = n_distinct(company)) %>%
  arrange(desc(no_of_companies)) %>% head(15)

# create the bar plot using barplot
barplot(layoff_by_city$no_of_companies, main = "Number of Companies did layoffs in the big Cities",
        xlab = "City", ylab = "Number of companies", col = "red",
        names.arg = layoff_by_city$location, las = 2, cex.names = 0.8)

# add the number of companies to each bar using the text function
text(x = 1:length(layoff_by_city$no_of_companies), y = layoff_by_city$no_of_companies,
     labels = layoff_by_city$no_of_companies, pos = 3.9, cex = 0.8)

This code first groups the data by location and counts the number of unique companies in each group. It then arranges the resulting data frame in descending order based on the number of companies and selects the top 20 rows. Finally, it creates a bar plot using the barplot function, with the number of companies on the y-axis and the location (city) on the x-axis. The plot is titled Number of Companies Undertook Lay Offs in the big Cities.

The San Francisco Bay Area (also known as Silicon Valley), New York City, and Bengalore are all renowned tech hubs that have seen significant numbers of layoffs in recent times. These cities are known for their high concentration of technology companies and are often considered the centers of innovation and growth in the tech industry. However, economic downturns and other factors can lead to layoffs in these areas, resulting in a reduction of the tech workforce in these cities. Now, If we closely observe then we won’t see any small cities in this list which says that all the big city area has high layoffs than smaller cities.

Code

top_industry <- df %>%
  group_by(industry) %>%
  summarise(total_laid_off = sum(total_laid_off)) %>%
  arrange(desc(total_laid_off))

Code

dim(top_industry)

[1] 28  2

Code

par(bg = "lightgray")
colors <- c("red", "blue", "green", "yellow", "orange")
barplot(top_industry$total_laid_off, names.arg = top_industry$industry,
        xlab = "Industry", ylab = "Layoffs", main = "Industry vs Layoffs", col = colors)

The layoffs had a notable effect on three specific industries: transportation, consumer, and retail. These industries were likely negatively impacted by the layoffs in some way such as a decrease in business or financial losses.

Code

df_industries <- df %>% 
  group_by(industry) %>% 
  summarize(total_laid_off = sum(total_laid_off)) %>% 
  arrange(total_laid_off)

ggplot(data = df_industries, aes(x = industry)) +
  geom_bar(aes(y = total_laid_off), fill = "red", stat = "identity") +
  labs(title = "Total Layoffs in different industries world wide since 2020", y = "Number of layoffs reported") +
  theme(plot.title = element_text(size = 14),
        axis.title = element_text(size = 12),
        axis.text.x = element_text(angle = 90, vjust = 0.5))

On average 25,000 employees from the Transportation industry were laid off from their respective companies. This was followed by the retail, consumer, finance, and real estate industries in this order.

Code

# create a vector of the top 10 companies with the most layoffs, based on the total_laid_off column
top10_idx <- order(df$total_laid_off, decreasing = TRUE)[1:10]

# subset the dataframe to only include the top 10 companies
df_top10 <- df[top10_idx, ]

# create a pie chart of the top 10 companies, using the total_laid_off column as the values and the company column as the labels
pie(df_top10$total_laid_off, labels = df_top10$company, main = "Top 10 companies that laid off")

Here we can see that the layoff was highest in Meta
after doing some research, from source we come to know that on November 9, 2022, Meta announced that it had decided to terminate the employment of 11,000 employees, which represents approximately 13% of its total workforce. However, the company did not publicly disclose the specific breakdown of the layoffs among different departments or job levels. It was rumored that the Recruiting and Business teams were disproportionately affected by the layoffs, but there was no official confirmation of this. It is not known how many employees in the Engineering department were affected by the layoffs, or how many junior and senior employees were impacted.

It seems that the Recruiting and Engineering departments at a company were particularly affected by recent layoffs. These two departments made up around 50% of the total number of self-reported layoffs. The Engineering department appears to have primarily laid off junior employees, while the Product, Marketing, and Sales departments seem to have mostly laid off senior employees. Of the laid-off engineers, it was stated that 46% will require visa support, which may refer to employees who are not citizens or permanent residents of the country in which the company is located and who are working there on a temporary visa. On the other hand, only 8% of laid-off employees from the Recruiting department are expected to require visa support. It is not clear from the information provided the context or circumstances of the layoffs or the specific reasons for the departmental and job level differences.

