As of 2023, South Africa's population increased and counted approximately 62.3 million inhabitants in total, of which the majority inhabited Gauteng, KwaZulu-Natal, and the Western-Eastern Cape. Gauteng (includes Johannesburg) is the smallest province in South Africa, though highly urbanized with a population of over 16 million people according to the estimates. Cape Town, on the other hand, is the largest city in South Africa with nearly 3.43 million inhabitants in the same year, whereas Durban counted 3.12 million citizens. However, looking at cities including municipalities, Johannesburg ranks first. High rate of young population South Africa has a substantial population of young people. In 2024, approximately 34.3 percent of the people were aged 19 years or younger. Those aged 60 or older, on the other hand, made-up over 10 percent of the total population. Distributing South African citizens by marital status, approximately half of the males and females were classified as single in 2021. Furthermore, 29.1 percent of the men were registered as married, whereas nearly 27 percent of the women walked down the aisle. Youth unemployment Youth unemployment fluctuated heavily between 2003 and 2022. In 2003, the unemployment rate stood at 36 percent, followed by a significant increase to 45.5 percent in 2010. However, it fluctuated again and as of 2022, over 51 percent of the youth were registered as unemployed. Furthermore, based on a survey conducted on the worries of South Africans, some 64 percent reported being worried about employment and the job market situation.

Historical dataset of population level and growth rate for the Cape Town, South Africa metro area from 1950 to 2025.

As of 2024, South Africa's population increased, counting approximately 63 million inhabitants. Of these, roughly 27.5 million were aged 0-24, while 654,000 people were 80 years or older. Gauteng and Cape Town are the most populated South Africa’s yearly population growth has been fluctuating since 2013, with the growth rate dropping below the world average in 2024. The majority of people lived in the borders of Gauteng, the smallest of the nine provinces in terms of land area. The number of people residing there amounted to 16.6 million in 2023. Although the Western Cape was the third-largest province, the city of Cape Town had the highest number of inhabitants in the country, at 3.4 million. An underemployed younger population South Africa has a large population under 14, who will be looking for job opportunities in the future. However, the country's labor market has had difficulty integrating these youngsters. Specifically, as of the fourth quarter of 2024, the unemployment rate reached close to 60 percent and 384 percent among people aged 15-24 and 25–34 years, respectively. In the same period, some 27 percent of the individuals between 15 and 24 years were economically active, while the labor force participation rate was higher among people aged 25 to 34, at 74.3 percent.

The 1970 South African Population Census was an enumeration of the population and housing in South Africa.The census collected data on dwellings and individuals' demographic, migration, family and employment details.

South Africa is the sixth African country with the largest population, counting approximately 60.5 million individuals as of 2021. In 2023, the largest city in South Africa was Cape Town. The capital of Western Cape counted 3.4 million inhabitants, whereas South Africa's second largest city was Durban (eThekwini Municipality), with 3.1 million inhabitants. Note that when observing the number of inhabitants by municipality, Johannesburg is counted as largest city/municipality of South Africa.

From four provinces to nine provinces

Before Nelson Mandela became president in 1994, the country had four provinces, Cape of Good Hope, Natal, Orange Free State, and Transvaal and 10 “homelands” (also called Bantustans). The four larger regions were for the white population while the homelands for its black population. This system was dismantled following the new constitution of South Africa in 1996 and reorganized into nine provinces. Currently, Gauteng is the most populated province with around 15.9 million people residing there, followed by KwaZulu-Natal with 11.68 million inhabiting the province. As of 2022, Black African individuals were almost 81 percent of the total population in the country, while colored citizens followed amounting to around 5.34 million.

A diverse population

Although the majority of South Africans are identified as Black, the country’s population is far from homogenous, with different ethnic groups usually residing in the different “homelands”. This can be recognizable through the various languages used to communicate between the household members and externally. IsiZulu was the most common language of the nation with around a quarter of the population using it in- and outside of households. IsiXhosa and Afrikaans ranked second and third with roughly 15 percent and 12 percent, respectively.

SD = standard deviation.1Bacteriologically confirmed and not on a drug-resistant treatment regimen.23% of HIV results were unknown overall.3HIV results only available for 57 adults.

Abstract

A tender was posted, by the Cape Town City Council, in November 2005 for a socio-economic survey and two focus groups to be conducted in both Khayelitsha and Mitchell's Plain. This tender was awarded to the Unit for Religion and Development at the University of Stellenbosch. The purpose was to update the 2001 Census information as well as to identify key priority issues and needs to inform integrated planning for the areas. In addition, the survey was intended to assess the impact of the Urban Renewal Programme in the respective communities. The objectives of the survey and focus groups were as follows:

• To evaluate the Urban Renewal Programme in the nodes of Khayelitsha and Mitchell’s Plain in order to improve the programme outcomes and the communication thereof;

• To develop a demographic and socio-economic profile of the community in terms of household size and composition, education, income and work status. A socio-economic and demographic profile is important in the identification of community needs to inform planning;

• To measure the communities’ perceptions on the value and importance of various services as well as their level of satisfaction with the delivery of these and other services;

• To identify the key needs of the respective communities in order to inform the City on appropriate investment in facilities, infrastructure and services.

Geographic coverage

Two renewal nodes in the Western Cape: Khayelitsha and Mitchell's Plain

Analysis unit

Households and individuals

Universe

All households and de jure household members within Khayelitsha or Mitchell's Plain.

Kind of data

Sample survey data

Sampling procedure

The survey is a stratified sample of 1 000 households from the study area. The sample was stratified on two levels: first, according to the number of households of the two geographical areas in the study area; and second, according to the number of formal and informal dwelling units in each geographical area (Mitchell’s Plain and Khayelitsha).

Regarding the first level of stratification by the number of households for each nodal area, a sample was selected totalling 453 households for the Mitchell’s Plain area and 547 for Khayelitsha. The second level of stratification by dwelling unit type was done within each nodal area, for Mitchell’s Plain totalling 12 informal dwelling units and 441 formal dwelling units, and for Khayelitsha totalling 311 informal dwelling units and 236 formal dwelling units. Formal and informal households were randomly selected from a small area layer (SAL) data set. This data set was created by combining all enumerated areas (EAs) with a population of less than 500 with adjacent EAs within the same sub-place by Statistics South Africa. Assigned to the SAL are the elected datasets from the 2001 Census, one of which is housing type. Because of the small sample size, comparison between geographic areas and/or different dwelling units within the areas may not be statistically significant.

Mode of data collection

Face-to-face [f2f]

Abstract

In the year 2000 a small team of social scientists from the Universities of Cape Town and Michigan collaborated on designing a survey with a special focus on labour market issues as a precursor to a Cape Area Panel Study with a special focus on youth planned for the year 2002. After much debate and taking due cognisance of time and budget constraints the team decided to target the magisterial district of Mitchell’s Plain within the Cape Metropole for the survey.

This decision was informed by data gleaned from the 1996 census which revealed that Mitchell’s Plain – demarcated a magisterial district in 1986 – contained almost thirty percent of the population in the Cape Metropolitan Council area. It straddled the two cities of Cape Town and Tygerberg and housed nearly 74% of the African and over 20% of the ‘coloured’ metropolitan population. It included the three established African townships of Langa, Gugulethu and Nyanga as well as informal settlements such as Crossroads and Browns Farm. It also included Khayelitsha an African township proclaimed in the early 1980s with the first houses being built in 1986. The 1996 census had recorded high unemployment rates of over 44%, for Africans and over 20% for Coloured people.

Geographic coverage

The survey covers the Khayelitsha and Mitchell's Plain areas of Cape Town, South Africa.

Analysis unit

The unit of analysis for this survey includes households and individuals.

Universe

The survey covers the African and Coloured populations of the Khayelitsha and Mitchell's Plain areas of Cape Town.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was designed to represent all adults (18 years of age and older) in the Mitchell’s Plain Magisterial district. As discussed above, the most cost-efficient method of interviewing residents of such a large area is to use a two-stage cluster sample. The first stage of this sample entails selecting clusters of households and the second stage entails the selection of the households themselves. For our clusters of households, we relied on the Enumerator Areas as defined by Statistics South Africa for the 1996 Population Census. These Enumerator Areas are neighbourhoods of roughly 50 to 200 households. They are drawn up by the Chief Directorate of Demography at Statistics South Africa. This directorate is responsible for developing and maintaining a GIS system that provides the maps that are used for conducting the five-yearly national population census (Statistics South Africa, 2001:42-44). Although Enumerator Area boundaries do not cross municipal boundaries, they do not correspond to any other administrative demarcations such as voting wards. Enumerator Areas are designed to be homogeneous with respect to housing type and size. For example, Enumerator Area boundaries within the Mitchell’s Plain Magisterial District do not usually cut across different types of settlements such as squatter camps, site and service settlements, hostels, formal council estates or privately built estates. Instead, each Enumerator Area is homogeneous with respect to any one of these housing types.