Total layoffs year wise-

Code

total_laid_year <- df %>%
  group_by(year) %>%
  summarize(total_laid_off = sum(total_laid_off))
total_laid_year

# A tibble: 3 × 2
   year total_laid_off
  <dbl>          <int>
1  2020          60960
2  2021           6490
3  2022         119755

Code

ggplot(data = total_laid_year, aes(x = year, y = total_laid_off)) +
  geom_bar(stat = "identity", color = "red") +
  labs(x = "year", y = "Layoffs") +
  ggtitle("layoffs year wise") +
  theme(plot.background = element_rect(fill = "yellow"))

Code

df %>%
  group_by(location) %>%
  summarise(total_laid_off = n_distinct(company)) %>%
  arrange(location)

# A tibble: 99 × 2
   location       total_laid_off
   <fct>                   <int>
 1 Ahmedabad                   1
 2 Amsterdam                   1
 3 Ann Arbor                   1
 4 Atlanta                     3
 5 Austin                     10
 6 Bangkok                     1
 7 Beijing                     1
 8 Belo Horizonte              2
 9 Bend                        1
10 Bengaluru                  25
# … with 89 more rows

Code

df %>%
  # Group the data by year and stage
  group_by(year, stage) %>%
  # Summarize the data by counting the number of distinct funds raised within each group
  summarise(n = n_distinct(company), .groups = "keep") %>%
  ggplot(mapping = aes(x = year, y = n, color = stage)) +
  geom_point() + 
  geom_line() +
  labs(y = "Total laid off", title = "Total laid off in stages-wise over years")

Code

df %>%
  group_by(year, stage) %>%
  summarise(n = n_distinct(funds_raised), .groups = "keep") %>%
  ggplot(mapping = aes(x = year, y = n, color = stage)) +
  geom_point() + 
  geom_line() +
  labs(y = "Funds Raised", title = "Funds raised included in the stage over the years")

Code

df %>% count(company) %>% arrange(desc(n))