The method of selection used was that of Probability Proportional to Size (PPS). The measure of size being the number of households in each Enumerator Area as measured by the 1996 Population Census. This method was chosen as it provides the most efficient way to obtain equal subsample sizes across two stages of selection, i.e. we are able to select the Enumerator Areas and then select from each Enumerator Area a constant number of households for all Enumerator Areas in the sample. The sample is implicitly stratified by location and by housing type.

A more detailed description of the sampling method and procedure for this survey can be found in the sampling method document available through this site under Other Study Materials.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire: Was aimed at establishing the household roster with the usual questions on age, gender and relationships. It was divided into two sections covering those aged 18 and older and those younger than 18. For the latter a separate set of questions covering education, health and work status was included.

The adult questionnaire: Was aimed to fit the international standard approach on the labour force by allocating the labour market status of ‘employee’ to all those ‘at work’ (for profit or family gain, in cash or in kind). One of the innovative aspects of the survey was that respondents were asked about all income-earning activities. In other words, they were not allocated into particular labour market categories during the process of the interview.

The adult questionnaire was divided into 13 sections:

• Section A on education and other characteristics covered age, racial classification, educational attainment, language, religion and health. • Section B on migration covered place of origin, relocation and destination. • Section C on intergenerational mobility aimed at capturing parental influence on the respondent. • Section D on employment history aimed at capturing the respondent’s work history. • Section E on wage employment attempted to capture respondents working for a wage or salary whether full-time, part-time, in the formal sector or the informal sector including those who had more than one job. • Section F on unemployment included questions on job search • Section G on self-employment included a question on more than one economic activity and the frequency of self-employment. • Section H on non-labour force participants was aimed at refining work status. • Section I on casual work aimed to capture not only those in irregular/short term employment but also people who might have more than one job. • Section J on helping other people with their business for gain was aimed at identifying respondents who assist others from time to time but who might not regard themselves as ‘working’. • Section K on reservation wages attempted to establish the lowest wage at which a respondent would accept work. • Section L on savings, borrowing and grants and investment income attempted to capture income derived from sources other than work • Section M on perceptions of distributive justice posed a number of attitudinal questions.

The Current Population Survey (CPS) was initiated by the South African Department of Statistics (now Statistics South Africa) in October 1977 to collect data on the "African" and "coloured" labour force in South Africa, excluding the Transkei and Bophuthatswana. The CPS draws data from a sample of nearly 10 000 dwellings.

Demographics of children and families.

This dataset includes imputation for missing data in key variables in the ten percent sample of the 2001 South African Census. Researchers at the Centre for the Analysis of South African Social Policy (CASASP) at the University of Oxford used sequential multiple regression techniques to impute income, education, age, gender, population group, occupation and employment status in the dataset. The main focus of the work was to impute income where it was missing or recorded as zero. The imputed results are similar to previous imputation work on the 2001 South African Census, including the single ‘hot-deck’ imputation carried out by Statistics South Africa.

Abstract

The survey, which covers the two neighbourhoods of Imizamo Yethu and Hout Bay Harbour (Hangberg) in the suburb of Hout Bay, Cape Town, was conducted in November and December 2005.

Geographic coverage

The survey covers two neighbourhoods in the suburb of Hout Bay, Cape Town.

Analysis unit

Households

Universe

The survey covers the population living in the two neighbourhoods of Imizamo Yethu and Hout Bay Harbour (Hangberg) in the suburb of Hout Bay, Cape Town

Kind of data

Sample survey data

Mode of data collection

Face-to-face [f2f]

Research instrument

Between March and September 2005 the Centre for Actuarial Research (CARe) the Southern Africa Labour and Development Research Unit (SALDRU) at the University of Cape Town devised the questionnaire which developed from a number of earlier drafts. It drew on those questionnaires used in the 1993 Project for Statistics on Living Standards and Development (PSLSD), the 1996 South African Census, the 1999 Integrated Family Survey (Langeberg Survey), the 2000 Khayelitsha/Mitchell’s Plain Survey (KMPS 2000), and the 2001 South African Census. The pre-final draft was extensively discussed with staff from Citizen Surveys, Cape Town, and considerably amended and reformatted. The questionnaire was presented and discussed at a workshop held at the University of Cape Town on the 8th of November 2005 and slight amendments were made.

Abstract

The current study was undertaken by the Sociology Department of the University of Stellenbosch and the Cape Metropolitan Council (CMC) to support spatial development within the Cape Metropolitan Area (CMA).

At the time, Cape Town faced considerable challenges affecting the outcome of its urban undertaking. These challenges centred on (1) outside migration and its impact on the local economy, delivery needs and the spatial structure of the city; and (2) internal population movements, especially those associated with the informally housed population and the more settled poor. These trends had potential outcomes which were difficult to predict accurately and carry the threat of upsetting the delicate planning models, which were being introduced. Housing delivery for the disadvantaged sectors of the CMA population was falling further behind as informal occupation of land and informal housing continued to spread and proliferate (CMC, 1997a). Housing lists were not moving, and land invasions continued to take place. This study tried to address the uncertainty around inside and outside migration in relation to settlement, and to contribute to the refinement of the CMA's spatial planning and implementation initiative.

Geographic coverage

The survey covered the Cape Metropolitan Area (CMA) within the Western Province of South Africa.

Analysis unit

Units of analysis in the survey were persons

Universe

The survey covered households in the Cape Metropolitan Area.

As part of the pre-survey qualitative research, 25 settlement areas were selected on the basis of a search for as much socio-economic and cultural diversity as possible. A ‘housing polygon’ map was made available to the research team in the selection. Distribution of these areas across the six Cape Metropolitan Area local council areas was also taken into consideration.

Kind of data

Sample survey data [ssd]

Sampling procedure

A sample survey of 1000 randomly- selected Cape Metropolitan Area residents was designed. First, a list of all Enumerator areas falling within the CMA was obtained from the Statistics South Africa office in Cape Town. Secondly, the number of Cape Metropolitan Enumerator areas falling within each magisterial district was counted and a stratified random sample of 25 settlement areas (using electronically- generated random number selection techniques) was selected. Thirdly, detailed EA maps (showing residential units, streets names as well as public and other non-residential buildings) were obtained from Statistics South Africa for each of the 25 selected areas and 40 dwelling units were selected on a random spatial basis from each of these enumerator areas maps.

Mode of data collection

Face-to-face [f2f]

BackgroundSouth Africa is one of the most ‘unequal’ societies in the world. Despite apartheid ending more than 20 years ago, material inequalities remain interwoven with ethnic/racial inequalities. There is limited research on the prevalence/predictors of common mental disorders (CMD) among young people. Adolescence is a unique time-point during which intervention may lead to improved mental health and reduced social problems later. The study objective was to assess mental health disparities in a representative sample of adolescents growing up in South Africa.MethodsCross-sectional associations of race/ethnicity and material disadvantage with CMD and Post Traumatic Stress Disorder (PTSD) were assessed in a stratified random sample representative of school-attendees, aged 14–15 years, in a large metropolitan area of Cape Town. Validated instruments assessed mental disorders; these included: Harvard Trauma Questionnaire (PTSD); Short Moods and Feelings Questionnaire (depression); Zung self-rated anxiety scale (anxiety). Self-ascribed ethnicity was determined using procedures similar to the South African census and previous national surveys.ResultsResponse rate was 88% (1034 of 1169 individuals). Adolescents experienced a high prevalence of depression (41%), anxiety (16%) and PTSD (21%). A gradient between material disadvantage and CMD/ PTSD was evident across all ethnic/racial groups. Respondents self-identifying as ‘black’ or ‘coloured’ were disadvantaged across most indicators. After adjusting for confounders, relative to white children, relative risk (RR) of CMD in black children was 2.27 (95% CI:1.24, 4.15) and for PTSD was RR: 2.21 (95% CI:1.73, 2.83). Relative risk of CMD was elevated in children self-identifying as ‘coloured’ (RR: 1.73, 95% CI:1.11, 2.70). Putative mediators (violence, racially motivated bullying, social support, self-esteem) partially accounted for differences in CMD and fully for PTSD.ConclusionsAdolescent mental health inequalities in Cape Town are associated with material disadvantage and self-identification with historically disadvantaged groups.