                          company n
1                            Uber 4
2                            Doma 3
3                        Glossier 3
4                        Intercom 3
5                         Katerra 3
6                           Latch 3
7                            Lyft 3
8                             Oda 3
9                         Peloton 3
10                         Redfin 3
11                        RenoRun 3
12                           Trax 3
13                      Unacademy 3
14                    Zeus Living 3
15                            2TM 2
16                     Better.com 2
17                           Bird 2
18                        Bizzabo 2
19                          Blend 2
20                         Bonsai 2
21                         Bounce 2
22                           Brex 2
23                        Carvana 2
24                        Compass 2
25                     Crypto.com 2
26                     Cybereason 2
27                          Ebanx 2
28                     Ethos Life 2
29                       Flyhomes 2
30                         Food52 2
31                       FrontRow 2
32                        Fundbox 2
33                         Gemini 2
34                         Gopuff 2
35                        Groupon 2
36                          Homie 2
37                      Hootsuite 2
38                          Hopin 2
39                         Infarm 2
40                        IronNet 2
41                           Juul 2
42                         Knotel 2
43                            Kry 2
44                         Leafly 2
45                Lighter Capital 2
46                           Loft 2
47                           Loom 2
48                     MakeMyTrip 2
49                         Mejuri 2
50                      Metromile 2
51                       MindBody 2
52                        Netflix 2
53                      New Relic 2
54                        On Deck 2
55                       OneTrust 2
56                       Opendoor 2
57                        Patreon 2
58                         Pollen 2
59                      Quantcast 2
60                    QuintoAndar 2
61                Rad Power Bikes 2
62                       RealSelf 2
63                         Ritual 2
64                         Rivian 2
65                      Robinhood 2
66                     Salesforce 2
67                        Salsify 2
68                          Sendy 2
69                          Smava 2
70                         Sonder 2
71                     Stitch Fix 2
72                         StockX 2
73                         Swiggy 2
74                       Truepill 2
75                         UiPath 2
76                        Vedantu 2
77                            Vee 2
78                    WanderJaunt 2
79                        Zilingo 2
80                         Zillow 2
81                         Zomato 2
82                          &Open 1
83                   10X Genomics 1
84                        1stdibs 1
85                         54gene 1
86                         6sense 1
87                             99 1
88                           Abra 1
89                          Acast 1
90                           Acko 1
91                            Ada 1
92                    Ada Support 1
93       Adaptive Biotechnologies 1
94                         AdRoll 1
95                          Ahead 1
96                         Airbnb 1
97                        Airtime 1
98                          Ajaib 1
99                      AlayaCare 1
100                        Albert 1
101             AliExpress Russia 1
102                 Alto Pharmacy 1
103                        Amazon 1
104                      Amperity 1
105                       Amplero 1
106                        Andela 1
107                        Anodot 1
108               Antidote Health 1
109                Apartment List 1
110                     App Annie 1
111                      AppLovin 1
112                    ApplyBoard 1
113                    Aqgromalin 1
114                          Aqua 1
115                 Aqua Security 1
116                       Argo AI 1
117                        Argyle 1
118              Arrive Logistics 1
119                         Asana 1
120                         Astra 1
121                          AtoB 1
122                          Atsu 1
123                          Aura 1
124           Automation Anywhere 1
125                     AvantStay 1
126                           Avo 1
127                          Away 1
128                           Aya 1
129                          B8ta 1
130                         Banxa 1
131                         Baton 1
132                         Bench 1
133           Berlin Brands Group 1
134                        Bestow 1
135                          Bevi 1
136                   Beyond Meat 1
137                   BeyondMinds 1
138                      BioMarin 1
139                        BitMEX 1
140                      BitOasis 1
141                      Bitpanda 1
142                         Bitso 1
143                      BitTitan 1
144                Blockchain.com 1
145                       BlockFi 1
146                    Blueground 1
147                      Bluprint 1
148                          Bolt 1
149                      BookClub 1
150                    BookMyShow 1
151                         Boozt 1
152                     Borrowell 1
153                       BounceX 1
154                        Branch 1
155                Branch Metrics 1
156                    Brave Care 1
157                       Breathe 1
158                      Breather 1
159              Bridge Connector 1
160               Bright Machines 1
161                  Bright Money 1
162                       Brighte 1
163                        Bringg 1
164                        Bryter 1
165                       Buenbit 1
166                       Builder 1
167                      Built In 1
168                       Bullish 1
169                        BusBud 1
170          Bustle Digital Group 1
171            Butler Hospitality 1
172                        Button 1
173             Buy.com / Rakuten 1
174                      BuzzFeed 1
175                        Byju's 1
176                         Cadre 1
177                     Calibrate 1
178                          Calm 1
179                         Cameo 1
180                 Candy Digital 1
181                         Canoo 1
182                     Capitolis 1
183                   CaptivateIQ 1
184                 Carbon Health 1
185                        Careem 1
186                  Career Karma 1
187                      CarGurus 1
188                     Carousell 1
189                        Cars24 1
190                         Carta 1
191                        Carwow 1
192                        Casper 1
193                         Cazoo 1
194                       Celsius 1
195                      Cerebral 1
196                     Chargebee 1
197                     Checkmarx 1
198                  Checkout.com 1
199                        Checkr 1
200                         Chime 1
201                  Chipper Cash 1
202                         Cisco 1
203                      CityMall 1
204                Clarify Health 1
205                     ClassPass 1
206                         Clear 1
207                     Clearbanc 1
208                       Clearco 1
209                     CleverTap 1
210                       ClickUp 1
211                         Clinc 1
212                    Cloudinary 1
213                        Clutch 1
214                        Code42 1
215                   Coding Dojo 1
216                        Cogito 1
217                      Coinbase 1
218                       CoinJar 1
219                    Coinsquare 1
220                     Community 1
221                       Compete 1
222                     ConsenSys 1
223                   Consider.co 1
224                     ContaAzul 1
225                    ContraFect 1
226                       Convene 1
227                        Convoy 1
228             Coterie Insurance 1
229                        Crayon 1
230                 Credit Sesame 1
231                     Crejo.Fun 1
232                   CrowdStreet 1
233                        Cruise 1
234                        CTO.ai 1
235                   Culture Amp 1
236                  Culture Trip 1
237                       CureFit 1
238                         Curve 1
239                         Cvent 1
240                          D2iQ 1
241                   Dapper Labs 1
242                          Dark 1
243                     DataRails 1
244                     DataRobot 1
245                         Deliv 1
246                     Deliveroo 1
247           Deliveroo Australia 1
248                    Demandbase 1
249                        Deputy 1
250                Descartes Labs 1
251                    DialSource 1
252                  Divergent 3D 1
253                   Divvy Homes 1
254                          Dock 1
255                         Docly 1
256                      DocuSign 1
257                     Domestika 1
258                          Domo 1
259                      DoorDash 1
260                         Dover 1
261                       Dropbox 1
262                       Dutchie 1
263                Dynamic Signal 1
264                        Earnin 1
265                      EasyPost 1
266                         Eatsy 1
267                        Ecobee 1
268           Eden / Managed By Q 1
269                          eGym 1
270                    Element AI 1
271                     Elementor 1
272                       Elinvar 1
273                      Embroker 1
274                       Emotive 1
275                         Enjoy 1
276                         Envoy 1
277                     Equitybee 1
278                     EquityZen 1
279                         eToro 1
280                    Eucalyptus 1
281                    Eventbrite 1
282                        Exodus 1
283                       ezCater 1
284                     FabHotels 1
285                        Fabric 1
286                        Facily 1
287                         Faire 1
288                        FarEye 1
289                  Fifth Season 1
290                  Finite State 1
291               Finleap Connect 1
292                        Fiverr 1
293                     Flipboard 1
294                      Flockjay 1
295                     Flytedesk 1
296               Flywheel Sports 1
297                       Flywire 1
298                       Foodsby 1
299                      Forma.ai 1
300                       Forward 1
301                     FourKites 1
302                       Foxtrot 1
303                     Freetrade 1
304                    Freshbooks 1
305                     Frontdesk 1
306             Funding Societies 1
307                            G2 1
308                        Gather 1
309                           Gem 1
310                     Getaround 1
311                     GetNinjas 1
312                  GetYourGuide 1
313                     Glassdoor 1
314                        Glitch 1
315                        GoBear 1
316                      GoHealth 1
317                         Gojek 1
318                        GoodRx 1
319                         GoPro 1
320                      Gorillas 1
321                   GoSpotCheck 1
322                    GoTo Group 1
323                          Grab 1
324                      GrayMeta 1
325           Greenhouse Software 1
326                          Grin 1
327              Group Nine Media 1
328                        Grover 1
329                        GumGum 1
330                       Gympass 1
331                   Happy Money 1
332                          Hash 1
333                   HealthMatch 1
334                      Help.com 1
335                        Heroes 1
336                         Hibob 1
337                  Highsnobiety 1
338               Hippo Insurance 1
339                     Hireology 1
340                      Hologram 1
341                          HOOQ 1
342                  HopSkipDrive 1
343                Horizn Studios 1
344                       Hotmart 1
345                         Houzz 1
346                         Hubba 1
347                        Hubilo 1
348                         Huobi 1
349                  Hyperscience 1
350                        Ibotta 1
351                          iFit 1
352                         Iflix 1
353                           Ike 1
354                      Illumina 1
355                Immersive Labs 1
356                     Immutable 1
357                        Impala 1
358              Impossible Foods 1
359             Incredible Health 1
360                      InDebted 1
361                   Industrious 1
362                    InfluxData 1
363                       InfoSum 1
364                    Innovaccer 1
365                     Inspirato 1
366                     Instamojo 1
367                   Instructure 1
368           Integral Ad Science 1
369                     Introhive 1
370                        Intuit 1
371                      Involves 1
372                  iPrice Group 1
373                     Iris Nova 1
374                           IRL 1
375                        iRobot 1
376                       Iron Ox 1
377                      Jam City 1
378                          Jama 1
379                    JetClosing 1
380                         Jetty 1
381                         Jimdo 1