Abstract

The Cape Area Panel Study (CAPS) is a longitudinal study of the lives of youths in metropolitan Cape Town, South Africa. The first wave of the study collected interviews from about 4800 randomly selected young people age 14-22 in August-December, 2002. Wave 1 also collected information on all members of these young people’s households, as well as a random sample of households that did not have members age 14-22. A third of the youth sample was re-interviewed in 2003 (Wave 2a) and the remaining two thirds were re-visited in 2004 (Wave 2b). The full youth sample was then re-interviewed in 2005 (Wave 3), 2006 (Wave 4) and 2009 (Wave 5). Wave 3 includes interviews with approximately 2000 co-resident parents of young adults, while wave 4 also includes interviews with a sample of older adults (all individuals from the original 2002 households who were born on or before 1 January 1956) and all children born to the female young adults. The fifth wave comprises all respondents interviewed in any of the Waves 2a, 3 or 4. In 2010 there were telephonic follow-ups or proxy interviewed that tried to capture those that were not successfully interviewed during the course of the 2009 fieldwork. The study covers a wide range of outcomes, including schooling, employment, health, family formation, and intergenerational support systems. CAPS began in 2002 as a collaborative project of the Population Studies Center in the Institute for Social Research at the University of Michigan and the Centre for Social Science Research at the University of Cape Town (UCT). Other units involved in subsequent waves include UCT’s Southern African Labour and Development Research Unit and the Research Program in Development Studies at Princeton University.

The secure version of CAPS 2002-2009 includes date of birth, location (ea number, placename), job and school names and locations, as well as variables used in the processing of the data. The secure version does not include information available in the public release dataset and researchers will have to merge these data with the publicly available data when doing their analyses.

Geographic coverage

The survey covered Metropolitan Cape Town.

Analysis unit

The unit of analysis for this survey is individuals.

Universe

The survey covered youths in Metropolitan Cape Town, South Africa.

Kind of data

Sample survey data [ssd]

Sampling procedure

The CAPS household sample was drawn through a two-stage process. First, the 'enumeration areas' (EAs) used for the 1996 Population Census were divided into three strata according to whether the population of each was predominantly African, predominantly coloured or predominantly white. A sample of primary sampling units (PSUs) was selected within each stratum with probability proportional to size. Within each PSU a sample of 25 screener households was drawn. The Overview and Technical Documentation for Waves 1-2-3-4-5 provides a more detailed discussion of the sampling design. Data users should take the stratification and clustering into account for all analyses. Strata and PSUs are identified by the majpop and cluster variables respectively.

Mode of data collection

Face-to-face [f2f]

Research instrument

• Wave 1 (2002) included a household questionnaire, a young adult questionnaire and a literacy and numeracy evaluation questionnaire
• Wave 2a (2003) and 2b (2004) both included young adult questionnaires only • Wave 3 (2005) included a household questionnaire, a parent questionnaire and a young adult questionnaire
• Wave 4 (2006) included a household questionnaire, an older adult questionnaire, a young adult questionnaire, a young adult proxy questionnaire and a child questionnaire
• Wave 5 (2009) included a young adult questionnaire, young adult telephonic questionnaire and a young adult proxy questionnaire

The questionaires and technical documentation for use with the secure version of CAPS 2002-2009 should be downloaded from the link to the public access dataset.

Response rate

Response rates for the survey are covered in Section 5 on non-response and attrition in the document "The Cape Area Panel Study: Overview and technical documentation: Waves 1-2-3-4-5 (2002-2009)."