382                        Jiobit 1
383                       Jobcase 1
384                          JOKR 1
385                          Jump 1
386                          Juni 1
387                         Kabam 1
388             Kayak / OpenTable 1
389                   KeepTruckin 1
390                        Kenoby 1
391                        Khoros 1
392                         Kiavi 1
393                   Kickstarter 1
394                        Kitopi 1
395                    Kitty Hawk 1
396                        Klarna 1
397                         Klook 1
398                         Knock 1
399               Kodiak Robotics 1
400                          Koho 1
401                     KoinWorks 1
402                 Komodo Health 1
403                       Kontist 1
404                        Kraken 1
405                          Kuda 1
406                        Kueski 1
407                      Lacework 1
408                   Ladder Life 1
409                      Lastline 1
410                       Lattice 1
411                       Lawgeex 1
412                          LEAD 1
413                       Legible 1
414                         Lemon 1
415                  Lending Club 1
416                   Lendingkart 1
417                        Lendis 1
418                         Lever 1
419                       Liftoff 1
420                    Lightricks 1
421                          Lime 1
422                      LinkedIn 1
423                      Linkfire 1
424                      Linktree 1
425                        Liv Up 1
426                    LivePerson 1
427                      Livspace 1
428                       Loftium 1
429                     Loftsmart 1
430                         Loggi 1
431                      Lokalise 1
432                          Loop 1
433                        Loopio 1
434                        LoopMe 1
435                      Lunchbox 1
436                         Lusha 1
437                          Lyst 1
438                MadeiraMadeira 1
439                    Magic Leap 1
440                    Mainstreet 1
441                  Malwarebytes 1
442                   Marketforce 1
443                   MasterClass 1
444                    Matterport 1
445                         Maven 1
446                     MediaMath 1
447                        Meesho 1
448                        Mercos 1
449                          Meta 1
450                        Metigy 1
451                         MFine 1
452                        Minted 1
453                      Mixpanel 1
454         Mobile Premier League 1
455                          Mogo 1
456                     Momentive 1
457                         Monzo 1
458                          Moss 1
459                      Movidesk 1
460                       Mozilla 1
461                         Mural 1
462                           Mux 1
463                           N26 1
464                        Namely 1
465                          Nate 1
466                          Navi 1
467                          Neon 1
468                Next Insurance 1
469                       Niantic 1
470                          Noom 1
471                      Notarize 1
472                           NS8 1
473                           NSO 1
474                        Numbrs 1
475                         NuoDB 1
476                          Nuri 1
477                          Nuro 1
478                       Nutanix 1
479                         NYDIG 1
480                         Nylas 1
481                         Ocavu 1
482                           Ola 1
483                         Olive 1
484                          Omie 1
485                        OneWeb 1
486                      Opencare 1
487                       OpenWeb 1
488                         OpenX 1
489                    Optimizely 1
490                         OrCam 1
491                       Oriente 1
492                  Oscar Health 1
493                OutboundEngine 1
494                      Outbrain 1
495                      Outreach 1
496                     Outschool 1
497                       Outside 1
498                      Overtime 1
499                     OwnBackup 1
500                  Oye Rickshaw 1
501                        Pacaso 1
502                      Packable 1
503                   PaisaBazaar 1
504                   Panda Squad 1
505                      Parsable 1
506                    PatientPop 1
507                 Pavilion Data 1
508                        PayJoy 1
509             Pear Therapeutics 1
510            Pear Therapeutics  1
511                     Peerspace 1
512                    PeerStreet 1
513                         Pendo 1
514                     People.ai 1
515                  Perimeter 81 1
516                        Perion 1
517                      PerkSpot 1
518                     Permutive 1
519                 PickYourTrail 1
520                       Picsart 1
521                     Pipedrive 1
522                          Pipl 1
523                         Pitch 1
524                         Plaid 1
525                      Planetly 1
526                         Plato 1
527                      Playdots 1
528                          Pleo 1
529                        Pliops 1
530                          Plum 1
531                    Pocketmath 1
532                  PolicyGenius 1
533                         Polly 1
534                Pomelo Fashion 1
535                     Postmates 1
536                        Preply 1
537                       Procore 1
538                     Project44 1
539                        Puppet 1
540                            Q4 1
541                       Quandoo 1
542                     Quanterix 1
543                        Quanto 1
544                        Quidax 1
545                        Qumulo 1
546                          Rasa 1
547                         Reali 1
548                      Recharge 1
549               Redesign Health 1
550                         Redox 1
551                          Reef 1
552                        Remote 1
553                   Remote Year 1
554   Repertoire Immune Medicines 1
555                  ResearchGate 1
556                       Revolut 1
557                         Rhino 1
558                       Rhumbix 1
559                        Ribbon 1
560                      Ridecell 1
561                         RigUp 1
562                  Rock Content 1
563                          Roku 1
564                Root Insurance 1
565                         Rover 1
566                          Rows 1
567               Rubicon Project 1
568                        Rubius 1
569                        Rupeek 1
570                     SafeGraph 1
571             Sage Therapeutics 1