Large carnivores play a vital role in structuring our ecosystems, yet they face mounting threats such as habitat loss, prey reduction and persecution. These threats reduce their global distribution and impacts their population numbers. Protected areas can offer refuge for large carnivores, however leopards (Panthera pardus), can persist outside of these areas and often occupy mixed-use landscapes. Our understanding of how leopards persist over time in mixed-use landscapes is limited, especially in the semi-arid regions of southern Africa. This study, to the best of my knowledge, is the only multi-session maximum likelihood spatial capture-recapture (SCR) analysis to have been conducted in a semi-arid environment outside of a protected area in Southern Africa. The study aimed to estimate leopard population changes over time and to investigate the possible drivers affecting density, using three surveys (2012, 2017, 2022), in the mixed-use landscape of the Little Karoo in the Western Cape, South Africa. In 2012, a total of 141 paired camera stations were used for a total of 13,050 trap days resulting in 29 unique leopard captures. In 2017, a total of 40 paired camera stations were used for a total of 2,128 trap days resulting in 18 unique leopard captures and in 2022 a total of 64 paired camera stations were used for a total of 8,997 trap days resulting in 37 unique leopard captures. The best performing density model indicated an increasing population trend over the study period which included a trend term on density (D~year) and an interaction term (individual session*sex) on λ0 (capture rate) and σ (spatial decay). Density estimates (Standard Error) for leopard populations for the three surveys 2012, 2017, and 2022, were 0.52 (± 0.11), 0.70 (± 0.08), and 0.95 (± 0.08) leopards per 100 km2, respectively. Terrain ruggedness, elevation, vegetation type and distance from major rivers were all important drivers in leopard density in the Little Karoo. Indicating that high lying areas provide suitable refuge for leopards and are key areas for movement corridor planning. These density estimates are similar to previous single maximum likelihood SCR density estimate studies in the Little Karoo and the Western Cape province. Results from this study indicate the leopards have persisted in the Little Karoo over the study period and suggest that the population may be increasing. Further research on what is driving this population shift is needed, but the results serve as an encouraging sign for leopard conservation in the Little Karoo.

Research conducted in partial fulfilment of requirements for the degree of Master of Science in Conservation Biology

As the world’s human population increases, transformation of natural landscapes into urban habitats continues to increase. In Africa, rates of human population growth and urbanisation are among the highest in the world, but the impacts of these processes on the continent’s biodiversity remain largely unexplored. Furthermore, the effects of ongoing anthropogenic climate change are likely to be severe and to interact with urbanisation.

Some organisms appear resilient to urbanisation, and even proliferate in human-modified environments. One such species is the peregrine falcon Falco peregrinus in Cape Town, South Africa. Using a long-term data set (1989-2014), we investigate the relationship between breeding attempts, timing of breeding and breeding performance under varying weather conditions. Exploring these issues along an urbanisation gradient, we focus on the role of artificially provided nest boxes, and their capacity to buffer against extreme weather events.

Pairs in more urbanised areas, and particularly those in nest boxes, were more likely to breed and to commence breeding earlier. Additionally, pairs using nest boxes were more likely to breed in years with higher rainfall. Warm and dry weather conditions generally advanced the timing of breeding, although this relationship with weather was not seen for urban pairs using nest boxes. Furthermore, weather did not impact breeding performance directly (breeding success and fledged brood size), but timing of breeding did, with earlier breeders producing more fledglings.

Our study shows that falcons breeding in specially provided nest boxes were less sensitive to local weather dynamics than pairs using more natural nest sites. This has important implications as it suggests that the managed provision of such nesting sites can help this key urban species to cope with extreme weather events, which are predicted to increase with climate change.

^p-value obtained using chi-square test (at 95% significance level).*The proportions (of men and women) are based on the overall total, 1220 (denominator). All other proportions are column percentages.** Missing data (n = 215) were not includeda & bNormal, Overweight and Obesity categories are defined by standard WHO cut-offs for BMI and WC[33]cNormal, Overweight and Obesity cut-offs for BF% were taken as = 36% (for women), and = 26% (for men) respectively[34].Socio-demographic and anthropometric characteristics of study participants.

Increases in initial loss to follow up (ILTFU) and post-treatment loss (PTL) between pre- & during-COVID-19 periods, disaggregated by demographic and clinical characteristics for all individuals diagnosed with DS-TB in Cape Town, South Africa.

The studies described in the referenced papers collected clinical and demographic data from adolescents aged 12-18 years from high schools in the area of Worcester, South Africa. This data item contains the clinical and demographic data collected.clinical_data.csv, clinical_data.xlsx and clinical_data.rds contain the actual clinical data. They are identical except that the first is a CSV file, the second an XLSX file and the third an RDS file. The RDS file is useful because it can be read into R using readRDS('clinical_data.rds') and then retains the original variable types for each of the columns. clinical_data-column_descriptions.txt describes what each of the columns are and contain.