572                          Sami 1
573                       Samsara 1
574                         Sanar 1
575                    Sandbox VR 1
576                    Sauce Labs 1
577                   ScaleFactor 1
578                         Scoop 1
579                       Segment 1
580                         Sema4 1
581                          SEND 1
582                        Sendle 1
583                     Sensibill 1
584                     ShareChat 1
585                        Shippo 1
586                        Shipsi 1
587                        Shogun 1
588                       Shop101 1
589                       Shopify 1
590                       Showpad 1
591                Sidecar Health 1
592                    SimilarWeb 1
593                  Simple Feast 1
594                        SIRCLO 1
595                       Sisense 1
596                          Skai 1
597                       Skedulo 1
598                        Skillz 1
599                    Skyscanner 1
600                          Snap 1
601                          Snyk 1
602                        Socure 1
603                          SoFi 1
604                        Sojern 1
605                        Soluto 1
606                         Sonos 1
607                    SoundHound 1
608                   Sourcegraph 1
609                          Spin 1
610                        Splunk 1
611                      SpotHero 1
612                         Spyce 1
613                        SQream 1
614                Stack Overflow 1
615                         Stash 1
616               Stash Financial 1
617                     Stashaway 1
618                   Stay Alfred 1
619                         Stedi 1
620                         Stord 1
621                      Storytel 1
622                        Stream 1
623                        Stripe 1
624                        Studio 1
625                   Submittable 1
626                      Substack 1
627                     SummerBio 1
628                         SumUp 1
629                        Sunday 1
630                     SundaySky 1
631                    Superhuman 1
632               Superpedestrian 1
633                       Swappie 1
634                    Sweetgreen 1
635                          SWVL 1
636                         Swyft 1
637                     SynapseFI 1
638                     Synapsica 1
639                  Synergysuite 1
640                      Synthego 1
641                          Syte 1
642                       Taboola 1
643                         Tally 1
644                 Tamara Mellon 1
645                        TaskUs 1
646                     Teachmint 1
647                      TealBook 1
648                      Teleport 1
649                        Textio 1
650                  The Athletic 1
651                   The Grommet 1
652                     The Guild 1
653               The Mom Project 1
654          The Predictive Index 1
655                  The RealReal 1
656                      The Sill 1
657                      TheSkimm 1
658                       Thimble 1
659                     Thinkific 1
660                     ThirdLove 1
661                       Thriver 1
662                     Thumbtack 1
663                 Tier Mobility 1
664                         TIFIN 1
665                         Toast 1
666                          Tomo 1
667                         Tonal 1
668                       Tonkean 1
669                       Top Hat 1
670                           Tor 1
671                   TouchBistro 1
672                      Transfix 1
673             Transmit Security 1
674                TravelTriangle 1
675                     Treehouse 1
676                         Trell 1
677                   TripActions 1
678                   TripAdvisor 1
679                    Triplebyte 1
680                    TripleLift 1
681                       TrueCar 1
682                     TrueLayer 1
683                       Truiloo 1
684                         Trybe 1
685                         Tufin 1
686                          Turo 1
687                    TutorMundi 1
688                         Twiga 1
689                        Twilio 1
690                       Twitter 1
691                          Ualá 1
692                      Uberflip 1
693                         Udaan 1
694                       Udacity 1
695                         Udayy 1
696                           Ula 1
697                       Unbabel 1
698                      Unbounce 1
699                      Uncapped 1
700                        Unison 1
701                         Unity 1
702           Unstoppable Domains 1
703                        UPshow 1
704                       Upstart 1
705             Urban Sports Club 1
706                      Usermind 1
707                         uShip 1
708                       Varonis 1
709                          Veev 1
710                      Vendease 1
711                        Verbit 1
712                        Veriff 1
713              VerSe Innovation 1
714         Vesalius Therapeutics 1
715                       Vezeeta 1
716                      VideoAmp 1
717              Virgin Hyperloop 1
718                           Voi 1
719                         Vouch 1
720                     Vox Media 1
721                    Voyage SMS 1
722                         Vroom 1
723                          VSCO 1
724                          VTEX 1
725                          Wave 1
726 Wave Sports and Entertainment 1
727                       Wayfair 1
728                      Wayflyer 1
729                          Waze 1
730                  Wealthsimple 1
731                         Weee! 1
732                 Welkin Health 1
733                        WeWork 1
734                         Wheel 1
735                   When I Work 1
736                         WHOOP 1
737              Wildlife Studios 1
738                          Wish 1
739                        Wonder 1
740                  Wonderschool 1
741                        Wonolo 1
742                    Wordstream 1
743                      Workable 1
744                      WorkRamp 1
745                     Worksmith 1
746                         Woven 1
747                   Xiaohongshu 1
748                          Yelp 1
749                         Yojak 1
750                         Yotpo 1
751                           Zak 1
752                          Zego 1
753                      Zeitgold 1
754                       Zencity 1
755                       Zendesk 1
756                      Zenefits 1
757                        Zenoti 1
758                        Zipcar 1
759                  ZipRecruiter 1
760                          Zoox 1
761                          Zume 1
762                        Zumper 1

Code

cor(df$funds_raised, df$total_laid_off)

[1] 0.1677711

Code

# Using the total_laid_off column as the x-axis and the funds_raised column as the y-axis
# Group the data by the company column and color the lines red
ggplot(df, aes(x = total_laid_off, y = funds_raised, group = company, color = "red")) +
  geom_line() +
  labs(x = "Total Laid Off", y = "Funds Raised", title = "Funds Raised vs. Total Laid Off") +
  theme(plot.title = element_text(hjust = 0.5))

Code

# Use the total_laid_off column as the x-axis and the funds_raised column as the y-axis
# Group the data by the company column and color the lines red
ggplot(df, aes(x = funds_raised, y = total_laid_off, group = company, color = "red")) +
  geom_line() +
  labs(x = "Funds Raised", y = "Total Laid Off", title = "Total Laid Off vs. Funds Raised") +
  theme(plot.title = element_text(hjust = 0.5))

Code

library(plotly)

# Creating a scatterplot
plot_ly(data = df, x = ~funds_raised, y = ~total_laid_off, type = "scatter", mode = "markers", text = ~company)

Code

# x-axis represents the amount of funds raised by each company
# y-axis represents the total number of employees laid off by each company

Code

library(plotly)

# Creating a scatterplot
plot_ly(data = df, x = ~total_laid_off, y = ~funds_raised, type = "scatter", mode = "markers", text = ~company)

Code

# x-axis represents the total number of employees laid off by each company
# y-axis represents the amount of funds raised by each company

The above plot shows that there is no relationship or dependence between the funds raised and the number of layoffs.

Code

unique(df$stage)

 [1] Series D       Unknown        IPO            Series C       Series E      
 [6] Series B       Private Equity Series A       Series J       Series F      
[11] Acquired       Series H       Series G       Seed           Series I      
15 Levels: Acquired IPO Private Equity Seed Series A Series B ... Unknown

Code

distinct(df, stage)

            stage
1        Series D
2         Unknown
3             IPO
4        Series C
5        Series E
6        Series B
7  Private Equity
8        Series A
9        Series J
10       Series F
11       Acquired
12       Series H
13       Series G
14           Seed
15       Series I

This is a list of all the stages that a company goes through, which in total consists of 15 stages.

Code

library(plotly)
# Create a new dataframe called df_stage that groups the df by the stage column and summarizes the total_laid_off column by summing the values
df_stage <- df %>%
  group_by(stage) %>%
  summarize(total_laid_off = sum(total_laid_off)) %>%
# Sorting the resulting dataframe in descending order
  arrange(desc(total_laid_off))
# create a horizontal bar chart using the df_stage. x-axis- total_laid_off & y-axis- stage
plot_ly(data = df_stage, x = ~total_laid_off, y = ~stage, type = "bar", orientation = "h", text = ~total_laid_off, color = c('orange')) %>%
  layout(title = "Layoffs & company stage", plot_bgcolor = 'lightblue')

The companies that were in the initial public offering (IPO) stage had the highest number of layoffs, followed by start-up companies whose stage was unknown. On the other hand, the least amount of layoffs occurred in seed stage companies. This pattern suggests that the companies that were further along in their development and had already gone public were more likely to experience layoffs, while the newer, smaller companies that were still in the early stages of development were less likely to do so.

Reflection

As someone who had no previous experience with R and no technical background with R language, this class, all the challenges and project were challenging for me. I chose to work with a Layoffs data set because it is the type of data that I am really familiar with since I have looking for a job from a long time and I was up to date with the market since 2019. To begin the analysis, the data set was cleaned and any missing values were handled. This step was important because it allowed for the creation of more accurate figures and calculations. The data was cleaned in order to facilitate this process. Once the data cleaning was completed, further analysis could be conducted.

One of the most challenging aspects for me was identifying the focus of the data set in terms of cleaning, transforming and visulizing the data, as well as figuring out where to start with the analysis of the data. This was the first time I was doing data analysis, I have mostly worked as a front end developer but never as a data scientist. I decided to focus on the basics of analysis in this project to challenge myself, and if I were to continue with this project, I would look for ways to cross-tabulate the data in more depth, such as by combining many different data points to see different data analysis.

There were a large number of missing records in the dataset, and the data from 1,300 companies is not sufficient to give us a comprehensive understanding of layoffs. If we could obtain data from at least 10,000 companies, it would be much more useful in terms of understanding the impact of layoffs. One idea I will implement is to compare the data set year by year and observe the yearly layoff trend for the entire world, as well as for the United States alone and for the rest of the world excluding the United States. I will also look at how various industries performed on a yearly basis in India, and by removing data about India from the rest of the world, I will examine the results again to see which fields are consistently performing well or poorly. Another idea is to examine the yearly layoff trends excluding both the United States and India.

Overall, I believe that this project was helpful in allowing me to do the application of what I learned in this class. As I completed the project, I felt that I had gained a basic understanding of the functionality of R and how to structure basic code of R and also how the data analysis works. I found that I was able to approach each challenge more easily as I progressed through the project, although I still feel as though I have only done little bit of R and many more amount to learn. My goal in learning R in depth so I will be able to contribute to the corporate world by learning all functions of R from this class that I am familiar with to read data, filter data, provide specific data, clean data, transform data and very importantly visulize the data. I think all the challenges and speciflcally this project has helped me to develop these skills.

Conclusion

The United States has experienced the highest number of layoffs compared to any other country, with India coming in second. Of all the layoffs that have occurred since 2020, more than 75% have been in the United States. In 2022, the consumer industry in the United States has been the hardest hit by layoffs, while the food industry has been most affected outside the United States. Layoffs have also been seen in the transportation and food industries globally. The travel industry has experienced considerably fewer layoffs globally in 2022 compared to the previous year.

According to the research, in the United States, 40% of the population has experienced a layoff at least once in their career. In the past two years, 28% of Americans have reported being laid off from their company. In July 2022, 160 tech companies underwent layoffs. In 2022, there were a total of 2.12 million layoffs recorded in the business and professional services industry. In 2021, there were a total of 17 million layoffs recorded across all industries in the US. The trade and transportation industry saw 1.46 million layoffs between June 2021 and June 2022. Among adults between the ages of 18 and 34, 61% reported experiencing significant pressure and anxiety related to the possibility of being laid off. Women are 25% less likely to be laid off by companies compared to men. In the third quarter of 2022, 23,158 layoffs were recorded. A survey found that 46% of employees felt unprepared for layoffs and separations.

Despite the significant changes and challenges brought about by the coronavirus pandemic, tech companies continue to prioritize engineering talent in their outbound recruitment efforts. It is possible that the pandemic has even intensified this trend, as startups may be focusing more on product development and less on sales and customer success due to slowed customer growth. Previous analysis has shown that tech layoffs have disproportionately affected sales and customer success roles. As a result, tech companies may be able to redirect their resources towards product development initiatives, leading to a sustained demand for engineers.

Bibliography

Layoffs.fyi. (n.d.). Layoffs and job loss tracker. Retrieved from https://layoffs.fyi/.

R Project. (n.d.). R: A language and environment for statistical computing. Retrieved from https://www.r-project.org/.

Kaggle. (n.d.). Layoffs 2022 [Data set]. Retrieved from https://www.kaggle.com/datasets/swaptr/layoffs-2022.

Smith, J. (2022, November 5). Meta layoffs analysis. Retrieved from https://community.powerbi.com/t5/Data-Stories-Gallery/Meta-layoffs-analysis-November-2022/m-p/2951409

Demand Sage. (n.d.). Layoff statistics. Retrieved from https://www.demandsage.com/layoff-statistics/